def build_reranker_model(model_opt, fields, gpu, checkpoint=None):
"""
Args:
model_opt: the option loaded from checkpoint.
fields: `Field` objects for the model.
gpu(bool): whether to use gpu.
checkpoint: the model gnerated by train phase, or a resumed snapshot
model from a stopped training.
Returns:
the NMTModel.
"""
assert model_opt.model_type in ["text"], \
("Unsupported model type %s" % (model_opt.model_type))
# Build encoder.
src_dict = fields["src"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'src')
src_embeddings = build_embeddings(model_opt, src_dict, feature_dicts)
reranker = build_reranker(model_opt, src_embeddings)
reranker = onmt.models.ReRankerModel(reranker)
# Build NMTModel(= encoder + decoder).
device = torch.device("cuda" if gpu else "cpu")
reranker.model_type = model_opt.model_type
# Load the model states from checkpoint or initialize them.
if checkpoint is not None:
reranker.load_state_dict(checkpoint['reranker'])
else:
if model_opt.param_init != 0.0:
for p in reranker.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
if model_opt.param_init_glorot:
for p in reranker.parameters():
if p.dim() > 1:
xavier_uniform_(p)
for p in reranker.parameters():
if p.dim() > 1:
xavier_uniform_(p)
if hasattr(reranker.reranker, 'embeddings'):
reranker.reranker.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_enc, model_opt.fix_word_vecs_enc)
reranker.to(device)
return reranker
def build_base_model(model_opt, fields, gpu, checkpoint=None):
"""
Args:
model_opt: the option loaded from checkpoint.
fields: `Field` objects for the model.
gpu(bool): whether to use gpu.
checkpoint: the model gnerated by train phase, or a resumed snapshot
model from a stopped training.
Returns:
the MemModel.
"""
# Build encoder.
src_dict = fields["src"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'src')
src_embeddings = build_embeddings(model_opt, src_dict, feature_dicts)
encoder = DistractorEncoder(
model_opt.rnn_type, model_opt.word_encoder_type,
model_opt.sent_encoder_type, model_opt.question_init_type,
model_opt.word_encoder_layers, model_opt.sent_encoder_layers,
model_opt.question_init_layers, model_opt.rnn_size, model_opt.dropout,
src_embeddings, model_opt.lambda_question, model_opt.lambda_answer)
# Build decoder.
tgt_dict = fields["tgt"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'tgt')
tgt_embeddings = build_embeddings(model_opt,
tgt_dict,
feature_dicts,
for_encoder=False)
# Share the embedding matrix - preprocess with share_vocab required.
if model_opt.share_embeddings:
# src/tgt vocab should be the same if `-share_vocab` is specified.
if src_dict != tgt_dict:
raise AssertionError('The `-share_vocab` should be set during '
'preprocess if you use share_embeddings!')
tgt_embeddings.word_lut.weight = src_embeddings.word_lut.weight
bidirectional_encoder = True if model_opt.question_init_type == 'brnn' else False
decoder = HierDecoder(model_opt.rnn_type, bidirectional_encoder,
model_opt.dec_layers, model_opt.rnn_size,
model_opt.global_attention, model_opt.dropout,
tgt_embeddings)
# Build ans_decoder.
ans_dict = fields["answer"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'answer')
ans_embeddings = build_embeddings(model_opt,
ans_dict,
feature_dicts,
for_encoder=False)
# Share the embedding matrix - preprocess with share_vocab required.
if model_opt.share_embeddings:
# src/tgt vocab should be the same if `-share_vocab` is specified.
if src_dict != ans_dict:
raise AssertionError('The `-share_vocab` should be set during '
'preprocess if you use share_embeddings!')
ans_embeddings.word_lut.weight = src_embeddings.word_lut.weight
ans_bidirectional_encoder = True if model_opt.question_init_type == 'brnn' else False
ans_decoder = HierDecoderAns(model_opt.rnn_type, ans_bidirectional_encoder,
model_opt.dec_layers, model_opt.rnn_size,
model_opt.global_attention, model_opt.dropout,
ans_embeddings)
# Build NMTModel(= encoder + decoder).
device = torch.device("cuda" if gpu else "cpu")
model = DGModel(encoder, decoder, ans_decoder)
# Build Generator.
gen_func = nn.LogSoftmax(dim=-1)
generator = nn.Sequential(
nn.Linear(model_opt.dec_rnn_size, len(fields["tgt"].vocab)), gen_func)
# Build AnsGenerator.
ans_gen_func = nn.LogSoftmax(dim=-1)
ans_generator = nn.Sequential(
nn.Linear(model_opt.dec_rnn_size, len(fields["answer"].vocab)),
ans_gen_func)
# Load the model states from checkpoint or initialize them.
if checkpoint is not None:
model.load_state_dict(checkpoint['model'], strict=False)
generator.load_state_dict(checkpoint['generator'], strict=False)
ans_generator.load_state_dict(checkpoint['ans_generator'],
strict=False)
else:
if model_opt.param_init != 0.0:
for p in model.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
for p in generator.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
if model_opt.param_init_glorot:
for p in model.parameters():
if p.dim() > 1:
xavier_uniform_(p)
for p in generator.parameters():
if p.dim() > 1:
xavier_uniform_(p)
if hasattr(model.encoder, 'embeddings'):
model.encoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_enc, model_opt.fix_word_vecs_enc)
if hasattr(model.decoder, 'embeddings'):
model.decoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_dec, model_opt.fix_word_vecs_dec)
if hasattr(model.ans_decoder, 'embeddings'):
model.ans_decoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_dec, model_opt.fix_word_vecs_dec)
# Add generator to model (this registers it as parameter of model).
model.generator = generator
model.ans_generator = ans_generator
model.to(device)
return model
def build_base_model(model_opt, fields, gpu, checkpoint=None):
"""
Args:
model_opt: the option loaded from checkpoint.
fields: `Field` objects for the model.
gpu(bool): whether to use gpu.
checkpoint: the model gnerated by train phase, or a resumed snapshot
model from a stopped training.
Returns:
the NMTModel.
"""
assert model_opt.model_type in ["text", "img", "audio"], \
("Unsupported model type %s" % (model_opt.model_type))
# for backward compatibility
if model_opt.rnn_size != -1:
model_opt.enc_rnn_size = model_opt.rnn_size
model_opt.dec_rnn_size = model_opt.rnn_size
if model_opt.model_type == 'text' and \
model_opt.enc_rnn_size != model_opt.dec_rnn_size:
raise AssertionError("""We do not support different encoder and
decoder rnn sizes for translation now.""")
# Build encoder.
if model_opt.model_type == "text":
src_dict = fields["src"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'src')
src_embeddings = build_embeddings(model_opt, src_dict, feature_dicts)
encoder = build_encoder(model_opt, src_embeddings)
if model_opt.refer:
ref_dict = fields["ref"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'ref')
ref_embeddings = build_embeddings(model_opt, ref_dict, feature_dicts)
refer = build_encoder(model_opt, ref_embeddings)
else:
refer = None
# Build decoder.
tgt_dict = fields["tgt"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'tgt')
tgt_embeddings = build_embeddings(model_opt,
tgt_dict,
feature_dicts,
for_encoder=False)
# Share the embedding matrix - preprocess with share_vocab required.
if model_opt.share_embeddings:
# src/tgt vocab should be the same if `-share_vocab` is specified.
if src_dict != tgt_dict:
raise AssertionError('The `-share_vocab` should be set during '
'preprocess if you use share_embeddings!')
tgt_embeddings.word_lut.weight = src_embeddings.word_lut.weight
decoder = build_decoder(model_opt, tgt_embeddings)
# Build NMTModel(= encoder + decoder).
device = torch.device("cuda" if gpu else "cpu")
model = onmt.models.NMTModel(encoder, decoder, refer)
# Build Generator.
if not model_opt.copy_attn:
if model_opt.generator_function == "sparsemax":
gen_func = onmt.modules.sparse_activations.LogSparsemax(dim=-1)
else:
gen_func = nn.LogSoftmax(dim=-1)
generator = nn.Sequential(
nn.Linear(model_opt.dec_rnn_size, len(fields["tgt"].vocab)),
gen_func)
if model_opt.share_decoder_embeddings:
generator[0].weight = decoder.embeddings.word_lut.weight
else:
generator = CopyGenerator(model_opt.dec_rnn_size, fields["tgt"].vocab)
# Load the model states from checkpoint or initialize them.
if checkpoint is not None:
# This preserves backward-compat for models using customed layernorm
def fix_key(s):
s = re.sub(r'(.*)\.layer_norm((_\d+)?)\.b_2',
r'\1.layer_norm\2.bias', s)
s = re.sub(r'(.*)\.layer_norm((_\d+)?)\.a_2',
r'\1.layer_norm\2.weight', s)
return s
checkpoint['model'] = \
{fix_key(k): v for (k, v) in checkpoint['model'].items()}
# end of patch for backward compatibility
model.load_state_dict(checkpoint['model'], strict=False)
generator.load_state_dict(checkpoint['generator'], strict=False)
else:
if model_opt.param_init != 0.0:
for p in model.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
for p in generator.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
if model_opt.param_init_glorot:
for p in model.parameters():
if p.dim() > 1:
xavier_uniform_(p)
for p in generator.parameters():
if p.dim() > 1:
xavier_uniform_(p)
if hasattr(model.encoder, 'embeddings'):
model.encoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_enc, model_opt.fix_word_vecs_enc)
if hasattr(model.decoder, 'embeddings'):
model.decoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_dec, model_opt.fix_word_vecs_dec)
# Add generator to model (this registers it as parameter of model).
model.generator = generator
model.to(device)
return model
def build_base_model(model_opt, fields, gpu, checkpoint=None):
"""
Args:
model_opt: the option loaded from checkpoint.
fields: `Field` objects for the model.
gpu(bool): whether to use gpu.
checkpoint: the model gnerated by train phase, or a resumed snapshot
model from a stopped training.
Returns:
the MemModel.
"""
# Build encoder.
src_dict = fields["src"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'src')
src_embeddings = build_embeddings(model_opt, src_dict, feature_dicts)
encoder = build_encoder(model_opt, src_embeddings)
# Build decoder.
tgt_dict = fields["tgt"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'tgt')
tgt_embeddings = build_embeddings(model_opt,
tgt_dict,
feature_dicts,
for_encoder=False)
# Share the embedding matrix - preprocess with share_vocab required.
if model_opt.share_embeddings:
# src/tgt vocab should be the same if `-share_vocab` is specified.
if src_dict != tgt_dict:
raise AssertionError('The `-share_vocab` should be set during '
'preprocess if you use share_embeddings!')
tgt_embeddings.word_lut.weight = src_embeddings.word_lut.weight
decoder = build_decoder(model_opt, tgt_embeddings)
# Build NMTModel(= encoder + decoder).
device = torch.device("cuda" if gpu else "cpu")
model = onmt.models.MemModel(encoder, decoder)
# Build Generator.
if not model_opt.copy_attn:
if model_opt.generator_function == "sparsemax":
gen_func = onmt.modules.sparse_activations.LogSparsemax(dim=-1)
else:
gen_func = nn.LogSoftmax(dim=-1)
generator = nn.Sequential(
nn.Linear(model_opt.dec_rnn_size, len(fields["tgt"].vocab)),
gen_func)
if model_opt.share_decoder_embeddings:
generator[0].weight = decoder.embeddings.word_lut.weight
elif model_opt.coref_vocab or model_opt.coref_attn:
generator = CorefGenerator(model_opt.dec_rnn_size, fields["tgt"].vocab,
fields["coref_tgt"].vocab)
else:
generator = CopyGenerator(model_opt.dec_rnn_size, fields["tgt"].vocab)
# Load the model states from checkpoint or initialize them.
if checkpoint is not None:
model.load_state_dict(checkpoint['model'], strict=False)
generator.load_state_dict(checkpoint['generator'], strict=False)
else:
if model_opt.param_init != 0.0:
for p in model.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
for p in generator.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
if model_opt.param_init_glorot:
for p in model.parameters():
if p.dim() > 1:
xavier_uniform_(p)
for p in generator.parameters():
if p.dim() > 1:
xavier_uniform_(p)
if hasattr(model.encoder, 'embeddings'):
model.encoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_enc, model_opt.fix_word_vecs_enc)
if hasattr(model.decoder, 'embeddings'):
model.decoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_dec, model_opt.fix_word_vecs_dec)
# Add generator to model (this registers it as parameter of model).
model.generator = generator
model.to(device)
return model
def build_base_model(model_opt, fields, gpu, checkpoint=None):
"""
Args:
model_opt: the option loaded from checkpoint.
fields: `Field` objects for the model.
gpu(bool): whether to use gpu.
checkpoint: the model gnerated by train phase, or a resumed snapshot
model from a stopped training.
Returns:
the NMTModel.
"""
assert model_opt.model_type in ["text", "img", "audio"], \
("Unsupported model type %s" % (model_opt.model_type))
# Build encoder.
if model_opt.model_type == "text":
src_dict = fields["src"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'src')
src_embeddings = build_embeddings(model_opt, src_dict, feature_dicts)
encoder = build_encoder(model_opt, src_embeddings)
elif model_opt.model_type == "img":
encoder = ImageEncoder(model_opt.enc_layers, model_opt.brnn,
model_opt.rnn_size, model_opt.dropout)
elif model_opt.model_type == "audio":
encoder = AudioEncoder(model_opt.enc_layers, model_opt.brnn,
model_opt.rnn_size, model_opt.dropout,
model_opt.sample_rate, model_opt.window_size)
# Build decoder.
tgt_dict = fields["tgt"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'tgt')
tgt_embeddings = build_embeddings(model_opt,
tgt_dict,
feature_dicts,
for_encoder=False)
# Share the embedding matrix - preprocess with share_vocab required.
if model_opt.share_embeddings:
# src/tgt vocab should be the same if `-share_vocab` is specified.
if src_dict != tgt_dict:
raise AssertionError('The `-share_vocab` should be set during '
'preprocess if you use share_embeddings!')
tgt_embeddings.word_lut.weight = src_embeddings.word_lut.weight
decoder = build_decoder(model_opt, tgt_embeddings)
# Build NMTModel(= encoder + decoder).
device = torch.device("cuda" if gpu else "cpu")
model = onmt.models.NMTModel(encoder, decoder)
model.model_type = model_opt.model_type
# Build Generator.
if not model_opt.copy_attn:
generator = nn.Sequential(
nn.Linear(model_opt.rnn_size, len(fields["tgt"].vocab)),
nn.LogSoftmax(dim=-1))
if model_opt.share_decoder_embeddings:
generator[0].weight = decoder.embeddings.word_lut.weight
else:
generator = CopyGenerator(model_opt.rnn_size, fields["tgt"].vocab)
# Load the model states from checkpoint or initialize them.
if checkpoint is not None:
model.load_state_dict(checkpoint['model'])
generator.load_state_dict(checkpoint['generator'])
else:
if model_opt.param_init != 0.0:
for p in model.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
for p in generator.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
if model_opt.param_init_glorot:
for p in model.parameters():
if p.dim() > 1:
xavier_uniform_(p)
for p in generator.parameters():
if p.dim() > 1:
xavier_uniform_(p)
if hasattr(model.encoder, 'embeddings'):
model.encoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_enc, model_opt.fix_word_vecs_enc)
if hasattr(model.decoder, 'embeddings'):
model.decoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_dec, model_opt.fix_word_vecs_dec)
if model_opt.share_embeddings:
assert model.encoder.embeddings.word_lut.weight \
is model.decoder.embeddings.word_lut.weight
# Add generator to model (this registers it as parameter of model).
model.generator = generator
model.to(device)
return model
def build_end2end_model(model_opt,
fields,
gpu,
checkpoint=None,
sel_checkpoint=None,
s2s_gen_checkpoint=None):
"""
Args:
model_opt: the option loaded from checkpoint.
fields: `Field` objects for the model.
gpu(bool): whether to use gpu.
checkpoint: the model gnerated by train phase, or a resumed snapshot
model from a stopped training.
sel_checkpoint: the model gnerated by selector pre-train phase.
Returns:
the E2EModel.
"""
assert model_opt.model_type in ["text"], \
("Unsupported model type %s" % (model_opt.model_type))
# Build selector.
src_dict = fields["src"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'src')
sel_src_embeddings = build_embeddings(model_opt, src_dict, feature_dicts)
selector = build_selector(model_opt, sel_src_embeddings)
# Build encoder
if model_opt.e2e_type == "separate_enc_sel":
if model_opt.selector_share_embeddings:
# the shared embeddings are in the encoder.embeddings
# TODO: change the state name to load the embeddings in the pretrained selector embeddings
assert model_opt.load_pretrained_selector_from == ''
src_embeddings = build_embeddings(model_opt, src_dict,
feature_dicts)
src_embeddings.word_lut.weight = sel_src_embeddings.word_lut.weight
else:
src_embeddings = build_embeddings(model_opt, src_dict,
feature_dicts)
encoder = build_encoder(model_opt, src_embeddings)
else:
# model_opt.e2e_type == "share_enc_sel"
src_embeddings = sel_src_embeddings
encoder = None
# Build decoder.
tgt_dict = fields["tgt"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'tgt')
tgt_embeddings = build_embeddings(model_opt,
tgt_dict,
feature_dicts,
for_encoder=False)
# Share the embedding matrix - preprocess with share_vocab required.
if model_opt.share_embeddings:
# src/tgt vocab should be the same if `-share_vocab` is specified.
if src_dict != tgt_dict:
raise AssertionError('The `-share_vocab` should be set during '
'preprocess if you use share_embeddings!')
tgt_embeddings.word_lut.weight = src_embeddings.word_lut.weight
decoder = build_decoder(model_opt, tgt_embeddings)
# Build E2EModel(= encoder + selector + decoder).
device = torch.device("cuda" if gpu else "cpu")
model = onmt.models.E2EModel(encoder,
selector,
decoder,
e2e_type=model_opt.e2e_type,
use_gt_sel_probs=model_opt.use_gt_sel_probs)
model.model_type = model_opt.model_type
# Build Generator.
if not model_opt.copy_attn:
generator = nn.Sequential(
nn.Linear(model_opt.rnn_size, len(fields["tgt"].vocab)),
nn.LogSoftmax(dim=-1))
if model_opt.share_decoder_embeddings:
generator[0].weight = decoder.embeddings.word_lut.weight
else:
generator = CopyGenerator(model_opt.rnn_size, fields["tgt"].vocab)
# Load the model states from checkpoint or initialize them.
if checkpoint is not None:
model.load_state_dict(checkpoint['end2end_model'])
generator.load_state_dict(checkpoint['generator'])
else:
if model_opt.param_init != 0.0:
for p in model.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
for p in generator.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
if model_opt.param_init_glorot:
for p in model.parameters():
if p.dim() > 1:
xavier_uniform_(p)
for p in generator.parameters():
if p.dim() > 1:
xavier_uniform_(p)
if sel_checkpoint is not None:
model.load_state_dict(sel_checkpoint['selector'], strict=False)
if s2s_gen_checkpoint is not None:
model.load_state_dict(s2s_gen_checkpoint['model'], strict=False)
generator.load_state_dict(s2s_gen_checkpoint['generator'])
# if hasattr(model.encoder, 'embeddings'):
# model.encoder.embeddings.load_pretrained_vectors(
# model_opt.pre_word_vecs_enc, model_opt.fix_word_vecs_enc)
# if hasattr(model.decoder, 'embeddings'):
# model.decoder.embeddings.load_pretrained_vectors(
# model_opt.pre_word_vecs_dec, model_opt.fix_word_vecs_dec)
# Add generator to model (this registers it as parameter of model).
model.generator = generator
model.to(device)
return model
def build_base_model(model_opt, fields, gpu, checkpoint=None):
"""
Args:
model_opt: the option loaded from checkpoint.
fields: `Field` objects for the model.
gpu(bool): whether to use gpu.
checkpoint: the model gnerated by train phase, or a resumed snapshot
model from a stopped training.
Returns:
the NMTModel.
"""
assert model_opt.model_type in ["text", "img", "audio"], \
("Unsupported model type %s" % (model_opt.model_type))
# Build encoder.
if model_opt.model_type == "text":
src_dict = fields["src"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'src')
src_embeddings = build_embeddings(model_opt, src_dict, feature_dicts)
encoder = build_encoder(
model_opt, src_embeddings
) # we added additional encoder: TransformerEncoderLM
elif model_opt.model_type == "img":
if ("image_channel_size" not in model_opt.__dict__):
image_channel_size = 3
else:
image_channel_size = model_opt.image_channel_size
encoder = ImageEncoder(model_opt.enc_layers, model_opt.brnn,
model_opt.enc_rnn_size, model_opt.dropout,
image_channel_size)
elif model_opt.model_type == "audio":
encoder = AudioEncoder(model_opt.rnn_type, model_opt.enc_layers,
model_opt.dec_layers, model_opt.brnn,
model_opt.enc_rnn_size, model_opt.dec_rnn_size,
model_opt.audio_enc_pooling, model_opt.dropout,
model_opt.sample_rate, model_opt.window_size)
# Build decoder.
tgt_dict = fields["tgt"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'tgt')
tgt_embeddings = build_embeddings(model_opt,
tgt_dict,
feature_dicts,
for_encoder=False)
# Share the embedding matrix - preprocess with share_vocab required.
if model_opt.share_embeddings:
# src/tgt vocab should be the same if `-share_vocab` is specified.
if src_dict != tgt_dict:
raise AssertionError('The `-share_vocab` should be set during '
'preprocess if you use share_embeddings!')
tgt_embeddings.word_lut.weight = src_embeddings.word_lut.weight
decoder = build_decoder(model_opt, tgt_embeddings)
lm_aux = model_opt.encoder_type == "transformerAuxLTR"
# Build NMTModel(= encoder + decoder).
device = torch.device("cuda" if gpu else "cpu")
# the model will return more stuff
model = onmt.models.NMTModel(encoder, decoder, lm_aux=lm_aux)
# Build Generator.
# Hmmm...generator is just hidden states -> word in vocab
# since we use shared embedding between encoder and decoder..plus shared embedding between
# decoder src to tgt...
if not model_opt.copy_attn:
if model_opt.generator_function == "sparsemax":
gen_func = onmt.modules.sparse_activations.LogSparsemax(dim=-1)
else:
gen_func = nn.LogSoftmax(dim=-1)
generator = nn.Sequential(
nn.Linear(model_opt.dec_rnn_size, len(fields["tgt"].vocab)),
gen_func)
if model_opt.share_decoder_embeddings:
generator[0].weight = decoder.embeddings.word_lut.weight
else:
generator = CopyGenerator(model_opt.dec_rnn_size, fields["tgt"].vocab)
# Build Source Generator
# not considering copy attention right now
if lm_aux:
if model_opt.generator_function == "sparsemax":
gen_func = onmt.modules.sparse_activations.LogSparsemax(dim=-1)
else:
gen_func = nn.LogSoftmax(dim=-1)
# source vocab does not have <s> </s>, but share_vocab might be different...
src_generator = nn.Sequential(
nn.Linear(model_opt.enc_rnn_size, len(fields["src"].vocab)),
gen_func)
# this would have made sure that both encoder and decoder share the same generator
if model_opt.share_decoder_embeddings:
src_generator[0].weight = src_embeddings.word_lut.weight
# Load the model states from checkpoint or initialize them.
if checkpoint is not None:
model.load_state_dict(checkpoint['model'], strict=False)
generator.load_state_dict(checkpoint['generator'], strict=False)
# if lm_aux:
# src_generator.load_state_dict(checkpoint['src_generator'], strict=False)
else:
if model_opt.param_init != 0.0:
for p in model.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
for p in generator.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
if lm_aux:
for p in src_generator.parameters():
p.data.uniform_(-model_opt.param_init,
model_opt.param_init)
if model_opt.param_init_glorot:
for p in model.parameters():
if p.dim() > 1:
xavier_uniform_(p)
for p in generator.parameters():
if p.dim() > 1:
xavier_uniform_(p)
if lm_aux:
for p in src_generator.parameters():
if p.dim() > 1:
xavier_uniform_(p)
if hasattr(model.encoder, 'embeddings'):
model.encoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_enc, model_opt.fix_word_vecs_enc)
if hasattr(model.decoder, 'embeddings'):
model.decoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_dec, model_opt.fix_word_vecs_dec)
# Add generator to model (this registers it as parameter of model).
model.generator = generator
if lm_aux:
model.src_generator = src_generator
model.to(device)
return model
def build_base_model(model_opt, fields, gpu, checkpoint=None):
"""
Args:
model_opt: the option loaded from checkpoint.
fields: `Field` objects for the model.
gpu(bool): whether to use gpu.
checkpoint: the model gnerated by train phase, or a resumed snapshot
model from a stopped training.
Returns:
the NMTModel.
"""
assert model_opt.model_type in ["text", "img", "audio"], \
("Unsupported model type %s" % (model_opt.model_type))
# Build encoder.
src_dict = fields["src"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'src')
src_embeddings = build_embeddings(model_opt, src_dict, feature_dicts)
encoder = build_encoder(model_opt, src_embeddings)
# Build decoder.
tgt_dict = fields["tgt"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'tgt')
tgt_embeddings = build_embeddings(model_opt,
tgt_dict,
feature_dicts,
for_encoder=False)
# Share the embedding matrix - preprocess with share_vocab required.
if model_opt.share_embeddings:
# src/tgt vocab should be the same if `-share_vocab` is specified.
if src_dict != tgt_dict:
raise AssertionError('The `-share_vocab` should be set during '
'preprocess if you use share_embeddings!')
tgt_embeddings.word_lut.weight = src_embeddings.word_lut.weight
decoder = build_decoder(model_opt, tgt_embeddings)
# Build NMTModel(= [session encoder] + encoder + decoder).
device = torch.device("cuda" if gpu else "cpu")
if model_opt.experiment == 'session':
# Build Session Encoder.
item_embeddings = build_embeddings(model_opt,
fields["src_item_sku"].vocab, [],
for_user=True)
user_log_embeddings = build_embeddings(model_opt,
fields["src_user_log"].vocab,
[],
for_user=True)
user_op_embeddings = build_embeddings(model_opt,
fields["src_operator"].vocab, [],
for_user=True)
user_site_cy_embeddings = build_embeddings(model_opt,
fields["src_site_cy"].vocab,
[],
for_user=True)
user_site_pro_embeddings = build_embeddings(
model_opt, fields["src_site_pro"].vocab, [], for_user=True)
user_site_ct_embeddings = build_embeddings(model_opt,
fields["src_site_ct"].vocab,
[],
for_user=True)
session_encoder = SessionEncoder(item_embeddings, user_log_embeddings,
user_op_embeddings,
user_site_cy_embeddings,
user_site_pro_embeddings,
user_site_ct_embeddings)
else:
session_encoder = None
model = onmt.models.NMTModel(session_encoder, encoder, decoder)
model.model_type = model_opt.model_type
# Build Generator. Copy Generator.
generator = CopyGenerator(model_opt.rnn_size, fields["tgt"].vocab,
model_opt.session_weight,
model_opt.explanation_weight)
# Load the model states from checkpoint or initialize them.
if checkpoint is not None:
model.load_state_dict(checkpoint['model'])
generator.load_state_dict(checkpoint['generator'])
else:
if model_opt.param_init != 0.0:
for p in model.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
for p in generator.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
if model_opt.param_init_glorot:
for p in model.parameters():
if p.dim() > 1:
xavier_uniform_(p)
for p in generator.parameters():
if p.dim() > 1:
xavier_uniform_(p)
if hasattr(model.encoder, 'embeddings'):
model.encoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_enc, model_opt.fix_word_vecs_enc)
if hasattr(model.decoder, 'embeddings'):
model.decoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_dec, model_opt.fix_word_vecs_dec)
# Add generator to model (this registers it as parameter of model).
model.generator = generator
model.to(device)
return model
def build_base_model(model_opt, fields, gpu, checkpoint=None):
"""
Args:
model_opt: the option loaded from checkpoint.
fields: `Field` objects for the model.
gpu(bool): whether to use gpu.
checkpoint: the model gnerated by train phase, or a resumed snapshot
model from a stopped training.
Returns:
the NMTModel.
"""
assert model_opt.model_type in ["text", "img", "audio"], \
("Unsupported model type %s" % (model_opt.model_type))
# for backward compatibility
if model_opt.rnn_size != -1:
model_opt.enc_rnn_size = model_opt.rnn_size
model_opt.dec_rnn_size = model_opt.rnn_size
if model_opt.model_type == 'text' and \
model_opt.enc_rnn_size != model_opt.dec_rnn_size:
raise AssertionError("""We do not support different encoder and
decoder rnn sizes for translation now.""")
# Build encoder.
if model_opt.model_type == "text":
src_dict = fields["src"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'src')
src_embeddings = build_embeddings(model_opt, src_dict, feature_dicts)
encoder = build_encoder(model_opt, src_embeddings)
elif model_opt.model_type == "img":
if ("image_channel_size" not in model_opt.__dict__):
image_channel_size = 3
else:
image_channel_size = model_opt.image_channel_size
encoder = ImageEncoder(model_opt.enc_layers, model_opt.brnn,
model_opt.enc_rnn_size, model_opt.dropout,
image_channel_size)
elif model_opt.model_type == "audio":
encoder = AudioEncoder(model_opt.rnn_type, model_opt.enc_layers,
model_opt.dec_layers, model_opt.brnn,
model_opt.enc_rnn_size, model_opt.dec_rnn_size,
model_opt.audio_enc_pooling, model_opt.dropout,
model_opt.sample_rate, model_opt.window_size)
# Build decoder.
tgt_dict = fields["tgt"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'tgt')
tgt_embeddings = build_embeddings(model_opt,
tgt_dict,
feature_dicts,
for_encoder=False)
# Share the embedding matrix - preprocess with share_vocab required.
if model_opt.share_embeddings:
# src/tgt vocab should be the same if `-share_vocab` is specified.
if src_dict != tgt_dict:
raise AssertionError('The `-share_vocab` should be set during '
'preprocess if you use share_embeddings!')
tgt_embeddings.word_lut.weight = src_embeddings.word_lut.weight
decoder = build_decoder(model_opt, tgt_embeddings)
# Build NMTModel(= encoder + decoder).
device = torch.device("cuda" if gpu else "cpu")
model = onmt.models.NMTModel(encoder, decoder)
# The generator function only matters at translation time, so it is not
# necessary to create it here anymore. At translation time, the model's
# model_opt will still have a value for generator_function or loss_alpha.
# This is sufficient to figure out what function to use at translation
# time.
generator = nn.Sequential(
nn.Linear(model_opt.dec_rnn_size, len(fields["tgt"].vocab)))
if model_opt.share_decoder_embeddings:
generator[0].weight = decoder.embeddings.word_lut.weight
# Load the model states from checkpoint or initialize them.
if checkpoint is not None:
# This preserves backward-compat for models using customed layernorm
def fix_key(s):
s = re.sub(r'(.*)\.layer_norm((_\d+)?)\.b_2',
r'\1.layer_norm\2.bias', s)
s = re.sub(r'(.*)\.layer_norm((_\d+)?)\.a_2',
r'\1.layer_norm\2.weight', s)
return s
checkpoint['model'] = \
{fix_key(k): v for (k, v) in checkpoint['model'].items()}
# end of patch for backward compatibility
model.load_state_dict(checkpoint['model'], strict=False)
generator.load_state_dict(checkpoint['generator'], strict=False)
else:
if model_opt.param_init != 0.0:
for p in model.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
for p in generator.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
if model_opt.param_init_glorot:
for p in model.parameters():
if p.dim() > 1:
xavier_uniform_(p)
for p in generator.parameters():
if p.dim() > 1:
xavier_uniform_(p)
if hasattr(model.encoder, 'embeddings'):
model.encoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_enc, model_opt.fix_word_vecs_enc)
if hasattr(model.decoder, 'embeddings'):
model.decoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_dec, model_opt.fix_word_vecs_dec)
# Add generator to model (this registers it as parameter of model).
model.generator = generator
model.to(device)
return model
def build_base_model(model_opt,
fields,
gpu,
length_model,
length_penalty_a,
length_penalty_b,
checkpoint=None):
"""
Args:
model_opt: the option loaded from checkpoint.
fields: `Field` objects for the model.
gpu(bool): whether to use gpu.
checkpoint: the model gnerated by train phase, or a resumed snapshot
model from a stopped training.
Returns:
the NMTModel.
"""
assert model_opt.model_type in ["text", "img", "audio"], \
("Unsupported model type %s" % (model_opt.model_type))
# for backward compatibility
if model_opt.rnn_size != -1:
model_opt.enc_rnn_size = model_opt.rnn_size
model_opt.dec_rnn_size = model_opt.rnn_size
if model_opt.model_type == 'text' and \
model_opt.enc_rnn_size != model_opt.dec_rnn_size:
raise AssertionError("""We do not support different encoder and
decoder rnn sizes for translation now.""")
# Build encoder.
if model_opt.model_type == "text":
src_dict = fields["src"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'src')
src_embeddings = build_embeddings(model_opt, src_dict, feature_dicts)
encoder = build_encoder(model_opt, src_embeddings)
elif model_opt.model_type == "img":
if ("image_channel_size" not in model_opt.__dict__):
image_channel_size = 3
else:
image_channel_size = model_opt.image_channel_size
encoder = ImageEncoder(model_opt.enc_layers, model_opt.brnn,
model_opt.enc_rnn_size, model_opt.dropout,
image_channel_size)
elif model_opt.model_type == "audio":
encoder = AudioEncoder(model_opt.rnn_type, model_opt.enc_layers,
model_opt.dec_layers, model_opt.brnn,
model_opt.enc_rnn_size, model_opt.dec_rnn_size,
model_opt.audio_enc_pooling, model_opt.dropout,
model_opt.sample_rate, model_opt.window_size)
# Build decoder.
tgt_dict = fields["tgt"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'tgt')
tgt_embeddings = build_embeddings(model_opt,
tgt_dict,
feature_dicts,
for_encoder=False)
# Share the embedding matrix - preprocess with share_vocab required.
if model_opt.share_embeddings:
# src/tgt vocab should be the same if `-share_vocab` is specified.
if src_dict != tgt_dict:
raise AssertionError('The `-share_vocab` should be set during '
'preprocess if you use share_embeddings!')
tgt_embeddings.word_lut.weight = src_embeddings.word_lut.weight
decoder = build_decoder(model_opt, tgt_embeddings)
# Build NMTModel(= encoder + decoder).
device = torch.device("cuda" if gpu else "cpu")
model = onmt.models.NMTModel(encoder, decoder)
# Build Generator.
if not model_opt.copy_attn:
if model_opt.generator_function == "sparsemax":
gen_func = onmt.modules.sparse_activations.LogSparsemax(dim=-1)
else:
gen_func = nn.LogSoftmax(dim=-1)
# generator = nn.Sequential(
# nn.Linear(model_opt.dec_rnn_size, len(fields["tgt"].vocab)),
# gen_func
# )
# MMM
class tune_out_prob(nn.Module):
def __init__(self):
super(tune_out_prob, self).__init__()
self.t_lens = None
self.eos_ind = None
self.batch_max_len = None
self.word_index = None
self.tgt_vocab_size = None
self.validation = False
def length_model_loss(self, scale, value, a, b):
#return -(value / scale) ** 2 - scale.log()
#return -((value / scale) **2)/2 - (2.5066*scale).log()
return -a * (value / scale)**2 + b #*abs(scale)
# return -((value / scale) ** 2)*scale + scale
#return -(value / scale)*4 + scale
def forward(self, x):
y = x.clone()
#mask = np.ones(x.size())
# for i in range(self.t_lens.size(-1)):
# y[i*self.batch_size + self.t_lens[i], self.eos_ind] = \
# y[i * self.batch_size + self.t_lens[i], self.eos_ind].clone() + math.log(0.9)
if self.training or self.validation: # training phase
y = y.view(self.batch_max_len, -1, self.tgt_vocab_size)
# eos_list = [(i * self.batch_max_len + self.t_lens.data.cpu().numpy()[i]) for i in
# range(self.t_lens.size(-1))]
# other_list = list(set(list(range(x.size(0)))) - set(eos_list))
# y[other_list, self.eos_ind] = -100
# y[eos_list, self.eos_ind] = 0
for wi in range(self.batch_max_len):
delta_p = (self.t_lens - wi - 1).float()
delta_p[delta_p < 0] = 0.05 * delta_p[delta_p < 0]
scale = (self.t_lens.float()).sqrt() / 2.0
penalties = self.length_model_loss(
scale, delta_p, length_penalty_a, length_penalty_b)
#penalties[penalties > 0] = 0
y[wi, :, self.eos_ind] += penalties
y = y.view(-1, self.tgt_vocab_size)
#mask[eos_list, self.eos_ind] = +2
#mask[other_list, self.eos_ind] = -2
else: # translation phase
if len(
x.size()
) == 3: # x of shape [ tgt_len, batch_size, vocab ] is a full sentence
# for i in range(len(self.t_lens)):
# other_list = list(set(list(range(x.size(0)))) - set(list([self.t_lens.data.cpu().numpy()[i]])))
# #mask[other_list, i, self.eos_ind] = -2
# y[other_list, i, self.eos_ind] = -100
# if self.t_lens[i] < x.size(0):
# #mask[self.t_lens[i], i, self.eos_ind] = +2
# y[self.t_lens[i], i, self.eos_ind] = 0
pass
else: # x of shape [(batch_size x beam_size) , vocab ] is only for one step
beam_size = x.size(0) // self.t_lens.numel()
wi = self.word_index
delta_p = (self.t_lens - wi - 2).float()
delta_p[delta_p < 0] = 0.005 * delta_p[delta_p < 0]
delta_p = delta_p.unsqueeze(1).expand(
self.t_lens.numel(), beam_size).flatten()
scale = (self.t_lens.float()).sqrt() / 2.0
scale = scale.unsqueeze(1).expand(
self.t_lens.numel(), beam_size).flatten()
penalties = self.length_model_loss(
scale, delta_p, length_penalty_a, length_penalty_b)
#penalties[penalties > 0] = 0
y[:, self.eos_ind] += penalties
#y[eos_list ^ 1, self.eos_ind] = -100
return y
#mask = torch.tensor(mask, dtype=x.dtype).to(device)
#x= x+mask
#return x
# y = x.clone()
# # 1. since y is the output of log_softmax, apply exponential
# # to convert it to probabilistic form
# y = torch.exp(y)
# # 2. tune probabilities
# eos_list = [(i * self.batch_max_len + self.t_lens.data.cpu().numpy()[i]) for i in
# range(self.t_lens.size(-1))]
# other_list = list(set(list(range(y.size(0)))) - set(eos_list))
#
# z = y.clone()
# # 2.1. tune probabilities for eos positions
# z[eos_list, self.eos_ind] = 1
# z[eos_list, 0:self.eos_ind] = 0
# z[eos_list, self.eos_ind+1:-1] = 0
#
# # 2.2. tune probabilities for non-eos positions
# p_val = z[other_list, self.eos_ind] / (self.tgt_vocab_size - 1)
# z[other_list, self.eos_ind] = 0
# non_eos_inds = list(set(list(range(self.tgt_vocab_size))) - set([self.eos_ind]))
# for i in range(len(other_list)):
# z[other_list[i], non_eos_inds] = y[other_list[i], non_eos_inds] + p_val[i]
#
# # 3. convert y back to log-probability form
# z = torch.log(z)
# return z
# MMM
if length_model == 'oracle' or length_model == 'fixed_ratio' or length_model == 'lstm':
generator = nn.Sequential(
nn.Linear(model_opt.dec_rnn_size, len(fields["tgt"].vocab)),
gen_func, tune_out_prob())
else:
generator = nn.Sequential(
nn.Linear(model_opt.dec_rnn_size, len(fields["tgt"].vocab)),
gen_func)
# generator = nn.Sequential(
# nn.Linear(model_opt.dec_rnn_size, len(fields["tgt"].vocab)),
# gen_func
# )
if model_opt.share_decoder_embeddings:
generator[0].weight = decoder.embeddings.word_lut.weight
else:
generator = CopyGenerator(model_opt.dec_rnn_size, fields["tgt"].vocab)
# Load the model states from checkpoint or initialize them.
if checkpoint is not None:
# This preserves backward-compat for models using customed layernorm
def fix_key(s):
s = re.sub(r'(.*)\.layer_norm((_\d+)?)\.b_2',
r'\1.layer_norm\2.bias', s)
s = re.sub(r'(.*)\.layer_norm((_\d+)?)\.a_2',
r'\1.layer_norm\2.weight', s)
return s
checkpoint['model'] = \
{fix_key(k): v for (k, v) in checkpoint['model'].items()}
# end of patch for backward compatibility
model.load_state_dict(checkpoint['model'], strict=False)
generator.load_state_dict(checkpoint['generator'], strict=False)
else:
if model_opt.param_init != 0.0:
for p in model.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
for p in generator.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
if model_opt.param_init_glorot:
for p in model.parameters():
if p.dim() > 1:
xavier_uniform_(p)
for p in generator.parameters():
if p.dim() > 1:
xavier_uniform_(p)
if hasattr(model.encoder, 'embeddings'):
model.encoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_enc, model_opt.fix_word_vecs_enc)
if hasattr(model.decoder, 'embeddings'):
model.decoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_dec, model_opt.fix_word_vecs_dec)
# Add generator to model (this registers it as parameter of model).
model.generator = generator
model.to(device)
return model
def build_base_model(model_opt, fields, gpu, checkpoint=None):
"""
Args:
model_opt: the option loaded from checkpoint.
fields: `Field` objects for the model.
gpu(bool): whether to use gpu.
checkpoint: the model gnerated by train phase, or a resumed snapshot
model from a stopped training.
Returns:
the NMTModel.
"""
assert model_opt.model_type == "text", ("Unsupported model type %s" %
(model_opt.model_type))
# Build encoder.
logger.info("build_base_model")
if model_opt.model_type == "text":
src_dict = fields[
"src"].vocab # torchtext.vocab.Vocab object: dict_keys(['vectors', 'stoi', 'freqs', 'itos'])
feature_dicts = inputters.collect_feature_vocabs(fields,
'src') # list: []
src_embeddings = build_embeddings(model_opt, src_dict, feature_dicts)
''' Embeddings(
(make_embedding): Sequential(
(emb_luts): Elementwise(
(0): Embedding(24997, 500, padding_idx=1)
)
)
)
'''
#logger.info("src embeddings")
#logger.info(src_embeddings)
logger.info("bulding question encoder")
encoder = build_encoder(model_opt, src_embeddings)
logger.info(encoder)
############### Modified ###############################
ans_dict = fields["ans"].vocab
ans_embeddings = build_embeddings(model_opt, ans_dict, feature_dicts)
logger.info("building answer encoder")
encoder_ans = build_encoder(model_opt, ans_embeddings)
logger.info(encoder_ans)
##########################################################s
# Build decoder.
tgt_dict = fields["tgt"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'tgt')
tgt_embeddings = build_embeddings(model_opt,
tgt_dict,
feature_dicts,
for_encoder=False)
# Share the embedding matrix - preprocess with share_vocab required.
if model_opt.share_embeddings:
# src/tgt vocab should be the same if `-share_vocab` is specified.
if src_dict != tgt_dict:
raise AssertionError('The `-share_vocab` should be set during '
'preprocess if you use share_embeddings!')
tgt_embeddings.word_lut.weight = src_embeddings.word_lut.weight
decoder = build_decoder(model_opt, tgt_embeddings)
# Build NMTModel(= encoder + decoder).
device = torch.device("cuda" if gpu else "cpu")
model = NMTModel(encoder, encoder_ans, decoder)
model.model_type = model_opt.model_type
# Build Generator.
if not model_opt.copy_attn:
generator = nn.Sequential(
nn.Linear(model_opt.rnn_size * 2, len(fields["tgt"].vocab)),
nn.LogSoftmax(dim=-1))
if model_opt.share_decoder_embeddings:
generator[0].weight = decoder.embeddings.word_lut.weight
else:
generator = CopyGenerator(model_opt.rnn_size, fields["tgt"].vocab)
# Load the model states from checkpoint or initialize them.
if checkpoint is not None:
model.load_state_dict(checkpoint['model'])
generator.load_state_dict(checkpoint['generator'])
else:
if model_opt.param_init != 0.0:
for p in model.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
for p in generator.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
if model_opt.param_init_glorot:
for p in model.parameters():
if p.dim() > 1:
xavier_uniform_(p)
for p in generator.parameters():
if p.dim() > 1:
xavier_uniform_(p)
if hasattr(model.encoder, 'embeddings'):
model.encoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_enc, model_opt.fix_word_vecs_enc)
if hasattr(model.decoder, 'embeddings'):
model.decoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_dec, model_opt.fix_word_vecs_dec)
# Add generator to model (this registers it as parameter of model).
model.generator = generator
model.to(device)
return model
def build_base_model(model_opt,
fields,
gpu,
FeatureValues,
FeatureTensors,
FeatureTypes,
FeaturesList,
FeatureNames,
FTInfos,
FeatureTypesNames,
SimulationLanguages,
checkpoint=None):
"""
Args:
model_opt: the option loaded from checkpoint.
fields: `Field` objects for the model.
gpu(bool): whether to use gpu.
WALS info
checkpoint: the model gnerated by train phase, or a resumed snapshot
model from a stopped training.
Returns:
the NMTModel.
"""
assert model_opt.model_type in ["text", "img", "audio"], \
("Unsupported model type %s" % (model_opt.model_type))
# Build encoder.
if model_opt.model_type == "text":
src_dict = fields["src"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'src')
src_embeddings = build_embeddings(model_opt, src_dict, feature_dicts)
encoder = build_encoder(model_opt, src_embeddings)
elif model_opt.model_type == "img":
if ("image_channel_size" not in model_opt.__dict__):
image_channel_size = 3
else:
image_channel_size = model_opt.image_channel_size
encoder = ImageEncoder(model_opt.enc_layers, model_opt.brnn,
model_opt.rnn_size, model_opt.dropout,
image_channel_size)
elif model_opt.model_type == "audio":
encoder = AudioEncoder(model_opt.enc_layers, model_opt.brnn,
model_opt.rnn_size, model_opt.dropout,
model_opt.sample_rate, model_opt.window_size)
# Build decoder.
tgt_dict = fields["tgt"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'tgt')
tgt_embeddings = build_embeddings(model_opt,
tgt_dict,
feature_dicts,
for_encoder=False)
# Share the embedding matrix - preprocess with share_vocab required.
if model_opt.share_embeddings:
# src/tgt vocab should be the same if `-share_vocab` is specified.
if src_dict != tgt_dict:
raise AssertionError('The `-share_vocab` should be set during '
'preprocess if you use share_embeddings!')
tgt_embeddings.word_lut.weight = src_embeddings.word_lut.weight
decoder = build_decoder(model_opt, tgt_embeddings)
# Wals
print(
'Building embeddings for each WALS feature and MLP models for each feature type...'
)
embeddings_list, embeddings_keys, mlp_list, mlp_keys = [], [], [], []
for FeatureType in FeatureTypes:
list_features = FeatureType[1]
for Feature in list_features:
globals()['embedding_%s' % Feature] = build_feature_embeddings(
gpu, FeatureTensors, FeaturesList, FeatureNames,
Feature) # 192 embedding structures, one for each feature.
embeddings_keys.append(Feature)
embeddings_list.append(globals()['embedding_%s' % Feature])
globals()['mlp_%s' % FeatureType[0]] = build_mlp_feature_type(
model_opt, FTInfos, FeatureTypesNames,
FeatureType[0]) # 11 MLPs, one for each feature type.
mlp_keys.append(FeatureType[0])
mlp_list.append(globals()['mlp_%s' % FeatureType[0]])
embeddings_dic_keys = dict(zip(embeddings_keys, embeddings_list))
EmbeddingFeatures = nn.ModuleDict(embeddings_dic_keys)
mlp_dic_keys = dict(zip(mlp_keys, mlp_list))
# Build NMTModel(= encoder + decoder).
device = torch.device("cuda" if gpu else "cpu")
if model_opt.wals_model == 'EncInitHidden_Target':
MLP2RNNHiddenSize_Target = build_mlp2rnnhiddensize_target(
model_opt, FTInfos)
print('Embeddings for WALS features and MLP models are built!')
model = EncoderInitialization(model_opt.wals_model, encoder, decoder,
MLP2RNNHiddenSize_Target,
EmbeddingFeatures, FeatureValues,
FeatureTypes, SimulationLanguages,
model_opt)
print(
"Model created: uses WALS features from the target language to initialize encoder's hidden state."
)
elif model_opt.wals_model == 'EncInitHidden_Both':
MLP2RNNHiddenSize_Both = build_mlp2rnnhiddensize_both(
model_opt, FTInfos)
print('Embeddings for WALS features and MLP models are built!')
model = EncoderInitialization(model_opt.wals_model, encoder, decoder,
MLP2RNNHiddenSize_Both,
EmbeddingFeatures, FeatureValues,
FeatureTypes, SimulationLanguages,
model_opt)
print(
"Model created: uses WALS features from the source and target languages to initialize encoder's hidden state."
)
elif model_opt.wals_model == 'DecInitHidden_Target':
MLP2RNNHiddenSize_Target = build_mlp2rnnhiddensize_target(
model_opt, FTInfos)
print('Embeddings for WALS features and MLP models are built!')
model = DecoderInitialization(model_opt.wals_model, encoder, decoder,
MLP2RNNHiddenSize_Target,
EmbeddingFeatures, FeatureValues,
FeatureTypes, SimulationLanguages,
model_opt)
print(
"Model created: adds WALS features from the target language to the encoder's output to initialize decoder's hidden state."
)
elif model_opt.wals_model == 'DecInitHidden_Both':
MLP2RNNHiddenSize_Both = build_mlp2rnnhiddensize_both(
model_opt, FTInfos)
print('Embeddings for WALS features and MLP models are built!')
model = DecoderInitialization(model_opt.wals_model, encoder, decoder,
MLP2RNNHiddenSize_Both,
EmbeddingFeatures, FeatureValues,
FeatureTypes, SimulationLanguages,
model_opt)
print(
"Model created: adds WALS features from the source and target languages to the encoder's output to initialize decoder's hidden state."
)
elif model_opt.wals_model == 'WalstoSource_Target':
MLP2WALSHiddenSize_Target = build_mlp2walshiddensize_target(
model_opt, FTInfos)
print('Embeddings for WALS features and MLP models are built!')
model = CombineWalsSourceWords(model_opt.wals_model, encoder, decoder,
MLP2WALSHiddenSize_Target,
EmbeddingFeatures, FeatureValues,
FeatureTypes, SimulationLanguages,
model_opt)
print(
"Model created: concatenates WALS features from the target language to source words embeddings."
)
elif model_opt.wals_model == 'WalstoSource_Both':
MLP2WALSHiddenSize_Both = build_mlp2walshiddensize_both(
model_opt, FTInfos)
print('Embeddings for WALS features and MLP models are built!')
model = CombineWalsSourceWords(model_opt.wals_model, encoder, decoder,
MLP2WALSHiddenSize_Both,
EmbeddingFeatures, FeatureValues,
FeatureTypes, SimulationLanguages,
model_opt)
print(
"Model created: concatenates WALS features from the source and target languages to source words embeddings."
)
elif model_opt.wals_model == 'WalstoTarget_Target':
MLP2WALSHiddenSize_Target = build_mlp2walshiddensize_target(
model_opt, FTInfos)
print('Embeddings for WALS features and MLP models are built!')
model = CombineWalsTargetWords(model_opt.wals_model, encoder, decoder,
MLP2WALSHiddenSize_Target,
EmbeddingFeatures, FeatureValues,
FeatureTypes, SimulationLanguages,
model_opt)
print(
"Model created: concatenates WALS features from the target language to target words embeddings."
)
elif model_opt.wals_model == 'WalstoTarget_Both':
MLP2WALSHiddenSize_Both = build_mlp2walshiddensize_both(
model_opt, FTInfos)
print('Embeddings for WALS features and MLP models are built!')
model = CombineWalsTargetWords(model_opt.wals_model, encoder, decoder,
MLP2WALSHiddenSize_Both,
EmbeddingFeatures, FeatureValues,
FeatureTypes, SimulationLanguages,
model_opt)
print(
"Model created: concatenates WALS features from the source and target languages to target words embeddings."
)
elif model_opt.wals_model == 'WalsDoublyAttentive_Target':
MLPFeatureTypes = nn.ModuleDict(mlp_dic_keys)
MLP_AttentionTarget = build_doublyattentive_target(model_opt)
print('Embeddings for WALS features and MLP models are built!')
model = WalsDoublyAttention(model_opt.wals_model, encoder, decoder,
MLP_AttentionTarget, MLPFeatureTypes,
EmbeddingFeatures, FeatureValues,
FeatureTypes, SimulationLanguages,
model_opt)
print(
"Model created: the WALS features from the target language are incorporated as an additional attention mechanism."
)
elif model_opt.wals_model == 'WalsDoublyAttentive_Both':
MLPFeatureTypes = nn.ModuleDict(mlp_dic_keys)
MLP_AttentionBoth = build_doublyattentive_both(model_opt)
print('Embeddings for WALS features and MLP models are built!')
model = WalsDoublyAttention(model_opt.wals_model, encoder, decoder,
MLP_AttentionBoth, MLPFeatureTypes,
EmbeddingFeatures, FeatureValues,
FeatureTypes, SimulationLanguages,
model_opt)
print(
"Model created: the WALS features from the source and target languages are incorporated as an additional attention mechanism."
)
elif model_opt.wals_model == 'WalstoDecHidden_Target':
MLP2WALSHiddenSize_Target = build_mlp2walshiddensize_target(
model_opt, FTInfos)
print('Embeddings for WALS features and MLP models are built!')
model = WalstoDecHidden(model_opt.wals_model, encoder, decoder,
MLP2WALSHiddenSize_Target, EmbeddingFeatures,
FeatureValues, FeatureTypes,
SimulationLanguages, model_opt)
print(
"Model created: concatenates WALS features from the target language to decoder hidden state."
)
elif model_opt.wals_model == 'WalstoDecHidden_Both':
MLP2WALSHiddenSize_Both = build_mlp2walshiddensize_both(
model_opt, FTInfos)
print('Embeddings for WALS features and MLP models are built!')
model = WalstoDecHidden(model_opt.wals_model, encoder, decoder,
MLP2WALSHiddenSize_Both, EmbeddingFeatures,
FeatureValues, FeatureTypes,
SimulationLanguages, model_opt)
print(
"Model created: concatenates WALS features from the source and target languages to decoder hidden state."
)
else:
raise Exception("WALS model type not yet implemented: %s" %
(opt.wals_model))
model.model_type = model_opt.model_type
# Build Generator.
if not model_opt.copy_attn:
if model_opt.generator_function == "sparsemax":
gen_func = onmt.modules.sparse_activations.LogSparsemax(dim=-1)
else:
gen_func = nn.LogSoftmax(dim=-1)
generator = nn.Sequential(
nn.Linear(model_opt.rnn_size, len(fields["tgt"].vocab)), gen_func)
if model_opt.share_decoder_embeddings:
generator[0].weight = decoder.embeddings.word_lut.weight
else:
generator = CopyGenerator(model_opt.rnn_size, fields["tgt"].vocab)
# Load the model states from checkpoint or initialize them.
if checkpoint is not None:
model.load_state_dict(checkpoint['model'])
generator.load_state_dict(checkpoint['generator'])
else:
if model_opt.param_init != 0.0:
for p in model.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
for p in generator.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
if model_opt.param_init_glorot:
for p in model.parameters():
if p.dim() > 1:
xavier_uniform_(p)
for p in generator.parameters():
if p.dim() > 1:
xavier_uniform_(p)
if hasattr(model.encoder, 'embeddings'):
model.encoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_enc, model_opt.fix_word_vecs_enc)
if hasattr(model.decoder, 'embeddings'):
model.decoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_dec, model_opt.fix_word_vecs_dec)
# Add generator to model (this registers it as parameter of model).
model.generator = generator
model.to(device)
return model
def build_lm_bias_base_model(model_opt,
fields,
gpu,
checkpoint=None,
lm_out_checkpoint=None,
lm_in_checkpoint=None):
"""
Args:
model_opt: the option loaded from checkpoint.
fields: `Field` objects for the model.
gpu(bool): whether to use gpu.
checkpoint: the model gnerated by train phase, or a resumed snapshot
model from a stopped training.
Returns:
the NMTModel.
"""
assert model_opt.model_type in ["text"], \
("Unsupported model type %s" % (model_opt.model_type))
# Build encoder.
if model_opt.model_type == "text":
src_dict = fields["src"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'src')
src_embeddings = build_embeddings(model_opt, src_dict, feature_dicts)
lm_out_src_embeddings = build_embeddings(model_opt, src_dict,
feature_dicts)
lm_in_src_embeddings = build_embeddings(model_opt, src_dict,
feature_dicts)
encoder = build_encoder(model_opt, src_embeddings)
lm_out_encoder = build_encoder(model_opt, lm_out_src_embeddings, "lm")
lm_in_encoder = build_encoder(model_opt, lm_in_src_embeddings, "lm")
# Build decoder.
tgt_dict = fields["tgt"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'tgt')
tgt_embeddings = build_embeddings(model_opt,
tgt_dict,
feature_dicts,
for_encoder=False)
lm_out_tgt_embeddings = build_embeddings(model_opt,
tgt_dict,
feature_dicts,
for_encoder=False)
lm_in_tgt_embeddings = build_embeddings(model_opt,
tgt_dict,
feature_dicts,
for_encoder=False)
# Share the embedding matrix - preprocess with share_vocab required.
if model_opt.share_embeddings:
# src/tgt vocab should be the same if `-share_vocab` is specified.
if src_dict != tgt_dict:
raise AssertionError('The `-share_vocab` should be set during '
'preprocess if you use share_embeddings!')
tgt_embeddings.word_lut.weight = src_embeddings.word_lut.weight
decoder = build_decoder(model_opt, tgt_embeddings)
lm_out_decoder = build_decoder(model_opt, lm_out_tgt_embeddings, "lm")
lm_in_decoder = build_decoder(model_opt, lm_in_tgt_embeddings, "lm")
# Build NMTModel(= encoder + decoder).
device = torch.device("cuda" if gpu else "cpu")
model = onmt.models.NMTModel(encoder, decoder)
lm_out_model = onmt.models.LMModel(lm_out_encoder, lm_out_decoder)
lm_in_model = onmt.models.LMModel(lm_in_encoder, lm_in_decoder)
model.model_type = model_opt.model_type
lm_out_model.model_type = model_opt.model_type
lm_in_model.model_type = model_opt.model_type
# Build Generator.
gen_func = nn.LogSoftmax(dim=-1)
generator = nn.Sequential(
nn.Linear(model_opt.rnn_size, len(fields["tgt"].vocab)), gen_func)
if model_opt.share_decoder_embeddings:
generator[0].weight = decoder.embeddings.word_lut.weight
lm_out_generator = nn.Sequential(
nn.Linear(model_opt.rnn_size, len(fields["tgt"].vocab)), gen_func)
lm_in_generator = nn.Sequential(
nn.Linear(model_opt.rnn_size, len(fields["tgt"].vocab)), gen_func)
# Load the model states from checkpoint or initialize them.
assert checkpoint is not None
load_model_dict = {k: checkpoint['model'][k] for k in checkpoint['model']}
model_dict = model.state_dict()
model_dict.update(load_model_dict)
model.load_state_dict(model_dict)
generator.load_state_dict(checkpoint['generator'])
assert lm_out_checkpoint['model'] is not None and lm_in_checkpoint[
'model'] is not None
load_model_dict = {
k: lm_out_checkpoint['model'][k]
for k in lm_out_checkpoint['model']
}
model_dict = lm_out_model.state_dict()
model_dict.update(load_model_dict)
lm_out_model.load_state_dict(model_dict)
lm_out_generator.load_state_dict(lm_out_checkpoint['generator'])
load_model_dict = {
k: lm_in_checkpoint['model'][k]
for k in lm_in_checkpoint['model']
}
model_dict = lm_in_model.state_dict()
model_dict.update(load_model_dict)
lm_in_model.load_state_dict(model_dict)
lm_in_generator.load_state_dict(lm_in_checkpoint['generator'])
# Add generator to model (this registers it as parameter of model).
model.generator = generator
model.lm_out = lm_out_model
model.lm_in = lm_in_model
model.lm_out.generator = lm_out_generator
model.lm_in.generator = lm_in_generator
for param in model.lm_out.parameters():
param.requires_grad = False
for param in model.lm_in.parameters():
param.requires_grad = False
model.to(device)
return model
def build_base_model(model_opt, fields, gpu, checkpoint=None):
"""
Args:
model_opt: the option loaded from checkpoint.
fields: `Field` objects for the model.
gpu(bool): whether to use gpu.
checkpoint: the model gnerated by train phase, or a resumed snapshot
model from a stopped training.
Returns:
the NMTModel.
"""
assert model_opt.model_type in ["text", "img", "audio", "vector"], \
("Unsupported model type %s" % (model_opt.model_type))
use_src_directly_for_dec = False
# Build encoder.
if model_opt.model_type == "text":
src_dict = fields["src"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'src')
src_embeddings = build_embeddings(model_opt, src_dict, feature_dicts)
encoder = build_encoder(model_opt, src_embeddings)
elif model_opt.model_type == "img":
if ("image_channel_size" not in model_opt.__dict__):
image_channel_size = 3
else:
image_channel_size = model_opt.image_channel_size
encoder = ImageEncoder(model_opt.enc_layers, model_opt.brnn,
model_opt.enc_rnn_size, model_opt.dropout,
image_channel_size)
elif model_opt.model_type == "audio":
encoder = AudioEncoder(model_opt.rnn_type, model_opt.enc_layers,
model_opt.dec_layers, model_opt.brnn,
model_opt.enc_rnn_size, model_opt.dec_rnn_size,
model_opt.audio_enc_pooling, model_opt.dropout,
model_opt.sample_rate, model_opt.window_size)
elif model_opt.model_type == "vector":
use_src_directly_for_dec = True
if not hasattr(fields["src"], 'vocab'):
fields["src"].vocab = fields["tgt"].vocab
src_dict = fields["src"].vocab
#self.word_lut.weight.requires_grad = False
feature_dicts = inputters.collect_feature_vocabs(fields, 'src')
tgt_embeddings = build_embeddings(model_opt, src_dict, feature_dicts)
if model_opt.encoder_type == "rnn" or model_opt.encoder_type == "brnn":
encoder = RNNEncoder(model_opt.rnn_type, model_opt.brnn,
model_opt.enc_layers, model_opt.enc_rnn_size,
model_opt.dropout, None, model_opt.bridge)
tgt_embeddings = None
elif model_opt.decoder_type == "cnn":
use_src_directly_for_dec = False
encoder = CNNEncoder(model_opt.enc_layers, model_opt.enc_rnn_size,
model_opt.cnn_kernel_width, model_opt.dropout,
None)
tgt_embeddings = None
else:
encoder = None
# Build decoder.
tgt_dict = fields["tgt"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'tgt')
if model_opt.model_type != "vector":
tgt_embeddings = build_embeddings(model_opt,
tgt_dict,
feature_dicts,
for_encoder=False)
# else:
# tgt_embeddings = None
# Share the embedding matrix - preprocess with share_vocab required.
if model_opt.share_embeddings:
# src/tgt vocab should be the same if `-share_vocab` is specified.
if src_dict != tgt_dict:
raise AssertionError('The `-share_vocab` should be set during '
'preprocess if you use share_embeddings!')
tgt_embeddings.word_lut.weight = src_embeddings.word_lut.weight
decoder = build_decoder(model_opt, tgt_embeddings)
# Build NMTModel(= encoder + decoder).
device = torch.device("cuda" if gpu else "cpu")
if model_opt.decoder_type.startswith("vecdif"):
model = onmt.models.VecModel(
encoder,
decoder,
use_src_directly_for_dec=use_src_directly_for_dec)
else:
model = onmt.models.NMTModel(
encoder,
decoder,
use_src_directly_for_dec=use_src_directly_for_dec)
# Build Generator.
if not model_opt.copy_attn:
if model_opt.generator_function == "sparsemax":
gen_func = onmt.modules.sparse_activations.LogSparsemax(dim=-1)
elif model_opt.generator_function == "sigmoid":
gen_func = nn.Sigmoid()
else:
gen_func = nn.LogSoftmax(dim=-1)
if model_opt.model_type == "vector":
if model_opt.generator_function == "none":
# if model_opt.final_vec_size != model_opt.dec_rnn_size:
# generator = nn.Sequential(
# nn.Linear(model_opt.dec_rnn_size, model_opt.final_vec_size))
# else:
generator = None
else:
generator = nn.Sequential(
nn.Linear(model_opt.dec_rnn_size,
model_opt.final_vec_size), gen_func)
else:
generator = nn.Sequential(
nn.Linear(model_opt.dec_rnn_size, len(fields["tgt"].vocab)),
gen_func)
if model_opt.share_decoder_embeddings:
generator[0].weight = decoder.embeddings.word_lut.weight
else:
generator = CopyGenerator(model_opt.dec_rnn_size, fields["tgt"].vocab)
# Load the model states from checkpoint or initialize them.
if checkpoint is not None:
model.load_state_dict(checkpoint['model'], strict=False)
if generator is not None:
generator.load_state_dict(checkpoint['generator'], strict=False)
else:
if model_opt.param_init != 0.0:
for p in model.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
if generator is not None:
for p in generator.parameters():
p.data.uniform_(-model_opt.param_init,
model_opt.param_init)
if model_opt.param_init_glorot:
for p in model.parameters():
if p.dim() > 1:
xavier_uniform_(p)
if generator is not None:
for p in generator.parameters():
if p.dim() > 1:
xavier_uniform_(p)
if hasattr(model.encoder,
'embeddings') and model_opt.model_type != "vector":
model.encoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_enc, model_opt.fix_word_vecs_enc)
if hasattr(model.decoder,
'embeddings') and model_opt.model_type != "vector":
model.decoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_dec, model_opt.fix_word_vecs_dec)
# Add generator to model (this registers it as parameter of model).
model.generator = generator
model.to(device)
return model
def build_base_model(model_opt, fields, gpu, checkpoint=None):
"""
Args:
model_opt: the option loaded from checkpoint.
fields: `Field` objects for the model.
gpu(bool): whether to use gpu.
checkpoint: the model gnerated by train phase, or a resumed snapshot
model from a stopped training.
Returns:
the NMTModel.
"""
assert model_opt.model_type in ["text", "img", "audio"], \
("Unsupported model type %s" % (model_opt.model_type))
# for backward compatibility
if model_opt.rnn_size != -1:
model_opt.enc_rnn_size = model_opt.rnn_size
model_opt.dec_rnn_size = model_opt.rnn_size
if model_opt.model_type == 'text' and \
model_opt.enc_rnn_size != model_opt.dec_rnn_size:
raise AssertionError("""We do not support different encoder and
decoder rnn sizes for translation now.""")
# Build encoder.
logger.info('Building encoder......')
if model_opt.model_type == "text":
src_dict = fields["src"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'src')
src_embeddings = build_embeddings(model_opt, src_dict, feature_dicts)
encoder = build_encoder(model_opt, src_embeddings)
elif model_opt.model_type == "img":
if ("image_channel_size" not in model_opt.__dict__):
image_channel_size = 3
else:
image_channel_size = model_opt.image_channel_size
encoder = ImageEncoder(model_opt.enc_layers,
model_opt.brnn,
model_opt.enc_rnn_size,
model_opt.dropout,
image_channel_size)
elif model_opt.model_type == "audio":
encoder = AudioEncoder(model_opt.rnn_type,
model_opt.enc_layers,
model_opt.dec_layers,
model_opt.brnn,
model_opt.enc_rnn_size,
model_opt.dec_rnn_size,
model_opt.audio_enc_pooling,
model_opt.dropout,
model_opt.sample_rate,
model_opt.window_size)
# Build decoder.
logger.info('Building decoder......')
tgt_dict = fields["tgt"].vocab
feature_dicts = inputters.collect_feature_vocabs(fields, 'tgt')
tgt_embeddings = build_embeddings(model_opt, tgt_dict,
feature_dicts, for_encoder=False)
# Share the embedding matrix - preprocess with share_vocab required.
if model_opt.share_embeddings:
# src/tgt vocab should be the same if `-share_vocab` is specified.
if src_dict != tgt_dict:
raise AssertionError('The `-share_vocab` should be set during '
'preprocess if you use share_embeddings!')
logger.info('** Sharing word embedding matrix between src/tgt')
tgt_embeddings.word_lut.weight = src_embeddings.word_lut.weight
elif src_dict == tgt_dict:
logger.info('WARNING: NOT SHARING WORD EMBEDDINGS FOR TIED VOCAB???')
exit(0)
decoder = build_decoder(model_opt, tgt_embeddings)
# Build NMTModel(= encoder + decoder).
logger.info('Building NMTModel......')
device = torch.device("cuda" if gpu else "cpu")
model = onmt.models.NMTModel(encoder, decoder)
# Build Generator.
logger.info('Building generator......')
# (standard generator)
#
# Given final hidden state (after attention) at t-th decoding step, return
#
# s_t = log(softmax(W h_t + b))
#
# where W is optionally tied to the decoder word embedding matrix.
if not model_opt.copy_attn:
if model_opt.generator_function == "sparsemax":
gen_func = onmt.modules.sparse_activations.LogSparsemax(dim=-1)
else:
gen_func = nn.LogSoftmax(dim=-1)
generator = nn.Sequential(
nn.Linear(model_opt.dec_rnn_size, len(fields["tgt"].vocab)),
gen_func
)
if model_opt.share_decoder_embeddings:
logger.info('** Sharing generator softmax with tgt word embedding')
generator[0].weight = decoder.embeddings.word_lut.weight
else:
logger.info('WARNING: NOT SHARING GENERATOR SOFTMAX WITH TGT WORD '
'EMBEDDING MATRIX - IS THERE A GOOD REASON?')
else:
generator = CopyGenerator(model_opt.dec_rnn_size,
fields["tgt"].vocab)
# Load the model states from checkpoint or initialize them.
if checkpoint is not None:
logger.info('Loade model states from checkpoint......')
model.load_state_dict(checkpoint['model'], strict=False)
generator.load_state_dict(checkpoint['generator'], strict=False)
else:
logger.info('Initializing parameters......')
if not model_opt.param_init_glorot:
logger.info('WARNING: NOT USING XAVIER INITIALIZATION? WILL JUST '
'USE UNIF(+- %.2f)' % (model_opt.param_init))
if model_opt.param_init != 0.0:
for p in model.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
for p in generator.parameters():
p.data.uniform_(-model_opt.param_init, model_opt.param_init)
if model_opt.param_init_glorot:
for p in model.parameters():
if p.dim() > 1:
xavier_uniform_(p)
for p in generator.parameters():
if p.dim() > 1:
xavier_uniform_(p)
if hasattr(model.encoder, 'embeddings'):
if model_opt.pre_word_vecs_enc:
logger.info('** Using pretrained encoder word embeddings')
model.encoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_enc, model_opt.fix_word_vecs_enc)
if hasattr(model.decoder, 'embeddings'):
if model_opt.pre_word_vecs_dec:
logger.info('** Using pretrained decoder word embeddings')
model.decoder.embeddings.load_pretrained_vectors(
model_opt.pre_word_vecs_dec, model_opt.fix_word_vecs_dec)
# Add generator to model (this registers it as parameter of model).
model.generator = generator
model.to(device)
return model
