Upload pipeline.py

pipeline.py

@@ -0,0 +1,169 @@
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from typing import Dict, List, Any
+import itertools
+from nltk import sent_tokenize
+# import torch
+import nltk
+
+class PreTrainedPipeline():
+
+    def __init__(self, path=""):
+        # IMPLEMENT_THIS
+        # Preload all the elements you are going to need at inference.
+        # For instance your model, processors, tokenizer that might be needed.
+        # This function is only called once, so do all the heavy processing I/O here"""        
+        nltk.download('punkt')
+        self.model = AutoModelForSeq2SeqLM.from_pretrained(path)
+        self.tokenizer = AutoTokenizer.from_pretrained(path)
+        
+        self.model_type="t5"
+        # self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        self.device =  "cpu"
+        
+        self.model.to(self.device)
+        
+
+
+    def __call__(self, inputs: str, max_words_per_answer: int = 3):
+        if len(inputs) == 0: return []
+        inputs = " ".join(inputs.split())
+        sents, answers = self._extract_answers(inputs)
+        flat_answers = list(itertools.chain(*answers))
+        
+        if len(flat_answers) == 0:
+          return []
+
+        questions, qg_examples = self.prepare_and_generate_questions(sents, answers)
+        output = [{'answer': example['answer'], 'question': que} for example, que in zip(qg_examples, questions)]
+        output = self.clean_generated_QAs(output, max_words_per_answer)
+        return output  
+    
+    def prepare_and_generate_questions(self, sents, answers):
+        qg_examples = self._prepare_inputs_for_qg_from_answers_hl(sents, answers)
+      
+        qg_inputs = [example['source_text'] for example in qg_examples]
+        questions = self._generate_questions(qg_inputs)
+        return questions, qg_examples
+
+
+    def clean_answers_list_of_lists(self, answers):
+        clean_answers = []
+        for answer_list in answers:
+            answer_list = answer_list[:-1]
+            answer_list = list(set([a.strip() for a in answer_list]))
+            clean_answers.append(answer_list)
+        return clean_answers
+
+
+    def _extract_answers(self, context):
+        sents, inputs = self._prepare_inputs_for_ans_extraction(context)
+        inputs = self._tokenize(inputs, padding=True, truncation=True)
+
+        outs = self.model.generate(
+            input_ids=inputs['input_ids'].to(self.device), 
+            attention_mask=inputs['attention_mask'].to(self.device), 
+            max_length=32,
+        )
+        
+        dec = [self.tokenizer.decode(ids, skip_special_tokens=False) for ids in outs]
+        answers = [item.split('<sep>') for item in dec]
+
+        answers = self.clean_answers_list_of_lists(answers)
+
+        return sents, answers
+
+
+    
+    def _prepare_inputs_for_ans_extraction(self, text):
+        sents = sent_tokenize(text)
+
+        inputs = []
+        for i in range(len(sents)):
+            source_text = "extract answers:"
+            for j, sent in enumerate(sents):
+                if i == j:
+                    sent = "<hl> %s <hl>" % sent
+                source_text = "%s %s" % (source_text, sent)
+                source_text = source_text.strip()
+            
+            if self.model_type == "t5":
+              source_text = source_text + " </s>"
+            inputs.append(source_text)
+
+        return sents, inputs      
+          
+    def _tokenize(self,
+        inputs,
+        padding=True,
+        truncation=True,
+        add_special_tokens=True,
+        max_length=512
+    ):
+        inputs = self.tokenizer.batch_encode_plus(
+            inputs, 
+            max_length=max_length,
+            add_special_tokens=add_special_tokens,
+            truncation=truncation,
+            padding="max_length" if padding else False,
+            pad_to_max_length=padding,
+            return_tensors="pt"
+        )
+        return inputs        
+
+    def _generate_questions(self, inputs):
+        inputs = self._tokenize(inputs, padding=True, truncation=True)
+        
+        outs = self.model.generate(
+            input_ids=inputs['input_ids'].to(self.device), 
+            attention_mask=inputs['attention_mask'].to(self.device), 
+            max_length=32,
+            num_beams=4,
+        )
+        
+        questions = [self.tokenizer.decode(ids, skip_special_tokens=True) for ids in outs]
+        return questions
+    
+    def _prepare_inputs_for_qg_from_answers_hl(self, sents, answers):
+        inputs = []
+        for i, answer in enumerate(answers):
+            if len(answer) == 0: continue
+            for answer_text in answer:
+                sent = sents[i]
+                sents_copy = sents[:]
+                answer_text = self.remove_pad(answer_text)
+                answer_text = answer_text.strip()
+                
+                try:
+                  ans_start_idx = sent.lower().index(answer_text.lower())
+                except ValueError:
+                  # Means the answer is not in the sentence so we skip this one
+                  continue
+                
+                sent = f"{sent[:ans_start_idx]} <hl> {answer_text} <hl> {sent[ans_start_idx + len(answer_text): ]}"
+                sents_copy[i] = sent
+                
+                source_text = " ".join(sents_copy)
+                source_text = f"generate question: {source_text}" 
+                if self.model_type == "t5":
+                    source_text = source_text + " </s>"
+                
+                inputs.append({"answer": answer_text, "source_text": source_text})
+        
+        return inputs
+
+    def clean_generated_QAs(self, generated_QAs, max_words_per_answer):
+      clean_QAs = []
+      answers_used = set()
+      # Only allow 1 question per answer, take the first case of it
+      for qa in generated_QAs:
+        answer_word_length = len(qa['answer'].strip().split())
+        if qa['answer'] in answers_used or answer_word_length > max_words_per_answer:
+          continue
+        answers_used.add(qa['answer'])
+        clean_QAs.append(qa)
+      return clean_QAs
+
+    def remove_pad(self, str):
+      if "<pad>" in str:
+        return str.replace("<pad>", "")
+      return str