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93 lines
3.2 KiB
Python
93 lines
3.2 KiB
Python
# Copyright © 2023 Apple Inc.
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import itertools
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import unittest
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import mlx.core as mx
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import mlx_tests
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import numpy as np
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class TestFFT(mlx_tests.MLXTestCase):
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def check_mx_np(self, op, a_np, axes, s):
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with self.subTest(op=op, axes=axes, s=s):
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op_np = getattr(np.fft, op)
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op_mx = getattr(mx.fft, op)
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out_np = op_np(a_np, s=s, axes=axes)
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a_mx = mx.array(a_np)
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out_mx = op_mx(a_mx, s=s, axes=axes)
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self.assertTrue(np.allclose(out_np, out_mx, atol=1e-5, rtol=1e-6))
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def test_fft(self):
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def check_mx_np(op_mx, op_np, a_np, **kwargs):
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out_np = op_np(a_np, **kwargs)
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a_mx = mx.array(a_np)
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out_mx = op_mx(a_mx, **kwargs)
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self.assertTrue(np.allclose(out_np, out_mx, atol=1e-5, rtol=1e-6))
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with mx.stream(mx.cpu):
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r = np.random.rand(100).astype(np.float32)
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i = np.random.rand(100).astype(np.float32)
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a_np = r + 1j * i
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check_mx_np(mx.fft.fft, np.fft.fft, a_np)
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# Check with slicing and padding
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r = np.random.rand(100).astype(np.float32)
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i = np.random.rand(100).astype(np.float32)
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a_np = r + 1j * i
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check_mx_np(mx.fft.fft, np.fft.fft, a_np, n=80)
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check_mx_np(mx.fft.fft, np.fft.fft, a_np, n=120)
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# Check different axes
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r = np.random.rand(100, 100).astype(np.float32)
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i = np.random.rand(100, 100).astype(np.float32)
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a_np = r + 1j * i
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check_mx_np(mx.fft.fft, np.fft.fft, a_np, axis=0)
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check_mx_np(mx.fft.fft, np.fft.fft, a_np, axis=1)
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# Check real fft
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a_np = np.random.rand(100).astype(np.float32)
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check_mx_np(mx.fft.rfft, np.fft.rfft, a_np)
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check_mx_np(mx.fft.rfft, np.fft.rfft, a_np, n=80)
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check_mx_np(mx.fft.rfft, np.fft.rfft, a_np, n=120)
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# Check real inverse
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r = np.random.rand(100, 100).astype(np.float32)
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i = np.random.rand(100, 100).astype(np.float32)
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a_np = r + 1j * i
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check_mx_np(mx.fft.ifft, np.fft.ifft, a_np)
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check_mx_np(mx.fft.ifft, np.fft.ifft, a_np, n=80)
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check_mx_np(mx.fft.ifft, np.fft.ifft, a_np, n=120)
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check_mx_np(mx.fft.irfft, np.fft.irfft, a_np)
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check_mx_np(mx.fft.irfft, np.fft.irfft, a_np, n=80)
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check_mx_np(mx.fft.irfft, np.fft.irfft, a_np, n=120)
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def test_fftn(self):
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with mx.stream(mx.cpu):
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r = np.random.randn(8, 8, 8).astype(np.float32)
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i = np.random.randn(8, 8, 8).astype(np.float32)
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a = r + 1j * i
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axes = [None, (1, 2), (2, 1), (0, 2)]
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shapes = [None, (10, 5), (5, 10)]
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ops = [
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"fft2",
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"ifft2",
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"rfft2",
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"irfft2",
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"fftn",
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"ifftn",
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"rfftn",
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"irfftn",
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]
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for op, ax, s in itertools.product(ops, axes, shapes):
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x = a
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if op in ["rfft2", "rfftn"]:
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x = r
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self.check_mx_np(op, x, axes=ax, s=s)
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if __name__ == "__main__":
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unittest.main()
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