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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequence, pack_sequence, PackedSequence
from stanza.models.common.biaffine import BiaffineScorer
from stanza.models.common.hlstm import HighwayLSTM
from stanza.models.common.dropout import WordDropout
from stanza.models.common.vocab import CompositeVocab
from stanza.models.common.char_model import CharacterModel
class Tagger(nn.Module):
def __init__(self, args, vocab, emb_matrix=None, share_hid=False):
super().__init__()
self.vocab = vocab
self.args = args
self.share_hid = share_hid
self.unsaved_modules = []
def add_unsaved_module(name, module):
self.unsaved_modules += [name]
setattr(self, name, module)
# input layers
input_size = 0
if self.args['word_emb_dim'] > 0:
# frequent word embeddings
self.word_emb = nn.Embedding(len(vocab['word']), self.args['word_emb_dim'], padding_idx=0)
input_size += self.args['word_emb_dim']
if not share_hid:
# upos embeddings
self.upos_emb = nn.Embedding(len(vocab['upos']), self.args['tag_emb_dim'], padding_idx=0)
if self.args['char'] and self.args['char_emb_dim'] > 0:
self.charmodel = CharacterModel(args, vocab)
self.trans_char = nn.Linear(self.args['char_hidden_dim'], self.args['transformed_dim'], bias=False)
input_size += self.args['transformed_dim']
if self.args['pretrain']:
# pretrained embeddings, by default this won't be saved into model file
add_unsaved_module('pretrained_emb', nn.Embedding.from_pretrained(torch.from_numpy(emb_matrix), freeze=True))
self.trans_pretrained = nn.Linear(emb_matrix.shape[1], self.args['transformed_dim'], bias=False)
input_size += self.args['transformed_dim']
# recurrent layers
self.taggerlstm = HighwayLSTM(input_size, self.args['hidden_dim'], self.args['num_layers'], batch_first=True, bidirectional=True, dropout=self.args['dropout'], rec_dropout=self.args['rec_dropout'], highway_func=torch.tanh)
self.drop_replacement = nn.Parameter(torch.randn(input_size) / np.sqrt(input_size))
self.taggerlstm_h_init = nn.Parameter(torch.zeros(2 * self.args['num_layers'], 1, self.args['hidden_dim']))
self.taggerlstm_c_init = nn.Parameter(torch.zeros(2 * self.args['num_layers'], 1, self.args['hidden_dim']))
# classifiers
self.upos_hid = nn.Linear(self.args['hidden_dim'] * 2, self.args['deep_biaff_hidden_dim'])
self.upos_clf = nn.Linear(self.args['deep_biaff_hidden_dim'], len(vocab['upos']))
self.upos_clf.weight.data.zero_()
self.upos_clf.bias.data.zero_()
if share_hid:
clf_constructor = lambda insize, outsize: nn.Linear(insize, outsize)
else:
self.xpos_hid = nn.Linear(self.args['hidden_dim'] * 2, self.args['deep_biaff_hidden_dim'] if not isinstance(vocab['xpos'], CompositeVocab) else self.args['composite_deep_biaff_hidden_dim'])
self.ufeats_hid = nn.Linear(self.args['hidden_dim'] * 2, self.args['composite_deep_biaff_hidden_dim'])
clf_constructor = lambda insize, outsize: BiaffineScorer(insize, self.args['tag_emb_dim'], outsize)
if isinstance(vocab['xpos'], CompositeVocab):
self.xpos_clf = nn.ModuleList()
for l in vocab['xpos'].lens():
self.xpos_clf.append(clf_constructor(self.args['composite_deep_biaff_hidden_dim'], l))
else:
self.xpos_clf = clf_constructor(self.args['deep_biaff_hidden_dim'], len(vocab['xpos']))
if share_hid:
self.xpos_clf.weight.data.zero_()
self.xpos_clf.bias.data.zero_()
self.ufeats_clf = nn.ModuleList()
for l in vocab['feats'].lens():
if share_hid:
self.ufeats_clf.append(clf_constructor(self.args['deep_biaff_hidden_dim'], l))
self.ufeats_clf[-1].weight.data.zero_()
self.ufeats_clf[-1].bias.data.zero_()
else:
self.ufeats_clf.append(clf_constructor(self.args['composite_deep_biaff_hidden_dim'], l))
# criterion
self.crit = nn.CrossEntropyLoss(ignore_index=0) # ignore padding
self.drop = nn.Dropout(args['dropout'])
self.worddrop = WordDropout(args['word_dropout'])
def forward(self, word, word_mask, wordchars, wordchars_mask, upos, xpos, ufeats, pretrained, word_orig_idx, sentlens, wordlens):
def pack(x):
return pack_padded_sequence(x, sentlens, batch_first=True)
inputs = []
if self.args['word_emb_dim'] > 0:
word_emb = self.word_emb(word)
word_emb = pack(word_emb)
inputs += [word_emb]
if self.args['pretrain']:
pretrained_emb = self.pretrained_emb(pretrained)
pretrained_emb = self.trans_pretrained(pretrained_emb)
pretrained_emb = pack(pretrained_emb)
inputs += [pretrained_emb]
def pad(x):
return pad_packed_sequence(PackedSequence(x, word_emb.batch_sizes), batch_first=True)[0]
if self.args['char'] and self.args['char_emb_dim'] > 0:
char_reps = self.charmodel(wordchars, wordchars_mask, word_orig_idx, sentlens, wordlens)
char_reps = PackedSequence(self.trans_char(self.drop(char_reps.data)), char_reps.batch_sizes)
inputs += [char_reps]
lstm_inputs = torch.cat([x.data for x in inputs], 1)
lstm_inputs = self.worddrop(lstm_inputs, self.drop_replacement)
lstm_inputs = self.drop(lstm_inputs)
lstm_inputs = PackedSequence(lstm_inputs, inputs[0].batch_sizes)
lstm_outputs, _ = self.taggerlstm(lstm_inputs, sentlens, hx=(self.taggerlstm_h_init.expand(2 * self.args['num_layers'], word.size(0), self.args['hidden_dim']).contiguous(), self.taggerlstm_c_init.expand(2 * self.args['num_layers'], word.size(0), self.args['hidden_dim']).contiguous()))
lstm_outputs = lstm_outputs.data
upos_hid = F.relu(self.upos_hid(self.drop(lstm_outputs)))
upos_pred = self.upos_clf(self.drop(upos_hid))
preds = [pad(upos_pred).max(2)[1]]
upos = pack(upos).data
loss = self.crit(upos_pred.view(-1, upos_pred.size(-1)), upos.view(-1))
if self.share_hid:
xpos_hid = upos_hid
ufeats_hid = upos_hid
clffunc = lambda clf, hid: clf(self.drop(hid))
else:
xpos_hid = F.relu(self.xpos_hid(self.drop(lstm_outputs)))
ufeats_hid = F.relu(self.ufeats_hid(self.drop(lstm_outputs)))
if self.training:
upos_emb = self.upos_emb(upos)
else:
upos_emb = self.upos_emb(upos_pred.max(1)[1])
clffunc = lambda clf, hid: clf(self.drop(hid), self.drop(upos_emb))
xpos = pack(xpos).data
if isinstance(self.vocab['xpos'], CompositeVocab):
xpos_preds = []
for i in range(len(self.vocab['xpos'])):
xpos_pred = clffunc(self.xpos_clf[i], xpos_hid)
loss += self.crit(xpos_pred.view(-1, xpos_pred.size(-1)), xpos[:, i].view(-1))
xpos_preds.append(pad(xpos_pred).max(2, keepdim=True)[1])
preds.append(torch.cat(xpos_preds, 2))
else:
xpos_pred = clffunc(self.xpos_clf, xpos_hid)
loss += self.crit(xpos_pred.view(-1, xpos_pred.size(-1)), xpos.view(-1))
preds.append(pad(xpos_pred).max(2)[1])
ufeats_preds = []
ufeats = pack(ufeats).data
for i in range(len(self.vocab['feats'])):
ufeats_pred = clffunc(self.ufeats_clf[i], ufeats_hid)
loss += self.crit(ufeats_pred.view(-1, ufeats_pred.size(-1)), ufeats[:, i].view(-1))
ufeats_preds.append(pad(ufeats_pred).max(2, keepdim=True)[1])
preds.append(torch.cat(ufeats_preds, 2))
return loss, preds
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