Principle:Huggingface Transformers Training Execution
| Knowledge Sources | |
|---|---|
| Domains | NLP, Training, Deep Learning |
| Last Updated | 2026-02-13 00:00 GMT |
Overview
Training execution is the iterative process of updating model parameters by computing gradients of a loss function with respect to the parameters over batches of training data.
Description
The training loop is the core of the supervised learning process. For each batch of training data, the loop performs a forward pass through the model to compute predictions, calculates a loss that measures the discrepancy between predictions and ground truth, computes gradients via backpropagation, and applies an optimization step to update model weights.
The HuggingFace Trainer abstracts this loop into a managed execution that additionally handles:
- Gradient accumulation -- Simulating larger batch sizes by accumulating gradients over multiple forward-backward passes before stepping the optimizer.
- Mixed-precision training -- Using lower-precision arithmetic (FP16/BF16) for faster computation while maintaining numerical stability.
- Gradient clipping -- Preventing gradient explosion by capping gradient norms.
- Checkpoint resumption -- Restoring optimizer states, scheduler states, and RNG states from a previous checkpoint.
- Distributed synchronization -- Coordinating gradient all-reduce across multiple GPUs or nodes.
- Logging and callbacks -- Emitting training metrics and triggering hooks at configurable intervals.
Usage
Execute training when:
- All preceding steps (data loading, tokenization, model loading, configuration, Trainer initialization) are complete.
- You want to fine-tune or continue pretraining a model.
- You need to resume an interrupted training run from a checkpoint.
Theoretical Basis
The training loop implements mini-batch stochastic gradient descent (SGD) or one of its adaptive variants (Adam, AdamW):
for epoch in range(num_epochs):
for batch in dataloader:
# Forward pass
logits = model(batch.input_ids, batch.attention_mask)
loss = loss_fn(logits, batch.labels)
# Backward pass
loss = loss / gradient_accumulation_steps
loss.backward()
if (step + 1) % gradient_accumulation_steps == 0:
# Gradient clipping
clip_grad_norm_(model.parameters(), max_grad_norm)
# Optimizer step
optimizer.step()
scheduler.step()
optimizer.zero_grad()
step += 1
AdamW update rule:
m_t = beta1 * m_{t-1} + (1 - beta1) * g_t # first moment
v_t = beta2 * v_{t-1} + (1 - beta2) * g_t^2 # second moment
m_hat = m_t / (1 - beta1^t) # bias correction
v_hat = v_t / (1 - beta2^t) # bias correction
theta_t = theta_{t-1} - lr * (m_hat / (sqrt(v_hat) + eps) + lambda * theta_{t-1})
where lambda is the decoupled weight decay coefficient, distinguishing AdamW from the original Adam with L2 regularization.