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chore: import upstream snapshot with attribution

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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.models import (
FairseqEncoder,
FairseqEncoderDecoderModel,
FairseqIncrementalDecoder,
register_model,
register_model_architecture,
)
from fairseq.modules import AdaptiveSoftmax, FairseqDropout
from torch import Tensor
DEFAULT_MAX_SOURCE_POSITIONS = 1e5
DEFAULT_MAX_TARGET_POSITIONS = 1e5
@register_model("lstm")
class LSTMModel(FairseqEncoderDecoderModel):
def __init__(self, encoder, decoder):
super().__init__(encoder, decoder)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
# fmt: off
parser.add_argument('--dropout', type=float, metavar='D',
help='dropout probability')
parser.add_argument('--encoder-embed-dim', type=int, metavar='N',
help='encoder embedding dimension')
parser.add_argument('--encoder-embed-path', type=str, metavar='STR',
help='path to pre-trained encoder embedding')
parser.add_argument('--encoder-freeze-embed', action='store_true',
help='freeze encoder embeddings')
parser.add_argument('--encoder-hidden-size', type=int, metavar='N',
help='encoder hidden size')
parser.add_argument('--encoder-layers', type=int, metavar='N',
help='number of encoder layers')
parser.add_argument('--encoder-bidirectional', action='store_true',
help='make all layers of encoder bidirectional')
parser.add_argument('--decoder-embed-dim', type=int, metavar='N',
help='decoder embedding dimension')
parser.add_argument('--decoder-embed-path', type=str, metavar='STR',
help='path to pre-trained decoder embedding')
parser.add_argument('--decoder-freeze-embed', action='store_true',
help='freeze decoder embeddings')
parser.add_argument('--decoder-hidden-size', type=int, metavar='N',
help='decoder hidden size')
parser.add_argument('--decoder-layers', type=int, metavar='N',
help='number of decoder layers')
parser.add_argument('--decoder-out-embed-dim', type=int, metavar='N',
help='decoder output embedding dimension')
parser.add_argument('--decoder-attention', type=str, metavar='BOOL',
help='decoder attention')
parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR',
help='comma separated list of adaptive softmax cutoff points. '
'Must be used with adaptive_loss criterion')
parser.add_argument('--share-decoder-input-output-embed', default=False,
action='store_true',
help='share decoder input and output embeddings')
parser.add_argument('--share-all-embeddings', default=False, action='store_true',
help='share encoder, decoder and output embeddings'
' (requires shared dictionary and embed dim)')
# Granular dropout settings (if not specified these default to --dropout)
parser.add_argument('--encoder-dropout-in', type=float, metavar='D',
help='dropout probability for encoder input embedding')
parser.add_argument('--encoder-dropout-out', type=float, metavar='D',
help='dropout probability for encoder output')
parser.add_argument('--decoder-dropout-in', type=float, metavar='D',
help='dropout probability for decoder input embedding')
parser.add_argument('--decoder-dropout-out', type=float, metavar='D',
help='dropout probability for decoder output')
# fmt: on
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
# make sure that all args are properly defaulted (in case there are any new ones)
base_architecture(args)
if args.encoder_layers != args.decoder_layers:
raise ValueError("--encoder-layers must match --decoder-layers")
max_source_positions = getattr(
args, "max_source_positions", DEFAULT_MAX_SOURCE_POSITIONS
)
max_target_positions = getattr(
args, "max_target_positions", DEFAULT_MAX_TARGET_POSITIONS
)
def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim):
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
embed_dict = utils.parse_embedding(embed_path)
utils.print_embed_overlap(embed_dict, dictionary)
return utils.load_embedding(embed_dict, dictionary, embed_tokens)
if args.encoder_embed_path:
pretrained_encoder_embed = load_pretrained_embedding_from_file(
args.encoder_embed_path, task.source_dictionary, args.encoder_embed_dim
)
else:
num_embeddings = len(task.source_dictionary)
pretrained_encoder_embed = Embedding(
num_embeddings, args.encoder_embed_dim, task.source_dictionary.pad()
)
if args.share_all_embeddings:
# double check all parameters combinations are valid
if task.source_dictionary != task.target_dictionary:
raise ValueError("--share-all-embeddings requires a joint dictionary")
if args.decoder_embed_path and (
args.decoder_embed_path != args.encoder_embed_path
):
raise ValueError(
"--share-all-embed not compatible with --decoder-embed-path"
)
if args.encoder_embed_dim != args.decoder_embed_dim:
raise ValueError(
"--share-all-embeddings requires --encoder-embed-dim to "
"match --decoder-embed-dim"
)
pretrained_decoder_embed = pretrained_encoder_embed
args.share_decoder_input_output_embed = True
else:
# separate decoder input embeddings
pretrained_decoder_embed = None
if args.decoder_embed_path:
pretrained_decoder_embed = load_pretrained_embedding_from_file(
args.decoder_embed_path,
task.target_dictionary,
args.decoder_embed_dim,
)
# one last double check of parameter combinations
if args.share_decoder_input_output_embed and (
args.decoder_embed_dim != args.decoder_out_embed_dim
):
raise ValueError(
"--share-decoder-input-output-embeddings requires "
"--decoder-embed-dim to match --decoder-out-embed-dim"
)
if args.encoder_freeze_embed:
pretrained_encoder_embed.weight.requires_grad = False
if args.decoder_freeze_embed:
pretrained_decoder_embed.weight.requires_grad = False
encoder = LSTMEncoder(
dictionary=task.source_dictionary,
embed_dim=args.encoder_embed_dim,
hidden_size=args.encoder_hidden_size,
num_layers=args.encoder_layers,
dropout_in=args.encoder_dropout_in,
dropout_out=args.encoder_dropout_out,
bidirectional=args.encoder_bidirectional,
pretrained_embed=pretrained_encoder_embed,
max_source_positions=max_source_positions,
)
decoder = LSTMDecoder(
dictionary=task.target_dictionary,
embed_dim=args.decoder_embed_dim,
hidden_size=args.decoder_hidden_size,
out_embed_dim=args.decoder_out_embed_dim,
num_layers=args.decoder_layers,
dropout_in=args.decoder_dropout_in,
dropout_out=args.decoder_dropout_out,
attention=utils.eval_bool(args.decoder_attention),
encoder_output_units=encoder.output_units,
pretrained_embed=pretrained_decoder_embed,
share_input_output_embed=args.share_decoder_input_output_embed,
adaptive_softmax_cutoff=(
utils.eval_str_list(args.adaptive_softmax_cutoff, type=int)
if args.criterion == "adaptive_loss"
else None
),
max_target_positions=max_target_positions,
residuals=False,
)
return cls(encoder, decoder)
def forward(
self,
src_tokens,
src_lengths,
prev_output_tokens,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
):
encoder_out = self.encoder(src_tokens, src_lengths=src_lengths)
decoder_out = self.decoder(
prev_output_tokens,
encoder_out=encoder_out,
incremental_state=incremental_state,
)
return decoder_out
class LSTMEncoder(FairseqEncoder):
"""LSTM encoder."""
def __init__(
self,
dictionary,
embed_dim=512,
hidden_size=512,
num_layers=1,
dropout_in=0.1,
dropout_out=0.1,
bidirectional=False,
left_pad=True,
pretrained_embed=None,
padding_idx=None,
max_source_positions=DEFAULT_MAX_SOURCE_POSITIONS,
):
super().__init__(dictionary)
self.num_layers = num_layers
self.dropout_in_module = FairseqDropout(
dropout_in, module_name=self.__class__.__name__
)
self.dropout_out_module = FairseqDropout(
dropout_out, module_name=self.__class__.__name__
)
self.bidirectional = bidirectional
self.hidden_size = hidden_size
self.max_source_positions = max_source_positions
num_embeddings = len(dictionary)
self.padding_idx = padding_idx if padding_idx is not None else dictionary.pad()
if pretrained_embed is None:
self.embed_tokens = Embedding(num_embeddings, embed_dim, self.padding_idx)
else:
self.embed_tokens = pretrained_embed
self.lstm = LSTM(
input_size=embed_dim,
hidden_size=hidden_size,
num_layers=num_layers,
dropout=self.dropout_out_module.p if num_layers > 1 else 0.0,
bidirectional=bidirectional,
)
self.left_pad = left_pad
self.output_units = hidden_size
if bidirectional:
self.output_units *= 2
def forward(
self,
src_tokens: Tensor,
src_lengths: Tensor,
enforce_sorted: bool = True,
):
"""
Args:
src_tokens (LongTensor): tokens in the source language of
shape `(batch, src_len)`
src_lengths (LongTensor): lengths of each source sentence of
shape `(batch)`
enforce_sorted (bool, optional): if True, `src_tokens` is
expected to contain sequences sorted by length in a
decreasing order. If False, this condition is not
required. Default: True.
"""
if self.left_pad:
# nn.utils.rnn.pack_padded_sequence requires right-padding;
# convert left-padding to right-padding
src_tokens = utils.convert_padding_direction(
src_tokens,
torch.zeros_like(src_tokens).fill_(self.padding_idx),
left_to_right=True,
)
bsz, seqlen = src_tokens.size()
# embed tokens
x = self.embed_tokens(src_tokens)
x = self.dropout_in_module(x)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
# pack embedded source tokens into a PackedSequence
packed_x = nn.utils.rnn.pack_padded_sequence(
x, src_lengths.cpu(), enforce_sorted=enforce_sorted
)
# apply LSTM
if self.bidirectional:
state_size = 2 * self.num_layers, bsz, self.hidden_size
else:
state_size = self.num_layers, bsz, self.hidden_size
h0 = x.new_zeros(*state_size)
c0 = x.new_zeros(*state_size)
packed_outs, (final_hiddens, final_cells) = self.lstm(packed_x, (h0, c0))
# unpack outputs and apply dropout
x, _ = nn.utils.rnn.pad_packed_sequence(
packed_outs, padding_value=self.padding_idx * 1.0
)
x = self.dropout_out_module(x)
assert list(x.size()) == [seqlen, bsz, self.output_units]
if self.bidirectional:
final_hiddens = self.combine_bidir(final_hiddens, bsz)
final_cells = self.combine_bidir(final_cells, bsz)
encoder_padding_mask = src_tokens.eq(self.padding_idx).t()
return tuple(
(
x, # seq_len x batch x hidden
final_hiddens, # num_layers x batch x num_directions*hidden
final_cells, # num_layers x batch x num_directions*hidden
encoder_padding_mask, # seq_len x batch
)
)
def combine_bidir(self, outs, bsz: int):
out = outs.view(self.num_layers, 2, bsz, -1).transpose(1, 2).contiguous()
return out.view(self.num_layers, bsz, -1)
def reorder_encoder_out(self, encoder_out, new_order):
return tuple(
(
encoder_out[0].index_select(1, new_order),
encoder_out[1].index_select(1, new_order),
encoder_out[2].index_select(1, new_order),
encoder_out[3].index_select(1, new_order),
)
)
def max_positions(self):
"""Maximum input length supported by the encoder."""
return self.max_source_positions
class AttentionLayer(nn.Module):
def __init__(self, input_embed_dim, source_embed_dim, output_embed_dim, bias=False):
super().__init__()
self.input_proj = Linear(input_embed_dim, source_embed_dim, bias=bias)
self.output_proj = Linear(
input_embed_dim + source_embed_dim, output_embed_dim, bias=bias
)
def forward(self, input, source_hids, encoder_padding_mask):
# input: bsz x input_embed_dim
# source_hids: srclen x bsz x source_embed_dim
# x: bsz x source_embed_dim
x = self.input_proj(input)
# compute attention
attn_scores = (source_hids * x.unsqueeze(0)).sum(dim=2)
# don't attend over padding
if encoder_padding_mask is not None:
attn_scores = (
attn_scores.float()
.masked_fill_(encoder_padding_mask, float("-inf"))
.type_as(attn_scores)
) # FP16 support: cast to float and back
attn_scores = F.softmax(attn_scores, dim=0) # srclen x bsz
# sum weighted sources
x = (attn_scores.unsqueeze(2) * source_hids).sum(dim=0)
x = torch.tanh(self.output_proj(torch.cat((x, input), dim=1)))
return x, attn_scores
class LSTMDecoder(FairseqIncrementalDecoder):
"""LSTM decoder."""
def __init__(
self,
dictionary,
embed_dim=512,
hidden_size=512,
out_embed_dim=512,
num_layers=1,
dropout_in=0.1,
dropout_out=0.1,
attention=True,
encoder_output_units=512,
pretrained_embed=None,
share_input_output_embed=False,
adaptive_softmax_cutoff=None,
max_target_positions=DEFAULT_MAX_TARGET_POSITIONS,
residuals=False,
):
super().__init__(dictionary)
self.dropout_in_module = FairseqDropout(
dropout_in, module_name=self.__class__.__name__
)
self.dropout_out_module = FairseqDropout(
dropout_out, module_name=self.__class__.__name__
)
self.hidden_size = hidden_size
self.share_input_output_embed = share_input_output_embed
self.need_attn = True
self.max_target_positions = max_target_positions
self.residuals = residuals
self.num_layers = num_layers
self.adaptive_softmax = None
num_embeddings = len(dictionary)
padding_idx = dictionary.pad()
if pretrained_embed is None:
self.embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
else:
self.embed_tokens = pretrained_embed
self.encoder_output_units = encoder_output_units
if encoder_output_units != hidden_size and encoder_output_units != 0:
self.encoder_hidden_proj = Linear(encoder_output_units, hidden_size)
self.encoder_cell_proj = Linear(encoder_output_units, hidden_size)
else:
self.encoder_hidden_proj = self.encoder_cell_proj = None
# disable input feeding if there is no encoder
# input feeding is described in arxiv.org/abs/1508.04025
input_feed_size = 0 if encoder_output_units == 0 else hidden_size
self.layers = nn.ModuleList(
[
LSTMCell(
input_size=input_feed_size + embed_dim
if layer == 0
else hidden_size,
hidden_size=hidden_size,
)
for layer in range(num_layers)
]
)
if attention:
# TODO make bias configurable
self.attention = AttentionLayer(
hidden_size, encoder_output_units, hidden_size, bias=False
)
else:
self.attention = None
if hidden_size != out_embed_dim:
self.additional_fc = Linear(hidden_size, out_embed_dim)
if adaptive_softmax_cutoff is not None:
# setting adaptive_softmax dropout to dropout_out for now but can be redefined
self.adaptive_softmax = AdaptiveSoftmax(
num_embeddings,
hidden_size,
adaptive_softmax_cutoff,
dropout=dropout_out,
)
elif not self.share_input_output_embed:
self.fc_out = Linear(out_embed_dim, num_embeddings, dropout=dropout_out)
def forward(
self,
prev_output_tokens,
encoder_out: Optional[Tuple[Tensor, Tensor, Tensor, Tensor]] = None,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
src_lengths: Optional[Tensor] = None,
):
x, attn_scores = self.extract_features(
prev_output_tokens, encoder_out, incremental_state
)
return self.output_layer(x), attn_scores
def extract_features(
self,
prev_output_tokens,
encoder_out: Optional[Tuple[Tensor, Tensor, Tensor, Tensor]] = None,
incremental_state: Optional[Dict[str, Dict[str, Optional[Tensor]]]] = None,
):
"""
Similar to *forward* but only return features.
"""
# get outputs from encoder
if encoder_out is not None:
encoder_outs = encoder_out[0]
encoder_hiddens = encoder_out[1]
encoder_cells = encoder_out[2]
encoder_padding_mask = encoder_out[3]
else:
encoder_outs = torch.empty(0)
encoder_hiddens = torch.empty(0)
encoder_cells = torch.empty(0)
encoder_padding_mask = torch.empty(0)
srclen = encoder_outs.size(0)
if incremental_state is not None and len(incremental_state) > 0:
prev_output_tokens = prev_output_tokens[:, -1:]
bsz, seqlen = prev_output_tokens.size()
# embed tokens
x = self.embed_tokens(prev_output_tokens)
x = self.dropout_in_module(x)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
# initialize previous states (or get from cache during incremental generation)
if incremental_state is not None and len(incremental_state) > 0:
prev_hiddens, prev_cells, input_feed = self.get_cached_state(
incremental_state
)
elif encoder_out is not None:
# setup recurrent cells
prev_hiddens = [encoder_hiddens[i] for i in range(self.num_layers)]
prev_cells = [encoder_cells[i] for i in range(self.num_layers)]
if self.encoder_hidden_proj is not None:
prev_hiddens = [self.encoder_hidden_proj(y) for y in prev_hiddens]
prev_cells = [self.encoder_cell_proj(y) for y in prev_cells]
input_feed = x.new_zeros(bsz, self.hidden_size)
else:
# setup zero cells, since there is no encoder
zero_state = x.new_zeros(bsz, self.hidden_size)
prev_hiddens = [zero_state for i in range(self.num_layers)]
prev_cells = [zero_state for i in range(self.num_layers)]
input_feed = None
assert (
srclen > 0 or self.attention is None
), "attention is not supported if there are no encoder outputs"
attn_scores: Optional[Tensor] = (
x.new_zeros(srclen, seqlen, bsz) if self.attention is not None else None
)
outs = []
for j in range(seqlen):
# input feeding: concatenate context vector from previous time step
if input_feed is not None:
input = torch.cat((x[j, :, :], input_feed), dim=1)
else:
input = x[j]
for i, rnn in enumerate(self.layers):
# recurrent cell
hidden, cell = rnn(input, (prev_hiddens[i], prev_cells[i]))
# hidden state becomes the input to the next layer
input = self.dropout_out_module(hidden)
if self.residuals:
input = input + prev_hiddens[i]
# save state for next time step
prev_hiddens[i] = hidden
prev_cells[i] = cell
# apply attention using the last layer's hidden state
if self.attention is not None:
assert attn_scores is not None
out, attn_scores[:, j, :] = self.attention(
hidden, encoder_outs, encoder_padding_mask
)
else:
out = hidden
out = self.dropout_out_module(out)
# input feeding
if input_feed is not None:
input_feed = out
# save final output
outs.append(out)
# Stack all the necessary tensors together and store
prev_hiddens_tensor = torch.stack(prev_hiddens)
prev_cells_tensor = torch.stack(prev_cells)
cache_state = torch.jit.annotate(
Dict[str, Optional[Tensor]],
{
"prev_hiddens": prev_hiddens_tensor,
"prev_cells": prev_cells_tensor,
"input_feed": input_feed,
},
)
self.set_incremental_state(incremental_state, "cached_state", cache_state)
# collect outputs across time steps
x = torch.cat(outs, dim=0).view(seqlen, bsz, self.hidden_size)
# T x B x C -> B x T x C
x = x.transpose(1, 0)
if hasattr(self, "additional_fc") and self.adaptive_softmax is None:
x = self.additional_fc(x)
x = self.dropout_out_module(x)
# srclen x tgtlen x bsz -> bsz x tgtlen x srclen
if not self.training and self.need_attn and self.attention is not None:
assert attn_scores is not None
attn_scores = attn_scores.transpose(0, 2)
else:
attn_scores = None
return x, attn_scores
def output_layer(self, x):
"""Project features to the vocabulary size."""
if self.adaptive_softmax is None:
if self.share_input_output_embed:
x = F.linear(x, self.embed_tokens.weight)
else:
x = self.fc_out(x)
return x
def get_cached_state(
self,
incremental_state: Dict[str, Dict[str, Optional[Tensor]]],
) -> Tuple[List[Tensor], List[Tensor], Optional[Tensor]]:
cached_state = self.get_incremental_state(incremental_state, "cached_state")
assert cached_state is not None
prev_hiddens_ = cached_state["prev_hiddens"]
assert prev_hiddens_ is not None
prev_cells_ = cached_state["prev_cells"]
assert prev_cells_ is not None
prev_hiddens = [prev_hiddens_[i] for i in range(self.num_layers)]
prev_cells = [prev_cells_[j] for j in range(self.num_layers)]
input_feed = cached_state[
"input_feed"
] # can be None for decoder-only language models
return prev_hiddens, prev_cells, input_feed
def reorder_incremental_state(
self,
incremental_state: Dict[str, Dict[str, Optional[Tensor]]],
new_order: Tensor,
):
if incremental_state is None or len(incremental_state) == 0:
return
prev_hiddens, prev_cells, input_feed = self.get_cached_state(incremental_state)
prev_hiddens = [p.index_select(0, new_order) for p in prev_hiddens]
prev_cells = [p.index_select(0, new_order) for p in prev_cells]
if input_feed is not None:
input_feed = input_feed.index_select(0, new_order)
cached_state_new = torch.jit.annotate(
Dict[str, Optional[Tensor]],
{
"prev_hiddens": torch.stack(prev_hiddens),
"prev_cells": torch.stack(prev_cells),
"input_feed": input_feed,
},
)
self.set_incremental_state(incremental_state, "cached_state", cached_state_new),
return
def max_positions(self):
"""Maximum output length supported by the decoder."""
return self.max_target_positions
def make_generation_fast_(self, need_attn=False, **kwargs):
self.need_attn = need_attn
def Embedding(num_embeddings, embedding_dim, padding_idx):
m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
nn.init.uniform_(m.weight, -0.1, 0.1)
nn.init.constant_(m.weight[padding_idx], 0)
return m
def LSTM(input_size, hidden_size, **kwargs):
m = nn.LSTM(input_size, hidden_size, **kwargs)
for name, param in m.named_parameters():
if "weight" in name or "bias" in name:
param.data.uniform_(-0.1, 0.1)
return m
def LSTMCell(input_size, hidden_size, **kwargs):
m = nn.LSTMCell(input_size, hidden_size, **kwargs)
for name, param in m.named_parameters():
if "weight" in name or "bias" in name:
param.data.uniform_(-0.1, 0.1)
return m
def Linear(in_features, out_features, bias=True, dropout=0.0):
"""Linear layer (input: N x T x C)"""
m = nn.Linear(in_features, out_features, bias=bias)
m.weight.data.uniform_(-0.1, 0.1)
if bias:
m.bias.data.uniform_(-0.1, 0.1)
return m
@register_model_architecture("lstm", "lstm")
def base_architecture(args):
args.dropout = getattr(args, "dropout", 0.1)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 512)
args.encoder_embed_path = getattr(args, "encoder_embed_path", None)
args.encoder_freeze_embed = getattr(args, "encoder_freeze_embed", False)
args.encoder_hidden_size = getattr(
args, "encoder_hidden_size", args.encoder_embed_dim
)
args.encoder_layers = getattr(args, "encoder_layers", 1)
args.encoder_bidirectional = getattr(args, "encoder_bidirectional", False)
args.encoder_dropout_in = getattr(args, "encoder_dropout_in", args.dropout)
args.encoder_dropout_out = getattr(args, "encoder_dropout_out", args.dropout)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 512)
args.decoder_embed_path = getattr(args, "decoder_embed_path", None)
args.decoder_freeze_embed = getattr(args, "decoder_freeze_embed", False)
args.decoder_hidden_size = getattr(
args, "decoder_hidden_size", args.decoder_embed_dim
)
args.decoder_layers = getattr(args, "decoder_layers", 1)
args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 512)
args.decoder_attention = getattr(args, "decoder_attention", "1")
args.decoder_dropout_in = getattr(args, "decoder_dropout_in", args.dropout)
args.decoder_dropout_out = getattr(args, "decoder_dropout_out", args.dropout)
args.share_decoder_input_output_embed = getattr(
args, "share_decoder_input_output_embed", False
)
args.share_all_embeddings = getattr(args, "share_all_embeddings", False)
args.adaptive_softmax_cutoff = getattr(
args, "adaptive_softmax_cutoff", "10000,50000,200000"
)
@register_model_architecture("lstm", "lstm_wiseman_iwslt_de_en")
def lstm_wiseman_iwslt_de_en(args):
args.dropout = getattr(args, "dropout", 0.1)
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 256)
args.encoder_dropout_in = getattr(args, "encoder_dropout_in", 0)
args.encoder_dropout_out = getattr(args, "encoder_dropout_out", 0)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 256)
args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 256)
args.decoder_dropout_in = getattr(args, "decoder_dropout_in", 0)
args.decoder_dropout_out = getattr(args, "decoder_dropout_out", args.dropout)
base_architecture(args)
@register_model_architecture("lstm", "lstm_luong_wmt_en_de")
def lstm_luong_wmt_en_de(args):
args.encoder_embed_dim = getattr(args, "encoder_embed_dim", 1000)
args.encoder_layers = getattr(args, "encoder_layers", 4)
args.encoder_dropout_out = getattr(args, "encoder_dropout_out", 0)
args.decoder_embed_dim = getattr(args, "decoder_embed_dim", 1000)
args.decoder_layers = getattr(args, "decoder_layers", 4)
args.decoder_out_embed_dim = getattr(args, "decoder_out_embed_dim", 1000)
args.decoder_dropout_out = getattr(args, "decoder_dropout_out", 0)
base_architecture(args)