How does AI Agent perform gradient accumulation and mixed precision training?

An AI Agent performs gradient accumulation and mixed precision training to optimize deep learning model training, especially when dealing with limited GPU memory or large batch sizes. Here’s how each technique works and how they can be combined:

1. Gradient Accumulation

Gradient accumulation is a technique used to simulate a larger batch size by accumulating gradients over multiple smaller mini-batches before performing a single weight update. This is useful when the GPU memory cannot handle the full batch size in one forward and backward pass.

How it works:

Instead of updating the model weights after every mini-batch, the gradients are accumulated (summed) over several iterations.
After accumulating gradients over N mini-batches, the optimizer performs a single parameter update as if it had processed a batch of size N × mini-batch size.
The gradients are typically zeroed out after the update.

Example:

Suppose your GPU can only handle a mini-batch of 8 samples due to memory constraints, but you want to train with an effective batch size of 64. You can accumulate gradients over 8 mini-batches (8 × 8 = 64) and then update the model weights once.

In code (PyTorch-like pseudocode):

optimizer.zero_grad()
for i, (inputs, labels) in enumerate(data_loader):
    outputs = model(inputs)
    loss = criterion(outputs, labels)
    loss.backward()  # Accumulate gradients

    if (i + 1) % accumulation_steps == 0:  # Update every N steps
        optimizer.step()
        optimizer.zero_grad()

2. Mixed Precision Training

Mixed precision training uses both 16-bit (float16) and 32-bit (float32) floating-point numbers during training to speed up computation and reduce memory usage while maintaining model accuracy.

How it works:

Most computations (e.g., matrix multiplications) are performed in float16 for faster processing and lower memory consumption.
Certain critical operations (e.g., weight updates, loss scaling) are kept in float32 to maintain numerical stability.
A loss scaler is often used to prevent underflow (very small gradients becoming zero) when using float16.

Example:

Using mixed precision, the same model can fit larger batches into GPU memory or train faster on Tensor Cores (in NVIDIA GPUs).

In code (PyTorch-like pseudocode with AMP - Automatic Mixed Precision):

scaler = torch.cuda.amp.GradScaler()  # For loss scaling

for inputs, labels in data_loader:
    optimizer.zero_grad()
    with torch.cuda.amp.autocast():  # Automatic float16/float32 switching
        outputs = model(inputs)
        loss = criterion(outputs, labels)
    scaler.scale(loss).backward()  # Scale loss and backpropagate
    scaler.step(optimizer)         # Unscale and update weights
    scaler.update()                # Adjust scaling factor

Combining Gradient Accumulation & Mixed Precision Training

To maximize efficiency, AI Agents often combine both techniques. This allows training with larger effective batch sizes while keeping memory usage low and leveraging faster computation with float16.

Example Workflow:

Use automatic mixed precision (AMP) to speed up forward/backward passes with float16.
Accumulate gradients over multiple mini-batches (e.g., 8 steps).
Perform a single weight update after gradient accumulation.

In pseudocode:

scaler = torch.cuda.amp.GradScaler()
optimizer.zero_grad()
for i, (inputs, labels) in enumerate(data_loader):
    with torch.cuda.amp.autocast():
        outputs = model(inputs)
        loss = criterion(outputs, labels)
    scaler.scale(loss).backward()  # Mixed precision + gradient accumulation

    if (i + 1) % accumulation_steps == 0:
        scaler.step(optimizer)     # Update weights
        scaler.update()
        optimizer.zero_grad()

Recommended Tencent Cloud Services

For implementing such advanced training techniques efficiently, Tencent Cloud offers optimized solutions:

Tencent Cloud TI Platform: Provides managed training environments with built-in support for mixed precision and distributed training.
Tencent Cloud GPU Instances (e.g., GN-series): Equipped with NVIDIA GPUs that support Tensor Cores for fast mixed precision training.
Tencent Cloud TKE (Tencent Kubernetes Engine): Useful for scaling AI workloads with distributed gradient accumulation.
Tencent Cloud Model Training Services: Simplify the setup of complex training pipelines, including automatic mixed precision and gradient accumulation optimizations.

These services help AI Agents train models faster and more efficiently while managing computational resources effectively.