160 lines
4.6 KiB
Python
160 lines
4.6 KiB
Python
import logging
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import math
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import os
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import base64
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import random
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import signal
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import pickle
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import numpy as np
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try:
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import torch
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except ImportError:
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torch = None
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try:
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import tensorflow as tf
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except ImportError:
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tf = None
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logger = logging.getLogger(__name__)
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# ****** Worker-related utils ******
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class Timeout(object):
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def __init__(self, seconds, max_failure=5):
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self.seconds = seconds
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self.max_failure = max_failure
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def __enter__(self):
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def signal_handler(signum, frame):
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raise TimeoutError()
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if self.seconds > 0:
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signal.signal(signal.SIGALRM, signal_handler)
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signal.alarm(self.seconds)
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return self
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def __exit__(self, exc_type, exc_value, traceback):
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signal.alarm(0)
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def reset(self):
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signal.alarm(self.seconds)
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def block(self):
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signal.alarm(0)
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def exceed_max_failure(self, num_failure):
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return num_failure > self.max_failure
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def batch_iter(data, batch_size=64, shuffled=True):
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assert 'x' in data and 'y' in data
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data_x = data['x']
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data_y = data['y']
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data_size = len(data_y)
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num_batches_per_epoch = math.ceil(data_size / batch_size)
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while True:
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shuffled_index = np.random.permutation(
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np.arange(data_size)) if shuffled else np.arange(data_size)
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for batch in range(num_batches_per_epoch):
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start_index = batch * batch_size
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end_index = min(data_size, (batch + 1) * batch_size)
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sample_index = shuffled_index[start_index:end_index]
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yield {'x': data_x[sample_index], 'y': data_y[sample_index]}
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def merge_dict_of_results(dict1, dict2):
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"""
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Merge two ``dict`` according to their keys, and concatenate their value.
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Args:
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dict1: ``dict`` to be merged
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dict2: ``dict`` to be merged
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Returns:
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dict1: Merged ``dict``.
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"""
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for key, value in dict2.items():
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if key not in dict1:
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if isinstance(value, dict):
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dict1[key] = merge_dict_of_results({}, value)
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else:
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dict1[key] = [value]
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else:
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if isinstance(value, dict):
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merge_dict_of_results(dict1[key], value)
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else:
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dict1[key].append(value)
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return dict1
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def param2tensor(param):
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# TODO: make it work in `message`
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if isinstance(param, list):
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param = torch.FloatTensor(param)
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elif isinstance(param, int):
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param = torch.tensor(param, dtype=torch.long)
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elif isinstance(param, float):
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param = torch.tensor(param, dtype=torch.float)
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elif isinstance(param, str):
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param = pickle.loads((base64.b64decode(param)))
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return param
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def merge_param_dict(raw_param, filtered_param):
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for key in filtered_param.keys():
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raw_param[key] = filtered_param[key]
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return raw_param
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def calculate_time_cost(instance_number,
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comm_size,
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comp_speed=None,
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comm_bandwidth=None,
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augmentation_factor=3.0):
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# Served as an example, this cost model is adapted from FedScale at
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# https://github.com/SymbioticLab/FedScale/blob/master/fedscale/core/
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# internal/client.py#L35 (Apache License Version 2.0)
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# Users can modify this function according to customized cost model
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if comp_speed is not None and comm_bandwidth is not None:
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comp_cost = augmentation_factor * instance_number * comp_speed
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comm_cost = 2.0 * comm_size / comm_bandwidth
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else:
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comp_cost = 0
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comm_cost = 0
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return comp_cost, comm_cost
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# ****** Runner-related utils ******
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def setup_seed(seed):
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np.random.seed(seed)
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random.seed(seed)
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if torch is not None:
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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torch.backends.cudnn.deterministic = True
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if tf is not None:
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tf.set_random_seed(seed)
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def get_resource_info(filename):
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if filename is None or not os.path.exists(filename):
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logger.info('The device information file is not provided')
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return None
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# Users can develop this loading function according to resource_info_file
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# As an example, we use the device_info provided by FedScale (FedScale:
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# Benchmarking Model and System Performance of Federated Learning
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# at Scale), which can be downloaded from
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# https://github.com/SymbioticLab/FedScale/blob/master/benchmark/dataset/
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# data/device_info/client_device_capacity The expected format is
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# { INDEX:{'computation': FLOAT_VALUE_1, 'communication': FLOAT_VALUE_2}}
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with open(filename, 'br') as f:
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device_info = pickle.load(f)
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return device_info
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