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Zhongwei Li
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skills/transformers/references/training.md
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500
skills/transformers/references/training.md
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# Training and Fine-Tuning
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## Overview
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Fine-tune pre-trained models on custom datasets using the Trainer API. The Trainer handles training loops, gradient accumulation, mixed precision, logging, and checkpointing.
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## Basic Fine-Tuning Workflow
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### Step 1: Load and Preprocess Data
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```python
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from datasets import load_dataset
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# Load dataset
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dataset = load_dataset("yelp_review_full")
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train_dataset = dataset["train"]
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eval_dataset = dataset["test"]
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# Tokenize
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
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def tokenize_function(examples):
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return tokenizer(
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examples["text"],
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padding="max_length",
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truncation=True,
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max_length=512
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)
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train_dataset = train_dataset.map(tokenize_function, batched=True)
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eval_dataset = eval_dataset.map(tokenize_function, batched=True)
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```
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### Step 2: Load Model
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```python
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from transformers import AutoModelForSequenceClassification
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model = AutoModelForSequenceClassification.from_pretrained(
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"bert-base-uncased",
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num_labels=5 # Number of classes
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)
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```
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### Step 3: Define Metrics
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```python
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import evaluate
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import numpy as np
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metric = evaluate.load("accuracy")
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def compute_metrics(eval_pred):
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logits, labels = eval_pred
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predictions = np.argmax(logits, axis=-1)
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return metric.compute(predictions=predictions, references=labels)
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```
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### Step 4: Configure Training
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```python
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from transformers import TrainingArguments
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training_args = TrainingArguments(
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output_dir="./results",
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eval_strategy="epoch",
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save_strategy="epoch",
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learning_rate=2e-5,
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per_device_train_batch_size=8,
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per_device_eval_batch_size=8,
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num_train_epochs=3,
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weight_decay=0.01,
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logging_dir="./logs",
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logging_steps=10,
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load_best_model_at_end=True,
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metric_for_best_model="accuracy",
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)
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```
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### Step 5: Create Trainer and Train
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```python
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from transformers import Trainer
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trainer = Trainer(
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model=model,
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args=training_args,
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train_dataset=train_dataset,
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eval_dataset=eval_dataset,
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compute_metrics=compute_metrics,
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)
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# Start training
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trainer.train()
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# Evaluate
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results = trainer.evaluate()
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print(results)
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```
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### Step 6: Save Model
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```python
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trainer.save_model("./fine_tuned_model")
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tokenizer.save_pretrained("./fine_tuned_model")
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# Or push to Hub
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trainer.push_to_hub("username/my-finetuned-model")
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```
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## TrainingArguments Parameters
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### Essential Parameters
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**output_dir**: Directory for checkpoints and logs
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```python
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output_dir="./results"
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```
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**num_train_epochs**: Number of training epochs
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```python
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num_train_epochs=3
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```
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**per_device_train_batch_size**: Batch size per GPU/CPU
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```python
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per_device_train_batch_size=8
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```
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**learning_rate**: Optimizer learning rate
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```python
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learning_rate=2e-5 # Common for BERT-style models
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learning_rate=5e-5 # Common for smaller models
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```
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**weight_decay**: L2 regularization
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```python
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weight_decay=0.01
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```
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### Evaluation and Saving
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**eval_strategy**: When to evaluate ("no", "steps", "epoch")
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```python
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eval_strategy="epoch" # Evaluate after each epoch
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eval_strategy="steps" # Evaluate every eval_steps
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```
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**save_strategy**: When to save checkpoints
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```python
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save_strategy="epoch"
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save_strategy="steps"
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save_steps=500
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```
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**load_best_model_at_end**: Load best checkpoint after training
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```python
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load_best_model_at_end=True
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metric_for_best_model="accuracy" # Metric to compare
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```
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### Optimization
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**gradient_accumulation_steps**: Accumulate gradients over multiple steps
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```python
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gradient_accumulation_steps=4 # Effective batch size = batch_size * 4
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```
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**fp16**: Enable mixed precision (NVIDIA GPUs)
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```python
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fp16=True
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```
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**bf16**: Enable bfloat16 (newer GPUs)
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```python
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bf16=True
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```
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**gradient_checkpointing**: Trade compute for memory
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```python
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gradient_checkpointing=True # Slower but uses less memory
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```
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**optim**: Optimizer choice
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```python
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optim="adamw_torch" # Default
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optim="adamw_8bit" # 8-bit Adam (requires bitsandbytes)
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optim="adafactor" # Memory-efficient alternative
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```
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### Learning Rate Scheduling
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**lr_scheduler_type**: Learning rate schedule
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```python
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lr_scheduler_type="linear" # Linear decay
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lr_scheduler_type="cosine" # Cosine annealing
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lr_scheduler_type="constant" # No decay
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lr_scheduler_type="constant_with_warmup"
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```
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**warmup_steps** or **warmup_ratio**: Warmup period
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```python
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warmup_steps=500
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# Or
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warmup_ratio=0.1 # 10% of total steps
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```
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### Logging
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**logging_dir**: TensorBoard logs directory
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```python
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logging_dir="./logs"
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```
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**logging_steps**: Log every N steps
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```python
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logging_steps=10
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```
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**report_to**: Logging integrations
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```python
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report_to=["tensorboard"]
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report_to=["wandb"]
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report_to=["tensorboard", "wandb"]
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```
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### Distributed Training
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**ddp_backend**: Distributed backend
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```python
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ddp_backend="nccl" # For multi-GPU
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```
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**deepspeed**: DeepSpeed config file
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```python
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deepspeed="ds_config.json"
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```
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## Data Collators
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Handle dynamic padding and special preprocessing:
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### DataCollatorWithPadding
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Pad sequences to longest in batch:
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```python
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from transformers import DataCollatorWithPadding
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data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
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trainer = Trainer(
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model=model,
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args=training_args,
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train_dataset=train_dataset,
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data_collator=data_collator,
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)
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```
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### DataCollatorForLanguageModeling
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For masked language modeling:
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```python
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from transformers import DataCollatorForLanguageModeling
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data_collator = DataCollatorForLanguageModeling(
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tokenizer=tokenizer,
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mlm=True,
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mlm_probability=0.15
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)
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```
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### DataCollatorForSeq2Seq
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For sequence-to-sequence tasks:
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```python
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from transformers import DataCollatorForSeq2Seq
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data_collator = DataCollatorForSeq2Seq(
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tokenizer=tokenizer,
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model=model,
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padding=True
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)
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```
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## Custom Training
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### Custom Trainer
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Override methods for custom behavior:
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```python
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from transformers import Trainer
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class CustomTrainer(Trainer):
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def compute_loss(self, model, inputs, return_outputs=False):
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labels = inputs.pop("labels")
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outputs = model(**inputs)
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logits = outputs.logits
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# Custom loss computation
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loss_fct = torch.nn.CrossEntropyLoss(weight=class_weights)
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loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1))
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return (loss, outputs) if return_outputs else loss
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```
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### Custom Callbacks
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Monitor and control training:
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```python
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from transformers import TrainerCallback
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class CustomCallback(TrainerCallback):
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def on_epoch_end(self, args, state, control, **kwargs):
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print(f"Epoch {state.epoch} completed")
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# Custom logic here
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return control
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trainer = Trainer(
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model=model,
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args=training_args,
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train_dataset=train_dataset,
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callbacks=[CustomCallback],
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)
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```
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## Advanced Training Techniques
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### Parameter-Efficient Fine-Tuning (PEFT)
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Use LoRA for efficient fine-tuning:
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```python
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from peft import LoraConfig, get_peft_model
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lora_config = LoraConfig(
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r=16,
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lora_alpha=32,
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target_modules=["query", "value"],
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lora_dropout=0.05,
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bias="none",
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task_type="SEQ_CLS"
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)
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model = get_peft_model(model, lora_config)
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model.print_trainable_parameters() # Shows reduced parameter count
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# Train normally with Trainer
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trainer = Trainer(model=model, args=training_args, ...)
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trainer.train()
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```
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### Gradient Checkpointing
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Reduce memory at cost of speed:
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```python
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model.gradient_checkpointing_enable()
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training_args = TrainingArguments(
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gradient_checkpointing=True,
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...
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)
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```
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### Mixed Precision Training
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```python
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training_args = TrainingArguments(
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fp16=True, # For NVIDIA GPUs with Tensor Cores
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# or
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bf16=True, # For newer GPUs (A100, H100)
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...
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)
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```
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### DeepSpeed Integration
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For very large models:
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```python
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# ds_config.json
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{
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"train_batch_size": 16,
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"gradient_accumulation_steps": 1,
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"optimizer": {
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"type": "AdamW",
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"params": {
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"lr": 2e-5
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}
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},
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"fp16": {
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"enabled": true
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},
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"zero_optimization": {
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"stage": 2
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}
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}
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```
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```python
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training_args = TrainingArguments(
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deepspeed="ds_config.json",
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...
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)
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```
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## Training Tips
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### Hyperparameter Tuning
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Common starting points:
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- **Learning rate**: 2e-5 to 5e-5 for BERT-like models, 1e-4 to 1e-3 for smaller models
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- **Batch size**: 8-32 depending on GPU memory
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- **Epochs**: 2-4 for fine-tuning, more for domain adaptation
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- **Warmup**: 10% of total steps
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Use Optuna for hyperparameter search:
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```python
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def model_init():
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return AutoModelForSequenceClassification.from_pretrained(
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"bert-base-uncased",
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num_labels=5
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)
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def optuna_hp_space(trial):
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return {
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"learning_rate": trial.suggest_float("learning_rate", 1e-5, 5e-5, log=True),
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"per_device_train_batch_size": trial.suggest_categorical("per_device_train_batch_size", [8, 16, 32]),
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"num_train_epochs": trial.suggest_int("num_train_epochs", 2, 5),
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}
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trainer = Trainer(model_init=model_init, args=training_args, ...)
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best_trial = trainer.hyperparameter_search(
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direction="maximize",
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backend="optuna",
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hp_space=optuna_hp_space,
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n_trials=10,
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)
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```
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### Monitoring Training
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Use TensorBoard:
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```bash
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tensorboard --logdir ./logs
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```
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Or Weights & Biases:
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```python
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import wandb
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wandb.init(project="my-project")
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training_args = TrainingArguments(
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report_to=["wandb"],
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...
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)
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```
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### Resume Training
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Resume from checkpoint:
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```python
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trainer.train(resume_from_checkpoint="./results/checkpoint-1000")
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```
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## Common Issues
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**CUDA out of memory:**
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- Reduce batch size
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- Enable gradient checkpointing
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- Use gradient accumulation
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- Use 8-bit optimizers
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**Overfitting:**
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- Increase weight_decay
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- Add dropout
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- Use early stopping
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- Reduce model size or training epochs
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**Slow training:**
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- Increase batch size
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- Enable mixed precision (fp16/bf16)
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- Use multiple GPUs
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- Optimize data loading
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## Best Practices
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1. **Start small**: Test on small dataset subset first
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2. **Use evaluation**: Monitor validation metrics
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3. **Save checkpoints**: Enable save_strategy
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4. **Log extensively**: Use TensorBoard or W&B
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5. **Try different learning rates**: Start with 2e-5
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6. **Use warmup**: Helps training stability
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7. **Enable mixed precision**: Faster training
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8. **Consider PEFT**: For large models with limited resources
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