def step_fn(ctx, inputs): """Clones the model and calls make_predict_function.""" if model._compile_distribution: distributed_training_utils.clone_model_on_replicas( model, current_strategy, mode, inputs=inputs) else: distributed_training_utils._build_distributed_network( model, current_strategy, mode, inputs) (grouped_inputs, grouped_outputs, grouped_updates, grouped_session_args) = current_strategy.extended.call_for_each_replica( _per_device_predict_function, args=(distributed_training_utils.get_distributed_model( model, ModeKeys.PREDICT),)) (all_inputs, all_outputs, all_updates, all_session_args) = distributed_training_utils.unwrap_values( current_strategy, grouped_inputs, grouped_outputs, grouped_updates, grouped_session_args) combined_fn = K.function( all_inputs, all_outputs, updates=all_updates, name='distributed_predict_function', **all_session_args) for label, output in zip(model.output_names, combined_fn.outputs): ctx.set_last_step_output(label, output) return combined_fn.updates_op
def step_fn(ctx, inputs): """Clones the model and calls make_predict_function.""" if model._compile_distribution: distributed_training_utils.clone_model_on_replicas( model, current_strategy, mode, inputs=inputs) else: distributed_training_utils._build_distributed_network( model, current_strategy, mode, inputs) (grouped_inputs, grouped_outputs, grouped_updates, grouped_session_args ) = current_strategy.extended.call_for_each_replica( _per_device_predict_function, args=(distributed_training_utils.get_distributed_model( model, ModeKeys.PREDICT), )) (all_inputs, all_outputs, all_updates, all_session_args) = distributed_training_utils.unwrap_values( current_strategy, grouped_inputs, grouped_outputs, grouped_updates, grouped_session_args) combined_fn = K.function(all_inputs, all_outputs, updates=all_updates, name='distributed_predict_function', **all_session_args) for label, output in zip(model.output_names, combined_fn.outputs): ctx.set_last_step_output(label, output) return combined_fn.updates_op
def _step_fn(ctx, inputs): """A step fn that returns update ops.""" inputs, targets = inputs _build_model(strategy, model, mode, inputs, targets) (grouped_inputs, grouped_outputs, grouped_updates, grouped_session_args) = strategy.extended.call_for_each_replica( _per_device_execution_function, args=(distributed_training_utils.get_distributed_model( model, mode), mode)) (all_inputs, all_outputs, all_updates, all_session_args) = distributed_training_utils.unwrap_values( strategy, grouped_inputs, grouped_outputs, grouped_updates, grouped_session_args) combined_fn = K.function(all_inputs, all_outputs, updates=all_updates, name='distributed_' + str(mode) + '_function', **all_session_args) for label, output in zip(output_labels, combined_fn.outputs): if label == 'loss': reduce_op = ds_reduce_util.ReduceOp.SUM else: # We reduce all other metrics using mean for now. This is temporary # workaround until new metrics are in place. reduce_op = ds_reduce_util.ReduceOp.MEAN ctx.set_last_step_output(label, output, reduce_op) # TODO(priyag, sourabhbajaj): Ignoring these things from the combined_fn: # feed_dict, session kwargs, run options, run_metadata for now. These should # be handled appropriately return combined_fn.updates_op
def testOptimizerWithCallbacks(self, distribution): with self.cached_session(): model = get_model() optimizer = gradient_descent_keras.SGD(0.01) loss = 'mse' model.compile(optimizer, loss, distribute=distribution) dataset = get_dataset(distribution) def schedule(_): return 0.001 model.fit( dataset, epochs=1, steps_per_epoch=2, verbose=0, callbacks=[keras.callbacks.LearningRateScheduler(schedule)]) grouped_models = distribution.unwrap( distributed_training_utils.get_distributed_model( model, ModeKeys.TRAIN)) with distribution.scope(): for m in grouped_models: self.assertAllClose(0.001, keras.backend.get_value( m.optimizer.lr), atol=1e-05, rtol=1e-05)
def _test_step_fn(inputs): """A fn that returns output of single test step.""" inputs, targets = inputs (distribution_strategy_context.get_replica_context().merge_call( _build_model, args=(model, mode, inputs, targets))) (_, outputs, updates, _) = (_per_device_execution_function( distributed_training_utils.get_distributed_model(model, mode), mode)) with ops.control_dependencies([updates]): return outputs
def step_fn(ctx, inputs): """A step fn that returns update ops.""" if mode == ModeKeys.PREDICT: targets = None else: inputs, targets = inputs if model._compile_distribution: distributed_training_utils.clone_model_on_replicas( model, strategy, mode, inputs=inputs, targets=targets) else: distributed_training_utils._build_distributed_network( model, strategy, mode, inputs, targets) (grouped_inputs, grouped_outputs, grouped_updates, grouped_session_args) = strategy.extended.call_for_each_replica( _per_device_execution_function, args=(distributed_training_utils.get_distributed_model(model, mode),)) (all_inputs, all_outputs, all_updates, all_session_args) = distributed_training_utils.unwrap_values( strategy, grouped_inputs, grouped_outputs, grouped_updates, grouped_session_args) combined_fn = K.function( all_inputs, all_outputs, updates=all_updates, name='distributed_' + str(mode) + '_function', **all_session_args) for label, output in zip(output_labels, combined_fn.outputs): if mode == ModeKeys.PREDICT: ctx.set_last_step_output(label, output) else: if label == 'loss': reduce_op = ds_reduce_util.ReduceOp.SUM else: # We reduce all other metrics using mean for now. This is temporary # workaround until new metrics are in place. reduce_op = ds_reduce_util.ReduceOp.MEAN ctx.set_last_step_output(label, output, reduce_op) # TODO(priyag, sourabhbajaj): Ignoring these things from the combined_fn: # feed_dict, session kwargs, run options, run_metadata for now. These should # be handled appropriately return combined_fn.updates_op
def step_fn(ctx, inputs): """Clones the model and calls make_eval_function.""" inputs, targets = inputs if model._compile_distribution: distributed_training_utils.clone_model_on_replicas( model, current_strategy, mode=mode, inputs=inputs, targets=targets) else: distributed_training_utils._build_distributed_network( model, current_strategy, mode, inputs, targets) (grouped_inputs, grouped_outputs, grouped_updates, grouped_session_args ) = current_strategy.extended.call_for_each_replica( _per_device_eval_function, args=(distributed_training_utils.get_distributed_model( model, ModeKeys.TEST), )) (all_inputs, all_outputs, all_updates, all_session_args) = distributed_training_utils.unwrap_values( current_strategy, grouped_inputs, grouped_outputs, grouped_updates, grouped_session_args) combined_fn = K.function(all_inputs, all_outputs, updates=all_updates, name='distributed_test_function', **all_session_args) for label, output in zip(model.metrics_names, combined_fn.outputs): if label == 'loss': reduce_op = ds_reduce_util.ReduceOp.SUM else: # We reduce all other metrics using mean for now. This is temporary # workaround until new metrics are in place. reduce_op = ds_reduce_util.ReduceOp.MEAN ctx.set_last_step_output(label, output, reduce_op) return combined_fn.updates_op
def testOptimizerWithCallbacks(self, distribution): with self.cached_session(): model = get_model() optimizer = gradient_descent_keras.SGD(0.01) loss = 'mse' model.compile(optimizer, loss, distribute=distribution) dataset = get_dataset(distribution) def schedule(_): return 0.001 model.fit(dataset, epochs=1, steps_per_epoch=2, verbose=0, callbacks=[keras.callbacks.LearningRateScheduler(schedule)]) grouped_models = distribution.unwrap( distributed_training_utils.get_distributed_model( model, ModeKeys.TRAIN)) with distribution.scope(): for m in grouped_models: self.assertAllClose(0.001, keras.backend.get_value( m.optimizer.lr), atol=1e-05, rtol=1e-05)
def step_fn(ctx, inputs): """Clones the model and calls make_fit_function.""" inputs, targets = inputs if model._compile_distribution: distributed_training_utils.clone_model_on_replicas( model, current_strategy, mode, inputs=inputs, targets=targets) else: distributed_training_utils._build_distributed_network( model, current_strategy, mode, inputs, targets) (grouped_inputs, grouped_outputs, grouped_updates, grouped_session_args) = current_strategy.extended.call_for_each_replica( _per_device_fit_function, args=(distributed_training_utils.get_distributed_model( model, ModeKeys.TRAIN),)) (all_inputs, all_outputs, all_updates, all_session_args) = distributed_training_utils.unwrap_values( current_strategy, grouped_inputs, grouped_outputs, grouped_updates, grouped_session_args) combined_fn = K.function( all_inputs, all_outputs, updates=all_updates, name='distributed_fit_function', **all_session_args) for label, output in zip(out_labels, combined_fn.outputs): if label == 'loss': reduce_op = ds_reduce_util.ReduceOp.SUM else: # We reduce all other metrics using mean for now. This is temporary # workaround until new metrics are in place. reduce_op = ds_reduce_util.ReduceOp.MEAN ctx.set_last_step_output(label, output, reduce_op) # TODO(priyag, sourabhbajaj): Ignoring these things from the combined_fn: # feed_dict, session kwargs, run options, run_metadata for now. These should # be handled appropriately return combined_fn.updates_op
def step_fn(ctx, inputs): """Clones the model and calls make_eval_function.""" inputs, targets = inputs if model._compile_distribution: distributed_training_utils.clone_model_on_replicas( model, current_strategy, mode=mode, inputs=inputs, targets=targets) else: distributed_training_utils._build_distributed_network( model, current_strategy, mode, inputs, targets) (grouped_inputs, grouped_outputs, grouped_updates, grouped_session_args) = current_strategy.extended.call_for_each_replica( _per_device_eval_function, args=(distributed_training_utils.get_distributed_model( model, ModeKeys.TEST),)) (all_inputs, all_outputs, all_updates, all_session_args) = distributed_training_utils.unwrap_values( current_strategy, grouped_inputs, grouped_outputs, grouped_updates, grouped_session_args) combined_fn = K.function( all_inputs, all_outputs, updates=all_updates, name='distributed_test_function', **all_session_args) for label, output in zip(model.metrics_names, combined_fn.outputs): if label == 'loss': reduce_op = ds_reduce_util.ReduceOp.SUM else: # We reduce all other metrics using mean for now. This is temporary # workaround until new metrics are in place. reduce_op = ds_reduce_util.ReduceOp.MEAN ctx.set_last_step_output(label, output, reduce_op) return combined_fn.updates_op