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from dataclasses import dataclass
import re
from operator import itemgetter
import torch
from torch import nn
from typing import Any, Dict, List, Literal, Optional, Tuple, Union
from transformers import BertPreTrainedModel, BertModel, BertTokenizerFast
from transformers.models.bert.modeling_bert import BertOnlyMLMHead
from transformers.utils import ModelOutput
from .BertForSyntaxParsing import BertSyntaxParsingHead, SyntaxLabels, SyntaxLogitsOutput, parse_logits as syntax_parse_logits
from .BertForPrefixMarking import BertPrefixMarkingHead, parse_logits as prefix_parse_logits, encode_sentences_for_bert_for_prefix_marking, get_prefixes_from_str
from .BertForMorphTagging import BertMorphTaggingHead, MorphLogitsOutput, MorphLabels, parse_logits as morph_parse_logits
import warnings
@dataclass
class JointParsingOutput(ModelOutput):
loss: Optional[torch.FloatTensor] = None
# logits will contain the optional predictions for the given labels
logits: Optional[Union[SyntaxLogitsOutput, None]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
# if no labels are given, we will always include the syntax logits separately
syntax_logits: Optional[SyntaxLogitsOutput] = None
ner_logits: Optional[torch.FloatTensor] = None
prefix_logits: Optional[torch.FloatTensor] = None
lex_logits: Optional[torch.FloatTensor] = None
morph_logits: Optional[MorphLogitsOutput] = None
# wrapper class to wrap a torch.nn.Module so that you can store a module in multiple linked
# properties without registering the parameter multiple times
class ModuleRef:
def __init__(self, module: torch.nn.Module):
self.module = module
def forward(self, *args, **kwargs):
return self.module.forward(*args, **kwargs)
def __call__(self, *args, **kwargs):
return self.module(*args, **kwargs)
class BertForJointParsing(BertPreTrainedModel):
_tied_weights_keys = ["predictions.decoder.bias", "cls.predictions.decoder.weight"]
def __init__(self, config, do_syntax=None, do_ner=None, do_prefix=None, do_lex=None, do_morph=None, syntax_head_size=64):
super().__init__(config)
self.bert = BertModel(config, add_pooling_layer=False)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# create all the heads as None, and then populate them as defined
self.syntax, self.ner, self.prefix, self.lex, self.morph = (None,)*5
if do_syntax is not None:
config.do_syntax = do_syntax
config.syntax_head_size = syntax_head_size
if do_ner is not None: config.do_ner = do_ner
if do_prefix is not None: config.do_prefix = do_prefix
if do_lex is not None: config.do_lex = do_lex
if do_morph is not None: config.do_morph = do_morph
# add all the individual heads
if config.do_syntax:
self.syntax = BertSyntaxParsingHead(config)
if config.do_ner:
self.num_labels = config.num_labels
self.classifier = nn.Linear(config.hidden_size, config.num_labels) # name it same as in BertForTokenClassification
self.ner = ModuleRef(self.classifier)
if config.do_prefix:
self.prefix = BertPrefixMarkingHead(config)
if config.do_lex:
self.cls = BertOnlyMLMHead(config) # name it the same as in BertForMaskedLM
self.lex = ModuleRef(self.cls)
if config.do_morph:
self.morph = BertMorphTaggingHead(config)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.cls.predictions.decoder if self.lex is not None else None
def set_output_embeddings(self, new_embeddings):
if self.lex is not None:
self.cls.predictions.decoder = new_embeddings
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
prefix_class_id_options: Optional[torch.Tensor] = None,
labels: Optional[Union[SyntaxLabels, MorphLabels, torch.Tensor]] = None,
labels_type: Optional[Literal['syntax', 'ner', 'prefix', 'lex', 'morph']] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
compute_syntax_mst: Optional[bool] = None
):
if return_dict is False:
warnings.warn("Specified `return_dict=False` but the flag is ignored and treated as always True in this model.")
if labels is not None and labels_type is None:
raise ValueError("Cannot specify labels without labels_type")
if labels_type == 'seg' and prefix_class_id_options is None:
raise ValueError('Cannot calculate prefix logits without prefix_class_id_options')
if compute_syntax_mst is not None and self.syntax is None:
raise ValueError("Cannot compute syntax MST when the syntax head isn't loaded")
bert_outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=True,
)
# calculate the extended attention mask for any child that might need it
extended_attention_mask = None
if attention_mask is not None:
extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_ids.size())
# extract the hidden states, and apply the dropout
hidden_states = self.dropout(bert_outputs[0])
logits = None
syntax_logits = None
ner_logits = None
prefix_logits = None
lex_logits = None
morph_logits = None
# Calculate the syntax
if self.syntax is not None and (labels is None or labels_type == 'syntax'):
# apply the syntax head
loss, syntax_logits = self.syntax(hidden_states, extended_attention_mask, labels, compute_syntax_mst)
logits = syntax_logits
# Calculate the NER
if self.ner is not None and (labels is None or labels_type == 'ner'):
ner_logits = self.ner(hidden_states)
logits = ner_logits
if labels is not None:
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
# Calculate the segmentation
if self.prefix is not None and (labels is None or labels_type == 'prefix'):
loss, prefix_logits = self.prefix(hidden_states, prefix_class_id_options, labels)
logits = prefix_logits
# Calculate the lexeme
if self.lex is not None and (labels is None or labels_type == 'lex'):
lex_logits = self.lex(hidden_states)
logits = lex_logits
if labels is not None:
loss_fct = nn.CrossEntropyLoss() # -100 index = padding token
loss = loss_fct(lex_logits.view(-1, self.config.vocab_size), labels.view(-1))
if self.morph is not None and (labels is None or labels_type == 'morph'):
loss, morph_logits = self.morph(hidden_states, labels)
logits = morph_logits
# no labels => logits = None
if labels is None: logits = None
return JointParsingOutput(
loss,
logits,
hidden_states=bert_outputs.hidden_states,
attentions=bert_outputs.attentions,
# all the predicted logits section
syntax_logits=syntax_logits,
ner_logits=ner_logits,
prefix_logits=prefix_logits,
lex_logits=lex_logits,
morph_logits=morph_logits
)
def predict(self, sentences: Union[str, List[str]], tokenizer: BertTokenizerFast, padding='longest', truncation=True, compute_syntax_mst=True, per_token_ner=False, output_style: Literal['json', 'ud', 'iahlt_ud'] = 'json'):
is_single_sentence = isinstance(sentences, str)
if is_single_sentence:
sentences = [sentences]
if output_style not in ['json', 'ud', 'iahlt_ud']:
raise ValueError('output_style must be in json/ud/iahlt_ud')
if output_style in ['ud', 'iahlt_ud'] and (self.prefix is None or self.morph is None or self.syntax is None or self.lex is None):
raise ValueError("Cannot output UD format when any of the prefix,morph,syntax, and lex heads aren't loaded.")
# predict the logits for the sentence
if self.prefix is not None:
inputs = encode_sentences_for_bert_for_prefix_marking(tokenizer, self.config.prefix_cfg, sentences, padding)
else:
inputs = tokenizer(sentences, padding=padding, truncation=truncation, return_offsets_mapping=True, return_tensors='pt')
offset_mapping = inputs.pop('offset_mapping')
# Copy the tensors to the right device, and parse!
inputs = {k:v.to(self.device) for k,v in inputs.items()}
output = self.forward(**inputs, return_dict=True, compute_syntax_mst=compute_syntax_mst)
input_ids = inputs['input_ids'].tolist() # convert once
final_output = [dict(text=sentence, tokens=combine_token_wordpieces(ids, offsets, tokenizer)) for sentence, ids, offsets in zip(sentences, input_ids, offset_mapping)]
# Syntax logits: each sentence gets a dict(tree: List[dict(word,dep_head,dep_head_idx,dep_func)], root_idx: int)
if output.syntax_logits is not None:
for sent_idx,parsed in enumerate(syntax_parse_logits(input_ids, sentences, tokenizer, output.syntax_logits)):
merge_token_list(final_output[sent_idx]['tokens'], parsed['tree'], 'syntax')
final_output[sent_idx]['root_idx'] = parsed['root_idx']
# Prefix logits: each sentence gets a list([prefix_segment, word_without_prefix]) - **WITH CLS & SEP**
if output.prefix_logits is not None:
for sent_idx,parsed in enumerate(prefix_parse_logits(input_ids, sentences, tokenizer, output.prefix_logits, self.config.prefix_cfg)):
merge_token_list(final_output[sent_idx]['tokens'], map(tuple, parsed[1:-1]), 'seg')
# Lex logits each sentence gets a list(tuple(word, lexeme))
if output.lex_logits is not None:
for sent_idx, parsed in enumerate(lex_parse_logits(input_ids, sentences, tokenizer, output.lex_logits)):
merge_token_list(final_output[sent_idx]['tokens'], map(itemgetter(1), parsed), 'lex')
# morph logits each sentences get a dict(text=str, tokens=list(dict(token, pos, feats, prefixes, suffix, suffix_feats?)))
if output.morph_logits is not None:
for sent_idx,parsed in enumerate(morph_parse_logits(input_ids, sentences, tokenizer, output.morph_logits)):
merge_token_list(final_output[sent_idx]['tokens'], parsed['tokens'], 'morph')
# NER logits each sentence gets a list(tuple(word, ner))
if output.ner_logits is not None:
for sent_idx,parsed in enumerate(ner_parse_logits(input_ids, sentences, tokenizer, output.ner_logits, self.config.id2label)):
if per_token_ner:
merge_token_list(final_output[sent_idx]['tokens'], map(itemgetter(1), parsed), 'ner')
final_output[sent_idx]['ner_entities'] = aggregate_ner_tokens(final_output[sent_idx], parsed)
if output_style in ['ud', 'iahlt_ud']:
final_output = convert_output_to_ud(final_output, self.config, style='htb' if output_style == 'ud' else 'iahlt')
if is_single_sentence:
final_output = final_output[0]
return final_output
def aggregate_ner_tokens(final_output, parsed):
entities = []
prev = None
for token_idx, (d, (word, pred)) in enumerate(zip(final_output['tokens'], parsed)):
# O does nothing
if pred == 'O': prev = None
# B- || I-entity != prev (different entity or none)
elif pred.startswith('B-') or pred[2:] != prev:
prev = pred[2:]
entities.append([[word], dict(label=prev, start=d['offsets']['start'], end=d['offsets']['end'], token_start=token_idx, token_end=token_idx)])
else:
entities[-1][0].append(word)
entities[-1][1]['end'] = d['offsets']['end']
entities[-1][1]['token_end'] = token_idx
return [dict(phrase=' '.join(words), **d) for words, d in entities]
def merge_token_list(src, update, key):
for token_src, token_update in zip(src, update):
token_src[key] = token_update
def combine_token_wordpieces(input_ids: List[int], offset_mapping: torch.Tensor, tokenizer: BertTokenizerFast):
offset_mapping = offset_mapping.tolist()
ret = []
special_toks = tokenizer.all_special_tokens
special_toks.remove(tokenizer.unk_token)
for token, offsets in zip(tokenizer.convert_ids_to_tokens(input_ids), offset_mapping):
if token in special_toks: continue
if token.startswith('##'):
ret[-1]['token'] += token[2:]
ret[-1]['offsets']['end'] = offsets[1]
else: ret.append(dict(token=token, offsets=dict(start=offsets[0], end=offsets[1])))
return ret
def ner_parse_logits(input_ids: List[List[int]], sentences: List[str], tokenizer: BertTokenizerFast, logits: torch.Tensor, id2label: Dict[int, str]):
predictions = torch.argmax(logits, dim=-1).tolist()
batch_ret = []
special_toks = tokenizer.all_special_tokens
special_toks.remove(tokenizer.unk_token)
for batch_idx in range(len(sentences)):
ret = []
batch_ret.append(ret)
tokens = tokenizer.convert_ids_to_tokens(input_ids[batch_idx])
for tok_idx in range(len(tokens)):
token = tokens[tok_idx]
if token in special_toks: continue
# wordpieces should just be appended to the previous word
# we modify the last token in ret
# by discarding the original end position and replacing it with the new token's end position
if token.startswith('##'):
continue
# for each token, we append a tuple containing: token, label, start position, end position
ret.append((token, id2label[predictions[batch_idx][tok_idx]]))
return batch_ret
def lex_parse_logits(input_ids: List[List[int]], sentences: List[str], tokenizer: BertTokenizerFast, logits: torch.Tensor):
predictions = torch.argsort(logits, dim=-1, descending=True)[..., :3].tolist()
batch_ret = []
special_toks = tokenizer.all_special_tokens
special_toks.remove(tokenizer.unk_token)
for batch_idx in range(len(sentences)):
intermediate_ret = []
tokens = tokenizer.convert_ids_to_tokens(input_ids[batch_idx])
for tok_idx in range(len(tokens)):
token = tokens[tok_idx]
if token in special_toks: continue
# wordpieces should just be appended to the previous word
if token.startswith('##'):
intermediate_ret[-1] = (intermediate_ret[-1][0] + token[2:], intermediate_ret[-1][1])
continue
intermediate_ret.append((token, tokenizer.convert_ids_to_tokens(predictions[batch_idx][tok_idx])))
# build the final output taking into account valid letters
ret = []
batch_ret.append(ret)
for (token, lexemes) in intermediate_ret:
# must overlap on at least 2 non ืืืื letters
possible_lets = set(c for c in token if c not in 'ืืืื')
final_lex = '[BLANK]'
for lex in lexemes:
if sum(c in possible_lets for c in lex) >= min([2, len(possible_lets), len([c for c in lex if c not in 'ืืืื'])]):
final_lex = lex
break
ret.append((token, final_lex))
return batch_ret
ud_prefixes_to_pos = {
'ืฉ': ['SCONJ'],
'ืืฉ': ['SCONJ'],
'ืืฉ': ['SCONJ'],
'ืืืฉ': ['SCONJ'],
'ืืฉ': ['SCONJ'],
'ืืฉ': ['SCONJ'],
'ื': ['CCONJ'],
'ื': ['ADP'],
'ื': ['DET', 'SCONJ'],
'ื': ['ADP', 'SCONJ'],
'ื': ['ADP'],
'ื': ['ADP', 'ADV'],
}
ud_suffix_to_htb_str = {
'Gender=Masc|Number=Sing|Person=3': '_ืืื',
'Gender=Masc|Number=Plur|Person=3': '_ืื',
'Gender=Fem|Number=Sing|Person=3': '_ืืื',
'Gender=Fem|Number=Plur|Person=3': '_ืื',
'Gender=Fem,Masc|Number=Plur|Person=1': '_ืื ืื ื',
'Gender=Fem,Masc|Number=Sing|Person=1': '_ืื ื',
'Gender=Masc|Number=Plur|Person=2': '_ืืชื',
'Gender=Masc|Number=Sing|Person=3': '_ืืื',
'Gender=Masc|Number=Sing|Person=2': '_ืืชื',
'Gender=Fem|Number=Sing|Person=2': '_ืืช',
'Gender=Masc|Number=Plur|Person=3': '_ืื'
}
def convert_output_to_ud(output_sentences, model_cfg, style: Literal['htb', 'iahlt']):
if style not in ['htb', 'iahlt']:
raise ValueError('style must be htb/iahlt')
final_output = []
for sent_idx, sentence in enumerate(output_sentences):
# next, go through each word and insert it in the UD format. Store in a temp format for the post process
intermediate_output = []
ranges = []
# store a mapping between each word index and the actual line it appears in
idx_to_key = {-1: 0}
for word_idx,word in enumerate(sentence['tokens']):
try:
# handle blank lexemes
if word['lex'] == '[BLANK]':
word['lex'] = word['seg'][-1]
except KeyError:
import json
print(json.dumps(sentence, ensure_ascii=False, indent=2))
exit(0)
start = len(intermediate_output)
# Add in all the prefixes
if len(word['seg']) > 1:
for pre in get_prefixes_from_str(word['seg'][0], model_cfg.prefix_cfg, greedy=True):
# pos - just take the first valid pos that appears in the predicted prefixes list.
pos = next((pos for pos in ud_prefixes_to_pos[pre] if pos in word['morph']['prefixes']), ud_prefixes_to_pos[pre][0])
dep, func = ud_get_prefix_dep(pre, word, word_idx)
intermediate_output.append(dict(word=pre, lex=pre, pos=pos, dep=dep, func=func, feats='_'))
# if there was an implicit heh, add it in dependent on the method
if not 'ื' in pre and intermediate_output[-1]['pos'] == 'ADP' and 'DET' in word['morph']['prefixes']:
if style == 'htb':
intermediate_output.append(dict(word='ื_', lex='ื', pos='DET', dep=word_idx, func='det', feats='_'))
elif style == 'iahlt':
intermediate_output[-1]['feats'] = 'Definite=Def|PronType=Art'
idx_to_key[word_idx] = len(intermediate_output) + 1
# add the main word in!
intermediate_output.append(dict(
word=word['seg'][-1], lex=word['lex'], pos=word['morph']['pos'],
dep=word['syntax']['dep_head_idx'], func=word['syntax']['dep_func'],
feats='|'.join(f'{k}={v}' for k,v in word['morph']['feats'].items())))
# if we have suffixes, this changes things
if word['morph']['suffix']:
# first determine the dependency info:
# For adp, num, det - they main word points to here, and the suffix points to the dependency
entry_to_assign_suf_dep = None
if word['morph']['pos'] in ['ADP', 'NUM', 'DET']:
entry_to_assign_suf_dep = intermediate_output[-1]
intermediate_output[-1]['func'] = 'case'
dep = word['syntax']['dep_head_idx']
func = word['syntax']['dep_func']
else:
# if pos is verb -> obj, num -> dep, default to -> nmod:poss
dep = word_idx
func = {'VERB': 'obj', 'NUM': 'dep'}.get(word['morph']['pos'], 'nmod:poss')
s_word, s_lex = word['seg'][-1], word['lex']
# update the word of the string and extract the string of the suffix!
# for IAHLT:
if style == 'iahlt':
# we need to shorten the main word and extract the suffix
# if it is longer than the lexeme - just take off the lexeme.
if len(s_word) > len(s_lex):
idx = len(s_lex)
# Otherwise, try to find the last letter of the lexeme, and fail that just take the last letter
else:
# take either len-1, or the last occurence (which can be -1 === len-1)
idx = min([len(s_word) - 1, s_word.rfind(s_lex[-1])])
# extract the suffix and update the main word
suf = s_word[idx:]
intermediate_output[-1]['word'] = s_word[:idx]
# for htb:
elif style == 'htb':
# main word becomes the lexeme, the suffix is based on the features
intermediate_output[-1]['word'] = (s_lex if s_lex != s_word else s_word[:-1]) + '_'
suf_feats = word['morph']['suffix_feats']
suf = ud_suffix_to_htb_str.get(f"Gender={suf_feats.get('Gender', 'Fem,Masc')}|Number={suf_feats.get('Number', 'Sing')}|Person={suf_feats.get('Person', '3')}", "_ืืื")
# for HTB, if the function is poss, then add a shel pointing to the next word
if func == 'nmod:poss' and s_lex != 'ืฉื':
intermediate_output.append(dict(word='_ืฉื_', lex='ืฉื', pos='ADP', dep=len(intermediate_output) + 2, func='case', feats='_', absolute_dep=True))
# add the main suffix in
intermediate_output.append(dict(word=suf, lex='ืืื', pos='PRON', dep=dep, func=func, feats='|'.join(f'{k}={v}' for k,v in word['morph']['suffix_feats'].items())))
if entry_to_assign_suf_dep:
entry_to_assign_suf_dep['dep'] = len(intermediate_output)
entry_to_assign_suf_dep['absolute_dep'] = True
end = len(intermediate_output)
ranges.append((start, end, word['token']))
# now that we have the intermediate output, combine it to the final output
cur_output = []
final_output.append(cur_output)
# first, add the headers
cur_output.append(f'# sent_id = {sent_idx + 1}')
cur_output.append(f'# text = {sentence["text"]}')
# add in all the actual entries
for start,end,token in ranges:
if end - start > 1:
cur_output.append(f'{start + 1}-{end}\t{token}\t_\t_\t_\t_\t_\t_\t_\t_')
for idx,output in enumerate(intermediate_output[start:end], start + 1):
# compute the actual dependency location
dep = output['dep'] if output.get('absolute_dep', False) else idx_to_key[output['dep']]
func = normalize_dep_rel(output['func'], style)
# and add the full ud string in
cur_output.append('\t'.join([
str(idx),
output['word'],
output['lex'],
output['pos'],
output['pos'],
output['feats'],
str(dep),
func,
'_', '_'
]))
return final_output
def normalize_dep_rel(dep, style: Literal['htb', 'iahlt']):
if style == 'iahlt':
if dep == 'compound:smixut': return 'compound'
if dep == 'nsubj:cop': return 'nsubj'
if dep == 'mark:q': return 'mark'
if dep == 'case:gen' or dep == 'case:acc': return 'case'
return dep
def ud_get_prefix_dep(pre, word, word_idx):
does_follow_main = False
# shin goes to the main word for verbs, otherwise follows the word
if pre.endswith('ืฉ'):
does_follow_main = word['morph']['pos'] != 'VERB'
func = 'mark'
# vuv goes to the main word if the function is in the list, otherwise follows
elif pre == 'ื':
does_follow_main = word['syntax']['dep_func'] not in ["conj", "acl:recl", "parataxis", "root", "acl", "amod", "list", "appos", "dep", "flatccomp"]
func = 'cc'
else:
# for adj, noun, propn, pron, verb - prefixes go to the main word
if word['morph']['pos'] in ["ADJ", "NOUN", "PROPN", "PRON", "VERB"]:
does_follow_main = False
# otherwise - prefix follows the word if the function is in the list
else: does_follow_main = word['syntax']['dep_func'] in ["compound:affix", "det", "aux", "nummod", "advmod", "dep", "cop", "mark", "fixed"]
func = 'case'
if pre == 'ื':
func = 'det' if 'DET' in word['morph']['prefixes'] else 'mark'
return (word['syntax']['dep_head_idx'] if does_follow_main else word_idx), func |