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lax_numpy_indexing_test.py
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lax_numpy_indexing_test.py
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# Copyright 2018 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.
import enum
from functools import partial
import itertools
import typing
from typing import Any, Optional
from absl.testing import absltest
from absl.testing import parameterized
import numpy as np
import jax
from jax import lax
from jax import numpy as jnp
from jax import ops
from jax._src import config
from jax._src import dtypes
from jax._src import test_util as jtu
from jax._src import util
from jax._src.lax import lax as lax_internal
from jax._src.util import NumpyComplexWarning
config.parse_flags_with_absl()
# We disable the whitespace continuation check in this file because otherwise it
# makes the test name formatting unwieldy.
# pylint: disable=bad-continuation
ARRAY_MSG = r"Using a non-tuple sequence for multidimensional indexing is not allowed.*arr\[array\(seq\)\]"
TUPLE_MSG = r"Using a non-tuple sequence for multidimensional indexing is not allowed.*arr\[tuple\(seq\)\]"
float_dtypes = jtu.dtypes.floating
default_dtypes = float_dtypes + jtu.dtypes.integer
all_dtypes = default_dtypes + jtu.dtypes.boolean
class IndexSpec(typing.NamedTuple):
shape: tuple[int, ...]
indexer: Any
out_shape: Optional[tuple[int, ...]] = None
def check_grads(f, args, order, atol=None, rtol=None, eps=None):
# TODO(mattjj,dougalm): add higher-order check
default_tol = 1e-6 if config.enable_x64.value else 1e-2
atol = atol or default_tol
rtol = rtol or default_tol
eps = eps or default_tol
jtu.check_jvp(f, partial(jax.jvp, f), args, atol, rtol, eps)
jtu.check_vjp(f, partial(jax.vjp, f), args, atol, rtol, eps)
STATIC_INDEXING_TESTS = [
("OneIntIndex", [
IndexSpec(shape=(3,), indexer=1, out_shape=()),
IndexSpec(shape=(3, 3), indexer=0, out_shape=(3,)),
IndexSpec(shape=(3, 4, 5), indexer=2, out_shape=(4, 5)),
IndexSpec(shape=(3,), indexer=-1, out_shape=()),
IndexSpec(shape=(3,), indexer=-2, out_shape=()),
]),
("TwoIntIndices", [
IndexSpec(shape=(3, 3), indexer=(2, 1), out_shape=()),
IndexSpec(shape=(3, 4, 5), indexer=(1, 2), out_shape=(5,)),
IndexSpec(shape=(3, 4, 5), indexer=(-1, 2), out_shape=(5,)),
]),
("ThreeIntIndices", [
IndexSpec(shape=(3, 4, 5), indexer=(1, 2, 3), out_shape=()),
]),
("OneSliceIndex", [
IndexSpec(shape=(10,), indexer=slice(1, 3), out_shape=(2,)),
IndexSpec(shape=(10,), indexer=slice(1, -1), out_shape=(8,)),
IndexSpec(shape=(10,), indexer=slice(None, -1), out_shape=(9,)),
IndexSpec(shape=(10,), indexer=slice(None, None, None), out_shape=(10,)),
IndexSpec(shape=(10, 8), indexer=slice(1, 3), out_shape=(2, 8)),
IndexSpec(shape=(10, 8), indexer=slice(1, None), out_shape=(9, 8)),
IndexSpec(shape=(10, 8), indexer=slice(None, 3), out_shape=(3, 8)),
IndexSpec(shape=(10, 8), indexer=slice(-3, None), out_shape=(3, 8)),
]),
("OneSliceIndexNegativeStride", [
IndexSpec(shape=(10,), indexer=slice(3, 1, -1), out_shape=(2,)),
IndexSpec(shape=(10,), indexer=slice(1, 8, -1), out_shape=(0,)),
IndexSpec(shape=(10,), indexer=slice(None, 1, -2), out_shape=(4,)),
IndexSpec(shape=(10,), indexer=slice(None, None, -1), out_shape=(10,)),
IndexSpec(shape=(10, 8), indexer=slice(3, 1, -1), out_shape=(2, 8)),
IndexSpec(shape=(10, 8), indexer=slice(0, 8, -1), out_shape=(0, 8)),
IndexSpec(shape=(10, 8), indexer=slice(None, None, -1), out_shape=(10, 8)),
]),
("OneSliceIndexNonUnitStride", [
IndexSpec(shape=(10,), indexer=slice(0, 8, 2), out_shape=(4,)),
IndexSpec(shape=(10,), indexer=slice(0, 8, 3), out_shape=(3,)),
IndexSpec(shape=(10,), indexer=slice(1, 3, 2), out_shape=(1,)),
IndexSpec(shape=(10,), indexer=slice(1, None, 2), out_shape=(5,)),
IndexSpec(shape=(10,), indexer=slice(None, 1, -2), out_shape=(4,)),
IndexSpec(shape=(10, 8), indexer=slice(1, 8, 3), out_shape=(3, 8)),
IndexSpec(shape=(10, 8), indexer=slice(None, None, 2), out_shape=(5, 8)),
IndexSpec(shape=(10, 8), indexer=slice(None, 1, -2), out_shape=(4, 8)),
IndexSpec(shape=(10, 8), indexer=slice(None, None, -2), out_shape=(5, 8)),
]),
("TwoSliceIndices", [
IndexSpec(shape=(10, 8), indexer=(slice(1, 3), slice(0, 2)),
out_shape=(2, 2)),
IndexSpec(shape=(10, 8), indexer=(slice(1, None), slice(None, 2)),
out_shape=(9, 2)),
IndexSpec(shape=(10, 8), indexer=(slice(None, None, -1), slice(None, 2)),
out_shape=(10, 2)),
IndexSpec(shape=(10, 8, 3), indexer=(slice(1, 3), slice(0, 2)),
out_shape=(2, 2, 3)),
IndexSpec(shape=(10, 8, 3), indexer=(slice(1, 3), slice(0, None)),
out_shape=(2, 8, 3)),
IndexSpec(shape=(10, 8, 3), indexer=(slice(1, None), slice(0, 2)),
out_shape=(9, 2, 3)),
]),
("OneColonIndex", [
IndexSpec(shape=(3,), indexer=slice(None), out_shape=(3,)),
IndexSpec(shape=(3, 4), indexer=slice(None), out_shape=(3, 4)),
]),
("MultipleColonIndices", [
IndexSpec(shape=(3, 4), indexer=(slice(None), slice(None)),
out_shape=(3, 4)),
IndexSpec(shape=(3, 4, 5), indexer=(slice(None), slice(None)),
out_shape=(3, 4, 5)),
]),
("MixedSliceIndices", [
IndexSpec(shape=(10, 4), indexer=(slice(None), slice(0, 2)),
out_shape=(10, 2)),
IndexSpec(shape=(10, 4), indexer=(1, slice(None)),
out_shape=(4,)),
]),
("EllipsisIndex", [
IndexSpec(shape=(3,), indexer=Ellipsis, out_shape=(3,)),
IndexSpec(shape=(3, 4), indexer=Ellipsis, out_shape=(3, 4)),
IndexSpec(shape=(3, 4, 5), indexer=(0, Ellipsis), out_shape=(4, 5)),
IndexSpec(shape=(3, 4, 5), indexer=(Ellipsis, 2, 3), out_shape=(3,)),
]),
("NoneIndex", [
IndexSpec(shape=(), indexer=None, out_shape=(1,)),
IndexSpec(shape=(), indexer=(None, None), out_shape=(1, 1)),
IndexSpec(shape=(), indexer=(Ellipsis, None), out_shape=(1,)),
IndexSpec(shape=(3,), indexer=None, out_shape=(1, 3)),
IndexSpec(shape=(3, 4), indexer=None, out_shape=(1, 3, 4)),
IndexSpec(shape=(3, 4), indexer=(Ellipsis, None), out_shape=(3, 4, 1)),
IndexSpec(shape=(3, 4), indexer=(0, None, Ellipsis), out_shape=(1, 4)),
IndexSpec(shape=(3, 4, 5), indexer=(1, None, Ellipsis), out_shape=(1, 4, 5)),
]),
("EmptyIndex", [
IndexSpec(shape=(), indexer=(), out_shape=()),
IndexSpec(shape=(3,), indexer=(), out_shape=(3,)),
IndexSpec(shape=(3, 4), indexer=(), out_shape=(3, 4)),
]),
("TupleOfIntAndSliceAndIntArray", [
IndexSpec(shape=(3, 2, 3), indexer=(0, slice(None), np.arange(3)),
out_shape=(3, 2)),
IndexSpec(shape=(3, 2, 3), indexer=(np.int32(1), slice(None), np.arange(3)),
out_shape=(3, 2)),
IndexSpec(shape=(3, 2, 3), indexer=(np.array(2), slice(None), np.arange(3)),
out_shape=(3, 2)),
]),
]
STATIC_INDEXING_OUT_OF_BOUNDS_TESTS = [
("OneIntIndex", [
IndexSpec(shape=(3,), indexer=-4, out_shape=()),
IndexSpec(shape=(3, 3), indexer=3, out_shape=(3,)),
IndexSpec(shape=(3, 4, 5), indexer=4, out_shape=(4, 5)),
]),
("TwoIntIndices", [
IndexSpec(shape=(3, 3), indexer=(2, -4), out_shape=()),
IndexSpec(shape=(3, 4, 5), indexer=(3, 2), out_shape=()),
IndexSpec(shape=(3, 4, 5), indexer=(-4, 4), out_shape=(5,)),
]),
]
ADVANCED_INDEXING_TESTS = [
("One1DIntArrayIndex", [
IndexSpec(shape=(3,), indexer=np.array([0, 1]), out_shape=(2,)),
IndexSpec(shape=(3, 3), indexer=np.array([1, 2, 1]), out_shape=(3, 3)),
IndexSpec(shape=(3, 4, 5), indexer=np.array([0, 2, 0, 1]),
out_shape=(4, 4, 5)),
IndexSpec(shape=(3,), indexer=np.array([-1, 1]), out_shape=(2,)),
IndexSpec(shape=(3,), indexer=np.array([-2, -1]), out_shape=(2,)),
IndexSpec(shape=(0,), indexer=np.array([], dtype=np.int32),
out_shape=(0,)),
]),
("One2DIntArrayIndex", [
IndexSpec(shape=(3,), indexer=np.array([[0, 0]]),out_shape=(1, 2)),
IndexSpec(shape=(3, 3), indexer=np.array([[1, 2, 1], [0, 1, -1]]),
out_shape=(2, 3, 3)),
IndexSpec(shape=(3, 4, 5), indexer=np.array([[0, 2, 0, 1], [-1, -2, 1, 0]]),
out_shape=(2, 4, 4, 5)),
]),
("Two1DIntArrayIndicesNoBroadcasting", [
IndexSpec(shape=(3, 3), indexer=(np.array([0, 1]), np.array([1, 2])),
out_shape=(2,)),
IndexSpec(shape=(3, 4, 5),
indexer=(np.array([0, 2, 0, 1]), np.array([-1, 0, -1, 2])),
out_shape=(4, 5)),
]),
("Two1DIntArrayIndicesWithBroadcasting", [
IndexSpec(shape=(3, 3), indexer=(np.array([[0, 1]]), np.array([1, 2])),
out_shape=(1, 2)),
IndexSpec(shape=(3, 4, 5),
indexer=(np.array([[0, 2, 0, 1]]), np.array([-1, 0, -1, 2])),
out_shape=(1, 4, 5)),
]),
("ArrayOfInts", [
IndexSpec(shape=(3,), indexer=np.array([0, 1, 0]), out_shape=(3,)),
IndexSpec(shape=(3, 4, 5), indexer=np.array([ 0, -1]), out_shape=(2, 4, 5)),
]),
("TupleOfListsOfPythonInts", [
IndexSpec(shape=(3, 4, 5), indexer=([0, 1],), out_shape=(2, 4, 5)),
IndexSpec(shape=(3, 4, 5), indexer=([[0], [-1]], [[2, 3, 0, 3]]),
out_shape=(2, 4, 5)),
]),
("TupleOfPythonIntsAndIntArrays", [
IndexSpec(shape=(3, 4, 5), indexer=(0, np.array([0, 1])), out_shape=(2, 5)),
IndexSpec(shape=(3, 4, 5), indexer=(0, 1, np.array([[2, 3, 0, 3]])),
out_shape=(1, 4)),
]),
("TupleOfListsOfPythonIntsAndIntArrays", [
IndexSpec(shape=(3, 4, 5), indexer=([0, 1], np.array([0])),
out_shape=(2, 5)),
IndexSpec(shape=(3, 4, 5), indexer=([[0], [-1]], np.array([[2, 3, 0, 3]])),
out_shape=(2, 4, 5)),
]),
]
ADVANCED_INDEXING_TESTS_NO_REPEATS = [
("One1DIntArrayIndex", [
IndexSpec(shape=(3,), indexer=np.array([0, 1]), out_shape=(2,)),
IndexSpec(shape=(3, 3), indexer=np.array([1, 2, 0]), out_shape=(3, 3)),
IndexSpec(shape=(3, 4, 5), indexer=np.array([0, 2, 1]),
out_shape=(3, 4, 5)),
IndexSpec(shape=(3,), indexer=np.array([-1, 1]), out_shape=(2,)),
IndexSpec(shape=(3,), indexer=np.array([-2, -1]), out_shape=(2,)),
IndexSpec(shape=(0,), indexer=np.array([], dtype=np.int32), out_shape=(0,)),
]),
("One2DIntArrayIndex", [
IndexSpec(shape=(3,), indexer=np.array([[0, 1]]), out_shape=(1, 2)),
IndexSpec(shape=(6, 6), indexer=np.array([[1, 2, 0], [3, 4, -1]]),
out_shape=(2, 3, 6)),
]),
("Two1DIntArrayIndicesNoBroadcasting", [
IndexSpec(shape=(3, 3), indexer=(np.array([0, 1]), np.array([1, 2])),
out_shape=(2,)),
IndexSpec(shape=(4, 5, 6),
indexer=(np.array([0, 2, 1, 3]), np.array([-1, 0, -2, 1])),
out_shape=(4, 6)),
]),
("Two1DIntArrayIndicesWithBroadcasting", [
IndexSpec(shape=(3, 3), indexer=(np.array([[0, 1]]), np.array([1, 2])),
out_shape=(1, 2)),
IndexSpec(shape=(4, 5, 6),
indexer=(np.array([[0, 2, -1, 1]]), np.array([-1, 0, -2, 2])),
out_shape=(1, 4, 6)),
]),
("ArrayOfInts", [
IndexSpec(shape=(3,), indexer=np.array([0, 2, 1]), out_shape=(3,)),
IndexSpec(shape=(3, 4, 5), indexer=np.array([ 0, -1]), out_shape=(2, 4, 5)),
]),
("TupleOfListsOfPythonInts", [
IndexSpec(shape=(3, 4, 5), indexer=([0, 1],), out_shape=(2, 4, 5)),
IndexSpec(shape=(3, 4, 5), indexer=([[0], [-1]], [[2, 3, 0]]),
out_shape=(2, 3, 5)),
]),
("TupleOfPythonIntsAndIntArrays", [
IndexSpec(shape=(3, 4, 5), indexer=(0, np.array([0, 1])), out_shape=(2, 5)),
IndexSpec(shape=(3, 4, 5), indexer=(0, 1, np.array([[2, 3, 0]])),
out_shape=(1, 3)),
]),
("TupleOfListsOfPythonIntsAndIntArrays", [
IndexSpec(shape=(3, 4, 5), indexer=([0, 1], np.array([0])),
out_shape=(2, 5)),
IndexSpec(shape=(3, 4, 5), indexer=([[0], [-1]], np.array([[2, 3, 0]])),
out_shape=(2, 3, 5)),
]),
]
ADVANCED_INDEXING_TESTS_NO_REPEATS_SORTED = [
("One1DIntArrayIndex", [
IndexSpec(shape=(3,), indexer=np.array([0, 1]), out_shape=(2,)),
IndexSpec(shape=(3, 3), indexer=np.array([0, 1, 2]), out_shape=(3, 3)),
IndexSpec(shape=(3, 4, 5), indexer=np.array([0, 1, 2]),
out_shape=(3, 4, 5)),
IndexSpec(shape=(3,), indexer=np.array([-1, 1]), out_shape=(2,)),
IndexSpec(shape=(3,), indexer=np.array([-2, -1]), out_shape=(2,)),
IndexSpec(shape=(0,), indexer=np.array([], dtype=np.int32), out_shape=(0,)),
]),
("One2DIntArrayIndex", [
IndexSpec(shape=(3,), indexer=np.array([[0, 1]]), out_shape=(1, 2)),
IndexSpec(shape=(6, 6), indexer=np.array([[-1, 0, 1],
[ 2, 3, 4]]), out_shape=(2, 3, 6)),
]),
("Two1DIntArrayIndicesNoBroadcasting", [
IndexSpec(shape=(3, 3), indexer=(np.array([0, 1]), np.array([1, 2])),
out_shape=(2,)),
IndexSpec(shape=(4, 5, 6),
indexer=(np.array([0, 1, 2, 3]), np.array([-2, -1, 0, 1])),
out_shape=(4, 6)),
]),
("Two1DIntArrayIndicesWithBroadcasting", [
IndexSpec(shape=(3, 3), indexer=(np.array([[0, 1]]), np.array([1, 2])),
out_shape=(1, 2)),
IndexSpec(shape=(4, 5, 6),
indexer=(np.array([[-1, 0, 1, 2]]), np.array([-2, -1, 0, 2])),
out_shape=(1, 4, 6)),
]),
("TupleOfListsOfPythonInts", [
IndexSpec(shape=(3, 4, 5), indexer=([0, 1],), out_shape=(2, 4, 5)),
IndexSpec(shape=(3, 4, 5), indexer=([[0], [-1]], [[0, 2, 3]]),
out_shape=(2, 3, 5)),
]),
("TupleOfPythonIntsAndIntArrays", [
IndexSpec(shape=(3, 4, 5), indexer=(0, np.array([0, 1])), out_shape=(2, 5)),
IndexSpec(shape=(3, 4, 5), indexer=(0, 1, np.array([[0, 2, 3]])),
out_shape=(1, 3)),
]),
("TupleOfListsOfPythonIntsAndIntArrays", [
IndexSpec(shape=(3, 4, 5), indexer=([0, 1], np.array([0])),
out_shape=(2, 5)),
IndexSpec(shape=(3, 4, 5), indexer=([[0], [-1]], np.array([[0, 2, 3]])),
out_shape=(2, 3, 5)),
]),
]
MIXED_ADVANCED_INDEXING_TESTS_NO_REPEATS = [
("SlicesAndOneIntArrayIndex", [
IndexSpec(shape=(2, 3), indexer=(np.array([0, 1]), slice(1, 2)),
out_shape=(2, 1)),
IndexSpec(shape=(2, 3), indexer=(slice(0, 2), np.array([0, 2])),
out_shape=(2, 2)),
IndexSpec(shape=(3, 4, 5),
indexer=(Ellipsis, np.array([0, 2]), slice(None)),
out_shape=(3, 2, 5)),
IndexSpec(shape=(3, 4, 5),
indexer=(Ellipsis, np.array([[0, 2], [1, 3]]), slice(None)),
out_shape=(3, 2, 2, 5)),
]),
("SlicesAndTwoIntArrayIndices", [
IndexSpec(shape=(3, 4, 5),
indexer=(Ellipsis, np.array([0, 2]), np.array([-1, 2])),
out_shape=(3, 2)),
IndexSpec(shape=(3, 4, 5),
indexer=(np.array([0, 2]), Ellipsis, np.array([-1, 2])),
out_shape=(2, 4)),
IndexSpec(shape=(3, 4, 5),
indexer=(np.array([0, 2]), np.array([-1, 2]), Ellipsis),
out_shape=(2, 5)),
IndexSpec(shape=(3, 4, 5),
indexer=(np.array([0, 2]), np.array([-1, 2]), slice(1, 3)),
out_shape=(2, 2)),
IndexSpec(shape=(3, 4, 5),
indexer=(np.array([0, 2]), slice(1, 3), np.array([-1, 2])),
out_shape=(2, 2)),
IndexSpec(shape=(3, 4, 5),
indexer=(np.array([ 0, 2, -2]), slice(None, None, 2),
np.array([-1, 2, 1])),
out_shape=(3, 2)),
]),
("NonesAndIntArrayIndices", [
IndexSpec(shape=(3, 4, 5),
indexer=(np.array([0, 2]), None, np.array([-1, 2])),
out_shape=(2, 1, 5)),
IndexSpec(shape=(3, 4, 5),
indexer=(np.array([0, 2]), None, None, np.array([-1, 2])),
out_shape=(2, 1, 1, 5)),
IndexSpec(shape=(3, 4, 5),
indexer=(Ellipsis, np.array([0, 2]), None, None,
np.array([-1, 2])),
out_shape=(2, 3, 1, 1)),
]),
("IntArrayWithInt32Type", [
IndexSpec(shape=(3, 4), indexer=(Ellipsis, np.array(1, dtype=np.int32)),
out_shape=(3,)),
]),
]
MIXED_ADVANCED_INDEXING_TESTS = MIXED_ADVANCED_INDEXING_TESTS_NO_REPEATS + [
("SlicesAndOneIntArrayIndex", [
IndexSpec(shape=(3, 4, 5),
indexer=(Ellipsis, np.array([[0, 2], [1, 1]]), slice(None)),
out_shape=(3, 2, 2, 5)),
]),
("SlicesAndTwoIntArrayIndices", [
IndexSpec(shape=(3, 4, 5),
indexer=(np.array([ 0, 2, -2]), slice(None, None, 2),
np.array([-1, 2, -1])),
out_shape=(3, 2)),
IndexSpec(shape=(3, 4, 5),
indexer=(np.array([[0, 2], [2, 0]]), Ellipsis,
np.array([[1, 0], [1, 0]])),
out_shape=(2, 2, 4)),
]),
]
MODES = ["clip", "drop", "promise_in_bounds"]
class IndexingTest(jtu.JaxTestCase):
"""Tests for Numpy indexing translation rules."""
@jtu.sample_product(
[dict(name=name, shape=shape, indexer=indexer)
for name, index_specs in STATIC_INDEXING_TESTS
for shape, indexer, _ in index_specs],
dtype=all_dtypes
)
def testStaticIndexing(self, name, shape, dtype, indexer):
rng = jtu.rand_default(self.rng())
args_maker = lambda: [rng(shape, dtype)]
np_fun = lambda x: np.asarray(x)[indexer]
jnp_fun = lambda x: jnp.asarray(x)[indexer]
self._CheckAgainstNumpy(np_fun, jnp_fun, args_maker)
self._CompileAndCheck(jnp_fun, args_maker)
# Tests x.at[...].get(...) as well.
jnp_fun = lambda x: jnp.asarray(x).at[indexer].get()
self._CheckAgainstNumpy(np_fun, jnp_fun, args_maker)
self._CompileAndCheck(jnp_fun, args_maker)
@jtu.sample_product(
funcname=["negative", "sin", "cos", "square", "sqrt", "log", "exp"],
)
def testIndexApply(self, funcname, size=10, dtype='float32'):
rng = jtu.rand_default(self.rng())
idx_rng = jtu.rand_int(self.rng(), -size, size)
np_func = getattr(np, funcname)
jnp_func = getattr(jnp, funcname)
@jtu.ignore_warning(category=RuntimeWarning)
def np_op(x, idx):
y = x.copy()
np_func.at(y, idx)
return y
def jnp_op(x, idx):
return jnp.asarray(x).at[idx].apply(jnp_func)
# Test with traced integer index
args_maker = lambda: [rng(size, dtype), idx_rng(size, int)]
tol = (
5e-5
if jtu.test_device_matches(["tpu"]) and funcname in ("log", "exp")
else None
)
self._CheckAgainstNumpy(np_op, jnp_op, args_maker, atol=tol)
self._CompileAndCheck(jnp_op, args_maker)
# Test with slice index
idx = slice(1, 5)
np_op_idx = partial(np_op, idx=idx)
jnp_op_idx = partial(jnp_op, idx=idx)
args_maker = lambda: [rng(size, dtype)]
self._CheckAgainstNumpy(np_op_idx, jnp_op_idx, args_maker, atol=tol,
rtol=tol)
self._CompileAndCheck(jnp_op_idx, args_maker)
def testIndexApplyBatchingBug(self):
# https://github.com/google/jax/issues/16655
arr = jnp.array([[1, 2, 3, 4, 5, 6]])
ind = jnp.array([3])
func = lambda a, i: a.at[i].apply(lambda x: x - 1)
expected = jnp.array(list(map(func, arr, ind)))
out = jax.vmap(func)(arr, ind)
self.assertArraysEqual(out, expected)
def testIndexUpdateScalarBug(self):
# https://github.com/google/jax/issues/14923
a = jnp.arange(10.)
out = a.at[0].apply(jnp.cos)
self.assertArraysEqual(out, a.at[0].set(1))
@jtu.sample_product(
[dict(name=name, shape=shape, indexer=indexer, mode=mode)
for mode in MODES
for name, index_specs in (
STATIC_INDEXING_TESTS if mode == "promise_in_bounds" else
STATIC_INDEXING_TESTS + STATIC_INDEXING_OUT_OF_BOUNDS_TESTS)
for shape, indexer, _ in index_specs
],
dtype=float_dtypes,
)
def testStaticIndexingGrads(self, name, shape, dtype, indexer, mode):
rng = jtu.rand_default(self.rng())
tol = 1e-2 if jnp.finfo(dtype).bits == 32 else None
arg = rng(shape, dtype)
# Use an arbitrary finite fill_value, since NaNs won't work in a numerical
# gradient test.
fun = lambda x: jnp.asarray(x).at[indexer].get(mode=mode, fill_value=7)**2
check_grads(fun, (arg,), 2, tol, tol, tol)
def _ReplaceSlicesWithTuples(self, idx):
"""Helper method to replace slices with tuples for dynamic indexing args."""
if isinstance(idx, slice):
triple = idx.start, idx.stop, idx.step
isnone = [i for i, elt in enumerate(triple) if elt is None]
zeros = itertools.repeat(0)
nones = itertools.repeat(None)
out = util.subvals(triple, zip(isnone, zeros))
return out, lambda out: slice(*util.subvals(out, zip(isnone, nones)))
elif isinstance(idx, (tuple, list)) and idx:
t = type(idx)
elts, packs = zip(*map(self._ReplaceSlicesWithTuples, idx))
return elts, lambda elts: t((pack(i) for pack, i in zip(packs, elts)))
else:
return idx, lambda x: x
@jtu.sample_product(
[dict(name=name, shape=shape, indexer=indexer)
for name, index_specs in [
("OneSliceIndex",
[IndexSpec(shape=(5,), indexer=slice(1, 3)),
IndexSpec(shape=(5, 4), indexer=slice(1, 3))]),
("TwoSliceIndices",
[IndexSpec(shape=(5, 4), indexer=(slice(1, 3), slice(0, 2))),
IndexSpec(shape=(5, 4, 3), indexer=(slice(1, 3), slice(0, 2)))]),
("NonUnitStrides", [
IndexSpec(shape=(3,), indexer=slice(None, None, -1)),
IndexSpec(shape=(3, 3), indexer=slice(0, 3, -2)),
IndexSpec(shape=(3, 4, 5), indexer=slice(0, 4, 2))
]),
("OnlyStartOrStopDynamic", [
IndexSpec(shape=(5, 4), indexer=(slice(None, 3), slice(0, 2))),
IndexSpec(shape=(5, 4, 3), indexer=(slice(1, 3), slice(0, None)))
]),
]
for shape, indexer, _ in index_specs
],
dtype=all_dtypes,
)
def testDynamicIndexingWithSlicesErrors(self, name, shape, dtype, indexer):
rng = jtu.rand_default(self.rng())
unpacked_indexer, pack_indexer = self._ReplaceSlicesWithTuples(indexer)
@jax.jit
def fun(x, unpacked_indexer):
indexer = pack_indexer(unpacked_indexer)
return x[indexer]
args_maker = lambda: [rng(shape, dtype), unpacked_indexer]
self.assertRaises(IndexError, lambda: fun(*args_maker()))
@jtu.sample_product(
[dict(name=name, shape=shape, indexer=indexer)
for name, index_specs in [
("OneIntIndex",
[IndexSpec(shape=(3,), indexer=1),
IndexSpec(shape=(3, 3), indexer=0),
IndexSpec(shape=(3, 4, 5), indexer=2),
IndexSpec(shape=(3,), indexer=-1),
IndexSpec(shape=(3,), indexer=-2)]),
("TwoIntIndices",
[IndexSpec(shape=(3, 3), indexer=(2, 1)),
IndexSpec(shape=(3, 4, 5), indexer=(1, 2)),
IndexSpec(shape=(3, 4, 5), indexer=(-1, 2))]),
("ThreeIntIndices",
[IndexSpec((3, 4, 5), indexer=(1, 2, 3))]),
]
for shape, indexer, _ in index_specs
],
dtype=all_dtypes,
)
def testDynamicIndexingWithIntegers(self, name, shape, dtype, indexer):
rng = jtu.rand_default(self.rng())
unpacked_indexer, pack_indexer = self._ReplaceSlicesWithTuples(indexer)
def np_fun(x, unpacked_indexer):
indexer = pack_indexer(unpacked_indexer)
return np.asarray(x)[indexer]
def jnp_fun(x, unpacked_indexer):
indexer = pack_indexer(unpacked_indexer)
return jnp.array(x)[indexer]
args_maker = lambda: [rng(shape, dtype), unpacked_indexer]
self._CheckAgainstNumpy(np_fun, jnp_fun, args_maker)
self._CompileAndCheck(jnp_fun, args_maker)
@jtu.sample_product(
[dict(name=name, shape=shape, indexer=indexer)
for name, index_specs in [
("OneIntIndex",
[IndexSpec(shape=(3,), indexer=1),
IndexSpec(shape=(3, 3), indexer=0),
IndexSpec(shape=(3, 4, 5), indexer=2),
IndexSpec(shape=(3,), indexer=-1),
IndexSpec(shape=(3,), indexer=-2),
]),
("TwoIntIndices",
[IndexSpec(shape=(3, 3), indexer=(2, 1)),
IndexSpec(shape=(3, 4, 5), indexer=(1, 2)),
IndexSpec(shape=(3, 4, 5), indexer=(-1, 2)),
]),
("ThreeIntIndices",
[IndexSpec((3, 4, 5), indexer=(1, 2, 3))]),
]
for shape, indexer, _ in index_specs
],
dtype=float_dtypes,
)
def testDynamicIndexingWithIntegersGrads(self, name, shape, dtype, indexer):
rng = jtu.rand_default(self.rng())
tol = 1e-2 if jnp.finfo(dtype).bits == 32 else None
unpacked_indexer, pack_indexer = self._ReplaceSlicesWithTuples(indexer)
@jax.jit
def fun(unpacked_indexer, x):
indexer = pack_indexer(unpacked_indexer)
return x[indexer]
arr = rng(shape, dtype)
check_grads(partial(fun, unpacked_indexer), (arr,), 2, tol, tol, tol)
@jtu.sample_product(
[dict(name=name, shape=shape, indexer=indexer)
for name, index_specs in ADVANCED_INDEXING_TESTS
for shape, indexer, _ in index_specs
],
dtype=all_dtypes,
)
def testAdvancedIntegerIndexing(self, name, shape, dtype, indexer):
rng = jtu.rand_default(self.rng())
args_maker = lambda: [rng(shape, dtype), indexer]
np_fun = lambda x, idx: np.asarray(x)[idx]
jnp_fun = lambda x, idx: jnp.asarray(x)[idx]
self._CheckAgainstNumpy(np_fun, jnp_fun, args_maker)
self._CompileAndCheck(jnp_fun, args_maker)
@jtu.sample_product(dtype=jtu.dtypes.unsigned + jtu.dtypes.integer)
def testIndicesNormalizationByType(self, dtype):
x = jnp.arange(10)
jaxpr = jax.make_jaxpr(x.__getitem__)(jnp.arange(3, dtype=dtype))
primitives = [eqn.primitive for eqn in jaxpr.eqns]
if np.issubdtype(dtype, np.unsignedinteger):
# Unsigned integers should not require lt, add, and select.
self.assertEqual(primitives, [lax.convert_element_type_p, lax.broadcast_in_dim_p, lax.gather_p])
else:
# May or may not contain convert_element_type.
self.assertIn(len(primitives), [5, 6])
self.assertEqual(primitives[:3], [lax.lt_p, lax.add_p, lax.select_n_p])
self.assertEqual(primitives[-2:], [lax.broadcast_in_dim_p, lax.gather_p])
@jtu.sample_product(
[dict(name=name, shape=shape, indexer=indexer)
for name, index_specs in [
("One1DIntArrayIndex",
[IndexSpec(shape=(3,), indexer=np.array([0, 1])),
IndexSpec(shape=(3, 3), indexer=np.array([1, 2, 1])),
IndexSpec(shape=(3, 4, 5), indexer=np.array([0, 2, 0, 1])),
IndexSpec(shape=(3,), indexer=np.array([-1, 1])),
IndexSpec(shape=(3,), indexer=np.array([-2, -1])),
]),
("One2DIntArrayIndex",
[IndexSpec(shape=(3,), indexer=np.array([[0, 0]])),
IndexSpec(shape=(3, 3), indexer=np.array([[1, 2, 1],
[0, 1, -1]])),
IndexSpec(shape=(3, 4, 5), indexer=np.array([[0, 2, 0, 1],
[-1, -2, 1, 0]])),
]),
("Two1DIntArrayIndicesNoBroadcasting",
[IndexSpec(shape=(3, 3), indexer=(np.array([0, 1]),
np.array([1, 2]))),
IndexSpec(shape=(3, 4, 5), indexer=(np.array([0, 2, 0, 1]),
np.array([-1, 0, -1, 2]))),
]),
("Two1DIntArrayIndicesWithBroadcasting",
[IndexSpec(shape=(3, 3), indexer=(np.array([[0, 1]]),
np.array([1, 2]))),
IndexSpec(shape=(3, 4, 5), indexer=(np.array([[0, 2, 0, 1]]),
np.array([-1, 0, -1, 2]))),
]),
("TupleOfPythonIntsAndIntArrays",
[IndexSpec(shape=(3, 4, 5), indexer=(0, np.array([0, 1]))),
IndexSpec(shape=(3, 4, 5), indexer=(0, 1,
np.array([[2, 3, 0, 3]]))),
]),
("TupleOfListsOfPythonIntsAndIntArrays",
[IndexSpec(shape=(3, 4, 5), indexer=([0, 1], np.array([0]))),
IndexSpec(shape=(3, 4, 5), indexer=([[0], [-1]],
np.array([[2, 3, 0, 3]]))),
]),
]
for shape, indexer, _ in index_specs
],
dtype=float_dtypes,
)
def testAdvancedIntegerIndexingGrads(self, name, shape, dtype, indexer):
rng = jtu.rand_default(self.rng())
tol = 1e-2 if jnp.finfo(dtype).bits == 32 else None
arg = rng(shape, dtype)
fun = lambda x: jnp.asarray(x)[indexer]
check_grads(fun, (arg,), 2, tol, tol, eps=1.)
@jtu.sample_product(
[dict(name=name, shape=shape, indexer=indexer)
for name, index_specs in MIXED_ADVANCED_INDEXING_TESTS
for shape, indexer, _ in index_specs
],
dtype=all_dtypes,
)
def testMixedAdvancedIntegerIndexing(self, name, shape, dtype, indexer):
rng = jtu.rand_default(self.rng())
indexer_with_dummies = [e if isinstance(e, np.ndarray) else ()
for e in indexer]
substitutes = [(i, e) for i, e in enumerate(indexer)
if not isinstance(e, np.ndarray)]
args_maker = lambda: [rng(shape, dtype), indexer_with_dummies]
def jnp_fun(x, indexer_with_dummies):
idx = type(indexer)(util.subvals(indexer_with_dummies, substitutes))
return jnp.asarray(x)[idx]
def np_fun(x, indexer_with_dummies):
idx = type(indexer)(util.subvals(indexer_with_dummies, substitutes))
return np.asarray(x)[idx]
self._CheckAgainstNumpy(np_fun, jnp_fun, args_maker)
self._CompileAndCheck(jnp_fun, args_maker)
def testAdvancedIndexingManually(self):
x = self.rng().randn(3, 4, 5)
index_array = np.array([0, 2, -1, 0])
op = lambda x, index_array: x[..., index_array, :]
cop = jax.jit(op)
a1 = op(x, index_array)
a2 = cop(x, index_array)
self.assertAllClose(a1, a2)
op = lambda x, index_array: x[..., index_array, :, index_array, None]
cop = jax.jit(op)
a1 = op(x, index_array)
a2 = cop(x, index_array)
self.assertAllClose(a1, a2)
op = lambda x, index_array: x[index_array, ..., index_array[:, None], None]
cop = jax.jit(op)
a1 = op(x, index_array)
a2 = cop(x, index_array)
self.assertAllClose(a1, a2)
def testUnpacking(self):
def foo(x):
a, b, c = x
return a + b + c
cfoo = jax.jit(foo)
a1 = foo(np.arange(3))
a2 = cfoo(np.arange(3))
self.assertAllClose(a1, a2)
def testBooleanIndexingArray1D(self):
idx = np.array([True, True, False])
x = jax.device_put(np.arange(3))
ans = x[idx]
expected = np.arange(3)[idx]
self.assertAllClose(ans, expected, check_dtypes=False)
def testBooleanIndexingList1D(self):
idx = [True, True, False]
x = jax.device_put(np.arange(3))
with self.assertRaisesRegex(TypeError, ARRAY_MSG):
x[idx]
def testBooleanIndexingArray2DBroadcast(self):
idx = np.array([True, True, False, True])
x = np.arange(8).reshape(4, 2)
ans = jax.device_put(x)[idx]
expected = x[idx]
self.assertAllClose(ans, expected, check_dtypes=False)
def testBooleanIndexingList2DBroadcast(self):
idx = [True, True, False, True]
x = np.arange(8).reshape(4, 2)
with self.assertRaisesRegex(TypeError, ARRAY_MSG):
jax.device_put(x)[idx]
def testBooleanIndexingArray2D(self):
idx = np.array([[True, False],
[False, True],
[False, False],
[True, True]])
x = np.arange(8).reshape(4, 2)
ans = jax.device_put(x)[idx]
expected = x[idx]
self.assertAllClose(ans, expected, check_dtypes=False)
def testBoolean1DIndexingWithEllipsis(self):
# Regression test for https://github.com/google/jax/issues/8412
x = np.arange(24).reshape(4, 3, 2)
idx = (..., np.array([True, False]))
ans = jnp.array(x)[idx]
expected = x[idx]
self.assertAllClose(ans, expected, check_dtypes=False)
def testBoolean1DIndexingWithEllipsis2(self):
# Regression test for https://github.com/google/jax/issues/9050
x = np.arange(3)
idx = (..., np.array([True, False, True]))
ans = jnp.array(x)[idx]
expected = x[idx]
self.assertAllClose(ans, expected, check_dtypes=False)
def testBoolean1DIndexingWithEllipsis3(self):
x = np.arange(6).reshape(2, 3)
idx = (0, ..., np.array([True, False, True]))
ans = jnp.array(x)[idx]
expected = x[idx]
self.assertAllClose(ans, expected, check_dtypes=False)
def testBoolean2DIndexingWithEllipsis(self):
x = np.arange(24).reshape(4, 3, 2)
idx = (..., np.array([[True, False], [True, False], [False, False]]))
ans = jnp.array(x)[idx]
expected = x[idx]
self.assertAllClose(ans, expected, check_dtypes=False)
def testBoolean1DIndexingWithTrailingEllipsis(self):
x = np.arange(24).reshape(4, 3, 2)
idx = (np.array([True, False, True, False]), ...)
ans = jnp.array(x)[idx]
expected = x[idx]
self.assertAllClose(ans, expected, check_dtypes=False)
def testBooleanIndexingDynamicShapeError(self):
x = np.zeros(3)
i = np.array([True, True, False])
self.assertRaises(IndexError, lambda: jax.jit(lambda x, i: x[i])(x, i))
def testScalarBooleanIndexingNotImplemented(self):
msg = "JAX arrays do not support boolean scalar indices"
with self.assertRaisesRegex(TypeError, msg):
jnp.arange(4)[True]
with self.assertRaisesRegex(TypeError, msg):
jnp.arange(4)[False]
with self.assertRaisesRegex(TypeError, msg):
jnp.arange(4)[..., True]
def testIssue187(self):
x = jnp.ones((5, 5))
x[[0, 2, 4], [0, 2, 4]] # doesn't crash
x = np.arange(25).reshape((5, 5))
ans = jax.jit(lambda x: x[[0, 2, 4], [0, 2, 4]])(x)
expected = x[[0, 2, 4], [0, 2, 4]]
self.assertAllClose(ans, expected, check_dtypes=False)
def testJVPOfGradOfIndexing(self):
# Should return a value, even though we didn't pass a symbolic zero as the
# index tangent.
x = jnp.ones((3, 4), jnp.float32)
i = jnp.ones((3,), jnp.int32)
f = lambda x, i: jnp.sum(x[i])
primals, tangents = jax.jvp(jax.grad(f), (x, i),
(x, np.zeros(i.shape, dtypes.float0)))
expected = np.broadcast_to(
np.array([0, 3, 0], dtype=np.float32)[:, None], (3, 4))
self.assertAllClose(expected, primals)
self.assertAllClose(np.zeros_like(x), tangents)
def testSimpleIndexingUsesSlice(self):
jaxpr = jax.make_jaxpr(lambda x: x[:2, :2])(jnp.ones((3, 4)))
self.assertEqual(len(jaxpr.jaxpr.eqns), 1)
self.assertEqual(jaxpr.jaxpr.eqns[-1].primitive, lax.slice_p)
jaxpr = jax.make_jaxpr(lambda x: x[0, :2, 1])(jnp.ones((3, 4, 5)))
self.assertEqual(len(jaxpr.jaxpr.eqns), 2)
self.assertEqual(jaxpr.jaxpr.eqns[-2].primitive, lax.slice_p)
self.assertEqual(jaxpr.jaxpr.eqns[-1].primitive, lax.squeeze_p)
jaxpr = jax.make_jaxpr(lambda x: x[0, 0])(jnp.ones((3, 4, 5)))
self.assertEqual(len(jaxpr.jaxpr.eqns), 2)
self.assertEqual(jaxpr.jaxpr.eqns[-2].primitive, lax.slice_p)
self.assertEqual(jaxpr.jaxpr.eqns[-1].primitive, lax.squeeze_p)
jaxpr = jax.make_jaxpr(lambda x: x[:, 1])(jnp.ones((3, 4, 5)))
self.assertEqual(len(jaxpr.jaxpr.eqns), 2)
self.assertEqual(jaxpr.jaxpr.eqns[-2].primitive, lax.slice_p)
self.assertEqual(jaxpr.jaxpr.eqns[-1].primitive, lax.squeeze_p)
# Simple reverses lower to lax.rev_p
jaxpr = jax.make_jaxpr(lambda x: x[:, ::-1])(jnp.ones((3, 4)))
self.assertEqual(len(jaxpr.jaxpr.eqns), 1)
self.assertEqual(jaxpr.jaxpr.eqns[0].primitive, lax.rev_p)
# Non-static indices produce a dynamic slice
jaxpr = jax.make_jaxpr(lambda x, i: x[i])(jnp.ones((4,)), 2)
self.assertEqual(len(jaxpr.jaxpr.eqns), 6)
self.assertEqual(jaxpr.jaxpr.eqns[-2].primitive, lax.dynamic_slice_p)
self.assertEqual(jaxpr.jaxpr.eqns[-1].primitive, lax.squeeze_p)
def testTrivialGatherIsntGenerated(self):
# https://github.com/google/jax/issues/1621
jaxpr = jax.make_jaxpr(lambda x: x[:, None])(np.arange(4))
self.assertEqual(len(jaxpr.jaxpr.eqns), 1)
self.assertNotIn('gather', str(jaxpr))
jaxpr = jax.make_jaxpr(lambda x: x[0:6:1])(np.arange(4))
self.assertEqual(len(jaxpr.jaxpr.eqns), 0)
jaxpr = jax.make_jaxpr(lambda x: x[:4])(np.arange(4))
self.assertEqual(len(jaxpr.jaxpr.eqns), 0)
jaxpr = jax.make_jaxpr(lambda x: x[::-1])(np.arange(4))
self.assertEqual(len(jaxpr.jaxpr.eqns), 1)
self.assertEqual(jaxpr.jaxpr.eqns[0].primitive, lax.rev_p)
def testOOBEmptySlice(self):
x = jnp.arange(4, dtype='float32')
self.assertArraysEqual(x[1:0], jnp.empty(0, dtype='float32'))
self.assertArraysEqual(x[-2:-10], jnp.empty(0, dtype='float32'))
self.assertArraysEqual(x[5:10], jnp.empty(0, dtype='float32'))
x = jnp.arange(6, dtype='float32').reshape(2, 3)
self.assertArraysEqual(x[-1:-4], jnp.empty((0, 3), dtype='float32'))
self.assertArraysEqual(x[:, 3:2], jnp.empty((2, 0), dtype='float32'))
def testIndexingEmptyDimension(self):
# Issue 2671: XLA error when indexing into dimension of size 0
x = jnp.ones((2, 0))
# The following work, even on axis 1 of size 0
with jax.numpy_rank_promotion('allow'):
_ = x[0, :] + x[0, None] + x[0, 1:] + x[0, 1:3:2]
with self.assertRaisesRegex(IndexError,
"index .* is out of bounds for axis .* with size 0"):
_ = np.ones((2, 0))[0, 0] # The numpy error
with self.assertRaisesRegex(IndexError,
"index is out of bounds for axis .* with size 0"):
_ = x[0, 0] # JAX indexing
with self.assertRaisesRegex(IndexError,
"index is out of bounds for axis .* with size 0"):
jax.jit(lambda i: x[0, i])(0) # JAX indexing under jit
def testBooleanIndexingWithEmptyResult(self):
# based on a TensorFlow Probability test that started failing after #1622
x = jnp.array([-1])
mask = jnp.array([False])
ans = x[mask] # doesn't crash
expected = np.array([-1])[np.array([False])]
self.assertAllClose(ans, expected, check_dtypes=False)
def testBooleanIndexingShapeMismatch(self):
# Regression test for https://github.com/google/jax/issues/7329
x = jnp.arange(4)
idx = jnp.array([True, False])
with self.assertRaisesRegex(IndexError, "boolean index did not match shape.*"):
x[idx]
def testBooleanIndexingWithNone(self):
# Regression test for https://github.com/google/jax/issues/18542
x = jnp.arange(6).reshape(2, 3)
idx = (None, jnp.array([True, False]))
ans = x[idx]
expected = jnp.arange(3).reshape(1, 1, 3)
self.assertAllClose(ans, expected)
def testBooleanIndexingWithNoneAndEllipsis(self):
# Regression test for https://github.com/google/jax/issues/18542
x = jnp.arange(6).reshape(2, 3)
mask = jnp.array([True, False, False])
ans = x[None, ..., mask]
expected = jnp.array([0, 3]).reshape(1, 2, 1)
self.assertAllClose(ans, expected)
def testBooleanIndexingWithEllipsisAndNone(self):
# Regression test for https://github.com/google/jax/issues/18542
x = jnp.arange(6).reshape(2, 3)
mask = jnp.array([True, False, False])
ans = x[..., None, mask]
expected = jnp.array([0, 3]).reshape(2, 1, 1)
self.assertAllClose(ans, expected)
def testNontrivialBooleanIndexing(self):