Training technique that enables larger effective batch sizes with limited memory
Core Idea: Gradient Accumulation is a technique that accumulates gradients across multiple forward and backward passes before performing a parameter update, allowing for larger effective batch sizes without increasing memory requirements proportionally.
Key Elements
Technical Implementation
- Performs multiple forward and backward passes without immediate weight updates
- Accumulates (sums) gradients across these passes
- Updates model weights only after the specified number of accumulation steps
- Effectively simulates training with a larger batch size
- Reduces peak memory usage during training
Key Parameters
- Gradient accumulation steps: number of forward/backward passes before update
- Effective batch size = batch size per device × accumulation steps × number of devices
- Learning rate often needs adjustment based on effective batch size
- Optimizer state memory remains constant regardless of accumulation steps
- Can be combined with distributed training for further scaling
Use Cases
- Training with larger effective batch sizes than would fit in memory
- Stabilizing training for models sensitive to batch size
- Compensating for limited GPU memory in consumer hardware
- Maintaining training quality when forced to use small per-device batches
- Enabling higher learning rates through larger effective batches
Common Pitfalls
- Bug in implementation can lead to incorrect gradient scaling
- Increased training time due to sequential batch processing
- Potential divergence if learning rate isn't properly scaled
- Synchronization issues in distributed training environments
- Batch normalization statistics may behave differently than with true large batches
Connections
- Related Concepts: Batch Size (this technique affects effective size), Learning Rate Scaling (often needed with this technique)
- Broader Context: Training Optimization Techniques (one method in this field)
- Applications: Unsloth (implements bug fixes for this), QLoRA (often used together)
- Components: Optimizer (works with the optimizer update step)
References
- "Deep Learning with Limited Numerical Precision" (Gupta et al.)
- Unsloth documentation on gradient accumulation bug fixes
- PyTorch documentation on gradient accumulation
#deeplearning #trainingtechniques #optimization #batchprocessing #memory
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