support instrcut training (#3230)

applications/ChatGPT/chatgpt/dataset/sft_dataset.py

@@ -119,10 +119,15 @@ def preprocess(
 class AlpacaDataset(Dataset):
     """Dataset for supervised fine-tuning."""
 
-    def __init__(self, data_path: str, tokenizer: transformers.PreTrainedTokenizer):
+    def __init__(self, data_path: str, tokenizer: transformers.PreTrainedTokenizer, max_length: int=None):
         super(AlpacaDataset, self).__init__()
         logger.info("Loading data...")
         list_data_dict = jload(data_path)
+        logger.info(f"Loaded {len(list_data_dict)} examples.")
+
+        if max_length is not None:
+            logger.info(f"Truncating data to max length {max_length}...")
+            list_data_dict = [example for example in list_data_dict if len(example["input"]) <= max_length]
 
         logger.info("Formatting inputs...")
         prompt_input, prompt_no_input = PROMPT_DICT["prompt_input"], PROMPT_DICT["prompt_no_input"]

applications/ChatGPT/chatgpt/models/base/actor.py

@@ -60,3 +60,6 @@ class Actor(LoRAModule):
         logits = output['logits']
         log_probs = log_probs_from_logits(logits[:, :-1, :], sequences[:, 1:])
         return log_probs[:, -num_actions:]
+
+    def get_base_model(self):
+        return self.model

applications/ChatGPT/chatgpt/models/llama/llama_lm.py

@@ -36,3 +36,5 @@ class LlamaLM(LM):
             
         super().__init__(model, lora_rank, lora_train_bias)
 
+    def forward(self, input_ids, attention_mask=None, labels=None, **kwargs):
+        return self.model(input_ids, attention_mask=attention_mask, labels=labels, **kwargs)

applications/ChatGPT/chatgpt/trainer/sft.py

@@ -61,13 +61,15 @@ class SFTTrainer(ABC):
             # train
             self.model.train()
             for batch_id, batch in enumerate(self.train_dataloader):
-                prompt_ids = batch["input_ids"]
-                p_mask = batch["attention_mask"]
-                labels = batch["labels"]
-                prompt_ids = prompt_ids.squeeze(1).cuda()
-                p_mask = p_mask.squeeze(1).cuda()
+                prompt_ids = batch["input_ids"].to(torch.cuda.current_device())
+                p_mask = batch["attention_mask"].to(torch.cuda.current_device())
+                labels = batch["labels"].to(torch.cuda.current_device())
+                # prompt_ids = prompt_ids.squeeze(1).cuda()
+                # p_mask = p_mask.squeeze(1).cuda()
                 # prompt_logits = self.model(prompt_ids, attention_mask=p_mask, labels=labels)
-                loss, prompt_logits = self.model(prompt_ids, attention_mask=p_mask, labels=labels)
+                outputs = self.model(prompt_ids, attention_mask=p_mask, labels=labels)
+                loss = outputs.loss
+                prompt_logits = outputs.logits
 
                 # loss = self.loss_fn(prompt_logits, labels)
                 self.strategy.backward(loss, self.model, self.optimizer)
@@ -83,13 +85,16 @@ class SFTTrainer(ABC):
                     loss_sum = 0
                     num_seen = 0
                     for batch in self.eval_dataloader:
-                        prompt_ids = batch["input_ids"]
-                        p_mask = batch["attention_mask"]
-                        prompt_ids = prompt_ids.squeeze(1).cuda()
-                        p_mask = p_mask.squeeze(1).cuda()
+                        prompt_ids = batch["input_ids"].to(torch.cuda.current_device())
+                        p_mask = batch["attention_mask"].to(torch.cuda.current_device())
+                        labels = batch["labels"].to(torch.cuda.current_device())
+                        # prompt_ids = prompt_ids.squeeze(1).cuda()
+                        # p_mask = p_mask.squeeze(1).cuda()
+
+                        outputs = self.model(prompt_ids, attention_mask=p_mask, labels=labels)
+                        loss = outputs.loss
+                        # prompt_logits = outputs.logits
 
-                        prompt_logits = self.model(prompt_ids, attention_mask=p_mask)
-                        loss = self.loss_fn(prompt_logits, prompt_ids)
                         loss_sum += loss.item()
                         num_seen += prompt_ids.size(0)
 

applications/ChatGPT/chatgpt/trainer/strategies/colossalai.py

@@ -9,7 +9,6 @@ from chatgpt.models.base import Actor
 from chatgpt.models.lora import LoraLinear
 from torch.optim import Optimizer
 
-
 from transformers.modeling_utils import PreTrainedModel
 from transformers.tokenization_utils_base import PreTrainedTokenizerBase
 

applications/ChatGPT/chatgpt/utils/tokenizer_utils.py

@@ -16,6 +16,8 @@ from typing import Dict
 
 import transformers
 
+from ..models.llama.llama_lm import LlamaLM
+
 DEFAULT_PAD_TOKEN = "[PAD]"
 DEFAULT_EOS_TOKEN = "</s>"
 DEFAULT_BOS_TOKEN = "</s>"
@@ -60,6 +62,10 @@ def smart_tokenizer_and_embedding_resize(
 
     if tokenizer.pad_token is None:
         num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict)
+
+        if isinstance(model, LlamaLM):
+            model = model.get_base_model()
+            
         model.resize_token_embeddings(len(tokenizer))
 
         if num_new_tokens > 0:

applications/ChatGPT/examples/train_sft.py

@@ -93,25 +93,27 @@ def train(args):
     elif 'alpaca' in args.dataset:
         train_dataset = AlpacaDataset(tokenizer=tokenizer, data_path=args.dataset)
         eval_dataset = None
-        eval_dataset
         data_collator = AlpacaDataCollator(tokenizer=tokenizer)
 
     if dist.is_initialized() and dist.get_world_size() > 1:
-        sampler = DistributedSampler(train_dataset, shuffle=True, seed=42, drop_last=True)
-        logger.info("Using Distributed Sampler")
+        train_sampler = DistributedSampler(train_dataset, shuffle=True, seed=42, drop_last=True)
+        if eval_dataset is not None:
+            eval_sampler = DistributedSampler(eval_dataset, shuffle=False, seed=42, drop_last=False)
     else:
-        sampler = None
+        train_sampler = None
+        eval_sampler = None
 
-    train_dataloader = DataLoader(train_dataset, shuffle=(sampler is None), sampler=sampler, batch_size=args.batch_size)
+    train_dataloader = DataLoader(train_dataset, shuffle=(train_sampler is None), sampler=train_sampler, batch_size=args.batch_size, collate_fn=data_collator)
     if eval_dataset is not None:
-        eval_dataloader = DataLoader(eval_dataset, batch_size=args.batch_size)
+        eval_dataloader = DataLoader(eval_dataset, shuffle=(eval_sampler is None), sampler=eval_sampler, batch_size=args.batch_size, collate_fn=data_collator)
+    else:
+        eval_dataloader = None
 
     trainer = SFTTrainer(model=model,
                          strategy=strategy,
                          optim=optim,
                          train_dataloader=train_dataloader,
                          eval_dataloader=eval_dataloader,
-                         sampler=sampler,
                          batch_size=args.batch_size,
                          max_epochs=args.max_epochs)
 
@@ -128,7 +130,7 @@ if __name__ == '__main__':
     parser.add_argument('--strategy',
                         choices=['naive', 'ddp', 'colossalai_gemini', 'colossalai_zero2'],
                         default='naive')
-    parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt'], default='bloom')
+    parser.add_argument('--model', choices=['gpt2', 'bloom', 'opt', 'llama'], default='bloom')
     parser.add_argument('--pretrain', type=str, default=None)
     parser.add_argument('--dataset', type=str, default='yizhongw/self_instruct')
     parser.add_argument('--save_path', type=str, default='sft_ckpt.pth')

applications/ChatGPT/examples/train_sft.sh

@@ -17,4 +17,10 @@ set_n_least_used_CUDA_VISIBLE_DEVICES 8
 
 #torchrun --standalone --nproc_per_node=2 train_sft.py --pretrain 'bigscience/bloomz-560m' --model 'bloom' --strategy colossalai_zero2 --log_interval 10
 #torchrun --standalone --nproc_per_node=8 train_sft.py  --model 'gpt2' --strategy colossalai_zero2 --batch_size 1 --log_interval 10
-torchrun --standalone --nproc_per_node=2 train_sft.py --pretrain "facebook/opt-350m" --model 'opt' --strategy colossalai_zero2 --log_interval 10
+torchrun --standalone --nproc_per_node=8 train_sft.py \
+    --pretrain "/data/personal/nus-mql/LLAMA-7B" \
+    --model 'llama' \
+    --strategy colossalai_zero2 \
+    --log_interval 10 \
+    --save_path /data/personal/nus-mql/Coati-7B \
+    --dataset /data/personal/nus-mql/stanford_alpaca/alpaca_data.json

applications/ChatGPT/version.txt

@@ -1 +1 @@
-0.1.0
+1.0.0