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https://github.com/ml-explore/mlx.git
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324 lines
11 KiB
Python
324 lines
11 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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try:
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import torch
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has_torch = True
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except ImportError as e:
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has_torch = False
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class TestFFT(mlx_tests.MLXTestCase):
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def check_mx_np(self, op_mx, op_np, a_np, atol=1e-5, rtol=1e-6, **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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np.testing.assert_allclose(out_np, out_mx, atol=atol, rtol=rtol)
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def test_fft(self):
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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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self.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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self.check_mx_np(mx.fft.fft, np.fft.fft, a_np, n=80)
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self.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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self.check_mx_np(mx.fft.fft, np.fft.fft, a_np, axis=0)
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self.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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self.check_mx_np(mx.fft.rfft, np.fft.rfft, a_np)
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self.check_mx_np(mx.fft.rfft, np.fft.rfft, a_np, n=80)
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self.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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self.check_mx_np(mx.fft.ifft, np.fft.ifft, a_np)
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self.check_mx_np(mx.fft.ifft, np.fft.ifft, a_np, n=80)
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self.check_mx_np(mx.fft.ifft, np.fft.ifft, a_np, n=120)
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x = np.fft.rfft(np.real(a_np))
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self.check_mx_np(mx.fft.irfft, np.fft.irfft, x)
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def test_fftn(self):
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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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if ax is None and s is not None:
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continue
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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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elif op == "irfft2":
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x = np.ascontiguousarray(np.fft.rfft2(r, axes=ax, s=s))
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elif op == "irfftn":
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x = np.ascontiguousarray(np.fft.rfftn(r, axes=ax, s=s))
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mx_op = getattr(mx.fft, op)
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np_op = getattr(np.fft, op)
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self.check_mx_np(mx_op, np_op, x, axes=ax, s=s)
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def _run_ffts(self, shape, atol=1e-4, rtol=1e-4):
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np.random.seed(9)
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r = np.random.rand(*shape).astype(np.float32)
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i = np.random.rand(*shape).astype(np.float32)
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a_np = r + 1j * i
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self.check_mx_np(mx.fft.fft, np.fft.fft, a_np, atol=atol, rtol=rtol)
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self.check_mx_np(mx.fft.ifft, np.fft.ifft, a_np, atol=atol, rtol=rtol)
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self.check_mx_np(mx.fft.rfft, np.fft.rfft, r, atol=atol, rtol=rtol)
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ia_np = np.fft.rfft(r)
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self.check_mx_np(
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mx.fft.irfft, np.fft.irfft, ia_np, atol=atol, rtol=rtol, n=shape[-1]
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)
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self.check_mx_np(mx.fft.irfft, np.fft.irfft, ia_np, atol=atol, rtol=rtol)
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def test_fft_shared_mem(self):
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nums = np.concatenate(
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[
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# small radix
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np.arange(2, 14),
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# powers of 2
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[2**k for k in range(4, 13)],
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# stockham
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[3 * 3 * 3, 3 * 11, 11 * 13 * 2, 7 * 4 * 13 * 11, 13 * 13 * 11],
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# rader
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[17, 23, 29, 17 * 8 * 3, 23 * 2, 1153, 1982],
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# bluestein
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[47, 83, 17 * 17],
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# large stockham
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[3159, 3645, 3969, 4004],
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]
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)
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for batch_size in (1, 3, 32):
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for num in nums:
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atol = 1e-4 if num < 1025 else 1e-3
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self._run_ffts((batch_size, num), atol=atol)
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@unittest.skip("Too slow for CI but useful for local testing.")
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def test_fft_exhaustive(self):
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nums = range(2, 4097)
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for batch_size in (1, 3, 32):
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for num in nums:
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print(num)
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atol = 1e-4 if num < 1025 else 1e-3
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self._run_ffts((batch_size, num), atol=atol)
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def test_fft_big_powers_of_two(self):
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# TODO: improve precision on big powers of two on GPU
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for k in range(12, 17):
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self._run_ffts((3, 2**k), atol=1e-3)
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for k in range(17, 20):
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self._run_ffts((3, 2**k), atol=1e-2)
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def test_fft_large_numbers(self):
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numbers = [
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1037, # prime > 2048
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18247, # medium size prime factors
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1259 * 11, # large prime factors
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7883, # large prime
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3**8, # large stockham decomposable
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3109, # bluestein
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4006, # large rader
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]
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for large_num in numbers:
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self._run_ffts((1, large_num), atol=1e-3)
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def test_fft_contiguity(self):
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r = np.random.rand(4, 8).astype(np.float32)
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i = np.random.rand(4, 8).astype(np.float32)
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a_np = r + 1j * i
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a_mx = mx.array(a_np)
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# non-contiguous in the FFT dim
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out_mx = mx.fft.fft(a_mx[:, ::2])
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out_np = np.fft.fft(a_np[:, ::2])
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np.testing.assert_allclose(out_np, out_mx, atol=1e-5, rtol=1e-5)
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# non-contiguous not in the FFT dim
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out_mx = mx.fft.fft(a_mx[::2])
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out_np = np.fft.fft(a_np[::2])
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np.testing.assert_allclose(out_np, out_mx, atol=1e-5, rtol=1e-5)
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out_mx = mx.broadcast_to(mx.reshape(mx.transpose(a_mx), (4, 8, 1)), (4, 8, 16))
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out_np = np.broadcast_to(np.reshape(np.transpose(a_np), (4, 8, 1)), (4, 8, 16))
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np.testing.assert_allclose(out_np, out_mx, atol=1e-5, rtol=1e-5)
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out2_mx = mx.fft.fft(mx.abs(out_mx) + 4)
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out2_np = np.fft.fft(np.abs(out_np) + 4)
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np.testing.assert_allclose(out2_mx, out2_np, atol=1e-5, rtol=1e-5)
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b_np = np.array([[0, 1, 2, 3]])
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out_mx = mx.abs(mx.fft.fft(mx.tile(mx.reshape(mx.array(b_np), (1, 4)), (4, 1))))
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out_np = np.abs(np.fft.fft(np.tile(np.reshape(np.array(b_np), (1, 4)), (4, 1))))
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np.testing.assert_allclose(out_mx, out_np, atol=1e-5, rtol=1e-5)
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def test_fft_into_ifft(self):
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n_fft = 8193
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mx.random.seed(0)
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segment = mx.random.normal(shape=[1, n_fft]) + 1j * mx.random.normal(
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shape=(1, n_fft)
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)
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segment = mx.fft.fft(segment, n=n_fft)
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r = mx.fft.ifft(segment, n=n_fft)
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r_np = np.fft.ifft(segment, n=n_fft)
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self.assertTrue(np.allclose(r, r_np, atol=1e-5, rtol=1e-5))
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def test_fft_throws(self):
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x = mx.array(3.0)
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with self.assertRaises(ValueError):
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mx.fft.irfftn(x)
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def test_fftshift(self):
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# Test 1D arrays
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r = np.random.rand(100).astype(np.float32)
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self.check_mx_np(mx.fft.fftshift, np.fft.fftshift, r)
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# Test with specific axis
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r = np.random.rand(4, 6).astype(np.float32)
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self.check_mx_np(mx.fft.fftshift, np.fft.fftshift, r, axes=[0])
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self.check_mx_np(mx.fft.fftshift, np.fft.fftshift, r, axes=[1])
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self.check_mx_np(mx.fft.fftshift, np.fft.fftshift, r, axes=[0, 1])
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# Test with negative axes
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self.check_mx_np(mx.fft.fftshift, np.fft.fftshift, r, axes=[-1])
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# Test with odd lengths
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r = np.random.rand(5, 7).astype(np.float32)
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self.check_mx_np(mx.fft.fftshift, np.fft.fftshift, r)
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self.check_mx_np(mx.fft.fftshift, np.fft.fftshift, r, axes=[0])
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# Test with complex input
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r = np.random.rand(8, 8).astype(np.float32)
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i = np.random.rand(8, 8).astype(np.float32)
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c = r + 1j * i
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self.check_mx_np(mx.fft.fftshift, np.fft.fftshift, c)
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def test_ifftshift(self):
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# Test 1D arrays
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r = np.random.rand(100).astype(np.float32)
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self.check_mx_np(mx.fft.ifftshift, np.fft.ifftshift, r)
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# Test with specific axis
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r = np.random.rand(4, 6).astype(np.float32)
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self.check_mx_np(mx.fft.ifftshift, np.fft.ifftshift, r, axes=[0])
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self.check_mx_np(mx.fft.ifftshift, np.fft.ifftshift, r, axes=[1])
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self.check_mx_np(mx.fft.ifftshift, np.fft.ifftshift, r, axes=[0, 1])
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# Test with negative axes
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self.check_mx_np(mx.fft.ifftshift, np.fft.ifftshift, r, axes=[-1])
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# Test with odd lengths
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r = np.random.rand(5, 7).astype(np.float32)
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self.check_mx_np(mx.fft.ifftshift, np.fft.ifftshift, r)
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self.check_mx_np(mx.fft.ifftshift, np.fft.ifftshift, r, axes=[0])
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# Test with complex input
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r = np.random.rand(8, 8).astype(np.float32)
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i = np.random.rand(8, 8).astype(np.float32)
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c = r + 1j * i
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self.check_mx_np(mx.fft.ifftshift, np.fft.ifftshift, c)
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def test_fftshift_errors(self):
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# Test invalid axes
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x = mx.array(np.random.rand(4, 4).astype(np.float32))
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with self.assertRaises(ValueError):
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mx.fft.fftshift(x, axes=[2])
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with self.assertRaises(ValueError):
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mx.fft.fftshift(x, axes=[-3])
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# Test empty array
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x = mx.array([])
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self.assertTrue(mx.array_equal(mx.fft.fftshift(x), x))
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@unittest.skipIf(not has_torch, "requires PyTorch")
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def test_fft_grads(self):
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real = [True, False]
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inverse = [True, False]
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axes = [
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(-1,),
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(-2, -1),
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]
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shapes = [
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(4, 4),
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(2, 4),
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(2, 7),
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(7, 7),
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]
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mxffts = {
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(True, True): mx.fft.irfftn,
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(True, False): mx.fft.rfftn,
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(False, True): mx.fft.ifftn,
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(False, False): mx.fft.fftn,
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}
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tffts = {
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(True, True): torch.fft.irfftn,
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(True, False): torch.fft.rfftn,
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(False, True): torch.fft.ifftn,
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(False, False): torch.fft.fftn,
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}
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for r, i, ax, sh in itertools.product(real, inverse, axes, shapes):
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def f(x):
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y = mxffts[r, i](x)
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return (mx.abs(y) ** 2).sum()
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def g(x):
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y = tffts[r, i](x)
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return (torch.abs(y) ** 2).sum()
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if r and not i:
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x = mx.random.normal(sh)
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else:
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x = mx.random.normal((*sh, 2)).view(mx.complex64).squeeze()
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fx = f(x)
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gx = g(torch.tensor(x))
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self.assertLess((fx - gx).abs().max() / gx.abs().mean(), 1e-4)
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dfdx = mx.grad(f)(x)
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dgdx = torch.func.grad(g)(torch.tensor(x))
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self.assertLess((dfdx - dgdx).abs().max() / dgdx.abs().mean(), 1e-4)
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if __name__ == "__main__":
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mlx_tests.MLXTestRunner()
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