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qasrl_model_pipeline.py

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+from typing import Optional
+import json
+from argparse import Namespace
+from pathlib import Path
+from transformers import Text2TextGenerationPipeline, AutoModelForSeq2SeqLM, AutoTokenizer
+
+def get_markers_for_model(is_t5_model: bool) -> Namespace:
+    special_tokens_constants = Namespace() 
+    if is_t5_model:
+        # T5 model have 100 special tokens by default
+        special_tokens_constants.separator_input_question_predicate = "<extra_id_1>"
+        special_tokens_constants.separator_output_answers = "<extra_id_3>"
+        special_tokens_constants.separator_output_questions = "<extra_id_5>"  # if using only questions
+        special_tokens_constants.separator_output_question_answer = "<extra_id_7>"
+        special_tokens_constants.separator_output_pairs = "<extra_id_9>"
+        special_tokens_constants.predicate_generic_marker = "<extra_id_10>" 
+        special_tokens_constants.predicate_verb_marker = "<extra_id_11>" 
+        special_tokens_constants.predicate_nominalization_marker = "<extra_id_12>" 
+
+    else:
+        special_tokens_constants.separator_input_question_predicate = "<question_predicate_sep>"
+        special_tokens_constants.separator_output_answers = "<answers_sep>"
+        special_tokens_constants.separator_output_questions = "<question_sep>"  # if using only questions
+        special_tokens_constants.separator_output_question_answer = "<question_answer_sep>"
+        special_tokens_constants.separator_output_pairs = "<qa_pairs_sep>"
+        special_tokens_constants.predicate_generic_marker = "<predicate_marker>" 
+        special_tokens_constants.predicate_verb_marker = "<verbal_predicate_marker>" 
+        special_tokens_constants.predicate_nominalization_marker = "<nominalization_predicate_marker>" 
+    return special_tokens_constants
+
+def load_trained_model(name_or_path):
+    import huggingface_hub as HFhub
+    tokenizer = AutoTokenizer.from_pretrained(name_or_path)
+    model = AutoModelForSeq2SeqLM.from_pretrained(name_or_path)  
+    # load preprocessing_kwargs from the model repo on HF hub, or from the local model directory
+    kwargs_filename = None
+    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
+        kwargs_filename = HFhub.hf_hub_download(repo_id=name_or_path, filename="preprocessing_kwargs.json")
+    elif Path(name_or_path).is_dir() and (Path(name_or_path) / "experiment_kwargs.json").exists():
+        kwargs_filename = Path(name_or_path) / "experiment_kwargs.json"
+    
+    if kwargs_filename:
+        preprocessing_kwargs = json.load(open(kwargs_filename)) 
+        # integrate into model.config (for decoding args, e.g. "num_beams"), and save also as standalone object for preprocessing
+        model.config.preprocessing_kwargs = Namespace(**preprocessing_kwargs)
+        model.config.update(preprocessing_kwargs)
+    return model, tokenizer
+
+
+class QASRL_Pipeline(Text2TextGenerationPipeline):
+    def __init__(self, model_repo: str, **kwargs):
+        model, tokenizer = load_trained_model(model_repo)
+        super().__init__(model, tokenizer, framework="pt")
+        self.is_t5_model = "t5" in model.config.model_type
+        self.special_tokens = get_markers_for_model(self.is_t5_model)
+        self.data_args = model.config.preprocessing_kwargs 
+        # backward compatibility - default keyword values implemeted in `run_summarization`, thus not saved in `preprocessing_kwargs`
+        if "predicate_marker_type" not in vars(self.data_args):
+            self.data_args.predicate_marker_type = "generic"
+        if "use_bilateral_predicate_marker" not in vars(self.data_args):
+            self.data_args.use_bilateral_predicate_marker = True
+        if "append_verb_form" not in vars(self.data_args):
+            self.data_args.append_verb_form = True
+        self._update_config(**kwargs)
+    
+    def _update_config(self, **kwargs):
+        " Update self.model.config with initialization parameters and necessary defaults. "
+        # set default values that will always override model.config, but can overriden by __init__ kwargs
+        kwargs["max_length"] = kwargs.get("max_length", 80)
+        # override model.config with kwargs
+        for k,v in kwargs.items():
+            self.model.config.__dict__[k] = v           
+        
+    def _sanitize_parameters(self, **kwargs):
+        preprocess_kwargs, forward_kwargs, postprocess_kwargs = {}, {}, {} 
+        if "predicate_marker" in kwargs:
+            preprocess_kwargs["predicate_marker"] = kwargs["predicate_marker"]
+        if "predicate_type" in kwargs:
+            preprocess_kwargs["predicate_type"] = kwargs["predicate_type"]
+        if "verb_form" in kwargs:
+            preprocess_kwargs["verb_form"] = kwargs["verb_form"]
+        return preprocess_kwargs, forward_kwargs, postprocess_kwargs
+
+    def preprocess(self, inputs, predicate_marker="<predicate>", predicate_type=None, verb_form=None):
+        # Here, inputs is string or list of strings; apply string postprocessing
+        if isinstance(inputs, str):
+            processed_inputs = self._preprocess_string(inputs, predicate_marker, predicate_type, verb_form)
+        elif hasattr(inputs, "__iter__"):
+            processed_inputs = [self._preprocess_string(s, predicate_marker, predicate_type, verb_form) for s in inputs]
+        else:
+            raise ValueError("inputs must be str or Iterable[str]")
+        # Now pass to super.preprocess for tokenization
+        return super().preprocess(processed_inputs)
+    
+    def _preprocess_string(self, seq: str, predicate_marker: str, predicate_type: Optional[str], verb_form: Optional[str]) -> str:
+        sent_tokens = seq.split(" ")
+        assert predicate_marker in sent_tokens, f"Input sentence must include a predicate-marker token ('{predicate_marker}') before the target predicate word"
+        predicate_idx = sent_tokens.index(predicate_marker)
+        sent_tokens.remove(predicate_marker)
+        sentence_before_predicate = " ".join([sent_tokens[i] for i in range(predicate_idx)])
+        predicate = sent_tokens[predicate_idx]
+        sentence_after_predicate = " ".join([sent_tokens[i] for i in range(predicate_idx+1, len(sent_tokens))])
+        
+        if self.data_args.predicate_marker_type == "generic":
+            predicate_marker = self.special_tokens.predicate_generic_marker    
+        #  In case we want special marker for each predicate type: """
+        elif self.data_args.predicate_marker_type == "pred_type":
+            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"
+            assert predicate_type in ("verbal", "nominal"), f"`predicate_type` must be either 'verbal' or 'nominal'; got '{predicate_type}'"
+            predicate_marker = {"verbal": self.special_tokens.predicate_verb_marker , 
+                                "nominal": self.special_tokens.predicate_nominalization_marker 
+                                }[predicate_type]
+
+        if self.data_args.use_bilateral_predicate_marker:
+            seq = f"{sentence_before_predicate} {predicate_marker} {predicate} {predicate_marker} {sentence_after_predicate}"
+        else:
+            seq = f"{sentence_before_predicate} {predicate_marker} {predicate} {sentence_after_predicate}"
+
+        # embed also verb_form
+        if self.data_args.append_verb_form and verb_form is None:
+            raise ValueError(f"For this model, you must provide the `verb_form` of the predicate when applying __call__(...)")
+        elif self.data_args.append_verb_form:
+            seq = f"{seq} {self.special_tokens.separator_input_question_predicate} {verb_form} "
+        else:
+            seq = f"{seq} "
+    
+        # append source prefix (for t5 models)
+        prefix = self._get_source_prefix(predicate_type)
+        
+        return prefix + seq
+    
+    def _get_source_prefix(self, predicate_type: Optional[str]):
+        if not self.is_t5_model or self.data_args.source_prefix is None:
+            return ''
+        if not self.data_args.source_prefix.startswith("<"):  # Regular prefix - not dependent on input row x
+            return self.data_args.source_prefix
+        if self.data_args.source_prefix == "<predicate-type>":
+            if predicate_type is None:
+                raise ValueError("source_prefix is '<predicate-type>' but input no `predicate_type`.")
+            else:
+                return f"Generate QAs for {predicate_type} QASRL: "
+    
+    def _forward(self, *args, **kwargs):
+        outputs = super()._forward(*args, **kwargs)
+        return outputs
+
+
+    def postprocess(self, model_outputs):
+        output_seq = self.tokenizer.decode(
+            model_outputs["output_ids"].squeeze(),
+            skip_special_tokens=False,
+            clean_up_tokenization_spaces=False,
+        )
+        output_seq = output_seq.strip(self.tokenizer.pad_token).strip(self.tokenizer.eos_token).strip()
+        qa_subseqs = output_seq.split(self.special_tokens.separator_output_pairs)
+        qas = [self._postrocess_qa(qa_subseq) for qa_subseq in qa_subseqs]
+        return {"generated_text": output_seq,
+                "QAs": qas}
+        
+    def _postrocess_qa(self, seq: str) -> str:
+        # split question and answers
+        if self.special_tokens.separator_output_question_answer in seq:
+            question, answer = seq.split(self.special_tokens.separator_output_question_answer)[:2]
+        else:
+            print("invalid format: no separator between question and answer found...")
+            return None
+            # question, answer = seq, '' # Or: backoff to only question  
+        # skip "_" slots in questions
+        question = ' '.join(t for t in question.split(' ') if t != '_')
+        answers = [a.strip() for a in answer.split(self.special_tokens.separator_output_answers)]
+        return {"question": question, "answers": answers}
+    
+    
+if __name__ == "__main__":
+    pipe = QASRL_Pipeline("kleinay/qanom-seq2seq-model-baseline")
+    res1 = pipe("The student was interested in Luke 's <predicate> research about sea animals .", verb_form="research", predicate_type="nominal")
+    res2 = pipe(["The doctor was interested in Luke 's <predicate> treatment .",
+                 "The Veterinary student was interested in Luke 's <predicate> treatment of sea animals ."], verb_form="treat", predicate_type="nominal", num_beams=10)
+    res3 = pipe("A number of professions have <predicate> developed that specialize in the treatment of mental disorders .", verb_form="develop", predicate_type="verbal")
+    print(res1)
+    print(res2)
+    print(res3)