configuration_mistral_advanced.py

from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from transformers.auto.configuration_auto import AutoConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging



if TYPE_CHECKING:
    from ... import PreTrainedTokenizerBase, TensorType

logger = logging.get_logger(__name__)

""" Mistral model configuration"""



MISTRAL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "mistralai/Mistral-7B-v0.1": "https://huggingface.co/mistralai/Mistral-7B-v0.1/resolve/main/config.json",
    "mistralai/Mistral-7B-Instruct-v0.1": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/resolve/main/config.json",
}

class EncoderDecoderConfig(PretrainedConfig):
    is_composition = True   

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        if "encoder" not in kwargs or "decoder" not in kwargs:
            raise ValueError(
                f"A configuraton of type {self.model_type} cannot be instantiated because "
                f"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}"
            )

        encoder_config = kwargs.pop("encoder")
        encoder_model_type = encoder_config.pop("model_type")
        decoder_config = kwargs.pop("decoder")
        decoder_model_type = decoder_config.pop("model_type")

        self.encoder = AutoConfig.for_model(encoder_model_type, **encoder_config)
        self.decoder = AutoConfig.for_model(decoder_model_type, **decoder_config)
        self.is_encoder_decoder = True
    @classmethod
    def from_encoder_decoder_configs(
        cls, encoder_config: PretrainedConfig, decoder_config: PretrainedConfig, **kwargs
    ) -> PretrainedConfig:
        r"""
        Instantiate a [`SpeechEncoderDecoderConfig`] (or a derived class) from a pre-trained encoder model
        configuration and decoder model configuration.

        Returns:
            [`SpeechEncoderDecoderConfig`]: An instance of a configuration object
        """
        logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config")
        decoder_config.is_decoder = True
        decoder_config.add_cross_attention = True

        return cls(encoder=encoder_config.to_dict(), decoder=decoder_config.to_dict(), **kwargs)

class VisionEncoderDecoderConfig(PretrainedConfig):
    r"""
    [`VisionEncoderDecoderConfig`] is the configuration class to store the configuration of a
    [`VisionEncoderDecoderModel`]. It is used to instantiate a Vision-Encoder-Text-Decoder model according to the
    specified arguments, defining the encoder and decoder configs.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        kwargs (*optional*):
            Dictionary of keyword arguments. Notably:

                - **encoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines
                  the encoder config.
                - **decoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines
                  the decoder config.

    Examples:

    ```python
    >>> from transformers import BertConfig, ViTConfig, VisionEncoderDecoderConfig, VisionEncoderDecoderModel

    >>> # Initializing a ViT & BERT style configuration
    >>> config_encoder = ViTConfig()
    >>> config_decoder = BertConfig()

    >>> config = VisionEncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)

    >>> # Initializing a ViTBert model (with random weights) from a ViT & google-bert/bert-base-uncased style configurations
    >>> model = VisionEncoderDecoderModel(config=config)

    >>> # Accessing the model configuration
    >>> config_encoder = model.config.encoder
    >>> config_decoder = model.config.decoder
    >>> # set decoder config to causal lm
    >>> config_decoder.is_decoder = True
    >>> config_decoder.add_cross_attention = True

    >>> # Saving the model, including its configuration
    >>> model.save_pretrained("my-model")

    >>> # loading model and config from pretrained folder
    >>> encoder_decoder_config = VisionEncoderDecoderConfig.from_pretrained("my-model")
    >>> model = VisionEncoderDecoderModel.from_pretrained("my-model", config=encoder_decoder_config)
    ```"""

    model_type = "vision-encoder-decoder"
    is_composition = True

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        if "encoder" not in kwargs or "decoder" not in kwargs:
            raise ValueError(
                f"A configuraton of type {self.model_type} cannot be instantiated because "
                f"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}"
            )

        encoder_config = kwargs.pop("encoder")
        encoder_model_type = encoder_config.pop("model_type")
        decoder_config = kwargs.pop("decoder")
        decoder_model_type = decoder_config.pop("model_type")

        self.encoder = AutoConfig.for_model(encoder_model_type, **encoder_config)
        self.decoder = AutoConfig.for_model(decoder_model_type, **decoder_config)
        self.is_encoder_decoder = True

    @classmethod
    def from_encoder_decoder_configs(
        cls, encoder_config: PretrainedConfig, decoder_config: PretrainedConfig, **kwargs
    ) -> PretrainedConfig:
        r"""
        Instantiate a [`VisionEncoderDecoderConfig`] (or a derived class) from a pre-trained encoder model
        configuration and decoder model configuration.

        Returns:
            [`VisionEncoderDecoderConfig`]: An instance of a configuration object
        """
        logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config")
        decoder_config.is_decoder = True
        decoder_config.add_cross_attention = True

        return cls(encoder=encoder_config.to_dict(), decoder=decoder_config.to_dict(), **kwargs)

class SpeechEncoderDecoderConfig(PretrainedConfig):
    r"""
    [`SpeechEncoderDecoderConfig`] is the configuration class to store the configuration of a
    [`SpeechEncoderDecoderModel`]. It is used to instantiate an Encoder Decoder model according to the specified
    arguments, defining the encoder and decoder configs.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        kwargs (*optional*):
            Dictionary of keyword arguments. Notably:

                - **encoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines
                  the encoder config.
                - **decoder** ([`PretrainedConfig`], *optional*) -- An instance of a configuration object that defines
                  the decoder config.

    Examples:

    ```python
    >>> from transformers import BertConfig, Wav2Vec2Config, SpeechEncoderDecoderConfig, SpeechEncoderDecoderModel

    >>> # Initializing a Wav2Vec2 & BERT style configuration
    >>> config_encoder = Wav2Vec2Config()
    >>> config_decoder = BertConfig()

    >>> config = SpeechEncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)

    >>> # Initializing a Wav2Vec2Bert model from a Wav2Vec2 & google-bert/bert-base-uncased style configurations
    >>> model = SpeechEncoderDecoderModel(config=config)

    >>> # Accessing the model configuration
    >>> config_encoder = model.config.encoder
    >>> config_decoder = model.config.decoder
    >>> # set decoder config to causal lm
    >>> config_decoder.is_decoder = True
    >>> config_decoder.add_cross_attention = True

    >>> # Saving the model, including its configuration
    >>> model.save_pretrained("my-model")

    >>> # loading model and config from pretrained folder
    >>> encoder_decoder_config = SpeechEncoderDecoderConfig.from_pretrained("my-model")
    >>> model = SpeechEncoderDecoderModel.from_pretrained("my-model", config=encoder_decoder_config)
    ```"""

    model_type = "speech-encoder-decoder"
    is_composition = True

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        if "encoder" not in kwargs or "decoder" not in kwargs:
            raise ValueError(
                f"A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and"
                f" `decoder` sub-configurations are passed, but only {kwargs}"
            )

        encoder_config = kwargs.pop("encoder")
        encoder_model_type = encoder_config.pop("model_type")
        decoder_config = kwargs.pop("decoder")
        decoder_model_type = decoder_config.pop("model_type")

        self.encoder = AutoConfig.for_model(encoder_model_type, **encoder_config)
        self.decoder = AutoConfig.for_model(decoder_model_type, **decoder_config)
        self.is_encoder_decoder = True

    @classmethod
    def from_encoder_decoder_configs(
        cls, encoder_config: PretrainedConfig, decoder_config: PretrainedConfig, **kwargs
    ) -> PretrainedConfig:
        r"""
        Instantiate a [`SpeechEncoderDecoderConfig`] (or a derived class) from a pre-trained encoder model
        configuration and decoder model configuration.

        Returns:
            [`SpeechEncoderDecoderConfig`]: An instance of a configuration object
        """
        logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config")
        decoder_config.is_decoder = True
        decoder_config.add_cross_attention = True

        return cls(encoder=encoder_config.to_dict(), decoder=decoder_config.to_dict(), **kwargs)

class MistralConfig(PretrainedConfig):
    is_composition = True   

    r"""
    This is the configuration class to store the configuration of a [`MistralModel`]. It is used to instantiate an
    Mistral model according to the specified arguments, defining the model architecture. Instantiating a configuration
    with the defaults will yield a similar configuration to that of the Mistral-7B-v0.1 or Mistral-7B-Instruct-v0.1.

    [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
    [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.


    Args:
        vocab_size (`int`, *optional*, defaults to 32000):
            Vocabulary size of the Mistral model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`MistralModel`]
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 14336):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 32):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 32):
            Number of attention heads for each attention layer in the Transformer encoder.
        num_key_value_heads (`int`, *optional*, defaults to 8):
            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
            by meanpooling all the original heads within that group. For more details checkout [this
            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
            The non-linear activation function (function or string) in the decoder.
        max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
            The maximum sequence length that this model might ever be used with. Mistral's sliding window attention
            allows sequence of up to 4096*32 tokens.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
            The epsilon used by the rms normalization layers.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        pad_token_id (`int`, *optional*):
            The id of the padding token.
        bos_token_id (`int`, *optional*, defaults to 1):
            The id of the "beginning-of-sequence" token.
        eos_token_id (`int`, *optional*, defaults to 2):
            The id of the "end-of-sequence" token.
        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
            Whether the model's input and output word embeddings should be tied.
        rope_theta (`float`, *optional*, defaults to 10000.0):
            The base period of the RoPE embeddings.
        sliding_window (`int`, *optional*, defaults to 4096):
            Sliding window attention window size. If not specified, will default to `4096`.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.

    ```python
    >>> from transformers import MistralModel, MistralConfig

    >>> # Initializing a Mistral 7B style configuration
    >>> configuration = MistralConfig()

    >>> # Initializing a model from the Mistral 7B style configuration
    >>> model = MistralModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = ["mistral","speech-encoder-decoder","image-encoder-decoder","mistral-encoder-decoder"]
    # model_type = "mistral-encoder-decoder"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size=32000,
        hidden_size=4096,
        intermediate_size=14336,
        num_hidden_layers=32,
        num_attention_heads=32,
        num_key_value_heads=8,
        hidden_act="silu",
        max_position_embeddings=4096 * 32,
        initializer_range=0.02,
        rms_norm_eps=1e-6,
        use_cache=True,
        pad_token_id=None,
        bos_token_id=1,
        eos_token_id=2,
        tie_word_embeddings=False,
        sliding_window=4096,
        attention_dropout=0.0,

# for yarn Later
        rope_theta=10000.0,
        rope_scaling=None,
# for thought generation Later
        max_thoughts=16,
        max_temperature=10,
        complexity_factor = 0.5,
        merged_talk_heads=True,
        merged_lm_and_talk_heads=False,
        merged_lm_and_think_heads=True,
        use_concat_talk_head=True,
        use_shallow_think=True,
        use_shallow_talk=False,
        use_complex_think_head=False,
        use_complex_talk_head=True,
        use_weighted_talk_head=True,
        hidden_dropout_prob=0.00,

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

        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        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.sliding_window = sliding_window

        # for backward compatibility
        if num_key_value_heads is None:
            num_key_value_heads = num_attention_heads

        self.num_key_value_heads = num_key_value_heads
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.attention_dropout = attention_dropout
# yarn  
        self.rope_scaling = rope_scaling
        self._rope_scaling_validation()
        self.rope_theta = rope_theta
#Thought gen
        self.max_thoughts = max_thoughts
        self.complexity_factor = complexity_factor
        self.max_temperature = max_temperature
        self.merged_talk_heads = merged_talk_heads
        self.merged_lm_and_talk_heads = merged_lm_and_talk_heads
        self.merged_lm_and_think_heads = merged_lm_and_think_heads
        self.use_concat_talk_head = use_concat_talk_head
        self.use_shallow_think = use_shallow_think
        self.use_shallow_talk = use_shallow_talk
        self.use_complex_think_head = use_complex_think_head
        self.use_complex_talk_head = use_complex_talk_head
        self.use_weighted_talk_head = use_weighted_talk_head
        self.hidden_dropout_prob = hidden_dropout_prob
#Encoder Decoder - Currently only a single EncoderDecoder is supported -Later will be eXpanded to suport both models 
        if "encoder" not in kwargs or "decoder" not in kwargs:
            raise ValueError(
                f"A configuraton of type {self.model_type} cannot be instantiated because "
                f"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}"
            )

        encoder_config = kwargs.pop("encoder")
        encoder_model_type = encoder_config.pop("model_type")
        decoder_config = kwargs.pop("decoder")
        decoder_model_type = decoder_config.pop("model_type")

        self.encoder = AutoConfig.for_model(encoder_model_type, **encoder_config)
        self.decoder = AutoConfig.for_model(decoder_model_type, **decoder_config)
        self.is_encoder_decoder = True

    @classmethod
    def from_encoder_decoder_configs(
        cls, encoder_config: PretrainedConfig, decoder_config: PretrainedConfig, **kwargs
    ) -> PretrainedConfig:
        r"""
        Instantiate a [`SpeechEncoderDecoderConfig`] (or a derived class) from a pre-trained encoder model
        configuration and decoder model configuration.

        Returns:
            [`SpeechEncoderDecoderConfig`]: An instance of a configuration object
        """
        logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config")
        decoder_config.is_decoder = True
        decoder_config.add_cross_attention = True

        return cls(encoder=encoder_config.to_dict(), decoder=decoder_config.to_dict(), **kwargs)
    
    def _rope_scaling_validation(self):
        """
        Validate the `rope_scaling` configuration.
        """
        if self.rope_scaling is None:
            return

        if not isinstance(self.rope_scaling, dict):
            raise ValueError(
                "`rope_scaling` must be a dictionary, "
                f"got {self.rope_scaling}"
            )
        rope_scaling_type = self.rope_scaling.get("type", None)
        rope_scaling_factor = self.rope_scaling.get("factor", None)
        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic", "yarn", "dynamic-yarn"]:
            raise ValueError(
                f"`rope_scaling`'s name field must be one of ['linear', 'dynamic', 'yarn', 'dynamic-yarn'], got {rope_scaling_type}"
            )
        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
            raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")
        if rope_scaling_type == "yarn" or rope_scaling_type == "dynamic-yarn":
            original_max_position_embeddings = self.rope_scaling.get("original_max_position_embeddings", None)
            if original_max_position_embeddings is None or not isinstance(original_max_position_embeddings, int):
                raise ValueError(f"`rope_scaling.original_max_position_embeddings` must be set to an int when using yarn, and dynamic-yarn")