# Compute weight gradients with accumulation
original_grad = weight_grad.clone()

deep_gemm.wgrad_gemm_fp8_fp8_fp32_nt(grad_input, act_input, weight_grad)

# Verify: grad_output^T @ activations + original_grad
reference_update = grad_output.float().t() @ activations.float()
expected_grad = original_grad + reference_update

error = torch.abs(weight_grad - expected_grad).max().item()
relative_error = error / torch.abs(expected_grad).max().item()

print(f"Weight gradient error: {error:.6f}")
print(f"Relative error: {relative_error*100:.3f}%")
print("✓ Weight gradient computation successful!")