Source code for texar.torch.modules.decoders.gpt2_decoder

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
GPT2 decoder.

from typing import Dict, Optional, Tuple, Union

import torch

from texar.torch.modules.decoders.decoder_helpers import Helper
from texar.torch.modules.decoders.transformer_decoders import \
    TransformerDecoder, TransformerDecoderOutput
from texar.torch.modules.embedders import PositionEmbedder, WordEmbedder
from texar.torch.modules.pretrained.gpt2 import PretrainedGPT2Mixin

__all__ = [

[docs]class GPT2Decoder(PretrainedGPT2Mixin): r"""Raw GPT2 Transformer for decoding 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.TransformerDecoder`. This module supports the architecture first proposed in `(Radford et al.)` GPT2. 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. """ _IS_DECODE = True 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 decoder (a TransformerDecoder) def func(tokens, positions): word_embeds = self.word_embedder(tokens) pos_embeds = self.position_embedder(positions) return word_embeds + pos_embeds class GPT2TransformerDecoder(TransformerDecoder): def embed_tokens(self, tokens: torch.LongTensor, positions: torch.LongTensor) -> torch.Tensor: return func(tokens, positions) self.decoder = GPT2TransformerDecoder( vocab_size=self._hparams.vocab_size, output_layer=self.word_embedder.embedding, hparams=self._hparams.decoder) self.init_pretrained_weights()
[docs] @staticmethod def default_hparams(): r"""Returns a dictionary of hyperparameters with default values. * The decoder 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 { "name": "gpt2_decoder", "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" }, # hparams for TransformerDecoder "decoder": { "dim": 768, "num_blocks": 12, "embedding_dropout": 0, "residual_dropout": 0, "multihead_attention": { "use_bias": True, "num_units": 768, "num_heads": 12, "dropout_rate": 0.0, "output_dim": 768 }, "initializer": { "type": "variance_scaling_initializer", "kwargs": { "factor": 1.0, "mode": "FAN_AVG", "uniform": True } }, "eps": 1e-5, "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" } }, } 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. `"eps"`: float Epsilon values for layer norm layers. `"position_size"`: int The maximum sequence length that this model might ever be used with. `"name"`: str Name of the module. """ return { 'decoder': { 'dim': 768, 'num_blocks': 12, 'embedding_dropout': 0, 'residual_dropout': 0, 'multihead_attention': { 'use_bias': True, 'num_units': 768, 'num_heads': 12, "dropout_rate": 0.0, 'output_dim': 768 }, 'initializer': { 'type': 'variance_scaling_initializer', 'kwargs': { 'factor': 1.0, 'mode': 'FAN_AVG', 'uniform': True } }, 'eps': 1e-5, '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' }, 'name': 'gpt2_decoder', '@no_typecheck': ['pretrained_model_name'], }
[docs] def forward(self, # type: ignore inputs: Optional[torch.Tensor] = None, sequence_length: Optional[torch.LongTensor] = None, memory: Optional[torch.Tensor] = None, memory_sequence_length: Optional[torch.LongTensor] = None, memory_attention_bias: Optional[torch.Tensor] = None, context: Optional[torch.Tensor] = None, context_sequence_length: Optional[torch.LongTensor] = None, helper: Optional[Helper] = None, decoding_strategy: str = 'train_greedy', max_decoding_length: Optional[int] = None, impute_finished: bool = False, infer_mode: Optional[bool] = None, beam_width: Optional[int] = None, length_penalty: float = 0., **kwargs) \ -> Union[ TransformerDecoderOutput, Tuple[TransformerDecoderOutput, torch.LongTensor], Dict[str, torch.Tensor]]: r"""Performs decoding. Has exact the same interfaces with :meth:`texar.torch.modules.TransformerDecoder.forward`. Please refer to it for the detailed usage. """ return self.decoder(inputs=inputs, sequence_length=sequence_length, memory=memory, memory_sequence_length=memory_sequence_length, memory_attention_bias=memory_attention_bias, context=context, context_sequence_length=context_sequence_length, helper=helper, decoding_strategy=decoding_strategy, max_decoding_length=max_decoding_length, impute_finished=impute_finished, infer_mode=infer_mode, beam_width=beam_width, length_penalty=length_penalty, **kwargs)