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edgelm/examples/pointer_generator/pointer_generator_src/__init__.py
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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import transformer_pg # noqa
edgelm/examples/pointer_generator/pointer_generator_src/transformer_pg.py
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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
from typing import Any, Dict, Optional, List, Tuple
import torch
import torch.nn as nn
from fairseq import utils
from fairseq.models import register_model, register_model_architecture
from fairseq.models.transformer import (
DEFAULT_MAX_SOURCE_POSITIONS,
DEFAULT_MAX_TARGET_POSITIONS,
TransformerDecoder,
TransformerEncoder,
TransformerModel,
base_architecture,
)
from torch import Tensor
logger = logging.getLogger(__name__)
@register_model("transformer_pointer_generator")
class TransformerPointerGeneratorModel(TransformerModel):
"""
Transformer model from `"Attention Is All You Need" (Vaswani et al, 2017)
<https://arxiv.org/abs/1706.03762>`_, augmented with a pointer-generator
network from `"Get To The Point: Summarization with Pointer-Generator
Networks" (See et al, 2017) <https://arxiv.org/abs/1704.04368>`_.
Args:
encoder (TransformerPointerGeneratorEncoder): the encoder
decoder (TransformerPointerGeneratorDecoder): the decoder
The Transformer pointer-generator model provides the following named
architectures and command-line arguments:
.. argparse::
:ref: fairseq.models.transformer_pointer_generator_parser
:prog:
"""
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
# fmt: off
TransformerModel.add_args(parser)
parser.add_argument('--alignment-heads', type=int, metavar='N',
help='number of attention heads to be used for '
'pointing')
parser.add_argument('--alignment-layer', type=int, metavar='I',
help='layer number to be used for pointing (0 '
'corresponding to the bottommost layer)')
parser.add_argument('--source-position-markers', type=int, metavar='N',
help='dictionary includes N additional items that '
'represent an OOV token at a particular input '
'position')
parser.add_argument('--force-generation', type=float, metavar='P',
default=None,
help='set the vocabulary distribution weight to P, '
'instead of predicting it from the input (1.0 '
'corresponding to generation, 0.0 to pointing)')
# fmt: on
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure all arguments are present in older models
base_architecture(args)
if args.encoder_layers_to_keep:
args.encoder_layers = len(args.encoder_layers_to_keep.split(","))
if args.decoder_layers_to_keep:
args.decoder_layers = len(args.decoder_layers_to_keep.split(","))
if getattr(args, "max_source_positions", None) is None:
args.max_source_positions = DEFAULT_MAX_SOURCE_POSITIONS
if getattr(args, "max_target_positions", None) is None:
args.max_target_positions = DEFAULT_MAX_TARGET_POSITIONS
if getattr(args, "source_position_markers", None) is None:
args.source_position_markers = args.max_source_positions
src_dict, tgt_dict = task.source_dictionary, task.target_dictionary
if src_dict != tgt_dict:
raise ValueError("Pointer-generator requires a joined dictionary")
def build_embedding(dictionary, embed_dim, path=None):
# The dictionary may include additional items that can be used in
# place of the normal OOV token and that all map to the same
# embedding. Using a different token for each input position allows
# one to restore the word identities from the original source text.
num_embeddings = len(dictionary) - args.source_position_markers
padding_idx = dictionary.pad()
unk_idx = dictionary.unk()
logger.info(
"dictionary indices from {0} to {1} will be mapped to {2}".format(
num_embeddings, len(dictionary) - 1, unk_idx
)
)
emb = Embedding(num_embeddings, embed_dim, padding_idx, unk_idx)
# if provided, load from preloaded dictionaries
if path:
embed_dict = utils.parse_embedding(path)
utils.load_embedding(embed_dict, dictionary, emb)
return emb
if args.share_all_embeddings:
if args.encoder_embed_dim != args.decoder_embed_dim:
raise ValueError(
"--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim"
)
if args.decoder_embed_path and (
args.decoder_embed_path != args.encoder_embed_path
):
raise ValueError(
"--share-all-embeddings not compatible with --decoder-embed-path"
)
encoder_embed_tokens = build_embedding(
src_dict, args.encoder_embed_dim, args.encoder_embed_path
)
decoder_embed_tokens = encoder_embed_tokens
args.share_decoder_input_output_embed = True
else:
encoder_embed_tokens = build_embedding(
src_dict, args.encoder_embed_dim, args.encoder_embed_path
)
decoder_embed_tokens = build_embedding(
tgt_dict, args.decoder_embed_dim, args.decoder_embed_path
)
encoder = cls.build_encoder(args, src_dict, encoder_embed_tokens)
decoder = cls.build_decoder(args, tgt_dict, decoder_embed_tokens)
return cls(args, encoder, decoder)
@classmethod
def build_encoder(cls, args, src_dict, embed_tokens):
return TransformerPointerGeneratorEncoder(args, src_dict, embed_tokens)
@classmethod
def build_decoder(cls, args, tgt_dict, embed_tokens):
return TransformerPointerGeneratorDecoder(args, tgt_dict, embed_tokens)
class TransformerPointerGeneratorEncoder(TransformerEncoder):
"""
Transformer encoder consisting of *args.encoder_layers* layers. Each layer
is a :class:`TransformerEncoderLayer`. The pointer-generator variant adds
the source tokens to the encoder output as these are otherwise not passed
to the decoder.
"""
def forward(
self,
src_tokens,
src_lengths: Optional[Tensor] = None,
return_all_hiddens: bool = False,
token_embeddings: Optional[Tensor] = None
):
"""
Runs the `forward()` method of the parent Transformer class. Then adds
the source tokens into the encoder output tuple.
While it might be more elegant that the model would pass the source
tokens to the `forward()` method of the decoder too, this would require
changes to `SequenceGenerator`.
Args:
src_tokens (torch.LongTensor): tokens in the source language of
shape `(batch, src_len)`
src_lengths (torch.LongTensor): lengths of each source sentence of
shape `(batch)`
return_all_hiddens (bool, optional): also return all of the
intermediate hidden states (default: False).
token_embeddings (torch.Tensor, optional): precomputed embeddings
default `None` will recompute embeddings
Returns:
namedtuple:
- **encoder_out** (Tensor): the last encoder layer's output of
shape `(src_len, batch, embed_dim)`
- **encoder_padding_mask** (ByteTensor): the positions of
padding elements of shape `(batch, src_len)`
- **encoder_embedding** (Tensor): the (scaled) embedding lookup
of shape `(batch, src_len, embed_dim)`
- **encoder_states** (List[Tensor]): all intermediate
hidden states of shape `(src_len, batch, embed_dim)`.
Only populated if *return_all_hiddens* is True.
- **src_tokens** (Tensor): input token ids of shape
`(batch, src_len)`
"""
encoder_out = self.forward_scriptable(src_tokens,
src_lengths,
return_all_hiddens,
token_embeddings)
# The Pytorch Mobile lite interpreter does not supports returning NamedTuple in
# `forward` so we use a dictionary instead.
# TorchScript does not support mixed values so the values are all lists.
# The empty list is equivalent to None.
return {
"encoder_out": encoder_out["encoder_out"], # T x B x C
"encoder_padding_mask": encoder_out["encoder_padding_mask"], # B x T
"encoder_embedding": encoder_out["encoder_embedding"], # B x T x C
"encoder_states": encoder_out["encoder_states"], # List[T x B x C]
"src_tokens": [src_tokens], # B x T
"src_lengths": [],
}
class TransformerPointerGeneratorDecoder(TransformerDecoder):
"""
Transformer decoder consisting of *args.decoder_layers* layers. Each layer
is a :class:`TransformerDecoderLayer`. The pointer-generator variant mixes
the output probabilities with an attention distribution in the output layer.
Args:
args (argparse.Namespace): parsed command-line arguments
dictionary (~fairseq.data.Dictionary): decoding dictionary
embed_tokens (torch.nn.Embedding): output embedding
"""
def __init__(self, args, dictionary, embed_tokens):
super().__init__(args, dictionary, embed_tokens, no_encoder_attn=False)
# In the pointer-generator model these arguments define the decoder
# layer and the number of attention heads that will be averaged to
# create the alignment for pointing.
self.alignment_heads = args.alignment_heads
self.alignment_layer = args.alignment_layer
input_embed_dim = embed_tokens.embedding_dim
# Generation probabilities / interpolation coefficients are predicted
# from the current decoder input embedding and the decoder output, which
# is the size of output_embed_dim.
p_gen_input_size = input_embed_dim + self.output_embed_dim
self.project_p_gens = nn.Linear(p_gen_input_size, 1)
nn.init.zeros_(self.project_p_gens.bias)
# The dictionary may include a separate entry for an OOV token in each
# input position, so that their identity can be restored from the
# original source text.
self.num_types = len(dictionary)
self.num_oov_types = args.source_position_markers
self.num_embeddings = self.num_types - self.num_oov_types
self.force_p_gen = args.force_generation
def forward(
self,
prev_output_tokens,
encoder_out: Optional[Dict[str, List[Tensor]]] = None,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
features_only: bool = False,
alignment_layer: Optional[int] = 0,
alignment_heads: Optional[int] = 1,
src_lengths: Optional[Any] = None,
return_all_hiddens: bool = False,
):
"""
Args:
prev_output_tokens (LongTensor): previous decoder outputs of shape
`(batch, tgt_len)`, for teacher forcing
encoder_out (optional): output from the encoder, used for
encoder-side attention
incremental_state (dict, optional): dictionary used for storing
state during :ref:`Incremental decoding`
features_only (bool, optional): only return features without
applying output layer (default: False)
alignment_layer (int, optional): 0-based index of the layer to be
used for pointing (default: 0)
alignment_heads (int, optional): number of attention heads to be
used for pointing (default: 1)
Returns:
tuple:
- the decoder's output of shape `(batch, tgt_len, vocab)`
- a dictionary with any model-specific outputs
"""
# The normal Transformer model doesn't pass the alignment_layer and
# alignment_heads parameters correctly. We use our local variables.
x, extra = self.extract_features(
prev_output_tokens,
encoder_out=encoder_out,
incremental_state=incremental_state,
alignment_layer=self.alignment_layer,
alignment_heads=self.alignment_heads,
)
if not features_only:
# Embedding the tokens again for generation probability prediction,
# so that we don't have to reimplement the whole extract_features()
# method.
if incremental_state is not None:
prev_output_tokens = prev_output_tokens[:, -1:]
prev_output_embed = self.embed_tokens(prev_output_tokens)
prev_output_embed *= self.embed_scale
predictors = torch.cat((prev_output_embed, x), 2)
p_gens = self.project_p_gens(predictors)
p_gens = torch.sigmoid(p_gens.float())
# Torchscript complains if encoder_out or attn are None because
# `output_layer()` signature expects tensors instead
attn: Optional[Tensor] = extra["attn"][0]
assert encoder_out is not None
assert attn is not None
x = self.output_layer(x, attn, encoder_out["src_tokens"][0], p_gens)
return x, extra
def output_layer(
self,
features: Tensor,
attn: Tensor,
src_tokens: Tensor,
p_gens: Tensor
) -> Tensor:
"""
Project features to the vocabulary size and mix with the attention
distributions.
"""
if self.force_p_gen is not None:
p_gens = self.force_p_gen
# project back to size of vocabulary
if self.adaptive_softmax is None:
logits = self.output_projection(features)
else:
logits = features
batch_size = logits.shape[0]
output_length = logits.shape[1]
assert logits.shape[2] == self.num_embeddings
assert src_tokens.shape[0] == batch_size
src_length = src_tokens.shape[1]
# The final output distribution will be a mixture of the normal output
# distribution (softmax of logits) and attention weights.
gen_dists = self.get_normalized_probs_scriptable(
(logits, None), log_probs=False, sample=None
)
gen_dists = torch.mul(gen_dists, p_gens)
padding_size = (batch_size, output_length, self.num_oov_types)
padding = gen_dists.new_zeros(padding_size)
gen_dists = torch.cat((gen_dists, padding), 2)
assert gen_dists.shape[2] == self.num_types
# Scatter attention distributions to distributions over the extended
# vocabulary in a tensor of shape [batch_size, output_length,
# vocab_size]. Each attention weight will be written into a location
# that is for other dimensions the same as in the index tensor, but for
# the third dimension it's the value of the index tensor (the token ID).
attn = torch.mul(attn.float(), 1 - p_gens)
index = src_tokens[:, None, :]
index = index.expand(batch_size, output_length, src_length)
attn_dists_size = (batch_size, output_length, self.num_types)
attn_dists = attn.new_zeros(attn_dists_size)
attn_dists.scatter_add_(2, index, attn.float())
# Final distributions, [batch_size, output_length, num_types].
return gen_dists + attn_dists
def get_normalized_probs(
self,
net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]],
log_probs: bool,
sample: Optional[Dict[str, Tensor]] = None,
):
"""
Get normalized probabilities (or log probs) from a net's output.
Pointer-generator network output is already normalized.
"""
probs = net_output[0]
# Make sure the probabilities are greater than zero when returning log
# probabilities.
return probs.clamp(1e-10, 1.0).log() if log_probs else probs
class Embedding(nn.Embedding):
r"""A simple lookup table that stores embeddings of a fixed dictionary and size.
This module is often used to store word embeddings and retrieve them using indices.
The input to the module is a list of indices, and the output is the corresponding
word embeddings. This subclass differs from the standard PyTorch Embedding class by
allowing additional vocabulary entries that will be mapped to the unknown token
embedding.
Args:
num_embeddings (int): size of the dictionary of embeddings
embedding_dim (int): the size of each embedding vector
padding_idx (int): Pads the output with the embedding vector at :attr:`padding_idx`
(initialized to zeros) whenever it encounters the index.
unk_idx (int): Maps all token indices that are greater than or equal to
num_embeddings to this index.
Attributes:
weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim)
initialized from :math:`\mathcal{N}(0, 1)`
Shape:
- Input: :math:`(*)`, LongTensor of arbitrary shape containing the indices to extract
- Output: :math:`(*, H)`, where `*` is the input shape and :math:`H=\text{embedding\_dim}`
.. note::
Keep in mind that only a limited number of optimizers support
sparse gradients: currently it's :class:`optim.SGD` (`CUDA` and `CPU`),
:class:`optim.SparseAdam` (`CUDA` and `CPU`) and :class:`optim.Adagrad` (`CPU`)
.. note::
With :attr:`padding_idx` set, the embedding vector at
:attr:`padding_idx` is initialized to all zeros. However, note that this
vector can be modified afterwards, e.g., using a customized
initialization method, and thus changing the vector used to pad the
output. The gradient for this vector from :class:`~torch.nn.Embedding`
is always zero.
"""
__constants__ = ["unk_idx"]
# Torchscript: Inheriting from Embedding class produces an error when exporting to Torchscript
# -> RuntimeError: Unable to cast Python instance to C++ type (compile in debug mode for details
# It's happening because max_norm attribute from nn.Embedding is None by default and it cannot be
# cast to a C++ type
def __init__(
self,
num_embeddings: int,
embedding_dim: int,
padding_idx: Optional[int],
unk_idx: int,
max_norm: Optional[float] = float("inf"),
):
super().__init__(num_embeddings, embedding_dim, padding_idx=padding_idx, max_norm=max_norm)
self.unk_idx = unk_idx
nn.init.normal_(self.weight, mean=0, std=embedding_dim ** -0.5)
nn.init.constant_(self.weight[padding_idx], 0)
def forward(self, input):
input = torch.where(
input >= self.num_embeddings, torch.ones_like(input) * self.unk_idx, input
)
return nn.functional.embedding(
input, self.weight, self.padding_idx, self.max_norm,
self.norm_type, self.scale_grad_by_freq, self.sparse
)
@register_model_architecture(
"transformer_pointer_generator", "transformer_pointer_generator"
)
def transformer_pointer_generator(args):
args.alignment_heads = getattr(args, "alignment_heads", 1)
args.alignment_layer = getattr(args, "alignment_layer", -1)
base_architecture(args)
if args.alignment_layer < 0:
args.alignment_layer = args.decoder_layers + args.alignment_layer
@register_model_architecture(
"transformer_pointer_generator", "transformer_pointer_generator_iwslt_de_en"
)
def transformer_pointer_generator_iwslt_de_en(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 1024)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4)
args.encoder_layers = getattr(args, "encoder_layers", 6)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 1024)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4)
args.decoder_layers = getattr(args, "decoder_layers", 6)
transformer_pointer_generator(args)
@register_model_architecture(
"transformer_pointer_generator", "transformer_pointer_generator_wmt_en_de"
)
def transformer_pointer_generator_wmt_en_de(args):
transformer_pointer_generator(args)
# Transformer pointer-generator with the base Transformer parameters as used in
# the "Attention Is All You Need" paper (Vaswani et al., 2017)
@register_model_architecture(
"transformer_pointer_generator",
"transformer_pointer_generator_vaswani_wmt_en_de_big",
)
def transformer_pointer_generator_vaswani_wmt_en_de_big(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1024)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 4096)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 16)
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", False)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1024)
args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 4096)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 16)
args.dropout = getattr(args, "dropout", 0.3)
transformer_pointer_generator(args)
@register_model_architecture(
"transformer_pointer_generator",
"transformer_pointer_generator_vaswani_wmt_en_fr_big",
)
def transformer_pointer_generator_vaswani_wmt_en_fr_big(args):
args.dropout = getattr(args, "dropout", 0.1)
transformer_pointer_generator_vaswani_wmt_en_de_big(args)
@register_model_architecture(
"transformer_pointer_generator", "transformer_pointer_generator_wmt_en_de_big"
)
def transformer_pointer_generator_wmt_en_de_big(args):
args.attention_dropout = getattr(args, "attention_dropout", 0.1)
transformer_pointer_generator_vaswani_wmt_en_de_big(args)
# default parameters used in tensor2tensor implementation
@register_model_architecture(
"transformer_pointer_generator", "transformer_pointer_generator_wmt_en_de_big_t2t"
)
def transformer_pointer_generator_wmt_en_de_big_t2t(args):
args.encoder_normalize_before = getattr(args, "encoder_normalize_before", True)
args.decoder_normalize_before = getattr(args, "decoder_normalize_before", True)
args.attention_dropout = getattr(args, "attention_dropout", 0.1)
args.activation_dropout = getattr(args, "activation_dropout", 0.1)
transformer_pointer_generator_vaswani_wmt_en_de_big(args)