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This tutorial is the second part of a two-part series that demonstrates how to
implement custom types of federated algorithms in TFF using the
Federated Core (FC), which serves as a foundation for
the Federated Learning (FL) layer (tff.learning).
We encourage you to first read the first part of this series, which introduce some of the key concepts and programming abstractions used here.
This second part of the series uses the mechanisms introduced in the first part to implement a simple version of federated training and evaluation algorithms.
We encourage you to review the image classification and text generation tutorials for a higher-level and more gentle introduction to TFF's Federated Learning APIs, as they will help you put the concepts we describe here in context.
Before we start
Before we start, try to run the following "Hello World" example to make sure your environment is correctly setup. If it doesn't work, please refer to the Installation guide for instructions.
pip install --quiet --upgrade tensorflow_federated
import collections
import numpy as np
import tensorflow as tf
import tensorflow_federated as tff
tf.compat.v1.enable_v2_behavior()
# TODO(b/148678573,b/148685415): must use the ReferenceExecutor because it
# supports unbounded references and tff.sequence_* intrinsics.
tff.framework.set_default_executor(tff.framework.ReferenceExecutor())
@tff.federated_computation
def hello_world():
return 'Hello, World!'
hello_world()
'Hello, World!'
Implementing Federated Averaging
As in
Federated Learning for Image Classification,
we are going to use the MNIST example, but since this is intended as a low-level
tutorial, we are going to bypass the Keras API and tff.simulation, write raw
model code, and construct a federated data set from scratch.
Preparing federated data sets
For the sake of a demonstration, we're going to simulate a scenario in which we have data from 10 users, and each of the users contributes knowledge how to recognize a different digit. This is about as non-i.i.d. as it gets.
First, let's load the standard MNIST data:
mnist_train, mnist_test = tf.keras.datasets.mnist.load_data()
[(x.dtype, x.shape) for x in mnist_train]
[(dtype('uint8'), (60000, 28, 28)), (dtype('uint8'), (60000,))]
The data comes as Numpy arrays, one with images and another with digit labels, both
with the first dimension going over the individual examples. Let's write a
helper function that formats it in a way compatible with how we feed federated
sequences into TFF computations, i.e., as a list of lists - the outer list
ranging over the users (digits), the inner ones ranging over batches of data in
each client's sequence. As is customary, we will structure each batch as a pair
of tensors named x and y, each with the leading batch dimension. While at
it, we'll also flatten each image into a 784-element vector and rescale the
pixels in it into the 0..1 range, so that we don't have to clutter the model
logic with data conversions.
NUM_EXAMPLES_PER_USER = 1000
BATCH_SIZE = 100
def get_data_for_digit(source, digit):
output_sequence = []
all_samples = [i for i, d in enumerate(source[1]) if d == digit]
for i in range(0, min(len(all_samples), NUM_EXAMPLES_PER_USER), BATCH_SIZE):
batch_samples = all_samples[i:i + BATCH_SIZE]
output_sequence.append({
'x':
np.array([source[0][i].flatten() / 255.0 for i in batch_samples],
dtype=np.float32),
'y':
np.array([source[1][i] for i in batch_samples], dtype=np.int32)
})
return output_sequence
federated_train_data = [get_data_for_digit(mnist_train, d) for d in range(10)]
federated_test_data = [get_data_for_digit(mnist_test, d) for d in range(10)]
As a quick sanity check, let's look at the Y tensor in the last batch of data
contributed by the fifth client (the one corresponding to the digit 5).
federated_train_data[5][-1]['y']
array([5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], dtype=int32)
Just to be sure, let's also look at the image corresponding to the last element of that batch.
from matplotlib import pyplot as plt
plt.imshow(federated_train_data[5][-1]['x'][-1].reshape(28, 28), cmap='gray')
plt.grid(False)
plt.show()
On combining TensorFlow and TFF
In this tutorial, for compactness we immediately decorate functions that
introduce TensorFlow logic with tff.tf_computation. However, for more complex
logic, this is not the pattern we recommend. Debugging TensorFlow can already be
a challenge, and debugging TensorFlow after it has been fully serialized and
then re-imported necessarily loses some metadata and limits interactivity,
making debugging even more of a challenge.
Therefore, we strongly recommend writing complex TF logic as stand-alone
Python functions (that is, without tff.tf_computation decoration). This way
the TensorFlow logic can be developed and tested using TF best practices and
tools (like eager mode), before serializing the computation for TFF (e.g., by invoking tff.tf_computation with a Python function as the argument).
Defining a loss function
Now that we have the data, let's define a loss function that we can use for
training. First, let's define the type of input as a TFF named tuple. Since the
size of data batches may vary, we set the batch dimension to None to indicate
that the size of this dimension is unknown.
BATCH_SPEC = collections.OrderedDict(
x=tf.TensorSpec(shape=[None, 784], dtype=tf.float32),
y=tf.TensorSpec(shape=[None], dtype=tf.int32))
BATCH_TYPE = tff.to_type(BATCH_SPEC)
str(BATCH_TYPE)
'<x=float32[?,784],y=int32[?]>'
You may be wondering why we can't just define an ordinary Python type. Recall
the discussion in part 1, where we
explained that while we can express the logic of TFF computations using Python,
under the hood TFF computations are not Python. The symbol BATCH_TYPE
defined above represents an abstract TFF type specification. It is important to
distinguish this abstract TFF type from concrete Python representation
types, e.g., containers such as dict or collections.namedtuple that may be
used to represent the TFF type in the body of a Python function. Unlike Python,
TFF has a single abstract type constructor tff.NamedTupleType for tuple-like
containers, with elements that can be individually named or left unnamed. This
type is also used to model formal parameters of computations, as TFF
computations can formally only declare one parameter and one result - you will
see examples of this shortly.
Let's now define the TFF type of model parameters, again as a TFF named tuple of weights and bias.
MODEL_SPEC = collections.OrderedDict(
weights=tf.TensorSpec(shape=[784, 10], dtype=tf.float32),
bias=tf.TensorSpec(shape=[10], dtype=tf.float32))
MODEL_TYPE = tff.to_type(MODEL_SPEC)
print(MODEL_TYPE)
<weights=float32[784,10],bias=float32[10]>
With those definitions in place, now we can define the loss for a given model, over a single batch. Note the usage of @tf.function decorator inside the @tff.tf_computation decorator. This allows us to write TF using Python like semantics even though were inside a tf.Graph context created by the tff.tf_computation decorator.
@tff.tf_computation(MODEL_TYPE, BATCH_TYPE)
@tf.function
def batch_loss(model, batch):
predicted_y = tf.nn.softmax(
tf.matmul(batch['x'], model['weights']) + model['bias'])
return -tf.reduce_mean(
tf.reduce_sum(
tf.one_hot(batch['y'], 10) * tf.math.log(predicted_y), axis=[1]))
As expected, computation batch_loss returns float32 loss given the model and
a single data batch. Note how the MODEL_TYPE and BATCH_TYPE have been lumped
together into a 2-tuple of formal parameters; you can recognize the type of
batch_loss as (<MODEL_TYPE,BATCH_TYPE> -> float32).
str(batch_loss.type_signature)
'(<<weights=float32[784,10],bias=float32[10]>,<x=float32[?,784],y=int32[?]>> -> float32)'
As a sanity check, let's construct an initial model filled with zeros and compute the loss over the batch of data we visualized above.
initial_model = collections.OrderedDict(
weights=np.zeros([784, 10], dtype=np.float32),
bias=np.zeros([10], dtype=np.float32))
sample_batch = federated_train_data[5][-1]
batch_loss(initial_model, sample_batch)
2.3025854
Note that we feed the TFF computation with the initial model defined as a
dict, even though the body of the Python function that defines it consumes
model parameters as model.weight and model.bias. The arguments of the call
to batch_loss aren't simply passed to the body of that function.
What happens when we invoke batch_loss?
The Python body of batch_loss has already been traced and serialized in the above cell where it was defined. TFF acts as the caller to batch_loss
at the computation definition time, and as the target of invocation at the time
batch_loss is invoked. In both roles, TFF serves as the bridge between TFF's
abstract type system and Python representation types. At the invocation time,
TFF will accept most standard Python container types (dict, list, tuple,
collections.namedtuple, etc.) as concrete representations of abstract TFF
tuples. Also, although as noted above, TFF computations formally only accept a
single parameter, you can use the familiar Python call syntax with positional
and/or keyword arguments in case where the type of the parameter is a tuple - it
works as expected.
Gradient descent on a single batch
Now, let's define a computation that uses this loss function to perform a single
step of gradient descent. Note how in defining this function, we use
batch_loss as a subcomponent. You can invoke a computation constructed with
tff.tf_computation inside the body of another computation, though typically
this is not necessary - as noted above, because serialization looses some
debugging information, it is often preferable for more complex computations to
write and test all the TensorFlow without the tff.tf_computation decorator.
@tff.tf_computation(MODEL_TYPE, BATCH_TYPE, tf.float32)
def batch_train(initial_model, batch, learning_rate):
# Define a group of model variables and set them to `initial_model`. Must
# be defined outside the @tf.function.
model_vars = collections.OrderedDict([
(name, tf.Variable(name=name, initial_value=value))
for name, value in initial_model.items()
])
optimizer = tf.keras.optimizers.SGD(learning_rate)
@tf.function
def _train_on_batch(model_vars, batch):
# Perform one step of gradient descent using loss from `batch_loss`.
with tf.GradientTape() as tape:
loss = forward_pass(model_vars, batch)
grads = tape.gradient(loss, model_vars)
optimizer.apply_gradients(
zip(tf.nest.flatten(grads), tf.nest.flatten(model_vars)))
return model_vars
return _train_on_batch(model_vars, batch)
str(batch_train.type_signature)
'(<<weights=float32[784,10],bias=float32[10]>,<x=float32[?,784],y=int32[?]>,float32> -> <weights=float32[784,10],bias=float32[10]>)'
When you invoke a Python function decorated with tff.tf_computation within the
body of another such function, the logic of the inner TFF computation is
embedded (essentially, inlined) in the logic of the outer one. As noted above,
if you are writing both computations, it is likely preferable to make the inner
function (batch_loss in this case) a regular Python or tf.function rather
than a tff.tf_computation. However, here we illustrate that calling one
tff.tf_computation inside another basically works as expected. This may be
necessary if, for example, you do not have the Python code defining
batch_loss, but only its serialized TFF representation.
Now, let's apply this function a few times to the initial model to see whether the loss decreases.
model = initial_model
losses = []
for _ in range(5):
model = batch_train(model, sample_batch, 0.1)
losses.append(batch_loss(model, sample_batch))
losses
[0.19690028, 0.13176318, 0.101132266, 0.08273812, 0.0703014]
Gradient descent on a sequence of local data
Now, since batch_train appears to work, let's write a similar training
function local_train that consumes the entire sequence of all batches from one
user instead of just a single batch. The new computation will need to now
consume tff.SequenceType(BATCH_TYPE) instead of BATCH_TYPE.
LOCAL_DATA_TYPE = tff.SequenceType(BATCH_TYPE)
@tff.federated_computation(MODEL_TYPE, tf.float32, LOCAL_DATA_TYPE)
def local_train(initial_model, learning_rate, all_batches):
# Mapping function to apply to each batch.
@tff.federated_computation(MODEL_TYPE, BATCH_TYPE)
def batch_fn(model, batch):
return batch_train(model, batch, learning_rate)
return tff.sequence_reduce(all_batches, initial_model, batch_fn)
str(local_train.type_signature)
'(<<weights=float32[784,10],bias=float32[10]>,float32,<x=float32[?,784],y=int32[?]>*> -> <weights=float32[784,10],bias=float32[10]>)'
There are quite a few details buried in this short section of code, let's go over them one by one.
First, while we could have implemented this logic entirely in TensorFlow,
relying on tf.data.Dataset.reduce to process the sequence similarly to how
we've done it earlier, we've opted this time to express the logic in the glue
language, as a tff.federated_computation. We've used the federated operator
tff.sequence_reduce to perform the reduction.
The operator tff.sequence_reduce is used similarly to
tf.data.Dataset.reduce. You can think of it as essentially the same as
tf.data.Dataset.reduce, but for use inside federated computations, which as
you may remember, cannot contain TensorFlow code. It is a template operator with
a formal parameter 3-tuple that consists of a sequence of T-typed elements,
the initial state of the reduction (we'll refer to it abstractly as zero) of
some type U, and the reduction operator of type (<U,T> -> U) that alters the
state of the reduction by processing a single element. The result is the final
state of the reduction, after processing all elements in a sequential order. In
our example, the state of the reduction is the model trained on a prefix of the
data, and the elements are data batches.
Second, note that we have again used one computation (batch_train) as a
component within another (local_train), but not directly. We can't use it as a
reduction operator because it takes an additional parameter - the learning rate.
To resolve this, we define an embedded federated computation batch_fn that
binds to the local_train's parameter learning_rate in its body. It is
allowed for a child computation defined this way to capture a formal parameter
of its parent as long as the child computation is not invoked outside the body
of its parent. You can think of this pattern as an equivalent of
functools.partial in Python.
The practical implication of capturing learning_rate this way is, of course,
that the same learning rate value is used across all batches.
Now, let's try the newly defined local training function on the entire sequence
of data from the same user who contributed the sample batch (digit 5).
locally_trained_model = local_train(initial_model, 0.1, federated_train_data[5])
Did it work? To answer this question, we need to implement evaluation.
Local evaluation
Here's one way to implement local evaluation by adding up the losses across all data batches (we could have just as well computed the average; we'll leave it as an exercise for the reader).
@tff.federated_computation(MODEL_TYPE, LOCAL_DATA_TYPE)
def local_eval(model, all_batches):
# TODO(b/120157713): Replace with `tff.sequence_average()` once implemented.
return tff.sequence_sum(
tff.sequence_map(
tff.federated_computation(lambda b: batch_loss(model, b), BATCH_TYPE),
all_batches))
str(local_eval.type_signature)
'(<<weights=float32[784,10],bias=float32[10]>,<x=float32[?,784],y=int32[?]>*> -> float32)'
Again, there are a few new elements illustrated by this code, let's go over them one by one.
First, we have used two new federated operators for processing sequences:
tff.sequence_map that takes a mapping function T->U and a sequence of
T, and emits a sequence of U obtained by applying the mapping function
pointwise, and tff.sequence_sum that just adds all the elements. Here, we map
each data batch to a loss value, and then add the resulting loss values to
compute the total loss.
Note that we could have again used tff.sequence_reduce, but this wouldn't be
the best choice - the reduction process is, by definition, sequential, whereas
the mapping and sum can be computed in parallel. When given a choice, it's best
to stick with operators that don't constrain implementation choices, so that
when our TFF computation is compiled in the future to be deployed to a specific
environment, one can take full advantage of all potential opportunities for a
faster, more scalable, more resource-efficient execution.
Second, note that just as in local_train, the component function we need
(batch_loss) takes more parameters than what the federated operator
(tff.sequence_map) expects, so we again define a partial, this time inline by
directly wrapping a lambda as a tff.federated_computation. Using wrappers
inline with a function as an argument is the recommended way to use
tff.tf_computation to embed TensorFlow logic in TFF.
Now, let's see whether our training worked.
print('initial_model loss =', local_eval(initial_model,
federated_train_data[5]))
print('locally_trained_model loss =',
local_eval(locally_trained_model, federated_train_data[5]))
initial_model loss = 23.025854 locally_trained_model loss = 0.4348469
Indeed, the loss decreased. But what happens if we evaluated it on another user's data?
print('initial_model loss =', local_eval(initial_model,
federated_train_data[0]))
print('locally_trained_model loss =',
local_eval(locally_trained_model, federated_train_data[0]))
initial_model loss = 23.025854 locally_trained_model loss = 74.500755
As expected, things got worse. The model was trained to recognize 5, and has
never seen a 0. This brings the question - how did the local training impact
the quality of the model from the global perspective?
Federated evaluation
This is the point in our journey where we finally circle back to federated types and federated computations - the topic that we started with. Here's a pair of TFF types definitions for the model that originates at the server, and the data that remains on the clients.
SERVER_MODEL_TYPE = tff.FederatedType(MODEL_TYPE, tff.SERVER)
CLIENT_DATA_TYPE = tff.FederatedType(LOCAL_DATA_TYPE, tff.CLIENTS)
With all the definitions introduced so far, expressing federated evaluation in TFF is a one-liner - we distribute the model to clients, let each client invoke local evaluation on its local portion of data, and then average out the loss. Here's one way to write this.
@tff.federated_computation(SERVER_MODEL_TYPE, CLIENT_DATA_TYPE)
def federated_eval(model, data):
return tff.federated_mean(
tff.federated_map(local_eval, [tff.federated_broadcast(model), data]))
We've already seen examples of tff.federated_mean and tff.federated_map
in simpler scenarios, and at the intuitive level, they work as expected, but
there's more in this section of code than meets the eye, so let's go over it
carefully.
First, let's break down the let each client invoke local evaluation on its
local portion of data part. As you may recall from the preceding sections,
local_eval has a type signature of the form (<MODEL_TYPE, LOCAL_DATA_TYPE> ->
float32).
The federated operator tff.federated_map is a template that accepts as a
parameter a 2-tuple that consists of the mapping function of some type T->U
and a federated value of type {T}@CLIENTS (i.e., with member constituents of
the same type as the parameter of the mapping function), and returns a result of
type {U}@CLIENTS.
Since we're feeding local_eval as a mapping function to apply on a per-client
basis, the second argument should be of a federated type {<MODEL_TYPE,
LOCAL_DATA_TYPE>}@CLIENTS, i.e., in the nomenclature of the preceding sections,
it should be a federated tuple. Each client should hold a full set of arguments
for local_eval as a member consituent. Instead, we're feeding it a 2-element
Python list. What's happening here?
Indeed, this is an example of an implicit type cast in TFF, similar to
implicit type casts you may have encountered elsewhere, e.g., when you feed an
int to a function that accepts a float. Implicit casting is used scarcily at
this point, but we plan to make it more pervasive in TFF as a way to minimize
boilerplate.
The implicit cast that's applied in this case is the equivalence between
federated tuples of the form {<X,Y>}@Z, and tuples of federated values
<{X}@Z,{Y}@Z>. While formally, these two are different type signatures,
looking at it from the programmers's perspective, each device in Z holds two
units of data X and Y. What happens here is not unlike zip in Python, and
indeed, we offer an operator tff.federated_zip that allows you to perform such
conversions explicity. When the tff.federated_map encounters a tuple as a
second argument, it simply invokes tff.federated_zip for you.
Given the above, you should now be able to recognize the expression
tff.federated_broadcast(model) as representing a value of TFF type
{MODEL_TYPE}@CLIENTS, and data as a value of TFF type
{LOCAL_DATA_TYPE}@CLIENTS (or simply CLIENT_DATA_TYPE), the two getting
filtered together through an implicit tff.federated_zip to form the second
argument to tff.federated_map.
The operator tff.federated_broadcast, as you'd expect, simply transfers data
from the server to the clients.
Now, let's see how our local training affected the average loss in the system.
print('initial_model loss =', federated_eval(initial_model,
federated_train_data))
print('locally_trained_model loss =',
federated_eval(locally_trained_model, federated_train_data))
initial_model loss = 23.025852 locally_trained_model loss = 54.43263
Indeed, as expected, the loss has increased. In order to improve the model for all users, we'll need to train in on everyone's data.
Federated training
The simplest way to implement federated training is to locally train, and then average the models. This uses the same building blocks and patters we've already discussed, as you can see below.
SERVER_FLOAT_TYPE = tff.FederatedType(tf.float32, tff.SERVER)
@tff.federated_computation(SERVER_MODEL_TYPE, SERVER_FLOAT_TYPE,
CLIENT_DATA_TYPE)
def federated_train(model, learning_rate, data):
return tff.federated_mean(
tff.federated_map(local_train, [
tff.federated_broadcast(model),
tff.federated_broadcast(learning_rate), data
]))
Note that in the full-featured implementation of Federated Averaging provided by
tff.learning, rather than averaging the models, we prefer to average model
deltas, for a number of reasons, e.g., the ability to clip the update norms,
for compression, etc.
Let's see whether the training works by running a few rounds of training and comparing the average loss before and after.
model = initial_model
learning_rate = 0.1
for round_num in range(5):
model = federated_train(model, learning_rate, federated_train_data)
learning_rate = learning_rate * 0.9
loss = federated_eval(model, federated_train_data)
print('round {}, loss={}'.format(round_num, loss))
round 0, loss=21.60552406311035 round 1, loss=20.365678787231445 round 2, loss=19.27480125427246 round 3, loss=18.31110954284668 round 4, loss=17.457256317138672
For completeness, let's now also run on the test data to confirm that our model generalizes well.
print('initial_model test loss =',
federated_eval(initial_model, federated_test_data))
print('trained_model test loss =', federated_eval(model, federated_test_data))
initial_model test loss = 22.795593 trained_model test loss = 17.278767
This concludes our tutorial.
Of course, our simplified example doesn't reflect a number of things you'd need
to do in a more realistic scenario - for example, we haven't computed metrics
other than loss. We encourage you to study
the implementation
of federated averaging in tff.learning as a more complete example, and as a
way to demonstrate some of the coding practices we'd like to encourage.