[chatgpt] add supervised learning fine-tune code

applications/ChatGPT/chatgpt/dataset/__init__.py

@@ -1,4 +1,5 @@
 from .reward_dataset import RmStaticDataset, HhRlhfDataset
 from .utils import is_rank_0
+from .sft_dataset import SFTDataset
 
-__all__ = ['RmStaticDataset', 'HhRlhfDataset','is_rank_0']
+__all__ = ['RmStaticDataset', 'HhRlhfDataset','is_rank_0', 'SFTDataset']

applications/ChatGPT/chatgpt/dataset/sft_dataset.py

@@ -0,0 +1,40 @@
+from typing import Callable
+import random
+from torch.utils.data import Dataset
+import torch.distributed as dist
+from tqdm import tqdm
+import torch
+
+from .utils import is_rank_0
+
+
+class SFTDataset(Dataset):
+    """
+    Dataset for sft model
+
+    Args:
+        dataset: dataset for supervised model
+        tokenizer: tokenizer for supervised model
+        max_length: max length of input
+    """
+
+    def __init__(self, dataset, tokenizer: Callable, max_length: int=512) -> None:
+        super().__init__()
+        self.prompts = []
+
+        for data in tqdm(dataset, disable=not is_rank_0()):
+            prompt = data['prompt'] + data['completion'] + "<|endoftext|>"
+            prompt_token = tokenizer(prompt,
+                                     max_length=max_length,
+                                     padding="max_length",
+                                     truncation=True,
+                                     return_tensors="pt")
+
+            self.prompts.append(prompt_token)
+
+    def __len__(self):
+        length = len(self.prompts)
+        return length
+
+    def __getitem__(self, idx):
+        return self.prompts[idx]

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

@@ -1,5 +1,6 @@
 from .actor import Actor
 from .critic import Critic
 from .reward_model import RewardModel
+from .lm import LM
 
-__all__ = ['Actor', 'Critic', 'RewardModel']
+__all__ = ['Actor', 'Critic', 'RewardModel', 'LM']

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

@@ -0,0 +1,33 @@
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..generation import generate
+from .actor import Actor
+
+
+class LM(Actor):
+    """
+    Language model base class.
+
+    Args:
+        model (nn.Module): Language Model.
+        lora_rank (int): LoRA rank.
+        lora_train_bias (str): LoRA bias training mode.
+    """
+
+    def __init__(self, model: nn.Module, lora_rank: int = 0, lora_train_bias: str = 'none') -> None:
+        super().__init__(model=model, lora_rank=lora_rank, lora_train_bias=lora_train_bias)
+
+    def forward(self,
+                sequences: torch.LongTensor,
+                attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
+        """Returns output log probs
+        """
+        output = self.model(sequences, attention_mask=attention_mask)
+        logits = output['logits']
+        log_probs = F.log_softmax(logits, dim=-1)
+        return log_probs
+

applications/ChatGPT/chatgpt/models/bloom/__init__.py

@@ -1,5 +1,6 @@
 from .bloom_actor import BLOOMActor
 from .bloom_critic import BLOOMCritic
 from .bloom_rm import BLOOMRM
+from .bloom_lm import BLOOMLM
 
-__all__ = ['BLOOMActor', 'BLOOMCritic', 'BLOOMRM']
+__all__ = ['BLOOMActor', 'BLOOMCritic', 'BLOOMRM', 'BLOOMLM']

applications/ChatGPT/chatgpt/models/bloom/bloom_lm.py

@@ -0,0 +1,36 @@
+from typing import Optional
+
+import torch
+from transformers import BloomConfig, BloomForCausalLM, BloomModel
+
+from ..base import LM
+
+
+class BLOOMLM(LM):
+    """
+    BLOOM language model.
+
+    Args:
+        pretrained (str): Pretrained model name or path.
+        config (BloomConfig): Model config.
+        checkpoint (bool): Enable gradient checkpointing.
+        lora_rank (int): LoRA rank.
+        lora_train_bias (str): LoRA bias training mode.
+    """
+
+    def __init__(self,
+                 pretrained: str = None,
+                 config: Optional[BloomConfig] = None,
+                 checkpoint: bool = False,
+                 lora_rank: int = 0,
+                 lora_train_bias: str = 'none') -> None:
+        if pretrained is not None:
+            model = BloomForCausalLM.from_pretrained(pretrained)
+        elif config is not None:
+            model = BloomForCausalLM(config)
+        else:
+            model = BloomForCausalLM(BloomConfig())
+        if checkpoint:
+            model.gradient_checkpointing_enable()
+        super().__init__(model, lora_rank, lora_train_bias)
+

applications/ChatGPT/chatgpt/models/gpt/__init__.py

@@ -1,5 +1,6 @@
 from .gpt_actor import GPTActor
 from .gpt_critic import GPTCritic
 from .gpt_rm import GPTRM
+from .gpt_lm import GPTLM
 
-__all__ = ['GPTActor', 'GPTCritic', 'GPTRM']
+__all__ = ['GPTActor', 'GPTCritic', 'GPTRM', 'GPTLM']

applications/ChatGPT/chatgpt/models/gpt/gpt_lm.py

@@ -0,0 +1,36 @@
+from typing import Optional
+
+from transformers.models.gpt2.configuration_gpt2 import GPT2Config
+from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel
+
+from ..base import LM
+
+
+class GPTLM(LM):
+    """
+    GPT language model.
+
+    Args:
+        pretrained (str): Pretrained model name or path.
+        config (GPT2Config): Model config.
+        checkpoint (bool): Enable gradient checkpointing.
+        lora_rank (int): Rank of the LoRa layer.
+        lora_train_bias (str): Bias training strategy for the LoRa layer.
+    """
+
+    def __init__(self,
+                 pretrained: Optional[str] = None,
+                 config: Optional[GPT2Config] = None,
+                 checkpoint: bool = False,
+                 lora_rank: int = 0,
+                 lora_train_bias: str = 'none') -> None:
+        if pretrained is not None:
+            model = GPT2LMHeadModel.from_pretrained(pretrained)
+        elif config is not None:
+            model = GPT2LMHeadModel(config)
+        else:
+            model = GPT2LMHeadModel(GPT2Config())
+        if checkpoint:
+            model.gradient_checkpointing_enable()
+        super().__init__(model, lora_rank, lora_train_bias)
+

applications/ChatGPT/chatgpt/models/opt/__init__.py

@@ -1,5 +1,6 @@
 from .opt_actor import OPTActor
 from .opt_critic import OPTCritic
 from .opt_rm import OPTRM
+from .opt_lm import OPTLM
 
-__all__ = ['OPTActor', 'OPTCritic', 'OPTRM']
+__all__ = ['OPTActor', 'OPTCritic', 'OPTRM', 'OPTLM']

applications/ChatGPT/chatgpt/models/opt/opt_lm.py

@@ -0,0 +1,36 @@
+from typing import Optional
+
+from transformers.models.opt.configuration_opt import OPTConfig
+from transformers.models.opt.modeling_opt import OPTForCausalLM
+
+from ..base import LM
+
+
+class OPTLM(LM):
+    """
+    OPT language model.
+
+    Args:
+        pretrained (str): Pretrained model name or path.
+        config (OPTConfig): Model config.
+        checkpoint (bool): Enable gradient checkpointing.
+        lora_rank (int): Rank of the low-rank approximation.
+        lora_train_bias (str): LoRA bias training mode.
+    """
+
+    def __init__(self,
+                 pretrained: Optional[str] = None,
+                 config: Optional[OPTConfig] = None,
+                 checkpoint: bool = False,
+                 lora_rank: int = 0,
+                 lora_train_bias: str = 'none') -> None:
+        if pretrained is not None:
+            model = OPTForCausalLM.from_pretrained(pretrained)
+        elif config is not None:
+            model = OPTForCausalLM(config)
+        else:
+            model = OPTForCausalLM(OPTConfig())
+        if checkpoint:
+            model.gradient_checkpointing_enable()
+        super().__init__(model, lora_rank, lora_train_bias)
+

applications/ChatGPT/chatgpt/trainer/__init__.py

@@ -1,5 +1,6 @@
 from .base import Trainer
 from .ppo import PPOTrainer
 from .rm import RewardModelTrainer
+from .sft import SFTTrainer
 
-__all__ = ['Trainer', 'PPOTrainer', 'RewardModelTrainer']
+__all__ = ['Trainer', 'PPOTrainer', 'RewardModelTrainer', 'SFTTrainer']

applications/ChatGPT/chatgpt/trainer/sft.py

@@ -0,0 +1,101 @@
+from abc import ABC
+from typing import Optional
+import loralib as lora
+import torch
+from chatgpt.dataset import SFTDataset
+from chatgpt.models.loss import GPTLMLoss
+from torch.optim import Adam, Optimizer
+from torch.utils.data import DataLoader
+from torch.utils.data.distributed import DistributedSampler
+from tqdm import tqdm
+import torch.distributed as dist
+from .strategies import Strategy
+from .utils import is_rank_0
+from colossalai.logging import get_dist_logger
+
+
+class SFTTrainer(ABC):
+    """
+        Trainer to use while training reward model.
+
+    Args:
+        model (torch.nn.Module): the model to train
+        strategy (Strategy): the strategy to use for training
+        optim(Optimizer): the optimizer to use for training
+        train_dataset (SFTDataset or SFTDistributedDataset): the dataset to use for training
+        eval_dataset (SFTDataset or SFTDistributedDataset): the dataset to use for evaluation
+        batch_size (int, defaults to 1): the batch size while training
+        max_epochs (int, defaults to 2): the number of epochs to train
+        optim_kwargs (dict, defaults to {'lr':1e-4}): the kwargs to use while initializing optimizer
+    """
+
+    def __init__(
+        self,
+        model,
+        strategy: Strategy,
+        optim: Optimizer,
+        train_dataset: SFTDataset,
+        eval_dataset: SFTDataset,
+        sampler: Optional[DistributedSampler] = None,
+        batch_size: int = 1,
+        max_epochs: int = 2,
+    ) -> None:
+        super().__init__()
+        self.strategy = strategy
+        self.epochs = max_epochs
+        self.train_dataset = train_dataset
+        self.eval_dataset = eval_dataset
+        self.sampler = sampler
+
+        self.train_dataloader = DataLoader(self.train_dataset, shuffle=(sampler is None),
+                                           sampler=sampler, batch_size=batch_size)
+        self.eval_dataloader = DataLoader(self.eval_dataset, batch_size=batch_size)
+
+        self.model = strategy.setup_model(model)
+        if "DDP" in str(self.strategy):
+            self.model = self.model.module
+        self.loss_fn = GPTLMLoss()
+        self.optimizer = strategy.setup_optimizer(optim, self.model)
+
+    def fit(self, logger, use_lora, log_interval=10):
+        epoch_bar = tqdm(range(self.epochs), desc='Train epoch', disable=not is_rank_0())
+        for epoch in range(self.epochs):
+            if isinstance(self.sampler, DistributedSampler):
+                self.sampler.set_epoch(epoch)
+            # train
+            self.model.train()
+            for batch_id, batch in enumerate(self.train_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_logits = self.model(prompt_ids, attention_mask=p_mask)
+
+                loss = self.loss_fn(prompt_logits, prompt_ids)
+                self.strategy.backward(loss, self.model, self.optimizer)
+                self.strategy.optimizer_step(self.optimizer)
+                self.optimizer.zero_grad()
+                if batch_id % log_interval == 0:
+                    logger.info(f'Train Epoch {epoch}/{self.epochs} Batch {batch_id} Rank {dist.get_rank()} loss {loss.item()}')
+
+            # eval
+            self.model.eval()
+            with torch.no_grad():
+                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_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)
+
+                loss_mean = loss_sum / num_seen
+                if dist.get_rank() == 0:
+                    logger.info(f'Eval Epoch {epoch}/{self.epochs} loss {loss_mean}')
+            epoch_bar.update()
+