wehub-resource-sync
198 lines
8.4 KiB
Python
198 lines
8.4 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Bart model configuration"""
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from __future__ import annotations
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from typing import Dict
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from paddlenlp.transformers.configuration_utils import PretrainedConfig
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from ...utils.log import logger
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__all__ = ["BART_PRETRAINED_INIT_CONFIGURATION", "BartConfig", "BART_PRETRAINED_RESOURCE_FILES_MAP"]
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BART_PRETRAINED_INIT_CONFIGURATION = {
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"bart-base": {
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"vocab_size": 50265,
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"bos_token_id": 0,
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"pad_token_id": 1,
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"eos_token_id": 2,
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"forced_eos_token_id": 2,
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"decoder_start_token_id": 2,
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"d_model": 768,
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"num_encoder_layers": 6,
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"num_decoder_layers": 6,
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"encoder_attention_heads": 12,
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"decoder_attention_heads": 12,
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"encoder_ffn_dim": 3072,
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"decoder_ffn_dim": 3072,
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"dropout": 0.1,
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"activation_function": "gelu",
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"attention_dropout": 0.1,
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"activation_dropout": 0.1,
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"max_position_embeddings": 1024,
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"init_std": 0.02,
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"scale_embedding": False,
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},
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"bart-large": {
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"vocab_size": 50265,
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"bos_token_id": 0,
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"pad_token_id": 1,
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"eos_token_id": 2,
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"forced_eos_token_id": 2,
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"decoder_start_token_id": 2,
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"d_model": 1024,
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"num_encoder_layers": 12,
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"num_decoder_layers": 12,
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"encoder_attention_heads": 16,
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"decoder_attention_heads": 16,
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"encoder_ffn_dim": 4096,
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"decoder_ffn_dim": 4096,
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"dropout": 0.1,
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"activation_function": "gelu",
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"attention_dropout": 0.1,
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"activation_dropout": 0.1,
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"max_position_embeddings": 1024,
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"init_std": 0.02,
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"scale_embedding": False,
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},
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}
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BART_PRETRAINED_RESOURCE_FILES_MAP = {
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"model_state": {
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"bart-base": "https://bj.bcebos.com/paddlenlp/models/transformers/bart/bart-base.pdparams",
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"bart-large": "https://bj.bcebos.com/paddlenlp/models/transformers/bart/bart-large.pdparams",
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}
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}
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class BartConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`BartModel`]. It is used to instantiate a BART
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the BART bart-base architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, optional):
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Vocabulary size of the BART model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`BartModel`] or [`TFBartModel`]. Default to 50265.
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d_model (`int`, optional):
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Dimensionality of the layers and the pooler layer. Default to 1024
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encoder_layers (`int`, optional):
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Number of encoder layers. Default to 6.
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decoder_layers (`int`, optional):
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Number of decoder layers. Default to 6.
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encoder_attention_heads (`int`, optional):
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Number of attention heads for each attention layer in the Transformer encoder. Default to 12.
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decoder_attention_heads (`int`, optional):
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Number of attention heads for each attention layer in the Transformer decoder. Default to 12.
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decoder_ffn_dim (`int`, optional):
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Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. Default to 3072.
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encoder_ffn_dim (`int`, optional):
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Dimensionality of the "intermediate" (often named feed-forward) layer in decoder. Default to 3072.
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activation_function (`str` or `function`, optional):
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The non-linear activation function in the feed-forward layer.
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``"gelu"``, ``"relu"`` and any other paddle supported activation functions are supported.
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Default to `"gelu"`.
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dropout (`float`, optional):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. Default to 0.1.
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attention_dropout (`float`, optional):
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The dropout ratio for the attention probabilities. Default to 0.1.
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activation_dropout (`float`, optional):
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The dropout ratio for activations inside the fully connected layer. Default to 0.1.
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max_position_embeddings (`int`, optional):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048). Default to 1024.
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init_std (`float`, optional):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices. Default to 0.02.
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num_labels (`int`, optional):
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The number of labels to use in [`BartForSequenceClassification`]. Default to 3.
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forced_eos_token_id (`int`, optional):
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The id of the token to force as the last generated token when `max_length` is reached. Usually set to
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`eos_token_id`. Default to 2.
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scale_embedding (`bool`, optional):
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Scale embeddings by diving by sqrt(d_model). Default to `False`.
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"""
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model_type = "bart"
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keys_to_ignore_at_inference = ["past_key_values"]
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attribute_map: Dict[str, str] = {
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"num_encoder_layers": "encoder_layers",
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"num_decoder_layers": "decoder_layers",
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"num_classes": "num_labels",
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}
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pretrained_init_configuration = BART_PRETRAINED_INIT_CONFIGURATION
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def __init__(
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self,
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vocab_size: int = 50265,
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max_position_embeddings: int = 1024,
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encoder_layers: int = 6,
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encoder_ffn_dim: int = 3072,
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encoder_attention_heads: int = 12,
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decoder_layers: int = 6,
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decoder_ffn_dim: int = 3072,
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decoder_attention_heads: int = 12,
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activation_function: str = "gelu",
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d_model: int = 768,
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dropout: float = 0.1,
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attention_dropout: float = 0.1,
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activation_dropout: float = 0.1,
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init_std: float = 0.02,
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pad_token_id: int = 1,
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bos_token_id: int = 0,
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eos_token_id: int = 2,
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is_encoder_decoder: bool = True,
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decoder_start_token_id: int = 2,
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forced_eos_token_id: int = 2,
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scale_embedding: bool = False,
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**kwargs
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):
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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is_encoder_decoder=is_encoder_decoder,
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decoder_start_token_id=decoder_start_token_id,
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forced_eos_token_id=forced_eos_token_id,
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**kwargs,
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)
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.d_model = d_model
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self.encoder_ffn_dim = encoder_ffn_dim
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self.encoder_layers = encoder_layers
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self.encoder_attention_heads = encoder_attention_heads
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self.decoder_ffn_dim = decoder_ffn_dim
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self.decoder_layers = decoder_layers
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self.decoder_attention_heads = decoder_attention_heads
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self.dropout = dropout
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self.attention_dropout = attention_dropout
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self.activation_dropout = activation_dropout
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self.activation_function = activation_function
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self.init_std = init_std
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self.num_hidden_layers = encoder_layers
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self.scale_embedding = scale_embedding
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# ensure backward compatibility for BART CNN models
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if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False):
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self.forced_bos_token_id = self.bos_token_id
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logger.warning(
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f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
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"The config can simply be saved and uploaded again to be fixed."
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)
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