def __init__(self, args, dictionary, embed_tokens, left_pad=True):
super().__init__(dictionary)
self.dropout = args.dropout
embed_dim = embed_tokens.embedding_dim
self.padding_idx = embed_tokens.padding_idx
self.embed_tokens = embed_tokens
self.embed_scale = math.sqrt(embed_dim)
self.embed_positions = fairseq_transformer.PositionalEmbedding(
1024,
embed_dim,
self.padding_idx,
left_pad=left_pad,
learned=args.encoder_learned_pos,
)
self.layers = nn.ModuleList([])
self.layers.extend([
fairseq_transformer.TransformerEncoderLayer(args)
for i in range(args.encoder_layers)
])
# Variable tracker
self.tracker = VariableTracker()
# Initialize adversarial mode
self.set_gradient_tracking_mode(False)
def __init__(self, args, dictionary, embed_tokens, left_pad=False):
super().__init__(dictionary)
self.dropout = args.dropout
self.share_input_output_embed = args.share_decoder_input_output_embed
embed_dim = embed_tokens.embedding_dim
padding_idx = embed_tokens.padding_idx
self.embed_tokens = embed_tokens
self.embed_scale = math.sqrt(embed_dim)
self.embed_positions = fairseq_transformer.PositionalEmbedding(
1024,
embed_dim,
padding_idx,
left_pad=left_pad,
learned=args.decoder_learned_pos,
)
self.layers = nn.ModuleList([])
self.layers.extend(
[
fairseq_transformer.TransformerDecoderLayer(args)
for i in range(args.decoder_layers)
]
)
if not self.share_input_output_embed:
self.embed_out = nn.Parameter(torch.Tensor(len(dictionary), embed_dim))
nn.init.normal_(self.embed_out, mean=0, std=embed_dim ** -0.5)
def __init__(self, args, src_dict, dst_dict, embed_tokens):
super().__init__(dst_dict)
self.dropout = args.dropout
self.share_input_output_embed = args.share_decoder_input_output_embed
embed_dim = embed_tokens.embedding_dim
padding_idx = embed_tokens.padding_idx
self.embed_tokens = embed_tokens
self.embed_scale = math.sqrt(embed_dim)
self.embed_positions = fairseq_transformer.PositionalEmbedding(
1024, embed_dim, padding_idx, learned=args.decoder_learned_pos)
self.layers = nn.ModuleList([])
self.layers.extend([
fairseq_transformer.TransformerDecoderLayer(args)
for i in range(args.decoder_layers)
])
self.adaptive_softmax = None
self.bottleneck_layer = None
out_embed_dim = embed_dim
if args.decoder_out_embed_dim is not None:
assert (
not args.share_all_embeddings
and not args.share_decoder_input_output_embed
), "--decoder-out-embed-dim is incompatible with sharing output embeddings!"
self.bottleneck_layer = fairseq_transformer.Linear(
embed_dim, args.decoder_out_embed_dim)
out_embed_dim = args.decoder_out_embed_dim
if args.adaptive_softmax_cutoff is not None:
self.adaptive_softmax = AdaptiveSoftmax(
len(dst_dict),
out_embed_dim,
options.eval_str_list(args.adaptive_softmax_cutoff, type=int),
dropout=args.dropout,
)
elif not self.share_input_output_embed:
self.embed_out = nn.Parameter(
torch.Tensor(len(dst_dict), out_embed_dim))
nn.init.normal_(self.embed_out, mean=0, std=out_embed_dim**-0.5)
self.vocab_reduction_module = None
if args.vocab_reduction_params:
assert (
self.adaptive_softmax is None
), "vocabulary reduction not compatible with adaptive softmax!"
self.vocab_reduction_module = vocab_reduction.VocabReduction(
src_dict,
dst_dict,
args.vocab_reduction_params,
fp16=args.fp16)
self.onnx_trace = False
def __init__(self, args, embed_tokens):
super().__init__()
self.dropout = args.dropout
embed_dim = embed_tokens.embedding_dim
self.padding_idx = embed_tokens.padding_idx
self.embed_tokens = embed_tokens
self.embed_scale = math.sqrt(embed_dim)
self.embed_positions = fairseq_transformer.PositionalEmbedding(
1024, embed_dim, self.padding_idx, learned=args.encoder_learned_pos
)
def __init__(
self,
args,
dictionary,
embed_tokens,
num_chars=50,
embed_dim=32,
char_cnn_params="[(128, 3), (128, 5)]",
char_cnn_nonlinear_fn="tanh",
char_cnn_pool_type="max",
char_cnn_num_highway_layers=0,
char_cnn_output_dim=-1,
use_pretrained_weights=False,
finetune_pretrained_weights=False,
weights_file=None,
):
super().__init__(dictionary)
convolutions_params = literal_eval(char_cnn_params)
self.char_cnn_encoder = char_encoder.CharCNNModel(
dictionary,
num_chars,
embed_dim,
convolutions_params,
char_cnn_nonlinear_fn,
char_cnn_pool_type,
char_cnn_num_highway_layers,
char_cnn_output_dim,
use_pretrained_weights,
finetune_pretrained_weights,
weights_file,
)
self.embed_tokens = embed_tokens
token_embed_dim = embed_tokens.embedding_dim
self.word_layer_norm = nn.LayerNorm(token_embed_dim)
char_embed_dim = (
char_cnn_output_dim
if char_cnn_output_dim != -1
else sum(out_dim for (out_dim, _) in convolutions_params)
)
self.char_layer_norm = nn.LayerNorm(char_embed_dim)
self.word_dim = char_embed_dim + token_embed_dim
self.char_scale = math.sqrt(char_embed_dim / self.word_dim)
self.word_scale = math.sqrt(token_embed_dim / self.word_dim)
if self.word_dim != args.encoder_embed_dim:
self.word_to_transformer_embed = fairseq_transformer.Linear(
self.word_dim, args.encoder_embed_dim
)
self.dropout = args.dropout
self.padding_idx = dictionary.pad()
self.embed_positions = fairseq_transformer.PositionalEmbedding(
1024,
args.encoder_embed_dim,
self.padding_idx,
learned=args.encoder_learned_pos,
)
self.transformer_encoder_given_embeddings = TransformerEncoderGivenEmbeddings(
args=args, proj_to_decoder=True
)
# Variable tracker
self.tracker = VariableTracker()
# Initialize adversarial mode
self.set_gradient_tracking_mode(False)
self.set_embed_noising_mode(False)
# disables sorting and word-length thresholding if True
# (enables ONNX tracing of length-sorted input with batch_size = 1)
self.onnx_export_model = False
def __init__(self, args, src_dict, dst_dict, embed_tokens):
super().__init__(dst_dict)
self.dropout = args.dropout
self.decoder_layerdrop = 0
if hasattr(args, "decoder_layerdrop") and args.decoder_layerdrop > 0:
self.decoder_layerdrop = args.decoder_layerdrop
self.share_input_output_embed = args.share_decoder_input_output_embed
embed_dim = embed_tokens.embedding_dim
padding_idx = embed_tokens.padding_idx
self.embed_tokens = embed_tokens
self.embed_scale = math.sqrt(embed_dim)
self.embed_positions = fairseq_transformer.PositionalEmbedding(
1024, embed_dim, padding_idx, learned=args.decoder_learned_pos)
self.aan = args.aan
decoder_layer_class = (AANDecoderLayer if self.aan else
fairseq_transformer.TransformerDecoderLayer)
self.layers = nn.ModuleList([])
self.layers.extend(
[decoder_layer_class(args) for i in range(args.decoder_layers)])
if hasattr(args,
"decoder_layers_to_keep") and args.decoder_layers_to_keep:
layers_to_keep = sorted(
int(x) for x in args.decoder_layers_to_keep.split(","))
self.decoder_layers_to_keep = {
layer_id: layer_idx
for layer_idx, layer_id in enumerate(layers_to_keep)
}
self.adaptive_softmax = None
self.bottleneck_layer = None
out_embed_dim = embed_dim
if args.decoder_out_embed_dim is not None:
assert (
not args.share_all_embeddings
and not args.share_decoder_input_output_embed
), "--decoder-out-embed-dim is incompatible with sharing output embeddings!"
self.bottleneck_layer = fairseq_transformer.Linear(
embed_dim, args.decoder_out_embed_dim)
out_embed_dim = args.decoder_out_embed_dim
if args.adaptive_softmax_cutoff is not None:
self.adaptive_softmax = AdaptiveSoftmax(
len(dst_dict),
out_embed_dim,
options.eval_str_list(args.adaptive_softmax_cutoff, type=int),
dropout=args.dropout,
)
elif not self.share_input_output_embed:
self.embed_out = nn.Parameter(
torch.Tensor(len(dst_dict), out_embed_dim))
nn.init.normal_(self.embed_out, mean=0, std=out_embed_dim**-0.5)
self.vocab_reduction_module = None
if args.vocab_reduction_params:
assert (
self.adaptive_softmax is None
), "vocabulary reduction not compatible with adaptive softmax!"
self.vocab_reduction_module = vocab_reduction.VocabReduction(
src_dict,
dst_dict,
args.vocab_reduction_params,
fp16=args.fp16)
self.onnx_trace = False
# Use quantizable nn.Linear for output projection instead of F.linear
self.output_projection = None
if self.vocab_reduction_module is None:
if self.share_input_output_embed:
self.output_projection = nn.Linear(
self.embed_tokens.weight.shape[1],
self.embed_tokens.weight.shape[0])
self.output_projection.weight = self.embed_tokens.weight
else:
self.output_projection = nn.Linear(self.embed_out.shape[1],
self.embed_out.shape[0])
self.output_projection.weight = self.embed_out
def __init__(
self,
args,
src_dict,
dst_dict,
embed_tokens,
no_encoder_attn=False,
left_pad=False,
final_norm=True,
):
super().__init__(dst_dict)
self.dropout = args.dropout
self.share_input_output_embed = args.share_decoder_input_output_embed
input_embed_dim = embed_tokens.embedding_dim
embed_dim = args.decoder_embed_dim
padding_idx = embed_tokens.padding_idx
self.max_target_positions = args.max_target_positions
self.embed_tokens = embed_tokens
self.embed_scale = math.sqrt(
embed_dim) # todo: try with input_embed_dim
self.project_in_dim = (Linear(input_embed_dim, embed_dim, bias=False)
if embed_dim != input_embed_dim else None)
self.embed_positions = fairseq_transformer.PositionalEmbedding(
1024, embed_dim, padding_idx, learned=args.decoder_learned_pos)
self.layers = nn.ModuleList([])
self.layers.extend([
TransformerAANDecoderLayer(args, no_encoder_attn)
for _ in range(args.decoder_layers)
])
self.adaptive_softmax = None
self.bottleneck_layer = None
out_embed_dim = embed_dim
if args.decoder_out_embed_dim is not None:
assert (
not args.share_all_embeddings
and not args.share_decoder_input_output_embed
), "--decoder-out-embed-dim is incompatible with sharing output embeddings!"
self.bottleneck_layer = Linear(embed_dim,
args.decoder_out_embed_dim)
out_embed_dim = args.decoder_out_embed_dim
if args.adaptive_softmax_cutoff is not None:
self.adaptive_softmax = AdaptiveSoftmax(
len(dst_dict),
out_embed_dim,
options.eval_str_list(args.adaptive_softmax_cutoff, type=int),
dropout=args.adaptive_softmax_dropout,
adaptive_inputs=embed_tokens
if args.tie_adaptive_weights else None,
factor=args.adaptive_softmax_factor,
tie_proj=args.tie_adaptive_proj,
)
elif not self.share_input_output_embed:
self.embed_out = nn.Parameter(
torch.Tensor(len(dst_dict), out_embed_dim))
nn.init.normal_(self.embed_out, mean=0, std=out_embed_dim**-0.5)
self.register_buffer("version", torch.Tensor([2]))
self.normalize = args.decoder_normalize_before and final_norm
if self.normalize:
self.layer_norm = LayerNorm(embed_dim)
self.vocab_reduction_module = None
if args.vocab_reduction_params:
assert (
self.adaptive_softmax is None
), "vocabulary reduction not compatible with adaptive softmax!"
self.vocab_reduction_module = vocab_reduction.VocabReduction(
src_dict,
dst_dict,
args.vocab_reduction_params,
fp16=args.fp16)
self.onnx_trace = False
