configuration_mixtral_clex.py

# coding=utf-8
# Copyright 2023 Mistral AI and the HuggingFace Inc. 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,
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""" Mistral model configuration"""

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
from transformers import MixtralConfig

logger = logging.get_logger(__name__)





class CLEXMixtralConfig(MixtralConfig):
    r"""
    This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
    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 LLaMA-7B.

    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 LLaMA model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`LlamaModel`]
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 11008):
            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*):
            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
            `num_attention_heads`.
        pretraining_tp (`int`, *optional*, defaults to `1`):
            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
            document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
            issue](https://github.com/pytorch/pytorch/issues/76232).
        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 2048):
            The maximum sequence length that this model might ever be used with. Typically set this to something large
            just in case (e.g., 512 or 1024 or 2048).
        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-12):
            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`.
        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
            Whether to tie weight embeddings
        rope_scaling (`Dict`, *optional*):
            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports three scaling
            strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
            is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
            these scaling strategies behave:
            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
            experimental feature, subject to breaking API changes in future versions.

        Example:

    ```python
    >>> from transformers import LlamaModel, LlamaConfig

    >>> # Initializing a LLaMA llama-7b style configuration
    >>> configuration = LlamaConfig()

    >>> # Initializing a model from the llama-7b style configuration
    >>> model = LlamaModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""
    model_type = "mixtral"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        rope_scaling=None,
        use_flashattn=True,
        log_scale=True,
        pretraining_tp=1,
        **kwargs,
    ):
        super().__init__(
            **kwargs,
        )
        self.pretraining_tp = pretraining_tp
        self.use_flashattn = use_flashattn
        self.log_scale = log_scale
        # self.rope_theta = 10000
        # self.max_position_embeddings = 4096
        # self.data_length = 4096
        self.rope_scaling = rope_scaling
        self._rope_scaling_validation()


    def _rope_scaling_validation(self):
        """
        Validate the `rope_scaling` configuration.
        """
        if self.rope_scaling is None:
            return

        # if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
        #     raise ValueError(
        #         "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
        #         f"got {self.rope_scaling}"
        #     )
        rope_scaling_type = self.rope_scaling.get("type", None)
        rope_scaling_max_factor = self.rope_scaling.get("max_factor", None)
        rope_scaling_param_factor = self.rope_scaling.get("param_factor", None)
        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic", "clex"]:
            raise ValueError(
                f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
            )
        # if rope_scaling_max_factor is None or not isinstance(rope_scaling_max_factor, float) or rope_scaling_max_factor <= 1.0:
        #     raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_max_factor}")
        # if rope_scaling_param_factor is None or not isinstance(rope_scaling_param_factor, float) or rope_scaling_param_factor <= 1.0:
        #     raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_param_factor}")