Create eval_ese_nli.py

examples/NLI/eval_ese_nli.py

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+# -*- coding: utf-8 -*-
+
+import sys
+import os
+import logging
+
+# Set up logger
+logging.basicConfig(format='%(asctime)s : %(message)s', level=logging.DEBUG)
+
+import torch
+import fcntl
+import time
+import argparse
+from prettytable import PrettyTable
+from transformers import AutoTokenizer, AutoModel
+import billm
+from peft import PeftModel
+from angle_emb import Pooler
+import numpy as np
+from sklearn.decomposition import PCA
+
+# Import SentEval
+sys.path.insert(0, './SentEval')
+import senteval
+
+
+PATH_TO_DATA = './SentEval/data'
+
+
+def print_table(task_names, scores):
+    tb = PrettyTable()
+    tb.field_names = task_names
+    tb.add_row(scores)
+    print(tb)
+
+
+def lock_and_write_file(file_path, content):
+    with open(file_path, 'a') as file:
+        while True:
+            try:
+                # Acquire an exclusive lock (non-blocking)
+                fcntl.flock(file, fcntl.LOCK_EX | fcntl.LOCK_NB)
+
+                # Perform your write operations here
+                file.write(content + '\n')
+                file.flush()
+
+            except IOError as e:
+                print("File is locked by another process. Can't write.")
+                time.sleep(1)
+            finally:
+                # Release the lock
+                fcntl.flock(file, fcntl.LOCK_UN)
+                break
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--billm_model_class', type=str, default=None)
+    parser.add_argument("--pooling_strategy", type=str, default='cls')
+    parser.add_argument("--layer_index", type=int, default=-1)
+    parser.add_argument("--embedding_start", type=int, default=0)
+    parser.add_argument("--embedding_size", type=int, default=None)
+    parser.add_argument("--model_name_or_path", type=str, help="Transformers' model name or path")
+    parser.add_argument("--max_length", type=int, default=512, help="max len")
+    parser.add_argument("--mode", type=str,
+                        choices=['dev', 'test', 'fasttest'],
+                        default='test',
+                        help="What evaluation mode to use (dev: fast mode, dev results; test: full mode, test results); fasttest: fast mode, test results")
+    parser.add_argument("--task_set", type=str,
+                        choices=['sts', 'transfer', 'full', 'na'],
+                        default='sts',
+                        help="What set of tasks to evaluate on. If not 'na', this will override '--tasks'")
+    parser.add_argument('--load_kbit', type=int,
+                        choices=[4,8,16],
+                        default=16,
+                        help="Load model in kbit")
+    parser.add_argument('--end_with_eos', type=int,
+                        choices=[0, 1],
+                        default=0,
+                        help="end with eos")
+
+    parser.add_argument('--avg', action='store_true')
+    parser.add_argument('--lora_weight', type=str, default=None)
+    parser.add_argument('--pretrained_model_path', type=str, default=None)
+    parser.add_argument('--checkpoint_path', type=str, default=None)
+
+
+    args = parser.parse_args()
+    # print('>>> args:', args)
+    if args.pretrained_model_path == 'None':
+        args.pretrained_model_path = None
+
+    if args.billm_model_class is not None:
+        print('>>>> load base llm:', args.model_name_or_path)
+        print('>>>> load lora from:', args.lora_weight)
+        classname = getattr(billm, args.billm_model_class)
+        backbone = classname.from_pretrained(
+            args.model_name_or_path, output_hidden_states=True, device_map='auto', torch_dtype=torch.float16)
+        if args.lora_weight:
+            backbone = PeftModel.from_pretrained(
+                backbone,
+                args.lora_weight,
+                torch_dtype=torch.float16,
+                device_map={'': 0},
+            )
+            backbone.print_trainable_parameters()
+        # args.pooling_strategy = 'last'
+    else:
+        backbone = AutoModel.from_pretrained(args.pretrained_model_path or args.model_name_or_path).cuda()
+
+    model = Pooler(backbone, pooling_strategy=args.pooling_strategy)
+
+    tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+    # Set up the tasks
+    if args.task_set == 'sts':
+        args.tasks = ['STS12', 'STS13', 'STS14', 'STS15', 'STS16', 'STSBenchmark', 'SICKRelatedness']
+        if args.mode == 'dev':
+            args.tasks = ['STSBenchmark-dev']
+    elif args.task_set == 'transfer':
+        args.tasks = ['MR', 'CR', 'MPQA', 'SUBJ', 'SST2', 'TREC', 'MRPC']
+    elif args.task_set == 'full':
+        args.tasks = ['STS12', 'STS13', 'STS14', 'STS15', 'STS16', 'STSBenchmark', 'SICKRelatedness']
+        args.tasks += ['MR', 'CR', 'MPQA', 'SUBJ', 'SST2', 'TREC', 'MRPC']
+
+    # Set params for SentEval
+    if args.mode == 'dev' or args.mode == 'fasttest':
+        # Fast mode
+        params = {'task_path': PATH_TO_DATA, 'usepytorch': True, 'kfold': 5, 'batch_size': 32}
+        params['classifier'] = {'nhid': 0, 'optim': 'rmsprop', 'batch_size': 32,
+                                         'tenacity': 3, 'epoch_size': 2}
+    elif args.mode == 'test':
+        # Full mode
+        params = {'task_path': PATH_TO_DATA, 'usepytorch': True, 'kfold': 10, 'batch_size': 16}  # 16
+        params['classifier'] = {'nhid': 0, 'optim': 'adam', 'batch_size': 64,
+                                         'tenacity': 5, 'epoch_size': 4}
+    else:
+        raise NotImplementedError
+
+    # SentEval prepare and batcher
+    def prepare(params, samples):
+        return
+
+    def batcher(params, batch, max_length=None):
+        # Handle rare token encoding issues in the dataset
+        if len(batch) >= 1 and len(batch[0]) >= 1 and isinstance(batch[0][0], bytes):
+            batch = [[word.decode('utf-8') for word in s] for s in batch]
+
+        sentences = [' '.join(s) for s in batch]
+        if max_length == 500:
+            sentences = [tokenizer.decode(tokenizer.encode(s, add_special_tokens=False)[:max_length]) for s in sentences]
+            max_length = 512
+
+        if args.end_with_eos:
+            tok = tokenizer(
+                sentences,
+                padding=False,
+                return_attention_mask=False,
+                max_length=args.max_length,
+                truncation=True)
+            tok['input_ids'] = [input_ids + [tokenizer.eos_token_id] for input_ids in tok['input_ids']]
+            tok = tokenizer.pad(tok, padding=True, return_attention_mask=True, return_tensors='pt')
+        else:
+            tok = tokenizer(
+                sentences,
+                padding='longest',
+                max_length=args.max_length,
+                truncation=True,
+                return_tensors='pt')
+        for k, v in tok.items():
+            tok[k] = v.to(backbone.device)
+        # print("!!!tok keys>>>", tok.keys())
+        with torch.no_grad():
+            outputs = model(tok, layer_index=args.layer_index)
+        # print('outputs>>>', outputs.shape)
+        outputs = outputs[:,  args.embedding_start:]
+        if args.embedding_size is not None:
+            return outputs[:, :args.embedding_size].float().detach().cpu().numpy()
+        return outputs.float().detach().cpu().numpy()
+
+    results = {}
+    for task in args.tasks:
+        se = senteval.engine.SE(params, batcher, prepare)
+        result = se.eval(task)
+        results[task] = result
+
+    # Print evaluation results
+    if args.mode == 'dev':
+        print("------ %s ------" % (args.mode))
+
+        task_names = []
+        scores = []
+        for task in ['STSBenchmark-dev']:
+            task_names.append(task)
+            if task in results:
+                scores.append("%.2f" % (results[task]['dev']['spearman'][0] * 100))
+            else:
+                scores.append("0.00")
+        print_table(task_names, scores)
+
+        if args.checkpoint_path is not None:
+            # evaluate checkpoints on dev
+            if os.path.exists(os.path.join(args.checkpoint_path, 'dev_results')):
+                max_scores = 0
+                with open(os.path.join(args.checkpoint_path, 'dev_results'), 'r') as f:
+                    for i in f:
+                        max_scores = max(max_scores, float(i.split()[1]))
+            else:
+                max_scores = 0
+
+            # save best checkpoint
+            if float(scores[-1]) >= max_scores:
+                import shutil
+                if args.lora_weight is not None:
+                    shutil.copytree(args.lora_weight, os.path.join(args.checkpoint_path, 'best_model'), dirs_exist_ok=True)
+                else:
+                    shutil.copytree(args.model_name_or_path, os.path.join(args.checkpoint_path, 'best_model'), dirs_exist_ok=True)
+
+            # log dev results
+            with open(os.path.join(args.checkpoint_path, 'dev_results'), 'a') as f:
+                prefix = args.mask_embedding_sentence_template if not args.avg else 'avg'
+                line = prefix + ' ' +str(scores[-1]) + ' ' + \
+                    args.lora_weight if args.lora_weight is not None else args.model_name_or_path
+                f.write( line + '\n')
+
+        task_names = []
+        scores = []
+        for task in ['MR', 'CR', 'SUBJ', 'MPQA', 'SST2', 'TREC', 'MRPC']:
+            task_names.append(task)
+            if task in results:
+                scores.append("%.2f" % (results[task]['devacc']))    
+            else:
+                scores.append("0.00")
+        task_names.append("Avg.")
+        scores.append("%.2f" % (sum([float(score) for score in scores]) / len(scores)))
+        if args.task_set in ['transfer', 'full']:
+            print_table(task_names, scores)
+
+
+    elif args.mode == 'test' or args.mode == 'fasttest':
+        print("------ %s ------" % (args.mode))
+
+        task_names = []
+        scores = []
+        for task in ['STS12', 'STS13', 'STS14', 'STS15', 'STS16', 'STSBenchmark', 'SICKRelatedness']:
+            task_names.append(task)
+            if task in results:
+                if task in ['STS12', 'STS13', 'STS14', 'STS15', 'STS16']:
+                    scores.append("%.2f" % (results[task]['all']['spearman']['all'] * 100))
+                else:
+                    scores.append("%.2f" % (results[task]['test']['spearman'].correlation * 100))
+            else:
+                scores.append("0.00")
+        task_names.append("Avg.")
+        scores.append("%.2f" % (sum([float(score) for score in scores]) / len(scores)))
+        if args.task_set in ['sts', 'full']:
+            print_table(task_names, scores)
+
+        task_names = []
+        scores = []
+        for task in ['MR', 'CR', 'SUBJ', 'MPQA', 'SST2', 'TREC', 'MRPC']:
+            task_names.append(task)
+            if task in results:
+                scores.append("%.2f" % (results[task]['acc']))
+            else:
+                scores.append("0.00")
+        task_names.append("Avg.")
+        scores.append("%.2f" % (sum([float(score) for score in scores]) / len(scores)))
+        if args.task_set in ['transfer', 'full']:
+            print_table(task_names, scores)
+
+
+if __name__ == "__main__":
+    main()