async_server.py

# Copyright 2020 Flower Labs GmbH. 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.
# ==============================================================================
"""Flower server."""


import concurrent.futures
from concurrent.futures import ThreadPoolExecutor
from threading import Lock, Thread, Timer
from logging import DEBUG, INFO, WARNING
from typing import Dict, List, Optional, Tuple, Union
from time import sleep, time

from flwr.common import (
    Code,
    DisconnectRes,
    EvaluateIns,
    EvaluateRes,
    FitIns,
    FitRes,
    Parameters,
    ReconnectIns,
    Scalar,
)
from flwr.common.logger import log
from flwr.common.typing import GetParametersIns, FitIns
from flwr.common import parameters_to_ndarrays, ndarrays_to_parameters
from flwr.server.client_manager import ClientManager
from flwr.server.client_proxy import ClientProxy
from flwr.server.history import History
from flwr.server.strategy import FedAvg, Strategy
import flwr.server.strategy.aggregate as agg
from flwr.server.server import Server
from flower_async.async_history import AsyncHistory

from flower_async.async_client_manager import AsyncClientManager
from flower_async.async_strategy import AsynchronousStrategy

FitResultsAndFailures = Tuple[
    List[Tuple[ClientProxy, FitRes]],
    List[Union[Tuple[ClientProxy, FitRes], BaseException]],
]
EvaluateResultsAndFailures = Tuple[
    List[Tuple[ClientProxy, EvaluateRes]],
    List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]],
]
ReconnectResultsAndFailures = Tuple[
    List[Tuple[ClientProxy, DisconnectRes]],
    List[Union[Tuple[ClientProxy, DisconnectRes], BaseException]],
]


class AsyncServer(Server):
    """Flower server implementing asynchronous FL."""

    def __init__(
        self,
        strategy: Strategy,
        client_manager: ClientManager, # AsyncClientManager,
        async_strategy: AsynchronousStrategy,
        base_conf_dict,
        total_train_time: int = 85,
        waiting_interval: int = 5,
        max_workers: int = 2,
    ):
        self.async_strategy = async_strategy
        self.total_train_time = total_train_time
        # number of seconds waited to start a new set of clients (and evaluate the previous ones)
        self.waiting_interval = waiting_interval
        self.strategy = strategy
        self._client_manager = client_manager
        self.max_workers = max_workers
        self.client_data_percs: Dict[str, List[float]] = {} # dictionary tracking the data percentages sent to the client
        for key, value in base_conf_dict.items():
            setattr(self, key, value)
        self.start_timestamp = 0.0
        self.end_timestamp = 0.0

        # super().__init__(*args, strategy=strategy, client_manager=client_manager)

    def set_new_params(self, new_params: Parameters):
        lock = Lock()
        with lock:
            self.parameters = new_params

    # pylint: disable=too-many-locals

    def fit(self, num_rounds: int, timeout: Optional[float]) -> History:
        """Run federated averaging for a number of rounds."""
        history = AsyncHistory()

        # Initialize parameters
        log(INFO, "Initializing global parameters")
        self.parameters = self._get_initial_parameters(timeout=timeout)
        log(INFO, "Evaluating initial parameters")
        res = self.strategy.evaluate(0, parameters=self.parameters)
        if res is not None:
            log(
                INFO,
                "initial parameters (loss, other metrics): %s, %s",
                res[0],
                res[1],
            )
            history.add_loss_centralized(timestamp=time(), loss=res[0])
            history.add_metrics_centralized(timestamp=time(), metrics=res[1])

        # Run federated learning for num_rounds
        log(INFO, "FL starting")
        executor = ThreadPoolExecutor(max_workers=self.max_workers)
        start_time = time()
        end_timestamp = time() + self.total_train_time
        self.end_timestamp = end_timestamp
        self.start_timestamp = time()
        counter = 1
        self.fit_round(
            server_round=0,
            timeout=timeout,
            executor=executor,
            end_timestamp=end_timestamp,
            history=history
        )
        while time() - start_time < self.total_train_time:
            # If the clients are to be started periodically, move fit_round here and remove the executor.submit lines from _handle_finished_future_after_fit
            sleep(self.waiting_interval)
            self.evaluate_centralized(counter, history)
            #self.evaluate_decentralized(counter, history, timeout)
            counter += 1

        executor.shutdown(wait=True, cancel_futures=True)
        log(INFO, "FL finished")
        end_time = time()
        elapsed = end_time - start_time
        log(INFO, "FL finished in %s", elapsed)
        return history

    def evaluate_centralized(self, current_round: int, history: History):
        res_cen = self.strategy.evaluate(
            current_round, parameters=self.parameters)
        if res_cen is not None:
            loss_cen, metrics_cen = res_cen
            metrics_cen['end_timestamp'] = self.end_timestamp
            metrics_cen['start_timestamp'] = self.start_timestamp
            history.add_loss_centralized(
                timestamp=time(), loss=loss_cen)
            history.add_metrics_centralized(
                timestamp=time(), metrics=metrics_cen
            )

    def evaluate_centralized_async(self, history: AsyncHistory):
        res_cen = self.strategy.evaluate(
            0, parameters=self.parameters)
        if res_cen is not None:
            loss_cen, metrics_cen = res_cen
            metrics_cen['end_timestamp'] = self.end_timestamp
            metrics_cen['start_timestamp'] = self.start_timestamp
            history.add_loss_centralized_async(
                timestamp=time(), loss=loss_cen)
            history.add_metrics_centralized_async(
                timestamp=time(), metrics=metrics_cen
            )

    def evaluate_decentralized(self, current_round: int, history: History, timeout: Optional[float]):
        """Evaluate model on a sample of available clients
        NOTE: Only call this method if clients are started periodically.
        This is not to be called if the clients are starting immediately after they finish! This is because the ray actor cannot process
        two concurrent requests to the same client. They get mixed up and future.result() in client_fit can return an
        EvaluateRes instead of FitRes.
        """
        res_fed = self.evaluate_round(
            server_round=current_round, timeout=timeout)
        if res_fed is not None:
            loss_fed, evaluate_metrics_fed, (results, _) = res_fed
            if loss_fed is not None:
                client_ids = [client.cid for client, _ in results]
                evaluate_metrics_fed['client_ids'] = client_ids
                history.add_loss_distributed(
                    timestamp=time(), loss=loss_fed
                )
                history.add_metrics_distributed(
                    timestamp=time(), metrics=evaluate_metrics_fed
                )

    def evaluate_round(
        self,
        server_round: int,
        timeout: Optional[float],
    ) -> Optional[
        Tuple[Optional[float], Dict[str, Scalar], EvaluateResultsAndFailures]
    ]:
        """Validate current global model on a number of clients."""
        # Get clients and their respective instructions from strategy
        client_instructions = self.strategy.configure_evaluate(
            server_round=server_round,
            parameters=self.parameters,
            client_manager=self._client_manager,
        )
        if not client_instructions:
            log(INFO, "evaluate_round %s: no clients selected, cancel", server_round)
            return None
        log(
            DEBUG,
            "evaluate_round %s: strategy sampled %s clients (out of %s)",
            server_round,
            len(client_instructions),
            self._client_manager.num_available(),
        )

        # Collect `evaluate` results from all clients participating in this round
        results, failures = evaluate_clients(
            client_instructions,
            max_workers=self.max_workers,
            timeout=timeout,
        )
        log(
            DEBUG,
            "evaluate_round %s received %s results and %s failures",
            server_round,
            len(results),
            len(failures),
        )
        # log(DEBUG, f"Evaluate results: {results}")

        # Aggregate the evaluation results
        aggregated_result: Tuple[
            Optional[float],
            Dict[str, Scalar],
        ] = self.strategy.aggregate_evaluate(server_round, results, failures)

        loss_aggregated, metrics_aggregated = aggregated_result
        return loss_aggregated, metrics_aggregated, (results, failures)

    def fit_round(
        self,
        server_round: int,
        timeout: Optional[float],
        executor: ThreadPoolExecutor,
        end_timestamp: float,
        history: AsyncHistory,
    ):  # -> Optional[Tuple[Optional[Parameters], Dict[str, Scalar], FitResultsAndFailures]]:
        """Perform a single round of federated averaging."""
        # Get clients and their respective instructions from strategy
        client_instructions = self.strategy.configure_fit(
            server_round=server_round,
            parameters=self.parameters,
            client_manager=self._client_manager,
        )
        for client_proxy, fitins in client_instructions:
            fitins.config = { **fitins.config, **self.get_config_for_client_fit(client_proxy.cid) }

        if not client_instructions:
            log(INFO, "fit_round %s: no clients selected, cancel", server_round)
            return None
        log(
            DEBUG,
            "fit_round %s: strategy sampled %s clients (out of %s)",
            server_round,
            len(client_instructions),
            self._client_manager.num_available(),
        )

        # Collect `fit` results from all clients participating in this round
        fit_clients(
            client_instructions=client_instructions,
            timeout=timeout,
            server=self,
            executor=executor,
            end_timestamp=end_timestamp,
            history=history,
        )

    def get_config_for_client_fit(self, client_id):
        config = {}
        if not self.is_streaming:
            return config
        curr_timestamp = time()
        if curr_timestamp > self.end_timestamp:
            return config
        if client_id not in self.client_data_percs:
            self.client_data_percs[client_id] = [0.0] # Clients start with 10% of the data (otherwise called with 0 samples)
        prev_data_perc = self.client_data_percs[client_id][-1]
        start_timestamp = self.end_timestamp - self.total_train_time
        data_perc = ( (time() - start_timestamp) / self.total_train_time ) * 0.9 + 0.1 # Linearly increase the data percentage from 10% to 100% over the total_train_time
        config['data_percentage'] = data_perc
        config['prev_data_percentage'] = prev_data_perc
        config['data_loading_strategy'] = self.data_loading_strategy
        if self.data_loading_strategy == 'fixed_nr':
            config['n_last_samples_for_data_loading_fit'] = self.n_last_samples_for_data_loading_fit
        self.client_data_percs[client_id].append(data_perc)
        return config

    def disconnect_all_clients(self, timeout: Optional[float]) -> None:
        """Send shutdown signal to all clients."""
        all_clients = self._client_manager.all()
        clients = [all_clients[k] for k in all_clients.keys()]
        instruction = ReconnectIns(seconds=None)
        client_instructions = [(client_proxy, instruction)
                               for client_proxy in clients]
        _ = reconnect_clients(
            client_instructions=client_instructions,
            max_workers=self.max_workers,
            timeout=timeout,
        )

    def _get_initial_parameters(self, timeout: Optional[float]) -> Parameters:
        """Get initial parameters from one of the available clients."""
        # Server-side parameter initialization
        parameters: Optional[Parameters] = self.strategy.initialize_parameters(
            client_manager=self._client_manager
        )
        if parameters is not None:
            log(INFO, "Using initial parameters provided by strategy")
            return parameters

        # Get initial parameters from one of the clients
        log(INFO, "Requesting initial parameters from one random client")
        random_client = self._client_manager.sample(1)[0]
        ins = GetParametersIns(config={})
        get_parameters_res = random_client.get_parameters(
            ins=ins, timeout=timeout)
        log(INFO, "Received initial parameters from one random client")
        return get_parameters_res.parameters


def reconnect_clients(
    client_instructions: List[Tuple[ClientProxy, ReconnectIns]],
    max_workers: Optional[int],
    timeout: Optional[float],
) -> ReconnectResultsAndFailures:
    """Instruct clients to disconnect and never reconnect."""
    with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:

        submitted_fs = {
            executor.submit(reconnect_client, client_proxy, ins, timeout)
            for client_proxy, ins in client_instructions
        }
        finished_fs, _ = concurrent.futures.wait(
            fs=submitted_fs,
            timeout=None,  # Handled in the respective communication stack
        )

    # Gather results
    results: List[Tuple[ClientProxy, DisconnectRes]] = []
    failures: List[Union[Tuple[ClientProxy,
                               DisconnectRes], BaseException]] = []
    for future in finished_fs:
        failure = future.exception()
        if failure is not None:
            failures.append(failure)
        else:
            result = future.result()
            results.append(result)
    return results, failures


def reconnect_client(
    client: ClientProxy,
    reconnect: ReconnectIns,
    timeout: Optional[float],
) -> Tuple[ClientProxy, DisconnectRes]:
    """Instruct client to disconnect and (optionally) reconnect later."""
    disconnect = client.reconnect(
        reconnect,
        timeout=timeout,
    )
    return client, disconnect


def handle_futures(futures, server):
    for future in futures:
        _handle_finished_future_after_fit(
            future=future, server=server
        )


def fit_clients(
    client_instructions: List[Tuple[ClientProxy, FitIns]],
    timeout: Optional[float],
    server: AsyncServer,
    executor: ThreadPoolExecutor,
    end_timestamp: float,
    history: AsyncHistory,
):
    """Refine parameters concurrently on all selected clients."""

    submitted_fs = {
        executor.submit(fit_client, client_proxy, ins, timeout)
        for client_proxy, ins in client_instructions
    }
    for f in submitted_fs:
        f.add_done_callback(
            lambda ftr: _handle_finished_future_after_fit(ftr, server=server, executor=executor, end_timestamp=end_timestamp, history=history),
        )


def fit_client(
    client: ClientProxy, ins: FitIns, timeout: Optional[float]
) -> Tuple[ClientProxy, FitRes]:
    """Refine parameters on a single client."""
    fit_res = client.fit(ins, timeout=timeout)
    return client, fit_res


def _handle_finished_future_after_fit(
    future: concurrent.futures.Future,
    server: AsyncServer,
    executor: ThreadPoolExecutor,
    end_timestamp: float,
    history: AsyncHistory,
) -> None:
    """Convert finished future into either a result or a failure."""
    # Check if there was an exception

    failure = future.exception()
    if failure is not None:
        print("Got a failure :(")
        return

    print("Got a result :)")
    result: Tuple[ClientProxy, FitRes] = future.result()
    clientProxy, res = result

    #server.client_manager().set_client_to_free(clientProxy.cid)
    process_start = time()
    # Check result status code
    if res.status.code == Code.OK:
        parameters_aggregated = server.async_strategy.average(
            server.parameters, res.parameters, res.metrics['t_diff'], res.num_examples)
        server.set_new_params(parameters_aggregated)
        
        history.add_metrics_distributed_fit_async(
            clientProxy.cid,{"sample_sizes": res.num_examples, **res.metrics }, timestamp=time()
        )
        # server.evaluate_centralized_async(history) # Evaluate the global model after the merge

    process_end = time()
    log(DEBUG, f"Time taken to process the result: {process_end - process_start} seconds")    
    if time() < end_timestamp:
        log(DEBUG, f"Yippie! Starting the client {clientProxy.cid} again \U0001f973")
        new_ins = FitIns(server.parameters, server.get_config_for_client_fit(clientProxy.cid))
        ftr = executor.submit(fit_client, client=clientProxy, ins=new_ins, timeout=None)
        ftr.add_done_callback(lambda ftr: _handle_finished_future_after_fit(ftr, server, executor, end_timestamp, history))


############################### FOR EVALUATION ####################################

def evaluate_clients(
    client_instructions: List[Tuple[ClientProxy, EvaluateIns]],
    max_workers: Optional[int],
    timeout: Optional[float],
) -> EvaluateResultsAndFailures:
    """Evaluate parameters concurrently on all selected clients."""
    with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
        submitted_fs = {
            executor.submit(evaluate_client, client_proxy, ins, timeout)
            for client_proxy, ins in client_instructions
        }
        finished_fs, _ = concurrent.futures.wait(
            fs=submitted_fs,
            timeout=None,  # Handled in the respective communication stack
        )

    # Gather results
    results: List[Tuple[ClientProxy, EvaluateRes]] = []
    failures: List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]] = []
    for future in finished_fs:
        _handle_finished_future_after_evaluate(
            future=future, results=results, failures=failures
        )
    return results, failures


def evaluate_client(
    client: ClientProxy,
    ins: EvaluateIns,
    timeout: Optional[float],
) -> Tuple[ClientProxy, EvaluateRes]:
    """Evaluate parameters on a single client."""
    evaluate_res = client.evaluate(ins, timeout=timeout)
    return client, evaluate_res


def _handle_finished_future_after_evaluate(
    future: concurrent.futures.Future,  # type: ignore
    results: List[Tuple[ClientProxy, EvaluateRes]],
    failures: List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]],
) -> None:
    """Convert finished future into either a result or a failure."""
    # Check if there was an exception
    failure = future.exception()
    if failure is not None:
        failures.append(failure)
        return

    # Successfully received a result from a client
    result: Tuple[ClientProxy, EvaluateRes] = future.result()
    _, res = result

    # Check result status code
    if res.status.code == Code.OK:
        results.append(result)
        return

    # Not successful, client returned a result where the status code is not OK
    failures.append(result)