549 lines
20 KiB
Python
549 lines
20 KiB
Python
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
|
|
#
|
|
# 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
|
|
#
|
|
# http://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 ShardedVariable."""
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import os
|
|
|
|
from tensorflow.python.client import session as session_lib
|
|
from tensorflow.python.compat import v2_compat
|
|
from tensorflow.python.distribute import sharded_variable
|
|
from tensorflow.python.eager import def_function
|
|
from tensorflow.python.framework import constant_op
|
|
from tensorflow.python.framework import dtypes
|
|
from tensorflow.python.framework import ops
|
|
from tensorflow.python.framework import sparse_tensor
|
|
from tensorflow.python.framework import tensor_shape
|
|
from tensorflow.python.framework import tensor_spec
|
|
from tensorflow.python.module import module
|
|
from tensorflow.python.ops import array_ops
|
|
from tensorflow.python.ops import embedding_ops
|
|
from tensorflow.python.ops import variables as variables_lib
|
|
from tensorflow.python.platform import test
|
|
from tensorflow.python.saved_model import load
|
|
from tensorflow.python.saved_model import loader
|
|
from tensorflow.python.saved_model import save
|
|
from tensorflow.python.saved_model import signature_constants
|
|
from tensorflow.python.saved_model import tag_constants
|
|
from tensorflow.python.training.tracking import tracking
|
|
from tensorflow.python.training.tracking import util
|
|
from tensorflow.python.util import nest
|
|
|
|
|
|
def _load_and_run(
|
|
model_dir,
|
|
inputs,
|
|
signature_key=signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY):
|
|
"""Load a SavedModel into a TF 1.x-style graph and run `signature_key`."""
|
|
graph = ops.Graph()
|
|
with graph.as_default(), session_lib.Session() as session:
|
|
meta_graph_def = loader.load(session, [tag_constants.SERVING], model_dir)
|
|
signature = meta_graph_def.signature_def[signature_key]
|
|
feed_dict = {}
|
|
for arg_name in inputs.keys():
|
|
input_tensor = session.graph.get_tensor_by_name(
|
|
signature.inputs[arg_name].name)
|
|
feed_dict[input_tensor] = inputs[arg_name]
|
|
output_dict = {}
|
|
for output_name, output_tensor_info in signature.outputs.items():
|
|
output_dict[output_name] = session.graph.get_tensor_by_name(
|
|
output_tensor_info.name)
|
|
return session.run(output_dict, feed_dict=feed_dict)
|
|
|
|
|
|
class PartitionerTest(test.TestCase):
|
|
|
|
def test_fixed_shards_partitioner(self):
|
|
partitioner = sharded_variable.FixedShardsPartitioner(num_shards=2)
|
|
got = partitioner(tensor_shape.TensorShape([10, 3]), dtypes.float32)
|
|
self.assertAllEqual(got, [2, 1])
|
|
|
|
def test_min_size_partitioner(self):
|
|
partitioner = sharded_variable.MinSizePartitioner(
|
|
min_shard_bytes=4, max_shards=2)
|
|
got = partitioner(tensor_shape.TensorShape([6, 1]), dtypes.float32)
|
|
self.assertAllEqual(got, [2, 1])
|
|
|
|
partitioner = sharded_variable.MinSizePartitioner(
|
|
min_shard_bytes=4, max_shards=10)
|
|
got = partitioner(tensor_shape.TensorShape([6, 1]), dtypes.float32)
|
|
self.assertAllEqual(got, [6, 1])
|
|
|
|
def test_max_size_partitioner(self):
|
|
partitioner = sharded_variable.MaxSizePartitioner(max_shard_bytes=4)
|
|
got = partitioner(tensor_shape.TensorShape([6, 1]), dtypes.float32)
|
|
self.assertAllEqual(got, [6, 1])
|
|
|
|
partitioner = sharded_variable.MaxSizePartitioner(
|
|
max_shard_bytes=4, max_shards=2)
|
|
got = partitioner(tensor_shape.TensorShape([6, 1]), dtypes.float32)
|
|
self.assertAllEqual(got, [2, 1])
|
|
|
|
partitioner = sharded_variable.MaxSizePartitioner(max_shard_bytes=1024)
|
|
got = partitioner(tensor_shape.TensorShape([6, 1]), dtypes.float32)
|
|
self.assertAllEqual(got, [1, 1])
|
|
|
|
|
|
class ShardedVariableTest(test.TestCase):
|
|
|
|
def test_sharded_variable_simple(self):
|
|
v0 = variables_lib.Variable([0])
|
|
v1 = variables_lib.Variable([1])
|
|
s = sharded_variable.ShardedVariable([v0, v1], name='s')
|
|
self.assertEqual(s.variables[0], v0)
|
|
self.assertEqual(s.variables[1], v1)
|
|
self.assertEqual(s.shape.as_list(), [2])
|
|
self.assertEqual(s.dtype, v0.dtype)
|
|
self.assertEqual(s.name, 's')
|
|
|
|
def test_assign(self):
|
|
v0 = variables_lib.Variable([[0, 0]])
|
|
v1 = variables_lib.Variable([[1, 1], [2, 2]])
|
|
v2 = variables_lib.Variable([[3, 3]])
|
|
s = sharded_variable.ShardedVariable([v0, v1, v2])
|
|
s.assign([[4, 4], [5, 5], [6, 6], [7, 7]])
|
|
self.assertAllEqual(self.evaluate(s.variables[0]), [[4, 4]])
|
|
self.assertAllEqual(self.evaluate(s.variables[1]), [[5, 5], [6, 6]])
|
|
self.assertAllEqual(self.evaluate(s.variables[2]), [[7, 7]])
|
|
|
|
def test_assign_add(self):
|
|
v0 = variables_lib.Variable([[0, 0]])
|
|
v1 = variables_lib.Variable([[1, 1], [2, 2]])
|
|
v2 = variables_lib.Variable([[3, 3]])
|
|
s = sharded_variable.ShardedVariable([v0, v1, v2])
|
|
s.assign_add([[1, 1], [1, 1], [2, 2], [2, 2]])
|
|
self.assertAllEqual(self.evaluate(s.variables[0]), [[1, 1]])
|
|
self.assertAllEqual(self.evaluate(s.variables[1]), [[2, 2], [4, 4]])
|
|
self.assertAllEqual(self.evaluate(s.variables[2]), [[5, 5]])
|
|
|
|
def test_assign_sub(self):
|
|
v0 = variables_lib.Variable([[0, 0]])
|
|
v1 = variables_lib.Variable([[1, 1], [2, 2]])
|
|
v2 = variables_lib.Variable([[3, 3]])
|
|
s = sharded_variable.ShardedVariable([v0, v1, v2])
|
|
s.assign_sub([[0, 0], [1, 1], [1, 1], [3, 3]])
|
|
self.assertAllEqual(self.evaluate(s.variables[0]), [[0, 0]])
|
|
self.assertAllEqual(self.evaluate(s.variables[1]), [[0, 0], [1, 1]])
|
|
self.assertAllEqual(self.evaluate(s.variables[2]), [[0, 0]])
|
|
|
|
def test_convert_to_tensor(self):
|
|
v0 = variables_lib.Variable([[0, 0]])
|
|
v1 = variables_lib.Variable([[1, 1], [2, 2]])
|
|
v2 = variables_lib.Variable([[3, 3]])
|
|
s = sharded_variable.ShardedVariable([v0, v1, v2])
|
|
t = ops.convert_to_tensor(s)
|
|
self.assertAllEqual(t, [[0, 0], [1, 1], [2, 2], [3, 3]])
|
|
|
|
def test_save_restore(self):
|
|
fname = os.path.join(self.get_temp_dir(), 'checkpoint')
|
|
variables = [
|
|
variables_lib.Variable([0]),
|
|
variables_lib.Variable([1]),
|
|
variables_lib.Variable([2]),
|
|
variables_lib.Variable([3])
|
|
]
|
|
s = sharded_variable.ShardedVariable(variables, name='s')
|
|
|
|
cp = util.Checkpoint(s=s)
|
|
self.assertEqual(self.evaluate(cp.s.variables[0]), [0])
|
|
cp.write(fname)
|
|
|
|
self.evaluate(cp.s.variables[0].assign([4]))
|
|
self.assertEqual(self.evaluate(cp.s.variables[0]), [4])
|
|
|
|
cp.restore(fname)
|
|
# Tests that the original weights are restored.
|
|
self.assertEqual(self.evaluate(cp.s.variables[0]), [0])
|
|
|
|
def test_save_restore_different_partitions(self):
|
|
fname = os.path.join(self.get_temp_dir(), 'checkpoint')
|
|
variables = [
|
|
variables_lib.Variable([0]),
|
|
variables_lib.Variable([1]),
|
|
variables_lib.Variable([2]),
|
|
variables_lib.Variable([3])
|
|
]
|
|
s = sharded_variable.ShardedVariable(variables, name='s')
|
|
|
|
cp = util.Checkpoint(s=s)
|
|
cp.write(fname)
|
|
|
|
variables2 = [variables_lib.Variable([0, 0, 0, 0])]
|
|
s2 = sharded_variable.ShardedVariable(variables2, name='s')
|
|
|
|
# Restore from 4 partitions into 1.
|
|
cp2 = util.Checkpoint(s=s2)
|
|
cp2.restore(fname)
|
|
self.assertAllEqual(self.evaluate(cp2.s.variables[0]), [0, 1, 2, 3])
|
|
|
|
self.evaluate(cp2.s.variables[0].assign([5, 10, 15, 20]))
|
|
cp2.write(fname)
|
|
|
|
# Restore 1 partition into 4.
|
|
cp.restore(fname)
|
|
self.assertEqual(self.evaluate(cp.s.variables[0]), [5])
|
|
self.assertEqual(self.evaluate(cp.s.variables[1]), [10])
|
|
self.assertEqual(self.evaluate(cp.s.variables[2]), [15])
|
|
self.assertEqual(self.evaluate(cp.s.variables[3]), [20])
|
|
|
|
def test_save_restore_4_to_2_partitions(self):
|
|
fname = os.path.join(self.get_temp_dir(), 'checkpoint')
|
|
variables = [
|
|
variables_lib.Variable([0]),
|
|
variables_lib.Variable([1]),
|
|
variables_lib.Variable([2]),
|
|
variables_lib.Variable([3])
|
|
]
|
|
s = sharded_variable.ShardedVariable(variables, name='s')
|
|
cp = util.Checkpoint(s=s)
|
|
cp.write(fname)
|
|
|
|
variables2 = [
|
|
variables_lib.Variable([0, 0]),
|
|
variables_lib.Variable([0, 0])
|
|
]
|
|
s2 = sharded_variable.ShardedVariable(variables2, name='s')
|
|
cp2 = util.Checkpoint(s=s2)
|
|
cp2.restore(fname)
|
|
# Assert that weights from the 4 partitions were loaded here.
|
|
self.assertLen(cp2.s.variables, 2)
|
|
self.assertAllEqual(self.evaluate(cp2.s.variables[0]), [0, 1])
|
|
self.assertAllEqual(self.evaluate(cp2.s.variables[1]), [2, 3])
|
|
|
|
def test_delayed_restore(self):
|
|
fname = os.path.join(self.get_temp_dir(), 'checkpoint')
|
|
model = tracking.AutoTrackable()
|
|
variables = [
|
|
variables_lib.Variable([0]),
|
|
variables_lib.Variable([1]),
|
|
variables_lib.Variable([2]),
|
|
variables_lib.Variable([3])
|
|
]
|
|
model.s = sharded_variable.ShardedVariable(variables)
|
|
cp = util.Checkpoint(model=model)
|
|
cp.write(fname)
|
|
|
|
model2 = tracking.AutoTrackable()
|
|
cp2 = util.Checkpoint(model=model2)
|
|
cp2.restore(fname)
|
|
variables2 = [
|
|
variables_lib.Variable([0]),
|
|
variables_lib.Variable([0]),
|
|
variables_lib.Variable([0]),
|
|
variables_lib.Variable([0])
|
|
]
|
|
model2.s = sharded_variable.ShardedVariable(variables2)
|
|
self.assertAllEqual(self.evaluate(model2.s.variables[0]), [0])
|
|
self.assertAllEqual(self.evaluate(model2.s.variables[1]), [1])
|
|
self.assertAllEqual(self.evaluate(model2.s.variables[2]), [2])
|
|
self.assertAllEqual(self.evaluate(model2.s.variables[3]), [3])
|
|
|
|
def test_delayed_restore_4_to_2_partitions(self):
|
|
fname = os.path.join(self.get_temp_dir(), 'checkpoint')
|
|
model = tracking.AutoTrackable()
|
|
variables = [
|
|
variables_lib.Variable([0]),
|
|
variables_lib.Variable([1]),
|
|
variables_lib.Variable([2]),
|
|
variables_lib.Variable([3])
|
|
]
|
|
model.s = sharded_variable.ShardedVariable(variables)
|
|
cp = util.Checkpoint(model=model)
|
|
cp.write(fname)
|
|
|
|
model2 = tracking.AutoTrackable()
|
|
cp2 = util.Checkpoint(model=model2)
|
|
cp2.restore(fname)
|
|
variables2 = [
|
|
variables_lib.Variable([0, 0]),
|
|
variables_lib.Variable([0, 0])
|
|
]
|
|
model2.s = sharded_variable.ShardedVariable(variables2)
|
|
self.assertAllEqual(self.evaluate(model2.s.variables[0]), [0, 1])
|
|
self.assertAllEqual(self.evaluate(model2.s.variables[1]), [2, 3])
|
|
|
|
def test_save_graph_def(self):
|
|
root = tracking.AutoTrackable()
|
|
v1 = variables_lib.Variable([3.])
|
|
v2 = variables_lib.Variable([2.])
|
|
root.v = sharded_variable.ShardedVariable([v1, v2])
|
|
root.train = def_function.function(
|
|
lambda x: embedding_ops.embedding_lookup_v2(root.v.variables, x))
|
|
# TODO(b/144057383): Remove the necessity of root.serve once saving context
|
|
# is made to tf.function cache.
|
|
root.serve = def_function.function(
|
|
lambda x: embedding_ops.embedding_lookup_v2(root.v.variables[0], x),
|
|
input_signature=[tensor_spec.TensorSpec([2], dtypes.int32, name='x')])
|
|
|
|
# Trace and use root.train
|
|
self.assertAllEqual([3., 2.], root.train([0, 1]).numpy())
|
|
|
|
save_dir = os.path.join(self.get_temp_dir(), 'saved_model')
|
|
save.save(root, save_dir, root.serve)
|
|
self.assertAllEqual([3., 2.],
|
|
_load_and_run(save_dir, {'x': [0, 1]})['output_0'])
|
|
|
|
# Continue using root.train for training
|
|
self.assertAllEqual([3., 2.], root.train([0, 1]).numpy())
|
|
|
|
def test_load_raises_error(self):
|
|
root = tracking.AutoTrackable()
|
|
v1 = variables_lib.Variable([3.])
|
|
v2 = variables_lib.Variable([2.])
|
|
root.v = sharded_variable.ShardedVariable([v1, v2])
|
|
|
|
save_dir = os.path.join(self.get_temp_dir(), 'saved_model')
|
|
save.save(root, save_dir)
|
|
|
|
with self.assertRaisesWithLiteralMatch(
|
|
ValueError, 'Loading `ShardedVariable` is not supported'):
|
|
load.load(save_dir)
|
|
|
|
def test_validation_errors(self):
|
|
with self.assertRaisesRegex(ValueError, 'Expected a list of '):
|
|
sharded_variable.ShardedVariable(
|
|
[variables_lib.Variable([0]), 'not-a-variable'])
|
|
|
|
with self.assertRaisesRegex(ValueError, 'must have the same dtype'):
|
|
sharded_variable.ShardedVariable([
|
|
variables_lib.Variable([0], dtype='int64'),
|
|
variables_lib.Variable([1], dtype='int32')
|
|
])
|
|
|
|
with self.assertRaisesRegex(ValueError, 'the same shapes except'):
|
|
sharded_variable.ShardedVariable([
|
|
variables_lib.Variable(array_ops.ones((5, 10))),
|
|
variables_lib.Variable(array_ops.ones((5, 20)))
|
|
])
|
|
|
|
with self.assertRaisesRegex(ValueError, '`SaveSliceInfo` should not'):
|
|
v = variables_lib.Variable([0])
|
|
v._set_save_slice_info(
|
|
variables_lib.Variable.SaveSliceInfo(
|
|
full_name='s', full_shape=[2], var_offset=[0], var_shape=[1]))
|
|
sharded_variable.ShardedVariable([v])
|
|
|
|
def test_as_function_input(self):
|
|
variables1 = [
|
|
variables_lib.Variable([1]),
|
|
variables_lib.Variable([1]),
|
|
]
|
|
s = sharded_variable.ShardedVariable(variables1)
|
|
variables2 = [
|
|
variables_lib.Variable([2]),
|
|
variables_lib.Variable([2]),
|
|
]
|
|
s2 = sharded_variable.ShardedVariable(variables2)
|
|
|
|
trace_count = [0]
|
|
|
|
@def_function.function
|
|
def func(sharded_var):
|
|
trace_count[0] = trace_count[0] + 1
|
|
sharded_var.assign([0, 0])
|
|
|
|
func(s)
|
|
self.assertAllEqual(ops.convert_to_tensor(s), [0, 0])
|
|
self.assertEqual(trace_count[0], 1)
|
|
func(s2)
|
|
self.assertAllEqual(ops.convert_to_tensor(s2), [0, 0])
|
|
self.assertEqual(trace_count[0], 1)
|
|
|
|
def test_flatten(self):
|
|
variables = [
|
|
variables_lib.Variable([0]),
|
|
variables_lib.Variable([1]),
|
|
]
|
|
s = sharded_variable.ShardedVariable(variables)
|
|
|
|
got = nest.flatten(s)
|
|
self.assertEqual(s, got[0])
|
|
|
|
got = nest.flatten(s, expand_composites=True)
|
|
self.assertAllEqual(variables, got)
|
|
|
|
def test_tf_module(self):
|
|
|
|
class Model(module.Module):
|
|
|
|
def __init__(self):
|
|
super().__init__()
|
|
variables = [
|
|
variables_lib.Variable([0]),
|
|
variables_lib.Variable([1]),
|
|
]
|
|
self.w = sharded_variable.ShardedVariable(variables)
|
|
|
|
model = Model()
|
|
|
|
self.assertLen(model.variables, 2)
|
|
self.assertEqual(model.variables[0], [0])
|
|
self.assertEqual(model.variables[1], [1])
|
|
self.assertAllEqual(model.variables, model.trainable_variables)
|
|
|
|
self.assertLen(model._checkpoint_dependencies, 1)
|
|
self.assertEqual(model._checkpoint_dependencies[0].ref, model.w)
|
|
|
|
def test_embedding_lookup(self):
|
|
v = [
|
|
variables_lib.Variable([[1., 2.], [3., 4.]]),
|
|
variables_lib.Variable([[5., 6.], [7., 8.]]),
|
|
variables_lib.Variable([[9., 10.]])
|
|
]
|
|
sv = sharded_variable.ShardedVariable(v)
|
|
|
|
@def_function.function
|
|
def lookup():
|
|
ids = constant_op.constant([0, 3, 4])
|
|
return embedding_ops.embedding_lookup_v2(sv, ids)
|
|
|
|
@def_function.function
|
|
def sparse_lookup():
|
|
sp_ids = sparse_tensor.SparseTensor(
|
|
indices=[[0, 0], [0, 1], [1, 0], [2, 2]],
|
|
values=[0, 3, 4, 1],
|
|
dense_shape=[3, 3])
|
|
return embedding_ops.embedding_lookup_sparse_v2(sv, sp_ids, None)
|
|
|
|
@def_function.function
|
|
def safe_sparse_lookup():
|
|
sp_ids = sparse_tensor.SparseTensor(
|
|
indices=[[0, 0], [0, 1], [1, 0], [2, 2]],
|
|
values=[0, -1, 4, 1],
|
|
dense_shape=[3, 3])
|
|
sp_weights = sparse_tensor.SparseTensor(
|
|
indices=[[0, 0], [0, 1], [1, 0], [2, 2]],
|
|
values=[1., 1., -1., 1.],
|
|
dense_shape=[3, 3])
|
|
return embedding_ops.safe_embedding_lookup_sparse_v2(
|
|
sv, sp_ids, sp_weights)
|
|
|
|
# TODO(chenkai): Add safe_sparse_lookup to the list. Currently
|
|
# ShardedVariable is converted to a tensor in safe_sparse_lookup.
|
|
for func in [lookup, sparse_lookup]:
|
|
num_gather_ops = 0
|
|
for op in func.get_concrete_function().graph.get_operations():
|
|
if op.type == 'ResourceGather':
|
|
num_gather_ops += 1
|
|
self.assertEqual(
|
|
num_gather_ops, len(v), 'Number of ResourceGather op does not match'
|
|
' expected, possibly due to ShardedVariable accidentally being'
|
|
' converted to tensor in embedding_lookup ops.')
|
|
|
|
self.assertAllEqual(lookup(), [[1., 2.], [7., 8.], [9., 10.]])
|
|
self.assertAllClose(sparse_lookup(), [[4., 5.], [9., 10.], [3., 4.]])
|
|
self.assertAllClose(safe_sparse_lookup(), [[1., 2.], [0., 0.], [3., 4.]])
|
|
|
|
def test_slicing(self):
|
|
v = [
|
|
variables_lib.Variable([[1, 2], [3, 4], [5, 6]]),
|
|
variables_lib.Variable([[7, 8], [9, 10], [11, 12]]),
|
|
variables_lib.Variable([[13, 14], [15, 16]])
|
|
]
|
|
sv = sharded_variable.ShardedVariable(v)
|
|
empty = v[0][0:0]
|
|
|
|
# Test cases: positive step
|
|
self.assertAllEqual(sv[:], array_ops.concat(v, axis=0))
|
|
self.assertAllEqual(sv[:2], [[1, 2], [3, 4]])
|
|
self.assertAllEqual(sv[-8:2], [[1, 2], [3, 4]])
|
|
self.assertAllEqual(sv[-10:2], [[1, 2], [3, 4]])
|
|
self.assertAllEqual(sv[5:], [[11, 12], [13, 14], [15, 16]])
|
|
self.assertAllEqual(sv[5:-1], [[11, 12], [13, 14]])
|
|
self.assertAllEqual(sv[::3], [[1, 2], [7, 8], [13, 14]])
|
|
self.assertAllEqual(sv[::5], [[1, 2], [11, 12]])
|
|
self.assertAllEqual(sv[1::6], [[3, 4], [15, 16]])
|
|
self.assertAllEqual(sv[1:5:6], [[3, 4]])
|
|
self.assertAllEqual(sv[1::7], [[3, 4]])
|
|
self.assertAllEqual(sv[2:7], [[5, 6], [7, 8], [9, 10], [11, 12], [13, 14]])
|
|
self.assertAllEqual(sv[2:7:2], [[5, 6], [9, 10], [13, 14]])
|
|
self.assertAllEqual(sv[2:7:3], [[5, 6], [11, 12]])
|
|
|
|
# Test cases: negative step
|
|
self.assertAllEqual(
|
|
sv[::-1], array_ops.reverse(array_ops.concat(v, axis=0), axis=[0]))
|
|
self.assertAllEqual(sv[2::-1], [[5, 6], [3, 4], [1, 2]])
|
|
self.assertAllEqual(sv[2:-8:-1], [[5, 6], [3, 4]])
|
|
self.assertAllEqual(sv[2:-10:-1], [[5, 6], [3, 4], [1, 2]])
|
|
self.assertAllEqual(sv[4::-1], [[9, 10], [7, 8], [5, 6], [3, 4], [1, 2]])
|
|
self.assertAllEqual(sv[-1:-3:-1], [[15, 16], [13, 14]])
|
|
self.assertAllEqual(sv[::-5], [[15, 16], [5, 6]])
|
|
self.assertAllEqual(sv[6::-6], [[13, 14], [1, 2]])
|
|
self.assertAllEqual(sv[6:5:-6], [[13, 14]])
|
|
self.assertAllEqual(sv[6::-7], [[13, 14]])
|
|
self.assertAllEqual(sv[7:1:-1],
|
|
[[15, 16], [13, 14], [11, 12], [9, 10], [7, 8], [5, 6]])
|
|
self.assertAllEqual(sv[7:1:-2], [[15, 16], [11, 12], [7, 8]])
|
|
self.assertAllEqual(sv[7:1:-4], [[15, 16], [7, 8]])
|
|
|
|
# Test cases: empty slice
|
|
self.assertAllEqual(sv[0:0], empty)
|
|
self.assertAllEqual(sv[5:3], empty)
|
|
self.assertAllEqual(sv[3:5:-1], empty)
|
|
self.assertAllEqual(sv[-1:0], empty)
|
|
self.assertAllEqual(sv[2:-1:-1], empty)
|
|
|
|
# Test cases: slicing other dimensions
|
|
self.assertAllEqual(sv[:, 0], [1, 3, 5, 7, 9, 11, 13, 15])
|
|
self.assertAllEqual(sv[:, 0:1], [[1], [3], [5], [7], [9], [11], [13], [15]])
|
|
|
|
# Test cases: normal indexing
|
|
self.assertAllEqual(sv[2], [5, 6])
|
|
self.assertAllEqual(sv[6], [13, 14])
|
|
self.assertAllEqual(sv[2, 1], 6)
|
|
self.assertAllEqual(sv[-2], [13, 14])
|
|
with self.assertRaisesRegex(IndexError, 'out of bounds'):
|
|
_ = sv[100]
|
|
with self.assertRaisesRegex(IndexError, 'out of bounds'):
|
|
_ = sv[-100]
|
|
|
|
# Test cases: Ellipsis
|
|
self.assertAllEqual(sv[...], array_ops.concat(v, axis=0))
|
|
self.assertAllEqual(sv[..., 0], [1, 3, 5, 7, 9, 11, 13, 15])
|
|
self.assertAllEqual(sv[0:1, ...], [[1, 2]])
|
|
|
|
# Test cases: newaxis
|
|
self.assertAllEqual(
|
|
sv[array_ops.newaxis, ...],
|
|
array_ops.expand_dims_v2(array_ops.concat(v, axis=0), axis=0))
|
|
|
|
# Test cases: boolean masks
|
|
self.assertAllEqual(sv[ops.convert_to_tensor(sv) > 10],
|
|
[11, 12, 13, 14, 15, 16])
|
|
|
|
# Test cases: tensor input
|
|
with self.assertRaisesRegex(TypeError, 'not allowed'):
|
|
_ = sv[constant_op.constant(1)::]
|
|
with self.assertRaisesRegex(TypeError, 'not allowed'):
|
|
_ = sv[:constant_op.constant(1):]
|
|
with self.assertRaisesRegex(TypeError, 'not allowed'):
|
|
_ = sv[constant_op.constant(1)]
|
|
|
|
# Test cases: inside tf.function
|
|
@def_function.function
|
|
def func():
|
|
a = sv[:, 0]
|
|
return a
|
|
|
|
self.assertAllEqual(func(), [1, 3, 5, 7, 9, 11, 13, 15])
|
|
|
|
|
|
if __name__ == '__main__':
|
|
v2_compat.enable_v2_behavior()
|
|
test.main()
|