Implement fft torchop

[bmehta001]

WIP

• Implement aten__fft_r2c, aten__fft_c2r, aten__fft_c2c
  r2c = forwards, could be one-sided
  c2r = backwards/inverse, never one-sided
  c2c could be either forwards/backwards, never one-sided

Must respect normalization method provided - however, op.DFT calls "backwards" normalization, if 'inverse' is set to True, so need to account for normalization being done by op.DFT

When running above functions across multiple axes, need to run FFT in reverse order through op.DFT one-by-one

Currently have issues with:

• c2r has extra parameter of last_dim_size, so must truncate/zero-pad to ensure last dimension size matches last_dim_size -- still debugging this part to avoid triggering errors

#1271

[codecov]

Codecov Report

Attention: Patch coverage is 96.00000% with 2 lines in your changes missing coverage. Please review.

Project coverage is 74.27%. Comparing base (e404922) to head (f2555ca).
Report is 9 commits behind head on main.

Files with missing lines	Patch %	Lines
onnxscript/function_libs/torch_lib/ops/fft.py	96.00%	1 Missing and 1 partial ⚠️

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[titaiwangms]

The error looks like a mismatch of complex (torch) vs real representation (ONNX). Maybe explore

onnxscript/tests/function_libs/torch_lib/ops_test.py

Line 197 in a36ec86

if isinstance(torch_output, torch.Tensor) and torch.is_complex(torch_output):

Or using

onnxscript/tests/function_libs/torch_lib/ops_test.py

Line 22 in a36ec86

2. Set `CREATE_REPRODUCTION_REPORT=1` to create markdown files for reproduction of

[justinchuby]

Output type needs to be converted to Complex Tensor

output doesn't need to be converted. We always use the real repr for complex values in onnx. The torch exporter will keep track of this information.

[justinchuby]

For the errors RuntimeError: out_size == signal_size[i + 1] || out_size == (signal_size[i + 1] / 2) + 1 INTERNAL ASSERT FAILED at "../aten/src/ATen/native/mkl/SpectralOps.cpp":468, please report a bug to PyTorch it's possible to change the test cases because current ones are invalid for pytorch.

[justinchuby]

@bmehta001 not sure why the cla bot is complaining even though you have join the org. Can you comment following the second (company) option?

[justinchuby]

Could you test (pytorch/pytorch#119360)

import torch

from torch import nn


class iRFFTModel(nn.Module):
  def __init__(self):
      super().__init__()

  def forward(self, x):
    return torch.fft.irfft(torch.view_as_complex(x), n=960)

example_tensor = torch.rand(481, 2)
irfft_model = iRFFTModel()
irfft_model_onnx_path = "irfft_model.onnx"
out_torch = irfft_model(example_tensor)
print("torch shape:", out_torch.shape)

torch.onnx.export(
    irfft_model,
    (example_tensor,),
    dynamo=True,
    verify=True
)

(pytorch/pytorch#139246)

import torch
from torch import nn
import torchaudio

class DataCov(nn.Module):
    def __init__(self):
        super(DataCov, self).__init__()

        self.transform = nn.Sequential(
            torchaudio.transforms.MelSpectrogram(
                sample_rate=48000, n_fft=1536, hop_length=768, f_min=20, f_max=20000
            )
        )

    def forward(self, x1):
        return self.transform(x1)


def load_data_cov():
    module = DataCov().to(torch.float32).to("cpu")
    module.eval()
    return module


data_cov = load_data_cov()
x = torch.randn((1, 1, 12 * 48000), dtype=torch.float32, device="cpu")
y = data_cov(x)
input_names = ["x"]
output_names = ["output"]

torch.onnx.export(
    data_cov,
    (x,),
    dynamo=True,
    verify=True,
)

(pytorch/pytorch#113444)

import torch
import torch.nn as nn


def fftconv(u, k, D):
    seqlen = u.shape[-1]
    fft_size = 2 * seqlen

    k_f = torch.fft.rfft(k, n=fft_size) / fft_size
    u_f = torch.fft.rfft(u.to(dtype=k.dtype), n=fft_size)

    if len(u.shape) > 3: k_f = k_f.unsqueeze(1)
    y = torch.fft.irfft(u_f * k_f, n=fft_size, norm='forward')[..., :seqlen]

    out = y + u * D.unsqueeze(-1)
    return out.to(dtype=u.dtype)


class Filter(nn.Module):
    def forward(self, x, k=None, bias=None):
        y = fftconv(x, k, bias)
        return y



filter = Filter().eval()
x_input, k_input, bias_input = torch.rand(1, 512, 1024), torch.rand(512, 1024), torch.rand(512)
export_output = torch.onnx.export(filter, (x_input, k_input, bias_input), dynamo=True, verify=True)

[bmehta001]

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[justinchuby]

I am going to take the liberty to merge this PR so that the main improvements can be made available. Please feel free to create follow ups when you get a chance to look at the pytorch examples. Thanks for the great work!

[justinchuby]

Note to self: this should be added back in a follow up