mirror of
https://github.com/ROCm/jax.git
synced 2025-04-28 01:56:05 +00:00
175 lines
6.6 KiB
Python
175 lines
6.6 KiB
Python
# Copyright 2023 The JAX Authors.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# https://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
"""Tests for jax.numpy.ufunc and its methods."""
|
|
|
|
from functools import partial
|
|
|
|
from absl.testing import absltest
|
|
|
|
import numpy as np
|
|
import jax
|
|
import jax.numpy as jnp
|
|
from jax._src import test_util as jtu
|
|
|
|
from jax import config
|
|
config.parse_flags_with_absl()
|
|
FLAGS = config.FLAGS
|
|
|
|
|
|
def scalar_add(x, y):
|
|
assert np.shape(x) == np.shape(y) == ()
|
|
return x + y
|
|
|
|
|
|
def scalar_mul(x, y):
|
|
assert np.shape(x) == np.shape(y) == ()
|
|
return x * y
|
|
|
|
|
|
SCALAR_FUNCS = [
|
|
{'func': scalar_add, 'nin': 2, 'nout': 1, 'identity': 0},
|
|
{'func': scalar_mul, 'nin': 2, 'nout': 1, 'identity': 1},
|
|
]
|
|
|
|
broadcast_compatible_shapes = [(), (1,), (3,), (1, 3), (4, 1), (4, 3)]
|
|
nonscalar_shapes = [(3,), (4,), (4, 3)]
|
|
|
|
def cast_outputs(fun):
|
|
def wrapped(*args, **kwargs):
|
|
dtype = np.asarray(args[0]).dtype
|
|
return jax.tree_map(lambda x: np.asarray(x, dtype=dtype), fun(*args, **kwargs))
|
|
return wrapped
|
|
|
|
|
|
class LaxNumpyUfuncTests(jtu.JaxTestCase):
|
|
@jtu.sample_product(
|
|
SCALAR_FUNCS,
|
|
lhs_shape=broadcast_compatible_shapes,
|
|
rhs_shape=broadcast_compatible_shapes,
|
|
dtype=jtu.dtypes.floating,
|
|
)
|
|
def test_call(self, func, nin, nout, identity, lhs_shape, rhs_shape, dtype):
|
|
jnp_fun = jnp.frompyfunc(func, nin=nin, nout=nout, identity=identity)
|
|
np_fun = cast_outputs(np.frompyfunc(func, nin=nin, nout=nout, identity=identity))
|
|
|
|
rng = jtu.rand_default(self.rng())
|
|
args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)]
|
|
|
|
self._CheckAgainstNumpy(jnp_fun, np_fun, args_maker)
|
|
self._CompileAndCheck(jnp_fun, args_maker)
|
|
|
|
@jtu.sample_product(
|
|
SCALAR_FUNCS,
|
|
lhs_shape=broadcast_compatible_shapes,
|
|
rhs_shape=broadcast_compatible_shapes,
|
|
dtype=jtu.dtypes.floating,
|
|
)
|
|
def test_outer(self, func, nin, nout, identity, lhs_shape, rhs_shape, dtype):
|
|
if (nin, nout) != (2, 1):
|
|
self.skipTest(f"outer requires (nin, nout)=(2, 1); got {(nin, nout)=}")
|
|
jnp_fun = jnp.frompyfunc(func, nin=nin, nout=nout, identity=identity).outer
|
|
np_fun = cast_outputs(np.frompyfunc(func, nin=nin, nout=nout, identity=identity).outer)
|
|
|
|
rng = jtu.rand_default(self.rng())
|
|
args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)]
|
|
|
|
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 broadcast_compatible_shapes
|
|
for axis in [None, *range(-len(shape), len(shape))]],
|
|
dtype=jtu.dtypes.floating,
|
|
)
|
|
def test_reduce(self, func, nin, nout, identity, shape, axis, dtype):
|
|
if (nin, nout) != (2, 1):
|
|
self.skipTest(f"reduce requires (nin, nout)=(2, 1); got {(nin, nout)=}")
|
|
jnp_fun = partial(jnp.frompyfunc(func, nin=nin, nout=nout, identity=identity).reduce, axis=axis)
|
|
np_fun = cast_outputs(partial(np.frompyfunc(func, nin=nin, nout=nout, identity=identity).reduce, axis=axis))
|
|
|
|
rng = jtu.rand_default(self.rng())
|
|
args_maker = lambda: [rng(shape, dtype)]
|
|
|
|
self._CheckAgainstNumpy(jnp_fun, np_fun, args_maker)
|
|
self._CompileAndCheck(jnp_fun, args_maker, check_cache_misses=False) # TODO(jakevdp): why the cache misses?
|
|
|
|
@jtu.sample_product(
|
|
SCALAR_FUNCS,
|
|
[{'shape': shape, 'axis': axis}
|
|
for shape in broadcast_compatible_shapes
|
|
for axis in range(-len(shape), len(shape))],
|
|
dtype=jtu.dtypes.floating,
|
|
)
|
|
def test_accumulate(self, func, nin, nout, identity, shape, axis, 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).accumulate, axis=axis)
|
|
np_fun = cast_outputs(partial(np.frompyfunc(func, nin=nin, nout=nout, identity=identity).accumulate, axis=axis))
|
|
|
|
rng = jtu.rand_default(self.rng())
|
|
args_maker = lambda: [rng(shape, dtype)]
|
|
|
|
self._CheckAgainstNumpy(jnp_fun, np_fun, args_maker)
|
|
self._CompileAndCheck(jnp_fun, args_maker, check_cache_misses=False) # TODO(jakevdp): why the cache misses?
|
|
|
|
@jtu.sample_product(
|
|
SCALAR_FUNCS,
|
|
shape=nonscalar_shapes,
|
|
idx_shape=[(), (2,)],
|
|
dtype=jtu.dtypes.floating,
|
|
)
|
|
def test_at(self, func, nin, nout, identity, shape, 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).at, inplace=False)
|
|
def np_fun(x, idx, y):
|
|
x_copy = x.copy()
|
|
np.frompyfunc(func, nin=nin, nout=nout, identity=identity).at(x_copy, idx, y)
|
|
return x_copy
|
|
|
|
rng = jtu.rand_default(self.rng())
|
|
idx_rng = jtu.rand_int(self.rng(), low=-shape[0], high=shape[0])
|
|
args_maker = lambda: [rng(shape, dtype), idx_rng(idx_shape, 'int32'), rng(idx_shape[1:], dtype)]
|
|
|
|
self._CheckAgainstNumpy(jnp_fun, np_fun, args_maker)
|
|
self._CompileAndCheck(jnp_fun, args_maker, check_cache_misses=False) # TODO(jakevdp): why the cache misses?
|
|
|
|
@jtu.sample_product(
|
|
SCALAR_FUNCS,
|
|
[{'shape': shape, 'axis': axis}
|
|
for shape in broadcast_compatible_shapes
|
|
for axis in [*range(-len(shape), len(shape))]],
|
|
idx_shape=[(0,), (3,), (5,)],
|
|
dtype=jtu.dtypes.floating,
|
|
)
|
|
def test_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, check_cache_misses=False) # TODO(jakevdp): why the cache misses?
|
|
|
|
|
|
if __name__ == "__main__":
|
|
absltest.main(testLoader=jtu.JaxTestLoader())
|