paddlepaddle--paddlenlp/paddlenlp/transformers/ctrl/configuration.py

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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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""" CTRL configuration"""
from __future__ import annotations

from typing import Dict

from paddlenlp.transformers.configuration_utils import PretrainedConfig

__all__ = ["CTRL_PRETRAINED_INIT_CONFIGURATION", "CTRLConfig", "CTRL_PRETRAINED_RESOURCE_FILES_MAP"]

CTRL_PRETRAINED_INIT_CONFIGURATION = {
    "ctrl": {
        "tie_word_embeddings": True,
        "intermediate_size": 8192,
        "embd_pdrop": 0.1,
        "initializer_range": 0.02,
        "layer_norm_epsilon": 1e-06,
        "hidden_size": 1280,
        "num_attention_heads": 16,
        "num_hidden_layers": 48,
        "max_position_embeddings": 50000,
        "resid_pdrop": 0.1,
        "vocab_size": 246534,
        "pad_token_id": None,
    },
    "sshleifer-tiny-ctrl": {
        "tie_word_embeddings": True,
        "intermediate_size": 2,
        "embd_pdrop": 0.1,
        "initializer_range": 0.02,
        "layer_norm_epsilon": 1e-06,
        "hidden_size": 16,
        "num_attention_heads": 2,
        "num_hidden_layers": 2,
        "max_position_embeddings": 50000,
        "resid_pdrop": 0.1,
        "vocab_size": 246534,
        "pad_token_id": None,
    },
}

CTRL_PRETRAINED_RESOURCE_FILES_MAP = {
    "model_state": {
        "ctrl": "https://bj.bcebos.com/paddlenlp/models/transformers/ctrl/model_state.pdparams",
        "sshleifer-tiny-ctrl": "https://bj.bcebos.com/paddlenlp/models/transformers/sshleifer-tiny-ctrl/model_state.pdparams",
    }
}


class CTRLConfig(PretrainedConfig):
    """
    This is the configuration class to store the configuration of a [`CTRLModel`]. It is used to
    instantiate a CTRL 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
    [ctrl] architecture from SalesForce.
    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 246534):
            Vocabulary size of the CTRL model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`CTRLModel`] or [`TFCTRLModel`].
        n_positions (`int`, *optional*, defaults to 256):
            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).
        n_embd (`int`, *optional*, defaults to 1280):
            Dimensionality of the embeddings and hidden states.
        dff (`int`, *optional*, defaults to 8192):
            Dimensionality of the inner dimension of the feed forward networks (FFN).
        n_layer (`int`, *optional*, defaults to 48):
            Number of hidden layers in the Transformer encoder.
        n_head (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer encoder.
        resid_pdrop (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        embd_pdrop (`int`, *optional*, defaults to 0.1):
            The dropout ratio for the embeddings.
        layer_norm_epsilon (`float`, *optional*, defaults to 1e-6):
            The epsilon to use in the layer normalization layers
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
    Examples:
    ```python
    >>> from transformers import CTRLConfig, CTRLModel
    >>> # Initializing a CTRL configuration
    >>> configuration = CTRLConfig()
    >>> # Initializing a model (with random weights) from the configuration
    >>> model = CTRLModel(configuration)
    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    pretrained_init_configuration = CTRL_PRETRAINED_INIT_CONFIGURATION
    model_type = "ctrl"
    attribute_map: Dict[str, str] = {
        "max_position_embeddings": "n_positions",
        "hidden_size": "n_embd",
        "num_attention_heads": "n_head",
        "num_hidden_layers": "n_layer",
        "intermediate_size": "dff",
        "num_classes": "num_labels",
    }

    def __init__(
        self,
        vocab_size=246534,
        n_positions=256,
        n_embd=1280,
        dff=8192,
        n_layer=48,
        n_head=16,
        resid_pdrop=0.1,
        embd_pdrop=0.1,
        layer_norm_epsilon=1e-6,
        initializer_range=0.02,
        use_cache=True,
        **kwargs,
    ):

        super().__init__(**kwargs)
        self.vocab_size = vocab_size
        self.n_positions = n_positions
        self.n_embd = n_embd
        self.n_layer = n_layer
        self.n_head = n_head
        self.dff = dff
        self.resid_pdrop = resid_pdrop
        self.embd_pdrop = embd_pdrop
        self.layer_norm_epsilon = layer_norm_epsilon
        self.initializer_range = initializer_range

        self.use_cache = use_cache