-
Notifications
You must be signed in to change notification settings - Fork 371
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #228 from gpengzhi/gpt2decoder
Add GPT2Decoder
- Loading branch information
Showing
5 changed files
with
573 additions
and
45 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,317 @@ | ||
| # 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 | ||
| # limitations under the License. | ||
| """ | ||
| GPT2 decoders. | ||
| """ | ||
|
|
||
| import tensorflow as tf | ||
|
|
||
| from texar.tf.modules.decoders.transformer_decoders import TransformerDecoder | ||
| from texar.tf.modules.embedders import PositionEmbedder, WordEmbedder | ||
| from texar.tf.modules.pretrained.gpt2 import PretrainedGPT2Mixin | ||
|
|
||
|
|
||
| __all__ = [ | ||
| "GPT2Decoder", | ||
| ] | ||
|
|
||
|
|
||
| class GPT2Decoder(PretrainedGPT2Mixin): | ||
| r"""Raw GPT2 Transformer for decoding sequences. Please see | ||
| :class:`~texar.tf.modules.PretrainedGPT2Mixin` for a brief description | ||
| of GPT2. | ||
| This module basically stacks | ||
| :class:`~texar.tf.modules.WordEmbedder`, | ||
| :class:`~texar.tf.modules.PositionEmbedder`, | ||
| :class:`~texar.tf.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.tf.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. | ||
| .. document private functions | ||
| .. automethod:: _build | ||
| """ | ||
| _IS_DECODE = True | ||
|
|
||
| def __init__(self, | ||
| pretrained_model_name=None, | ||
| cache_dir=None, | ||
| hparams=None): | ||
|
|
||
| super().__init__(hparams=hparams) | ||
|
|
||
| self.load_pretrained_config(pretrained_model_name, cache_dir) | ||
|
|
||
| with tf.variable_scope(self.variable_scope): | ||
|
|
||
| # 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) | ||
| self.decoder = TransformerDecoder( | ||
| vocab_size=self._hparams.vocab_size, | ||
| output_layer=tf.transpose(self.word_embedder.embedding, (1, 0)), | ||
| hparams=self._hparams.decoder) | ||
|
|
||
| def embed_tokens(self, tokens, positions): | ||
| word_embeds = self.word_embedder(tokens) | ||
| pos_embeds = self.position_embedder(positions) | ||
| return word_embeds + pos_embeds | ||
|
|
||
| @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 decoder 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, | ||
| "use_gpt_config": True, | ||
| "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 | ||
| } | ||
| }, | ||
| "poswise_feedforward": { | ||
| "layers": [ | ||
| { | ||
| "type": "Dense", | ||
| "kwargs": { | ||
| "activation": "gelu", | ||
| "name": "intermediate", | ||
| "units": 3072, | ||
| "use_bias": True | ||
| } | ||
| }, | ||
| { | ||
| "type": "Dense", | ||
| "kwargs": { | ||
| "activation": None, | ||
| "name": "output", | ||
| "units": 3072, | ||
| "use_bias": True | ||
| } | ||
| } | ||
| ], | ||
| "name": "ffn" | ||
| } | ||
| }, | ||
| "name": "gpt2_decoder", | ||
| } | ||
| 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. | ||
| `"name"`: str | ||
| Name of the module. | ||
| """ | ||
| return { | ||
| 'decoder': { | ||
| 'name': 'decoder', | ||
| 'dim': 768, | ||
| 'num_blocks': 12, | ||
| 'embedding_dropout': 0, | ||
| 'residual_dropout': 0, | ||
| 'multihead_attention': { | ||
| 'name': 'self', | ||
| '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 | ||
| } | ||
| }, | ||
| 'poswise_feedforward': { | ||
| 'layers': [ | ||
| { | ||
| 'type': 'Dense', | ||
| 'kwargs': { | ||
| 'activation': 'gelu', | ||
| 'name': 'intermediate', | ||
| 'units': 3072, | ||
| 'use_bias': True | ||
| } | ||
| }, | ||
| { | ||
| 'type': 'Dense', | ||
| 'kwargs': { | ||
| 'activation': None, | ||
| 'name': 'output', | ||
| 'units': 768, | ||
| 'use_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'], | ||
| } | ||
|
|
||
| def _build(self, | ||
| decoding_strategy='train_greedy', | ||
| inputs=None, | ||
| memory=None, | ||
| memory_sequence_length=None, | ||
| memory_attention_bias=None, | ||
| beam_width=None, | ||
| length_penalty=0., | ||
| start_tokens=None, | ||
| end_token=None, | ||
| context=None, | ||
| context_sequence_length=None, | ||
| softmax_temperature=None, | ||
| max_decoding_length=None, | ||
| impute_finished=False, | ||
| helper=None, | ||
| mode=None): | ||
| r"""Performs decoding. Has exact the same interfaces with | ||
| :meth:`texar.tf.modules.TransformerDecoder._build` except inputs | ||
| which is a tensor with shape `[batch_size, max_time]`. Please refer to | ||
| it for the detailed usage. | ||
| """ | ||
| if inputs is not None: | ||
| batch_size, max_time = inputs.shape.as_list() | ||
| time = tf.expand_dims(tf.range(max_time), 0) | ||
| time = tf.broadcast_to(time, [batch_size, max_time]) | ||
| inputs = self.embed_tokens(inputs, time) | ||
|
|
||
| outputs = self.decoder._build( | ||
| decoding_strategy=decoding_strategy, | ||
| inputs=inputs, | ||
| memory=memory, | ||
| memory_sequence_length=memory_sequence_length, | ||
| memory_attention_bias=memory_attention_bias, | ||
| beam_width=beam_width, | ||
| length_penalty=length_penalty, | ||
| start_tokens=start_tokens, | ||
| end_token=end_token, | ||
| context=context, | ||
| context_sequence_length=context_sequence_length, | ||
| softmax_temperature=softmax_temperature, | ||
| max_decoding_length=max_decoding_length, | ||
| impute_finished=impute_finished, | ||
| embedding=lambda a, b: self.embed_tokens(a, b), | ||
| helper=helper, | ||
| mode=mode) | ||
|
|
||
| if not self._built: | ||
| self._add_internal_trainable_variables() | ||
| self._built = True | ||
|
|
||
| self.init_pretrained_weights(self.variable_scope.name, | ||
| load_output_layer=True) | ||
|
|
||
| return outputs |
Oops, something went wrong.