manoharkdas commited on
Commit
df3eae0
1 Parent(s): dfd8182

Create new file

Browse files
Files changed (1) hide show
  1. qasrl_model_pipeline.py +182 -0
qasrl_model_pipeline.py ADDED
@@ -0,0 +1,182 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from typing import Optional
2
+ import json
3
+ from argparse import Namespace
4
+ from pathlib import Path
5
+ from transformers import Text2TextGenerationPipeline, AutoModelForSeq2SeqLM, AutoTokenizer
6
+
7
+ def get_markers_for_model(is_t5_model: bool) -> Namespace:
8
+ special_tokens_constants = Namespace()
9
+ if is_t5_model:
10
+ # T5 model have 100 special tokens by default
11
+ special_tokens_constants.separator_input_question_predicate = "<extra_id_1>"
12
+ special_tokens_constants.separator_output_answers = "<extra_id_3>"
13
+ special_tokens_constants.separator_output_questions = "<extra_id_5>" # if using only questions
14
+ special_tokens_constants.separator_output_question_answer = "<extra_id_7>"
15
+ special_tokens_constants.separator_output_pairs = "<extra_id_9>"
16
+ special_tokens_constants.predicate_generic_marker = "<extra_id_10>"
17
+ special_tokens_constants.predicate_verb_marker = "<extra_id_11>"
18
+ special_tokens_constants.predicate_nominalization_marker = "<extra_id_12>"
19
+
20
+ else:
21
+ special_tokens_constants.separator_input_question_predicate = "<question_predicate_sep>"
22
+ special_tokens_constants.separator_output_answers = "<answers_sep>"
23
+ special_tokens_constants.separator_output_questions = "<question_sep>" # if using only questions
24
+ special_tokens_constants.separator_output_question_answer = "<question_answer_sep>"
25
+ special_tokens_constants.separator_output_pairs = "<qa_pairs_sep>"
26
+ special_tokens_constants.predicate_generic_marker = "<predicate_marker>"
27
+ special_tokens_constants.predicate_verb_marker = "<verbal_predicate_marker>"
28
+ special_tokens_constants.predicate_nominalization_marker = "<nominalization_predicate_marker>"
29
+ return special_tokens_constants
30
+
31
+ def load_trained_model(name_or_path):
32
+ import huggingface_hub as HFhub
33
+ tokenizer = AutoTokenizer.from_pretrained(name_or_path)
34
+ model = AutoModelForSeq2SeqLM.from_pretrained(name_or_path)
35
+ # load preprocessing_kwargs from the model repo on HF hub, or from the local model directory
36
+ kwargs_filename = None
37
+ if name_or_path.startswith("kleinay/"): # and 'preprocessing_kwargs.json' in HFhub.list_repo_files(name_or_path): # the supported version of HFhub doesn't support list_repo_files
38
+ kwargs_filename = HFhub.hf_hub_download(repo_id=name_or_path, filename="preprocessing_kwargs.json")
39
+ elif Path(name_or_path).is_dir() and (Path(name_or_path) / "experiment_kwargs.json").exists():
40
+ kwargs_filename = Path(name_or_path) / "experiment_kwargs.json"
41
+
42
+ if kwargs_filename:
43
+ preprocessing_kwargs = json.load(open(kwargs_filename))
44
+ # integrate into model.config (for decoding args, e.g. "num_beams"), and save also as standalone object for preprocessing
45
+ model.config.preprocessing_kwargs = Namespace(**preprocessing_kwargs)
46
+ model.config.update(preprocessing_kwargs)
47
+ return model, tokenizer
48
+
49
+
50
+ class QASRL_Pipeline(Text2TextGenerationPipeline):
51
+ def __init__(self, model_repo: str, **kwargs):
52
+ model, tokenizer = load_trained_model(model_repo)
53
+ super().__init__(model, tokenizer, framework="pt")
54
+ self.is_t5_model = "t5" in model.config.model_type
55
+ self.special_tokens = get_markers_for_model(self.is_t5_model)
56
+ self.data_args = model.config.preprocessing_kwargs
57
+ # backward compatibility - default keyword values implemeted in `run_summarization`, thus not saved in `preprocessing_kwargs`
58
+ if "predicate_marker_type" not in vars(self.data_args):
59
+ self.data_args.predicate_marker_type = "generic"
60
+ if "use_bilateral_predicate_marker" not in vars(self.data_args):
61
+ self.data_args.use_bilateral_predicate_marker = True
62
+ if "append_verb_form" not in vars(self.data_args):
63
+ self.data_args.append_verb_form = True
64
+ self._update_config(**kwargs)
65
+
66
+ def _update_config(self, **kwargs):
67
+ " Update self.model.config with initialization parameters and necessary defaults. "
68
+ # set default values that will always override model.config, but can overriden by __init__ kwargs
69
+ kwargs["max_length"] = kwargs.get("max_length", 80)
70
+ # override model.config with kwargs
71
+ for k,v in kwargs.items():
72
+ self.model.config.__dict__[k] = v
73
+
74
+ def _sanitize_parameters(self, **kwargs):
75
+ preprocess_kwargs, forward_kwargs, postprocess_kwargs = {}, {}, {}
76
+ if "predicate_marker" in kwargs:
77
+ preprocess_kwargs["predicate_marker"] = kwargs["predicate_marker"]
78
+ if "predicate_type" in kwargs:
79
+ preprocess_kwargs["predicate_type"] = kwargs["predicate_type"]
80
+ if "verb_form" in kwargs:
81
+ preprocess_kwargs["verb_form"] = kwargs["verb_form"]
82
+ return preprocess_kwargs, forward_kwargs, postprocess_kwargs
83
+
84
+ def preprocess(self, inputs, predicate_marker="<predicate>", predicate_type=None, verb_form=None):
85
+ # Here, inputs is string or list of strings; apply string postprocessing
86
+ if isinstance(inputs, str):
87
+ processed_inputs = self._preprocess_string(inputs, predicate_marker, predicate_type, verb_form)
88
+ elif hasattr(inputs, "__iter__"):
89
+ processed_inputs = [self._preprocess_string(s, predicate_marker, predicate_type, verb_form) for s in inputs]
90
+ else:
91
+ raise ValueError("inputs must be str or Iterable[str]")
92
+ # Now pass to super.preprocess for tokenization
93
+ return super().preprocess(processed_inputs)
94
+
95
+ def _preprocess_string(self, seq: str, predicate_marker: str, predicate_type: Optional[str], verb_form: Optional[str]) -> str:
96
+ sent_tokens = seq.split(" ")
97
+ assert predicate_marker in sent_tokens, f"Input sentence must include a predicate-marker token ('{predicate_marker}') before the target predicate word"
98
+ predicate_idx = sent_tokens.index(predicate_marker)
99
+ sent_tokens.remove(predicate_marker)
100
+ sentence_before_predicate = " ".join([sent_tokens[i] for i in range(predicate_idx)])
101
+ predicate = sent_tokens[predicate_idx]
102
+ sentence_after_predicate = " ".join([sent_tokens[i] for i in range(predicate_idx+1, len(sent_tokens))])
103
+
104
+ if self.data_args.predicate_marker_type == "generic":
105
+ predicate_marker = self.special_tokens.predicate_generic_marker
106
+ # In case we want special marker for each predicate type: """
107
+ elif self.data_args.predicate_marker_type == "pred_type":
108
+ assert predicate_type is not None, "For this model, you must provide the `predicate_type` either when initializing QASRL_Pipeline(...) or when applying __call__(...) on it"
109
+ assert predicate_type in ("verbal", "nominal"), f"`predicate_type` must be either 'verbal' or 'nominal'; got '{predicate_type}'"
110
+ predicate_marker = {"verbal": self.special_tokens.predicate_verb_marker ,
111
+ "nominal": self.special_tokens.predicate_nominalization_marker
112
+ }[predicate_type]
113
+
114
+ if self.data_args.use_bilateral_predicate_marker:
115
+ seq = f"{sentence_before_predicate} {predicate_marker} {predicate} {predicate_marker} {sentence_after_predicate}"
116
+ else:
117
+ seq = f"{sentence_before_predicate} {predicate_marker} {predicate} {sentence_after_predicate}"
118
+
119
+ # embed also verb_form
120
+ if self.data_args.append_verb_form and verb_form is None:
121
+ raise ValueError(f"For this model, you must provide the `verb_form` of the predicate when applying __call__(...)")
122
+ elif self.data_args.append_verb_form:
123
+ seq = f"{seq} {self.special_tokens.separator_input_question_predicate} {verb_form} "
124
+ else:
125
+ seq = f"{seq} "
126
+
127
+ # append source prefix (for t5 models)
128
+ prefix = self._get_source_prefix(predicate_type)
129
+
130
+ return prefix + seq
131
+
132
+ def _get_source_prefix(self, predicate_type: Optional[str]):
133
+ if not self.is_t5_model or self.data_args.source_prefix is None:
134
+ return ''
135
+ if not self.data_args.source_prefix.startswith("<"): # Regular prefix - not dependent on input row x
136
+ return self.data_args.source_prefix
137
+ if self.data_args.source_prefix == "<predicate-type>":
138
+ if predicate_type is None:
139
+ raise ValueError("source_prefix is '<predicate-type>' but input no `predicate_type`.")
140
+ else:
141
+ return f"Generate QAs for {predicate_type} QASRL: "
142
+
143
+ def _forward(self, *args, **kwargs):
144
+ outputs = super()._forward(*args, **kwargs)
145
+ return outputs
146
+
147
+
148
+ def postprocess(self, model_outputs):
149
+ output_seq = self.tokenizer.decode(
150
+ model_outputs["output_ids"].squeeze(),
151
+ skip_special_tokens=False,
152
+ clean_up_tokenization_spaces=False,
153
+ )
154
+ output_seq = output_seq.strip(self.tokenizer.pad_token).strip(self.tokenizer.eos_token).strip()
155
+ qa_subseqs = output_seq.split(self.special_tokens.separator_output_pairs)
156
+ qas = [self._postrocess_qa(qa_subseq) for qa_subseq in qa_subseqs]
157
+ return {"generated_text": output_seq,
158
+ "QAs": qas}
159
+
160
+ def _postrocess_qa(self, seq: str) -> str:
161
+ # split question and answers
162
+ if self.special_tokens.separator_output_question_answer in seq:
163
+ question, answer = seq.split(self.special_tokens.separator_output_question_answer)[:2]
164
+ else:
165
+ print("invalid format: no separator between question and answer found...")
166
+ return None
167
+ # question, answer = seq, '' # Or: backoff to only question
168
+ # skip "_" slots in questions
169
+ question = ' '.join(t for t in question.split(' ') if t != '_')
170
+ answers = [a.strip() for a in answer.split(self.special_tokens.separator_output_answers)]
171
+ return {"question": question, "answers": answers}
172
+
173
+
174
+ if __name__ == "__main__":
175
+ pipe = QASRL_Pipeline("kleinay/qanom-seq2seq-model-baseline")
176
+ res1 = pipe("The student was interested in Luke 's <predicate> research about sea animals .", verb_form="research", predicate_type="nominal")
177
+ res2 = pipe(["The doctor was interested in Luke 's <predicate> treatment .",
178
+ "The Veterinary student was interested in Luke 's <predicate> treatment of sea animals ."], verb_form="treat", predicate_type="nominal", num_beams=10)
179
+ res3 = pipe("A number of professions have <predicate> developed that specialize in the treatment of mental disorders .", verb_form="develop", predicate_type="verbal")
180
+ print(res1)
181
+ print(res2)
182
+ print(res3)