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323 lines
12 KiB
Python
323 lines
12 KiB
Python
# Copyright 2018 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 collections
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import functools
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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 np
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import scipy
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import scipy.special as osp_special
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import jax
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import jax.numpy as jnp
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from jax._src import test_util as jtu
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from jax.scipy import special as lsp_special
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jax.config.parse_flags_with_absl()
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all_shapes = [(), (4,), (3, 4), (3, 1), (1, 4), (2, 1, 4)]
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OpRecord = collections.namedtuple(
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"OpRecord",
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["name", "nargs", "dtypes", "rng_factory", "test_autodiff", "nondiff_argnums", "test_name"])
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def op_record(name, nargs, dtypes, rng_factory, test_grad, nondiff_argnums=(), test_name=None):
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test_name = test_name or name
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nondiff_argnums = tuple(sorted(set(nondiff_argnums)))
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return OpRecord(name, nargs, dtypes, rng_factory, test_grad, nondiff_argnums, test_name)
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float_dtypes = jtu.dtypes.floating
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int_dtypes = jtu.dtypes.integer
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# TODO(phawkins): we should probably separate out the function domains used for
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# autodiff tests from the function domains used for equivalence testing. For
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# example, logit should closely match its scipy equivalent everywhere, but we
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# don't expect numerical gradient tests to pass for inputs very close to 0.
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JAX_SPECIAL_FUNCTION_RECORDS = [
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op_record(
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"beta", 2, float_dtypes, jtu.rand_default, False
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),
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op_record(
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"betaln", 2, float_dtypes, jtu.rand_default, False
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),
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op_record(
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"betainc", 3, float_dtypes, jtu.rand_positive, False
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),
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op_record(
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"gamma", 1, float_dtypes, jtu.rand_default, True
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),
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op_record(
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"digamma", 1, float_dtypes, jtu.rand_positive, True
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),
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op_record(
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"gammainc", 2, float_dtypes, jtu.rand_positive, True
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),
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op_record(
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"gammaincc", 2, float_dtypes, jtu.rand_positive, True
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),
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op_record(
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"gammasgn", 1, float_dtypes, jtu.rand_default, True
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),
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op_record(
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"erf", 1, float_dtypes, jtu.rand_small_positive, True
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),
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op_record(
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"erfc", 1, float_dtypes, jtu.rand_small_positive, True
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),
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op_record(
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"erfinv", 1, float_dtypes, jtu.rand_small_positive, True
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),
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op_record(
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"expit", 1, float_dtypes, jtu.rand_small_positive, True
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),
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# TODO: gammaln has slightly high error.
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op_record(
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"gammaln", 1, float_dtypes, jtu.rand_positive, False
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),
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op_record(
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"factorial", 1, float_dtypes, jtu.rand_default, True
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),
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op_record(
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"fresnel", 1, float_dtypes,
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functools.partial(jtu.rand_default, scale=30), True
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),
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op_record(
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"i0", 1, float_dtypes, jtu.rand_default, True
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),
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op_record(
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# Note: values near zero can fail numeric gradient tests.
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"i0e", 1, float_dtypes,
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functools.partial(jtu.rand_not_small, offset=0.1), True
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),
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op_record(
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"i1", 1, float_dtypes, jtu.rand_default, True
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),
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op_record(
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"i1e", 1, float_dtypes, jtu.rand_default, True
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),
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op_record(
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"logit", 1, float_dtypes,
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functools.partial(jtu.rand_uniform, low=0.05, high=0.95), True),
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op_record(
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"log_ndtr", 1, float_dtypes, jtu.rand_default, True
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),
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op_record(
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"ndtri", 1, float_dtypes,
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functools.partial(jtu.rand_uniform, low=0.0, high=1.0), True,
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),
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op_record(
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"ndtr", 1, float_dtypes, jtu.rand_default, True
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),
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# TODO(phawkins): gradient of entr yields NaNs.
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op_record(
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"entr", 1, float_dtypes, jtu.rand_default, False
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),
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op_record(
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"polygamma", 2, (int_dtypes, float_dtypes),
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jtu.rand_positive, True, (0,)),
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op_record(
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"xlogy", 2, float_dtypes, jtu.rand_positive, True
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),
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op_record(
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"xlog1py", 2, float_dtypes, jtu.rand_default, True
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),
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op_record("zeta", 2, float_dtypes, jtu.rand_positive, True),
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# TODO: float64 produces aborts on gpu, potentially related to use of jnp.piecewise
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op_record(
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"expi", 1, [np.float32],
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functools.partial(jtu.rand_not_small, offset=0.1), True),
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op_record("exp1", 1, [np.float32], jtu.rand_positive, True),
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op_record(
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"expn", 2, (int_dtypes, [np.float32]), jtu.rand_positive, True, (0,)),
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op_record("kl_div", 2, float_dtypes, jtu.rand_positive, True),
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op_record(
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"rel_entr", 2, float_dtypes, jtu.rand_positive, True,
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),
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op_record("poch", 2, float_dtypes, jtu.rand_positive, True),
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op_record(
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"hyp1f1", 3, float_dtypes,
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functools.partial(jtu.rand_uniform, low=0.5, high=30), True
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),
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op_record("log_softmax", 1, float_dtypes, jtu.rand_default, True),
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op_record("softmax", 1, float_dtypes, jtu.rand_default, True),
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]
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def _pretty_special_fun_name(case):
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shapes_str = "_".join("x".join(map(str, shape)) if shape else "s"
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for shape in case["shapes"])
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dtypes_str = "_".join(np.dtype(d).name for d in case["dtypes"])
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name = f"_{case['op']}_{shapes_str}_{dtypes_str}"
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return dict(**case, testcase_name=name)
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class LaxScipySpecialFunctionsTest(jtu.JaxTestCase):
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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(itertools.chain.from_iterable(
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map(_pretty_special_fun_name, jtu.sample_product_testcases(
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[dict(op=rec.name, rng_factory=rec.rng_factory,
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test_autodiff=rec.test_autodiff,
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nondiff_argnums=rec.nondiff_argnums)],
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shapes=itertools.combinations_with_replacement(all_shapes, rec.nargs),
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dtypes=(itertools.combinations_with_replacement(rec.dtypes, rec.nargs)
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if isinstance(rec.dtypes, list) else itertools.product(*rec.dtypes)),
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))
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for rec in JAX_SPECIAL_FUNCTION_RECORDS
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))
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@jax.numpy_rank_promotion('allow') # This test explicitly exercises implicit rank promotion.
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@jax.numpy_dtype_promotion('standard') # This test explicitly exercises dtype promotion
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def testScipySpecialFun(self, op, rng_factory, shapes, dtypes,
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test_autodiff, nondiff_argnums):
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scipy_op = getattr(osp_special, op)
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lax_op = getattr(lsp_special, op)
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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-4)
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if test_autodiff:
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def partial_lax_op(*vals):
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list_args = list(vals)
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for i in nondiff_argnums:
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list_args.insert(i, args[i])
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return lax_op(*list_args)
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assert list(nondiff_argnums) == sorted(set(nondiff_argnums))
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diff_args = [x for i, x in enumerate(args) if i not in nondiff_argnums]
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jtu.check_grads(partial_lax_op, diff_args, order=1,
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atol=.1 if jtu.test_device_matches(["tpu"]) else 1e-3,
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rtol=.1, eps=1e-3)
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@jtu.sample_product(
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n=[0, 1, 2, 3, 10, 50]
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)
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def testScipySpecialFunBernoulli(self, n):
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dtype = jnp.zeros(0).dtype # default float dtype.
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scipy_op = lambda: osp_special.bernoulli(n).astype(dtype)
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lax_op = functools.partial(lsp_special.bernoulli, n)
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args_maker = lambda: []
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self._CheckAgainstNumpy(scipy_op, lax_op, args_maker, atol=0, rtol=1E-5)
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self._CompileAndCheck(lax_op, args_maker, atol=0, rtol=1E-5)
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def testGammaSign(self):
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dtype = jnp.zeros(0).dtype # default float dtype.
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typ = dtype.type
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testcases = [
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(np.arange(-10, 0).astype(dtype), np.array([np.nan] * 10, dtype=dtype)),
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(np.nextafter(np.arange(-5, 0).astype(dtype), typ(-np.inf)),
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np.array([1, -1, 1, -1, 1], dtype=dtype)),
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(np.nextafter(np.arange(-5, 0).astype(dtype), typ(np.inf)),
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np.array([-1, 1, -1, 1, -1], dtype=dtype)),
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(np.arange(0, 10).astype(dtype), np.ones((10,), dtype)),
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(np.nextafter(np.arange(0, 10).astype(dtype), typ(np.inf)),
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np.ones((10,), dtype)),
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(np.nextafter(np.arange(1, 10).astype(dtype), typ(-np.inf)),
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np.ones((9,), dtype)),
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(np.array([-np.inf, -0.0, 0.0, np.inf, np.nan]),
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np.array([np.nan, -1.0, 1.0, 1.0, np.nan]))
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]
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for inp, out in testcases:
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self.assertArraysEqual(out, lsp_special.gammasgn(inp))
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self.assertArraysEqual(out, jnp.sign(lsp_special.gamma(inp)))
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if jtu.parse_version(scipy.__version__) >= (1, 15):
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self.assertArraysEqual(out, osp_special.gammasgn(inp))
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self.assertAllClose(osp_special.gammasgn(inp),
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lsp_special.gammasgn(inp))
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def testNdtriExtremeValues(self):
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# Testing at the extreme values (bounds (0. and 1.) and outside the bounds).
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dtype = jnp.zeros(0).dtype # default float dtype.
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args_maker = lambda: [np.arange(-10, 10).astype(dtype)]
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rtol = 1E-3 if jtu.test_device_matches(["tpu"]) else 1e-5
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self._CheckAgainstNumpy(osp_special.ndtri, lsp_special.ndtri, args_maker, rtol=rtol)
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self._CompileAndCheck(lsp_special.ndtri, args_maker, rtol=rtol)
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def testRelEntrExtremeValues(self):
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# Testing at the extreme values (bounds (0. and 1.) and outside the bounds).
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dtype = jnp.zeros(0).dtype # default float dtype.
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args_maker = lambda: [np.array([-2, -2, -2, -1, -1, -1, 0, 0, 0]).astype(dtype),
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np.array([-1, 0, 1, -1, 0, 1, -1, 0, 1]).astype(dtype)]
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rtol = 1E-3 if jtu.test_device_matches(["tpu"]) else 1e-5
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self._CheckAgainstNumpy(osp_special.rel_entr, lsp_special.rel_entr, args_maker, rtol=rtol)
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self._CompileAndCheck(lsp_special.rel_entr, args_maker, rtol=rtol)
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def testBetaParameterDeprecation(self):
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with self.assertNoWarnings():
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lsp_special.beta(1, 1)
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lsp_special.beta(1, b=1)
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lsp_special.beta(a=1, b=1)
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with self.assertRaises(TypeError):
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lsp_special.beta(x=1, y=1)
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def testExpnTracerLeaks(self):
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# Regression test for https://github.com/jax-ml/jax/issues/26972
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with jax.checking_leaks():
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lsp_special.expi(jnp.ones(()))
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def testExpiDisableJit(self):
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# Regression test for https://github.com/jax-ml/jax/issues/27019
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x = jnp.array([-0.5])
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with jax.disable_jit(True):
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result_nojit = lsp_special.expi(x)
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with jax.disable_jit(False):
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result_jit = lsp_special.expi(x)
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self.assertAllClose(result_jit, result_nojit)
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def testGammaIncBoundaryValues(self):
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dtype = jax.numpy.zeros(0).dtype # default float dtype.
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nan = float('nan')
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inf = float('inf')
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if jtu.parse_version(scipy.__version__) >= (1, 16):
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samples_slice = slice(None)
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else:
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# disable samples that contradict with scipy/scipy#22441
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samples_slice = slice(None, -1)
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args_maker = lambda: [np.array([0, 0, 0, 1, nan, 1, nan, 0, 1, nan][samples_slice]).astype(dtype),
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np.array([0, 1, 2, 0, 1, nan, nan, inf, inf, inf][samples_slice]).astype(dtype)]
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rtol = 1E-3 if jtu.test_device_matches(["tpu"]) else 1e-5
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self._CheckAgainstNumpy(lsp_special.gammainc, osp_special.gammainc, args_maker, rtol=rtol)
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self._CompileAndCheck(lsp_special.gammainc, args_maker, rtol=rtol)
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def testGammaIncCBoundaryValues(self):
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dtype = jax.numpy.zeros(0).dtype # default float dtype.
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nan = float('nan')
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inf = float('inf')
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if jtu.parse_version(scipy.__version__) >= (1, 16):
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samples_slice = slice(None)
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else:
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# disable samples that contradict with scipy/scipy#22441
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samples_slice = slice(None, -1)
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args_maker = lambda: [np.array([0, 0, 0, 1, nan, 1, nan, 0, 1, 1, nan][samples_slice]).astype(dtype),
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np.array([0, 1, 2, 0, 1, nan, nan, inf, inf, -1, inf][samples_slice]).astype(dtype)]
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rtol = 1E-3 if jtu.test_device_matches(["tpu"]) else 1e-5
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self._CheckAgainstNumpy(lsp_special.gammaincc, osp_special.gammaincc, args_maker, rtol=rtol)
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self._CompileAndCheck(lsp_special.gammaincc, args_maker, rtol=rtol)
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if __name__ == "__main__":
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absltest.main(testLoader=jtu.JaxTestLoader())
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