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fedavg_quantization.py
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fedavg_quantization.py
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import chex
import jax
import jax.numpy as jnp # JAX NumPy
import numpy as np
import tensorflow_datasets as tfds # TFDS for MNIST
import wandb
from evosax import NetworkMapper
from backprop import sl
from args import get_args
from utils import helpers
from flax.core import FrozenDict
from evosax import ParameterReshaper
import os
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
# compress the array with quantization based on array distribution
def quantize(array, min_val, max_val, n_bits):
# max_val = array.max()
# min_val = array.min()
step = (max_val - min_val) / (2 ** n_bits - 1)
array = ((array - min_val) / step).round()
return array
# dequantization array
def dequantize(array, min_val, max_val, n_bits):
step = (max_val - min_val) / (2 ** n_bits - 1)
array = array * step + min_val
return array
def sparsify(array, percentage):
original = array
array = jnp.abs(array.flatten())
array = jnp.sort(array)
threshold = array[int(len(array) * percentage)]
array = jnp.where(jnp.abs(original) < threshold, 0, original)
return array
# L2 distance
def l2(x, y):
return -1 * jnp.sqrt(jnp.sum((x - y) ** 2)) # / jnp.sqrt(jnp.sum(x ** 2))
class TaskManager:
def __init__(self, rng: chex.PRNGKey, args):
wandb.run.name = '{}-{}-{} b{} c{} s{} q{} -- {}' \
.format(args.dataset, args.algo,
args.dist,
args.batch_size, args.n_clients,
args.seed, args.quantize_bits, wandb.run.id)
wandb.run.save()
self.train_ds, self.test_ds = sl.get_fed_datasets(args.dataset, args.n_clients, 2, args.dist == 'IID')
rng = jax.random.PRNGKey(0)
rng, init_rng = jax.random.split(rng)
self.learning_rate = wandb.config.lr
self.momentum = wandb.config.momentum
network = NetworkMapper[wandb.config.network_name](**wandb.config.network_config)
self.state = sl.create_train_state(init_rng, network, self.learning_rate, self.momentum)
del init_rng # Must not be used anymore.
self.param_count = sum(x.size for x in jax.tree_leaves(self.state.params))
self.num_epochs = wandb.config.n_rounds
self.batch_size = wandb.config.batch_size
self.client_epoch = wandb.config.client_epoch
self.n_clients = args.n_clients
min_cut = 10000
# if args.dataset == 'mnist':
# min_cut = 5421
self.X = jnp.array([train['image'][:min_cut] for train in self.train_ds])
self.y = jnp.array([train['label'][:min_cut] for train in self.train_ds])
self.args = args
self.n_bits = args.quantize_bits
self.param_reshaper = ParameterReshaper(self.state.params, n_devices=1)
def run(self, rng: chex.PRNGKey):
for epoch in range(0, self.num_epochs + 1):
# Use a separate PRNG key to permute image data during shuffling
rng, input_rng = jax.random.split(rng)
# Run an optimization step over a training batch
# clients = [self.state for i in range(5)]
clients, loss, acc = jax.vmap(sl.train_epoch, in_axes=(None, 0, 0, None, None))(self.state,
self.X,
self.y,
self.batch_size, input_rng)
for c_epoch in range(self.client_epoch):
input_rng, c_rng = jax.random.split(input_rng)
clients, loss, acc = jax.vmap(sl.train_epoch, in_axes=(0, 0, 0, None, None))(clients,
self.X,
self.y,
self.batch_size, c_rng)
wandb.log({
'Epoch': epoch * self.client_epoch + c_epoch,
'Train Loss': loss.mean(),
'Train Accuracy': acc.mean(),
})
server = self.param_reshaper.network_to_flat(self.state.params)
target_server = jax.vmap(self.param_reshaper.network_to_flat)(clients.params)
target_server = (target_server - server)
min_val, max_val = jax.vmap(jnp.min)(target_server), jax.vmap(jnp.max)(target_server)
target_server = jax.vmap(sparsify, in_axes=(0, None))(target_server, self.args.percentage)
# target_server = jax.vmap(quantize, in_axes=(0, 0, 0, None))(target_server, min_val, max_val, self.n_bits)
# target_server = jax.vmap(dequantize, in_axes=(0, 0, 0, None))(target_server, min_val, max_val, self.n_bits)
target_server = jax.vmap(jnp.mean)(target_server.T)
# target_server = jax.vmap(quantize, in_axes=(0, None))(target_server, self.n_bits)
# target_server = dequantize(target_server, min_val.mean(), max_val.mean(), self.n_bits)
target_server = sparsify(target_server, self.args.percentage)
target_server = target_server + server
params = self.param_reshaper.reshape_single_net(target_server)
self.state = self.state.replace(params=FrozenDict(params))
rng, eval_rng = jax.random.split(rng)
test_loss, test_accuracy = sl.eval_model(params, self.test_ds, eval_rng)
remining_params = self.param_count * (1 - self.args.percentage)
wandb.log({
'Round': epoch,
'Test Loss': test_loss,
'Global Accuracy': test_accuracy,
# 'Communication': epoch * 2 * self.param_count / (32 / self.n_bits),
'Communication': epoch * 2 * remining_params * ((self.n_bits + np.log2(self.param_count))/ 32),
})
def run():
print(jax.devices())
args = get_args()
config = helpers.load_config(args.config)
wandb.init(project='evofed-publish', config=args)
wandb.config.update(config)
args = wandb.config
rng = jax.random.PRNGKey(args.seed)
rng, rng_init, rng_run = jax.random.split(rng, 3)
manager = TaskManager(rng_init, args)
manager.run(rng_run)
if __name__ == '__main__':
# wandb.agent('tdt4lz81', function=run, project='evofed', count=10)
run()