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202 lines
6.9 KiB
Python
202 lines
6.9 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.special as osp_special
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import jax
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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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from jax 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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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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"betaln", 2, float_dtypes, jtu.rand_positive, 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_positive, 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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"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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"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=0.05, high=0.95), 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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]
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class LaxScipySpcialFunctionsTest(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.parameters(itertools.chain.from_iterable(
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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=jtu.if_device_under_test("tpu", .1, 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 = jax.numpy.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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if __name__ == "__main__":
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absltest.main(testLoader=jtu.JaxTestLoader())
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