494 lines
21 KiB
Python
494 lines
21 KiB
Python
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#!/usr/bin/env python3
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"""
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Styling Training Pipeline using Unsloth and SFTTrainer
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Supports style transfer tasks with LoRA fine-tuning
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"""
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import os
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import sys
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import json
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import logging
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import argparse
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from pathlib import Path
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from typing import Dict, Any, Optional
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import yaml
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# Add the project root to the path
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sys.path.append(str(Path(__file__).parent.parent.parent))
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from utils.config.config_manager import ConfigManager
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#from utils.logging.logging import setup_logging
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# Training imports
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import torch
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from datasets import load_from_disk, Dataset
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from unsloth import FastLanguageModel, is_bfloat16_supported
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from trl import SFTTrainer
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from transformers import TrainingArguments
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logger = logging.getLogger(__name__)
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class StylingTrainer:
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"""Styling task trainer using Unsloth and SFTTrainer"""
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def __init__(self, config: Dict[str, Any]):
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self.config = config
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self.model = None
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self.tokenizer = None
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self.trainer = None
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# Set device
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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logger.info(f"Using device: {self.device}")
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# Training parameters
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self.max_seq_length = config.get('max_seq_length', 2048)
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self.dtype = config.get('dtype', None)
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self.load_in_4bit = config.get('load_in_4bit', True)
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self.hf_token = config.get('hf_token', None)
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# LoRA parameters
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self.lora_r = config.get('lora_r', 16)
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self.lora_alpha = config.get('lora_alpha', 16)
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self.lora_dropout = config.get('lora_dropout', 0)
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self.target_modules = config.get('target_modules', [
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"q_proj", "k_proj", "v_proj", "o_proj",
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"gate_proj", "up_proj", "down_proj"
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])
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# Training arguments
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self.batch_size = config.get('batch_size', 2)
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self.gradient_accumulation_steps = config.get('gradient_accumulation_steps', 4)
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self.learning_rate = config.get('learning_rate', 2e-4)
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self.num_epochs = config.get('num_epochs', 1)
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self.max_steps = config.get('max_steps', None)
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self.warmup_ratio = config.get('warmup_ratio', 0.1)
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# Set a default warmup_steps value
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self.warmup_steps = config.get('warmup_steps', 10)
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self.weight_decay = config.get('weight_decay', 0.01)
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self.seed = config.get('seed', 3407)
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# Output paths
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self.output_dir = config.get('output_dir', './outputs')
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self.model_output_dir = config.get('model_output_dir', './models/styling')
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def load_model_and_tokenizer(self):
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"""Load the pre-trained model and tokenizer"""
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logger.info("Loading model and tokenizer...")
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try:
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self.model, self.tokenizer = FastLanguageModel.from_pretrained(
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model_name=self.config['model_name'],
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max_seq_length=self.max_seq_length,
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dtype=self.dtype,
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load_in_4bit=self.load_in_4bit,
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token=self.hf_token
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)
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logger.info(f"✅ Model loaded: {self.config['model_name']}")
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logger.info(f"✅ Tokenizer loaded with vocab size: {self.tokenizer.vocab_size}")
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except Exception as e:
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logger.error(f"❌ Error loading model: {e}")
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raise
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def setup_lora(self):
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"""Setup LoRA for efficient fine-tuning"""
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logger.info("Setting up LoRA configuration...")
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try:
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self.model = FastLanguageModel.get_peft_model(
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self.model,
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r=self.lora_r,
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target_modules=self.target_modules,
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lora_alpha=self.lora_alpha,
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lora_dropout=self.lora_dropout,
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bias="none",
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use_gradient_checkpointing="unsloth",
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random_state=self.seed,
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use_rslora=False,
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loftq_config=None
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)
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logger.info(f"✅ LoRA configured with r={self.lora_r}, alpha={self.lora_alpha}")
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except Exception as e:
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logger.error(f"❌ Error setting up LoRA: {e}")
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raise
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def load_dataset(self, dataset_path: str) -> Dataset:
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"""Load the training dataset"""
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logger.info(f"Loading dataset from: {dataset_path}")
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try:
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if Path(dataset_path).exists():
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# Check if it's a HuggingFace dataset directory
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if (Path(dataset_path) / "dataset_info.json").exists():
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# Load from HuggingFace dataset directory
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dataset = load_from_disk(dataset_path)
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logger.info(f"Loaded HuggingFace dataset from disk: {len(dataset)} samples")
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else:
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# Load from processed data files (JSONL format)
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logger.info("Loading from processed data files...")
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from datasets import Dataset
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import json
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all_data = []
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data_dir = Path(dataset_path)
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# Look for train.jsonl, validation.jsonl, test.jsonl
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for split_file in ["train.jsonl", "validation.jsonl", "test.jsonl"]:
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file_path = data_dir / split_file
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if file_path.exists():
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logger.info(f"Loading {split_file}...")
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with open(file_path, 'r', encoding='utf-8') as f:
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for line in f:
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if line.strip():
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data = json.loads(line)
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all_data.append(data)
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if not all_data:
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raise ValueError(f"No data found in {dataset_path}")
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# Create HuggingFace dataset
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dataset = Dataset.from_list(all_data)
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logger.info(f"Created HuggingFace dataset from {len(all_data)} samples")
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else:
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# Try loading from HuggingFace Hub
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logger.info(f"Attempting to load from HuggingFace Hub: {dataset_path}")
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dataset = Dataset.load_dataset(dataset_path, split="train")
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logger.info(f"Loaded from HuggingFace Hub: {len(dataset)} samples")
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logger.info(f"Dataset loaded: {len(dataset)} samples")
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logger.info(f"Dataset features: {dataset.features}")
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# Verify required fields exist
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required_fields = ["instruction", "input", "output"]
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missing_fields = [field for field in required_fields if field not in dataset.features]
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if missing_fields:
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raise ValueError(f"Missing required fields in dataset: {missing_fields}")
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return dataset
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except Exception as e:
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logger.error(f"Error loading dataset: {e}")
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raise
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def setup_trainer(self, train_dataset: Dataset):
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"""Setup the SFTTrainer"""
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print("Setting up SFTTrainer...")
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try:
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# First, map the dataset to create the text field with EOS token
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def formatting_prompts_func(examples):
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instructions = examples["instruction"]
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inputs = examples["input"]
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outputs = examples["output"]
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texts = []
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for instruction, input_text, output in zip(instructions, inputs, outputs):
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# Must add EOS_TOKEN, otherwise your generation will go on forever!
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alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that follows the instruction
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### Instruction:
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{}
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### Input:
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{}
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### Response:
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{}"""
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text = alpaca_prompt.format(instruction, input_text, output) + self.tokenizer.eos_token
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texts.append(text)
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return {"text": texts}
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# Apply the formatting function to create the text field
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print("Mapping dataset to create text field with EOS token...")
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formatted_dataset = train_dataset.map(formatting_prompts_func, batched=True, remove_columns=train_dataset.column_names)
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print(f"Dataset mapped successfully. New features: {formatted_dataset.features}")
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print(f"Sample text field: {formatted_dataset[0]['text'][:100]}...")
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# Debug logging to identify parameter issues
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print("Training parameters for TrainingArguments:")
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print(f" batch_size: {self.batch_size} (type: {type(self.batch_size)})")
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print(f" gradient_accumulation_steps: {self.gradient_accumulation_steps} (type: {type(self.gradient_accumulation_steps)})")
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print(f" warmup_steps: {self.warmup_steps} (type: {type(self.warmup_steps)})")
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print(f" num_epochs: {self.num_epochs} (type: {type(self.num_epochs)})")
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print(f" max_steps: {self.max_steps} (type: {type(self.max_steps)})")
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print(f" learning_rate: {self.learning_rate} (type: {type(self.learning_rate)})")
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print(f" weight_decay: {self.weight_decay} (type: {type(self.weight_decay)})")
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print(f" seed: {self.seed} (type: {type(self.seed)})")
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print("Creating TrainingArguments...")
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# Training arguments - using the exact working configuration
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training_args = TrainingArguments(
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per_device_train_batch_size=self.batch_size,
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gradient_accumulation_steps=self.gradient_accumulation_steps,
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warmup_steps=self.warmup_steps,
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num_train_epochs=self.num_epochs,
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max_steps=self.max_steps if self.max_steps is not None else 60, # Use default if None
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learning_rate=self.learning_rate,
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fp16=not is_bfloat16_supported(),
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bf16=is_bfloat16_supported(),
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logging_steps=1,
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optim="adamw_8bit",
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weight_decay=self.weight_decay,
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lr_scheduler_type="linear",
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seed=self.seed,
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output_dir=self.output_dir,
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report_to="none", # Disable wandb for now
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)
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print("TrainingArguments created successfully!")
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print("SFTTrainer parameters:")
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print(f" model: {type(self.model)}")
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print(f" tokenizer: {type(self.tokenizer)}")
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print(f" train_dataset: {type(formatted_dataset)} with {len(formatted_dataset)} samples")
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print(f" dataset_text_field: text")
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print(f" max_seq_length: {self.max_seq_length} (type: {type(self.max_seq_length)})")
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print(f" dataset_num_proc: 2")
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print(f" packing: False")
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print(f" args: {type(training_args)}")
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print("Creating SFTTrainer...")
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# Create trainer with the formatted dataset
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self.trainer = SFTTrainer(
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model=self.model,
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tokenizer=self.tokenizer,
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train_dataset=formatted_dataset, # Use the formatted dataset
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dataset_text_field="text", # The field we just created
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max_seq_length=int(self.max_seq_length) if self.max_seq_length is not None else 2048,
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dataset_num_proc=2,
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packing=False, # Can make training 5x faster for short sequences
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args=training_args
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)
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print("SFTTrainer configured successfully")
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except Exception as e:
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print(f"Error setting up trainer: {e}")
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import traceback
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print("Full error traceback:")
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traceback.print_exc()
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raise
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def train(self, dataset_path: str):
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"""Run the training process"""
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print("🚀 Starting training process...")
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try:
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# Load model and tokenizer
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print("Loading model and tokenizer...")
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self.load_model_and_tokenizer()
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# Setup LoRA
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print("Setting up LoRA...")
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self.setup_lora()
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# Load dataset
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print(f"Loading dataset from: {dataset_path}")
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train_dataset = self.load_dataset(dataset_path)
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# Setup trainer
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print("Setting up trainer...")
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self.setup_trainer(train_dataset)
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# Start training
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print("Starting training...")
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trainer_stats = self.trainer.train()
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print("✅ Training completed successfully!")
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print(f"Training stats: {trainer_stats}")
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# Save the model
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self.save_model()
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return trainer_stats
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except Exception as e:
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print(f"❌ Training failed: {e}")
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import traceback
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print("Full error traceback:")
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traceback.print_exc()
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raise
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def save_model(self):
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"""Save the trained model"""
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print("Saving trained model...")
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try:
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# Create output directory
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Path(self.model_output_dir).mkdir(parents=True, exist_ok=True)
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# Save model and tokenizer
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self.model.save_pretrained(self.model_output_dir)
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self.tokenizer.save_pretrained(self.model_output_dir)
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# Save training config
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config_path = Path(self.model_output_dir) / "training_config.json"
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with open(config_path, 'w') as f:
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json.dump(self.config, f, indent=2)
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print(f"✅ Model saved to: {self.model_output_dir}")
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print(f"✅ You can now use this model for inference with: --config {self.model_output_dir}")
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except Exception as e:
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print(f"❌ Error saving model: {e}")
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raise
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def prepare_for_inference(self):
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"""Prepare model for inference"""
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logger.info("Preparing model for inference...")
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try:
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FastLanguageModel.for_inference(self.model)
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logger.info("✅ Model prepared for inference")
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except Exception as e:
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logger.error(f"❌ Error preparing for inference: {e}")
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raise
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def load_training_config(yaml_path: str) -> Dict[str, Any]:
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"""Load training configuration from YAML file"""
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try:
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with open(yaml_path, 'r') as f:
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config = yaml.safe_load(f)
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training_config = {}
|
||
|
|
|
||
|
|
# Model configuration - extract from model section
|
||
|
|
if 'model' in config:
|
||
|
|
model_config = config['model']
|
||
|
|
training_config.update({
|
||
|
|
'model_name': model_config.get('name', 'unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit'),
|
||
|
|
'max_seq_length': int(model_config.get('max_seq_length', 2048)),
|
||
|
|
'dtype': model_config.get('dtype', None),
|
||
|
|
'load_in_4bit': model_config.get('load_in_4bit', True),
|
||
|
|
'hf_token': model_config.get('token', None)
|
||
|
|
})
|
||
|
|
|
||
|
|
# Training configuration - extract from training section
|
||
|
|
if 'training' in config:
|
||
|
|
training_data = config['training']
|
||
|
|
print("DEBUG: Training data from YAML:")
|
||
|
|
print(f" num_epochs: {training_data.get('num_epochs')} (type: {type(training_data.get('num_epochs'))})")
|
||
|
|
print(f" batch_size: {training_data.get('batch_size')} (type: {type(training_data.get('batch_size'))})")
|
||
|
|
print(f" learning_rate: {training_data.get('learning_rate')} (type: {type(training_data.get('learning_rate'))})")
|
||
|
|
print(f" weight_decay: {training_data.get('weight_decay')} (type: {type(training_data.get('weight_decay'))})")
|
||
|
|
print(f" warmup_steps: {training_data.get('warmup_steps')} (type: {type(training_data.get('warmup_steps'))})")
|
||
|
|
print(f" max_steps: {training_data.get('max_steps')} (type: {type(training_data.get('max_steps'))})")
|
||
|
|
print(f" gradient_accumulation_steps: {training_data.get('gradient_accumulation_steps')} (type: {type(training_data.get('gradient_accumulation_steps'))})")
|
||
|
|
print(f" seed: {training_data.get('seed')} (type: {type(training_data.get('seed'))})")
|
||
|
|
print(f" model_output_dir: {training_data.get('model_output_dir')} (type: {type(training_data.get('model_output_dir'))})")
|
||
|
|
|
||
|
|
training_config.update({
|
||
|
|
'num_epochs': int(training_data.get('num_epochs', 1)),
|
||
|
|
'batch_size': int(training_data.get('batch_size', 2)),
|
||
|
|
'learning_rate': float(training_data.get('learning_rate', 2e-4)),
|
||
|
|
'weight_decay': float(training_data.get('weight_decay', 0.01)),
|
||
|
|
'warmup_steps': int(training_data.get('warmup_steps', 5)),
|
||
|
|
'max_steps': int(training_data.get('max_steps', 60)),
|
||
|
|
'gradient_accumulation_steps': int(training_data.get('gradient_accumulation_steps', 4)),
|
||
|
|
'lr_scheduler_type': training_data.get('lr_scheduler_type', 'linear'),
|
||
|
|
'seed': int(training_data.get('seed', 3407)),
|
||
|
|
'model_output_dir': training_data.get('model_output_dir', './models/styling')
|
||
|
|
})
|
||
|
|
|
||
|
|
# Data configuration - use output_dir from data section
|
||
|
|
if 'data' in config:
|
||
|
|
data_config = config['data']
|
||
|
|
output_dir = data_config.get('output_dir', './data/processed/styling')
|
||
|
|
training_config.update({
|
||
|
|
'data_output_dir': output_dir,
|
||
|
|
'dataset_path': output_dir, # Default dataset path is the output_dir
|
||
|
|
'style_instruction': data_config.get('instruction', 'Rewrite the following text in a formal style')
|
||
|
|
})
|
||
|
|
|
||
|
|
# LoRA configuration
|
||
|
|
training_config.update({
|
||
|
|
'lora_r': 16,
|
||
|
|
'lora_alpha': 16,
|
||
|
|
'lora_dropout': 0,
|
||
|
|
'target_modules': [
|
||
|
|
"q_proj", "k_proj", "v_proj", "o_proj",
|
||
|
|
"gate_proj", "up_proj", "down_proj"
|
||
|
|
],
|
||
|
|
'output_dir': './outputs',
|
||
|
|
'model_output_dir': './models/styling'
|
||
|
|
})
|
||
|
|
|
||
|
|
print("DEBUG: Final training_config:")
|
||
|
|
for key, value in training_config.items():
|
||
|
|
print(f" {key}: {value} (type: {type(value)})")
|
||
|
|
|
||
|
|
return training_config
|
||
|
|
|
||
|
|
except Exception as e:
|
||
|
|
logger.error(f"Error loading training config: {e}")
|
||
|
|
raise
|
||
|
|
|
||
|
|
def main():
|
||
|
|
"""Main training function"""
|
||
|
|
parser = argparse.ArgumentParser(description="Styling Training Pipeline")
|
||
|
|
|
||
|
|
# Configuration
|
||
|
|
parser.add_argument("--config", type=str, required=True, help="Path to YAML configuration file")
|
||
|
|
parser.add_argument("--dataset", type=str, help="Path to training dataset (HF dataset path or local path)")
|
||
|
|
parser.add_argument("--output-dir", type=str, help="Output directory for model")
|
||
|
|
parser.add_argument("--epochs", type=int, help="Number of training epochs")
|
||
|
|
parser.add_argument("--batch-size", type=int, help="Training batch size")
|
||
|
|
parser.add_argument("--learning-rate", type=float, help="Learning rate")
|
||
|
|
parser.add_argument("--max-steps", type=int, help="Maximum training steps")
|
||
|
|
|
||
|
|
args = parser.parse_args()
|
||
|
|
|
||
|
|
# Setup logging
|
||
|
|
# setup_logging() # Commented out as per user's change
|
||
|
|
|
||
|
|
try:
|
||
|
|
# Load configuration
|
||
|
|
logger.info(f"Loading configuration from: {args.config}")
|
||
|
|
training_config = load_training_config(args.config)
|
||
|
|
|
||
|
|
# Override with CLI arguments
|
||
|
|
if args.output_dir:
|
||
|
|
training_config['model_output_dir'] = args.output_dir
|
||
|
|
if args.epochs:
|
||
|
|
training_config['num_epochs'] = int(args.epochs)
|
||
|
|
if args.batch_size:
|
||
|
|
training_config['batch_size'] = int(args.batch_size)
|
||
|
|
if args.learning_rate:
|
||
|
|
training_config['learning_rate'] = float(args.learning_rate)
|
||
|
|
if args.max_steps:
|
||
|
|
training_config['max_steps'] = int(args.max_steps)
|
||
|
|
|
||
|
|
# Determine dataset path: CLI argument takes precedence, then YAML config
|
||
|
|
dataset_path = args.dataset or training_config.get('dataset_path')
|
||
|
|
if not dataset_path:
|
||
|
|
logger.error("No dataset path provided. Use --dataset or ensure output_dir is set in YAML config.")
|
||
|
|
sys.exit(1)
|
||
|
|
|
||
|
|
logger.info("Training configuration:")
|
||
|
|
for key, value in training_config.items():
|
||
|
|
logger.info(f" {key}: {value}")
|
||
|
|
logger.info(f" Dataset path: {dataset_path}")
|
||
|
|
|
||
|
|
# Initialize trainer
|
||
|
|
trainer = StylingTrainer(training_config)
|
||
|
|
|
||
|
|
# Start training
|
||
|
|
trainer.train(dataset_path)
|
||
|
|
|
||
|
|
logger.info("Training completed successfully!")
|
||
|
|
|
||
|
|
except Exception as e:
|
||
|
|
logger.error(f"Training failed: {e}")
|
||
|
|
sys.exit(1)
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
main()
|