Optimize model architecture and GPU kernels for maximum computational efficiency.

Operator Fusion

torch.compile

import torch

model = YourTransformerModel()

# Compile with maximum optimization
compiled_model = torch.compile(
    model,
    mode="max-autotune",  # Finds best kernel configurations
    fullgraph=True        # Compile entire model graph
)

# Typically 2-4x speedup for transformers
# First call is slow (compilation), subsequent calls are fast
output = compiled_model(input_data)

Custom CUDA Kernels

import triton
import triton.language as tl

@triton.jit
def fused_softmax_kernel(
    input_ptr, output_ptr,
    n_cols, BLOCK_SIZE: tl.constexpr
):
    row_idx = tl.program_id(0)
    row_start = row_idx * n_cols
    
    col_offsets = tl.arange(0, BLOCK_SIZE)
    mask = col_offsets < n_cols
    
    # Load row
    row = tl.load(input_ptr + row_start + col_offsets, mask=mask, other=-float('inf'))
    
    # Compute softmax in single pass
    row_max = tl.max(row, axis=0)
    row_exp = tl.exp(row - row_max)
    row_sum = tl.sum(row_exp, axis=0)
    softmax_output = row_exp / row_sum
    
    tl.store(output_ptr + row_start + col_offsets, softmax_output, mask=mask)

def softmax(x):
    n_rows, n_cols = x.shape
    output = torch.empty_like(x)
    grid = (n_rows,)
    fused_softmax_kernel[grid](x, output, n_cols, BLOCK_SIZE=triton.next_power_of_2(n_cols))
    return output

Memory Optimization

Gradient Checkpointing

from torch.utils.checkpoint import checkpoint

class MemoryEfficientTransformer(nn.Module):
    def __init__(self, num_layers):
        super().__init__()
        self.layers = nn.ModuleList([TransformerLayer() for _ in range(num_layers)])
    
    def forward(self, x):
        for layer in self.layers:
            # Recompute activations during backward instead of storing
            x = checkpoint(layer, x, use_reentrant=False)
        return x

# Trade-off: 40% memory reduction, 20-30% compute overhead
# Required for training large models on limited VRAM

Mixed Precision Training

from torch.cuda.amp import autocast, GradScaler

scaler = GradScaler()

for batch in dataloader:
    optimizer.zero_grad()
    
    with autocast(dtype=torch.bfloat16):  # BF16 on Ampere+
        output = model(batch)
        loss = criterion(output)
    
    # Scale gradients to prevent underflow
    scaler.scale(loss).backward()
    scaler.unscale_(optimizer)
    
    # Clip gradients
    torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
    
    scaler.step(optimizer)
    scaler.update()

# BF16: 2x memory reduction, 2x compute speedup on Ampere+