FP16 half precision for p100

p100.py

import torch

def optimize_model_for_p100(model, use_amp=True):
    # Move model to GPU
    model = model.cuda()
    
    # Use FP16 precision (P100 supports this, but without Tensor Cores)
    if use_amp:
        model = model.half()
        
    # Set model to evaluation mode
    model = model.eval()
    
    # Disable gradient computation for inference
    torch.set_grad_enabled(False)
    
    # Optional: Script/trace the model for better performance
    try:
        scripted_model = torch.jit.script(model)
        return scripted_model
    except Exception as e:
        print(f"Model scripting failed: {e}")
        return model

# Example usage
def inference_pipeline(model, input_data, batch_size=32):
    # Optimize model for P100
    optimized_model = optimize_model_for_p100(model)
    
    # Process data in batches to manage memory
    results = []
    for i in range(0, len(input_data), batch_size):
        batch = input_data[i:i+batch_size].cuda()
        if optimized_model.half():
            batch = batch.half()  # Convert inputs to match model precision
        
        # Use CUDA events for timing
        start = torch.cuda.Event(enable_timing=True)
        end = torch.cuda.Event(enable_timing=True)
        
        start.record()
        with torch.cuda.amp.autocast(enabled=True):  # Use AMP if available
            output = optimized_model(batch)
        end.record()
        
        torch.cuda.synchronize()
        print(f"Batch inference time: {start.elapsed_time(end):.2f} ms")
        
        results.append(output.cpu())  # Move results back to CPU
    
    return torch.cat(results, dim=0)