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configuration.py
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# Copyright (c) 2023 PaddlePaddle Authors. 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,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
from ..configuration_utils import PretrainedConfig
__all__ = ["GAUAlPHA_PRETRAINED_INIT_CONFIGURATION", "GAUAlphaConfig", "GAUAlPHA_PRETRAINED_RESOURCE_FILES_MAP"]
GAUAlPHA_PRETRAINED_INIT_CONFIGURATION = {
"chinese_GAU-alpha-char_L-24_H-768": {
"vocab_size": 12000,
"hidden_size": 768,
"intermediate_size": 1536,
"num_hidden_layers": 24,
"max_position_embeddings": 512,
"type_vocab_size": 2,
"attention_key_size": 128,
"norm_eps": 1e-12,
"pad_token_id": 0,
"hidden_dropout_prob": 0.1,
"attention_probs_dropout_prob": 0.1,
"hidden_act": "swish",
"use_bias": False,
"normalization": "softmax_plus",
"attention_scale": True,
},
}
GAUAlPHA_PRETRAINED_RESOURCE_FILES_MAP = {
"model_state": {
"chinese_GAU-alpha-char_L-24_H-768": "https://bj.bcebos.com/paddlenlp/models/transformers/gau_alpha/chinese_GAU-alpha-char_L-24_H-768/model_state.pdparams",
}
}
class GAUAlphaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GAUAlphaModel`]. It is used to
instantiate a GAUAlpha 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 GAUAlpha
chinese_GAU-alpha-char_L-24_H-768 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 30522):
Vocabulary size of the GAUAlpha model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`GAUAlphaModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`int`, *optional*, defaults to 512):
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).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`GAUAlphaModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
[Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
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`.
classifier_dropout (`float`, *optional*):
The dropout ratio for the classification head.
Examples:
```python
>>> from paddlenlp.transformers import GAUAlphaModel, GAUAlphaConfig
>>> # Initializing a GAUAlpha chinese_GAU-alpha-char_L-24_H-768style configuration
>>> configuration = GAUAlphaConfig()
>>> # Initializing a model from the style configuration
>>> model = GAUAlphaModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "gau_alpha"
pretrained_init_configuration = GAUAlPHA_PRETRAINED_INIT_CONFIGURATION
def __init__(
self,
vocab_size: int = 30522,
hidden_size: int = 768,
num_hidden_layers: int = 12,
num_attention_heads: int = 12,
task_id=0,
intermediate_size: int = 3072,
hidden_act: str = "gelu",
hidden_dropout_prob: float = 0.1,
attention_probs_dropout_prob: float = 0.1,
max_position_embeddings: int = 512,
task_type_vocab_size: int = 3,
type_vocab_size: int = 16,
attention_key_size=128,
initializer_range: float = 0.02,
pad_token_id: int = 0,
pool_act: str = "tanh",
activation: str = "swish",
normalization: str = "softmax_plus",
fuse: bool = False,
layer_norm_eps=1e-12,
norm_eps=1e-12,
use_cache=False,
use_task_id=True,
use_bias=False,
attention_scale=True,
**kwargs
):
super().__init__(pad_token_id=pad_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.task_id = task_id
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.task_type_vocab_size = task_type_vocab_size
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.pool_act = pool_act
self.fuse = fuse
self.layer_norm_eps = layer_norm_eps
self.norm_eps = norm_eps
self.use_cache = use_cache
self.use_task_id = use_task_id
self.use_bias = use_bias
self.activation = activation
self.attention_key_size = attention_key_size
self.normalization = normalization
self.attention_scale = attention_scale