Create Programming Assistant Training Script

finetune/Programming Assistant Training Script

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+
+import copy
+import random
+import logging
+from dataclasses import dataclass, field
+from typing import Optional, Dict, Sequence
+
+import torch
+import torch.distributed
+from transformers import Trainer, AutoTokenizer, AutoModelForCausalLM, HfArgumentParser, TrainingArguments
+from datasets import load_dataset
+
+IGNORE_INDEX = -100
+EOT_TOKEN = "<|EOT|>"
+LOGGING_FORMAT = '%(asctime)s - %(levelname)s - %(message)s'
+
+# Set up logging
+logging.basicConfig(level=logging.INFO, format=LOGGING_FORMAT)
+
+
+def build_instruction_prompt(instruction: str) -> str:
+    """
+    Build a formatted instruction prompt for the model to respond to.
+    """
+    return f'''
+You are an AI programming assistant, utilizing the DeepSeek Coder model, developed by DeepSeek Company, 
+and you only answer questions related to computer science. For politically sensitive questions, security 
+and privacy issues, and other non-computer science questions, you will refuse to answer.
+### Instruction:
+{instruction.strip()}
+### Response:
+'''.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 CustomTrainingArguments(TrainingArguments):
+    cache_dir: Optional[str] = field(default=None)
+    optim: str = field(default="adamw_torch")
+    model_max_length: int = field(
+        default=512,
+        metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."},
+    )
+
+
+def safe_save_model(trainer: Trainer, output_dir: str):
+    """
+    Collects the state dict and saves it 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()}
+        trainer._save(output_dir, state_dict=cpu_state_dict)
+    logging.info(f"Model saved to {output_dir}")
+
+
+def tokenize_texts(strings: Sequence[str], tokenizer: AutoTokenizer) -> 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 = [tokenized.input_ids[0] for tokenized in tokenized_list]
+    input_ids_lens = [
+        tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item() for tokenized in tokenized_list
+    ]
+
+    return dict(
+        input_ids=input_ids,
+        input_ids_lens=input_ids_lens,
+    )
+
+
+def preprocess_data(
+    sources: Sequence[str],
+    targets: Sequence[str],
+    tokenizer: AutoTokenizer,
+) -> Dict:
+    """
+    Preprocess the data by tokenizing it and preparing the labels.
+    """
+    examples = [s + t for s, t in zip(sources, targets)]
+    examples_tokenized, sources_tokenized = [tokenize_texts(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:
+    """
+    Collator for supervised fine-tuning.
+    """
+    tokenizer: AutoTokenizer
+
+    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.nn.utils.rnn.pad_sequence([torch.tensor(x) for x in input_ids], 
+                                                    batch_first=True, 
+                                                    padding_value=self.tokenizer.pad_token_id)
+        labels = torch.nn.utils.rnn.pad_sequence([torch.tensor(x) for x in 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 tokenize_for_training(examples, tokenizer):
+    """
+    Tokenize the examples for training.
+    """
+    sources = [build_instruction_prompt(instruction) for instruction in examples['instruction']]
+    targets = [f"{output}\n{EOT_TOKEN}" for output in examples['output']]
+    return preprocess_data(sources, targets, tokenizer)
+
+
+def train():
+    """
+    Main training loop.
+    """
+    parser = HfArgumentParser((ModelArguments, DataArguments, CustomTrainingArguments))
+    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # Initialize tokenizer and model
+    tokenizer = 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
+    )
+    
+    model = AutoModelForCausalLM.from_pretrained(model_args.model_name_or_path, torch_dtype=torch.bfloat16)
+
+    # Load dataset
+    raw_train_datasets = load_dataset('json', data_files=data_args.data_path, split="train", cache_dir=training_args.cache_dir)
+    train_dataset = raw_train_datasets.map(
+        tokenize_for_training,
+        batched=True,
+        batch_size=3000,
+        num_proc=32,
+        remove_columns=raw_train_datasets.column_names,
+        desc="Tokenizing",
+        fn_kwargs={"tokenizer": tokenizer}
+    )
+
+    logging.info(f"Loaded dataset with {len(train_dataset)} samples.")
+
+    data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer)
+    trainer = Trainer(model=model, tokenizer=tokenizer, args=training_args, train_dataset=train_dataset, data_collator=data_collator)
+
+    # Start training
+    trainer.train()
+    trainer.save_state()
+    safe_save_model(trainer, training_args.output_dir)
+
+
+if __name__ == "__main__":
+    train()