DeepSeek-Coder-main/finetune/finetune_deepseekcoder.py

import copy
import random
from dataclasses import dataclass, field
from typing import Optional, Dict, Sequence

import torch
import torch.distributed
import transformers
import wandb
from transformers import Trainer
from datasets import load_dataset


IGNORE_INDEX = -100
EOT_TOKEN = "<|EOT|>"

def build_instruction_prompt(instruction: str):
    return '''
You are an AI programming assistant specializing in Solana blockchain development, utilizing the DeepSeek Coder model. You excel at writing, reviewing, and explaining Solana smart contracts, Rust programs, and web3 applications. For questions not related to Solana development or blockchain programming, you will politely decline to answer.
### Instruction:
{}
### Response:
'''.format(instruction.strip()).lstrip()

@dataclass
class ModelArguments:
    model_name_or_path: Optional[str] = field(default="deepseek-ai/deepseek-coder-6.7b-instruct")

@dataclass
class DataArguments:
    data_path: str = field(default=None, metadata={"help": "Path to the training data."})


@dataclass
class TrainingArguments(transformers.TrainingArguments):
    cache_dir: Optional[str] = field(default=None)
    optim: str = field(default="adamw_torch")
    model_max_length: int = field(
        default=2048,  # Increased for longer Solana code contexts
        metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."},
    )

def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output_dir: str):
    """Collects the state dict and dump to disk."""
    state_dict = trainer.model.state_dict()
    if trainer.args.should_save:
        cpu_state_dict = {key: value.cpu() for key, value in state_dict.items()}
        del state_dict
        trainer._save(output_dir, state_dict=cpu_state_dict)  # noqa


def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer) -> Dict:
    """Tokenize a list of strings."""
    tokenized_list = [
        tokenizer(
            text,
            return_tensors="pt",
            padding="longest",
            max_length=tokenizer.model_max_length,
            truncation=True,
        )
        for text in strings
    ]

    input_ids = labels = [tokenized.input_ids[0] for tokenized in tokenized_list]
    input_ids_lens = labels_lens = [
        tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item() for tokenized in tokenized_list
    ]

    return dict(
        input_ids=input_ids,
        labels=labels,
        input_ids_lens=input_ids_lens,
        labels_lens=labels_lens,
    )


def preprocess(
    sources: Sequence[str],
    targets: Sequence[str],
    tokenizer: transformers.PreTrainedTokenizer,
) -> Dict:
    """Preprocess the data by tokenizing."""
    examples = [s + t for s, t in zip(sources, targets)]
    examples_tokenized, sources_tokenized = [_tokenize_fn(strings, tokenizer) 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"]):
        label[:source_len] = IGNORE_INDEX
    return dict(input_ids=input_ids, labels=labels)

@dataclass
class DataCollatorForSupervisedDataset(object):
    """Collate examples for supervised fine-tuning."""
    tokenizer: transformers.PreTrainedTokenizer

    def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
        input_ids, labels = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels"))
        input_ids = [torch.tensor(x) for x in input_ids]
        input_ids = torch.nn.utils.rnn.pad_sequence(
            input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id
        )
        labels = [torch.tensor(x) for x in labels]
        labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX)
        
        return dict(
            input_ids=input_ids,
            labels=labels,
            attention_mask=input_ids.ne(self.tokenizer.pad_token_id),
        )

def train_tokenize_function(examples, tokenizer):
    sources = [
        build_instruction_prompt(instruction)
        for instruction in examples['instruction']
    ]
    targets = [f"{output}\n{EOT_TOKEN}" for output in examples['output']]
    data_dict = preprocess(sources, targets, tokenizer)
    return data_dict

class CustomTrainer(Trainer):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.best_loss = float('inf')

    def compute_loss(self, model, inputs, return_outputs=False):
        loss, outputs = super().compute_loss(model, inputs, return_outputs=True)
        
        # Log metrics to wandb
        if self.state.global_step % self.args.logging_steps == 0:
            wandb.log({
                "loss": loss.item(),
                "learning_rate": self.lr_scheduler.get_last_lr()[0],
                "epoch": self.state.epoch,
                "global_step": self.state.global_step
            })
            
            # Save best model
            if loss.item() < self.best_loss:
                self.best_loss = loss.item()
                self.save_model("best_model")
                
        return (loss, outputs) if return_outputs else loss

def train():
    parser = transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments))
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
    
    # Initialize wandb
    if training_args.local_rank == 0:
        wandb.init(
            project="solana-deepseek-coder",
            config={
                "model_name": model_args.model_name_or_path,
                "learning_rate": training_args.learning_rate,
                "batch_size": training_args.per_device_train_batch_size,
                "max_steps": training_args.max_steps,
                "warmup_steps": training_args.warmup_steps
            }
        )
        print('='*100)
        print(training_args)
    
    tokenizer = transformers.AutoTokenizer.from_pretrained(
        model_args.model_name_or_path,
        model_max_length=training_args.model_max_length,
        padding_side="right",
        use_fast=True,
        trust_remote_code=True
    )

    print("PAD Token:", tokenizer.pad_token, tokenizer.pad_token_id)
    print("BOS Token", tokenizer.bos_token, tokenizer.bos_token_id)
    print("EOS Token", tokenizer.eos_token, tokenizer.eos_token_id)

    if training_args.local_rank == 0:
        print("Load tokenizer from {} over.".format(model_args.model_name_or_path))

    model = transformers.AutoModelForCausalLM.from_pretrained(
        model_args.model_name_or_path,
        torch_dtype=torch.bfloat16,
        device_map="auto"  # Enable automatic device mapping
    )

    if training_args.local_rank == 0:
        print("Load model from {} over.".format(model_args.model_name_or_path))

    raw_train_datasets = load_dataset(
        'json',
        data_files=data_args.data_path,
        split="train",
        cache_dir=training_args.cache_dir
    )
    if training_args.local_rank > 0: 
        torch.distributed.barrier()
        
    train_dataset = raw_train_datasets.map(
        train_tokenize_function,
        batched=True,
        batch_size=3000,
        num_proc=32,
        remove_columns=raw_train_datasets.column_names,
        load_from_cache_file=True,
        desc="Running Encoding",
        fn_kwargs={ "tokenizer": tokenizer }
    )

    if training_args.local_rank == 0:
        torch.distributed.barrier()
    
    if training_args.local_rank == 0:
        print("Training dataset samples:", len(train_dataset))
        for index in random.sample(range(len(train_dataset)), 3):
            print(f"Sample {index} of the training set: {train_dataset[index]['input_ids']}, {train_dataset[index]['labels']}.")
            print(f"Sample {index} of the training set: {tokenizer.decode(list(train_dataset[index]['input_ids']))}.")

    data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer)
    data_module = dict(train_dataset=train_dataset, eval_dataset=None, data_collator=data_collator)

    trainer = CustomTrainer(model=model, tokenizer=tokenizer, args=training_args, **data_module)

    trainer.train()
    trainer.save_state()
    safe_save_model_for_hf_trainer(trainer=trainer, output_dir=training_args.output_dir)

    if training_args.local_rank == 0:
        wandb.finish()


if __name__ == "__main__":
    train()