GPU Acceleration & Mixed Precision Training

import torch
import torch.nn as nn
import torch.optim as optim
from torch.cuda.amp import autocast, GradScaler

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# DEVICE SELECTION — works on CUDA, MPS (Mac M1/M2), or CPU
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
if torch.cuda.is_available():
    device = torch.device("cuda")
    print(f"Using GPU: {torch.cuda.get_device_name(0)}")
    print(f"VRAM: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB")
elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
    device = torch.device("mps")  # Apple Silicon
    print("Using Apple Silicon MPS")
else:
    device = torch.device("cpu")
    print("Using CPU — training will be slower")

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# AUTOMATIC MIXED PRECISION (AMP)
# FP32 (32-bit float): used for weight updates — numerically stable
# FP16 (16-bit float): used for forward pass — 2x faster, 2x less VRAM
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

model = nn.Sequential(nn.Linear(512, 256), nn.ReLU(), nn.Linear(256, 10)).to(device)
optimizer = optim.AdamW(model.parameters(), lr=1e-3)
loss_fn = nn.CrossEntropyLoss()
scaler = GradScaler()  # scales loss to prevent fp16 underflow

def train_step_amp(X: torch.Tensor, y: torch.Tensor) -> float:
    """Training step with Automatic Mixed Precision."""
    X, y = X.to(device), y.to(device)
    optimizer.zero_grad()

    # Forward pass in FP16 (autocast automatically switches dtypes)
    with autocast():
        logits = model(X)           # computation in fp16
        loss = loss_fn(logits, y)   # fp32 for numerical stability

    # Backward in FP32 (scaler handles this)
    scaler.scale(loss).backward()   # scale loss to prevent fp16 underflow
    scaler.unscale_(optimizer)      # unscale gradients before clipping
    torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
    scaler.step(optimizer)          # update weights (skips if gradients are NaN/Inf)
    scaler.update()                 # adjust scale factor for next iteration

    return loss.item()

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# GPU MEMORY MANAGEMENT
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

def gpu_info() -> None:
    if torch.cuda.is_available():
        allocated = torch.cuda.memory_allocated() / 1e9
        reserved  = torch.cuda.memory_reserved() / 1e9
        print(f"GPU Memory — Allocated: {allocated:.2f} GB | Reserved: {reserved:.2f} GB")

# Free GPU cache between experiments
torch.cuda.empty_cache()

# gradient checkpointing — trade compute for memory
# Recomputes activations during backward instead of storing them
# Reduces memory by ~sqrt(n_layers), costs ~33% more compute
# from torch.utils.checkpoint import checkpoint
# x = checkpoint(layer, x)  # inside forward()

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# OUT-OF-MEMORY ERROR DEBUGGING
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

def find_max_batch_size(model, input_shape, start=256, device=device):
    """Binary search for max batch size that fits in GPU memory."""
    batch_size = start
    while batch_size > 0:
        try:
            x = torch.randn(batch_size, *input_shape, device=device)
            _ = model(x)
            print(f"✅ Batch size {batch_size} fits in memory")
            return batch_size
        except RuntimeError as e:
            if "out of memory" in str(e):
                torch.cuda.empty_cache()
                batch_size //= 2
                print(f"↓ OOM at {batch_size*2}, trying {batch_size}")
            else:
                raise
    return 1