Remove legacy benchmarking code

Author: Frank Odom

tests/test_fft_conv.py

@@ -71,79 +71,3 @@ def test_fft_conv(
     _assert_almost_equal(y0, y1)
     _assert_almost_equal(y0, y2)
     _assert_almost_equal(y1, y2)
-
-
-# def benchmark_conv(
-#     signal: Tensor,
-#     kernel: Tensor,
-#     bias: Tensor,
-#     padding: Union[int, Iterable[int]] = 0,
-#     stride: Union[int, Iterable[int]] = 1,
-#     groups: int = 1,
-# ):
-#     print(f"Signal size: {signal.shape}")
-#     print(f"Kernel size: {kernel.shape}")
-
-#     torch_conv = {1: f.conv1d, 2: f.conv2d, 3: f.conv3d}[signal.ndim - 2]
-#     direct_time = benchmark(
-#         torch_conv,
-#         signal,
-#         kernel,
-#         bias=bias,
-#         padding=padding,
-#         stride=stride,
-#         groups=groups,
-#     )
-#     fourier_time = benchmark(
-#         fft_conv,
-#         signal,
-#         kernel,
-#         bias=bias,
-#         padding=padding,
-#         stride=stride,
-#         groups=groups,
-#     )
-#     print(f"Direct time: {direct_time}")
-#     print(f"Fourier time: {fourier_time}")
-
-#     y0 = torch_conv(
-#         signal, kernel, bias=bias, padding=padding, stride=stride, groups=groups
-#     )
-#     y1 = fft_conv(
-#         signal, kernel, bias=bias, padding=padding, stride=stride, groups=groups
-#     )
-#     abs_error = torch.abs(y0 - y1)
-#     print(f"Output size: {y0.size()}")
-#     print(f"Abs Error Mean: {abs_error.mean():.3E}")
-#     print(f"Abs Error Std Dev: {abs_error.std():.3E}")
-
-
-# print("\n--- 1D Convolution ---")
-# benchmark_conv(
-#     signal=torch.randn(4, 4, 4091),
-#     kernel=torch.randn(6, 2, 1025),
-#     bias=torch.randn(6),
-#     padding=512,
-#     stride=3,
-#     groups=2,
-# )
-
-# print("\n--- 2D Convolution ---")
-# benchmark_conv(
-#     signal=torch.randn(4, 4, 256, 235),
-#     kernel=torch.randn(6, 2, 22, 21),
-#     bias=torch.randn(6),
-#     padding=(9, 10),
-#     stride=(2, 3),
-#     groups=2,
-# )
-
-# print("\n--- 3D Convolution ---")
-# benchmark_conv(
-#     signal=torch.randn(4, 4, 96, 72, 61),
-#     kernel=torch.randn(6, 2, 12, 7, 9),
-#     bias=torch.randn(6),
-#     padding=(2, 3, 4),
-#     stride=(1, 2, 3),
-#     groups=2,
-# )