add community example dictionary (#3465)

applications/Chat/examples/community/README.md

@@ -0,0 +1 @@
+# Community Examples

applications/Chat/examples/community/peft/README.md

@@ -10,7 +10,7 @@ Since the current pypi peft package(0.2) has some bugs, please install the peft
 git clone https://github.com/huggingface/peft
 cd peft
 pip install .
-``` 
+```
 
 # Usage
 For SFT training, just call train_peft_sft.py
@@ -21,4 +21,4 @@ For stage-3 rlhf training, call train_peft_prompts.py.
 Its arguments are almost idential to train_prompts.py. The only difference is that I use text files to indicate the prompt and pretrained data file. The models are included in easy_models.py. Currently only bloom models are tested, but technically gpt2/opt/llama should be supported.
 
 # Dataformat
-Please refer the formats in test_sft.txt, test_prompts.txt, test_pretrained.txt.
+Please refer the formats in test_sft.txt, test_prompts.txt, test_pretrained.txt.

applications/Chat/examples/community/peft/easy_dataset.py

@@ -1,19 +1,17 @@
 import copy
+import json
 from typing import Dict, Sequence
+
+import torch
 from datasets import load_dataset
 from torch.utils.data import Dataset
-from transformers import AutoTokenizer
-import torch
 from tqdm import tqdm
-import json
-
-from tqdm import tqdm
-import json
+from transformers import AutoTokenizer
 
 IGNORE_INDEX = -100
 
 
-def _tokenize_fn(strings: Sequence[str], tokenizer: AutoTokenizer,max_length :int = 512) -> Dict:
+def _tokenize_fn(strings: Sequence[str], tokenizer: AutoTokenizer, max_length: int = 512) -> Dict:
     """Tokenize a list of strings."""
     tokenized_list = [
         tokenizer(
@@ -36,15 +34,12 @@ def _tokenize_fn(strings: Sequence[str], tokenizer: AutoTokenizer,max_length :in
     )
 
 
-def preprocess(
-    sources: Sequence[str],
-    targets: Sequence[str],
-    tokenizer: AutoTokenizer,
-    max_length :int = 512
-) -> Dict:
+def preprocess(sources: Sequence[str], targets: Sequence[str], tokenizer: AutoTokenizer, max_length: int = 512) -> Dict:
     """Preprocess the data by tokenizing."""
     examples = [s + t for s, t in zip(sources, targets)]
-    examples_tokenized, sources_tokenized = [_tokenize_fn(strings, tokenizer,max_length) for strings in (examples, sources)]
+    examples_tokenized, sources_tokenized = [
+        _tokenize_fn(strings, tokenizer, max_length) for strings in (examples, sources)
+    ]
     input_ids = examples_tokenized["input_ids"]
     labels = copy.deepcopy(input_ids)
     for label, source_len in zip(labels, sources_tokenized["input_ids_lens"]):
@@ -53,59 +48,60 @@ def preprocess(
 
 
 class EasySupervisedDataset(Dataset):
-    def __init__(self, data_file :str, tokenizer :AutoTokenizer,max_length :int = 512) -> None:
-        super(EasySupervisedDataset,self).__init__()
-        with open(data_file,"r",encoding="UTF-8") as f:
+
+    def __init__(self, data_file: str, tokenizer: AutoTokenizer, max_length: int = 512) -> None:
+        super(EasySupervisedDataset, self).__init__()
+        with open(data_file, "r", encoding="UTF-8") as f:
             all_lines = f.readlines()
         #split to source and target ,source the characters before "回答：" including "回答：", target the characters after "回答："
-        sources,targets = [],[]
+        sources, targets = [], []
         for line in all_lines:
             if "回答：" in line:
                 sep_index = line.index("回答：")
-                sources.append(line[:sep_index+3])
-                targets.append(line[sep_index+3:]+tokenizer.eos_token)
+                sources.append(line[:sep_index + 3])
+                targets.append(line[sep_index + 3:] + tokenizer.eos_token)
             else:
                 sources.append(line)
-                targets.append(""+tokenizer.eos_token)
-        data_dict = preprocess(sources, targets, tokenizer,max_length)
+                targets.append("" + tokenizer.eos_token)
+        data_dict = preprocess(sources, targets, tokenizer, max_length)
 
         self.input_ids = data_dict["input_ids"]
         self.labels = data_dict["labels"]
         self.data_file = data_file
-    
+
     def __len__(self):
         return len(self.input_ids)
 
     def __getitem__(self, i) -> Dict[str, torch.Tensor]:
         return dict(input_ids=self.input_ids[i], labels=self.labels[i])
-    
+
     def __repr__(self):
         return f"LawSupervisedDataset(data_file={self.data_file}, input_ids_len={len(self.input_ids)}, labels_len={len(self.labels)})"
-    
+
     def __str__(self):
         return f"LawSupervisedDataset(data_file={self.data_file}, input_ids_len={len(self.input_ids)}, labels_len={len(self.labels)})"
 
+
 class EasyPromptsDataset(Dataset):
-    def __init__(self,data_file :str, tokenizer :AutoTokenizer, max_length :int = 96) -> None:
-        super(EasyPromptsDataset,self).__init__()
-        with open(data_file,"r",encoding="UTF-8") as f:
+
+    def __init__(self, data_file: str, tokenizer: AutoTokenizer, max_length: int = 96) -> None:
+        super(EasyPromptsDataset, self).__init__()
+        with open(data_file, "r", encoding="UTF-8") as f:
             all_lines = f.readlines()
-            all_lines = [line if "回答：" not in line else line[:line.index("回答：")+3] for line in all_lines]
+            all_lines = [line if "回答：" not in line else line[:line.index("回答：") + 3] for line in all_lines]
         self.prompts = [
-            tokenizer(line,
-                              return_tensors='pt',
-                              max_length=max_length,
-                              padding='max_length',
-                              truncation=True)['input_ids'].to(torch.cuda.current_device()).squeeze(0)
+            tokenizer(line, return_tensors='pt', max_length=max_length, padding='max_length',
+                      truncation=True)['input_ids'].to(torch.cuda.current_device()).squeeze(0)
             for line in tqdm(all_lines)
         ]
         self.data_file = data_file
+
     def __len__(self):
         return len(self.prompts)
-    
+
     def __getitem__(self, idx):
         return self.prompts[idx]
-    
+
     def __repr__(self):
         return f"LawPromptsDataset(data_file={self.data_file}, prompts_len={len(self.prompts)})"
 
@@ -114,8 +110,9 @@ class EasyPromptsDataset(Dataset):
 
 
 class EasyRewardDataset(Dataset):
-    def __init__(self,train_file :str,tokenizer :AutoTokenizer, special_token = None,max_length = 512) -> None:
-        super(EasyRewardDataset,self).__init__()
+
+    def __init__(self, train_file: str, tokenizer: AutoTokenizer, special_token=None, max_length=512) -> None:
+        super(EasyRewardDataset, self).__init__()
         self.chosen = []
         self.reject = []
         if special_token is None:
@@ -124,11 +121,11 @@ class EasyRewardDataset(Dataset):
             self.end_token = special_token
         print(self.end_token)
         #read all lines in the train_file to a list
-        with open(train_file,"r",encoding="UTF-8") as f:
+        with open(train_file, "r", encoding="UTF-8") as f:
             all_lines = f.readlines()
         for line in tqdm(all_lines):
             data = json.loads(line)
-            prompt = "提问："+data['prompt']+" 回答："
+            prompt = "提问：" + data['prompt'] + " 回答："
 
             chosen = prompt + data['chosen'] + self.end_token
             chosen_token = tokenizer(chosen,
@@ -159,7 +156,7 @@ class EasyRewardDataset(Dataset):
     def __getitem__(self, idx):
         return self.chosen[idx]["input_ids"], self.chosen[idx]["attention_mask"], self.reject[idx][
             "input_ids"], self.reject[idx]["attention_mask"]
-        
+
     #python representation of the object and the string representation of the object
     def __repr__(self):
         return f"LawRewardDataset(chosen_len={len(self.chosen)}, reject_len={len(self.reject)})"
@@ -167,27 +164,30 @@ class EasyRewardDataset(Dataset):
     def __str__(self):
         return f"LawRewardDataset(chosen_len={len(self.chosen)}, reject_len={len(self.reject)})"
 
+
 '''
 Easy SFT just accept a text file which can be read line by line. However the datasest will group texts together to max_length so LLM will learn the texts meaning better.
 If individual lines are not related, just set is_group_texts to False.
 '''
+
+
 class EasySFTDataset(Dataset):
-    
-    def __init__(self,data_file :str,tokenizer :AutoTokenizer,max_length = 512,is_group_texts = True) -> None:
+
+    def __init__(self, data_file: str, tokenizer: AutoTokenizer, max_length=512, is_group_texts=True) -> None:
         super().__init__()
         #read the data_file line by line
-        with open(data_file,"r",encoding="UTF-8") as f:
+        with open(data_file, "r", encoding="UTF-8") as f:
             #encode the text data line by line and put raw python list input_ids only to raw_input_ids list
             raw_input_ids = []
             for line in f:
                 encoded_ids = tokenizer.encode(line)
                 #if the encoded_ids is longer than max_length, then split it into several parts
                 if len(encoded_ids) > max_length:
-                    for i in range(0,len(encoded_ids),max_length):
-                        raw_input_ids.append(encoded_ids[i:i+max_length])
+                    for i in range(0, len(encoded_ids), max_length):
+                        raw_input_ids.append(encoded_ids[i:i + max_length])
                 else:
                     raw_input_ids.append(encoded_ids)
-        
+
         grouped_inpup_ids = []
         current_input_ids = []
         attention_mask = []
@@ -199,44 +199,42 @@ class EasySFTDataset(Dataset):
                     #pad the current_input_ids to max_length with tokenizer.pad_token_id
                     padded_length = max_length - len(current_input_ids)
                     current_input_ids.extend([tokenizer.pad_token_id] * padded_length)
-                    grouped_inpup_ids.append(torch.tensor(current_input_ids,dtype=torch.long))
-                    attention_mask.append(torch.tensor([1] * (max_length - padded_length) + [0] * padded_length,dtype=torch.long))
+                    grouped_inpup_ids.append(torch.tensor(current_input_ids, dtype=torch.long))
+                    attention_mask.append(
+                        torch.tensor([1] * (max_length - padded_length) + [0] * padded_length, dtype=torch.long))
                     current_input_ids = []
                 else:
                     current_input_ids.extend(input_ids)
             if len(current_input_ids) > 0:
                 padded_length = max_length - len(current_input_ids)
                 current_input_ids.extend([tokenizer.pad_token_id] * padded_length)
-                grouped_inpup_ids.append(torch.tensor(current_input_ids,dtype=torch.long))
-                attention_mask.append(torch.tensor([1] * (max_length - padded_length) + [0] * padded_length,dtype=torch.long))
+                grouped_inpup_ids.append(torch.tensor(current_input_ids, dtype=torch.long))
+                attention_mask.append(
+                    torch.tensor([1] * (max_length - padded_length) + [0] * padded_length, dtype=torch.long))
         else:
             #just append the raw_input_ids to max_length
             for input_ids in raw_input_ids:
                 padded_length = max_length - len(input_ids)
                 input_ids.extend([tokenizer.pad_token_id] * padded_length)
-                attention_mask.append(torch.tensor([1] * (max_length - padded_length) + [0] * padded_length,dtype=torch.long))
-                grouped_inpup_ids.append(torch.tensor(input_ids,dtype=torch.long))
+                attention_mask.append(
+                    torch.tensor([1] * (max_length - padded_length) + [0] * padded_length, dtype=torch.long))
+                grouped_inpup_ids.append(torch.tensor(input_ids, dtype=torch.long))
         self.input_ids = grouped_inpup_ids
         self.labels = copy.deepcopy(self.input_ids)
         self.file_name = data_file
         self.attention_mask = attention_mask
-        
+
     def __len__(self):
         return len(self.input_ids)
-    
+
     #get item from dataset
-    def __getitem__(self,idx):
-        return dict(input_ids=self.input_ids[idx],labels=self.labels[idx],attention_mask=self.attention_mask[idx])
-    
+    def __getitem__(self, idx):
+        return dict(input_ids=self.input_ids[idx], labels=self.labels[idx], attention_mask=self.attention_mask[idx])
+
     #generate the dataset description to be printed by print in python
     def __repr__(self):
         return f"EasySFTDataset(len={len(self)},\nfile_name is {self.file_name})"
-    
+
     #generate the dataset description to be printed by print in python
     def __str__(self):
         return f"EasySFTDataset(len={len(self)},\nfile_name is {self.file_name})"
-     
-    
-    
-
- 

applications/Chat/examples/community/peft/easy_models.py

@@ -3,12 +3,12 @@ from typing import Optional, Tuple, Union
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from torch.nn.modules import Module
-
 from coati.models.generation import generate
-from coati.models.utils import log_probs_from_logits,masked_mean
-from transformers import BloomConfig,BloomForCausalLM
+from coati.models.utils import log_probs_from_logits, masked_mean
 from peft import PeftModel
+from torch.nn.modules import Module
+from transformers import BloomConfig, BloomForCausalLM
+
 
 class Actor(Module):
     """
@@ -87,11 +87,10 @@ class BLOOMActor(Actor):
         else:
             model = BloomForCausalLM(BloomConfig())
         if lora_path is not None:
-            model = PeftModel.from_pretrained(model,lora_path)
+            model = PeftModel.from_pretrained(model, lora_path)
         if checkpoint:
             model.gradient_checkpointing_enable()
         super().__init__(model)
-        
+
     def print_trainable_parameters(self):
         self.get_base_model().print_trainable_parameters()
-

applications/Chat/examples/community/peft/train_peft_prompts.py

@@ -5,21 +5,22 @@ import torch
 import torch.distributed as dist
 from coati.dataset import DataCollatorForSupervisedDataset, PromptDataset, SupervisedDataset
 from coati.models.bloom import BLOOMRM, BLOOMCritic
-from easy_models import BLOOMActor
 from coati.models.gpt import GPTRM, GPTActor, GPTCritic
 from coati.models.llama import LlamaActor, LlamaCritic, LlamaRM
 from coati.models.opt import OPTRM, OPTActor, OPTCritic
 from coati.trainer import PPOTrainer
 from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy
 from coati.utils import prepare_llama_tokenizer_and_embedding
+from easy_dataset import EasyPromptsDataset, EasySupervisedDataset
+from easy_models import BLOOMActor
+from peft import PeftModel
 from torch.optim import Adam
 from torch.utils.data import DataLoader
 from torch.utils.data.distributed import DistributedSampler
 from transformers import AutoTokenizer, BloomTokenizerFast, GPT2Tokenizer, LlamaTokenizer
 
 from colossalai.nn.optimizer import HybridAdam
-from peft import PeftModel
-from easy_dataset import EasyPromptsDataset,EasySupervisedDataset
+
 
 def main(args):
     # configure strategy
@@ -41,7 +42,7 @@ def main(args):
     if args.model == 'bloom':
         # initial_model = BLOOMActor(pretrained=args.pretrain)
         print('Using peft lora to load Bloom model as inital_model')
-        initial_model = BLOOMActor(pretrained=args.pretrain,lora_path=args.sft_lora_path)
+        initial_model = BLOOMActor(pretrained=args.pretrain, lora_path=args.sft_lora_path)
         print('Using peft lora to load Bloom model as initial_model (Done)')
     else:
         raise ValueError(f'Unsupported actor model "{args.model}"')
@@ -54,7 +55,7 @@ def main(args):
     if rm_model_name == 'gpt2':
         reward_model = GPTRM(pretrained=args.rm_pretrain)
     elif rm_model_name == 'bloom':
-        print("load bloom reward model ",args.rm_pretrain)
+        print("load bloom reward model ", args.rm_pretrain)
         reward_model = BLOOMRM(pretrained=args.rm_pretrain)
     elif rm_model_name == 'opt':
         reward_model = OPTRM(pretrained=args.rm_pretrain)
@@ -75,7 +76,7 @@ def main(args):
         if args.model == 'bloom':
             # actor = BLOOMActor(pretrained=args.pretrain, lora_rank=args.lora_rank)
             print('Using peft lora to load Bloom model as Actor')
-            actor = BLOOMActor(pretrained=args.pretrain,lora_path=args.sft_lora_path)
+            actor = BLOOMActor(pretrained=args.pretrain, lora_path=args.sft_lora_path)
             print('Using peft lora to load Bloom model as Actor (Done)')
         else:
             raise ValueError(f'Unsupported actor model "{args.model}"')
@@ -83,7 +84,7 @@ def main(args):
         if rm_model_name == 'gpt2':
             critic = GPTCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True)
         elif rm_model_name == 'bloom':
-            print("load bloom critic ",args.rm_pretrain," lora_rank ",args.lora_rank," use_action_mask ",True)
+            print("load bloom critic ", args.rm_pretrain, " lora_rank ", args.lora_rank, " use_action_mask ", True)
             critic = BLOOMCritic(pretrained=args.rm_pretrain, lora_rank=args.lora_rank, use_action_mask=True)
             print("load bloom critic (Done) ")
         elif rm_model_name == 'opt':
@@ -130,7 +131,7 @@ def main(args):
 
     data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer)
 
-    prompt_dataset = EasyPromptsDataset(args.prompt_path,tokenizer)
+    prompt_dataset = EasyPromptsDataset(args.prompt_path, tokenizer)
     if dist.is_initialized() and dist.get_world_size() > 1:
         prompt_sampler = DistributedSampler(prompt_dataset, shuffle=True, seed=42, drop_last=True)
     else:

applications/Chat/examples/community/peft/train_peft_sft.py

@@ -14,19 +14,19 @@ from coati.trainer import SFTTrainer
 from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy, NaiveStrategy
 from coati.utils import prepare_llama_tokenizer_and_embedding
 from datasets import load_dataset
+from easy_dataset import EasyDataset
+from peft import LoraConfig, PeftModel, TaskType, get_peft_model
 from torch.optim import Adam
 from torch.utils.data import DataLoader
+from torch.utils.data.dataloader import default_collate
 from torch.utils.data.distributed import DistributedSampler
-from transformers import AutoTokenizer, BloomTokenizerFast,AutoModelForCausalLM
+from transformers import AutoModelForCausalLM, AutoTokenizer, BloomTokenizerFast
 from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer
 
 from colossalai.logging import get_dist_logger
 from colossalai.nn.optimizer import HybridAdam
 from colossalai.tensor import ColoParameter
 
-from torch.utils.data.dataloader import default_collate
-from peft import LoraConfig, TaskType,get_peft_model,PeftModel
-from easy_dataset import EasyDataset
 
 def train(args):
     # configure strategy
@@ -48,17 +48,20 @@ def train(args):
         #if the args.save_path exists and args.save_path+'/adapter_config.json' exists, we'll load the adapter_config.json
         if os.path.exists(args.save_path) and os.path.exists(args.save_path+'/adapter_config.json') \
             and os.path.exists(args.save_path+'/adapter_model.bin'):
-            print("loading from saved peft model ",args.save_path)
+            print("loading from saved peft model ", args.save_path)
             model = PeftModel.from_pretrained(model, args.save_path)
         else:
             #we'll use peft lora library to do the lora
             lora_rank = args.lora_rank if args.lora_rank > 0 else 32
             #config lora with rank of lora_rank
-            lora_config = LoraConfig(task_type=TaskType.CAUSAL_LM, inference_mode=False, r=lora_rank, lora_alpha=32, lora_dropout=0.1)
+            lora_config = LoraConfig(task_type=TaskType.CAUSAL_LM,
+                                     inference_mode=False,
+                                     r=lora_rank,
+                                     lora_alpha=32,
+                                     lora_dropout=0.1)
             model = get_peft_model(model, lora_config)
         model.print_trainable_parameters()
 
-
     # configure tokenizer
     if args.model == 'gpt2':
         tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
@@ -103,12 +106,12 @@ def train(args):
     logger.set_level('WARNING')
 
     # configure dataset
-    law_dataset = EasyDataset(args.dataset,tokenizer=tokenizer,is_group_texts=not args.is_short_text)
+    law_dataset = EasyDataset(args.dataset, tokenizer=tokenizer, is_group_texts=not args.is_short_text)
     train_dataset = law_dataset
     print(train_dataset)
     eval_dataset = None
     if args.eval_dataset is not None:
-        eval_dataset = EasyDataset(args.eval_dataset,tokenizer=tokenizer,is_group_texts=not args.is_short_text)
+        eval_dataset = EasyDataset(args.eval_dataset, tokenizer=tokenizer, is_group_texts=not args.is_short_text)
     data_collator = default_collate
     if dist.is_initialized() and dist.get_world_size() > 1:
         train_sampler = DistributedSampler(train_dataset,
@@ -181,7 +184,7 @@ if __name__ == '__main__':
     parser.add_argument('--log_interval', type=int, default=100, help="how many steps to log")
     parser.add_argument('--lr', type=float, default=5e-6)
     parser.add_argument('--accimulation_steps', type=int, default=8)
-    parser.add_argument('--enable_peft_lora',action='store_true', default=False)
-    parser.add_argument("--is_short_text",action='store_true', default=False)
+    parser.add_argument('--enable_peft_lora', action='store_true', default=False)
+    parser.add_argument("--is_short_text", action='store_true', default=False)
     args = parser.parse_args()
     train(args)