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tensorflow--tensorflow/tensorflow/python/distribute/values_test.py
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# Copyright 2018 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 the distributed values library."""
import copy
import os
from absl.testing import parameterized
import numpy as np
from tensorflow.core.protobuf import config_pb2
from tensorflow.python import tf2
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.distribute import combinations
from tensorflow.python.distribute import strategy_combinations
from tensorflow.python.distribute import test_util as ds_test_util
from tensorflow.python.distribute import tpu_strategy
from tensorflow.python.distribute import tpu_values
from tensorflow.python.distribute import values as values_lib
from tensorflow.python.eager import context
from tensorflow.python.eager import def_function
from tensorflow.python.eager import test
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
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import sparse_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables as variables_lib
from tensorflow.python.training import saver as saver_lib
def _device_str(d):
return "/device:GPU:" + str(d)
def _nested_value(d):
return ("a" + d, ["b" + d, {"c": "d" + d, "e": "f" + d}, "g" + d], "h" + d)
def mirrored_and_tpu_strategy_combinations():
return combinations.combine(
distribution=[
strategy_combinations.mirrored_strategy_with_gpu_and_cpu,
strategy_combinations.mirrored_strategy_with_two_gpus_no_merge_call,
strategy_combinations.tpu_strategy,
strategy_combinations.tpu_strategy_packed_var,
strategy_combinations.tpu_strategy_spmd,
],
mode=["graph", "eager"])
class DistributedValuesTest(test.TestCase, parameterized.TestCase):
@combinations.generate(
combinations.combine(
distribution=(strategy_combinations.all_strategies_minus_default +
strategy_combinations.multiworker_strategies),
mode=["eager"]
))
def testMakeDistributedValueFromTensor(self, distribution):
if not tf2.enabled():
self.skipTest("Only V2 is supported.")
single_value = constant_op.constant(1)
def value_fn(ctx):
del ctx
return single_value
distributed_values = (
distribution.experimental_distribute_values_from_function(value_fn))
self.assertAllEqual(
ds_test_util.gather(distribution, distributed_values),
constant_op.constant(1., shape=(distribution.num_replicas_in_sync)))
@combinations.generate(
combinations.combine(
distribution=(strategy_combinations.all_strategies_minus_default +
strategy_combinations.multiworker_strategies),
mode=["eager"]
))
def testMakeDistributedValueSingleNumpyArrayConstant(self, distribution):
if not tf2.enabled():
self.skipTest("Only V2 is supported.")
array_value = np.array([1., 2., 3.])
def value_fn(ctx):
del ctx
return array_value
distributed_values = (
distribution.experimental_distribute_values_from_function(value_fn))
self.assertAllEqual(
ds_test_util.gather(distribution, distributed_values).numpy(),
[[1., 2., 3.]] * distribution.num_replicas_in_sync)
@combinations.generate(
combinations.combine(
distribution=(strategy_combinations.all_strategies_minus_default +
strategy_combinations.multiworker_strategies),
mode=["eager"]
))
def testMakeDistributedValueTupleConstant(self, distribution):
if not tf2.enabled():
self.skipTest("Only V2 is supported.")
tuple_value = (1., 2., 3.)
def value_fn(ctx):
del ctx
return tuple_value
distributed_values = (
distribution.experimental_distribute_values_from_function(value_fn))
distributed_values = ds_test_util.gather(distribution, distributed_values)
# Expected output for 2 replicas:
# ([1.0, 1.0], [2.0, 2.0], [3.0, 3.0])
expected = tuple([v for i in range(distribution.num_replicas_in_sync)]
for v in tuple_value)
self.assertAllEqual(distributed_values, expected)
@combinations.generate(
combinations.combine(
distribution=(strategy_combinations.all_strategies_minus_default +
strategy_combinations.multiworker_strategies),
mode=["eager"]
))
def testMakeDistributedValueNestedStructurePerReplica(self, distribution):
if not tf2.enabled():
self.skipTest("Only V2 is supported.")
tuple_value = (1., 2., 3.)
def value_fn(ctx):
per_replica = []
for val in tuple_value:
per_replica.append(val * ctx.replica_id_in_sync_group)
return tuple(per_replica)
distributed_values = (
distribution.experimental_distribute_values_from_function(value_fn))
distributed_values = ds_test_util.gather(distribution, distributed_values)
# Expected output for 2 replicas:
# ([0.0, 1.0], [0.0, 2.0], [0.0, 3.0])
expected = tuple([v * i for i in range(distribution.num_replicas_in_sync)]
for v in tuple_value)
self.assertAllEqual(distributed_values, expected)
# NOTE(priyag): Cannot test this with MultiWorkerMirroredStrategy because
# collective ops do not support SparseTensors.
@combinations.generate(
combinations.combine(
distribution=strategy_combinations.all_strategies_minus_default,
mode=["eager"]
))
def testMakeDistributedValueSpareTensor(self, distribution):
if not tf2.enabled():
self.skipTest("Only V2 is supported.")
def value_fn(ctx):
del ctx
return sparse_tensor.SparseTensor(
indices=[[0, 0], [1, 2]], values=[1, 2], dense_shape=[3, 4])
distributed_values = (
distribution.experimental_distribute_values_from_function(value_fn))
local_results = distribution.experimental_local_results(distributed_values)
for i in range(distribution.num_replicas_in_sync):
self.assertAllEqual(
sparse_ops.sparse_tensor_to_dense(local_results[i]),
[[1, 0, 0, 0], [0, 0, 2, 0], [0, 0, 0, 0]])
@combinations.generate(
combinations.combine(
distribution=(strategy_combinations.all_strategies_minus_default +
strategy_combinations.multiworker_strategies),
mode=["eager"]
))
def testMakeDistributedValueExtractFromArray(self, distribution):
if not tf2.enabled():
self.skipTest("Only V2 is supported.")
multiple_values = range(distribution.num_replicas_in_sync)
def value_fn(ctx):
return multiple_values[ctx.replica_id_in_sync_group]
distributed_values = (
distribution.experimental_distribute_values_from_function(value_fn))
distributed_values = ds_test_util.gather(distribution, distributed_values)
expected = range(distribution.num_replicas_in_sync)
self.assertAllEqual(distributed_values, expected)
@combinations.generate(
combinations.combine(
distribution=(strategy_combinations.all_strategies_minus_default +
strategy_combinations.multiworker_strategies),
mode=["eager"]
))
def testMakeDistributedValueAndRun(self, distribution):
if not tf2.enabled():
self.skipTest("Only V2 is supported.")
@def_function.function
def run():
multiple_values = range(distribution.num_replicas_in_sync)
def value_fn(ctx):
return multiple_values[ctx.replica_id_in_sync_group]
distributed_values = (
distribution.experimental_distribute_values_from_function(value_fn))
def computation(x):
return math_ops.square(x)
outputs = ds_test_util.gather(
distribution,
distribution.run(computation, args=(distributed_values,)))
return outputs
results = run()
expected = [i**2 for i in range(distribution.num_replicas_in_sync)]
self.assertAllEqual(results, expected)
@combinations.generate(
combinations.combine(
distribution=[
strategy_combinations.mirrored_strategy_with_gpu_and_cpu,
strategy_combinations
.mirrored_strategy_with_two_gpus_no_merge_call,
strategy_combinations.tpu_strategy,
strategy_combinations.tpu_strategy_packed_var,
strategy_combinations.central_storage_strategy_with_two_gpus,
] + strategy_combinations.multiworker_strategies,
mode=["eager"]))
def testMakeDistributedValueDefaultDevicePlacement(self, distribution):
if not tf2.enabled():
self.skipTest("Only V2 is supported.")
def value_fn(ctx):
del ctx
return constant_op.constant(1.0)
distributed_values = (
distribution.experimental_distribute_values_from_function(value_fn))
default_device = array_ops.identity(constant_op.constant(1.0)).device
for i in range(len(distribution.extended.worker_devices)):
self.assertAllEqual(distributed_values._values[i].device, default_device)
@combinations.generate(
combinations.combine(
distribution=[
strategy_combinations.mirrored_strategy_with_gpu_and_cpu,
strategy_combinations
.mirrored_strategy_with_two_gpus_no_merge_call,
strategy_combinations.tpu_strategy,
strategy_combinations.tpu_strategy_packed_var,
strategy_combinations.central_storage_strategy_with_two_gpus,
] + strategy_combinations.multiworker_strategies,
mode=["eager"],
op_type=[constant_op.constant, array_ops.identity]))
def testMakeDistributedValueExplicitDevicePlacement(self, distribution,
op_type):
if not tf2.enabled():
self.skipTest("Only V2 is supported.")
worker_devices = distribution.extended.worker_devices
def value_fn(ctx):
# In multi client setup, worker_devices is just the devices on that
# worker.
worker_device_id = ctx.replica_id_in_sync_group % len(worker_devices)
with ops.device(worker_devices[worker_device_id]):
return op_type(1.0)
distributed_values = (
distribution.experimental_distribute_values_from_function(value_fn))
for i in range(len(distribution.extended.worker_devices)):
self.assertAllEqual(distributed_values._values[i].device,
worker_devices[i])
class PerReplicaTest(test.TestCase, parameterized.TestCase):
@combinations.generate(
combinations.combine(
distribution=[
strategy_combinations.mirrored_strategy_with_gpu_and_cpu,
strategy_combinations
.mirrored_strategy_with_two_gpus_no_merge_call,
strategy_combinations.tpu_strategy,
strategy_combinations.tpu_strategy_packed_var,
strategy_combinations.central_storage_strategy_with_two_gpus,
] + strategy_combinations.multiworker_strategies,
mode=["eager"]))
def testUsePerReplicaInvalidContextGivesError(self, distribution):
if not tf2.enabled():
self.skipTest("Only V2 is supported.")
multiple_values = range(distribution.num_replicas_in_sync)
def value_fn(ctx):
return multiple_values[ctx.replica_id_in_sync_group]
distributed_values = (
distribution.experimental_distribute_values_from_function(value_fn))
with self.assertRaisesRegex(ValueError, "not inside a replica context"):
math_ops.cast(distributed_values, dtypes.float32)
class PerWorkerResourceTest(test.TestCase, parameterized.TestCase):
@combinations.generate(
combinations.combine(dataset_fn_as_tf_function=[True, False]))
def testMapFnTracing(self, dataset_fn_as_tf_function):
# For a PerWorkerResource to correctly behave when used in dataset.map,
# it has to be that the map_fn is not traced only once such that
# PerWorkerResource.local_table can return the correct resource. This test
# can detect the potential breakage of this behavior on TAP.
self._traced_once = 0
def map_fn(x):
self._traced_once += 1
return x
def dataset_fn():
dataset = dataset_ops.DatasetV2.from_tensors([0, 1, 2]).repeat().batch(
2, drop_remainder=True)
dataset = dataset.map(map_fn)
return dataset
datasets = []
number_of_input_pipelines = 5
if dataset_fn_as_tf_function:
dataset_fn = def_function.function(dataset_fn)
expected_tracing_times = 1
else:
expected_tracing_times = number_of_input_pipelines
for _ in range(number_of_input_pipelines):
datasets.append(dataset_fn())
self.assertEqual(self._traced_once, expected_tracing_times)
class DistributedDelegateTest(test.TestCase):
@test_util.run_in_graph_and_eager_modes
def testGetAttr(self):
class Foo(object):
def __init__(self, x):
self.x = x
v = values_lib.DistributedDelegate((Foo(7), Foo(8)))
self.assertEqual(7, v.x)
with self.assertRaises(AttributeError):
_ = v.y
@test_util.run_in_graph_and_eager_modes
def testOperatorOverride(self):
v = values_lib.DistributedDelegate((7, 8))
# v should act like int(7).
self.assertEqual(8, v + 1)
self.assertEqual(10, 3 + v)
self.assertEqual(14, v + v)
self.assertEqual(5, v - 2)
self.assertEqual(6, 13 - v)
self.assertEqual(0, v - v)
self.assertEqual(14, v * 2)
self.assertEqual(21, 3 * v)
self.assertEqual(49, v * v)
self.assertEqual(3.5, v / 2)
self.assertEqual(1.5, 10.5 / v)
self.assertEqual(3, v // 2)
self.assertEqual(2, 15 // v)
self.assertEqual(1, v % 2)
self.assertEqual(2, 16 % v)
# pylint: disable=g-generic-assert
self.assertTrue(v < 12)
self.assertTrue(v <= 12)
self.assertFalse(v > 12)
self.assertFalse(v >= 12)
self.assertFalse(12 < v)
self.assertFalse(12 <= v)
self.assertTrue(12 > v)
self.assertTrue(12 >= v)
# pylint: enable=g-generic-assert
self.assertEqual(3, v & 3)
self.assertEqual(3, 11 & v)
self.assertEqual(15, v | 8)
self.assertEqual(23, 16 | v)
self.assertEqual(4, v ^ 3)
self.assertEqual(12, 11 ^ v)
self.assertEqual(343, pow(v, 3))
self.assertEqual(3, pow(v, 3, 10))
self.assertEqual(128, pow(2, v))
self.assertEqual(-7, -v)
self.assertEqual(~7, ~v)
self.assertEqual(7, abs(v))
with self.assertRaises(TypeError):
_ = v[2]
@test_util.run_in_graph_and_eager_modes
def testCopy(self):
class Foo(object):
def __init__(self, x):
self.x = x
v = values_lib.DistributedDelegate((Foo(7), Foo(8)))
v_shallow_copy = copy.copy(v)
self.assertEqual(v.x, v_shallow_copy.x)
v_deep_copy = copy.deepcopy(v)
self.assertEqual(v.x, v_deep_copy.x)
_TPU_STRATEGIES = (tpu_strategy.TPUStrategy, tpu_strategy.TPUStrategyV1)
def _make_replica_local(method, strategy=None):
if strategy is None:
devices = ("/device:GPU:0", "/device:CPU:0")
else:
devices = strategy.extended.worker_devices
v = []
for d, n, init in zip(devices, ["v", "v/replica"], [1., 2.]):
with ops.device(d):
v.append(variable_scope.get_variable(
name=n, initializer=init, use_resource=True))
if (strategy is not None) and isinstance(strategy, _TPU_STRATEGIES):
var_cls = tpu_values.TPUSyncOnReadVariable
else:
var_cls = values_lib.SyncOnReadVariable
replica_local = var_cls(strategy, v, method)
return v, replica_local
class DistributedVariableTest(test.TestCase, parameterized.TestCase):
def tearDown(self):
super().tearDown()
context._reset_context()
def _assign_replica_local(self, v, new):
for var, n in zip(v, new):
with ops.device(var.device):
self.evaluate(var.assign(n))
def _save_return_saver(self, sess, var):
saver = saver_lib.Saver(var_list=[var])
test_dir = self.get_temp_dir()
prefix = os.path.join(test_dir, "ckpt")
return saver.save(sess, prefix), saver
def _save(self, sess, var):
save_path, _ = self._save_return_saver(sess, var)
return save_path
config = config_pb2.ConfigProto()
config.allow_soft_placement = True
@test_util.run_in_graph_and_eager_modes(config=config)
def testProperties(self):
if context.num_gpus() < 1 and context.executing_eagerly():
self.skipTest("A GPU is not available for this test in eager mode.")
v, replica_local = _make_replica_local(
variable_scope.VariableAggregation.SUM)
self.assertEqual(v[0].constraint, replica_local.constraint)
self.assertEqual(v[0].name, replica_local.name)
self.assertEqual(v[0].dtype, replica_local.dtype)
self.assertEqual(v[0].shape, replica_local.shape)
self.assertEqual(variable_scope.VariableAggregation.SUM,
replica_local.aggregation)
@combinations.generate(
combinations.combine(
distribution=[
strategy_combinations.mirrored_strategy_with_gpu_and_cpu
],
mode=["eager"]))
def testCanPassToDefFun(self, distribution):
@def_function.function
def add1(x):
return x + 1.
with distribution.scope():
v = variables_lib.Variable(
1.,
aggregation=variables_lib.VariableAggregation.MEAN,
synchronization=variables_lib.VariableSynchronization.ON_READ)
self.assertEqual(2., self.evaluate(add1(v)))
@combinations.generate(mirrored_and_tpu_strategy_combinations())
def testTensorConversion(self, distribution):
with context.graph_mode():
_, replica_local = _make_replica_local(
variable_scope.VariableAggregation.SUM, distribution)
converted = ops.convert_to_tensor(replica_local, as_ref=False)
self.assertIsInstance(converted, tensor.Tensor)
self.assertEqual(converted.dtype, replica_local.dtype)
converted = ops.convert_to_tensor(replica_local, as_ref=True)
# Resources variable are converted to tensors as well when as_ref is True.
self.assertIsInstance(converted, tensor.Tensor)
self.assertEqual(converted.dtype, replica_local.dtype)
@combinations.generate(combinations.combine(
distribution=[
strategy_combinations.mirrored_strategy_with_gpu_and_cpu,
strategy_combinations.mirrored_strategy_with_two_gpus_no_merge_call,
strategy_combinations.tpu_strategy,
strategy_combinations.tpu_strategy_packed_var,
], mode=["eager"]))
def testValueInCrossReplicaContext(self, distribution):
value_list, replica_local = _make_replica_local(
variable_scope.VariableAggregation.ONLY_FIRST_REPLICA, distribution)
self.assertIsInstance(replica_local.value(), tensor.Tensor)
self.assertEqual(self.evaluate(replica_local.value()),
self.evaluate(value_list[0].value()))
@combinations.generate(
combinations.combine(
distribution=[
strategy_combinations.mirrored_strategy_with_gpu_and_cpu,
strategy_combinations.tpu_strategy_packed_var,
],
mode=["eager"]))
def testValueInDefaultReplicaContext(self, distribution):
with distribution.scope():
v1 = variables_lib.Variable(
0.0,
aggregation=variables_lib.VariableAggregation.SUM,
synchronization=variables_lib.VariableSynchronization.ON_READ)
v2 = variables_lib.Variable(
0.0,
aggregation=variables_lib.VariableAggregation.SUM,
synchronization=variables_lib.VariableSynchronization.ON_READ)
@def_function.function
def replica_fn():
v1.assign_add(1.0)
v2.assign_add(2.0)
distribution.run(replica_fn)
sum_v = v1 + v2
self.assertEqual(sum_v, 6.0)
@combinations.generate(
combinations.combine(
distribution=[
strategy_combinations.tpu_strategy_packed_var,
],
mode=["eager"]))
def testValueInFunctionCrossReplicaContext(self, distribution):
with distribution.scope():
v1 = variables_lib.Variable(
0.0,
aggregation=variables_lib.VariableAggregation.NONE,
synchronization=variables_lib.VariableSynchronization.ON_WRITE)
@def_function.function
def assign_fn():
v1.assign(1.0)
assign_fn()
self.assertEqual(v1, 1.0)
# Make sure the function graph has composite variable as inputs.
graph_def = assign_fn.get_concrete_function().graph.as_graph_def()
self.assertRegex(str(graph_def), "device:COMPOSITE:0")
@combinations.generate(
combinations.combine(
distribution=[
strategy_combinations.tpu_strategy_packed_var,
],
mode=["eager"]))
def testReplicatedValueNameDeterministic(self, distribution):
with distribution.scope():
v1 = variables_lib.Variable(0.0, name="test_var_1")
v2 = variables_lib.Variable(0.0, name="test_var_2")
def fn():
v1.assign_add(1.0)
v2.assign_add(2.0)
return v1 + v2
@def_function.function
def dist_run_fn():
a = distribution.run(fn)
return a
concrete_fn = dist_run_fn.get_concrete_function()
inputs = concrete_fn.graph.inputs
self.assertLen(inputs, 2)
# Before cl/433948982, input name will include a non-deterministic uid,
# e.g. "test_var_1_139726389910864/handle/inputs_0:0"
self.assertEqual(inputs[0].name, "test_var_1/handle/inputs_0:0")
self.assertEqual(inputs[1].name, "test_var_2/handle/inputs_0:0")
@combinations.generate(mirrored_and_tpu_strategy_combinations())
def testSaveAndRestoreReplicaLocalSumOneGraph(self, distribution):
with self.cached_session() as sess:
v, replica_local = _make_replica_local(
variable_scope.VariableAggregation.SUM, distribution)
# Overwrite the initial values.
self._assign_replica_local(v, [3., 4.])
with distribution.scope():
# Saves the current value of v[0] + v[1], 7.
save_path, saver = self._save_return_saver(sess, replica_local)
# Change the values between save and restore.
self._assign_replica_local(v, [5., 6.])
# Restores the saved value of 7. which gets divided equally
# between the variables.
saver.restore(sess, save_path)
self.assertEqual([3.5, 3.5], self.evaluate([v[0], v[1]]))
@combinations.generate(mirrored_and_tpu_strategy_combinations())
def testSaveAndRestoreReplicaLocalMeanOneGraph(self, distribution):
if context.num_gpus() < 1 and context.executing_eagerly():
self.skipTest("A GPU is not available for this test in eager mode.")
with self.cached_session() as sess:
v, replica_local = _make_replica_local(
variable_scope.VariableAggregation.MEAN, distribution)
# Overwrite the initial values.
self._assign_replica_local(v, [3., 4.])
with distribution.scope():
# Saves the current value of (v[0] + v[1])/2, 3.5.
save_path, saver = self._save_return_saver(sess, replica_local)
# Change the values between save and restore.
self._assign_replica_local(v, [5., 6.])
# Restores the saved value of 3.5 to both variables.
saver.restore(sess, save_path)
self.assertEqual([3.5, 3.5], self.evaluate([v[0], v[1]]))
def _save_replica_local_mean(self, distribution):
"""Save variables with mirroring, returns save_path."""
with self.session(graph=ops.Graph()) as sess:
v, replica_local = _make_replica_local(
variable_scope.VariableAggregation.MEAN, distribution)
# Overwrite the initial values.
self._assign_replica_local(v, [3., 4.])
with distribution.scope():
# Saves the current value of (v[0] + v[1])/2, 3.5
save_path = self._save(sess, replica_local)
# Change the values between save and restore.
self._assign_replica_local(v, [5., 6.])
return save_path
def _save_replica_local_sum(self, distribution):
"""Save variables with mirroring, returns save_path."""
with self.session(graph=ops.Graph()) as sess:
v, replica_local = _make_replica_local(
variable_scope.VariableAggregation.SUM, distribution)
# Overwrite the initial values.
self._assign_replica_local(v, [1.5, 2.])
with distribution.scope():
# Saves the current value of v[0] + v[1], 3.5
save_path = self._save(sess, replica_local)
# Change the values between save and restore.
self._assign_replica_local(v, [5., 6.])
return save_path
def _save_normal(self):
"""Save variables without mirroring, returns save_path."""
with self.session(graph=ops.Graph()) as sess:
var = variable_scope.get_variable(
name="v", initializer=1., use_resource=True)
# Overwrite the initial value.
self.evaluate(var.assign(3.5))
# Saves the current value of var, 3.5.
save_path = self._save(sess, var)
# Change the values between save and restore.
self.evaluate(var.assign(5.))
return save_path
def _restore_normal(self, save_path):
"""Restore to variables without mirroring in a fresh graph."""
with self.session(graph=ops.Graph()) as sess:
var = variable_scope.get_variable(
name="v", initializer=7., use_resource=True)
# Overwrite the initial value.
self.evaluate(var.assign(8.))
# Restores the saved value of 3.5 to `var`.
saver = saver_lib.Saver(var_list=[var])
saver.restore(sess, save_path)
self.assertEqual(3.5, self.evaluate(var))
def _restore_replica_local_mean(self, save_path, distribution):
"""Restore to variables with mirroring in a fresh graph."""
with self.session(graph=ops.Graph()) as sess:
v, replica_local = _make_replica_local(
variable_scope.VariableAggregation.MEAN, distribution)
# Overwrite the initial values.
self._assign_replica_local(v, [7., 8.])
with distribution.scope():
# Restores the saved value of 3.5 to both variables.
saver = saver_lib.Saver(var_list=[replica_local])
saver.restore(sess, save_path)
self.assertEqual([3.5, 3.5], self.evaluate([v[0], v[1]]))
def _restore_replica_local_sum(self, save_path, distribution):
"""Restore to variables with mirroring in a fresh graph."""
with self.session(graph=ops.Graph()) as sess:
v, replica_local = _make_replica_local(
variable_scope.VariableAggregation.SUM, distribution)
# Overwrite the initial values.
self._assign_replica_local(v, [7., 8.])
with distribution.scope():
# Restores the saved value of 3.5 to both variables.
saver = saver_lib.Saver(var_list=[replica_local])
saver.restore(sess, save_path)
self.assertEqual([1.75, 1.75], self.evaluate([v[0], v[1]]))
@combinations.generate(mirrored_and_tpu_strategy_combinations())
def testSaveReplicaLocalRestoreReplicaLocalMean(self, distribution):
save_path = self._save_replica_local_mean(distribution)
self._restore_replica_local_mean(save_path, distribution)
@combinations.generate(mirrored_and_tpu_strategy_combinations())
def testSaveReplicaLocalRestoreReplicaLocalSum(self, distribution):
save_path = self._save_replica_local_sum(distribution)
self._restore_replica_local_sum(save_path, distribution)
@combinations.generate(mirrored_and_tpu_strategy_combinations())
def testSaveReplicaLocalMeanRestoreNormal(self, distribution):
save_path = self._save_replica_local_mean(distribution)
self._restore_normal(save_path)
@combinations.generate(mirrored_and_tpu_strategy_combinations())
def testSaveReplicaLocalSumRestoreNormal(self, distribution):
save_path = self._save_replica_local_sum(distribution)
self._restore_normal(save_path)
@combinations.generate(mirrored_and_tpu_strategy_combinations())
def testSaveNormalRestoreReplicaLocalMean(self, distribution):
save_path = self._save_normal()
self._restore_replica_local_mean(save_path, distribution)
@combinations.generate(mirrored_and_tpu_strategy_combinations())
def testSaveNormalRestoreReplicaLocalSum(self, distribution):
save_path = self._save_normal()
self._restore_replica_local_sum(save_path, distribution)
if __name__ == "__main__":
ds_test_util.main()
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