What is the role of Unsloth in accelerating fine-tuning processes? How does it improve memory efficiency and speed?
How do you handle padding in sequences during training? ### Discuss strategies for effective padding management.
What is the significance of setting a maximum sequence length? How does it affect model performance and memory usage?
Explain Proximal Policy Optimization (PPO) and its application in fine-tuning LLMs? What are its advantages compared to other optimization algorithms?
What is Direct Preference Optimization (DPO), and how does it differ from PPO How do you perform progress logging during model training?
What is the purpose of the Accelerate library in training LLMs? How does it facilitate distributed training?
Can you explain how to use the SFTTrainer class from Hugging Face's trl library? What are its key features?
- Overfitting: This occurs when a model learns the training data too well, leading to high accuracy on that dataset but poor generalization to new data.
- Catastrophic Forgetting: Description: When fine-tuning on a new task, the model may lose its ability to perform well on previously learned tasks.
- Hyperparameter Misconfiguration: Description: Incorrect settings for hyperparameters such as learning rate, batch size, and number of epochs can lead to suboptimal model performance.
Llama 3.1 models is primarily based on cross-entropy loss, which is standard for training language models.
This loss function measures the dissimilarity between the predicted probability distribution of the model and the actual distribution of the target labels.
While cross-entropy loss is used during training, various evaluation metrics (like accuracy, F1 score, etc.) are employed post-training to assess model performance on specific tasks
While Fine-tuning LLMs using distributed training across multiple nodes and GPUs using PyTorch FSDP (Fully Sharded Data Parallel),
Is it possible to use HuggingFace Transformers and SFT Trainer for distributed training?
How data should be distributed? Is data should be split before running training?
How data loader knows which records should be loaded? Each node will get different dataset or same dataset?
Data Distribution:
- The data is automatically sharded across nodes using the DistributedDataset class
- Each node gets a unique subset of the data based on its rank and world size
- The data is split at runtime, so you don't need to split it beforehand
- Each node processes different data to avoid redundancy
Data Loading:
- The dataloader knows which records to load through the rank-based sharding
- Each node's dataset is determined by: shard_size = total_data // world_size
- Data indices for each node: start_idx = rank * shard_size
FSDP Configuration:
- Uses mixed precision training (FP16) for efficiency
- Implements transformer-specific auto wrap policy
- Enables activation checkpointing to save memory
- Configures backward prefetching for better performance
Distributed Training Setup:
- Initializes distributed environment using NCCL backend
- Sets up proper device mapping for multi-GPU training
- Handles proper model distribution across GPUs using FSDP
torchrun --nproc_per_node=8 --nnodes=4 --node_rank=0 --master_addr="master_ip" --master_port=29500 train.pyShow code sample - Training script across multiple nodes (Generated by Claude.ai)
import os
import torch
import torch.distributed as dist
from torch.utils.data import Dataset, DataLoader
from torch.distributed.fsdp import (
FullyShardedDataParallel as FSDP,
MixedPrecision,
StateDictType,
FullStateDictConfig,
)
from torch.distributed.fsdp.wrap import (
transformer_auto_wrap_policy,
size_based_auto_wrap_policy,
)
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
Trainer,
TrainingArguments,
DataCollatorForLanguageModeling,
)
from datasets import load_dataset
from typing import Dict, List
def setup_distributed():
""" Initialize distributed training environment """
if "RANK" in os.environ and "WORLD_SIZE" in os.environ:
rank = int(os.environ["RANK"])
world_size = int(os.environ["WORLD_SIZE"])
local_rank = int(os.environ["LOCAL_RANK"])
dist.init_process_group("nccl")
torch.cuda.set_device(local_rank)
return rank, world_size, local_rank
else:
raise ValueError("RANK and WORLD_SIZE environment variables must be set")
class DistributedDataset(Dataset):
def __init__(self, dataset, rank: int, world_size: int, tokenizer):
"""
Initialize dataset with sharding for distributed training
Args:
dataset: HuggingFace dataset
rank: Current process rank
world_size: Total number of processes
tokenizer: HuggingFace tokenizer
"""
# Shard the dataset
self.shard_size = len(dataset) // world_size
start_idx = rank * self.shard_size
end_idx = start_idx + self.shard_size if rank != world_size - 1 else len(dataset)
self.data = dataset[start_idx:end_idx]
self.tokenizer = tokenizer
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
item = self.data[idx]
# Assuming 'text' is the column name in your dataset
encoded = self.tokenizer(
item['text'],
truncation=True,
max_length=512,
padding='max_length',
return_tensors='pt'
)
return {
'input_ids': encoded['input_ids'].squeeze(),
'attention_mask': encoded['attention_mask'].squeeze()
}
def get_fsdp_config():
"""Configure FSDP settings"""
mixed_precision_policy = MixedPrecision(
param_dtype=torch.float16,
reduce_dtype=torch.float16,
buffer_dtype=torch.float16,
)
return {
"mixed_precision": mixed_precision_policy,
"auto_wrap_policy": transformer_auto_wrap_policy,
"sharding_strategy": "FULL_SHARD",
"cpu_offload": False,
"backward_prefetch": "BACKWARD_PRE",
"activation_checkpointing": True,
}
def main():
# Setup distributed environment
rank, world_size, local_rank = setup_distributed()
# Initialize model and tokenizer
model_name = "facebook/opt-350m" # Example model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Wrap model with FSDP
fsdp_config = get_fsdp_config()
model = FSDP(
model,
device_id=torch.cuda.current_device(),
**fsdp_config
)
# Load and prepare dataset
dataset = load_dataset("your_dataset_name")
train_dataset = DistributedDataset(
dataset['train'],
rank,
world_size,
tokenizer
)
# Training arguments
training_args = TrainingArguments(
output_dir="./output",
per_device_train_batch_size=8,
gradient_accumulation_steps=4,
num_train_epochs=3,
learning_rate=5e-5,
fp16=True,
logging_steps=100,
save_steps=1000,
local_rank=local_rank,
)
# Data collator
data_collator = DataCollatorForLanguageModeling(
tokenizer=tokenizer,
mlm=False
)
# Initialize trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
data_collator=data_collator,
)
# Start training
trainer.train()
# Save model (only on rank 0)
if rank == 0:
save_policy = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT, save_policy):
state_dict = model.state_dict()
torch.save(state_dict, "model_checkpoint.pt")
if __name__ == "__main__":
main()While fine-tune LLMs using the SFT Trainer for mixed tasks (such as text generation, question answering, and summarization), how to pass multiple evaluation metrics task-wise to compute performance?
Set up the SFT Trainer in Hugging Face's transformers library to fine-tune a model for mixed tasks (text generation, question answering, and summarization) while specifying multiple evaluation metrics.
Show code sample (Generated by Claude.ai)
```python from transformers import Trainer, TrainingArguments, AutoModelForSeq2SeqLM, AutoTokenizer from datasets import load_dataset, load_metric from typing import Dict, List, Union import numpy as npclass MultiTaskTrainer(Trainer): """Custom Trainer class for handling multiple tasks""" def init(self, *args, **kwargs): # Extract task-specific metrics before passing remaining args to parent self.metrics = kwargs.pop('metrics', {}) super().init(*args, **kwargs)
def compute_metrics(self, eval_pred):
"""Compute metrics for all tasks"""
predictions, labels = eval_pred
# First element in labels is assumed to be task_ids
task_ids = labels[:, 0].tolist()
# Remove task_ids from labels
labels = labels[:, 1:]
# Convert logits to predictions
predictions = np.argmax(predictions, axis=-1)
# Decode predictions and labels
decoded_preds = self.tokenizer.batch_decode(predictions, skip_special_tokens=True)
decoded_labels = self.tokenizer.batch_decode(labels, skip_special_tokens=True)
# Group predictions by task
task_predictions: Dict[str, List] = {
"text_generation": [],
"question_answering": [],
"summarization": []
}
task_labels: Dict[str, List] = {
"text_generation": [],
"question_answering": [],
"summarization": []
}
# Map task IDs to task types
task_map = {0: "text_generation", 1: "question_answering", 2: "summarization"}
# Group predictions and labels by task
for pred, label, task_id in zip(decoded_preds, decoded_labels, task_ids):
task_type = task_map[task_id]
task_predictions[task_type].append(pred)
task_labels[task_type].append(label)
# Compute metrics for each task
results = {}
for task_type in task_predictions:
if len(task_predictions[task_type]) > 0:
task_metric = self.metrics[task_type].compute(
predictions=task_predictions[task_type],
references=task_labels[task_type]
)
# Add task prefix to metric names
results.update({
f"{task_type}_{k}": v
for k, v in task_metric.items()
})
return results
class MultiTaskDataset: """Dataset class that handles multiple tasks""" def init(self, dataset, tokenizer, max_length=512): self.dataset = dataset self.tokenizer = tokenizer self.max_length = max_length
def __len__(self):
return len(self.dataset)
def __getitem__(self, idx):
item = self.dataset[idx]
# Determine task type from dataset
task_type = item['task_type'] # Assume dataset has this field
task_id = {
"text_generation": 0,
"question_answering": 1,
"summarization": 2
}[task_type]
# Encode input and output
inputs = self.tokenizer(
item['input_text'],
truncation=True,
max_length=self.max_length,
padding='max_length',
return_tensors='pt'
)
outputs = self.tokenizer(
item['output_text'],
truncation=True,
max_length=self.max_length,
padding='max_length',
return_tensors='pt'
)
# Add task_id as first token of labels
labels = outputs['input_ids'].clone()
labels = torch.cat([torch.tensor([[task_id]]), labels], dim=1)
return {
'input_ids': inputs['input_ids'].squeeze(),
'attention_mask': inputs['attention_mask'].squeeze(),
'labels': labels.squeeze()
}
def main(): # Load model and tokenizer model_name = "your-model-name" # Replace with your model name model = AutoModelForSeq2SeqLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name)
# Load metrics
metrics = {
"text_generation": load_metric("bleu"),
"question_answering": load_metric("squad"),
"summarization": load_metric("rouge")
}
# Load dataset
dataset = load_dataset("your-dataset-name")
# Create multi-task datasets
train_dataset = MultiTaskDataset(dataset['train'], tokenizer)
eval_dataset = MultiTaskDataset(dataset['validation'], tokenizer)
# Define training arguments
training_args = TrainingArguments(
output_dir="./results",
evaluation_strategy="epoch",
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
num_train_epochs=3,
logging_dir='./logs',
)
# Initialize the custom trainer
trainer = MultiTaskTrainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
tokenizer=tokenizer,
metrics=metrics
)
# Train and evaluate
trainer.train()
trainer.evaluate()
if name == "main": main()
</details>
### GPTQ vs. BitsandBytes (NF4)
NF4 is part of the bitsandbytes library,
- **Normalization**: Weights are normalized before quantization, allowing for efficient representation of common values.
- **4-bit Quantization**: Weights are quantized to 4 bits with evenly spaced levels based on normalized weights.
- **Dequantization During Computation**: Although weights are stored in 4-bit format, they are dequantized during computation to enhance performance.
GPTQ stands for Post-Training Quantization,
- **Dynamic Dequantization**: Weights are dynamically dequantized to float16 during inference, which improves performance while keeping memory requirements low.
- **Custom Kernels**: Utilizes specialized kernels for matrix-vector operations, resulting in faster processing speeds compared to other methods like bitsandbytes and GGML.
- **Performance Metrics**: In tests with models like Llama-7B, GPTQ showed lower perplexity (PPL) scores and higher token generation rates than both GGML and NF4
#### Skip Connections vs. Residual
there are two fundamental ways that one could use skip connections through different non-sequential layers:
a) **addition** as in residual architectures,
b) **concatenation** as in densely connected architectures.
<img src="https://theaisummer.com/static/8d19d048cd68d6dce362e025cf3b635a/1ac66/skip-connection.png"/>
<img src="https://theaisummer.com/static/b8156f7a258e0c46eb1e5e7b6bb591bf/ad12c/resnet-concatenation.png" />