241 lines
11 KiB
Python
241 lines
11 KiB
Python
# coding=utf-8
|
|
# Copyright 2019-present, Facebook, Inc and the HuggingFace Inc. team.
|
|
#
|
|
# 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.
|
|
""" XLM configuration"""
|
|
from collections import OrderedDict
|
|
from typing import Mapping
|
|
|
|
from ...configuration_utils import PretrainedConfig
|
|
from ...onnx import OnnxConfig
|
|
from ...utils import logging
|
|
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
|
|
from ..deprecated._archive_maps import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
|
|
|
|
|
|
class XLMConfig(PretrainedConfig):
|
|
"""
|
|
This is the configuration class to store the configuration of a [`XLMModel`] or a [`TFXLMModel`]. It is used to
|
|
instantiate a XLM 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
|
|
[FacebookAI/xlm-mlm-en-2048](https://huggingface.co/FacebookAI/xlm-mlm-en-2048) 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 30145):
|
|
Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
|
|
`inputs_ids` passed when calling [`XLMModel`] or [`TFXLMModel`].
|
|
emb_dim (`int`, *optional*, defaults to 2048):
|
|
Dimensionality of the encoder layers and the pooler layer.
|
|
n_layer (`int`, *optional*, defaults to 12):
|
|
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.
|
|
dropout (`float`, *optional*, defaults to 0.1):
|
|
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
|
|
attention_dropout (`float`, *optional*, defaults to 0.1):
|
|
The dropout probability for the attention mechanism
|
|
gelu_activation (`bool`, *optional*, defaults to `True`):
|
|
Whether or not to use *gelu* for the activations instead of *relu*.
|
|
sinusoidal_embeddings (`bool`, *optional*, defaults to `False`):
|
|
Whether or not to use sinusoidal positional embeddings instead of absolute positional embeddings.
|
|
causal (`bool`, *optional*, defaults to `False`):
|
|
Whether or not the model should behave in a causal manner. Causal models use a triangular attention mask in
|
|
order to only attend to the left-side context instead if a bidirectional context.
|
|
asm (`bool`, *optional*, defaults to `False`):
|
|
Whether or not to use an adaptive log softmax projection layer instead of a linear layer for the prediction
|
|
layer.
|
|
n_langs (`int`, *optional*, defaults to 1):
|
|
The number of languages the model handles. Set to 1 for monolingual models.
|
|
use_lang_emb (`bool`, *optional*, defaults to `True`)
|
|
Whether to use language embeddings. Some models use additional language embeddings, see [the multilingual
|
|
models page](http://huggingface.co/transformers/multilingual.html#xlm-language-embeddings) for information
|
|
on how to use them.
|
|
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).
|
|
embed_init_std (`float`, *optional*, defaults to 2048^-0.5):
|
|
The standard deviation of the truncated_normal_initializer for initializing the embedding matrices.
|
|
init_std (`int`, *optional*, defaults to 50257):
|
|
The standard deviation of the truncated_normal_initializer for initializing all weight matrices except the
|
|
embedding matrices.
|
|
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
|
|
The epsilon used by the layer normalization layers.
|
|
bos_index (`int`, *optional*, defaults to 0):
|
|
The index of the beginning of sentence token in the vocabulary.
|
|
eos_index (`int`, *optional*, defaults to 1):
|
|
The index of the end of sentence token in the vocabulary.
|
|
pad_index (`int`, *optional*, defaults to 2):
|
|
The index of the padding token in the vocabulary.
|
|
unk_index (`int`, *optional*, defaults to 3):
|
|
The index of the unknown token in the vocabulary.
|
|
mask_index (`int`, *optional*, defaults to 5):
|
|
The index of the masking token in the vocabulary.
|
|
is_encoder(`bool`, *optional*, defaults to `True`):
|
|
Whether or not the initialized model should be a transformer encoder or decoder as seen in Vaswani et al.
|
|
summary_type (`string`, *optional*, defaults to "first"):
|
|
Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
|
|
|
|
Has to be one of the following options:
|
|
|
|
- `"last"`: Take the last token hidden state (like XLNet).
|
|
- `"first"`: Take the first token hidden state (like BERT).
|
|
- `"mean"`: Take the mean of all tokens hidden states.
|
|
- `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2).
|
|
- `"attn"`: Not implemented now, use multi-head attention.
|
|
summary_use_proj (`bool`, *optional*, defaults to `True`):
|
|
Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
|
|
|
|
Whether or not to add a projection after the vector extraction.
|
|
summary_activation (`str`, *optional*):
|
|
Argument used when doing sequence summary. Used in the sequence classification and multiple choice models.
|
|
|
|
Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation.
|
|
summary_proj_to_labels (`bool`, *optional*, defaults to `True`):
|
|
Used in the sequence classification and multiple choice models.
|
|
|
|
Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes.
|
|
summary_first_dropout (`float`, *optional*, defaults to 0.1):
|
|
Used in the sequence classification and multiple choice models.
|
|
|
|
The dropout ratio to be used after the projection and activation.
|
|
start_n_top (`int`, *optional*, defaults to 5):
|
|
Used in the SQuAD evaluation script.
|
|
end_n_top (`int`, *optional*, defaults to 5):
|
|
Used in the SQuAD evaluation script.
|
|
mask_token_id (`int`, *optional*, defaults to 0):
|
|
Model agnostic parameter to identify masked tokens when generating text in an MLM context.
|
|
lang_id (`int`, *optional*, defaults to 1):
|
|
The ID of the language used by the model. This parameter is used when generating text in a given language.
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import XLMConfig, XLMModel
|
|
|
|
>>> # Initializing a XLM configuration
|
|
>>> configuration = XLMConfig()
|
|
|
|
>>> # Initializing a model (with random weights) from the configuration
|
|
>>> model = XLMModel(configuration)
|
|
|
|
>>> # Accessing the model configuration
|
|
>>> configuration = model.config
|
|
```"""
|
|
|
|
model_type = "xlm"
|
|
attribute_map = {
|
|
"hidden_size": "emb_dim",
|
|
"num_attention_heads": "n_heads",
|
|
"num_hidden_layers": "n_layers",
|
|
"n_words": "vocab_size", # For backward compatibility
|
|
}
|
|
|
|
def __init__(
|
|
self,
|
|
vocab_size=30145,
|
|
emb_dim=2048,
|
|
n_layers=12,
|
|
n_heads=16,
|
|
dropout=0.1,
|
|
attention_dropout=0.1,
|
|
gelu_activation=True,
|
|
sinusoidal_embeddings=False,
|
|
causal=False,
|
|
asm=False,
|
|
n_langs=1,
|
|
use_lang_emb=True,
|
|
max_position_embeddings=512,
|
|
embed_init_std=2048**-0.5,
|
|
layer_norm_eps=1e-12,
|
|
init_std=0.02,
|
|
bos_index=0,
|
|
eos_index=1,
|
|
pad_index=2,
|
|
unk_index=3,
|
|
mask_index=5,
|
|
is_encoder=True,
|
|
summary_type="first",
|
|
summary_use_proj=True,
|
|
summary_activation=None,
|
|
summary_proj_to_labels=True,
|
|
summary_first_dropout=0.1,
|
|
start_n_top=5,
|
|
end_n_top=5,
|
|
mask_token_id=0,
|
|
lang_id=0,
|
|
pad_token_id=2,
|
|
bos_token_id=0,
|
|
**kwargs,
|
|
):
|
|
"""Constructs XLMConfig."""
|
|
self.vocab_size = vocab_size
|
|
self.emb_dim = emb_dim
|
|
self.n_layers = n_layers
|
|
self.n_heads = n_heads
|
|
self.dropout = dropout
|
|
self.attention_dropout = attention_dropout
|
|
self.gelu_activation = gelu_activation
|
|
self.sinusoidal_embeddings = sinusoidal_embeddings
|
|
self.causal = causal
|
|
self.asm = asm
|
|
self.n_langs = n_langs
|
|
self.use_lang_emb = use_lang_emb
|
|
self.layer_norm_eps = layer_norm_eps
|
|
self.bos_index = bos_index
|
|
self.eos_index = eos_index
|
|
self.pad_index = pad_index
|
|
self.unk_index = unk_index
|
|
self.mask_index = mask_index
|
|
self.is_encoder = is_encoder
|
|
self.max_position_embeddings = max_position_embeddings
|
|
self.embed_init_std = embed_init_std
|
|
self.init_std = init_std
|
|
self.summary_type = summary_type
|
|
self.summary_use_proj = summary_use_proj
|
|
self.summary_activation = summary_activation
|
|
self.summary_proj_to_labels = summary_proj_to_labels
|
|
self.summary_first_dropout = summary_first_dropout
|
|
self.start_n_top = start_n_top
|
|
self.end_n_top = end_n_top
|
|
self.mask_token_id = mask_token_id
|
|
self.lang_id = lang_id
|
|
|
|
if "n_words" in kwargs:
|
|
self.n_words = kwargs["n_words"]
|
|
|
|
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, **kwargs)
|
|
|
|
|
|
# Copied from transformers.models.bert.configuration_bert.BertOnnxConfig
|
|
class XLMOnnxConfig(OnnxConfig):
|
|
@property
|
|
def inputs(self) -> Mapping[str, Mapping[int, str]]:
|
|
if self.task == "multiple-choice":
|
|
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
|
|
else:
|
|
dynamic_axis = {0: "batch", 1: "sequence"}
|
|
return OrderedDict(
|
|
[
|
|
("input_ids", dynamic_axis),
|
|
("attention_mask", dynamic_axis),
|
|
("token_type_ids", dynamic_axis),
|
|
]
|
|
)
|