docs: Expand training customization examples

docs/source/customization.md

@@ -1,6 +1,6 @@
 # Training customization
 
-TRL is designed with modularity in mind so that users are able to efficiently customize the training loop for their needs. Below are some examples on how you can apply and test different techniques.  Note: Although these examples use the DPOTrainer, the customization applies to most (if not all) trainers.
+TRL is designed with modularity in mind so that users are able to efficiently customize the training loop for their needs. Below are examples on how you can apply and test different techniques. Note: Although these examples use the [`DPOTrainer`], these customization methods apply to most (if not all) trainers in TRL.
 
 ## Use different optimizers and schedulers
 
@@ -117,3 +117,152 @@ When training large models, you should better handle the accelerator cache by it
 ```python
 training_args = DPOConfig(..., optimize_device_cache=True)
 ```
+
+## Add custom callbacks
+
+You can customize the training loop by adding callbacks for logging, monitoring, or early stopping. Callbacks allow you to execute custom code at specific points during training.
+
+```python
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer, TrainerCallback
+from trl import DPOConfig, DPOTrainer
+
+
+class CustomLoggingCallback(TrainerCallback):
+    def on_log(self, args, state, control, logs=None, **kwargs):
+        if logs is not None:
+            print(f"Step {state.global_step}: {logs}")
+
+
+model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
+dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train")
+training_args = DPOConfig(output_dir="Qwen2.5-0.5B-DPO")
+
+trainer = DPOTrainer(
+    model=model,
+    args=training_args,
+    train_dataset=dataset,
+    tokenizer=tokenizer,
+    callbacks=[CustomLoggingCallback()],
+)
+trainer.train()
+```
+
+## Add custom evaluation metrics
+
+You can define custom evaluation metrics to track during training. This is useful for monitoring model performance on specific tasks.
+
+```python
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from trl import DPOConfig, DPOTrainer
+
+
+def compute_metrics(eval_preds):
+    # Custom metric computation
+    logits, labels = eval_preds
+    # Add your metric computation here
+    return {"custom_metric": 0.0}
+
+
+model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
+train_dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train")
+eval_dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="test[:10%]")
+training_args = DPOConfig(output_dir="Qwen2.5-0.5B-DPO", eval_strategy="steps", eval_steps=100)
+
+trainer = DPOTrainer(
+    model=model,
+    args=training_args,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    tokenizer=tokenizer,
+    compute_metrics=compute_metrics,
+)
+trainer.train()
+```
+
+## Use mixed precision training
+
+Mixed precision training can significantly speed up training and reduce memory usage. You can enable it by setting `bf16=True` or `fp16=True` in the training config.
+
+```python
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from trl import DPOConfig, DPOTrainer
+
+model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
+dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train")
+
+# Use bfloat16 precision (recommended for modern GPUs)
+training_args = DPOConfig(output_dir="Qwen2.5-0.5B-DPO", bf16=True)
+
+trainer = DPOTrainer(
+    model=model,
+    args=training_args,
+    train_dataset=dataset,
+    tokenizer=tokenizer,
+)
+trainer.train()
+```
+
+Note: Use `bf16=True` for Ampere GPUs (A100, RTX 30xx) or newer, and `fp16=True` for older GPUs.
+
+## Use gradient accumulation
+
+When training with limited GPU memory, gradient accumulation allows you to simulate larger batch sizes by accumulating gradients over multiple steps before updating weights.
+
+```python
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from trl import DPOConfig, DPOTrainer
+
+model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
+dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train")
+
+# Simulate a batch size of 32 with per_device_train_batch_size=4 and gradient_accumulation_steps=8
+training_args = DPOConfig(
+    output_dir="Qwen2.5-0.5B-DPO",
+    per_device_train_batch_size=4,
+    gradient_accumulation_steps=8,
+)
+
+trainer = DPOTrainer(
+    model=model,
+    args=training_args,
+    train_dataset=dataset,
+    tokenizer=tokenizer,
+)
+trainer.train()
+```
+
+## Use a custom data collator
+
+You can provide a custom data collator to handle special data preprocessing or padding strategies.
+
+```python
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from trl import DPOConfig, DPOTrainer
+from trl.trainer.dpo_trainer import DataCollatorForPreference
+
+model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
+dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train")
+training_args = DPOConfig(output_dir="Qwen2.5-0.5B-DPO")
+
+# Create a custom data collator with specific padding token
+data_collator = DataCollatorForPreference(pad_token_id=tokenizer.pad_token_id)
+
+trainer = DPOTrainer(
+    model=model,
+    args=training_args,
+    train_dataset=dataset,
+    tokenizer=tokenizer,
+    data_collator=data_collator,
+)
+trainer.train()
+```