class SingleLossStepTest(test.TestCase, parameterized.TestCase): @combinations.generate( combinations.times( strategy_combinations.distributions_and_v1_optimizers(), combinations.combine( mode=strategy_combinations.graph_and_eager_modes), combinations.combine(is_tpu=[False])) + combinations.combine( distribution=[strategy_combinations.tpu_strategy], optimizer_fn=strategy_combinations.optimizers_v1, mode=["graph"], is_tpu=[True])) def testTrainNetwork(self, distribution, optimizer_fn, is_tpu): with distribution.scope(): single_loss_step, layer = single_loss_example( optimizer_fn, distribution, use_bias=True, iterations_per_step=2) if context.executing_eagerly(): single_loss_step.initialize() run_step = single_loss_step else: with self.cached_session() as sess: sess.run(single_loss_step.initialize()) run_step = sess.make_callable(single_loss_step()) self.evaluate(variables.global_variables_initializer()) weights, biases = [], [] for _ in range(5): run_step() weights.append(self.evaluate(layer.kernel)) biases.append(self.evaluate(layer.bias)) error = abs(numpy.add(numpy.squeeze(weights), numpy.squeeze(biases)) - 1) is_not_increasing = all(y <= x for x, y in zip(error, error[1:])) self.assertTrue(is_not_increasing)
class MonitorTest(test.TestCase, parameterized.TestCase): @combinations.generate( combinations.times( strategy_combinations.distributions_and_v1_optimizers(), combinations.combine( mode=strategy_combinations.graph_and_eager_modes))) def testTrainNetwork(self, distribution, optimizer_fn): with distribution.scope(): single_loss_step, layer = single_loss_example(optimizer_fn, distribution) if context.executing_eagerly(): monitor = monitor_lib.Monitor(single_loss_step, None) else: with self.cached_session() as sess: monitor = monitor_lib.Monitor(single_loss_step, sess) monitor.run_steps(1) self.assertEqual(1, len(layer.trainable_variables)) mirrored_weight_variable = layer.trainable_variables[0] start_error = self.evaluate(mirrored_weight_variable) start_error = abs(numpy.array(start_error) - 1) monitor.run_steps(9) end_error = self.evaluate(mirrored_weight_variable) end_error = abs(numpy.array(end_error) - 1) self.assertGreaterEqual(start_error, end_error) def testPassingASessionInEager(self): distribution = one_device_strategy.OneDeviceStrategy( "/device:CPU:0") step_function, _ = single_loss_example( lambda: gradient_descent.GradientDescentOptimizer(0.2), distribution) with session.Session() as sess, context.eager_mode(): with self.assertRaisesRegexp(ValueError, "Should not provide"): _ = monitor_lib.Monitor(step_function, sess) def testNotPassingASessionInGraph(self): distribution = one_device_strategy.OneDeviceStrategy( "/device:CPU:0") step_function, _ = single_loss_example( lambda: gradient_descent.GradientDescentOptimizer(0.2), distribution) with context.graph_mode(), ops.Graph().as_default(): with self.assertRaisesRegexp(ValueError, "Should provide"): _ = monitor_lib.Monitor(step_function, session=None)
class MinimizeLossStepTest(test.TestCase, parameterized.TestCase): def _get_iterator(self, strategy, input_fn): iterator = strategy.make_input_fn_iterator(lambda _: input_fn()) self.evaluate(iterator.initializer) return iterator @combinations.generate( combinations.times( strategy_combinations.distributions_and_v1_optimizers(), combinations.combine(mode=["graph"], use_callable_loss=[True, False]) + combinations.combine(mode=["eager"], use_callable_loss=[True])) + combinations.times( strategy_combinations.distributions_and_v2_optimizers(), combinations.combine(mode=["graph", "eager"], use_callable_loss=[True])) + combinations.combine(distribution=[strategy_combinations.tpu_strategy], optimizer_fn=strategy_combinations.optimizers_v2, mode=["graph"], use_callable_loss=[True]) + combinations.combine(distribution=[strategy_combinations.tpu_strategy], optimizer_fn=strategy_combinations.optimizers_v1, mode=["graph"], use_callable_loss=[True, False])) def testTrainNetwork(self, distribution, optimizer_fn, use_callable_loss): with distribution.scope(): optimizer = optimizer_fn() model_fn, dataset_fn, layer = minimize_loss_example( optimizer, use_bias=True, use_callable_loss=use_callable_loss) def step_fn(ctx, inputs): del ctx # Unused return distribution.group( distribution.extended.call_for_each_replica( model_fn, args=(inputs, ))) iterator = self._get_iterator(distribution, dataset_fn) def run_step(): return distribution.extended.experimental_run_steps_on_iterator( step_fn, iterator, iterations=2).run_op if not context.executing_eagerly(): with self.cached_session() as sess: run_step = sess.make_callable(run_step()) self.evaluate(variables_lib.global_variables_initializer()) weights, biases = [], [] for _ in range(5): run_step() weights.append(self.evaluate(layer.kernel)) biases.append(self.evaluate(layer.bias)) error = abs( numpy.add(numpy.squeeze(weights), numpy.squeeze(biases)) - 1) is_not_increasing = all(y <= x for x, y in zip(error, error[1:])) self.assertTrue(is_not_increasing) @combinations.generate( combinations.times( strategy_combinations.distributions_and_v1_optimizers(), combinations.combine(mode=["graph"], use_callable_loss=[True, False]) + combinations.combine(mode=["eager"], use_callable_loss=[True])) + combinations.times( strategy_combinations.distributions_and_v2_optimizers(), combinations.combine(mode=["graph", "eager"], use_callable_loss=[True]))) def testTrainNetworkByCallForEachReplica(self, distribution, optimizer_fn, use_callable_loss): with distribution.scope(): optimizer = optimizer_fn() model_fn, dataset_fn, layer = minimize_loss_example( optimizer, use_bias=True, use_callable_loss=use_callable_loss) iterator = self._get_iterator(distribution, dataset_fn) def run_step(): return distribution.group( distribution.extended.call_for_each_replica( model_fn, args=(iterator.get_next(), ))) if not context.executing_eagerly(): with self.cached_session() as sess: run_step = sess.make_callable(run_step()) self.evaluate(variables_lib.global_variables_initializer()) weights, biases = [], [] for _ in range(10): run_step() weights.append(self.evaluate(layer.kernel)) biases.append(self.evaluate(layer.bias)) error = abs( numpy.add(numpy.squeeze(weights), numpy.squeeze(biases)) - 1) is_not_increasing = all(y <= x for x, y in zip(error, error[1:])) self.assertTrue(is_not_increasing) @combinations.generate( combinations.times( strategy_combinations.distributions_and_v1_and_v2_optimizers(), combinations.combine(mode=["graph", "eager"])) + combinations.combine( distribution=[strategy_combinations.tpu_strategy], optimizer_fn=strategy_combinations.optimizers_v1_and_v2, mode=["graph"])) def testOptimizerInsideModelFn(self, distribution, optimizer_fn): if (not context.executing_eagerly() and control_flow_v2_toggles.control_flow_v2_enabled()): self.skipTest("b/138751864") created_variables = [] trainable_variables = [] def appending_creator(next_creator, **kwargs): v = next_creator(**kwargs) created_variables.append(v.name) if "trainable" in kwargs and kwargs["trainable"]: trainable_variables.append(v.name) return v # Creator scope needs to be set before it's used inside # `distribution.scope`. with variable_scope.variable_creator_scope( appending_creator), distribution.scope(): optimizer = optimizer_fn() model_fn, dataset_fn, _ = minimize_loss_example( optimizer, use_bias=True, use_callable_loss=True) def step_fn(ctx, inputs): del ctx # Unused return distribution.group( distribution.extended.call_for_each_replica( model_fn, args=(inputs, ))) iterator = self._get_iterator(distribution, dataset_fn) def run_step(): return distribution.extended.experimental_run_steps_on_iterator( step_fn, iterator, iterations=1).run_op if not context.executing_eagerly(): with self.cached_session() as sess: run_step = sess.make_callable(run_step()) self.evaluate(variables_lib.global_variables_initializer()) run_step() def get_expected_variables(num_parameter_devices): name = optimizer._name if isinstance(optimizer, optimizer_v2.OptimizerV2): variables = VAR_MAP_V2[name] else: variables = VAR_MAP_V1[name] extended_variables = [ v + "/replica_{}".format(replica) for v in variables for replica in range(1, num_parameter_devices) ] variables = list(variables) + extended_variables return set(v + ":0" for v in variables) self.assertEqual( get_expected_variables( len(distribution.extended.parameter_devices)), set(created_variables)) @combinations.generate( combinations.times( combinations.combine(momentum=[0.8, 0.9, 0.99], renorm=[False, True]), combinations.times( strategy_combinations.distributions_and_v1_and_v2_optimizers(), combinations.combine( mode=["graph", "eager"], # TODO(isaprykin): Allow False here. Currently subsequent # replicas will re-execute UPDATE_OPS of previous replicas. update_ops_in_cross_replica_mode=[True])) + combinations.combine( distribution=[strategy_combinations.tpu_strategy], optimizer_fn=strategy_combinations.optimizers_v1_and_v2, mode=["graph"], update_ops_in_cross_replica_mode=[False]))) def testTrainNetworkWithBatchNorm(self, distribution, optimizer_fn, momentum, renorm, update_ops_in_cross_replica_mode): """Verifies that moving mean updates are reduced across replicas.""" with distribution.scope(): num_replicas = distribution.num_replicas_in_sync model_fn, dataset_fn, batchnorm = batchnorm_example( optimizer_fn, batch_per_epoch=num_replicas, momentum=momentum, renorm=renorm, update_ops_in_replica_mode=not update_ops_in_cross_replica_mode ) def step_fn(ctx, inputs): del ctx # Unused fetches = distribution.experimental_local_results( distribution.extended.call_for_each_replica( model_fn, args=(inputs, ))) if update_ops_in_cross_replica_mode: fetches += tuple( ops.get_collection(ops.GraphKeys.UPDATE_OPS)) return control_flow_ops.group(fetches) iterator = self._get_iterator(distribution, dataset_fn) def run_step(): return distribution.extended.experimental_run_steps_on_iterator( step_fn, iterator, iterations=1).run_op if not context.executing_eagerly(): with self.cached_session() as sess: run_step = sess.make_callable(run_step()) self.evaluate(variables_lib.global_variables_initializer()) expected_moving_means = [0.] * 8 def averaged_batch_mean(i): # Each batch has shape [16, 8] where the ith element in jth list is # (8 * j + i + replica_id * 100). So the batch mean in each replica is # (60 + i + replica_id * 100). So here comes its batch mean over all # replicas: return 60. + i + (num_replicas - 1.) / 2. * 100. for _ in range(10): run_step() moving_means = self.evaluate(batchnorm.moving_mean) # We make sure that the moving_mean is updated as if the sample mean is # calculated over all replicas. for i, expected_moving_mean in enumerate( expected_moving_means): expected_moving_means[i] -= ( (expected_moving_mean - averaged_batch_mean(i)) * (1.0 - momentum)) self.assertNear(expected_moving_means[i], moving_means[i], 0.0001) @combinations.generate( combinations.times( combinations.combine(loss_reduction=[ losses_impl.Reduction.SUM, losses_impl.Reduction.MEAN, losses_impl.Reduction.SUM_OVER_BATCH_SIZE, losses_impl.Reduction.SUM_OVER_NONZERO_WEIGHTS ]), combinations.times( combinations.combine(distribution=[ strategy_combinations.one_device_strategy, strategy_combinations.mirrored_strategy_with_gpu_and_cpu, strategy_combinations.mirrored_strategy_with_two_gpus ]), combinations.times( combinations.combine(optimizer_fn=strategy_combinations. gradient_descent_optimizer_v1_fn), combinations.combine(mode=["graph"], use_callable_loss=[True, False]) + combinations.combine(mode=["eager"], use_callable_loss=[True])) + combinations.times( combinations.combine( optimizer_fn=strategy_combinations. gradient_descent_optimizer_keras_v2_fn), combinations.combine(mode=["graph", "eager"], use_callable_loss=[True]))) + combinations.combine( distribution=[strategy_combinations.tpu_strategy], optimizer_fn=strategy_combinations. gradient_descent_optimizer_v1_fn, mode=["graph"], use_callable_loss=[True, False]) + combinations.combine( distribution=[strategy_combinations.tpu_strategy], optimizer_fn=strategy_combinations. gradient_descent_optimizer_keras_v2_fn, mode=["graph"], use_callable_loss=[True]))) def testMeanVsSum(self, distribution, optimizer_fn, loss_reduction, use_callable_loss): with distribution.scope(): all_vars = [] def model_fn(inputs): x, y = inputs w = variable_scope.get_variable("w", initializer=[[2.]]) all_vars.append(w) def loss_fn(): # Use fixed initialization to make the steps deterministic. predict = math_ops.matmul(x, w) loss = losses_impl.mean_squared_error( y, predict, reduction=loss_reduction) if loss_reduction == losses_impl.Reduction.SUM: return loss return loss / distribution.num_replicas_in_sync optimizer = optimizer_fn( ) # GradientDescent with 0.2 learning rate if isinstance(optimizer, optimizer_v2.OptimizerV2): return optimizer.minimize(loss_fn, [w]) else: if use_callable_loss: return optimizer.minimize(loss_fn) else: return optimizer.minimize(loss_fn()) def dataset_fn(): features = dataset_ops.Dataset.from_tensors([[2.], [7.]]) labels = dataset_ops.Dataset.from_tensors([[6.], [21.]]) return dataset_ops.Dataset.zip((features, labels)).repeat() def step_fn(ctx, inputs): del ctx # Unused return distribution.group( distribution.extended.call_for_each_replica( model_fn, args=(inputs, ))) iterator = self._get_iterator(distribution, dataset_fn) def run_step(): return distribution.extended.experimental_run_steps_on_iterator( step_fn, iterator, iterations=1).run_op if not context.executing_eagerly(): with self.cached_session() as sess: run_step = sess.make_callable(run_step()) self.evaluate(variables_lib.global_variables_initializer()) run_step() v = all_vars[0] self.assertTrue(all(v is vi for vi in all_vars[1:])) weight = numpy.squeeze(self.evaluate(v)) # Our model is: # predict = x * w # loss = (predict - y)^2 # dloss/dpredict = 2*(predict - y) # dloss/dw = 2 * x^T @ (predict - y) # For our batch size of 2, assuming sum loss reduction: # x = [2, 7] # y = [6, 21] # w_initial = 2 # predict = [4, 14] # predict - y = [-2, -7] # dloss/dw = 2 <[2, 7], [-2, -7]> = - 2(4 + 49) = -106 # So unreplicated the update to w with lr=0.001 is -0.2 * -106 = 0.106 # with sum loss reduction, or 0.053 with mean. if loss_reduction == losses_impl.Reduction.SUM: # Note that the "distribution.num_replicas_in_sync" factor will go away # once we split the input across replicas, instead of pulling a complete # batch of input per replica. self.assertNear(weight, 2 + 0.106 * distribution.num_replicas_in_sync, 0.0001) else: # One of the mean loss reductions. self.assertNear(weight, 2 + 0.053, 0.0001) @combinations.generate( combinations.times( strategy_combinations.distributions_and_v1_and_v2_optimizers(), combinations.combine(mode=["graph", "eager"]), combinations.combine(is_tpu=[False])) + combinations.combine( distribution=[strategy_combinations.tpu_strategy], optimizer_fn=strategy_combinations.optimizers_v1_and_v2, mode=["graph"], is_tpu=[True])) def testRunStepsWithOutputContext(self, distribution, optimizer_fn, is_tpu): with distribution.scope(): def dataset_fn(): dataset = dataset_ops.Dataset.from_tensors([[1.]]).repeat() # TODO(priyag): batch with drop_remainder=True causes shapes to be # fully defined for TPU. Remove this when XLA supports dynamic shapes. return dataset.batch(batch_size=1, drop_remainder=True) optimizer = optimizer_fn() layer = core.Dense(1, use_bias=True) key1 = "foo" value1 = "bar" def model_fn(output_context, x): """A very simple model written by the user.""" def loss_fn(): y = array_ops.reshape(layer(x), []) - constant_op.constant(1.) return y * y if isinstance(optimizer, optimizer_v2.OptimizerV2): train_op = optimizer.minimize( loss_fn, lambda: layer.trainable_variables) else: train_op = optimizer.minimize(loss_fn) loss = loss_fn() output_context.set_last_step_output( name="replica_loss_reduced", output=loss, reduce_op=reduce_util.ReduceOp.MEAN) output_context.set_non_tensor_output(key1, value1) return (train_op, loss) def step_fn(output_context, inputs): (train_op, loss) = distribution.extended.call_for_each_replica( model_fn, args=(output_context, inputs)) output_context.set_last_step_output( name="cross_replica_loss_reduced", output=loss, reduce_op=reduce_util.ReduceOp.MEAN) output_context.set_last_step_output( name="cross_replica_loss_not_reduced", output=loss) return distribution.group(train_op) iterator = self._get_iterator(distribution, dataset_fn) def run_step(): initial_loss = lambda: constant_op.constant(1e7) # Initial values corresponding to reduced losses are just single # tensors. But for non reduced losses, we need to have initial # values that are of the same structure as non reduced losses. In # MirroredStrategy, this will be a list of losses, in TPUStrategy # it will be single tensor. Using `call_for_each_replica` followed # by `experimental_local_results` gives us the desired initial # value structure. not_reduced = distribution.experimental_local_results( distribution.extended.call_for_each_replica(initial_loss)) initial_loop_values = { "replica_loss_reduced": initial_loss(), "cross_replica_loss_reduced": initial_loss(), "cross_replica_loss_not_reduced": not_reduced, } ctx = distribution.extended.experimental_run_steps_on_iterator( step_fn, iterator, iterations=2, initial_loop_values=initial_loop_values) self.assertEqual({key1: (value1, )}, ctx.non_tensor_outputs) self._verify_loss_output( initial_loss(), loss_output=ctx.last_step_outputs["replica_loss_reduced"], reduced=True, distribution=distribution) self._verify_loss_output( initial_loss(), loss_output=ctx. last_step_outputs["cross_replica_loss_reduced"], reduced=True, distribution=distribution) self._verify_loss_output( initial_loss(), loss_output=ctx. last_step_outputs["cross_replica_loss_not_reduced"], reduced=False, distribution=distribution) return (ctx.run_op, ctx.last_step_outputs["replica_loss_reduced"]) if not context.executing_eagerly(): with self.cached_session() as sess: run_step = sess.make_callable(run_step()) self.evaluate(variables_lib.global_variables_initializer()) weights, biases, losses = [], [], [] for _ in range(5): _, loss = run_step() losses.append(loss) weights.append(self.evaluate(layer.kernel)) biases.append(self.evaluate(layer.bias)) loss_is_not_increasing = all(y <= x for x, y in zip(losses, losses[1:])) self.assertTrue(loss_is_not_increasing) error = abs( numpy.add(numpy.squeeze(weights), numpy.squeeze(biases)) - 1) error_is_not_increasing = all(y <= x for x, y in zip(error, error[1:])) self.assertTrue(error_is_not_increasing) def _verify_loss_output(self, initial_loss, loss_output, reduced, distribution): if not reduced: self.assertLen( distribution.experimental_local_results(loss_output), distribution.num_replicas_in_sync) loss_tensor = distribution.reduce(reduce_util.ReduceOp.MEAN, loss_output, axis=None) else: unwrapped_output = distribution.experimental_local_results( loss_output) self.assertLen(unwrapped_output, 1) loss_tensor = unwrapped_output[0] self.assertEqual(initial_loss.dtype, loss_tensor.dtype) self.assertEqual(initial_loss.shape, loss_tensor.shape) @combinations.generate( strategy_combinations.distributions_and_v2_optimizers()) def test_empty_var_list(self, distribution, optimizer_fn): opt = optimizer_fn() with distribution.scope(): def run_fn(): opt.minimize(lambda: constant_op.constant(1.), []) opt.apply_gradients([]) distribution.run(run_fn)