tensorflow/python/distribute/sharded_variable_test.py
# 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."""
import os
from absl.testing import parameterized
import numpy as np
from tensorflow.python.checkpoint import checkpoint as util
from tensorflow.python.client import session as session_lib
from tensorflow.python.compat import v2_compat
from tensorflow.python.distribute import combinations
from tensorflow.python.distribute import distribute_lib
from tensorflow.python.distribute import parameter_server_strategy_v2
from tensorflow.python.distribute import sharded_variable
from tensorflow.python.distribute.cluster_resolver import cluster_resolver as cluster_resolver_lib
from tensorflow.python.distribute.test_util import get_cluster_def
from tensorflow.python.distribute.test_util import TestClusterParams
from tensorflow.python.eager import context
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 indexed_slices
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 control_flow_ops
from tensorflow.python.ops import embedding_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_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.trackable import autotrackable
from tensorflow.python.training import server_lib
from tensorflow.python.util import nest
# We create one cluster to share between tests. The cluster should be large
# enough to accommodate all the tests. Adjust the following constants as needed
# but be aware of resource limitations in OSS tests.
test_cluster_params = TestClusterParams(None, 2, 3)
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, parameterized.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])
ret = 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]])
self.assertIs(ret, s)
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])
ret = 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]])
self.assertIs(ret, s)
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])
ret = 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]])
self.assertIs(ret, s)
def test_scatter_add_uneven_partition(self):
v = variables_lib.Variable(array_ops.zeros((32, 1)))
sparse_delta = indexed_slices.IndexedSlices(
values=constant_op.constant([[0.], [1.], [2.], [3.], [4.], [5.]]),
indices=constant_op.constant([0, 10, 11, 12, 30, 31]))
v0 = variables_lib.Variable(array_ops.zeros((11, 1)))
v1 = variables_lib.Variable(array_ops.zeros((11, 1)))
v2 = variables_lib.Variable(array_ops.zeros((10, 1)))
sv = sharded_variable.ShardedVariable([v0, v1, v2])
v.scatter_add(sparse_delta)
sv.scatter_add(sparse_delta)
self.assertAllEqual(v, ops.convert_to_tensor(sv))
@def_function.function
def func():
v.scatter_add(sparse_delta)
sv.scatter_add(sparse_delta)
func()
self.assertAllEqual(v, ops.convert_to_tensor(sv))
@parameterized.parameters('scatter_add', 'scatter_div', 'scatter_max',
'scatter_min', 'scatter_mul', 'scatter_sub',
'scatter_update')
def test_scatter_ops_even_partition(self, op):
v = variables_lib.Variable(array_ops.zeros((30, 1)))
# Make sure values does not contain 0 due to testing `scatter_div`!
sparse_delta = indexed_slices.IndexedSlices(
values=constant_op.constant([[1.], [2.], [3.], [4.], [5.]]),
indices=constant_op.constant([0, 10, 12, 21, 22]))
v0 = variables_lib.Variable(array_ops.zeros((10, 1)))
v1 = variables_lib.Variable(array_ops.zeros((10, 1)))
v2 = variables_lib.Variable(array_ops.zeros((10, 1)))
sv = sharded_variable.ShardedVariable([v0, v1, v2])
getattr(v, op)(sparse_delta, name='scatter_v')
getattr(sv, op)(sparse_delta, name='scatter_sv')
self.assertAllEqual(v, ops.convert_to_tensor(sv))
@def_function.function
def func():
getattr(v, op)(sparse_delta, name='scatter_v')
getattr(sv, op)(sparse_delta, name='scatter_sv')
func()
self.assertAllEqual(v, ops.convert_to_tensor(sv))
def test_batch_scatter_update(self):
v = variables_lib.Variable(array_ops.zeros((32, 1)))
sparse_delta = indexed_slices.IndexedSlices(
values=constant_op.constant([[0.], [1.], [2.], [3.], [4.], [5.]]),
indices=constant_op.constant([10, 11, 12, 13, 14, 15]))
v0 = variables_lib.Variable(array_ops.zeros((11, 1)))
v1 = variables_lib.Variable(array_ops.zeros((11, 1)))
v2 = variables_lib.Variable(array_ops.zeros((10, 1)))
sv = sharded_variable.ShardedVariable([v0, v1, v2])
v.batch_scatter_update(sparse_delta)
sv.batch_scatter_update(sparse_delta)
self.assertAllEqual(v, ops.convert_to_tensor(sv))
@def_function.function
def func():
v.batch_scatter_update(sparse_delta)
sv.batch_scatter_update(sparse_delta)
func()
self.assertAllEqual(v, ops.convert_to_tensor(sv))
def test_sparse_read(self):
v = variables_lib.Variable(array_ops.zeros((30, 1)))
indices = constant_op.constant([0, 10, 12, 21, 22])
v0 = variables_lib.Variable(array_ops.zeros((10, 1)))
v1 = variables_lib.Variable(array_ops.zeros((10, 1)))
v2 = variables_lib.Variable(array_ops.zeros((10, 1)))
sv = sharded_variable.ShardedVariable([v0, v1, v2])
self.assertAllEqual(v.sparse_read(indices), sv.sparse_read(indices))
@def_function.function
def func():
return v.sparse_read(indices), sv.sparse_read(indices)
got, expect = func()
self.assertAllEqual(got, expect)
def test_control_dep_on_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])
@def_function.function
def func():
ret = s.assign([[4, 4], [5, 5], [6, 6], [7, 7]])
with ops.control_dependencies([ret]):
a = array_ops.ones((1, 1))
with ops.control_dependencies([control_flow_ops.group(ret)]):
b = array_ops.ones((1, 1))
return a, b
func()
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 = autotrackable.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 = autotrackable.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 = autotrackable.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 = autotrackable.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 = autotrackable.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_validation_errors(self):
with self.assertRaisesRegex(TypeError, 'should be a non-empty list of'):
sharded_variable.ShardedVariable(None)
with self.assertRaisesRegex(TypeError, 'should be a non-empty list of'):
sharded_variable.ShardedVariable(
[variables_lib.Variable([0]), 'not-a-variable'])
with self.assertRaisesRegex(TypeError, 'should be a non-empty list of'):
sharded_variable.ShardedVariable([])
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.assertIs(s, got[0])
got = nest.flatten(s, expand_composites=True)
expected = nest.flatten(variables, expand_composites=True)
self.assertEqual(got, expected)
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._trackable_children(), 1)
self.assertIs(model._trackable_children().popitem()[1], 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)
for func in [lookup, sparse_lookup, safe_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 '
f'({num_gather_ops}) does not match expected ({len(v)}), 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):
data = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14],
[15, 16]]
v = [
variables_lib.Variable(data[:3]),
variables_lib.Variable(data[3:6]),
variables_lib.Variable(data[6:])
]
sv = sharded_variable.ShardedVariable(v)
empty = v[0][0:0]
# Test cases: all individual indices
for ix in range(len(data)):
self.assertAllEqual(sv[ix].numpy(), data[ix])
# 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])
def test_operator_overload(self):
v1 = [
variables_lib.Variable([1.]),
variables_lib.Variable([2.]),
]
sv1 = sharded_variable.ShardedVariable(v1)
v2 = [
variables_lib.Variable([1.]),
variables_lib.Variable([2.]),
]
sv2 = sharded_variable.ShardedVariable(v2)
equal = sv1 == sv2
self.assertAllEqual(equal, [True, True])
self.assertAllEqual(sv1 + sv2, [2.0, 4.0])
def test_shards_have_container_set(self):
v1 = [
variables_lib.Variable([1.]),
variables_lib.Variable([2.]),
]
sv1 = sharded_variable.ShardedVariable(v1)
for v in sv1.variables:
self.assertTrue(hasattr(v, '_sharded_container'))
self.assertIs(v._sharded_container(), sv1)
def test_numpy(self):
v1 = [
variables_lib.Variable([1.]),
variables_lib.Variable([2.]),
]
sv1 = sharded_variable.ShardedVariable(v1)
sv1_np = sv1.numpy()
self.assertIsInstance(sv1_np, np.ndarray)
self.assertAllEqual(sv1_np, np.array([1., 2.]))
class ShardedVariableSaveLoadTest(test.TestCase, parameterized.TestCase):
def setUp(self):
super().setUp()
cluster_def = get_cluster_def(test_cluster_params, num_workers=2, num_ps=3)
self.cluster_resolver = cluster_resolver_lib.SimpleClusterResolver(
server_lib.ClusterSpec(cluster_def))
def tearDown(self):
super().tearDown()
# Reset context to disconnect from the cluster.
context._reset_context()
def _create_strategy(self, num_shards):
if num_shards > 1:
strategy = parameter_server_strategy_v2.ParameterServerStrategyV2(
self.cluster_resolver,
variable_partitioner=sharded_variable.FixedShardsPartitioner(
num_shards))
else:
strategy = distribute_lib._get_default_strategy()
return strategy
@combinations.generate(
combinations.combine(
shard_config=[[2, 2], [2, 3], [3, 2], [2, 1], [1, 1]],
))
def testSaveAndLoadSingleVariable(self, shard_config):
"""Test saving and loading ShardedVariable with different numbers of shards.
Loading tf.Variables into multiple Shards is not yet supported
Args:
shard_config: The number of shards to use before and after loading. For
example, [2, 1] means to create and save the variable with 2 shards and
load it into 1 shard (i.e., a regular tf.Variable).
"""
strategy = self._create_strategy(shard_config[0])
with strategy.scope():
var = variables_lib.Variable([1., 2., 3., 4., 5., 6.])
# Save variable
model_dir = self.get_temp_dir()
save.save(var, model_dir)
strategy2 = self._create_strategy(shard_config[1])
with strategy2.scope():
# Load variable
loaded = load.load(model_dir)
# Assert all values loaded, values are same
if shard_config[1] > 1:
loaded = array_ops.concat(loaded.variables, axis=0)
self.assertLen(loaded.numpy(), 6)
if shard_config[0] > 1:
var = array_ops.concat(var.variables, axis=0)
self.assertAllClose(var.numpy(), loaded.numpy())
def testSaveAndLoadModuleUnderStrategy(self):
class Dense(module.Module):
def __init__(self):
self.kernel = variables_lib.Variable(
random_ops.random_uniform((6, 6)), name='kernel')
self.bias = variables_lib.Variable(
random_ops.random_uniform((6,)), name='bias')
@def_function.function
def __call__(self, x):
out = math_ops.matmul(self.kernel, x)
out = out + self.bias
return out
x = constant_op.constant(
math_ops.range(6, dtype=dtypes.float32), shape=[6, 1])
strategy = self._create_strategy(2)
with strategy.scope():
layer = Dense()
expect = layer(x)
model_dir = self.get_temp_dir()
save.save(layer, model_dir)
strategy2 = self._create_strategy(3)
with strategy2.scope():
loaded_layer = load.load(model_dir)
# Should fail with informative error
with self.assertRaisesRegex(ValueError, 'run a loaded non-Keras'):
got = loaded_layer(x)
# Loading without a strategy should work, because the tf.function is traced
# with a single variable as input
loaded_layer = load.load(model_dir)
got = loaded_layer(x)
self.assertAllClose(got, expect)
if __name__ == '__main__':
v2_compat.enable_v2_behavior()
test.main()