# 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 json
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]
def encode_msg(msg: dict):
return bytes(json.dumps(msg), "utf-8")
[docs]
class MockSecureClientRunner(AppRunner, FLComponent):
def __init__(self, sample_size=1000):
FLComponent.__init__(self)
self.training_stopped = False
self.asked_to_stop = False
self.sample_size = sample_size
[docs]
def run(self, ctx: dict):
self.logger.info("START TRAINING")
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
# fake bcst
data = {
"op": "none",
}
msg_data = encode_msg(data)
self.logger.info("sending non-GH broadcast")
start = time.time()
seq += 1
result = client.send_broadcast(
seq_num=seq,
rank=rank,
data=msg_data,
root=0,
)
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 != msg_data:
self.logger.error("ERROR: broadcast result does not match request")
else:
self.logger.info("OK: broadcast result matched!")
# gh bcst
data = {
"op": "gh",
"size": self.sample_size,
}
self.logger.info("sending broadcast")
start = time.time()
seq += 1
result = client.send_broadcast(
seq_num=seq,
rank=rank,
data=encode_msg(data),
root=0,
)
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)}")
else:
self.logger.info("OK: broadcast result received!")
self.logger.info("sending allgatherV")
start = time.time()
seq += 1
data = {"op": "aggr", "groups": None}
result = client.send_allgatherv(seq_num=seq, rank=rank, data=encode_msg(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)}")
else:
self.logger.info("OK: allgatherV result received!")
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