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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 fairseq import utils
from fairseq.models import (
FairseqMultiModel,
register_model,
register_model_architecture,
)
from fairseq.models.transformer import (
Embedding,
base_architecture,
)
from fairseq.models.multilingual_transformer import (
MultilingualTransformerModel,
base_multilingual_architecture,
)
from fairseq.utils import safe_hasattr
from collections import OrderedDict
@register_model("multilingual_transformer_from_mbart")
class MultilingualTransformerModelFromMbart(MultilingualTransformerModel):
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
from fairseq.tasks.multilingual_translation import MultilingualTranslationTask
assert isinstance(task, MultilingualTranslationTask)
# make sure all arguments are present in older models
base_multilingual_architecture(args)
if not safe_hasattr(args, "max_source_positions"):
args.max_source_positions = 1024
if not safe_hasattr(args, "max_target_positions"):
args.max_target_positions = 1024
src_langs = [lang_pair.split("-")[0] for lang_pair in task.model_lang_pairs]
tgt_langs = [lang_pair.split("-")[1] for lang_pair in task.model_lang_pairs]
if args.share_encoders:
args.share_encoder_embeddings = True
if args.share_decoders:
args.share_decoder_embeddings = True
def build_embedding(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
# build shared embeddings (if applicable)
shared_encoder_embed_tokens, shared_decoder_embed_tokens = None, None
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"
)
shared_encoder_embed_tokens = FairseqMultiModel.build_shared_embeddings(
dicts=task.dicts,
langs=task.langs,
embed_dim=args.encoder_embed_dim,
build_embedding=build_embedding,
pretrained_embed_path=args.encoder_embed_path,
)
shared_decoder_embed_tokens = shared_encoder_embed_tokens
args.share_decoder_input_output_embed = True
else:
if args.share_encoder_embeddings:
shared_encoder_embed_tokens = FairseqMultiModel.build_shared_embeddings(
dicts=task.dicts,
langs=src_langs,
embed_dim=args.encoder_embed_dim,
build_embedding=build_embedding,
pretrained_embed_path=args.encoder_embed_path,
)
if args.share_decoder_embeddings:
shared_decoder_embed_tokens = FairseqMultiModel.build_shared_embeddings(
dicts=task.dicts,
langs=tgt_langs,
embed_dim=args.decoder_embed_dim,
build_embedding=build_embedding,
pretrained_embed_path=args.decoder_embed_path,
)
# encoders/decoders for each language
lang_encoders, lang_decoders = {}, {}
def get_encoder(lang):
if lang not in lang_encoders:
if shared_encoder_embed_tokens is not None:
encoder_embed_tokens = shared_encoder_embed_tokens
else:
encoder_embed_tokens = build_embedding(
task.dicts[lang],
args.encoder_embed_dim,
args.encoder_embed_path,
)
lang_encoders[lang] = MultilingualTransformerModel._get_module_class(
True, args, task.dicts[lang], encoder_embed_tokens, src_langs
)
return lang_encoders[lang]
def get_decoder(lang):
if lang not in lang_decoders:
if shared_decoder_embed_tokens is not None:
decoder_embed_tokens = shared_decoder_embed_tokens
else:
decoder_embed_tokens = build_embedding(
task.dicts[lang],
args.decoder_embed_dim,
args.decoder_embed_path,
)
lang_decoders[lang] = MultilingualTransformerModel._get_module_class(
False, args, task.dicts[lang], decoder_embed_tokens, tgt_langs
)
return lang_decoders[lang]
# shared encoders/decoders (if applicable)
shared_encoder, shared_decoder = None, None
if args.share_encoders:
shared_encoder = get_encoder(src_langs[0])
if args.share_decoders:
shared_decoder = get_decoder(tgt_langs[0])
encoders, decoders = OrderedDict(), OrderedDict()
for lang_pair, src, tgt in zip(task.model_lang_pairs, src_langs, tgt_langs):
encoders[lang_pair] = (
shared_encoder if shared_encoder is not None else get_encoder(src)
)
decoders[lang_pair] = (
shared_decoder if shared_decoder is not None else get_decoder(tgt)
)
return MultilingualTransformerModelFromMbart(encoders, decoders)
def load_state_dict(self, state_dict, strict=True, model_cfg=None):
state_dict_subset = state_dict.copy()
lang_pairs = set([x.split(".")[1] for x in state_dict.keys()])
finetune_mode = not any("neutral" in lp for lp in lang_pairs)
if finetune_mode:
# load a pre-trained mBART/BART model
# we need this code because mBART/BART are not of type FairseqMultiModel but FairseqModel
# so we hackishly load the weights by replicating them for all lang pairs
print("loading pre-trained BART")
self_state_dict = self.state_dict()
for k, v in state_dict.items():
for lang_pair in self.models:
new_key = k if "models." in k else f"models.{lang_pair}.{k}"
# print(new_key)
if self_state_dict[new_key].shape == v.shape:
state_dict_subset[new_key] = v
elif any(
w in k
for w in [
"encoder.embed_tokens.weight",
"decoder.embed_tokens.weight",
"decoder.output_projection.weight",
]
):
# why vocab_size - 5? because there are `vocab_size` tokens from the language
# and 5 additional tokens in the denoising task: eos,bos,pad,unk,mask.
# but in the translation task there are only `vocab_size` + 4 (no mask).
print(
f"{k}: {self_state_dict[new_key].shape} != {v.shape}",
end="",
flush=True,
)
vocab_size = v.shape[0] - 5
state_dict_subset[new_key] = self_state_dict[new_key]
state_dict_subset[new_key] = v[: vocab_size + 4]
print(f" => fixed by using first {vocab_size + 4} dims")
else:
raise ValueError("unable to load model due to mimatched dims!")
del state_dict_subset[k]
else:
print("loading pre-trained emotion translation model")
for k, _ in state_dict.items():
assert k.startswith("models.")
lang_pair = k.split(".")[1]
if lang_pair not in self.models:
del state_dict_subset[k]
super().load_state_dict(state_dict_subset, strict=strict, model_cfg=model_cfg)
@register_model_architecture("transformer", "transformer_small")
def transformer_small(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 512)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4)
args.encoder_layers = getattr(args, "encoder_layers", 3)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 512)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4)
args.decoder_layers = getattr(args, "decoder_layers", 3)
base_architecture(args)
@register_model_architecture(
"multilingual_transformer_from_mbart", "multilingual_small"
)
def multilingual_small(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_ffn_embed_dim = getattr(args, "encoder_ffn_embed_dim", 512)
args.encoder_attention_heads = getattr(args, "encoder_attention_heads", 4)
args.encoder_layers = getattr(args, "encoder_layers", 3)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
args.decoder_ffn_embed_dim = getattr(args, "decoder_ffn_embed_dim", 512)
args.decoder_attention_heads = getattr(args, "decoder_attention_heads", 4)
args.decoder_layers = getattr(args, "decoder_layers", 3)
base_multilingual_architecture(args)
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