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distil_logits.py
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distil_logits.py
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import os
import torch
import torch.nn.functional as F
from datasets import load_dataset
from trl import SFTTrainer, SFTConfig
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments
from accelerate import Accelerator
import yaml
# Configuration
config = {
"project_name": "distil-logits",
"dataset": {
"name": "mlabonne/FineTome-100k", # Only sharegpt format is currently supported.
"split": "train",
"num_samples" : 5_000,
"seed": 42
},
"models": {
"teacher": "Qwen/Qwen2-1.5B",
"student": "Qwen/Qwen2-0.5B"
},
"tokenizer": {
"max_length": 1024,
"chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
},
"training": {
"output_dir": "./results",
"num_train_epochs": 3,
"per_device_train_batch_size": 1,
"gradient_accumulation_steps": 8,
"save_steps": 1000,
"logging_steps": 1,
"learning_rate": 2e-5,
"weight_decay": 0.05,
"warmup_ratio": 0.1,
"lr_scheduler_type": "cosine",
"resume_from_checkpoint": None, # Set to a path or True to resume from the latest checkpoint
"fp16": False,
"bf16": True
},
"distillation": {
"temperature": 2.0,
"alpha": 0.5
},
"model_config": {
"use_flash_attention": True
}
}
# Set up environment
os.environ['WANDB_PROJECT'] = config["project_name"]
accelerator = Accelerator()
device = accelerator.device
# Load and preprocess dataset
dataset = load_dataset(config["dataset"]["name"], split=config["dataset"]["split"])
dataset = dataset.shuffle(seed=config["dataset"]["seed"])
if "num_samples" in config["dataset"]:
dataset = dataset.select(range(config["dataset"]["num_samples"]))
# Load tokenizers
teacher_tokenizer = AutoTokenizer.from_pretrained(config["models"]["teacher"])
student_tokenizer = AutoTokenizer.from_pretrained(config["models"]["student"])
# Apply chat template to student tokenizer
student_tokenizer.chat_template = config["tokenizer"]["chat_template"]
def sharegpt_format(example):
conversations = example['conversations']
message = []
if isinstance(conversations, list):
for conversation in conversations:
if isinstance(conversation, dict):
if conversation.get('from') == 'human':
message.append({"role": "user", "content": conversation.get('value', '')})
elif conversation.get('from') == 'gpt':
message.append({"role": "assistant", "content": conversation.get('value', '')})
elif conversation.get('from') == 'system':
message.insert(0, {"role": "system", "content": conversation.get('value', '')})
if not any(msg.get('role') == 'system' for msg in message):
message.insert(0, {"role": "system", "content": "You are a helpful assistant."})
text = student_tokenizer.apply_chat_template(message, tokenize=False, add_generation_prompt=True)
return {"text": text}
# Preprocess and tokenize the dataset
print("Preprocessing and tokenizing dataset...")
original_columns = dataset.column_names
dataset = dataset.map(sharegpt_format, remove_columns=original_columns)
def tokenize_function(examples):
return student_tokenizer(examples["text"], truncation=True, max_length=config["tokenizer"]["max_length"], padding="max_length")
tokenized_dataset = dataset.map(tokenize_function, batched=True, num_proc=8, remove_columns=["text"])
tokenized_dataset = tokenized_dataset.train_test_split(test_size=0.1)
print("Dataset preparation complete. Loading models...")
# Load models with configurable flash attention
model_kwargs = {"torch_dtype": torch.bfloat16}
if config["model_config"]["use_flash_attention"]:
model_kwargs["attn_implementation"] = "flash_attention_2"
teacher_model = AutoModelForCausalLM.from_pretrained(config["models"]["teacher"], **model_kwargs)
student_model = AutoModelForCausalLM.from_pretrained(config["models"]["student"], **model_kwargs)
# Optionally freeze layers of the student model based on spectrum configuration
if "spectrum" in config and "layers_to_unfreeze" in config["spectrum"]:
def freeze_student_spectrum(model, unfrozen_layers_file):
with open(unfrozen_layers_file, 'r') as file:
unfrozen_layers = yaml.safe_load(file)['unfrozen_parameters']
for name, param in model.named_parameters():
if not any(layer in name for layer in unfrozen_layers):
param.requires_grad = False
else:
param.requires_grad = True
# Apply freezing to student model
freeze_student_spectrum(student_model, config["spectrum"]["layers_to_unfreeze"])
else:
print("Spectrum configuration not found. All layers of the student model will be trainable.")
def pad_logits(student_logits, teacher_logits):
student_size, teacher_size = student_logits.size(-1), teacher_logits.size(-1)
if student_size != teacher_size:
pad_size = abs(student_size - teacher_size)
pad_tensor = torch.zeros((*teacher_logits.shape[:-1], pad_size), dtype=teacher_logits.dtype, device=teacher_logits.device)
return (torch.cat([student_logits, pad_tensor], dim=-1), teacher_logits) if student_size < teacher_size else (student_logits, torch.cat([teacher_logits, pad_tensor], dim=-1))
return student_logits, teacher_logits
class LogitsTrainer(SFTTrainer):
def compute_loss(self, model, inputs, return_outputs=False):
inputs = {k: v.to(model.device) if hasattr(v, 'to') else v for k, v in inputs.items()}
self.teacher_model = self.teacher_model.to(model.device)
student_model = model.module if hasattr(model, 'module') else model
teacher_model = self.teacher_model.module if hasattr(self.teacher_model, 'module') else self.teacher_model
student_outputs = student_model(**inputs)
with torch.no_grad():
teacher_outputs = teacher_model(**inputs)
custom_loss = self.distillation_loss(student_outputs.logits, teacher_outputs.logits, inputs, student_outputs.loss)
return (custom_loss, student_outputs) if return_outputs else custom_loss
def distillation_loss(self, student_logits, teacher_logits, inputs, original_loss):
student_logits, teacher_logits = pad_logits(student_logits.to(self.model.device), teacher_logits.to(self.model.device))
student_logits_scaled = student_logits / config["distillation"]["temperature"]
teacher_logits_scaled = teacher_logits / config["distillation"]["temperature"]
loss_kd = F.kl_div(
F.log_softmax(student_logits_scaled, dim=-1),
F.softmax(teacher_logits_scaled, dim=-1),
reduction='batchmean'
) * (config["distillation"]["temperature"] ** 2) / config["tokenizer"]["max_length"]
return config["distillation"]["alpha"] * loss_kd + (1 - config["distillation"]["alpha"]) * original_loss
# Training arguments
training_arguments = TrainingArguments(**config["training"])
# Create the custom SFT Trainer
trainer = LogitsTrainer(
model=student_model,
train_dataset=tokenized_dataset["train"],
eval_dataset=tokenized_dataset["test"],
tokenizer=student_tokenizer,
args=training_arguments,
max_seq_length=config["tokenizer"]["max_length"],
dataset_text_field="text",
)
# Add the teacher model to the trainer
trainer.teacher_model = teacher_model
# Prepare for distributed training
trainer = accelerator.prepare(trainer)
# Train the model
trainer.train(resume_from_checkpoint=config["training"]["resume_from_checkpoint"])
# Save the final model
trainer.save_model(config["training"]["output_dir"])