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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 typing import Dict, List, Optional, Tuple
import torch
import torch.nn as nn
from fairseq import utils, options
from fairseq.dataclass.utils import gen_parser_from_dataclass
from fairseq.distributed import fsdp_wrap
from fairseq.models import FairseqEncoderDecoderModel
from fairseq.models.transformer import (
TransformerEncoderBase,
TransformerDecoderBase,
TransformerConfig,
)
from torch import Tensor
from fairseq.modules import AdaptiveInput
class TransformerModelBase(FairseqEncoderDecoderModel):
"""
Transformer model from `"Attention Is All You Need" (Vaswani, et al, 2017)
<https://arxiv.org/abs/1706.03762>`_.
Args:
encoder (TransformerEncoder): the encoder
decoder (TransformerDecoder): the decoder
The Transformer model provides the following named architectures and
command-line arguments:
.. argparse::
:ref: fairseq.models.transformer_parser
:prog:
"""
def __init__(self, cfg, encoder, decoder):
super().__init__(encoder, decoder)
self.cfg = cfg
self.supports_align_args = True
@classmethod
def add_args(cls, parser):
"""Add model-specific arguments to the parser."""
# we want to build the args recursively in this case.
gen_parser_from_dataclass(
parser, TransformerConfig(), delete_default=False, with_prefix=""
)
@classmethod
def build_model(cls, cfg, task):
"""Build a new model instance."""
# -- TODO T96535332
# bug caused by interaction between OmegaConf II and argparsing
cfg.decoder.input_dim = int(cfg.decoder.input_dim)
cfg.decoder.output_dim = int(cfg.decoder.output_dim)
# --
if cfg.encoder.layers_to_keep:
cfg.encoder.layers = len(cfg.encoder.layers_to_keep.split(","))
if cfg.decoder.layers_to_keep:
cfg.decoder.layers = len(cfg.decoder.layers_to_keep.split(","))
src_dict, tgt_dict = task.source_dictionary, task.target_dictionary
if cfg.adaptive_input is False:
if cfg.share_all_embeddings:
if src_dict != tgt_dict:
raise ValueError("--share-all-embeddings requires a joined dictionary")
if cfg.encoder.embed_dim != cfg.decoder.embed_dim:
raise ValueError(
"--share-all-embeddings requires --encoder-embed-dim to match --decoder-embed-dim"
)
if cfg.decoder.embed_path and (
cfg.decoder.embed_path != cfg.encoder.embed_path
):
raise ValueError(
"--share-all-embeddings not compatible with --decoder-embed-path"
)
encoder_embed_tokens = cls.build_embedding(
cfg, src_dict, cfg.encoder.embed_dim, cfg.encoder.embed_path
)
decoder_embed_tokens = encoder_embed_tokens
cfg.share_decoder_input_output_embed = True
else:
encoder_embed_tokens = cls.build_embedding(
cfg, src_dict, cfg.encoder.embed_dim, cfg.encoder.embed_path
)
decoder_embed_tokens = cls.build_embedding(
cfg, tgt_dict, cfg.decoder.embed_dim, cfg.decoder.embed_path
)
else:
encoder_embed_tokens = AdaptiveInput(
len(task.source_dictionary),
task.source_dictionary.pad(),
cfg.encoder.embed_dim,
cfg.adaptive_input_factor,
cfg.encoder.embed_dim,
options.eval_str_list(cfg.adaptive_input_cutoff, type=int),
cfg.quant_noise_pq,
cfg.quant_noise_pq_block_size,
)
decoder_embed_tokens = encoder_embed_tokens
if cfg.tie_adaptive_weights:
assert cfg.adaptive_input
assert cfg.adaptive_input_factor == cfg.adaptive_softmax_factor
assert (
cfg.adaptive_softmax_cutoff == cfg.adaptive_input_cutoff
), "{} != {}".format(
cfg.adaptive_softmax_cutoff, cfg.adaptive_input_cutoff
)
assert cfg.decoder_input_dim == cfg.decoder_output_dim
if cfg.offload_activations:
cfg.checkpoint_activations = True # offloading implies checkpointing
encoder = cls.build_encoder(cfg, src_dict, encoder_embed_tokens)
decoder = cls.build_decoder(cfg, tgt_dict, decoder_embed_tokens, encoder_layers=encoder.layers)
if not cfg.share_all_embeddings:
# fsdp_wrap is a no-op when --ddp-backend != fully_sharded
encoder = fsdp_wrap(encoder, min_num_params=cfg.min_params_to_wrap)
decoder = fsdp_wrap(decoder, min_num_params=cfg.min_params_to_wrap)
return cls(cfg, encoder, decoder)
@classmethod
def build_embedding(cls, cfg, dictionary, embed_dim, path=None):
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
emb = Embedding(num_embeddings, embed_dim, padding_idx)
# if provided, load from preloaded dictionaries
if path:
embed_dict = utils.parse_embedding(path)
utils.load_embedding(embed_dict, dictionary, emb)
return emb
@classmethod
def build_encoder(cls, cfg, src_dict, embed_tokens):
return TransformerEncoderBase(cfg, src_dict, embed_tokens)
@classmethod
def build_decoder(cls, cfg, tgt_dict, embed_tokens, encoder_layers=None):
return TransformerDecoderBase(
cfg,
tgt_dict,
embed_tokens,
no_encoder_attn=cfg.no_cross_attention,
encoder_layers=encoder_layers
)
# TorchScript doesn't support optional arguments with variable length (**kwargs).
# Current workaround is to add union of all arguments in child classes.
def forward(
self,
src_tokens,
src_lengths,
prev_output_tokens,
return_all_hiddens: bool = True,
features_only: bool = False,
alignment_layer: Optional[int] = None,
alignment_heads: Optional[int] = None,
):
"""
Run the forward pass for an encoder-decoder model.
Copied from the base class, but without ``**kwargs``,
which are not supported by TorchScript.
"""
encoder_out = self.encoder(
src_tokens, src_lengths=src_lengths, return_all_hiddens=return_all_hiddens
)
decoder_out = self.decoder(
prev_output_tokens,
encoder_out=encoder_out,
features_only=features_only,
alignment_layer=alignment_layer,
alignment_heads=alignment_heads,
src_lengths=src_lengths,
return_all_hiddens=return_all_hiddens,
)
return decoder_out
# Since get_normalized_probs is in the Fairseq Model which is not scriptable,
# I rewrite the get_normalized_probs from Base Class to call the
# helper function in the Base Class.
@torch.jit.export
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."""
return self.get_normalized_probs_scriptable(net_output, log_probs, sample)
def Embedding(num_embeddings, embedding_dim, padding_idx):
m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
nn.init.constant_(m.weight[padding_idx], 0)
return m
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