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316 lines
12 KiB
Python
316 lines
12 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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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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from jax._src.numpy.ufunc_api import get_if_single_primitive
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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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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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FASTPATH_FUNCS = [
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{'func': jnp.add, 'nin': 2, 'nout': 1, 'identity': 0,
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'reducer': jax.lax.reduce_sum_p, 'accumulator': jax.lax.cumsum_p},
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{'func': jnp.multiply, 'nin': 2, 'nout': 1, 'identity': 1,
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'reducer': jax.lax.reduce_prod_p, 'accumulator': jax.lax.cumprod_p},
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]
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NON_FASTPATH_FUNCS = [
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{'func': lambda a, b: jnp.add(a, a), 'nin': 2, 'nout': 1, 'identity': 0},
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{'func': lambda a, b: jnp.multiply(b, a), 'nin': 2, 'nout': 1, 'identity': 1},
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{'func': jax.jit(lambda a, b: jax.jit(jnp.multiply)(b, a)), 'nin': 2, 'nout': 1, 'identity': 1},
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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_ufunc_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(SCALAR_FUNCS)
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def test_ufunc_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', '_call']:
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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_ufunc_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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def test_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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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_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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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_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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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_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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FASTPATH_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_reduce_fastpath(self, func, nin, nout, identity, shape, axis, dtype, reducer, accumulator):
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del accumulator # unused
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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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rng = jtu.rand_default(self.rng())
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args = (rng(shape, dtype),)
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jnp_fun = partial(jnp.frompyfunc(func, nin=nin, nout=nout, identity=identity).reduce, axis=axis)
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self.assertEqual(get_if_single_primitive(jnp_fun, *args), reducer)
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@jtu.sample_product(
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NON_FASTPATH_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_non_fastpath(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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rng = jtu.rand_default(self.rng())
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args = (rng(shape, dtype),)
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_ = func(0, 0) # function should not error.
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reduce_fun = partial(jnp.frompyfunc(func, nin=nin, nout=nout, identity=identity).reduce, axis=axis)
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self.assertIsNone(get_if_single_primitive(reduce_fun, *args))
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accum_fun = partial(jnp.frompyfunc(func, nin=nin, nout=nout, identity=identity).accumulate, axis=axis)
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self.assertIsNone(get_if_single_primitive(accum_fun, *args))
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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_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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FASTPATH_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_accumulate_fastpath(self, func, nin, nout, identity, shape, axis, dtype, reducer, accumulator):
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del reducer # unused
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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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rng = jtu.rand_default(self.rng())
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args = (rng(shape, dtype),)
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jnp_fun = partial(jnp.frompyfunc(func, nin=nin, nout=nout, identity=identity).accumulate, axis=axis)
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self.assertEqual(get_if_single_primitive(jnp_fun, *args), accumulator)
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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_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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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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idx_shape=[(0,), (3,), (5,)],
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dtype=jtu.dtypes.floating,
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)
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def test_reduceat(self, func, nin, nout, identity, shape, axis, 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).reduceat, axis=axis)
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np_fun = cast_outputs(partial(np.frompyfunc(func, nin=nin, nout=nout, identity=identity).reduceat, axis=axis))
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rng = jtu.rand_default(self.rng())
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idx_rng = jtu.rand_int(self.rng(), low=0, high=shape[axis])
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args_maker = lambda: [rng(shape, dtype), idx_rng(idx_shape, 'int32')]
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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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if __name__ == "__main__":
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absltest.main(testLoader=jtu.JaxTestLoader())
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