# 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. # ============================================================================== """Library for testing DistributionStrategy descendants.""" import functools import os import tempfile import numpy as np from tensorflow.core.protobuf import config_pb2 from tensorflow.core.util import event_pb2 from tensorflow.python.client import session as session_lib from tensorflow.python.data.ops import dataset_ops from tensorflow.python.distribute import collective_all_reduce_strategy as mwms_lib from tensorflow.python.distribute import distribute_lib from tensorflow.python.distribute import distribute_utils from tensorflow.python.distribute import mirrored_strategy as mirrored_lib from tensorflow.python.distribute import reduce_util from tensorflow.python.distribute import tpu_strategy from tensorflow.python.eager import backprop from tensorflow.python.eager import context from tensorflow.python.eager import def_function from tensorflow.python.eager import test from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import test_util from tensorflow.python.lib.io import tf_record from tensorflow.python.ops import array_ops from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import init_ops from tensorflow.python.ops import init_ops_v2 from tensorflow.python.ops import math_ops from tensorflow.python.ops import summary_ops_v2 as summary_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variable_v1 from tensorflow.python.ops import variables from tensorflow.python.platform import gfile from tensorflow.python.training import optimizer from tensorflow.python.training import training_util from tensorflow.python.util import nest from tensorflow.python.util import tf_inspect class _TestException(Exception): pass # Conditionally wrap the fn in a def_function.function (so it runs in graph # mode). def _maybe_run_in_function(fn, run_in_function=False): if not run_in_function or not context.executing_eagerly(): return fn else: return def_function.function()(fn) # May be the argument to either distribution.extended.call_for_each_replica() or # get_replica_context().merge_call() def _raise_exception_fn(_=None): raise _TestException() # Must be the argument to a distribution.extended.call_for_each_replica() call, # calls a get_replica_context().merge_call() that raises an exception. def _merge_raises_fn(): distribute_lib.get_replica_context().merge_call(_raise_exception_fn) # Must be the argument to a get_replica_context().merge_call() call, calls # dist.extended.call_for_each_replica() with a function that raises an # exception. def _call_raises_fn(dist): dist.extended.call_for_each_replica(_raise_exception_fn) # Must be the argument to a distribution.extended.call_for_each_replica() call, # calls a get_replica_context().merge_call() that calls a # call_for_each_replica() that raises an exception. def _merge_call_raises_fn(): distribute_lib.get_replica_context().merge_call(_call_raises_fn) # Must be the argument to a get_replica_context().merge_call() call, calls # dist.extended.call_for_each_replica() with a function that calls a # get_replica_context().merge_call() that raises an exception. def _call_merge_raises_fn(dist): dist.extended.call_for_each_replica(_merge_raises_fn) # Must be the argument to a distribution.extended.call_for_each_replica() call, # calls a get_replica_context().merge_call() that calls a # call_for_each_replica() that calls a get_replica_context().merge_call() that # raises an exception. def _merge_call_merge_raises_fn(): distribute_lib.get_replica_context().merge_call(_call_merge_raises_fn) def _events_from_logdir(test_case, logdir): """Reads summary events from log directory.""" test_case.assertTrue(gfile.Exists(logdir)) files = gfile.ListDirectory(logdir) test_case.assertLen(files, 1) records = list(tf_record.tf_record_iterator(os.path.join(logdir, files[0]))) result = [] for r in records: event = event_pb2.Event() event.ParseFromString(r) result.append(event) return result def create_variable_like_keras_layer(name, shape, dtype): """Utility for create variables that works like variable in keras layer.""" initializer = functools.partial( init_ops_v2.GlorotUniform(), shape, dtype=dtype) return variables.Variable( initial_value=initializer, name=name, trainable=True) def is_optimizer_v2_instance(optimizer_obj): # For a optimizer instance, the v2 implementation has var_list as a required # argument. arg_spec = tf_inspect.getfullargspec(optimizer_obj.minimize) return "var_list" in arg_spec.args[:-len(arg_spec.defaults)] def is_mirrored_strategy(strategy: distribute_lib.Strategy) -> bool: return isinstance( strategy, (mirrored_lib.MirroredStrategy, mirrored_lib.MirroredStrategyV1)) def is_multi_worker_mirrored_strategy( strategy: distribute_lib.Strategy) -> bool: return isinstance(strategy, (mwms_lib.CollectiveAllReduceStrategy, mwms_lib.CollectiveAllReduceStrategyV1)) def is_tpu_strategy(strategy: distribute_lib.Strategy) -> bool: return isinstance(strategy, (tpu_strategy.TPUStrategy, tpu_strategy.TPUStrategyV1, tpu_strategy.TPUStrategyV2)) class DistributionTestBase(test.TestCase): """Some tests that should work with any DistributionStrategy.""" def _test_minimize_loss_eager(self, d): with d.scope(): kernel = create_variable_like_keras_layer( name="kernel", shape=(1, 1), dtype=dtypes.float32) def loss(x): y = array_ops.reshape( math_ops.mat_mul(x, kernel), []) - array_ops.identity(1.) return y * y # TODO(isaprykin): Extract implicit_grad+get_filtered_grad_fn into a # common `implicit_grad` function and put it in DistributionStrategy. grad_fn = backprop.implicit_grad(loss) grad_fn = optimizer.get_filtered_grad_fn(grad_fn) def update(v, g): return v.assign_sub(0.2 * g) one = array_ops.identity([[1.]]) def step(): """Perform one optimization step.""" # Run forward & backward to get gradients, variables list. g_v = d.extended.call_for_each_replica(grad_fn, args=(one,)) # Update the variables using the gradients and the update() function. before_list = [] after_list = [] for g, v in g_v: fetched = d.extended.read_var(v) before_list.append(fetched) # control_dependencies irrelevant but harmless in eager execution with ops.control_dependencies([fetched]): g = d.extended.reduce_to( reduce_util.ReduceOp.SUM, g, destinations=v) with ops.control_dependencies( d.extended.update(v, update, args=(g,), group=False)): after_list.append(d.extended.read_var(v)) return before_list, after_list for i in range(10): b, a = step() if i == 0: before, = b # pylint: disable=unbalanced-tuple-unpacking after, = a # pylint: disable=unbalanced-tuple-unpacking error_before = abs(before.numpy() - 1) error_after = abs(after.numpy() - 1) # Error should go down self.assertLess(error_after, error_before) def _test_minimize_loss_graph(self, d, soft_placement=False, learning_rate=0.2): config = config_pb2.ConfigProto() config.allow_soft_placement = soft_placement config.gpu_options.per_process_gpu_memory_fraction = 0.3 with context.graph_mode(), \ ops.Graph().as_default(), \ self.cached_session(config=config) as sess, \ d.scope(): kernel = create_variable_like_keras_layer( name="kernel", shape=(1, 1), dtype=dtypes.float32) def loss(x): y = array_ops.reshape( math_ops.mat_mul(x, kernel), []) - array_ops.identity(1.) return y * y grad_fn = backprop.implicit_grad(loss) def update(v, g): return v.assign_sub(learning_rate * g) one = array_ops.identity([[1.]]) def step(): """Perform one optimization step.""" # Run forward & backward to get gradients, variables list. g_v = d.extended.call_for_each_replica(grad_fn, args=(one,)) # Update the variables using the gradients and the update() function. before_list = [] after_list = [] for g, v in g_v: fetched = d.extended.read_var(v) before_list.append(fetched) with ops.control_dependencies([fetched]): g = d.extended.reduce_to( reduce_util.ReduceOp.SUM, g, destinations=v) with ops.control_dependencies( d.extended.update(v, update, args=(g,), group=False)): after_list.append(d.extended.read_var(v)) return before_list, after_list before_out, after_out = step() variables.global_variables_initializer().run() for i in range(10): b, a = sess.run((before_out, after_out)) if i == 0: before, = b after, = a error_before = abs(before - 1) error_after = abs(after - 1) # Error should go down self.assertLess(error_after, error_before) def _test_summary_for_replica_zero_only(self, d): logdir = tempfile.mkdtemp() def run_fn(): """Function executed for each replica.""" with summary_writer.as_default(): replica_id = distribute_lib.get_replica_context().replica_id_in_sync_group return summary_ops.write("a", replica_id) with self.cached_session() as sess, d.scope(), \ summary_ops.always_record_summaries(): # We need global_step because summary writing op *always* has global_step # as input, even when we always record summary or never record summary. global_step = training_util.get_or_create_global_step() if not context.executing_eagerly(): # When executing eagerly, variables are initialized immediately after # creation, and its initializer will be None. global_step.initializer.run() summary_ops.set_step(0) summary_writer = summary_ops.create_file_writer(logdir) output = d.extended.call_for_each_replica(run_fn) unwrapped = d.unwrap(output) if not context.executing_eagerly(): sess.run(summary_writer.init()) sess.run(unwrapped) sess.run(summary_writer.close()) events = _events_from_logdir(self, logdir) # There will be 2 entries: 1 summary file header entry, and 1 entry # written by replica 0. self.assertLen(events, 2) self.assertEqual(events[1].summary.value[0].tag, "a") self.assertEqual(events[1].summary.value[0].simple_value, 0.0) def _test_replica_id(self, d): with d.scope(): expected_devices = [False] * len(d.extended.worker_devices) def mark_devices_fn(): replica_id = self.evaluate( distribute_lib.get_replica_context().replica_id_in_sync_group) self.assertLess(replica_id, len(d.extended.worker_devices)) self.assertFalse(expected_devices[replica_id]) expected_devices[replica_id] = True d.extended.call_for_each_replica(mark_devices_fn) self.assertAllEqual(expected_devices, [True] * len(d.extended.worker_devices)) def _test_call_and_merge_exceptions(self, dist): with dist.scope(): with self.assertRaises(_TestException): dist.extended.call_for_each_replica(_raise_exception_fn) with self.assertRaises(_TestException): dist.extended.call_for_each_replica(_merge_raises_fn) with self.assertRaises(_TestException): dist.extended.call_for_each_replica(_merge_call_raises_fn) with self.assertRaises(_TestException): dist.extended.call_for_each_replica(_merge_call_merge_raises_fn) def _input_fn_to_test_input_context(self, dataset_or_callable_fn, expected_num_replicas_in_sync, expected_num_input_pipelines, expected_input_pipeline_id): # Use a list of one element as counter so that it can be captured by the # `_input_fn`. This counter is incremented by 1 each time an input_fn is # called. We use this counter to check whether the `input_pipeline_id` # matches the counter in the in-graph replication. worker_id_counter = [0] def _input_fn(input_context): """Input fn for testing.""" self.assertIsNotNone(input_context) self.assertEqual(expected_num_replicas_in_sync, input_context.num_replicas_in_sync) self.assertEqual(expected_num_input_pipelines, input_context.num_input_pipelines) if expected_input_pipeline_id is not None: self.assertEqual(expected_input_pipeline_id, input_context.input_pipeline_id) else: self.assertEqual(worker_id_counter[0], input_context.input_pipeline_id) worker_id_counter[0] += 1 return dataset_or_callable_fn() return _input_fn def _test_input_fn_iterable( self, strategy, input_fn, expected_values, ignore_order=False): assert_same = self.assertCountEqual if ignore_order else self.assertEqual iterable = strategy.distribute_datasets_from_function(input_fn) if context.executing_eagerly(): iterator = iter(iterable) for expected_value in expected_values: computed_value = self.evaluate( list(strategy.experimental_local_results(next(iterator)))) assert_same(expected_value, computed_value) with self.assertRaises(StopIteration): self.evaluate(strategy.experimental_local_results(next(iterator))) # After re-initializing the iterator, should be able to iterate again. iterator = iter(iterable) for expected_value in expected_values: computed_value = self.evaluate( list(strategy.experimental_local_results(next(iterator)))) assert_same(expected_value, computed_value) else: iterator = dataset_ops.make_initializable_iterator(iterable) self._test_input_fn_iterator(iterator, strategy.extended.worker_devices, expected_values, test_reinitialize=True, ignore_order=ignore_order) def _test_input_fn_iterator(self, iterator, devices, expected_values, sess=None, test_reinitialize=True, ignore_order=False): evaluate = lambda x: sess.run(x) if sess else self.evaluate(x) evaluate(iterator.initializer) for expected_value in expected_values: next_element = iterator.get_next() computed_value = evaluate( [distribute_utils.select_replica(r, next_element) for r in range(len(devices))]) if ignore_order: self.assertCountEqual(expected_value, computed_value) else: self.assertEqual(expected_value, computed_value) with self.assertRaises(errors.OutOfRangeError): next_element = iterator.get_next() evaluate( [distribute_utils.select_replica(r, next_element) for r in range(len(devices))]) # After re-initializing the iterator, should be able to iterate again. if test_reinitialize: evaluate(iterator.initializer) for expected_value in expected_values: next_element = iterator.get_next() computed_value = evaluate([ distribute_utils.select_replica(r, next_element) for r in range(len(devices)) ]) if ignore_order: self.assertCountEqual(expected_value, computed_value) else: self.assertEqual(expected_value, computed_value) def _test_global_step_update(self, strategy): with strategy.scope(): global_step = variable_scope.get_variable( "global_step", shape=[], dtype=dtypes.int64, initializer=init_ops.zeros_initializer(), trainable=False, aggregation=variables.VariableAggregation.ONLY_FIRST_REPLICA) self.evaluate(variables.global_variables_initializer()) def model_fn(): train_op = global_step.assign_add(1) value = global_step.read_value() return train_op, value train_ops, value = strategy.extended.call_for_each_replica(model_fn) self.evaluate(strategy.group(train_ops)) global_step_tensors = strategy.experimental_local_results(value) global_step_values = self.evaluate(global_step_tensors) self.assertEqual((1,) * len(global_step_tensors), global_step_values) def _test_numpy_dataset(self, strategy, session=None, run_in_function=False): if not isinstance(strategy, distribute_lib.StrategyV1): self.skipTest("n/a: V1 only") cached_session = session or self.cached_session() with strategy.scope(), cached_session as sess: x = np.asarray([[1, 2], [6, 12], [2, 4], [5, 10], [3, 6], [4, 8]]) y = np.asarray([5, 4, 3, 2, 1, 0]) batch_size = 6 if not strategy.extended._global_batch_size: # pylint: disable=protected-access batch_size = batch_size // strategy.num_replicas_in_sync ds = strategy.extended.experimental_make_numpy_dataset( (x, y), session=sess or self.cached_session()) ds = ds.repeat(2) # 2 epochs # We need to use the drop_remainder argument to get a known static # input shape which is required for TPUs. drop_remainder = strategy.extended.experimental_require_static_shapes ds = ds.batch(batch_size, drop_remainder=drop_remainder) i = strategy.make_dataset_iterator(ds) self.evaluate(i.initializer) def run_and_concatenate(strategy, i): x, y = strategy.experimental_run( _maybe_run_in_function(lambda z: z, run_in_function), i) x, y = self.evaluate((strategy.experimental_local_results(x), strategy.experimental_local_results(y))) return np.concatenate(x), np.concatenate(y) x_1, y_1 = run_and_concatenate(strategy, i) self.assertAllEqual(x, x_1) self.assertAllEqual(y, y_1) x_2, y_2 = run_and_concatenate(strategy, i) self.assertAllEqual(x, x_2) self.assertAllEqual(y, y_2) with self.assertRaises(errors.OutOfRangeError): run_and_concatenate(strategy, i) def _test_trainable_variable(self, strategy): for cls in [variable_v1.VariableV1, variables.Variable]: with strategy.scope(): v1 = cls(1.0) self.assertEqual(True, v1.trainable) v2 = cls(1.0, synchronization=variables.VariableSynchronization.ON_READ) self.assertEqual(False, v2.trainable) v3 = cls(1.0, synchronization=variables.VariableSynchronization.ON_READ, trainable=True) self.assertEqual(True, v3.trainable) v4 = cls(1.0, synchronization=variables.VariableSynchronization.ON_READ, trainable=False) self.assertEqual(False, v4.trainable) class OneDeviceDistributionTestBase(test.TestCase): """Some tests that should work with any one-device DistributionStrategy.""" def _test_run(self, strategy): out1 = strategy.run(lambda: array_ops.identity(4.)) self.assertAllEqual([4.], self.evaluate(strategy.unwrap(out1))) out2 = strategy.run(lambda x: {"a": x * 2, "b": x * x}, args=(out1,)) out2_vals = self.evaluate(nest.map_structure(strategy.unwrap, out2)) self.assertAllEqual([8.], out2_vals["a"]) self.assertAllEqual([16.], out2_vals["b"]) out3 = strategy.run(lambda b, a: a + 2 * b + 2, kwargs=out2) self.assertAllEqual([42.], self.evaluate(strategy.unwrap(out3))) def _test_all_reduce_sum(self, strategy): self._test_collective_comms( strategy, _all_sum, inputs=(4., [42., 43.]), expected=(4., [42., 43.])) def _test_all_reduce_sum_gradients(self, strategy): self._test_collective_comms_gradients( strategy, _all_sum, inputs=[4.], expected_grads=[4.]) def _test_all_reduce_sum_gradient_tape(self, strategy): self._test_collective_comms_gradient_tape( strategy, _all_sum, inputs=[4.], expected_grads=[4.]) def _test_all_reduce_mean(self, strategy): self._test_collective_comms( strategy, _all_mean, inputs=(2., [21., 22.]), expected=(2., [21., 22.])) def _test_all_reduce_mean_gradients(self, strategy): self._test_collective_comms_gradients( strategy, _all_mean, inputs=[5.], expected_grads=[5.]) def _test_all_reduce_mean_gradient_tape(self, strategy): self._test_collective_comms_gradient_tape( strategy, _all_mean, inputs=[5.], expected_grads=[5.]) def _test_collective_comms(self, strategy, comm_fn, inputs, expected): inputs = strategy.make_input_fn_iterator( lambda _: dataset_ops.Dataset.from_tensors(inputs)) self.evaluate(inputs.initialize()) outputs = self.evaluate( list( map(strategy.experimental_local_results, strategy.experimental_run(comm_fn, inputs)))) self.assertAllEqual([expected[0]], outputs[0]) self.assertAllEqual([expected[1]], outputs[1]) def _test_collective_comms_gradients(self, strategy, comm_fn, inputs, expected_grads): if context.executing_eagerly(): self.skipTest("`tf.gradients` is not supported with eager execution.") def step(c): x = array_ops.identity(42.) y = comm_fn(x) * c return gradients_impl.gradients(y, [x])[0] inputs = strategy.make_input_fn_iterator( lambda _: dataset_ops.Dataset.from_tensors(inputs)) self.evaluate(inputs.initialize()) self.assertAllEqual( expected_grads, self.evaluate( strategy.experimental_local_results( strategy.experimental_run(step, inputs)))) def _test_collective_comms_gradient_tape(self, strategy, comm_fn, inputs, expected_grads): def step(c): x = array_ops.identity(42.) with backprop.GradientTape() as tape: tape.watch(x) y = comm_fn(x) * c return tape.gradient(y, x) inputs = strategy.make_input_fn_iterator( lambda _: dataset_ops.Dataset.from_tensors(inputs)) self.evaluate(inputs.initialize()) self.assertAllEqual( expected_grads, self.evaluate( strategy.experimental_local_results( strategy.experimental_run(step, inputs)))) def _test_device_and_input_device_are_colocated(self, strategy): if context.executing_eagerly(): self.skipTest( "cross-device tests are not supported with eager execution.") workers, _ = test_util.create_local_cluster(2, 0) inputs = strategy.make_input_fn_iterator( lambda _: dataset_ops.Dataset.range(5)) comm_fn = lambda x: x + 1 run_op = strategy.experimental_run(comm_fn, inputs) with session_lib.Session(target=workers[1].target) as sess: sess.run(inputs.initialize()) sess.run(run_op) def _test_device_and_input_device_are_colocated_with_function(self, strategy): if context.executing_eagerly(): self.skipTest( "cross-device tests are not supported with eager execution.") workers, _ = test_util.create_local_cluster(2, 0) inputs = strategy.make_input_fn_iterator( lambda _: dataset_ops.Dataset.range(5)) comm_fn = lambda x: x + 1 experimental_run = def_function.function()(strategy.experimental_run) with ops.device("/job:worker/replica:0/task:1/device:CPU:0"): # The tf.function must be defined on the right device as well. run_op = experimental_run(comm_fn, inputs) with session_lib.Session(target=workers[1].target) as sess: sess.run(inputs.initialize()) sess.run(run_op) class TwoDeviceDistributionTestBase(test.TestCase): """Some tests that should work with any two-device DistributionStrategy.""" def _test_run(self, strategy, run_in_function=False): out1 = strategy.run(_maybe_run_in_function( lambda: distribute_lib.get_replica_context().replica_id_in_sync_group + 1, run_in_function)) self.assertAllEqual([1, 2], self.evaluate(strategy.unwrap(out1))) out2 = strategy.run(_maybe_run_in_function( lambda x: {"a": x * 2, "b": x * x}, run_in_function), args=(out1,)) out2_vals = self.evaluate(nest.map_structure(strategy.unwrap, out2)) self.assertAllEqual([2, 4], out2_vals["a"]) self.assertAllEqual([1, 4], out2_vals["b"]) out3 = strategy.run(_maybe_run_in_function( lambda b, a: a + 2 * b + 2, run_in_function), kwargs=out2) self.assertAllEqual([6, 14], self.evaluate(strategy.unwrap(out3))) def _test_all_reduce_sum(self, strategy, run_in_function=False): self._test_collective_comms( strategy, _all_sum, inputs=([1., 3.], [[39., 2.], [3., 41.]]), expected=(4., [42., 43.]), run_in_function=run_in_function) def _test_all_reduce_sum_gradients(self, strategy, run_in_function=False): self._test_collective_comms_gradients( strategy, _all_sum, inputs=[1., 3.], expected_grads=[4., 4.], run_in_function=run_in_function) def _test_all_reduce_sum_gradient_tape(self, strategy, run_in_function=False): self._test_collective_comms_gradient_tape( strategy, _all_sum, inputs=[1., 3.], expected_grads=[4., 4.], run_in_function=run_in_function) def _test_all_reduce_mean(self, strategy, run_in_function=False): self._test_collective_comms( strategy, _all_mean, inputs=([1., 3.], [[39., 2.], [3., 41.]]), expected=(2., [21., 21.5]), run_in_function=run_in_function) def _test_all_reduce_mean_gradients(self, strategy, run_in_function=False): self._test_collective_comms_gradients( strategy, _all_mean, inputs=[1., 3.], expected_grads=[2., 2.], run_in_function=run_in_function) def _test_all_reduce_mean_gradient_tape(self, strategy, run_in_function=False): self._test_collective_comms_gradient_tape( strategy, _all_mean, inputs=[1., 3.], expected_grads=[2., 2.], run_in_function=run_in_function) def _test_collective_comms(self, strategy, comm_fn, inputs, expected, run_in_function=False): inputs = strategy.make_input_fn_iterator( lambda _: dataset_ops.Dataset.from_tensor_slices(inputs)) self.evaluate(inputs.initialize()) outputs = self.evaluate( list( map(strategy.experimental_local_results, strategy.experimental_run( _maybe_run_in_function(comm_fn, run_in_function), inputs)))) self.assertAllEqual([expected[0], expected[0]], outputs[0]) self.assertAllEqual([expected[1], expected[1]], outputs[1]) def _test_collective_comms_gradients(self, strategy, comm_fn, inputs, expected_grads, run_in_function=False): if context.executing_eagerly() and not run_in_function: self.skipTest("`tf.gradients` is not supported with eager execution " "without using tf.functions.") def step(c): x = array_ops.identity(42.) y = comm_fn(x) * c return gradients_impl.gradients(y, [x])[0] inputs = strategy.make_input_fn_iterator( lambda _: dataset_ops.Dataset.from_tensor_slices(inputs)) self.evaluate(inputs.initialize()) self.assertAllEqual( expected_grads, self.evaluate( strategy.experimental_local_results( strategy.experimental_run( _maybe_run_in_function(step, run_in_function), inputs)))) def _test_collective_comms_gradient_tape(self, strategy, comm_fn, inputs, expected_grads, run_in_function=False): def step(c): x = array_ops.identity(42.) with backprop.GradientTape() as tape: tape.watch(x) y = comm_fn(x) * c return tape.gradient(y, x) inputs = strategy.make_input_fn_iterator( lambda _: dataset_ops.Dataset.from_tensor_slices(inputs)) self.evaluate(inputs.initialize()) self.assertAllEqual( expected_grads, self.evaluate( strategy.experimental_local_results( strategy.experimental_run( _maybe_run_in_function(step, run_in_function), inputs)))) class RemoteSingleWorkerMirroredStrategyBase(DistributionTestBase): """Tests for a Remote single worker.""" def _get_num_gpus(self): pass def _testNumReplicasInSync(self, distribution): self.assertEqual(self._get_num_gpus(), distribution.num_replicas_in_sync) def _testMinimizeLoss(self, distribution): if context.executing_eagerly(): self._test_minimize_loss_eager(distribution) else: self._test_minimize_loss_graph(distribution, learning_rate=0.05) def _testDeviceScope(self, distribution): with distribution.scope(): a = array_ops.identity(1.) with ops.device("/cpu:0"): b = array_ops.identity(1.) if context.executing_eagerly(): device = "/job:worker/replica:0/task:0/device:CPU:0" else: device = "/job:worker/replica:0/task:0" self.assertEqual(a.device, device) self.assertEqual(b.device, "/job:worker/replica:0/task:0/device:CPU:0") def _testMakeInputFnIteratorWithDataset(self, distribution): dataset_fn = lambda: dataset_ops.Dataset.range(100) num_gpus = self._get_num_gpus() # pylint: disable=assignment-from-no-return num_workers = 1 expected_values = [[i+j for j in range(num_gpus)] * num_workers for i in range(0, 100, num_gpus)] # Dummy cached_session is used in Eager with self.cached_session() as sess: # `expected_input_pipeline_id` is None because the input_fn will be called # multiple times, each with a different input_pipeline_id. input_fn = self._input_fn_to_test_input_context( dataset_fn, expected_num_replicas_in_sync=num_workers*num_gpus, expected_num_input_pipelines=num_workers, expected_input_pipeline_id=None) iterator = distribution.make_input_fn_iterator(input_fn) self._test_input_fn_iterator( iterator, distribution.extended.worker_devices, expected_values, sess) def _testMakeInputFnIteratorWithCallable(self, distribution): def fn(): dataset = dataset_ops.Dataset.range(100) it = dataset_ops.make_one_shot_iterator(dataset) return it.get_next num_gpus = self._get_num_gpus() # pylint: disable=assignment-from-no-return num_workers = 1 expected_values = [] for i in range(0, 100, num_gpus): expected_values.append([i+j for j in range(num_gpus)] * num_workers) # Dummy cached_session is used in Eager with self.cached_session() as sess: # `expected_input_pipeline_id` is None because the input_fn will be called # multiple times, each with a different input_pipeline_id. input_fn = self._input_fn_to_test_input_context( fn, expected_num_replicas_in_sync=num_workers*num_gpus, expected_num_input_pipelines=num_workers, expected_input_pipeline_id=None) iterator = distribution.make_input_fn_iterator(input_fn) self._test_input_fn_iterator( iterator, distribution.extended.worker_devices, expected_values, sess, test_reinitialize=False, ignore_order=True) def _all_sum(value): ctx = distribute_lib.get_replica_context() return ctx.all_reduce(reduce_util.ReduceOp.SUM, value) def _all_mean(value): ctx = distribute_lib.get_replica_context() return ctx.all_reduce(reduce_util.ReduceOp.MEAN, value)