Upload evaluate_gpt.py

evaluate_gpt.py

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+# # -*- coding: utf-8 -*-
+# """evaluate.ipynb
+
+# Automatically generated by Colaboratory.
+
+# Original file is located at
+# 		https://colab.research.google.com/drive/1_WZN6_5mgwRgg484xzXMSwCXBQXfr8Vj
+# """
+
+# # -*- coding: utf-8 -*-
+
+# """# code here"""
+# print("**************OUTPUT FILE PATH UPDATED FOR SEED 42 hinglish ******************")
+
+import numpy as np
+import timeit
+import torch
+#from torch.utils.data import DataLoader, TensorDataset, RandomSampler
+import json, argparse
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+import pandas as pd
+from torch.utils.data import Dataset, DataLoader
+import transformers
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+print('use transformers version = ',transformers.__version__) # make sure it is 2.6.0
+
+
+def add_special_tokens(tokenizer):
+	""" Returns GPT2 tokenizer after adding separator and padding tokens """
+	#tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+	special_tokens = {'bos_token':'<|startoftext|>','eos_token':'<|endoftext|>', 'pad_token':'<|pad|>','sep_token':'<|summarize|>'}
+	num_add_toks = tokenizer.add_special_tokens(special_tokens)
+	return tokenizer
+
+
+class GPT21024Dataset(Dataset):
+
+	#def __init__(self, root_dir, ids_file, mode='train',length=None):
+	def __init__(self, text, ctext, tokenizer, source_len, summ_len):
+		self.tokenizer = add_special_tokens(tokenizer)
+		# self.data = dataframe
+		self.source_len = source_len
+		self.summ_len = summ_len
+		# self.text = self.data['summary-hinglish']   ## the summary
+		# self.ctext = self.data['dialogue-hinglish']  ## ctext is the article to be summarized
+		self.text = text   ## the summary
+		self.ctext = ctext
+		
+		
+	def __len__(self):
+		return len(self.ctext)
+		#return self.len
+
+	def __getitem__(self,index):
+
+		##articles
+		ctext = str(self.ctext[index])
+		ctext = ' '.join(ctext.split())
+
+		##summaries
+		
+		text = str(self.text[index])
+		text = ' '.join(text.split())
+		
+		
+		tok_data={}
+		tok_data['article']= ctext
+		tok_data['summary']= text
+	
+		input_ids= '<|startoftext|>' + tok_data['article'] + '<|summarize|>' 
+		summary= tok_data['summary']
+		
+		content = self.tokenizer.encode(input_ids, max_length = 512, padding='max_length',truncation=True)
+		summary_target_ids= self.tokenizer.encode( summary, max_length = 512, padding='max_length',truncation=True)
+		
+		#texts[:len(content)] = content
+		texts = torch.tensor(content)
+		summary_target_ids=torch.tensor(summary_target_ids)
+		sample = {'article': texts, 'actual_summary': summary_target_ids, 'sum_idx': len(self.tokenizer.encode(tok_data['article']))}
+		return sample
+
+def gpt_eval(
+	verbose=True,
+	model_name_path=None,
+	src_txt=None,
+	tar_txt=None,
+	gen_path=None,
+	scor_path=None,
+	batch_size=4
+):
+		"""
+		"""
+		predictions=[]
+		actuals=[]
+		
+		model = GPT2LMHeadModel.from_pretrained(model_name_path)
+		tokenizer = GPT2Tokenizer.from_pretrained(model_name_path)
+	
+		# Add a [CLS] to the vocabulary (we should train it also!)
+		#special_tokens = {'bos_token':'<|startoftext|>','eos_token':'<|endoftext|>','pad_token':'<pad>','additional_special_tokens':['<|keyword|>','<|summarize|>']}
+		#tokenizer.add_special_tokens(special_tokens)
+	
+		"""
+		special_tokens = {'pad_token':'<|pad|>','sep_token':'<|summarize|>'}
+		tokenizer.add_special_tokens(special_tokens)
+	
+		#assert len(tokenizer) == 50261, "tokenizer size is not 50261"
+		model.resize_token_embeddings(len(tokenizer))
+		print(' ')
+		"""
+	
+		model = model.to(device)
+		model.eval()
+		"""
+		input_text =  input_text +' <|summarize|>'
+		input_token = tokenizer.encode(input_text)
+		input_token_torch = torch.tensor(input_token, dtype=torch.long)
+		"""
+		
+		val_params = {
+		'batch_size':batch_size,
+		'shuffle': False,
+		'num_workers': 0
+		}
+
+		sp= open(src_txt,'r')
+		src= sp.readlines()
+		sp.close()
+		tp = open(tar_txt, 'r')
+		tar=tp.readlines()
+		tp.close()
+		val_set = GPT21024Dataset(tar, src,tokenizer, 512, 150)
+		val_loader = DataLoader(val_set, **val_params)
+		
+		with torch.no_grad():
+			for _, data in enumerate(val_loader, 0):
+				
+				
+				
+				target_output = data['actual_summary'].to(device, dtype = torch.long)
+				input_ids = data['article']
+				input_ids=input_ids.to(device)
+				#print(input_ids)
+	
+				print(f'Length of the input context: {len(input_ids[0])}')
+				print(f'BEAM SIZE: {4}')
+				#input_ids.unsqueeze(0).to(device)
+				generated_output = model.generate(
+					input_ids=input_ids,
+					max_length= 582,
+					min_length = 562 ,
+					temperature=1.0,
+					decoder_start_token_id= '<|summarize|>',
+					num_beams=4,
+					num_return_sequences=1)
+		
+				# print(f' Generated_output: {generated_output}')
+				
+				preds=[]
+				target=[]
+				ids=[]
+				for g in generated_output:
+					preds.append(tokenizer.decode(g[len(input_ids[0]):] ,  skip_special_tokens=True))
+				
+				#preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in                              generated_output]
+				for t in target_output:
+					target.append(tokenizer.decode(t ,  skip_special_tokens=True))
+				#target = [tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True)for t in y]
+				if _%100==0:
+					print(f'Completed {_}')
+
+				predictions.extend(preds)
+				actuals.extend(target)
+		
+		gp= open(gen_path, 'w')
+		for pre in predictions:
+			gp.write(pre+"\n")
+		gp.close()
+
+
+