# Copyright (c) 2023, NVIDIA CORPORATION. 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.
import os
import time
import nvflare.app_opt.xgboost.histogram_based_v2.proto.federated_pb2 as pb2
from nvflare.apis.fl_component import FLComponent
from nvflare.app_opt.xgboost.histogram_based_v2.defs import Constant
from nvflare.app_opt.xgboost.histogram_based_v2.grpc_client import GrpcClient
from nvflare.app_opt.xgboost.histogram_based_v2.runners.xgb_runner import AppRunner
[docs]
class MockClientRunner(AppRunner, FLComponent):
def __init__(self):
FLComponent.__init__(self)
self.training_stopped = False
self.asked_to_stop = False
[docs]
def run(self, ctx: dict):
# raise RuntimeError("ABORTED")
server_addr = ctx.get(Constant.RUNNER_CTX_SERVER_ADDR)
rank = ctx.get(Constant.RUNNER_CTX_RANK)
num_rounds = ctx.get(Constant.RUNNER_CTX_NUM_ROUNDS)
client = GrpcClient(server_addr=server_addr)
client.start()
rank = rank
seq = 0
total_time = 0
total_reqs = 0
for i in range(num_rounds):
if self.asked_to_stop:
self.logger.info("training aborted")
self.training_stopped = True
return
self.logger.info(f"Test round {i}")
data = os.urandom(1000000)
self.logger.info("sending allgather")
start = time.time()
result = client.send_allgather(seq_num=seq + 1, rank=rank, data=data)
total_reqs += 1
total_time += time.time() - start
if not isinstance(result, pb2.AllgatherReply):
self.logger.error(f"expect reply to be pb2.AllgatherReply but got {type(result)}")
elif result.receive_buffer != data:
self.logger.error("allgather result does not match request")
else:
self.logger.info("OK: allgather result matches request!")
self.logger.info("sending allgatherV")
start = time.time()
result = client.send_allgatherv(seq_num=seq + 2, rank=rank, data=data)
total_reqs += 1
total_time += time.time() - start
if not isinstance(result, pb2.AllgatherVReply):
self.logger.error(f"expect reply to be pb2.AllgatherVReply but got {type(result)}")
elif result.receive_buffer != data:
self.logger.error("allgatherV result does not match request")
else:
self.logger.info("OK: allgatherV result matches request!")
self.logger.info("sending allreduce")
start = time.time()
result = client.send_allreduce(
seq_num=seq + 3,
rank=rank,
data=data,
reduce_op=2,
data_type=2,
)
total_reqs += 1
total_time += time.time() - start
if not isinstance(result, pb2.AllreduceReply):
self.logger.error(f"expect reply to be pb2.AllreduceReply but got {type(result)}")
elif result.receive_buffer != data:
self.logger.error("allreduce result does not match request")
else:
self.logger.info("OK: allreduce result matches request!")
print("OK: allreduce result matches request!")
self.logger.info("sending broadcast")
start = time.time()
result = client.send_broadcast(
seq_num=seq + 4,
rank=rank,
data=data,
root=3,
)
total_reqs += 1
total_time += time.time() - start
if not isinstance(result, pb2.BroadcastReply):
self.logger.error(f"expect reply to be pb2.BroadcastReply but got {type(result)}")
elif result.receive_buffer != data:
self.logger.error("ERROR: broadcast result does not match request")
else:
self.logger.info("OK: broadcast result matches request!")
seq += 4
time.sleep(1.0)
time_per_req = total_time / total_reqs
self.logger.info(f"DONE: {total_reqs=} {total_time=} {time_per_req=}")
print(f"DONE: {total_reqs=} {total_time=} {time_per_req=}")
self.training_stopped = True
[docs]
def stop(self):
self.asked_to_stop = True
[docs]
def is_stopped(self) -> (bool, int):
return self.training_stopped, 0