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445 lines
17 KiB
Python
445 lines
17 KiB
Python
# Copyright 2023 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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"""Tests for jax.numpy.ufunc and its methods."""
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import itertools
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from functools import partial
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from absl.testing import absltest
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import numpy as np
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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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jax.config.parse_flags_with_absl()
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def scalar_add(x, y):
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assert np.shape(x) == np.shape(y) == ()
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return x + y
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def scalar_div(x, y):
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assert np.shape(x) == np.shape(y) == ()
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return x / y
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def scalar_mul(x, y):
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assert np.shape(x) == np.shape(y) == ()
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return x * y
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def scalar_sub(x, y):
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assert np.shape(x) == np.shape(y) == ()
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return x - y
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SCALAR_FUNCS = [
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{'func': scalar_add, 'nin': 2, 'nout': 1, 'identity': 0},
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{'func': scalar_div, 'nin': 2, 'nout': 1, 'identity': None},
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{'func': scalar_mul, 'nin': 2, 'nout': 1, 'identity': 1},
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{'func': scalar_sub, 'nin': 2, 'nout': 1, 'identity': None},
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]
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def _jnp_ufunc_props(name):
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jnp_func = getattr(jnp, name)
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assert isinstance(jnp_func, jnp.ufunc)
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np_func = getattr(np, name)
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dtypes = [np.dtype(c) for c in "Ffi?" if f"{c}{c}->{c}" in np_func.types]
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return [dict(name=name, dtype=dtype) for dtype in dtypes]
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JAX_NUMPY_UFUNCS = [
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name for name in dir(jnp) if isinstance(getattr(jnp, name), jnp.ufunc)
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]
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JAX_NUMPY_UFUNCS_WITH_DTYPES = list(itertools.chain.from_iterable(
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_jnp_ufunc_props(name) for name in JAX_NUMPY_UFUNCS
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))
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broadcast_compatible_shapes = [(), (1,), (3,), (1, 3), (4, 1), (4, 3)]
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nonscalar_shapes = [(3,), (4,), (4, 3)]
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def cast_outputs(fun):
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def wrapped(*args, **kwargs):
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dtype = np.asarray(args[0]).dtype
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return jax.tree.map(lambda x: np.asarray(x, dtype=dtype), fun(*args, **kwargs))
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return wrapped
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class LaxNumpyUfuncTests(jtu.JaxTestCase):
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@jtu.sample_product(SCALAR_FUNCS)
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def test_frompyfunc_properties(self, func, nin, nout, identity):
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jnp_fun = jnp.frompyfunc(func, nin=nin, nout=nout, identity=identity)
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self.assertEqual(jnp_fun.identity, identity)
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self.assertEqual(jnp_fun.nin, nin)
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self.assertEqual(jnp_fun.nout, nout)
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self.assertEqual(jnp_fun.nargs, nin)
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@jtu.sample_product(name=JAX_NUMPY_UFUNCS)
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def test_ufunc_properties(self, name):
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jnp_fun = getattr(jnp, name)
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np_fun = getattr(np, name)
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self.assertEqual(jnp_fun.identity, np_fun.identity)
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self.assertEqual(jnp_fun.nin, np_fun.nin)
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self.assertEqual(jnp_fun.nout, np_fun.nout)
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self.assertEqual(jnp_fun.nargs, np_fun.nargs - 1) # -1 because NumPy accepts `out`
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@jtu.sample_product(SCALAR_FUNCS)
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def test_frompyfunc_properties_readonly(self, func, nin, nout, identity):
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jnp_fun = jnp.frompyfunc(func, nin=nin, nout=nout, identity=identity)
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for attr in ['nargs', 'nin', 'nout', 'identity', '_func']:
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getattr(jnp_fun, attr) # no error on attribute access.
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with self.assertRaises(AttributeError):
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setattr(jnp_fun, attr, None) # error when trying to mutate.
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@jtu.sample_product(name=JAX_NUMPY_UFUNCS)
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def test_ufunc_properties_readonly(self, name):
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jnp_fun = getattr(jnp, name)
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for attr in ['nargs', 'nin', 'nout', 'identity', '_func']:
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getattr(jnp_fun, attr) # no error on attribute access.
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with self.assertRaises(AttributeError):
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setattr(jnp_fun, attr, None) # error when trying to mutate.
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@jtu.sample_product(SCALAR_FUNCS)
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def test_frompyfunc_hash(self, func, nin, nout, identity):
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jnp_fun = jnp.frompyfunc(func, nin=nin, nout=nout, identity=identity)
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jnp_fun_2 = jnp.frompyfunc(func, nin=nin, nout=nout, identity=identity)
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self.assertEqual(jnp_fun, jnp_fun_2)
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self.assertEqual(hash(jnp_fun), hash(jnp_fun_2))
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other_fun = jnp.frompyfunc(jnp.add, nin=2, nout=1, identity=0)
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self.assertNotEqual(jnp_fun, other_fun)
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# Note: don't test hash for non-equality because it may collide.
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@jtu.sample_product(
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SCALAR_FUNCS,
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lhs_shape=broadcast_compatible_shapes,
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rhs_shape=broadcast_compatible_shapes,
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dtype=jtu.dtypes.floating,
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)
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@jax.numpy_rank_promotion('allow') # This test explicitly exercises implicit rank promotion.
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def test_frompyfunc_call(self, func, nin, nout, identity, lhs_shape, rhs_shape, dtype):
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jnp_fun = jnp.frompyfunc(func, nin=nin, nout=nout, identity=identity)
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np_fun = cast_outputs(np.frompyfunc(func, nin=nin, nout=nout, identity=identity))
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rng = jtu.rand_default(self.rng())
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args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)]
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self._CheckAgainstNumpy(jnp_fun, np_fun, args_maker)
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self._CompileAndCheck(jnp_fun, args_maker)
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@jtu.sample_product(
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JAX_NUMPY_UFUNCS_WITH_DTYPES,
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lhs_shape=broadcast_compatible_shapes,
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rhs_shape=broadcast_compatible_shapes,
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)
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@jax.numpy_rank_promotion('allow') # This test explicitly exercises implicit rank promotion.
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def test_ufunc_call(self, name, dtype, lhs_shape, rhs_shape):
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jnp_fun = getattr(jnp, name)
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np_fun = getattr(np, name)
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rng = jtu.rand_default(self.rng())
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args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)]
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self._CheckAgainstNumpy(jnp_fun, np_fun, args_maker)
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self._CompileAndCheck(jnp_fun, args_maker)
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@jtu.sample_product(
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SCALAR_FUNCS,
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lhs_shape=broadcast_compatible_shapes,
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rhs_shape=broadcast_compatible_shapes,
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dtype=jtu.dtypes.floating,
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)
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def test_frompyfunc_outer(self, func, nin, nout, identity, lhs_shape, rhs_shape, dtype):
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if (nin, nout) != (2, 1):
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self.skipTest(f"outer requires (nin, nout)=(2, 1); got {(nin, nout)=}")
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jnp_fun = jnp.frompyfunc(func, nin=nin, nout=nout, identity=identity).outer
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np_fun = cast_outputs(np.frompyfunc(func, nin=nin, nout=nout, identity=identity).outer)
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rng = jtu.rand_default(self.rng())
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args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)]
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self._CheckAgainstNumpy(jnp_fun, np_fun, args_maker)
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self._CompileAndCheck(jnp_fun, args_maker)
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@jtu.sample_product(
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JAX_NUMPY_UFUNCS_WITH_DTYPES,
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lhs_shape=broadcast_compatible_shapes,
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rhs_shape=broadcast_compatible_shapes,
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)
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def test_ufunc_outer(self, name, lhs_shape, rhs_shape, dtype):
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jnp_fun = getattr(jnp, name)
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np_fun = getattr(np, name)
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if (jnp_fun.nin, jnp_fun.nout) != (2, 1):
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self.skipTest(f"outer requires (nin, nout)=(2, 1); got {(jnp_fun.nin, jnp_fun.nout)=}")
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rng = jtu.rand_default(self.rng())
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args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)]
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self._CheckAgainstNumpy(jnp_fun.outer, np_fun.outer, args_maker)
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self._CompileAndCheck(jnp_fun.outer, args_maker)
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@jtu.sample_product(
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SCALAR_FUNCS,
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[{'shape': shape, 'axis': axis}
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for shape in nonscalar_shapes
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for axis in [None, *range(-len(shape), len(shape))]],
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dtype=jtu.dtypes.floating,
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)
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def test_frompyfunc_reduce(self, func, nin, nout, identity, shape, axis, dtype):
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if (nin, nout) != (2, 1):
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self.skipTest(f"reduce requires (nin, nout)=(2, 1); got {(nin, nout)=}")
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jnp_fun = partial(jnp.frompyfunc(func, nin=nin, nout=nout, identity=identity).reduce, axis=axis)
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np_fun = cast_outputs(partial(np.frompyfunc(func, nin=nin, nout=nout, identity=identity).reduce, axis=axis))
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rng = jtu.rand_default(self.rng())
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args_maker = lambda: [rng(shape, dtype)]
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self._CheckAgainstNumpy(jnp_fun, np_fun, args_maker)
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self._CompileAndCheck(jnp_fun, args_maker)
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@jtu.sample_product(
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JAX_NUMPY_UFUNCS_WITH_DTYPES,
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[{'shape': shape, 'axis': axis}
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for shape in nonscalar_shapes
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for axis in [None, *range(-len(shape), len(shape))]],
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)
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def test_ufunc_reduce(self, name, shape, axis, dtype):
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jnp_fun = getattr(jnp, name)
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np_fun = getattr(np, name)
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if (jnp_fun.nin, jnp_fun.nout) != (2, 1):
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self.skipTest(f"reduce requires (nin, nout)=(2, 1); got {(jnp_fun.nin, jnp_fun.nout)=}")
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jnp_fun_reduce = partial(jnp_fun.reduce, axis=axis)
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np_fun_reduce = partial(np_fun.reduce, axis=axis)
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rng = jtu.rand_default(self.rng())
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args_maker = lambda: [rng(shape, dtype)]
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self._CheckAgainstNumpy(jnp_fun_reduce, np_fun_reduce, args_maker)
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self._CompileAndCheck(jnp_fun_reduce, args_maker)
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@jtu.sample_product(
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SCALAR_FUNCS,
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[{'shape': shape, 'axis': axis}
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for shape in nonscalar_shapes
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for axis in [None, *range(-len(shape), len(shape))]],
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dtype=jtu.dtypes.floating,
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)
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def test_frompyfunc_reduce_where(self, func, nin, nout, identity, shape, axis, dtype):
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if (nin, nout) != (2, 1):
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self.skipTest(f"reduce requires (nin, nout)=(2, 1); got {(nin, nout)=}")
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# Need initial if identity is None
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initial = 1 if identity is None else None
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def jnp_fun(arr, where):
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return jnp.frompyfunc(func, nin, nout, identity=identity).reduce(
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arr, where=where, axis=axis, initial=initial)
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@cast_outputs
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def np_fun(arr, where):
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# Workaround for https://github.com/numpy/numpy/issues/24530
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# TODO(jakevdp): remove this when possible.
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initial_workaround = identity if initial is None else initial
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return np.frompyfunc(func, nin=nin, nout=nout, identity=identity).reduce(
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arr, where=where, axis=axis, initial=initial_workaround)
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rng = jtu.rand_default(self.rng())
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rng_where = jtu.rand_bool(self.rng())
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args_maker = lambda: [rng(shape, dtype), rng_where(shape, bool)]
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self._CheckAgainstNumpy(jnp_fun, np_fun, args_maker)
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self._CompileAndCheck(jnp_fun, args_maker)
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@jtu.sample_product(
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JAX_NUMPY_UFUNCS_WITH_DTYPES,
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[{'shape': shape, 'axis': axis}
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for shape in nonscalar_shapes
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for axis in [None, *range(-len(shape), len(shape))]],
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)
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def test_ufunc_reduce_where(self, name, shape, axis, dtype):
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jnp_fun = getattr(jnp, name)
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np_fun = getattr(np, name)
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if (jnp_fun.nin, jnp_fun.nout) != (2, 1):
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self.skipTest(f"reduce requires (nin, nout)=(2, 1); got {(jnp_fun.nin, jnp_fun.nout)=}")
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if jnp_fun.identity is None:
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self.skipTest("reduce with where requires identity")
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jnp_fun_reduce = lambda a, where: jnp_fun.reduce(a, axis=axis, where=where)
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np_fun_reduce = lambda a, where: np_fun.reduce(a, axis=axis, where=where)
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rng = jtu.rand_default(self.rng())
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rng_where = jtu.rand_bool(self.rng())
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args_maker = lambda: [rng(shape, dtype), rng_where(shape, bool)]
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self._CheckAgainstNumpy(jnp_fun_reduce, np_fun_reduce, args_maker)
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self._CompileAndCheck(jnp_fun_reduce, args_maker)
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@jtu.sample_product(
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SCALAR_FUNCS,
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[{'shape': shape, 'axis': axis}
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for shape in nonscalar_shapes
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for axis in range(-len(shape), len(shape))],
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dtype=jtu.dtypes.floating,
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)
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def test_frompyfunc_accumulate(self, func, nin, nout, identity, shape, axis, dtype):
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if (nin, nout) != (2, 1):
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self.skipTest(f"accumulate requires (nin, nout)=(2, 1); got {(nin, nout)=}")
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jnp_fun = partial(jnp.frompyfunc(func, nin=nin, nout=nout, identity=identity).accumulate, axis=axis)
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np_fun = cast_outputs(partial(np.frompyfunc(func, nin=nin, nout=nout, identity=identity).accumulate, axis=axis))
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rng = jtu.rand_default(self.rng())
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args_maker = lambda: [rng(shape, dtype)]
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self._CheckAgainstNumpy(jnp_fun, np_fun, args_maker)
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self._CompileAndCheck(jnp_fun, args_maker)
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@jtu.sample_product(
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JAX_NUMPY_UFUNCS_WITH_DTYPES,
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[{'shape': shape, 'axis': axis}
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for shape in nonscalar_shapes
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for axis in range(-len(shape), len(shape))]
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)
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def test_ufunc_accumulate(self, name, shape, axis, dtype):
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jnp_fun = getattr(jnp, name)
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np_fun = getattr(np, name)
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if (jnp_fun.nin, jnp_fun.nout) != (2, 1):
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self.skipTest(f"accumulate requires (nin, nout)=(2, 1); got {(jnp_fun.nin, jnp_fun.nout)=}")
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rng = jtu.rand_default(self.rng())
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args_maker = lambda: [rng(shape, dtype)]
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jnp_fun_accumulate = partial(jnp_fun.accumulate, axis=axis)
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def np_fun_accumulate(x):
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# numpy accumulate has different dtype casting behavior.
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result = np_fun.accumulate(x, axis=axis)
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return result if x.dtype == bool else result.astype(x.dtype)
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self._CheckAgainstNumpy(jnp_fun_accumulate, np_fun_accumulate, args_maker)
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self._CompileAndCheck(jnp_fun_accumulate, args_maker)
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@jtu.sample_product(
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SCALAR_FUNCS,
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shape=nonscalar_shapes,
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idx_shape=[(), (2,)],
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dtype=jtu.dtypes.floating,
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)
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def test_frompyfunc_at(self, func, nin, nout, identity, shape, idx_shape, dtype):
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if (nin, nout) != (2, 1):
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self.skipTest(f"accumulate requires (nin, nout)=(2, 1); got {(nin, nout)=}")
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jnp_fun = partial(jnp.frompyfunc(func, nin=nin, nout=nout, identity=identity).at, inplace=False)
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def np_fun(x, idx, y):
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x_copy = x.copy()
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np.frompyfunc(func, nin=nin, nout=nout, identity=identity).at(x_copy, idx, y)
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return x_copy
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rng = jtu.rand_default(self.rng())
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idx_rng = jtu.rand_int(self.rng(), low=-shape[0], high=shape[0])
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args_maker = lambda: [rng(shape, dtype), idx_rng(idx_shape, 'int32'), rng(idx_shape[1:], dtype)]
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self._CheckAgainstNumpy(jnp_fun, np_fun, args_maker)
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self._CompileAndCheck(jnp_fun, args_maker)
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@jtu.sample_product(
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JAX_NUMPY_UFUNCS_WITH_DTYPES,
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shape=nonscalar_shapes,
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idx_shape=[(), (2,)],
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)
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def test_ufunc_at(self, name, shape, idx_shape, dtype):
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jnp_fun = getattr(jnp, name)
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np_fun = getattr(np, name)
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if (jnp_fun.nin, jnp_fun.nout) != (2, 1):
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self.skipTest(f"accumulate requires (nin, nout)=(2, 1); got {(jnp_fun.nin, jnp_fun.nout)=}")
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rng = jtu.rand_default(self.rng())
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idx_rng = jtu.rand_int(self.rng(), low=-shape[0], high=shape[0])
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args_maker = lambda: [rng(shape, dtype), idx_rng(idx_shape, 'int32'), rng(idx_shape[1:], dtype)]
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jnp_fun_at = partial(jnp_fun.at, inplace=False)
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def np_fun_at(x, idx, y):
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x_copy = x.copy()
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np_fun.at(x_copy, idx, y)
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return x_copy
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self._CheckAgainstNumpy(jnp_fun_at, np_fun_at, args_maker)
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self._CompileAndCheck(jnp_fun_at, args_maker)
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def test_frompyfunc_at_broadcasting(self):
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# Regression test for https://github.com/google/jax/issues/18004
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args_maker = lambda: [np.ones((5, 3)), np.array([0, 4, 2]),
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np.arange(9.0).reshape(3, 3)]
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def np_fun(x, idx, y):
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x_copy = np.copy(x)
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np.add.at(x_copy, idx, y)
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return x_copy
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jnp_fun = partial(jnp.frompyfunc(jnp.add, nin=2, nout=1, identity=0).at, inplace=False)
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|
|
|
self._CheckAgainstNumpy(jnp_fun, np_fun, args_maker)
|
|
self._CompileAndCheck(jnp_fun, args_maker)
|
|
|
|
@jtu.sample_product(
|
|
SCALAR_FUNCS,
|
|
[{'shape': shape, 'axis': axis}
|
|
for shape in nonscalar_shapes
|
|
for axis in [*range(-len(shape), len(shape))]],
|
|
idx_shape=[(0,), (3,), (5,)],
|
|
dtype=jtu.dtypes.floating,
|
|
)
|
|
def test_frompyfunc_reduceat(self, func, nin, nout, identity, shape, axis, idx_shape, dtype):
|
|
if (nin, nout) != (2, 1):
|
|
self.skipTest(f"accumulate requires (nin, nout)=(2, 1); got {(nin, nout)=}")
|
|
jnp_fun = partial(jnp.frompyfunc(func, nin=nin, nout=nout, identity=identity).reduceat, axis=axis)
|
|
np_fun = cast_outputs(partial(np.frompyfunc(func, nin=nin, nout=nout, identity=identity).reduceat, axis=axis))
|
|
|
|
rng = jtu.rand_default(self.rng())
|
|
idx_rng = jtu.rand_int(self.rng(), low=0, high=shape[axis])
|
|
args_maker = lambda: [rng(shape, dtype), idx_rng(idx_shape, 'int32')]
|
|
|
|
self._CheckAgainstNumpy(jnp_fun, np_fun, args_maker)
|
|
self._CompileAndCheck(jnp_fun, args_maker)
|
|
|
|
@jtu.sample_product(
|
|
JAX_NUMPY_UFUNCS_WITH_DTYPES,
|
|
[{'shape': shape, 'axis': axis}
|
|
for shape in nonscalar_shapes
|
|
for axis in [*range(-len(shape), len(shape))]],
|
|
idx_shape=[(0,), (3,), (5,)],
|
|
)
|
|
def test_ufunc_reduceat(self, name, shape, axis, idx_shape, dtype):
|
|
jnp_fun = getattr(jnp, name)
|
|
np_fun = getattr(np, name)
|
|
if (jnp_fun.nin, jnp_fun.nout) != (2, 1):
|
|
self.skipTest(f"accumulate requires (nin, nout)=(2, 1); got {(jnp_fun.nin, jnp_fun.nout)=}")
|
|
if name in ['add', 'multiply'] and dtype == bool:
|
|
# TODO(jakevdp): figure out how to fix thest cases.
|
|
self.skipTest(f"known failure for {name}.reduceat with {dtype=}")
|
|
|
|
rng = jtu.rand_default(self.rng())
|
|
idx_rng = jtu.rand_int(self.rng(), low=0, high=shape[axis])
|
|
args_maker = lambda: [rng(shape, dtype), idx_rng(idx_shape, 'int32')]
|
|
|
|
def np_fun_reduceat(x, i):
|
|
# Numpy has different casting behavior.
|
|
return np_fun.reduceat(x, i).astype(x.dtype)
|
|
|
|
self._CheckAgainstNumpy(jnp_fun.reduceat, np_fun_reduceat, args_maker)
|
|
self._CompileAndCheck(jnp_fun.reduceat, args_maker)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
absltest.main(testLoader=jtu.JaxTestLoader())
|