def __init__(self, feature_size, self_attention_layer,
cross_attention_layer, feed_forward_layer, adapter_dict,
adapter_bottleneck_size, dropout_rate):
super(TransformerDecoderLayer, self).__init__()
self.feature_size = feature_size
self.self_attention_layer = self_attention_layer
self.cross_attention_layer = cross_attention_layer
self.feed_forward_layer = feed_forward_layer
adapters = nn.ModuleDict({})
sublayer_connection_for_adapter = nn.ModuleDict({})
_adapters = collections.OrderedDict()
_sublayer_connection_for_adapter = collections.OrderedDict()
for domain, size in zip(adapter_dict, adapter_bottleneck_size):
_adapters[domain] = PositionWiseFeedForward(input_dim=feature_size,
ff_dim=size,
dropout=dropout_rate)
_sublayer_connection_for_adapter[domain] = SublayerConnection(
feature_size, dropout_rate)
adapters.update(_adapters)
sublayer_connection_for_adapter.update(
_sublayer_connection_for_adapter)
self.adapters = adapters
self.sublayer_connection_for_adapter = sublayer_connection_for_adapter
self.sublayer_with_cache = clones(
SublayerConnectionWithCache(feature_size, dropout_rate), 2)
self.sublayer = SublayerConnection(feature_size, dropout_rate)
def make_transformer_with_split_position(model_config, vocab):
attention = MultiHeadedAttention(head_num=model_config['head_num'],
feature_size=model_config['feature_size'],
dropout=model_config['dropout_rate'])
attention_with_cache = MultiHeadedAttentionWithCache(
head_num=model_config['head_num'],
feature_size=model_config['feature_size'],
dropout=model_config['dropout_rate'])
feed_forward = PositionWiseFeedForward(
input_dim=model_config['feature_size'],
ff_dim=model_config['feedforward_dim'],
dropout=model_config['dropout_rate'])
model = TransformerWithSplitPosition(
src_embedding_layer=Embeddings(
emb_size=model_config['feature_size'],
vocab_size=len(vocab['src']),
dropout=model_config['dropout_rate'],
linear_combination=model_config['position_linear_combination'],
),
trg_embedding_layer=Embeddings(
emb_size=model_config['feature_size'],
vocab_size=len(vocab['trg']),
dropout=model_config['dropout_rate'],
linear_combination=model_config['position_linear_combination'],
),
encoder=TransformerEncoder(
layer=TransformerEncoderLayer(
feature_size=model_config['feature_size'],
self_attention_layer=copy.deepcopy(attention),
feed_forward_layer=copy.deepcopy(feed_forward),
dropout_rate=model_config['dropout_rate']),
feature_size=model_config['feature_size'],
num_layers=model_config['num_layers'],
),
decoder=TransformerDecoder(
layer=TransformerDecoderLayer(
feature_size=model_config['feature_size'],
self_attention_layer=copy.deepcopy(attention_with_cache),
cross_attention_layer=copy.deepcopy(attention_with_cache),
feed_forward_layer=copy.deepcopy(feed_forward),
dropout_rate=model_config['dropout_rate']),
num_layers=model_config['num_layers'],
feature_size=model_config['feature_size'],
),
generator=SimpleGenerator(feature_size=model_config['feature_size'],
vocab_size=len(vocab['trg'])),
vocab=vocab,
share_decoder_embedding=model_config['share_decoder_embedding'],
share_enc_dec_embedding=model_config['share_enc_dec_embedding'],
)
for p in model.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
return model
def init_adapter(
self,
feature_size,
dropout_rate,
adapter_dict,
adapter_bottleneck_size,
):
# define two new sublayer_connection for each domain with domain-specific layer norm
domain_specific_sublayer_connection = nn.ModuleDict({})
_domain_specific_sublayer_connection = collections.OrderedDict()
for domain in adapter_dict:
_domain_specific_sublayer_connection[domain] = clones(
SublayerConnection(feature_size, dropout_rate), 2)
domain_specific_sublayer_connection.update(
_domain_specific_sublayer_connection)
self.domain_specific_sublayer_connection = domain_specific_sublayer_connection
# define two adapter layer for each sub_layer
domain_specific_adapter_for_self_attn = nn.ModuleDict({})
_domain_specific_adapter_for_self_attn = collections.OrderedDict()
domain_specific_adapter_for_ffn = nn.ModuleDict({})
_domain_specific_adapter_for_ffn = collections.OrderedDict()
domain_specific_sublayer_connection_for_adapter = nn.ModuleDict({})
_domain_specific_sublayer_connection_for_adapter = collections.OrderedDict(
)
for domain, domain_sz in zip(adapter_dict, adapter_bottleneck_size):
_domain_specific_adapter_for_self_attn[
domain] = PositionWiseFeedForward(feature_size, domain_sz,
dropout_rate)
_domain_specific_adapter_for_ffn[domain] = PositionWiseFeedForward(
feature_size, domain_sz, dropout_rate)
_domain_specific_sublayer_connection_for_adapter[domain] = clones(
SublayerConnection(feature_size, dropout_rate), 2)
domain_specific_adapter_for_self_attn.update(
_domain_specific_adapter_for_self_attn)
domain_specific_adapter_for_ffn.update(
_domain_specific_adapter_for_ffn)
domain_specific_sublayer_connection_for_adapter.update(
_domain_specific_sublayer_connection_for_adapter)
self.domain_specific_adapter_for_self_attn = domain_specific_adapter_for_self_attn
self.domain_specific_adapter_for_ffn = domain_specific_adapter_for_ffn
self.domain_specific_sublayer_connection_for_adapter = domain_specific_sublayer_connection_for_adapter
def make_transformer_language_model(model_config, vocab):
attention = MultiHeadedAttention(head_num=model_config['head_num'],
feature_size=model_config['feature_size'],
dropout=model_config['dropout_rate'])
feed_forward = PositionWiseFeedForward(
input_dim=model_config['feature_size'],
ff_dim=model_config['feedforward_dim'],
dropout=model_config['dropout_rate'])
model = TransformerLanguageModel(
embedding_layer=Embeddings(vocab_size=len(vocab['text']),
emb_size=model_config['feature_size'],
dropout=model_config['dropout_rate'],
max_len=5000),
decoder=TransformerEncoder(
layer=TransformerEncoderLayer(
feature_size=model_config['feature_size'],
self_attention_layer=copy.deepcopy(attention),
feed_forward_layer=copy.deepcopy(feed_forward),
dropout_rate=model_config['dropout_rate'],
layer_norm_rescale=model_config['layer_norm_rescale']),
feature_size=model_config['feature_size'],
num_layers=model_config['num_layers'],
layer_norm_rescale=model_config['layer_norm_rescale'],
),
generator=SimpleGenerator(feature_size=model_config['feature_size'],
vocab_size=len(vocab['text']),
bias=model_config['generator_bias']),
vocab=vocab,
share_embedding=model_config['share_embedding'],
)
for p in model.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
return model
def make_transformer_with_full_adapter(model_config, vocab):
attention = MultiHeadedAttention(head_num=model_config['head_num'],
feature_size=model_config['feature_size'],
dropout=model_config['dropout_rate'])
attention_with_cache = MultiHeadedAttentionWithCache(
head_num=model_config['head_num'],
feature_size=model_config['feature_size'],
dropout=model_config['dropout_rate'])
feed_forward = PositionWiseFeedForward(
input_dim=model_config['feature_size'],
ff_dim=model_config['feedforward_dim'],
dropout=model_config['dropout_rate'])
model = TransformerWithFullAdapter(
src_embedding_layer=EmbeddingWithAdapter(
vocab_size=len(vocab['src']),
emb_size=model_config['feature_size'],
dropout=model_config['dropout_rate'],
domain_adapter_dict=model_config['domain_adapter_dict'],
max_len=5000),
trg_embedding_layer=EmbeddingWithAdapter(
vocab_size=len(vocab['src']),
emb_size=model_config['feature_size'],
dropout=model_config['dropout_rate'],
domain_adapter_dict=model_config['domain_adapter_dict'],
max_len=5000),
encoder=TransformerEncoderWithStackedAdapter(
layer=TransformerEncoderLayerWithStackedAdapter(
feature_size=model_config['feature_size'],
self_attention_layer=copy.deepcopy(attention),
feed_forward_layer=copy.deepcopy(feed_forward),
domain_adapter_dict=model_config['domain_adapter_dict'],
dropout_rate=model_config['dropout_rate'],
),
feature_size=model_config['feature_size'],
num_layers=model_config['num_layers'],
),
decoder=TransformerDecoderWithStackedAdapter(
layer=TransformerDecoderLayerWithStackedAdapter(
feature_size=model_config['feature_size'],
self_attention_layer=copy.deepcopy(attention_with_cache),
cross_attention_layer=copy.deepcopy(attention_with_cache),
feed_forward_layer=copy.deepcopy(feed_forward),
domain_adapter_dict=model_config['domain_adapter_dict'],
dropout_rate=model_config['dropout_rate']),
num_layers=model_config['num_layers'],
feature_size=model_config['feature_size'],
),
generator=SimpleGeneratorWithAdapter(
feature_size=model_config['feature_size'],
vocab_size=len(vocab['trg']),
domain_adapter_dict=model_config['domain_adapter_dict'],
bias=model_config['generator_bias']),
vocab=vocab,
share_decoder_embedding=model_config['share_decoder_embedding'],
share_enc_dec_embedding=model_config['share_enc_dec_embedding'],
domain_dict=model_config['domain_adapter_dict'],
)
for p in model.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
for name, param in model.named_parameters():
# if param.dim() > 1 and 'adapter' in name:
# nn.init.zeros_(param)
if 'memory_score_bias' in name:
nn.init.xavier_uniform_(param)
return model
def make_transformer_with_classifier_adapter(model_config, vocab):
attention = MultiHeadedAttention(head_num=model_config['head_num'],
feature_size=model_config['feature_size'],
dropout=model_config['dropout_rate'])
attention_with_cache = MultiHeadedAttentionWithCache(
head_num=model_config['head_num'],
feature_size=model_config['feature_size'],
dropout=model_config['dropout_rate'])
feed_forward = PositionWiseFeedForward(
input_dim=model_config['feature_size'],
ff_dim=model_config['feedforward_dim'],
dropout=model_config['dropout_rate'])
if model_config['classifier_type'] == 'simple':
classifier = SimpleClassifier(
input_dim=model_config['feature_size'],
feature_size=model_config['classify_feature_size'],
class_num=model_config['domain_class_num'],
)
elif model_config['classifier_type'] == 'cnn':
classifier = CNNClassifier(num_class=model_config['domain_class_num'],
input_dim=model_config['feature_size'],
kernel_nums=model_config['kernel_nums'],
kernel_sizes=model_config['kernel_sizes'],
dropout_rate=model_config['dropout_rate'])
else:
classifier = None
model = TransformerWithClassifierAdapter(
src_embedding_layer=Embeddings(vocab_size=len(vocab['src']),
emb_size=model_config['feature_size'],
dropout=model_config['dropout_rate'],
max_len=5000),
trg_embedding_layer=Embeddings(vocab_size=len(vocab['trg']),
emb_size=model_config['feature_size'],
dropout=model_config['dropout_rate'],
max_len=5000),
encoder=TransformerEncoderWithClassifierAdapter(
layer=TransformerEncoderLayerWithClassifierAdapter(
feature_size=model_config['feature_size'],
self_attention_layer=copy.deepcopy(attention),
feed_forward_layer=copy.deepcopy(feed_forward),
domain_adapter_dict=model_config['domain_adapter_dict'],
dropout_rate=model_config['dropout_rate'],
),
feature_size=model_config['feature_size'],
num_layers=model_config['num_layers'],
domain_label_dict=model_config['domain_dict']),
decoder=TransformerDecoderWithClassifierAdapter(
layer=TransformerDecoderLayerWithClassifierAdapter(
feature_size=model_config['feature_size'],
self_attention_layer=copy.deepcopy(attention_with_cache),
cross_attention_layer=copy.deepcopy(attention_with_cache),
feed_forward_layer=copy.deepcopy(feed_forward),
domain_adapter_dict=model_config['domain_adapter_dict'],
dropout_rate=model_config['dropout_rate']),
num_layers=model_config['num_layers'],
feature_size=model_config['feature_size'],
domain_label_dict=model_config['domain_dict'],
),
generator=SimpleGenerator(feature_size=model_config['feature_size'],
vocab_size=len(vocab['trg']),
bias=model_config['generator_bias']),
emb_classifier=copy.deepcopy(classifier),
domain_mask=model_config['domain_mask'],
vocab=vocab,
share_decoder_embedding=model_config['share_decoder_embedding'],
share_enc_dec_embedding=model_config['share_enc_dec_embedding'],
)
for p in model.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
for name, param in model.named_parameters():
# if param.dim() > 1 and 'adapter' in name:
# nn.init.zeros_(param)
if 'memory_score_bias' in name:
nn.init.xavier_uniform_(param)
return model
def init_adapter(self, feature_size, dropout_rate, adapter_dict, adapter_bottleneck_size):
domain_specific_sublayer_with_cache = nn.ModuleDict({})
_domain_specific_sublayer_with_cache = collections.OrderedDict()
for domain in adapter_dict:
_domain_specific_sublayer_with_cache[domain] = clones(
SublayerConnectionWithCache(feature_size, dropout_rate), 2)
domain_specific_sublayer_with_cache.update(_domain_specific_sublayer_with_cache)
self.domain_specific_sublayer_with_cache = domain_specific_sublayer_with_cache
domain_specific_sublayer = nn.ModuleDict({})
_domain_specific_sublayer = collections.OrderedDict()
for domain in adapter_dict:
_domain_specific_sublayer[domain] = SublayerConnection(feature_size, dropout_rate)
domain_specific_sublayer.update(_domain_specific_sublayer)
self.domain_specific_sublayer = domain_specific_sublayer
# define three adapter for each sub layer
domain_specific_adapter_for_self_attn = nn.ModuleDict({})
_domain_specific_adapter_for_self_attn = collections.OrderedDict()
domain_specific_adapter_for_cross_attn = nn.ModuleDict({})
_domain_specific_adapter_for_cross_attn = collections.OrderedDict()
domain_specific_adapter_for_ffn = nn.ModuleDict({})
_domain_specific_adapter_for_ffn = collections.OrderedDict()
domain_specific_sublayer_for_self_attn_adapter = nn.ModuleDict({})
domain_specific_sublayer_for_cross_attn_adapter = nn.ModuleDict({})
domain_specific_sublayer_for_ffn_adapter = nn.ModuleDict({})
_domain_specific_sublayer_for_self_attn_adapter = collections.OrderedDict()
_domain_specific_sublayer_for_cross_attn_adapter = collections.OrderedDict()
_domain_specific_sublayer_for_ffn_adapter = collections.OrderedDict()
for domain, domain_sz in zip(adapter_dict, adapter_bottleneck_size):
_domain_specific_adapter_for_self_attn[domain] = PositionWiseFeedForward(feature_size,
domain_sz,
dropout_rate)
_domain_specific_adapter_for_cross_attn[domain] = PositionWiseFeedForward(feature_size,
domain_sz,
dropout_rate)
_domain_specific_adapter_for_ffn[domain] = PositionWiseFeedForward(feature_size,
domain_sz,
dropout_rate)
_domain_specific_sublayer_for_self_attn_adapter[domain] = SublayerConnectionWithCache(feature_size, dropout_rate)
_domain_specific_sublayer_for_cross_attn_adapter[domain] = SublayerConnectionWithCache(feature_size, dropout_rate)
_domain_specific_sublayer_for_ffn_adapter[domain] = SublayerConnection(feature_size, dropout_rate)
domain_specific_adapter_for_self_attn.update(_domain_specific_adapter_for_self_attn)
domain_specific_adapter_for_cross_attn.update(_domain_specific_adapter_for_cross_attn)
domain_specific_adapter_for_ffn.update(_domain_specific_adapter_for_ffn)
domain_specific_sublayer_for_self_attn_adapter.update(_domain_specific_sublayer_for_self_attn_adapter)
domain_specific_sublayer_for_cross_attn_adapter.update(_domain_specific_sublayer_for_cross_attn_adapter)
domain_specific_sublayer_for_ffn_adapter.update(_domain_specific_sublayer_for_ffn_adapter)
self.domain_specific_adapter_for_self_attn = domain_specific_adapter_for_self_attn
self.domain_specific_adapter_for_ffn = domain_specific_adapter_for_ffn
self.domain_specific_adapter_for_cross_attn = domain_specific_adapter_for_cross_attn
self.domain_specific_sublayer_for_self_attn_adapter = domain_specific_sublayer_for_self_attn_adapter
self.domain_specific_sublayer_for_cross_attn_adapter = domain_specific_sublayer_for_cross_attn_adapter
self.domain_specific_sublayer_for_ffn_adapter = domain_specific_sublayer_for_ffn_adapter
def make_transformer_with_mix_adapter_update(model_config, vocab):
attention = MultiHeadedAttention(
head_num=model_config['head_num'],
feature_size=model_config['feature_size'],
dropout=model_config['dropout_rate']
)
attention_with_cache = MultiHeadedAttentionWithCache(
head_num=model_config['head_num'],
feature_size=model_config['feature_size'],
dropout=model_config['dropout_rate']
)
feed_forward = PositionWiseFeedForward(
input_dim=model_config['feature_size'],
ff_dim=model_config['feedforward_dim'],
dropout=model_config['dropout_rate']
)
adapter = None
if 'domain_adapter_dict' in model_config:
adapter = MixtureOfAdapter(
domain_adapter_dict=model_config['domain_adapter_dict'],
feature_size=model_config['feature_size'],
dropout_rate=model_config['dropout_rate'],
# domain_list=model_config['domain_list'],
# domain_inner_gate_list=model_config['domain_inner_gate_list'],
# gate_activate_func=model_config['adapter_gate_activate'],
# stack_between_adapter_and_experts=model_config['stack_between_adapter_and_experts'] if
# 'stack_between_adapter_and_experts' in model_config else False
)
enc_adapter = adapter
if 'enc_domain_adapter_dict' in model_config:
enc_adapter = MixtureOfAdapter(
domain_adapter_dict=model_config['enc_domain_adapter_dict'],
feature_size=model_config['feature_size'],
dropout_rate=model_config['dropout_rate'],
)
dec_adapter = adapter
if 'dec_domain_adapter_dict' in model_config:
dec_adapter = MixtureOfAdapter(
domain_adapter_dict=model_config['dec_domain_adapter_dict'],
feature_size=model_config['feature_size'],
dropout_rate=model_config['dropout_rate'],
)
model = TransformerWithMixAdapter(
src_embedding_layer=Embeddings(
vocab_size=len(vocab['src']),
emb_size=model_config['feature_size'],
dropout=model_config['dropout_rate'],
max_len=5000
),
trg_embedding_layer=Embeddings(
vocab_size=len(vocab['trg']),
emb_size=model_config['feature_size'],
dropout=model_config['dropout_rate'],
max_len=5000
),
encoder=TransformerEncoderWithMixAdapter(
layer=TransformerEncoderLayerWithMixAdapter(feature_size=model_config['feature_size'],
self_attention_layer=copy.deepcopy(attention),
feed_forward_layer=copy.deepcopy(feed_forward),
adapters=copy.deepcopy(enc_adapter),
dropout_rate=model_config['dropout_rate'],
),
feature_size=model_config['feature_size'],
num_layers=model_config['num_layers'],
),
decoder=TransformerDecoderWithMixAdapter(
layer=TransformerDecoderLayerWithMixAdapter(feature_size=model_config['feature_size'],
self_attention_layer=copy.deepcopy(
attention_with_cache),
cross_attention_layer=copy.deepcopy(
attention_with_cache),
feed_forward_layer=copy.deepcopy(feed_forward),
adapters=copy.deepcopy(dec_adapter),
dropout_rate=model_config['dropout_rate'],
),
num_layers=model_config['num_layers'],
feature_size=model_config['feature_size'],
),
generator=SimpleGenerator(feature_size=model_config['feature_size'],
vocab_size=len(vocab['trg']),
bias=model_config['generator_bias']),
vocab=vocab,
share_decoder_embedding=model_config['share_decoder_embedding'],
share_enc_dec_embedding=model_config['share_enc_dec_embedding'],
)
# for p in model.parameters():
# if p.dim() > 1:
# nn.init.xavier_uniform_(p)
for name, param in model.named_parameters():
if param.dim() > 1 and 'W' not in name and 'B' not in name:
nn.init.xavier_uniform_(param)
else:
if param.dim() > 1:
print('module self init', name, param.size())
# for name, param in model.named_parameters():
# if param.dim() > 1 and 'adapter' in name:
# # nn.init.zeros_(param)
# # if 'adapter' in name:
# print('init adapter', name)
# nn.init.xavier_uniform_(param, 0.001)
return model
def make_transformer_with_diff_size_stacked_adapter(model_config, vocab):
attention = MultiHeadedAttention(head_num=model_config['head_num'],
feature_size=model_config['feature_size'],
dropout=model_config['dropout_rate'])
attention_with_cache = MultiHeadedAttentionWithCache(
head_num=model_config['head_num'],
feature_size=model_config['feature_size'],
dropout=model_config['dropout_rate'])
feed_forward = PositionWiseFeedForward(
input_dim=model_config['feature_size'],
ff_dim=model_config['feedforward_dim'],
dropout=model_config['dropout_rate'])
encoder_layers = []
decoder_layers = []
for i in range(0, model_config['num_layers']):
adapter_dict = {}
if 'encoder.' + str(i) in model_config['domain_adapter_dict'].keys():
adapter_dict = model_config['domain_adapter_dict']['encoder.' +
str(i)]
else:
adapter_dict = model_config['domain_adapter_dict']['default']
encoder_layer = TransformerEncoderLayerWithDiffSizeStackedAdapter(
feature_size=model_config['feature_size'],
self_attention_layer=copy.deepcopy(attention),
feed_forward_layer=copy.deepcopy(feed_forward),
domain_adapter_dict=adapter_dict,
dropout_rate=model_config['dropout_rate'],
)
encoder_layers.append(encoder_layer)
adapter_dict = {}
if 'decoder.' + str(i) in model_config['domain_adapter_dict'].keys():
adapter_dict = model_config['domain_adapter_dict']['decoder.' +
str(i)]
else:
adapter_dict = model_config['domain_adapter_dict']['default']
decoder_layer = TransformerDecoderLayerWithDiffSizeStackedAdapter(
feature_size=model_config['feature_size'],
self_attention_layer=copy.deepcopy(attention_with_cache),
cross_attention_layer=copy.deepcopy(attention_with_cache),
feed_forward_layer=copy.deepcopy(feed_forward),
domain_adapter_dict=adapter_dict,
dropout_rate=model_config['dropout_rate'])
decoder_layers.append(decoder_layer)
model = TransformerWithAdapter(
src_embedding_layer=Embeddings(vocab_size=len(vocab['src']),
emb_size=model_config['feature_size'],
dropout=model_config['dropout_rate'],
max_len=5000),
trg_embedding_layer=Embeddings(vocab_size=len(vocab['trg']),
emb_size=model_config['feature_size'],
dropout=model_config['dropout_rate'],
max_len=5000),
encoder=TransformerEncoderWithDiffSizeStackedAdapter(
layers=encoder_layers,
feature_size=model_config['feature_size'],
num_layers=model_config['num_layers'],
),
decoder=TransformerDecoderWithDiffSizeStackedAdapter(
layers=decoder_layers,
num_layers=model_config['num_layers'],
feature_size=model_config['feature_size'],
),
generator=SimpleGenerator(feature_size=model_config['feature_size'],
vocab_size=len(vocab['trg']),
bias=model_config['generator_bias']),
vocab=vocab,
share_decoder_embedding=model_config['share_decoder_embedding'],
share_enc_dec_embedding=model_config['share_enc_dec_embedding'],
)
# for name, param in model.named_parameters():
# print(name)
for p in model.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
for name, param in model.named_parameters():
# if param.dim() > 1 and 'adapter' in name:
# nn.init.zeros_(param)
if 'memory_score_bias' in name:
nn.init.xavier_uniform_(param)
return model
