mirror of
https://github.com/ROCm/jax.git
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fffdb2daa8
* Make check_dtypes, atol, and rtol keyword-only arguments in jax.test_util APIs. Default to check_dtypes=True. Remove explicit usages of check_dtypes=True from tests. This mostly just removes visual noise from tests. Testing for exact type equality is the sensible default, although there are cases where opting out makes sense. No functional changes intended. * Fix a number of lax reference implementations to preserve types.
182 lines
7.2 KiB
Python
182 lines
7.2 KiB
Python
# Copyright 2018 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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import collections
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import functools
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from functools import partial
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import itertools
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from absl.testing import absltest
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from absl.testing import parameterized
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import numpy as onp
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import scipy.special as osp_special
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from jax import api
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from jax import test_util as jtu
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from jax.scipy import special as lsp_special
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from jax.config import config
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config.parse_flags_with_absl()
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FLAGS = config.FLAGS
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all_shapes = [(), (4,), (3, 4), (3, 1), (1, 4), (2, 1, 4)]
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float_dtypes = [onp.float32, onp.float64]
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complex_dtypes = [onp.complex64]
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int_dtypes = [onp.int32, onp.int64]
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bool_dtypes = [onp.bool_]
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default_dtypes = float_dtypes + int_dtypes
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numeric_dtypes = float_dtypes + complex_dtypes + int_dtypes
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OpRecord = collections.namedtuple(
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"OpRecord",
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["name", "nargs", "dtypes", "rng_factory", "test_autodiff", "test_name"])
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def op_record(name, nargs, dtypes, rng_factory, test_grad, test_name=None):
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test_name = test_name or name
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return OpRecord(name, nargs, dtypes, rng_factory, test_grad, test_name)
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JAX_SPECIAL_FUNCTION_RECORDS = [
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# TODO: digamma has no JVP implemented.
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op_record("betaln", 2, float_dtypes, jtu.rand_positive, False),
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op_record("betainc", 3, float_dtypes, jtu.rand_positive, False),
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op_record("digamma", 1, float_dtypes, jtu.rand_positive, False),
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op_record("gammainc", 2, float_dtypes, jtu.rand_positive, True),
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op_record("gammaincc", 2, float_dtypes, jtu.rand_positive, True),
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op_record("erf", 1, float_dtypes, jtu.rand_small_positive, True),
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op_record("erfc", 1, float_dtypes, jtu.rand_small_positive, True),
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op_record("erfinv", 1, float_dtypes, jtu.rand_small_positive, True),
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op_record("expit", 1, float_dtypes, jtu.rand_small_positive, True),
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# TODO: gammaln has slightly high error.
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op_record("gammaln", 1, float_dtypes, jtu.rand_positive, False),
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op_record("logit", 1, float_dtypes, jtu.rand_uniform, True),
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op_record("log_ndtr", 1, float_dtypes, jtu.rand_default, True),
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op_record("ndtri", 1, float_dtypes, partial(jtu.rand_uniform, low=0.05,
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high=0.95),
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True),
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op_record("ndtr", 1, float_dtypes, jtu.rand_default, True),
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# TODO(phawkins): gradient of entr yields NaNs.
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op_record("entr", 1, float_dtypes, jtu.rand_default, False),
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op_record("xlogy", 2, float_dtypes, jtu.rand_default, True),
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op_record("xlog1py", 2, float_dtypes, jtu.rand_default, True),
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]
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CombosWithReplacement = itertools.combinations_with_replacement
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class LaxBackedScipyTests(jtu.JaxTestCase):
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"""Tests for LAX-backed Scipy implementation."""
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def _GetArgsMaker(self, rng, shapes, dtypes):
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return lambda: [rng(shape, dtype) for shape, dtype in zip(shapes, dtypes)]
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@parameterized.named_parameters(jtu.cases_from_list(
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{"testcase_name": "_inshape={}_axis={}_keepdims={}".format(
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jtu.format_shape_dtype_string(shape, dtype), axis, keepdims),
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# TODO(b/133842870): re-enable when exp(nan) returns NaN on CPU.
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"rng_factory": jtu.rand_some_inf_and_nan
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if jtu.device_under_test() != "cpu"
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else jtu.rand_default,
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"shape": shape, "dtype": dtype,
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"axis": axis, "keepdims": keepdims}
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for shape in all_shapes for dtype in float_dtypes
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for axis in range(-len(shape), len(shape))
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for keepdims in [False, True]))
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@jtu.skip_on_flag("jax_xla_backend", "xrt")
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def testLogSumExp(self, rng_factory, shape, dtype, axis, keepdims):
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rng = rng_factory(self.rng())
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# TODO(mattjj): test autodiff
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def scipy_fun(array_to_reduce):
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return osp_special.logsumexp(array_to_reduce, axis, keepdims=keepdims)
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def lax_fun(array_to_reduce):
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return lsp_special.logsumexp(array_to_reduce, axis, keepdims=keepdims)
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args_maker = lambda: [rng(shape, dtype)]
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self._CheckAgainstNumpy(scipy_fun, lax_fun, args_maker)
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self._CompileAndCheck(lax_fun, args_maker)
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@parameterized.named_parameters(itertools.chain.from_iterable(
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jtu.cases_from_list(
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{"testcase_name": jtu.format_test_name_suffix(
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rec.test_name, shapes, dtypes),
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"rng_factory": rec.rng_factory, "shapes": shapes, "dtypes": dtypes,
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"test_autodiff": rec.test_autodiff,
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"scipy_op": getattr(osp_special, rec.name),
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"lax_op": getattr(lsp_special, rec.name)}
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for shapes in CombosWithReplacement(all_shapes, rec.nargs)
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for dtypes in CombosWithReplacement(rec.dtypes, rec.nargs))
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for rec in JAX_SPECIAL_FUNCTION_RECORDS))
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def testScipySpecialFun(self, scipy_op, lax_op, rng_factory, shapes, dtypes,
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test_autodiff):
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rng = rng_factory(self.rng())
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args_maker = self._GetArgsMaker(rng, shapes, dtypes)
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args = args_maker()
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self.assertAllClose(scipy_op(*args), lax_op(*args), atol=1e-3, rtol=1e-3,
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check_dtypes=False)
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self._CompileAndCheck(lax_op, args_maker, rtol=1e-5)
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if test_autodiff:
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jtu.check_grads(lax_op, args, order=1,
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atol=jtu.if_device_under_test("tpu", .1, 1e-3),
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rtol=.1, eps=1e-3)
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@parameterized.named_parameters(jtu.cases_from_list(
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{"testcase_name": "_inshape={}_d={}".format(
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jtu.format_shape_dtype_string(shape, dtype), d),
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"rng_factory": jtu.rand_positive, "shape": shape, "dtype": dtype,
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"d": d}
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for shape in all_shapes
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for dtype in float_dtypes
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for d in [1, 2, 5]))
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def testMultigammaln(self, rng_factory, shape, dtype, d):
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def scipy_fun(a):
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return osp_special.multigammaln(a, d)
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def lax_fun(a):
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return lsp_special.multigammaln(a, d)
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rng = rng_factory(self.rng())
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args_maker = lambda: [rng(shape, dtype) + (d - 1) / 2.]
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self._CheckAgainstNumpy(scipy_fun, lax_fun, args_maker,
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tol={onp.float32: 1e-3, onp.float64: 1e-14})
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self._CompileAndCheck(lax_fun, args_maker)
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def testIssue980(self):
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x = onp.full((4,), -1e20, dtype=onp.float32)
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self.assertAllClose(onp.zeros((4,), dtype=onp.float32),
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lsp_special.expit(x))
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def testXlogyShouldReturnZero(self):
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self.assertAllClose(lsp_special.xlogy(0., 0.), 0., check_dtypes=False)
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def testGradOfXlogyAtZero(self):
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partial_xlogy = functools.partial(lsp_special.xlogy, 0.)
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self.assertAllClose(api.grad(partial_xlogy)(0.), 0., check_dtypes=False)
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def testXlog1pyShouldReturnZero(self):
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self.assertAllClose(lsp_special.xlog1py(0., -1.), 0., check_dtypes=False)
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def testGradOfXlog1pyAtZero(self):
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partial_xlog1py = functools.partial(lsp_special.xlog1py, 0.)
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self.assertAllClose(api.grad(partial_xlog1py)(-1.), 0., check_dtypes=False)
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if __name__ == "__main__":
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absltest.main()
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