def from_config(
cls,
config: WordFeatConfig,
metadata: Optional[FieldMeta] = None,
tensorizer: Optional[Tensorizer] = None,
init_from_saved_state: Optional[bool] = False,
):
"""Factory method to construct an instance of WordEmbedding from
the module's config object and the field's metadata object.
Args:
config (WordFeatConfig): Configuration object specifying all the
parameters of WordEmbedding.
metadata (FieldMeta): Object containing this field's metadata.
Returns:
type: An instance of WordEmbedding.
"""
if tensorizer is not None:
if config.vocab_from_pretrained_embeddings:
raise ValueError(
"In new data design, to add tokens from a pretrained embeddings "
"file to the vocab, specify `vocab_file` in the token tensorizer."
)
embeddings_weight = None
# We don't need to load pretrained embeddings if we know the
# embedding weights are going to be loaded from a snapshot.
if config.pretrained_embeddings_path and not init_from_saved_state:
pretrained_embedding = PretrainedEmbedding(
config.pretrained_embeddings_path, # doesn't support fbpkg
lowercase_tokens=config.lowercase_tokens,
skip_header=config.skip_header,
)
embeddings_weight = pretrained_embedding.initialize_embeddings_weights(
tensorizer.vocab.idx,
tensorizer.vocab.unk_token,
config.embed_dim,
config.embedding_init_strategy,
)
num_embeddings = len(tensorizer.vocab)
unk_token_idx = tensorizer.vocab.get_unk_index()
vocab = tensorizer.vocab
else: # This else condition should go away after metadata goes away.
num_embeddings = metadata.vocab_size
embeddings_weight = metadata.pretrained_embeds_weight
unk_token_idx = metadata.unk_token_idx
vocab = metadata.vocab
return cls(
num_embeddings=num_embeddings,
embedding_dim=config.embed_dim,
embeddings_weight=embeddings_weight,
init_range=config.embedding_init_range,
unk_token_idx=unk_token_idx,
mlp_layer_dims=config.mlp_layer_dims,
padding_idx=config.padding_idx,
vocab=vocab,
)
def from_config(
cls,
config: WordFeatConfig,
metadata: Optional[FieldMeta] = None,
tensorizer: Optional[Tensorizer] = None,
):
"""Factory method to construct an instance of WordEmbedding from
the module's config object and the field's metadata object.
Args:
config (WordFeatConfig): Configuration object specifying all the
parameters of WordEmbedding.
metadata (FieldMeta): Object containing this field's metadata.
Returns:
type: An instance of WordEmbedding.
"""
if tensorizer is not None:
embeddings_weight = None
if config.pretrained_embeddings_path:
pretrained_embedding = PretrainedEmbedding(
config.pretrained_embeddings_path, # doesn't support fbpkg
lowercase_tokens=tensorizer.tokenizer.lowercase,
)
if config.vocab_from_pretrained_embeddings:
if not config.vocab_from_train_data: # Reset token counter.
tensorizer.vocab_builder._counter = collections.Counter(
)
tensorizer.vocab_builder.add_all(
pretrained_embedding.embed_vocab)
tensorizer.vocab = tensorizer.vocab_builder.make_vocab()
embeddings_weight = pretrained_embedding.initialize_embeddings_weights(
tensorizer.vocab.idx,
UNK,
config.embed_dim,
config.embedding_init_strategy,
)
num_embeddings = len(tensorizer.vocab)
unk_token_idx = tensorizer.vocab.idx[UNK]
else: # This else condition should go away after metadata goes away.
num_embeddings = metadata.vocab_size
embeddings_weight = metadata.pretrained_embeds_weight
unk_token_idx = metadata.unk_token_idx
return cls(
num_embeddings=num_embeddings,
embedding_dim=config.embed_dim,
embeddings_weight=embeddings_weight,
init_range=config.embedding_init_range,
unk_token_idx=unk_token_idx,
mlp_layer_dims=config.mlp_layer_dims,
)
def __init__(
self,
pretrained_embeddings_path: str,
embedding_dim: int,
mlp_layer_dims: Optional[Sequence[int]] = None,
lowercase_tokens: bool = False,
skip_header: bool = True,
delimiter: str = " ",
vocab: ScriptVocabulary = None,
) -> None:
super().__init__()
vocab = vocab or build_vocab(pretrained_embeddings_path)
pretrained_embedding = PretrainedEmbedding(
pretrained_embeddings_path,
lowercase_tokens=lowercase_tokens,
skip_header=skip_header,
delimiter=delimiter,
)
embeddings_weight = pretrained_embedding.initialize_embeddings_weights(
vocab.idx, # tensorizer.vocab.idx,
vocab.unk_token, # tensorizer.vocab.unk_token,
embedding_dim,
EmbedInitStrategy.RANDOM,
)
num_embeddings = len(vocab.idx)
self.embedding = nn.Embedding(
num_embeddings,
embedding_dim,
_weight=embeddings_weight,
padding_idx=vocab.get_pad_index(),
)
# Initialize unk embedding with zeros
# to guard the model against randomized decisions based on unknown words
unk_token_idx = vocab.get_unk_index()
if unk_token_idx >= 0:
self.embedding.weight.data[unk_token_idx].fill_(0.0)
# Create MLP layers
if mlp_layer_dims is None:
mlp_layer_dims = []
self.mlp = nn.Sequential(
*(
nn.Sequential(nn.Linear(m, n), nn.ReLU())
for m, n in zip([embedding_dim] + list(mlp_layer_dims), mlp_layer_dims)
)
)
self.output_dim = mlp_layer_dims[-1] if mlp_layer_dims else embedding_dim
def from_config(
cls,
config: Config,
tensorizer: Tensorizer = None,
init_from_saved_state: Optional[bool] = False,
):
"""Factory method to construct an instance of WordEmbedding from
the module's config object and the field's metadata object.
Args:
config (WordSeqEmbedding.Config): Configuration object specifying all the
parameters of WordEmbedding.
Returns:
type: An instance of WordSeqEmbedding.
"""
embeddings_weight = None
# We don't need to load pretrained embeddings if we know the
# embedding weights are going to be loaded from a snapshot.
if config.pretrained_embeddings_path and not init_from_saved_state:
pretrained_embedding = PretrainedEmbedding(
config.pretrained_embeddings_path, # doesn't support fbpkg
lowercase_tokens=config.lowercase_tokens,
skip_header=config.skip_header,
delimiter=config.delimiter,
)
embeddings_weight = pretrained_embedding.initialize_embeddings_weights(
tensorizer.vocab.idx,
tensorizer.vocab.unk_token,
config.word_embed_dim,
config.embedding_init_strategy,
)
num_embeddings = len(tensorizer.vocab)
unk_token_idx = tensorizer.vocab.get_unk_index()
vocab = tensorizer.vocab
vocab_pad_idx = vocab.get_pad_index(value=-1)
if vocab_pad_idx == -1:
vocab_pad_idx = None
return cls(
lstm_config=config.lstm,
num_embeddings=num_embeddings,
word_embed_dim=config.word_embed_dim,
embeddings_weight=embeddings_weight,
init_range=config.embedding_init_range,
init_std=config.embeddding_init_std,
unk_token_idx=unk_token_idx,
padding_idx=config.padding_idx or vocab_pad_idx,
vocab=vocab,
)
def test_assign_pretrained_weights(self):
embeddings_ref = PretrainedEmbedding()
embeddings_ref.load_cached_embeddings(EMBED_CACHED_PATH)
VOCAB = ["UNK", "aloha", "the"]
embed_vocab_to_idx = {tok: i for i, tok in enumerate(VOCAB)}
pretrained_embeds = embeddings_ref.initialize_embeddings_weights(
embed_vocab_to_idx, "UNK", EMBED_DIM, EmbedInitStrategy.RANDOM
)
assert pretrained_embeds.shape[0] == len(VOCAB)
assert pretrained_embeds.shape[1] == EMBED_DIM
np.testing.assert_array_almost_equal(
pretrained_embeds[1].numpy(),
[-0.43124, 0.014934, -0.50635, 0.60506, 0.56051],
) # embedding vector for 'aloha'
np.testing.assert_array_almost_equal(
pretrained_embeds[2].numpy(),
[-0.39153, -0.19803, 0.2573, -0.18617, 0.25551],
) # embedding vector for 'the'
def from_config(
cls,
config: WordFeatConfig,
metadata: Optional[FieldMeta] = None,
tensorizer: Optional[Tensorizer] = None,
init_from_saved_state: Optional[bool] = False,
):
"""Factory method to construct an instance of WordEmbedding from
the module's config object and the field's metadata object.
Args:
config (WordFeatConfig): Configuration object specifying all the
parameters of WordEmbedding.
metadata (FieldMeta): Object containing this field's metadata.
Returns:
type: An instance of WordEmbedding.
"""
if tensorizer is not None:
embeddings_weight = None
if config.pretrained_embeddings_path and (
# We don't need to load pretrained embeddings if we know the
# embedding weights are going to be loaded from a snapshot. The
# exception is if we rely on the pretrained embeddings to give us
# the vocab, in which case, we have to load it regardless.
config.vocab_from_pretrained_embeddings
or not init_from_saved_state):
pretrained_embedding = PretrainedEmbedding(
config.pretrained_embeddings_path, # doesn't support fbpkg
lowercase_tokens=config.lowercase_tokens,
)
if config.vocab_from_pretrained_embeddings:
# pretrained embeddings will get a freq count of 1
assert config.min_freq == 1, (
"If `vocab_from_pretrained_embeddings` is set, the vocab's "
"`min_freq` must be 1")
if not config.vocab_from_train_data: # Reset token counter.
tensorizer.vocab_builder._counter = collections.Counter(
)
pretrained_vocab = pretrained_embedding.embed_vocab
if config.vocab_size:
pretrained_vocab = pretrained_vocab[:config.vocab_size]
tensorizer.vocab_builder.add_all(pretrained_vocab)
tensorizer.vocab = tensorizer.vocab_builder.make_vocab()
embeddings_weight = pretrained_embedding.initialize_embeddings_weights(
tensorizer.vocab.idx,
UNK,
config.embed_dim,
config.embedding_init_strategy,
)
num_embeddings = len(tensorizer.vocab)
unk_token_idx = tensorizer.vocab.idx[UNK]
else: # This else condition should go away after metadata goes away.
num_embeddings = metadata.vocab_size
embeddings_weight = metadata.pretrained_embeds_weight
unk_token_idx = metadata.unk_token_idx
return cls(
num_embeddings=num_embeddings,
embedding_dim=config.embed_dim,
embeddings_weight=embeddings_weight,
init_range=config.embedding_init_range,
unk_token_idx=unk_token_idx,
mlp_layer_dims=config.mlp_layer_dims,
)