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135 lines
4.8 KiB
Python
135 lines
4.8 KiB
Python
# Copyright 2021 The JAX Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# https://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import itertools
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import numpy as np
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from absl.testing import absltest
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import jax
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from jax._src import config
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from jax._src import test_util as jtu
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import jax.scipy.fft as jsp_fft
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import scipy.fft as osp_fft
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jax.config.parse_flags_with_absl()
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float_dtypes = jtu.dtypes.floating
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real_dtypes = float_dtypes + jtu.dtypes.integer + jtu.dtypes.boolean
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def _get_dctn_test_axes(shape):
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axes = [[]]
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ndims = len(shape)
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axes.append(None)
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for naxes in range(1, min(ndims, 3) + 1):
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axes.extend(itertools.combinations(range(ndims), naxes))
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for index in range(1, ndims + 1):
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axes.append((-index,))
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return axes
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def _get_dctn_test_s(shape, axes):
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s_list = [None]
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if axes is not None:
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s_list.extend(itertools.product(*[[shape[ax]+i for i in range(-shape[ax]+1, shape[ax]+1)] for ax in axes]))
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return s_list
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class LaxBackedScipyFftTests(jtu.JaxTestCase):
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"""Tests for LAX-backed scipy.fft implementations"""
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@jtu.sample_product(
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dtype=real_dtypes,
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shape=[(10,), (2, 5)],
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n=[None, 1, 7, 13, 20],
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axis=[-1, 0],
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norm=[None, 'ortho', 'backward'],
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)
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def testDct(self, shape, dtype, n, axis, norm):
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rng = jtu.rand_default(self.rng())
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args_maker = lambda: (rng(shape, dtype),)
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jnp_fn = lambda a: jsp_fft.dct(a, n=n, axis=axis, norm=norm)
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np_fn = lambda a: osp_fft.dct(a, n=n, axis=axis, norm=norm)
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self._CheckAgainstNumpy(np_fn, jnp_fn, args_maker, check_dtypes=False,
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tol=1e-4)
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self._CompileAndCheck(jnp_fn, args_maker, atol=1e-4)
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@jtu.sample_product(
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[dict(shape=shape, axes=axes, s=s)
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for shape in [(10,), (10, 10), (9,), (2, 3, 4), (2, 3, 4, 5)]
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for axes in _get_dctn_test_axes(shape)
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for s in _get_dctn_test_s(shape, axes)],
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dtype=real_dtypes,
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norm=[None, 'ortho', 'backward'],
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)
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def testDctn(self, shape, dtype, s, axes, norm):
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rng = jtu.rand_default(self.rng())
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args_maker = lambda: (rng(shape, dtype),)
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jnp_fn = lambda a: jsp_fft.dctn(a, s=s, axes=axes, norm=norm)
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np_fn = lambda a: osp_fft.dctn(a, s=s, axes=axes, norm=norm)
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self._CheckAgainstNumpy(np_fn, jnp_fn, args_maker, check_dtypes=False,
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tol=1e-4)
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self._CompileAndCheck(jnp_fn, args_maker, atol=1e-4)
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@jtu.sample_product(
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dtype=real_dtypes,
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shape=[(10,), (2, 5)],
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n=[None, 1, 7, 13, 20],
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axis=[-1, 0],
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norm=[None, 'ortho', 'backward'],
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)
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# TODO(phawkins): these tests are failing on T4 GPUs in CI with a
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# CUDA_ERROR_ILLEGAL_ADDRESS.
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@jtu.skip_on_devices("cuda")
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def testiDct(self, shape, dtype, n, axis, norm):
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rng = jtu.rand_default(self.rng())
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args_maker = lambda: (rng(shape, dtype),)
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jnp_fn = lambda a: jsp_fft.idct(a, n=n, axis=axis, norm=norm)
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np_fn = lambda a: osp_fft.idct(a, n=n, axis=axis, norm=norm)
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self._CheckAgainstNumpy(np_fn, jnp_fn, args_maker, check_dtypes=False,
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tol=1e-4)
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self._CompileAndCheck(jnp_fn, args_maker, atol=1e-4)
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@jtu.sample_product(
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[dict(shape=shape, axes=axes, s=s)
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for shape in [(10,), (10, 10), (9,), (2, 3, 4), (2, 3, 4, 5)]
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for axes in _get_dctn_test_axes(shape)
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for s in _get_dctn_test_s(shape, axes)],
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dtype=real_dtypes,
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norm=[None, 'ortho', 'backward'],
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)
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# TODO(phawkins): these tests are failing on T4 GPUs in CI with a
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# CUDA_ERROR_ILLEGAL_ADDRESS.
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@jtu.skip_on_devices("cuda")
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def testiDctn(self, shape, dtype, s, axes, norm):
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rng = jtu.rand_default(self.rng())
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args_maker = lambda: (rng(shape, dtype),)
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jnp_fn = lambda a: jsp_fft.idctn(a, s=s, axes=axes, norm=norm)
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np_fn = lambda a: osp_fft.idctn(a, s=s, axes=axes, norm=norm)
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self._CheckAgainstNumpy(np_fn, jnp_fn, args_maker, check_dtypes=False,
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tol=1e-4)
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self._CompileAndCheck(jnp_fn, args_maker, atol=1e-4)
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def testIdctNormalizationPrecision(self):
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# reported in https://github.com/jax-ml/jax/issues/23895
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if not config.enable_x64.value:
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raise self.skipTest("requires jax_enable_x64=true")
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x = np.ones(3, dtype="float64")
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n = 10
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expected = osp_fft.idct(x, n=n, type=2)
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actual = jsp_fft.idct(x, n=n, type=2)
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self.assertArraysAllClose(actual, expected, atol=1e-14)
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if __name__ == "__main__":
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absltest.main(testLoader=jtu.JaxTestLoader())
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