[hotfix] gpt example titans bug #2493 (#2494)

examples/language/gpt/titans/configs/gpt2_small_zero3_pp1d.py

@@ -12,11 +12,11 @@ TENSOR_SHAPE = (BATCH_SIZE // NUM_MICRO_BATCHES, SEQ_LEN, HIDDEN_SIZE)
 
 # if you do no want zero, just comment out this dictionary
 zero = dict(model_config=dict(tensor_placement_policy='cuda', shard_strategy=TensorShardStrategy()),
-            optimizer_config=dict(initial_scale=2**16))
+            optimizer_config=dict(initial_scale=2**5))
 
 optimizer = dict(
     type=HybridAdam,
-    lr=0.00015,
+    lr=0.000015,
     weight_decay=1e-2,
 )
 

examples/language/gpt/titans/dataset/webtext.py

+import json
+import os
+from typing import Optional
+
+import torch
+from torch.utils.data import Dataset
+from transformers import GPT2Tokenizer
+
+from colossalai.registry import DATASETS
+
+
+@DATASETS.register_module
+class WebtextDataset(Dataset):
+
+    def __init__(self, path: Optional[str] = None, seq_len=1024) -> None:
+        super().__init__()
+        if path is not None:
+            root = os.path.dirname(path)
+            encoded_data_cache_path = os.path.join(root, f'gpt_webtext_{seq_len}.pt')
+            if os.path.isfile(encoded_data_cache_path):
+                seq_len_, data, attention_mask = torch.load(encoded_data_cache_path)
+                if seq_len_ == seq_len:
+                    self.data = data
+                    self.attention_mask = attention_mask
+                    return
+            raw_data = []
+            with open(path) as f:
+                for line in f.readlines():
+                    raw_data.append(json.loads(line)['text'])
+            tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+            tokenizer.pad_token = tokenizer.unk_token
+            encoded_data = tokenizer(raw_data, padding=True, truncation=True, max_length=seq_len, return_tensors='pt')
+            self.data = encoded_data['input_ids']
+            self.attention_mask = encoded_data['attention_mask']
+        else:
+            self.data = torch.randint(0, 50257, (10240, seq_len))
+            self.attention_mask = torch.ones_like(self.data)
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, index):
+        return {'input_ids': self.data[index], 'attention_mask': self.attention_mask[index]}, self.data[index]

examples/language/gpt/titans/run.sh

 export DATA=/data/scratch/gpt_data/small-gpt-dataset.json
-colossalai run --nproc_per_node=4 train_gpt.py --config ./configs/gpt2_small_zero3_pp1d.py --from_torch
+DUMMY_DATA=--use_dummy_dataset
+colossalai run --nproc_per_node=2 train_gpt.py --config ./configs/gpt2_small_zero3_pp1d.py --from_torch $DUMMY_DATA

examples/language/gpt/titans/train_gpt.py

@@ -3,6 +3,7 @@ import os
 
 import torch
 import torch.nn as nn
+from dataset.webtext import WebtextDataset
 from titans.model.gpt import GPTLMLoss
 
 import colossalai
@@ -30,7 +31,7 @@ VOCAB_SIZE = 50257
 def main():
     parser = colossalai.get_default_parser()
     parser.add_argument('--from_torch', default=False, action='store_true')
-    parser.add_argument('--use_dummy_dataset', default=True, action='store_true')
+    parser.add_argument('--use_dummy_dataset', default=False, action='store_true')
     args = parser.parse_args()
     disable_existing_loggers()
     if args.from_torch:
@@ -39,52 +40,16 @@ def main():
         colossalai.launch_from_slurm(config=args.config, host=args.host, port=29500, seed=42)
     logger = get_dist_logger()
 
-    if not args.use_dummy_dataset:
-        data_path = os.environ['DATA']
-        logger.info(f'Build data loader from path {data_path}', ranks=[0])
-        from dataset.webtext import WebtextDataset
-        train_ds = WebtextDataset(os.environ['DATA'], seq_len=gpc.config.SEQ_LEN)
-        train_dataloader = utils.get_dataloader(train_ds,
-                                                seed=42,
-                                                batch_size=gpc.config.BATCH_SIZE,
-                                                pin_memory=True,
-                                                shuffle=True,
-                                                drop_last=True)
-    else:
-        # build a dummy train_dataloader
-        logger.info('Build data loader using dummy data', ranks=[0])
-
-        def get_data(batch_size, seq_len, vocab_size):
-            input_ids = torch.randint(0, vocab_size, (batch_size, seq_len), device=torch.cuda.current_device())
-            attention_mask = torch.ones_like(input_ids)
-            return input_ids, attention_mask
-
-        # 10 iterations
-        input_ids, attn_mask = get_data(gpc.config.BATCH_SIZE * 10, gpc.config.SEQ_LEN, VOCAB_SIZE)
-        from torch.utils.data import DataLoader, Dataset
-
-        class TextSamplerDataset(Dataset):
-
-            def __init__(self, data, seq_len):
-                super().__init__()
-                self.data = data
-                self.seq_len = seq_len
-
-            def __getitem__(self, index):
-                rand_start = torch.randint(0, self.data.size(0) - self.seq_len, (1,))
-                full_seq = self.data[rand_start:rand_start + self.seq_len + 1].long()
-                return full_seq.cuda()
-
-            def __len__(self):
-                return self.data.size(0) // self.seq_len
-
-        def cycle(loader):
-            while True:
-                for data in loader:
-                    yield data
+    data_path = None if args.use_dummy_dataset else os.environ['DATA']
+    logger.info(f'Build data loader from path {data_path}', ranks=[0])
 
-        train_dataset = TextSamplerDataset(input_ids, gpc.config.SEQ_LEN)
-        train_dataloader = DataLoader(train_dataset, batch_size=gpc.config.BATCH_SIZE)
+    train_ds = WebtextDataset(path=data_path, seq_len=gpc.config.SEQ_LEN)
+    train_dataloader = utils.get_dataloader(train_ds,
+                                            seed=42,
+                                            batch_size=gpc.config.BATCH_SIZE,
+                                            pin_memory=True,
+                                            shuffle=True,
+                                            drop_last=True)
 
     logger.info('Build model', ranks=[0])
     use_pipeline = is_using_pp()