# Copyright 2018 The JAX Authors.

#

# 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

#

# https://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.

from functools import partial

import time

from jax import NamedSharding

import numpy as np

import numpy.random as npr

import jax

from jax import jit, grad

from jax.sharding import PartitionSpec as P, AxisType, reshard

from jax.scipy.special import logsumexp

import jax.numpy as jnp

import datasets

def init_random_params(scale, layer_sizes, rng=npr.RandomState(0)):

return [

(scale * rng.randn(m, n), scale * rng.randn(n))

for m, n in zip(layer_sizes[:-1], layer_sizes[1:])

]

def predict(params, inputs):

activations = inputs

for w, b in params[:-1]:

outputs = jnp.dot(activations, w) + b

activations = jnp.tanh(outputs)

final_w, final_b = params[-1]

logits = jnp.dot(activations, final_w) + final_b

return logits - logsumexp(logits, axis=1, keepdims=True)

def loss(params, batch):

inputs, targets = batch

preds = predict(params, inputs)

return -jnp.mean(jnp.sum(preds * targets, axis=1))

@partial(jax.jit, donate_argnums=0)

def train_step(params, batch):

grads = grad(loss)(params, batch)

return [

(w - step_size * dw, b - step_size * db) for (w, b), (dw, db) in zip(params, grads)

]

@jit

def accuracy(params, batch):

inputs, targets = batch

target_class = jnp.argmax(targets, axis=1)

predicted_class = jnp.argmax(predict(params, inputs), axis=1)

return jnp.mean(predicted_class == target_class)

if __name__ == "__main__":

layer_sizes = [784, 1024, 1024, 10]

param_scale = 0.1

step_size = 0.001

num_epochs = 10

batch_size = 128

train_images, train_labels, test_images, test_labels = datasets.mnist()

num_train = train_images.shape[0]

num_devices = jax.device_count()

print(f"Using {num_devices} devices")

if batch_size % num_devices != 0:

batch_size = (batch_size // num_devices) * num_devices

print(f"Adjusting batch size to {batch_size} for divisibility")

num_complete_batches, leftover = divmod(num_train, batch_size)

num_batches = num_complete_batches + bool(leftover)

devices = np.array(jax.devices())

mesh = jax.make_mesh(

(jax.device_count(),), ("batch",), axis_types=(AxisType.Explicit,)

)

replicated_sharding = NamedSharding(mesh, P())

data_sharding = NamedSharding(mesh, P("batch"))

def data_stream():

rng = npr.RandomState(0)

while True:

perm = rng.permutation(num_train)

for i in range(num_batches):

batch_idx = perm[i * batch_size : (i + 1) * batch_size]

images_np, labels_np = train_images[batch_idx], train_labels[batch_idx]

current_batch_size = images_np.shape[0]

if current_batch_size < batch_size:

pad_len = batch_size - current_batch_size

images_np = np.concatenate([images_np, images_np[:pad_len]], axis=0)

labels_np = np.concatenate([labels_np, labels_np[:pad_len]], axis=0)

images = jax.device_put(images_np, data_sharding)

labels = jax.device_put(labels_np, data_sharding)

yield images, labels

batches = data_stream()

params = init_random_params(param_scale, layer_sizes)

replicated_params = jax.device_put(params, replicated_sharding)

for epoch in range(num_epochs):

start_time = time.time()

for i in range(num_batches - 1):

print(f"Batch no {i+1} of {num_batches}")

batch = next(batches)

with jax.set_mesh(mesh):

replicated_params = train_step(replicated_params, batch)

epoch_time = time.time() - start_time

# Reshard train_images, train_labels, test_images, test_labels

sharded_train_images = reshard(train_images, data_sharding)

sharded_train_labels = reshard(train_labels, data_sharding)

sharded_test_images = reshard(test_images, data_sharding)

sharded_test_labels = reshard(test_labels, data_sharding)

train_acc = accuracy(

replicated_params, (sharded_train_images, sharded_train_labels)

)

test_acc = accuracy(replicated_params, (sharded_test_images, sharded_test_labels))

print(f"Epoch {epoch} in {epoch_time:0.2f} sec")

print(f"Training set accuracy {train_acc}")

print(f"Test set accuracy {test_acc}")

if epoch < num_epochs - 1:

batches = data_stream()

print(f"Batch no {0} of {num_batches}")

replicated_params = train_step(replicated_params, next(batches))