configuration_phimoe.py

# coding=utf-8
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""" PyTorch Phi-MoE model."""


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


logger = logging.get_logger(__name__)


PHIMOE_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "microsoft/Phi-3.5-moe-instruct": "https://huggingface.co/microsoft/Phi-3.5-moe-instruct/resolve/main/config.json",
}

class PhiMoEConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`PhiMoEModel`]. It is used to instantiate a Phi-MoE
    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
    [microsoft/Phi-3.5-moe-instruct](https://huggingface.co/microsoft/Phi-3.5-moe-instruct).

    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 32064):
            Vocabulary size of the PhiMoE model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`PhiMoEModel`]
        hidden_size (`int`, *optional*, defaults to 4096):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 6400):
            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. Mixtral'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-05):
            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.
        rope_scaling (`dict`, *optional*):
            The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must
            contain the following keys: `type`, `short_factor`, `long_factor`, `short_mscale`, `long_mscale` and
            `original_max_position_embeddings`. The `type` must be `longrope`, the `short_mscale` and `long_scale` must
            be numbers, the `short_factor` and `long_factor` must be lists of numbers with the same length as half of
            the attention head size and the `original_max_position_embeddings` must be an integer.
        sliding_window (`int`, *optional*):
            Sliding window attention window size. If not specified, will default to `262144`.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        num_experts_per_tok (`int`, *optional*, defaults to 2):
            The number of experts to root per-token, can be also interpreted as the `top-p` routing
            parameter
        num_local_experts (`int`, *optional*, defaults to 16):
            Number of experts per Sparse MLP layer.
        output_router_logits (`bool`, *optional*, defaults to `False`):
            Whether or not the router logits should be returned by the model. Enabeling this will also
            allow the model to output the auxiliary loss. See [here]() for more details
        router_aux_loss_coef (`float`, *optional*, defaults to 0.0):
            The aux loss factor for the total loss.
        router_jitter_noise (`float`, *optional*, defaults to 0.01):
            Amount of noise to add to the router.

    ```python
    >>> from transformers import PhiMoEModel, PhiMoEConfig

    >>> # Initializing a Phi-3 style configuration
    >>> configuration = PhiMoEConfig.from_pretrained("microsoft/Phi-3.5-moe-instruct")

    >>> # Initializing a model from the configuration
    >>> model = PhiMoEModel(configuration)

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

    def __init__(
        self,
        vocab_size=32064,
        hidden_size=4096,
        intermediate_size=6400,
        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-5,
        use_cache=True,
        pad_token_id=None,
        bos_token_id=1,
        eos_token_id=2,
        tie_word_embeddings=False,
        rope_theta=1e6,
        rope_scaling=None,
        sliding_window=None,
        attention_dropout=0.0,
        num_experts_per_tok=2,
        num_local_experts=16,
        output_router_logits=False,
        router_aux_loss_coef=0.001,
        router_jitter_noise=0.01,
        input_jitter_noise=0.0,
        attention_bias = False,
        lm_head_bias = False,
        **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
        self.attention_bias = attention_bias
        self.lm_head_bias = lm_head_bias
        # 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.rope_theta = rope_theta
        self.attention_dropout = attention_dropout

        self.num_experts_per_tok = num_experts_per_tok
        self.num_local_experts = num_local_experts
        self.output_router_logits = output_router_logits
        self.router_aux_loss_coef = router_aux_loss_coef
        self.router_jitter_noise = router_jitter_noise
        self.input_jitter_noise = input_jitter_noise

        self.rope_scaling = rope_scaling
        self._rope_scaling_validation()

        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,
        )

    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) != 6:
            raise ValueError(
                "`rope_scaling` must be a dictionary with three fields, `type`, `short_factor`, `long_factor`, "
                f"`short_mscale`, `long_mscale` and `original_max_position_embeddings`, got {self.rope_scaling}"
            )
        rope_scaling_type = self.rope_scaling.get("type", None)
        rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
        rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
        rope_scaling_short_mscale = self.rope_scaling.get("short_mscale", None)
        rope_scaling_long_mscale = self.rope_scaling.get("long_mscale", None)
        original_max_position_embeddings = self.rope_scaling.get("original_max_position_embeddings", None)
        if rope_scaling_type is None or rope_scaling_type not in ["longrope"]:
            raise ValueError(f"`rope_scaling`'s type field must be one of ['longrope'], got {rope_scaling_type}")
        if not (
            isinstance(rope_scaling_short_factor, list)
            and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)
        ):
            raise ValueError(
                f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"
            )
        if not len(rope_scaling_short_factor) == self.hidden_size // self.num_attention_heads // 2:
            raise ValueError(
                f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}"
            )
        if not (
            isinstance(rope_scaling_long_factor, list)
            and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)
        ):
            raise ValueError(
                f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"
            )
        if not len(rope_scaling_long_factor) == self.hidden_size // self.num_attention_heads // 2:
            raise ValueError(
                f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}"
            )
        if not isinstance(rope_scaling_short_mscale, (int, float)):
            raise ValueError(
                f"`rope_scaling`'s short_mscale field must be a number, got {rope_scaling_short_mscale}"
            )
        if not isinstance(rope_scaling_long_mscale, (int, float)):
            raise ValueError(
                f"`rope_scaling`'s long_mscale field must be a number, got {rope_scaling_long_mscale}"
            )
        if not isinstance(original_max_position_embeddings, int):
            raise ValueError(
                f"`rope_scaling`'s original_max_position_embeddings field must be an integer, got {original_max_position_embeddings}"
            )