add fedavg quantization

Author: mahi97

fedavg_quantization.py

@@ -0,0 +1,145 @@
+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()