# Copyright 2019 The Texar 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
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"""
GPT2 encoders.
"""
from typing import Optional, Union
import torch
from texar.torch.modules.embedders.embedders import WordEmbedder
from texar.torch.modules.embedders.position_embedders import PositionEmbedder
from texar.torch.modules.encoders.encoder_base import EncoderBase
from texar.torch.modules.encoders.transformer_encoder import TransformerEncoder
from texar.torch.modules.pretrained.gpt2 import PretrainedGPT2Mixin
__all__ = [
"GPT2Encoder",
]
[docs]class GPT2Encoder(EncoderBase, PretrainedGPT2Mixin):
r"""Raw GPT2 Transformer for encoding sequences. Please see
:class:`~texar.torch.modules.PretrainedGPT2Mixin` for a brief description
of GPT2.
This module basically stacks
:class:`~texar.torch.modules.WordEmbedder`,
:class:`~texar.torch.modules.PositionEmbedder`,
:class:`~texar.torch.modules.TransformerEncoder`.
Args:
pretrained_model_name (optional): a `str`, the name
of pre-trained model (e.g., ``gpt2-small``). Please refer to
:class:`~texar.torch.modules.PretrainedGPT2Mixin` for
all supported models.
If `None`, the model name in :attr:`hparams` is used.
cache_dir (optional): the path to a folder in which the
pre-trained models will be cached. If `None` (default),
a default directory (``texar_data`` folder under user's home
directory) will be used.
hparams (dict or HParams, optional): Hyperparameters. Missing
hyperparameter will be set to default values. See
:meth:`default_hparams` for the hyperparameter structure
and default values.
"""
def __init__(self,
pretrained_model_name: Optional[str] = None,
cache_dir: Optional[str] = None,
hparams=None):
super().__init__(hparams=hparams)
self.load_pretrained_config(pretrained_model_name, cache_dir)
# Word embedding
self.word_embedder = WordEmbedder(
vocab_size=self._hparams.vocab_size,
hparams=self._hparams.embed)
# Position embedding
self.position_embedder = PositionEmbedder(
position_size=self._hparams.position_size,
hparams=self._hparams.position_embed)
# The GPT2 encoder (a TransformerEncoder)
self.encoder = TransformerEncoder(hparams=self._hparams.encoder)
self.init_pretrained_weights(load_output_layer=False)
[docs] @staticmethod
def default_hparams():
r"""Returns a dictionary of hyperparameters with default values.
* The encoder arch is determined by the constructor argument
:attr:`pretrained_model_name` if it's specified. In this case,
`hparams` are ignored.
* Otherwise, the encoder arch is determined by
`hparams['pretrained_model_name']` if it's specified. All other
configurations in `hparams` are ignored.
* If the above two are `None`, the encoder arch is defined by the
configurations in `hparams` and weights are randomly initialized.
.. code-block:: python
{
"pretrained_model_name": "gpt2-small",
"vocab_size": 50257,
"context_size": 1024,
"embedding_size": 768,
"embed": {
"dim": 768,
"name": "word_embeddings"
},
"position_size": 1024,
"position_embed": {
"dim": 768,
"name": "position_embeddings"
},
"encoder": {
"dim": 768,
"num_blocks": 12,
"use_bert_config": False,
"embedding_dropout": 0,
"residual_dropout": 0,
"multihead_attention": {
"use_bias": True,
"num_units": 768,
"num_heads": 12,
"output_dim": 768
},
"eps": 1e-6,
"initializer": {
"type": "variance_scaling_initializer",
"kwargs": {
"factor": 1.0,
"mode": "FAN_AVG",
"uniform": True
}
},
"poswise_feedforward": {
"layers": [
{
"type": "Linear",
"kwargs": {
"in_features": 768,
"out_features": 3072,
"bias": True
}
},
{
"type": "GPTGELU",
"kwargs": {}
},
{
"type": "Linear",
"kwargs": {
"in_features": 3072,
"out_features": 768,
"bias": True
}
}
],
"name": "ffn"
}
},
"initializer": None,
"name": "gpt2_encoder",
}
Here:
The default parameters are values for 124M GPT2 model.
`"pretrained_model_name"`: str or None
The name of the pre-trained GPT2 model. If None, the model
will be randomly initialized.
`"embed"`: dict
Hyperparameters for word embedding layer.
`"vocab_size"`: int
The vocabulary size of `inputs` in `GPT2Model`.
`"position_embed"`: dict
Hyperparameters for position embedding layer.
`"position_size"`: int
The maximum sequence length that this model might ever be used with.
`"decoder"`: dict
Hyperparameters for the TransformerDecoder.
See :func:`~texar.torch.modules.TransformerDecoder.default_hparams`
for details.
`"eps"`: float
Epsilon values for layer norm layers.
`"initializer"`: dict, optional
Hyperparameters of the default initializer that initializes
variables created in this module.
See :func:`~texar.torch.core.get_initializer` for details.
`"name"`: str
Name of the module.
"""
return {
'encoder': {
'dim': 768,
'num_blocks': 12,
'use_bert_config': False,
'embedding_dropout': 0,
'residual_dropout': 0,
'multihead_attention': {
'use_bias': True,
'num_units': 768,
'num_heads': 12,
'output_dim': 768
},
'eps': 1e-6,
'initializer': {
'type': 'variance_scaling_initializer',
'kwargs': {
'factor': 1.0,
'mode': 'FAN_AVG',
'uniform': True
}
},
'poswise_feedforward': {
'layers': [
{
'type': 'Linear',
'kwargs': {
'in_features': 768,
'out_features': 3072,
'bias': True
}
},
{
'type': 'GPTGELU',
'kwargs': {}
},
{
'type': 'Linear',
'kwargs': {
'in_features': 3072,
'out_features': 768,
'bias': True
}
}
],
'name': 'ffn'
},
},
'pretrained_model_name': 'gpt2-small',
'vocab_size': 50257,
'context_size': 1024,
'embedding_size': 768,
'embed': {
'dim': 768,
'name': 'word_embeddings'
},
'position_size': 1024,
'position_embed': {
'dim': 768,
'name': 'position_embeddings'
},
'initializer': None,
'name': 'gpt2_encoder',
'@no_typecheck': ['pretrained_model_name'],
}
[docs] def forward(self, # type: ignore
inputs: Union[torch.Tensor, torch.LongTensor],
sequence_length: Optional[torch.LongTensor] = None):
r"""Encodes the inputs.
Args:
inputs: Either a **2D Tensor** of shape `[batch_size, max_time]`,
containing the ids of tokens in input sequences, or
a **3D Tensor** of shape `[batch_size, max_time, vocab_size]`,
containing soft token ids (i.e., weights or probabilities)
used to mix the embedding vectors.
sequence_length (optional): A 1D Tensor of shape `[batch_size]`.
Input tokens beyond respective sequence lengths are masked
out automatically.
Returns:
outputs: A Tensor of shape
`[batch_size, max_time, dim]` containing the encoded vectors.
"""
if inputs.dim() == 2:
word_embeds = self.word_embedder(ids=inputs)
elif inputs.dim() == 3:
word_embeds = self.word_embedder(soft_ids=inputs)
else:
raise ValueError("'inputs' should be a 2D or 3D tensor.")
batch_size = inputs.size(0)
pos_length = inputs.new_full(
(batch_size,), inputs.size(1), dtype=torch.long)
pos_embeds = self.position_embedder(sequence_length=pos_length)
inputs_embeds = word_embeds + pos_embeds
if sequence_length is None:
sequence_length = inputs.new_full(
(batch_size,), inputs.size(1), dtype=torch.long)
output = self.encoder(
inputs=inputs_embeds, sequence_length=sequence_length)
return output
@property
def output_size(self):
r"""The feature size of :meth:`forward` output.
"""
return self._hparams.encoder.dim