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https://github.com/ROCm/jax.git
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bbf8129aa6
* Change test tolerance logic not to choose tolerance values based on flags (in particular, --jax_enable_x64). We would like to move away from having global flags to enable 64-bit mode. We therefore need other methods to select test tolerances. Instead, use a per-type default tolerance, and allow tests to pass per-type dictionaries of tolerances as atol and rtol values. Fix up a number of tolerances to make tests pass. * Fix test tolerances. * Fix dtype canonicalization for test tolerances. * Relax core test_vjp tolerance.
111 lines
4.1 KiB
Python
111 lines
4.1 KiB
Python
# Copyright 2019 Google LLC
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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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import unittest
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import numpy as onp
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from absl.testing import absltest
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from absl.testing import parameterized
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from jax import numpy as np
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from jax import test_util as jtu
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from jax.config import config
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config.parse_flags_with_absl()
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float_dtypes = [onp.float32, onp.float64]
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# TODO(b/144573940): onp.complex128 isn't supported by XLA, and the JAX
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# implementation casts to complex64.
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complex_dtypes = [onp.complex64]
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inexact_dtypes = float_dtypes + complex_dtypes
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int_dtypes = [onp.int32, onp.int64]
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bool_dtypes = [onp.bool_]
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all_dtypes = float_dtypes + complex_dtypes + int_dtypes + bool_dtypes
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def _get_fftn_test_axes(shape):
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axes = [[]]
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ndims = len(shape)
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# XLA's FFT op only supports up to 3 innermost dimensions.
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if ndims <= 3: axes.append(None)
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for naxes in range(1, min(ndims, 3) + 1):
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axes.append(range(ndims - naxes, ndims))
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return axes
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class FftTest(jtu.JaxTestCase):
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@parameterized.named_parameters(jtu.cases_from_list(
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{"testcase_name": "_inverse={}_shape={}_axes={}".format(
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inverse, jtu.format_shape_dtype_string(shape, dtype), axes),
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"axes": axes, "shape": shape, "dtype": dtype, "rng_factory": rng_factory,
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"inverse": inverse}
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for inverse in [False, True]
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for rng_factory in [jtu.rand_default]
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for dtype in all_dtypes
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for shape in [(10,), (10, 10), (2, 3, 4), (2, 3, 4, 5)]
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for axes in _get_fftn_test_axes(shape)))
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def testFftn(self, inverse, shape, dtype, axes, rng_factory):
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rng = rng_factory()
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args_maker = lambda: (rng(shape, dtype),)
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np_op = np.fft.ifftn if inverse else np.fft.fftn
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onp_op = onp.fft.ifftn if inverse else onp.fft.fftn
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np_fn = lambda a: np_op(a, axes=axes)
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onp_fn = lambda a: onp_op(a, axes=axes)
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# Numpy promotes to complex128 aggressively.
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self._CheckAgainstNumpy(onp_fn, np_fn, args_maker, check_dtypes=False,
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tol=1e-4)
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self._CompileAndCheck(np_fn, args_maker, check_dtypes=True)
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# Test gradient for differentiable types.
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if dtype in inexact_dtypes:
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# TODO(skye): can we be more precise?
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tol = 1e-1
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jtu.check_grads(np_fn, args_maker(), order=1, atol=tol, rtol=tol)
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jtu.check_grads(np_fn, args_maker(), order=2, atol=tol, rtol=tol)
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@parameterized.named_parameters(jtu.cases_from_list(
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{"testcase_name": "_inverse={}".format(inverse),
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"inverse": inverse}
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for inverse in [False, True]))
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def testFftnErrors(self, inverse):
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rng = jtu.rand_default()
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name = 'ifftn' if inverse else 'fftn'
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func = np.fft.ifftn if inverse else np.fft.fftn
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self.assertRaisesRegex(
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ValueError,
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"jax.np.fft.{} only supports 1D, 2D, and 3D FFTs over the innermost axes. "
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"Got axes None with input rank 4.".format(name),
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lambda: func(rng([2, 3, 4, 5], dtype=onp.float64), axes=None))
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self.assertRaisesRegex(
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ValueError,
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"jax.np.fft.{} only supports 1D, 2D, and 3D FFTs over the innermost axes. "
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"Got axes \\[0\\] with input rank 4.".format(name),
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lambda: func(rng([2, 3, 4, 5], dtype=onp.float64), axes=[0]))
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self.assertRaisesRegex(
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ValueError,
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"jax.np.fft.{} does not support repeated axes. Got axes \\[1, 1\\].".format(name),
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lambda: func(rng([2, 3], dtype=onp.float64), axes=[1, 1]))
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self.assertRaises(
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IndexError, lambda: func(rng([2, 3], dtype=onp.float64), axes=[2]))
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if __name__ == "__main__":
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absltest.main()
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