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604 lines
30 KiB
Python
604 lines
30 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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from functools import partial
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import itertools
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import operator
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from unittest import SkipTest
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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 jax
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import jax.ops
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from jax import lax
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from jax import numpy as jnp
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from jax._src import dtypes
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from jax._src 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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FLAGS = config.FLAGS
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nonempty_nonscalar_array_shapes = [(4,), (3, 4), (3, 1), (1, 4), (2, 1, 4), (2, 3, 4)]
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nonempty_array_shapes = [()] + nonempty_nonscalar_array_shapes
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one_dim_array_shapes = [(1,), (6,), (12,)]
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empty_array_shapes = [(0,), (0, 4), (3, 0),]
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scalar_shapes = [jtu.NUMPY_SCALAR_SHAPE, jtu.PYTHON_SCALAR_SHAPE]
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array_shapes = nonempty_array_shapes + empty_array_shapes
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nonzerodim_shapes = nonempty_nonscalar_array_shapes + empty_array_shapes
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nonempty_shapes = scalar_shapes + nonempty_array_shapes
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all_shapes = scalar_shapes + array_shapes
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float_dtypes = jtu.dtypes.all_floating
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complex_dtypes = jtu.dtypes.complex
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int_dtypes = jtu.dtypes.all_integer
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unsigned_dtypes = jtu.dtypes.all_unsigned
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bool_dtypes = jtu.dtypes.boolean
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default_dtypes = float_dtypes + int_dtypes
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inexact_dtypes = float_dtypes + complex_dtypes
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number_dtypes = float_dtypes + complex_dtypes + int_dtypes + unsigned_dtypes
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all_dtypes = number_dtypes + bool_dtypes
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python_scalar_dtypes = [jnp.bool_, jnp.int_, jnp.float_, jnp.complex_]
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# uint64 is problematic because with any uint type it promotes to float:
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int_dtypes_no_uint64 = [d for d in int_dtypes + unsigned_dtypes if d != np.uint64]
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def _valid_dtypes_for_shape(shape, dtypes):
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# Not all (shape, dtype) pairs are valid. In particular, Python scalars only
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# have one type in each category (float, bool, etc.)
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if shape is jtu.PYTHON_SCALAR_SHAPE:
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return [t for t in dtypes if t in python_scalar_dtypes]
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return dtypes
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OpRecord = collections.namedtuple(
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"OpRecord",
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["name", "nargs", "dtypes", "shapes", "rng_factory", "diff_modes",
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"test_name", "check_dtypes", "tolerance", "inexact", "kwargs"])
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def op_record(name, nargs, dtypes, shapes, rng_factory, diff_modes,
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test_name=None, check_dtypes=True,
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tolerance=None, inexact=False, kwargs=None):
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test_name = test_name or name
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return OpRecord(name, nargs, dtypes, shapes, rng_factory, diff_modes,
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test_name, check_dtypes, tolerance, inexact, kwargs)
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JAX_ONE_TO_ONE_OP_RECORDS = [
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op_record("abs", 1, all_dtypes,
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all_shapes, jtu.rand_default, ["rev"]),
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op_record("add", 2, all_dtypes, all_shapes, jtu.rand_default, ["rev"]),
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op_record("ceil", 1, float_dtypes, all_shapes, jtu.rand_default, []),
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op_record("ceil", 1, int_dtypes + unsigned_dtypes, all_shapes,
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jtu.rand_default, [], check_dtypes=False),
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op_record("conj", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"]),
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op_record("equal", 2, all_dtypes, all_shapes, jtu.rand_some_equal, []),
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op_record("exp", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"],
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inexact=True),
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op_record("fabs", 1, float_dtypes, all_shapes, jtu.rand_default, ["rev"]),
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op_record("float_power", 2, inexact_dtypes, all_shapes,
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partial(jtu.rand_default, scale=1), ["rev"],
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tolerance={jnp.bfloat16: 1e-2, np.float32: 1e-3,
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np.float64: 1e-12, np.complex64: 2e-4,
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np.complex128: 1e-12}, check_dtypes=False),
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op_record("floor", 1, float_dtypes, all_shapes, jtu.rand_default, []),
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op_record("floor", 1, int_dtypes + unsigned_dtypes, all_shapes,
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jtu.rand_default, [], check_dtypes=False),
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op_record("greater", 2, all_dtypes, all_shapes, jtu.rand_some_equal, []),
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op_record("greater_equal", 2, all_dtypes, all_shapes, jtu.rand_some_equal, []),
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op_record("i0", 1, float_dtypes, all_shapes, jtu.rand_default, [],
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check_dtypes=False, tolerance={np.float16: 3e-3}),
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op_record("ldexp", 2, int_dtypes, all_shapes, jtu.rand_default, [], check_dtypes=False),
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op_record("less", 2, all_dtypes, all_shapes, jtu.rand_some_equal, []),
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op_record("less_equal", 2, all_dtypes, all_shapes, jtu.rand_some_equal, []),
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op_record("log", 1, number_dtypes, all_shapes, jtu.rand_positive, ["rev"],
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inexact=True),
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op_record("logical_and", 2, all_dtypes, all_shapes, jtu.rand_bool, []),
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op_record("logical_not", 1, all_dtypes, all_shapes, jtu.rand_bool, []),
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op_record("logical_or", 2, all_dtypes, all_shapes, jtu.rand_bool, []),
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op_record("logical_xor", 2, all_dtypes, all_shapes, jtu.rand_bool, []),
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op_record("maximum", 2, all_dtypes, all_shapes, jtu.rand_some_inf, []),
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op_record("minimum", 2, all_dtypes, all_shapes, jtu.rand_some_inf, []),
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op_record("multiply", 2, all_dtypes, all_shapes, jtu.rand_default, ["rev"]),
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op_record("negative", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"]),
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op_record("nextafter", 2, [f for f in float_dtypes if f != jnp.bfloat16],
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all_shapes, jtu.rand_default, ["rev"], inexact=True, tolerance=0),
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op_record("not_equal", 2, all_dtypes, all_shapes, jtu.rand_some_equal, ["rev"]),
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op_record("array_equal", 2, number_dtypes, all_shapes, jtu.rand_some_equal, ["rev"]),
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op_record("array_equiv", 2, number_dtypes, all_shapes, jtu.rand_some_equal, ["rev"]),
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op_record("reciprocal", 1, inexact_dtypes, all_shapes, jtu.rand_default, []),
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op_record("subtract", 2, number_dtypes, all_shapes, jtu.rand_default, ["rev"]),
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op_record("signbit", 1, default_dtypes + bool_dtypes, all_shapes,
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jtu.rand_some_inf_and_nan, ["rev"]),
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op_record("trunc", 1, float_dtypes, all_shapes, jtu.rand_some_inf_and_nan, []),
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op_record("trunc", 1, int_dtypes + unsigned_dtypes, all_shapes,
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jtu.rand_some_inf_and_nan, [], check_dtypes=False),
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op_record("sin", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"],
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inexact=True),
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op_record("cos", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"],
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inexact=True),
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op_record("tan", 1, number_dtypes, all_shapes,
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partial(jtu.rand_uniform, low=-1.5, high=1.5), ["rev"],
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inexact=True),
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op_record("sinh", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"],
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inexact=True),
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op_record("cosh", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"],
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inexact=True),
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# TODO(b/142975473): on CPU, tanh for complex128 is only accurate to
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# ~float32 precision.
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# TODO(b/143135720): on GPU, tanh has only ~float32 precision.
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op_record("tanh", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"],
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tolerance={np.float64: 1e-7, np.complex128: 1e-7},
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inexact=True),
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op_record("arcsin", 1, number_dtypes, all_shapes, jtu.rand_small, ["rev"],
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inexact=True, tolerance={np.complex128: 2e-15}),
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op_record("arccos", 1, number_dtypes, all_shapes, jtu.rand_small, ["rev"],
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inexact=True),
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op_record("arctan", 1, number_dtypes, all_shapes, jtu.rand_small, ["rev"],
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inexact=True),
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op_record("arctan2", 2, float_dtypes, all_shapes, jtu.rand_small, ["rev"],
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inexact=True, check_dtypes=False),
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op_record("arcsinh", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"],
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inexact=True, tolerance={np.complex64: 2E-4, np.complex128: 2E-14}),
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op_record("arccosh", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"],
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inexact=True, tolerance={np.complex64: 2E-2, np.complex128: 2E-12}),
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op_record("arctanh", 1, number_dtypes, all_shapes, jtu.rand_small, ["rev"],
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inexact=True, tolerance={np.float64: 1e-9}),
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]
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JAX_COMPOUND_OP_RECORDS = [
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# angle has inconsistent 32/64-bit return types across numpy versions.
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op_record("angle", 1, number_dtypes, all_shapes, jtu.rand_default, [],
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check_dtypes=False, inexact=True),
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op_record("angle", 1, number_dtypes, all_shapes, jtu.rand_default, [],
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check_dtypes=False, inexact=True, test_name="angle_deg", kwargs={'deg': True}),
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op_record("atleast_1d", 1, default_dtypes, all_shapes, jtu.rand_default, []),
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op_record("atleast_2d", 1, default_dtypes, all_shapes, jtu.rand_default, []),
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op_record("atleast_3d", 1, default_dtypes, all_shapes, jtu.rand_default, []),
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op_record("cbrt", 1, default_dtypes, all_shapes, jtu.rand_some_inf, ["rev"],
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inexact=True),
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op_record("conjugate", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"]),
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op_record("deg2rad", 1, float_dtypes, all_shapes, jtu.rand_default, []),
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op_record("divide", 2, number_dtypes, all_shapes, jtu.rand_nonzero, ["rev"],
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inexact=True),
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op_record("divmod", 2, int_dtypes + float_dtypes, all_shapes,
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jtu.rand_nonzero, []),
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op_record("exp2", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"],
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tolerance={jnp.bfloat16: 4e-2, np.float16: 1e-2}, inexact=True),
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# TODO(b/142975473): on CPU, expm1 for float64 is only accurate to ~float32
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# precision.
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op_record("expm1", 1, number_dtypes, all_shapes, jtu.rand_positive, [],
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test_name="expm1_large", tolerance={np.float64: 1e-8}, inexact=True),
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op_record("expm1", 1, number_dtypes, all_shapes, jtu.rand_small_positive,
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[], tolerance={np.float64: 1e-8}, inexact=True),
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op_record("fix", 1, float_dtypes, all_shapes, jtu.rand_default, []),
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op_record("fix", 1, int_dtypes + unsigned_dtypes, all_shapes,
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jtu.rand_default, [], check_dtypes=False),
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op_record("floor_divide", 2, default_dtypes + unsigned_dtypes,
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all_shapes, jtu.rand_nonzero, ["rev"]),
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op_record("fmin", 2, number_dtypes, all_shapes, jtu.rand_some_nan, []),
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op_record("fmax", 2, number_dtypes, all_shapes, jtu.rand_some_nan, []),
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op_record("fmod", 2, default_dtypes, all_shapes, jtu.rand_some_nan, []),
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op_record("heaviside", 2, default_dtypes, all_shapes, jtu.rand_default, [],
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inexact=True),
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op_record("hypot", 2, default_dtypes, all_shapes, jtu.rand_default, [],
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inexact=True),
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op_record("kron", 2, number_dtypes, nonempty_shapes, jtu.rand_default, []),
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op_record("outer", 2, number_dtypes, all_shapes, jtu.rand_default, []),
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op_record("imag", 1, number_dtypes, all_shapes, jtu.rand_some_inf, []),
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op_record("iscomplex", 1, number_dtypes, all_shapes, jtu.rand_some_inf, []),
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op_record("isfinite", 1, inexact_dtypes, all_shapes, jtu.rand_some_inf_and_nan, []),
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op_record("isinf", 1, inexact_dtypes, all_shapes, jtu.rand_some_inf_and_nan, []),
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op_record("isnan", 1, inexact_dtypes, all_shapes, jtu.rand_some_inf_and_nan, []),
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op_record("isneginf", 1, float_dtypes, all_shapes, jtu.rand_some_inf_and_nan, []),
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op_record("isposinf", 1, float_dtypes, all_shapes, jtu.rand_some_inf_and_nan, []),
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op_record("isreal", 1, number_dtypes, all_shapes, jtu.rand_some_inf, []),
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op_record("isrealobj", 1, number_dtypes, all_shapes, jtu.rand_some_inf, []),
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op_record("log2", 1, number_dtypes, all_shapes, jtu.rand_positive, ["rev"],
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inexact=True),
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op_record("log10", 1, number_dtypes, all_shapes, jtu.rand_positive, ["rev"],
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inexact=True),
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op_record("log1p", 1, number_dtypes, all_shapes, jtu.rand_positive, [],
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test_name="log1p_large", tolerance={np.float64: 1e-12},
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inexact=True),
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op_record("log1p", 1, number_dtypes, all_shapes, jtu.rand_small_positive, [],
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tolerance={np.float64: 1e-12}, inexact=True),
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op_record("logaddexp", 2, float_dtypes, all_shapes,
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jtu.rand_some_inf_and_nan, ["rev"],
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tolerance={np.float64: 1e-12}, inexact=True),
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op_record("logaddexp2", 2, float_dtypes, all_shapes,
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jtu.rand_some_inf_and_nan, ["rev"],
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tolerance={np.float16: 1e-2, np.float64: 2e-14}, inexact=True),
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op_record("polyval", 2, number_dtypes, nonempty_nonscalar_array_shapes,
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jtu.rand_default, [], check_dtypes=False,
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tolerance={dtypes.bfloat16: 4e-2, np.float16: 2e-2,
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np.float64: 1e-12}),
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op_record("positive", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"]),
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op_record("power", 2, number_dtypes, all_shapes, jtu.rand_positive, ["rev"],
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tolerance={np.complex128: 1e-14}, check_dtypes=False),
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op_record("rad2deg", 1, float_dtypes, all_shapes, jtu.rand_default, []),
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op_record("ravel", 1, all_dtypes, all_shapes, jtu.rand_default, ["rev"]),
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op_record("real", 1, number_dtypes, all_shapes, jtu.rand_some_inf, []),
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op_record("remainder", 2, default_dtypes, all_shapes, jtu.rand_some_zero, [],
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tolerance={np.float16: 1e-2}),
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op_record("mod", 2, default_dtypes, all_shapes, jtu.rand_some_zero, []),
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op_record("modf", 1, float_dtypes, all_shapes, jtu.rand_default, []),
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op_record("modf", 1, int_dtypes + unsigned_dtypes, all_shapes,
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jtu.rand_default, [], check_dtypes=False),
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op_record("rint", 1, inexact_dtypes, all_shapes, jtu.rand_some_inf_and_nan,
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[]),
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op_record("rint", 1, int_dtypes + unsigned_dtypes, all_shapes,
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jtu.rand_default, [], check_dtypes=False),
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op_record("sign", 1, number_dtypes, all_shapes, jtu.rand_some_inf_and_nan, []),
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# numpy 1.16 has trouble mixing uint and bfloat16, so we test these separately.
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op_record("copysign", 2, default_dtypes + unsigned_dtypes,
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all_shapes, jtu.rand_some_inf_and_nan, [], check_dtypes=False),
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op_record("sinc", 1, [t for t in number_dtypes if t != jnp.bfloat16],
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all_shapes, jtu.rand_default, ["rev"],
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tolerance={np.complex64: 1e-5}, inexact=True,
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check_dtypes=False),
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op_record("square", 1, number_dtypes, all_shapes, jtu.rand_default, ["rev"]),
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op_record("sqrt", 1, number_dtypes, all_shapes, jtu.rand_positive, ["rev"],
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inexact=True),
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op_record("transpose", 1, all_dtypes, all_shapes, jtu.rand_default, ["rev"],
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check_dtypes=False),
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op_record("true_divide", 2, all_dtypes, all_shapes, jtu.rand_nonzero,
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["rev"], inexact=True),
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op_record("ediff1d", 3, [np.int32], all_shapes, jtu.rand_default, [], check_dtypes=False),
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# TODO(phawkins): np.unwrap does not correctly promote its default period
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# argument under NumPy 1.21 for bfloat16 inputs. It works fine if we
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# explicitly pass a bfloat16 value that does not need promition. We should
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# probably add a custom test harness for unwrap that tests the period
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# argument anyway.
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op_record("unwrap", 1, [t for t in float_dtypes if t != dtypes.bfloat16],
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nonempty_nonscalar_array_shapes,
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jtu.rand_default, ["rev"],
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# numpy.unwrap always returns float64
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check_dtypes=False,
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# numpy cumsum is inaccurate, see issue #3517
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tolerance={dtypes.bfloat16: 1e-1, np.float16: 1e-1}),
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op_record("isclose", 2, [t for t in all_dtypes if t != jnp.bfloat16],
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all_shapes, jtu.rand_small_positive, []),
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op_record("gcd", 2, int_dtypes_no_uint64, all_shapes, jtu.rand_default, []),
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op_record("lcm", 2, int_dtypes_no_uint64, all_shapes, jtu.rand_default, []),
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op_record("lcm", 2, [np.int8], all_shapes, jtu.rand_not_small, [])
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]
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JAX_BITWISE_OP_RECORDS = [
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op_record("bitwise_and", 2, int_dtypes + unsigned_dtypes, all_shapes,
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jtu.rand_fullrange, []),
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op_record("bitwise_not", 1, int_dtypes + unsigned_dtypes, all_shapes,
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jtu.rand_fullrange, []),
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op_record("invert", 1, int_dtypes + unsigned_dtypes, all_shapes,
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jtu.rand_fullrange, []),
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op_record("bitwise_or", 2, int_dtypes + unsigned_dtypes, all_shapes,
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jtu.rand_fullrange, []),
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op_record("bitwise_xor", 2, int_dtypes + unsigned_dtypes, all_shapes,
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jtu.rand_fullrange, []),
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]
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JAX_OPERATOR_OVERLOADS = [
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op_record("__add__", 2, number_dtypes, all_shapes, jtu.rand_default, []),
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op_record("__sub__", 2, number_dtypes, all_shapes, jtu.rand_default, []),
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op_record("__mul__", 2, number_dtypes, all_shapes, jtu.rand_default, []),
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op_record("__eq__", 2, number_dtypes, all_shapes, jtu.rand_default, []),
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op_record("__ne__", 2, number_dtypes, all_shapes, jtu.rand_default, []),
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op_record("__lt__", 2, default_dtypes, all_shapes, jtu.rand_default, []),
|
|
op_record("__le__", 2, default_dtypes, all_shapes, jtu.rand_default, []),
|
|
op_record("__gt__", 2, default_dtypes, all_shapes, jtu.rand_default, []),
|
|
op_record("__ge__", 2, default_dtypes, all_shapes, jtu.rand_default, []),
|
|
op_record("__pos__", 1, number_dtypes, all_shapes, jtu.rand_default, []),
|
|
op_record("__neg__", 1, number_dtypes, all_shapes, jtu.rand_default, []),
|
|
op_record("__pow__", 2, inexact_dtypes, all_shapes, jtu.rand_positive, [],
|
|
tolerance={np.float32: 2e-4, np.complex64: 2e-4, np.complex128: 1e-14}),
|
|
op_record("__mod__", 2, default_dtypes, all_shapes, jtu.rand_nonzero, [],
|
|
tolerance={np.float16: 1e-1}),
|
|
op_record("__floordiv__", 2, default_dtypes, all_shapes,
|
|
jtu.rand_nonzero, []),
|
|
op_record("__truediv__", 2, number_dtypes, all_shapes, jtu.rand_nonzero, [],
|
|
inexact=True),
|
|
op_record("__abs__", 1, number_dtypes, all_shapes, jtu.rand_default, []),
|
|
# TODO(mattjj): __invert__ fails on bool dtypes because ~True == -2
|
|
op_record("__invert__", 1, int_dtypes, all_shapes, jtu.rand_default, []),
|
|
# TODO(mattjj): investigate these failures
|
|
# op_record("__or__", 2, number_dtypes, all_shapes, jtu.rand_bool, []),
|
|
# op_record("__and__", 2, number_dtypes, all_shapes, jtu.rand_default, []),
|
|
# op_record("__xor__", 2, number_dtypes, all_shapes, jtu.rand_bool, []),
|
|
# op_record("__divmod__", 2, number_dtypes, all_shapes, jtu.rand_nonzero, []),
|
|
op_record("__lshift__", 2, int_dtypes_no_uint64, all_shapes, partial(jtu.rand_int, high=8), []),
|
|
op_record("__rshift__", 2, int_dtypes_no_uint64, all_shapes, partial(jtu.rand_int, high=8), []),
|
|
]
|
|
|
|
JAX_RIGHT_OPERATOR_OVERLOADS = [
|
|
op_record("__radd__", 2, number_dtypes, all_shapes, jtu.rand_default, []),
|
|
op_record("__rsub__", 2, number_dtypes, all_shapes, jtu.rand_default, []),
|
|
op_record("__rmul__", 2, number_dtypes, all_shapes, jtu.rand_default, []),
|
|
op_record("__rpow__", 2, inexact_dtypes, all_shapes, jtu.rand_positive, [],
|
|
tolerance={np.float32: 2e-4, np.complex64: 1e-3}),
|
|
op_record("__rmod__", 2, default_dtypes, all_shapes, jtu.rand_nonzero, [],
|
|
tolerance={np.float16: 1e-1}),
|
|
op_record("__rfloordiv__", 2, default_dtypes, all_shapes,
|
|
jtu.rand_nonzero, []),
|
|
op_record("__rtruediv__", 2, number_dtypes, all_shapes, jtu.rand_nonzero, [],
|
|
inexact=True),
|
|
# op_record("__ror__", 2, number_dtypes, all_shapes, jtu.rand_bool, []),
|
|
# op_record("__rand__", 2, number_dtypes, all_shapes, jtu.rand_default, []),
|
|
# op_record("__rxor__", 2, number_dtypes, all_shapes, jtu.rand_bool, []),
|
|
# op_record("__rdivmod__", 2, number_dtypes, all_shapes, jtu.rand_nonzero, []),
|
|
op_record("__rlshift__", 2, int_dtypes_no_uint64, all_shapes, partial(jtu.rand_int, high=8), []),
|
|
op_record("__rrshift__", 2, int_dtypes_no_uint64, all_shapes, partial(jtu.rand_int, high=8), [])
|
|
]
|
|
|
|
class _OverrideEverything:
|
|
pass
|
|
|
|
for rec in JAX_OPERATOR_OVERLOADS + JAX_RIGHT_OPERATOR_OVERLOADS:
|
|
if rec.nargs == 2:
|
|
setattr(_OverrideEverything, rec.name, lambda self, other: self)
|
|
|
|
class _OverrideNothing:
|
|
pass
|
|
|
|
for rec in JAX_OPERATOR_OVERLOADS + JAX_RIGHT_OPERATOR_OVERLOADS:
|
|
if rec.nargs == 2:
|
|
setattr(_OverrideNothing, rec.name, lambda self, other: NotImplemented)
|
|
|
|
|
|
def _dtypes_are_compatible_for_bitwise_ops(args):
|
|
if len(args) <= 1:
|
|
return True
|
|
is_signed = lambda dtype: jnp.issubdtype(dtype, np.signedinteger)
|
|
width = lambda dtype: jnp.iinfo(dtype).bits
|
|
x, y = args
|
|
if width(x) > width(y):
|
|
x, y = y, x
|
|
# The following condition seems a little ad hoc, but seems to capture what
|
|
# numpy actually implements.
|
|
return (
|
|
is_signed(x) == is_signed(y)
|
|
or (width(x) == 32 and width(y) == 32)
|
|
or (width(x) == 32 and width(y) == 64 and is_signed(y)))
|
|
|
|
def _shapes_are_broadcast_compatible(shapes):
|
|
try:
|
|
lax.broadcast_shapes(*(() if s in scalar_shapes else s for s in shapes))
|
|
except ValueError:
|
|
return False
|
|
else:
|
|
return True
|
|
|
|
def _shapes_are_equal_length(shapes):
|
|
return all(len(shape) == len(shapes[0]) for shape in shapes[1:])
|
|
|
|
|
|
class JaxNumpyOperatorTests(jtu.JaxTestCase):
|
|
"""Tests for LAX-backed Numpy operators."""
|
|
|
|
def _GetArgsMaker(self, rng, shapes, dtypes, np_arrays=True):
|
|
def f():
|
|
out = [rng(shape, dtype or jnp.float_)
|
|
for shape, dtype in zip(shapes, dtypes)]
|
|
if np_arrays:
|
|
return out
|
|
return [jnp.asarray(a) if isinstance(a, (np.ndarray, np.generic)) else a
|
|
for a in out]
|
|
return f
|
|
|
|
@parameterized.parameters(itertools.chain.from_iterable(
|
|
jtu.sample_product_testcases(
|
|
[dict(op_name=rec.name, rng_factory=rec.rng_factory,
|
|
check_dtypes=rec.check_dtypes, tolerance=rec.tolerance,
|
|
inexact=rec.inexact, kwargs=rec.kwargs or {})],
|
|
[dict(shapes=shapes, dtypes=dtypes)
|
|
for shapes in filter(
|
|
_shapes_are_broadcast_compatible,
|
|
itertools.combinations_with_replacement(rec.shapes, rec.nargs))
|
|
for dtypes in itertools.product(
|
|
*(_valid_dtypes_for_shape(s, rec.dtypes) for s in shapes))],
|
|
)
|
|
for rec in itertools.chain(JAX_ONE_TO_ONE_OP_RECORDS,
|
|
JAX_COMPOUND_OP_RECORDS)))
|
|
@jax.numpy_rank_promotion('allow') # This test explicitly exercises implicit rank promotion.
|
|
def testOp(self, op_name, rng_factory, shapes, dtypes, check_dtypes,
|
|
tolerance, inexact, kwargs):
|
|
np_op = partial(getattr(np, op_name), **kwargs)
|
|
jnp_op = partial(getattr(jnp, op_name), **kwargs)
|
|
np_op = jtu.ignore_warning(category=RuntimeWarning,
|
|
message="invalid value.*")(np_op)
|
|
np_op = jtu.ignore_warning(category=RuntimeWarning,
|
|
message="divide by zero.*")(np_op)
|
|
|
|
rng = rng_factory(self.rng())
|
|
args_maker = self._GetArgsMaker(rng, shapes, dtypes, np_arrays=False)
|
|
tol = max(jtu.tolerance(dtype, tolerance) for dtype in dtypes)
|
|
tol = functools.reduce(jtu.join_tolerance,
|
|
[tolerance, tol, jtu.default_tolerance()])
|
|
|
|
with jtu.strict_promotion_if_dtypes_match(dtypes):
|
|
self._CheckAgainstNumpy(jtu.promote_like_jnp(np_op, inexact), jnp_op,
|
|
args_maker, check_dtypes=check_dtypes, tol=tol)
|
|
self._CompileAndCheck(jnp_op, args_maker, check_dtypes=check_dtypes,
|
|
atol=tol, rtol=tol)
|
|
|
|
@parameterized.parameters(itertools.chain.from_iterable(
|
|
jtu.sample_product_testcases(
|
|
[dict(name=rec.name, rng_factory=rec.rng_factory, tol=rec.tolerance)],
|
|
[dict(shapes=shapes, dtypes=dtypes)
|
|
for shapes in filter(
|
|
_shapes_are_broadcast_compatible,
|
|
itertools.combinations_with_replacement(rec.shapes, rec.nargs))
|
|
for dtypes in itertools.product(
|
|
*(_valid_dtypes_for_shape(s, rec.dtypes) for s in shapes))],
|
|
)
|
|
for rec in JAX_OPERATOR_OVERLOADS))
|
|
@jax.numpy_rank_promotion('allow') # This test explicitly exercises implicit rank promotion.
|
|
def testOperatorOverload(self, name, rng_factory, shapes, dtypes, tol):
|
|
rng = rng_factory(self.rng())
|
|
# np and jnp arrays have different type promotion rules; force the use of
|
|
# jnp arrays.
|
|
args_maker = self._GetArgsMaker(rng, shapes, dtypes, np_arrays=False)
|
|
fun = lambda *xs: getattr(operator, name.strip('_'))(*xs)
|
|
with jtu.strict_promotion_if_dtypes_match(dtypes):
|
|
self._CompileAndCheck(fun, args_maker, atol=tol, rtol=tol)
|
|
|
|
@parameterized.parameters(itertools.chain.from_iterable(
|
|
jtu.sample_product_testcases(
|
|
[dict(name=rec.name, rng_factory=rec.rng_factory,
|
|
op_tolerance=rec.tolerance)],
|
|
[dict(shapes=shapes, dtypes=dtypes)
|
|
for shapes in filter(
|
|
_shapes_are_broadcast_compatible,
|
|
itertools.combinations_with_replacement(rec.shapes, rec.nargs))
|
|
for dtypes in itertools.product(
|
|
*(_valid_dtypes_for_shape(s, rec.dtypes) for s in shapes))],
|
|
)
|
|
for rec in JAX_RIGHT_OPERATOR_OVERLOADS))
|
|
@jax.numpy_rank_promotion('allow') # This test explicitly exercises implicit rank promotion.
|
|
def testRightOperatorOverload(self, name, rng_factory, shapes, dtypes,
|
|
op_tolerance):
|
|
if shapes[1] is jtu.PYTHON_SCALAR_SHAPE:
|
|
raise SkipTest("scalars not implemented") # TODO(mattjj): clean up
|
|
rng = rng_factory(self.rng())
|
|
args_maker = self._GetArgsMaker(rng, shapes, dtypes, np_arrays=False)
|
|
fun = lambda fst, snd: getattr(snd, name)(fst)
|
|
tol = max(jtu.tolerance(dtype, op_tolerance) for dtype in dtypes)
|
|
with jtu.strict_promotion_if_dtypes_match(dtypes):
|
|
self._CompileAndCheck( fun, args_maker, atol=tol, rtol=tol)
|
|
|
|
@jtu.sample_product(
|
|
name=[rec.name for rec in JAX_OPERATOR_OVERLOADS if rec.nargs == 2],
|
|
othertype=[dict, list, tuple, set],
|
|
)
|
|
def testOperatorOverloadErrors(self, name, othertype):
|
|
# Test that binary operators with builtin collections raise a TypeError
|
|
# and report the types in the correct order.
|
|
data = [(1, 2), (2, 3)]
|
|
arr = jnp.array(data)
|
|
other = othertype(data)
|
|
|
|
msg = f"unsupported operand type.* 'ArrayImpl' and '{othertype.__name__}'"
|
|
with self.assertRaisesRegex(TypeError, msg):
|
|
getattr(arr, name)(other)
|
|
|
|
@jtu.sample_product(
|
|
name=[rec.name for rec in JAX_RIGHT_OPERATOR_OVERLOADS if rec.nargs == 2],
|
|
othertype=[dict, list, tuple, set],
|
|
)
|
|
def testRightOperatorOverloadErrors(self, name, othertype):
|
|
# Test that binary operators with builtin collections raise a TypeError
|
|
# and report the types in the correct order.
|
|
data = [(1, 2), (2, 3)]
|
|
arr = jnp.array(data)
|
|
other = othertype(data)
|
|
|
|
msg = f"unsupported operand type.* '{othertype.__name__}' and 'ArrayImpl'"
|
|
with self.assertRaisesRegex(TypeError, msg):
|
|
getattr(arr, name)(other)
|
|
|
|
@jtu.sample_product(
|
|
[dict(op_name=rec.name, rng_factory=rec.rng_factory, dtype=dtype)
|
|
for rec in JAX_OPERATOR_OVERLOADS if rec.nargs == 2
|
|
for dtype in rec.dtypes],
|
|
)
|
|
def testBinaryOperatorDefers(self, op_name, rng_factory, dtype):
|
|
rng = rng_factory(self.rng())
|
|
arg = jax.device_put(rng((), dtype))
|
|
op = getattr(operator, op_name)
|
|
|
|
other = _OverrideEverything()
|
|
assert op(other, arg) is other
|
|
assert op(arg, other) is other
|
|
|
|
other = _OverrideNothing()
|
|
if op_name == "__eq__":
|
|
assert op(other, arg) is False
|
|
assert op(arg, other) is False
|
|
elif op_name == "__ne__":
|
|
assert op(other, arg) is True
|
|
assert op(arg, other) is True
|
|
else:
|
|
with self.assertRaises(TypeError):
|
|
op(other, arg)
|
|
with self.assertRaises(TypeError):
|
|
op(arg, other)
|
|
|
|
@parameterized.parameters(itertools.chain.from_iterable(
|
|
jtu.sample_product_testcases(
|
|
[dict(name=rec.name, rng_factory=rec.rng_factory)],
|
|
shapes=filter(
|
|
_shapes_are_broadcast_compatible,
|
|
itertools.combinations_with_replacement(rec.shapes, rec.nargs)),
|
|
dtypes=filter(
|
|
_dtypes_are_compatible_for_bitwise_ops,
|
|
itertools.combinations_with_replacement(rec.dtypes, rec.nargs)),
|
|
)
|
|
for rec in JAX_BITWISE_OP_RECORDS))
|
|
@jax.numpy_rank_promotion('allow') # This test explicitly exercises implicit rank promotion.
|
|
def testBitwiseOp(self, name, rng_factory, shapes, dtypes):
|
|
np_op = getattr(np, name)
|
|
jnp_op = getattr(jnp, name)
|
|
rng = rng_factory(self.rng())
|
|
args_maker = self._GetArgsMaker(rng, shapes, dtypes)
|
|
with jtu.strict_promotion_if_dtypes_match(dtypes):
|
|
self._CheckAgainstNumpy(jtu.promote_like_jnp(np_op), jnp_op, args_maker)
|
|
self._CompileAndCheck(jnp_op, args_maker)
|
|
|
|
@jtu.sample_product(
|
|
[dict(dtypes=dtypes, shapes=shapes)
|
|
for shapes in filter(
|
|
_shapes_are_broadcast_compatible,
|
|
# TODO numpy always promotes to shift dtype for zero-dim shapes:
|
|
itertools.combinations_with_replacement(nonzerodim_shapes, 2))
|
|
for dtypes in itertools.product(
|
|
*(_valid_dtypes_for_shape(s, int_dtypes_no_uint64) for s in shapes))
|
|
],
|
|
op=[jnp.left_shift, jnp.right_shift],
|
|
)
|
|
@jax.numpy_rank_promotion('allow') # This test explicitly exercises implicit rank promotion.
|
|
def testShiftOpAgainstNumpy(self, op, dtypes, shapes):
|
|
dtype, shift_dtype = dtypes
|
|
signed_mix = np.issubdtype(dtype, np.signedinteger) != \
|
|
np.issubdtype(shift_dtype, np.signedinteger)
|
|
has_32 = any(np.iinfo(d).bits == 32 for d in dtypes)
|
|
promoting_to_64 = has_32 and signed_mix
|
|
if promoting_to_64 and not config.x64_enabled:
|
|
self.skipTest("np.right_shift/left_shift promoting to int64"
|
|
"differs from jnp in 32 bit mode.")
|
|
|
|
info, shift_info = map(np.iinfo, dtypes)
|
|
x_rng = jtu.rand_int(self.rng(), low=info.min, high=info.max + 1)
|
|
# NumPy requires shifts to be non-negative and below the bit width:
|
|
shift_rng = jtu.rand_int(self.rng(), high=max(info.bits, shift_info.bits))
|
|
args_maker = lambda: (x_rng(shapes[0], dtype), shift_rng(shapes[1], shift_dtype))
|
|
|
|
np_op = getattr(np, op.__name__)
|
|
|
|
with jtu.strict_promotion_if_dtypes_match(dtypes):
|
|
self._CompileAndCheck(op, args_maker)
|
|
self._CheckAgainstNumpy(np_op, op, args_maker)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
absltest.main(testLoader=jtu.JaxTestLoader())
|