configuration_speech_to_text_2.py

# coding=utf-8
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""" Speech2Text model configuration"""

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


logger = logging.get_logger(__name__)


class Speech2Text2Config(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`Speech2Text2ForCausalLM`]. It is used to
    instantiate an Speech2Text2 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 Speech2Text2
    [facebook/s2t-wav2vec2-large-en-de](https://huggingface.co/facebook/s2t-wav2vec2-large-en-de) architecture.

    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 50265):
            Vocabulary size of the Speech2Text model. Defines the number of different tokens that can be represented by
            the `inputs_ids` passed when calling [`Speech2TextModel`]
        d_model (`int`, *optional*, defaults to 1024):
            Dimensionality of the layers and the pooler layer.
        decoder_layers (`int`, *optional*, defaults to 12):
            Number of decoder layers.
        decoder_attention_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer decoder.
        decoder_ffn_dim (`int`, *optional*, defaults to 4096):
            Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
        activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the pooler. If string, `"gelu"`, `"relu"`,
            `"silu"` and `"gelu_new"` are supported.
        dropout (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, and pooler.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        activation_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for activations inside the fully connected layer.
        init_std (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
            https://arxiv.org/abs/1909.11556>`__ for more details.
        decoder_layerdrop (`float`, *optional*, defaults to 0.0):
            The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)
            for more details.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        max_target_positions (`int`, *optional*, defaults to 1024):
            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).

    Example:

    ```python
    >>> from transformers import Speech2Text2Config, Speech2Text2ForCausalLM

    >>> # Initializing a Speech2Text2 s2t_transformer_s style configuration
    >>> configuration = Speech2Text2Config()

    >>> # Initializing a model (with random weights) from the s2t_transformer_s style configuration
    >>> model = Speech2Text2ForCausalLM(configuration)

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

    model_type = "speech_to_text_2"
    keys_to_ignore_at_inference = ["past_key_values"]
    attribute_map = {"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}

    def __init__(
        self,
        vocab_size=10000,
        decoder_layers=6,
        decoder_ffn_dim=2048,
        decoder_attention_heads=4,
        decoder_layerdrop=0.0,
        use_cache=True,
        activation_function="relu",
        d_model=256,
        dropout=0.1,
        attention_dropout=0.0,
        activation_dropout=0.0,
        init_std=0.02,
        decoder_start_token_id=2,
        scale_embedding=True,
        pad_token_id=1,
        bos_token_id=0,
        eos_token_id=2,
        max_target_positions=1024,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.d_model = d_model
        self.decoder_ffn_dim = decoder_ffn_dim
        self.decoder_layers = decoder_layers
        self.decoder_attention_heads = decoder_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.decoder_layerdrop = decoder_layerdrop
        self.use_cache = use_cache
        self.num_hidden_layers = decoder_layers
        self.scale_embedding = scale_embedding  # scale factor will be sqrt(d_model) if True
        self.max_target_positions = max_target_positions

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