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functional.py
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functional.py
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# Copyright 2021, The TensorFlow Federated 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
#
# http://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.
"""Module for creating functional implementations of a `tff.learning.models.VariableModel`.
This version of the model parameterizes its `predict_on_batch` method by model
weights, rather than storing them in the model. This allows for greater
flexibility in model portability. In addition, the `loss` function is added and
decoupled from the `forward_pass` method. This improves non-supervised
techniques and integration with Jax ML frameworks. The `forward_pass` method is
removed from the `FunctionalModel` interface.
To use with `tff.learning.algorithms` and other APIs that construct learning
processes expecting stateful models, wrap the functional model with
`tff.learning.models.model_from_functional`.
"""
import collections
from collections.abc import Callable, Mapping, Sequence
import inspect
from typing import Any, Optional, TypeVar, Union
import numpy as np
import tensorflow as tf
from tensorflow_federated.python.common_libs import py_typecheck
from tensorflow_federated.python.learning.metrics import keras_finalizer
from tensorflow_federated.python.learning.metrics import keras_utils
from tensorflow_federated.python.learning.metrics import types
from tensorflow_federated.python.learning.models import variable
from tensorflow_federated.python.tensorflow_libs import variable_utils
Weight = Union[np.ndarray, int, float]
WeightStruct = Union[Sequence[Weight], Mapping[str, Weight]]
ModelWeights = tuple[WeightStruct, WeightStruct]
InitializeMetricsStateFn = Callable[[], types.MetricsState]
UpdateMetricsStateFn = Callable[
[types.MetricsState, Any, Any, Any], types.MetricsState
]
FinalizeMetricsFn = Callable[[types.MetricsState], Any]
GenericMetricsState = TypeVar('GenericMetricsState', bound=types.MetricsState)
class CallableNotTensorFlowFunctionError(TypeError):
"""Error raised when a callable is not decorated as a tf.function."""
class ValueMustNotBeTFError(TypeError):
"""Error raised a value must not be a `tf.Tensor` or `tf.Variable`."""
@tf.function
def empty_metrics_state() -> types.MetricsState:
return collections.OrderedDict()
@tf.function
def noop_update_metrics(
state: types.MetricsState,
labels: Any,
batch_output: variable.BatchOutput,
sample_weight: Optional[Any] = None,
) -> types.MetricsState:
del state # Unused.
del labels # Unused.
del batch_output # Unused.
del sample_weight # Unused.
return collections.OrderedDict()
@tf.function
def noop_finalize_metrics(
state: types.MetricsState,
) -> collections.OrderedDict[str, Any]:
del state # Unused.
return collections.OrderedDict()
class FunctionalModel:
"""A model that parameterizes forward pass by model weights."""
def __init__(
self,
*, # Require all arguments be named.
initial_weights: ModelWeights,
predict_on_batch_fn: Callable[[ModelWeights, Any, bool], Any],
loss_fn: Callable[[Any, Any, Any], Any],
metrics_fns: tuple[
InitializeMetricsStateFn, UpdateMetricsStateFn, FinalizeMetricsFn
] = (empty_metrics_state, noop_update_metrics, noop_finalize_metrics),
input_spec: Any,
):
"""Initializes a `FunctionalModel`.
Example model implementing linear regression:
```
w, b = np.zeros(shape=[1,3]), np.zeros([1])
trainable_weights = (w, b)
non_trainable_weights = ()
initial_weights = (trainable_weights, non_trainable_weights)
@tf.function
def predict_on_batch(model_weights, x, training):
del training # Unused.
trainable, non_trainable = model_weights
w, b = trainable
return tf.matmul(x, w, transpose_b=True) + b
def loss(output, label, sample_weight=None):
del sample_weight
return tf.math.reduce_mean(tf.math.pow(output - label, 2.))
model = FunctionalModel(
initial_weights, predict_on_batch, loss
(tf.TensorSpec(shape=[None, 3], dtype=tf.float32),
tf.TensorSpec(shape=[None, 1], dtype=tf.float32))
)
```
Args:
initial_weights: A 2-tuple `(trainable, non_trainable)` where the two
elements are sequences of weights. Weights must be values convertable to
`tf.Tensor` (e.g. `numpy.ndarray`, Python sequences, etc), but _not_
`tf.Tensor` values.
predict_on_batch_fn: A `tf.function` decorated callable that takes three
arguments, `model_weights` the same structure as `initial_weights`, `x`
the first element of `batch_input` (or `input_spec`), and `training` a
boolean determinig whether the call is during a training pass (e.g. for
Dropout, BatchNormalization, etc). It must return either a tensor of
predictions or a structure whose first element (as determined by
`tf.nest.flatten()`) is a tensor of predictions.
loss_fn: A callable that takes three arguments, `output` tensor(s) as
output of `predict_on_batch` that is interpretable by the loss function,
`label` the second element of `batch_input`, and optional
`sample_weight` that weights the output.
metrics_fns: A 3-tuple of callables that initialize the metrics state,
update the metrics state, and finalize the metrics values respectively.
This can be the result of `
tff.learning.metrics.create_functional_metric_fns`or custom user written
callables.
input_spec: A 2-tuple of `(x, y)` where each element is a nested structure
of `tf.TensorSpec`. `x` corresponds to batched model inputs that define
the shape and dtype of `x` to `predict_on_batch_fn`, while `y`
corresponds to batched labels for those inputs that define the shape and
dtype of `label` to `loss_fn`.
"""
def check_tf_function_decorated(fn: Any, arg_name: str) -> None:
if not hasattr(fn, 'get_concrete_function'):
type_string = py_typecheck.type_string(type(fn))
raise CallableNotTensorFlowFunctionError(
f'{arg_name} does not have a `get_concrete_function` attribute '
'meaning it is not a callable decorated with `tf.function`. '
f'Got a {type_string} with value {fn!r}.'
)
def check_non_tf_value(value):
if tf.is_tensor(value) or isinstance(value, tf.Variable):
raise ValueMustNotBeTFError(
'initial_weights may not contain TensorFlow values '
f'(tf.Tensor or tf.Variable). Got: {type(value)!r}. Try '
'converting to a np.ndarray by using the `.numpy()` '
'attribute for tf.Tensor, or `.read_value().numpy()` '
'for tf.Variable.'
)
tf.nest.map_structure(check_non_tf_value, initial_weights)
self._initial_weights = initial_weights
check_tf_function_decorated(predict_on_batch_fn, 'predict_on_batch_fn')
self._predict_on_batch_fn = predict_on_batch_fn
self._loss_fn = loss_fn
self._input_spec = input_spec
(
self._initialize_metrics_state,
self._update_metrics_state,
self._finalize_metrics,
) = metrics_fns
@property
def initial_weights(self) -> ModelWeights:
return self._initial_weights
@tf.function
def predict_on_batch(
self, model_weights: ModelWeights, x: Any, training: bool = True
):
"""Returns tensor(s) interpretable by the loss function."""
return self._predict_on_batch_fn(model_weights, x, training)
def loss(
self, output: Any, label: Any, sample_weight: Optional[Any] = None
) -> float:
"""Returns the loss value based on the model output and the label."""
return self._loss_fn(output, label, sample_weight)
@tf.function
def initialize_metrics_state(self) -> types.MetricsState:
return self._initialize_metrics_state()
@tf.function
def update_metrics_state(
self,
state: GenericMetricsState,
labels: Any,
batch_output: variable.BatchOutput,
sample_weight: Optional[Any] = None,
) -> GenericMetricsState:
return self._update_metrics_state(
state,
labels=labels,
batch_output=batch_output,
sample_weight=sample_weight,
)
@tf.function
def finalize_metrics(
self, state: types.MetricsState
) -> collections.OrderedDict[str, Any]:
return self._finalize_metrics(state)
@property
def input_spec(self):
return self._input_spec
class _ModelFromFunctional(variable.VariableModel):
"""A `tff.learning.models.VariableModel` wrapping a `tff.learning.model.FunctionalModel`."""
def __init__(
self,
functional_model: FunctionalModel,
metric_builders: Sequence[Callable[[], tf.keras.metrics.Metric]] = (),
):
self._functional_model = functional_model
# Construct `tf.Variable` to optimize during the learning process.
trainable, non_trainable = functional_model.initial_weights
self._trainable_variables = tuple(tf.Variable(x) for x in trainable)
self._non_trainable_variables = tuple(
tf.Variable(x, trainable=False) for x in non_trainable
)
self._model_weights = (
self._trainable_variables,
self._non_trainable_variables,
)
self._num_examples = tf.Variable(0, trainable=False)
self._loss_sum = tf.Variable(0.0, trainable=False)
if not metric_builders:
self._metric_builders = []
self._metrics = []
else:
self._metric_builders = metric_builders
self._metrics = [constructor() for constructor in metric_builders]
# Raise an error if there are duplicate metric names
metric_names = [metric.name for metric in self._metrics]
duplicates = set(
name for name in metric_names if metric_names.count(name) > 1
)
if duplicates:
raise ValueError(
f'{duplicates} appeared in the metric names more than once, '
'each metric should have a unique name.'
)
@property
def trainable_variables(self) -> tuple[tf.Variable, ...]:
return self._trainable_variables
@property
def non_trainable_variables(self) -> tuple[tf.Variable, ...]:
return self._non_trainable_variables
@property
def local_variables(self) -> tuple[tf.Variable, ...]:
metrics_variables = [self._loss_sum, self._num_examples]
for metric in self._metrics:
metrics_variables.extend(metric.variables)
return tuple(metrics_variables)
@property
def input_spec(self):
return self._functional_model.input_spec
@tf.function
def forward_pass(self, batch_input, training=True):
if isinstance(batch_input, Mapping):
x = batch_input['x']
y = batch_input['y']
else:
x, y = batch_input
batch_output = self._functional_model.predict_on_batch(
model_weights=tf.nest.map_structure(
lambda v: v.read_value(), self._model_weights
),
x=x,
training=training,
)
batch_loss = self._functional_model.loss(output=batch_output, label=y)
predictions = tf.nest.flatten(batch_output)[0]
batch_num_examples = tf.shape(predictions)[0]
self._num_examples.assign_add(batch_num_examples)
self._loss_sum.assign_add(
batch_loss * tf.cast(batch_num_examples, tf.float32)
)
for metric in self._metrics:
metric.update_state(y_true=y, y_pred=batch_output)
forward_pass_output = variable.BatchOutput(
loss=batch_loss,
predictions=batch_output,
num_examples=batch_num_examples,
)
return forward_pass_output
@tf.function
def predict_on_batch(self, x, training=True):
return self._functional_model.predict_on_batch(
model_weights=tf.nest.map_structure(
lambda v: v.read_value(), self._model_weights
),
x=x,
training=training,
)
@tf.function
def report_local_unfinalized_metrics(
self,
) -> collections.OrderedDict[str, list[tf.Tensor]]:
outputs = collections.OrderedDict(
loss=[self._loss_sum, tf.cast(self._num_examples, tf.float32)]
)
for metric in self._metrics:
outputs[metric.name] = [v.read_value() for v in metric.variables]
return outputs
def metric_finalizers(
self,
) -> collections.OrderedDict[str, keras_finalizer.KerasMetricFinalizer]:
finalizers = collections.OrderedDict(
# `loss` result is computed by `loss_sum` / `num_examples`.
loss=tf.function(func=lambda x: x[0] / x[1])
)
for metric_builder in self._metric_builders:
metric_name = metric_builder().name
finalizers[metric_name] = keras_finalizer.create_keras_metric_finalizer(
metric_builder
)
return finalizers
@tf.function
def reset_metrics(self):
for metric in self._metrics:
metric.reset_state()
additional_metrics_variables = [self._loss_sum, self._num_examples]
for var in additional_metrics_variables:
var.assign(tf.zeros_like(var))
def model_from_functional(
functional_model: FunctionalModel,
metric_constructors: Sequence[Callable[[], tf.keras.metrics.Metric]] = (),
) -> variable.VariableModel:
"""Converts a `FunctionalModel` to a `tff.learning.models.VariableModel`.
WARNING: The `metrics_constructors` argument will *replace* any metrics that
were originally attached to the `FunctionalModel` with new metrics.
Args:
functional_model: A `tff.learning.models.FunctionalModel` to convert.
metric_constructors: An optional sequence of callables that return newly
constructed `tf.keras.metrics.Metric` objects to attached to the output
`tff.learning.models.VariableModel`.
Returns:
A new `tff.learning.models.VariableModel` with the same behavior as
`functional_model`.
"""
return _ModelFromFunctional(functional_model, metric_constructors)
class KerasFunctionalModelError(Exception):
"""An error raised when a FunctionalModel backed by Keras is used outside TFF."""
def functional_model_from_keras(
keras_model: Union[tf.keras.Model, Callable[[], tf.keras.Model]],
loss_fn: tf.keras.losses.Loss,
input_spec: Union[Sequence[Any], Mapping[str, Any]],
metrics_constructor: Optional[
Union[
keras_utils.MetricConstructor,
keras_utils.MetricsConstructor,
keras_utils.MetricConstructors,
]
] = None,
) -> FunctionalModel:
"""Converts a `tf.keras.Model` to a `tff.learning.models.FunctionalModel`.
NOTE: This method only supports models where calling that model with
`training=True` and `training=False` produce the same graph. Keras layers
such as batch normalization will fail because they require updating internal
state when `training=True` which is not supported.
This method doesn't support loss functions scaled by sample weights at the
current state. Keras models with non-None sample weights will fail because
sample weights aren't supported in model serialization and deserialization.
IMPORTANT: The returned model must only be used in a graph context (for
example inside a `tff.tf_computation` decorated callable). It will raise an
error otherwise.
Args:
keras_model: A `tf.keras.Model` object, should be uncompiled. If compiled,
the metrics, optimizer, and loss function will be ignored. Note: models
that have multiple outputs will send all outputs to the `loss_fn`.
loss_fn: A `tf.keras.losses.Loss` object.
input_spec: A structure of `tf.TensorSpec` defining the input to the model.
metrics_constructor: An optional callable that must be compatible with
`tff.learning.metrics.create_functional_metric_fns`.
Returns:
A `tff.learning.models.FunctionalModel`.
Raises:
KerasFunctionalModelError: If the following conditions: 1) the Keras model
contains a batch normalization layer, 2) the Keras model is with
non-trainable variable, 3) error occurs when converting the Keras model, 4)
the Keras model shares variable across layers, 5) the FunctionalModel is
used outside of a tff.tf_computation decorated callable or a graph context,
6) the Keras model contains a loss function with non-None sample weights.
"""
# We're going to do something fancy here:
#
# 1. Get a copy of all the variables, in the order they are created during
# model construction, when in a graph context.
# 2. Use this ordering to construct a type signature of the model weights in
# such a way that we can inject TENSORS (those that are coming in as
# arguments) in place of variable creation during a call to
# `tf.keras.models.clone_model()`, which gives us a newly constructed Keras
# model in the context we want.
# 3. Profit by having variableless graphs!
#
# **WARNING** Caveats:
#
# 1. This model _must_ be used inside a graph context (e.g. a
# `tff.tf_computation` decorated callable, aka a `tff.Computation`). Keras
# appears to create extra variables in the eager context that are not part
# of the user specified model, and end up not being compatible.
#
# 2. We have found that this trick does NOT work with non-trainable variables
# that are updated during training. Namely layers such as
# BatchNormalization try to update means/variances during training and are
# not compatible with this approach. We generally recommend
# GroupNormalization in place of BatchNormalization at the current time.
#
# 3. This does not support multiple outputs with different loss functions, or
# laywerise regularization losses TODO: b/156629927.
if isinstance(keras_model, tf.keras.Model):
for layer in keras_model.layers:
# There may be other layers that are problematic, at this time updating
# the mean/variance in batchnorm layer is the only known such instance.
if isinstance(layer, tf.keras.layers.BatchNormalization):
raise KerasFunctionalModelError(
'Keras model contains a batch normalization layer, which is '
'incompatible with `tff.learning.models.FunctionalModel`. Consider '
'using group normalization instead.'
)
if keras_model.non_trainable_variables:
raise KerasFunctionalModelError(
'Received a Keras model with non-trainable variables. Keras models'
' with non-trainable variables are currently not supported by'
' FunctionalModel. Most training algorithms (e.g. Federated'
' Averaging) will not aggregate them, and they are not updated'
' locally by the optimizer. We can relax this in the future if we'
' have APIs that support updating non-trainable variables.'
)
elif not callable(keras_model):
raise ValueError(
'`keras_model` must be a `tf.keras.Model` or a no-arg '
'callable that returns a `tf.keras.Model`.'
)
# TODO: b/269671316 - more work needed to support non-None sample_weight
# during model serialization and deserialization.
keras_sample_weight = (
inspect.signature(loss_fn).parameters['sample_weight'].default
)
if keras_sample_weight is not None:
raise KerasFunctionalModelError(
'Received a non-None model_weight. Non-None model_weight is not'
'supported in the current model serialization and deserialization.'
)
# Clone the keras model inside a graph context so that we only get the
# variables for the layers (otherwise keras adds other non-user variables). We
# also setup ops to inject the current model weights, because the cloned model
# will be re-initialized from scratch.
with tf.Graph().as_default() as g:
with variable_utils.record_variable_creation_scope() as captured_variables:
if isinstance(keras_model, tf.keras.Model):
try:
cloned_model = tf.keras.models.clone_model(keras_model)
except RuntimeError as e:
raise KerasFunctionalModelError(
'Encountered a error converting the Keras model. Often this '
'occurs when the `tf.keras.Model` has a layer that receives '
'inputs from other layers directly (e.g. shared embeddings).'
'To avoid the problem, wrap the `tf.keras.Model` construction in '
'a no-arg callable (e.g. lambda) and pass that callable to '
'`functional_model_from_keras`'
) from e
if len(cloned_model.variables) != len(keras_model.variables):
raise KerasFunctionalModelError(
'The input Keras model is likely sharing variables across layers '
'which is unsupported. Cloning the model will duplicate these '
'variables and result in unexpected training gradients.'
)
else:
cloned_model = keras_model()
# Ensure our cloned model has the same weights as the current model.
# We'll feed in the current model waits into the palceholders for
# assignmnet in a session below.
def assign_placeholder(v):
p = tf.compat.v1.placeholder(dtype=v.dtype)
return v.assign(p), p
assign_ops, placeholders = zip(
*(assign_placeholder(v) for v in cloned_model.variables)
)
trainable_variables = tuple(v for v in captured_variables if v.trainable)
non_trainable_variables = tuple(
v for v in captured_variables if not v.trainable
)
# Here we get the initial weights from the incoming keras model in the order
# they are constructed; and also ensure that the values are set to the
# incoming model weights rather than their fresh initialization.
if isinstance(keras_model, tf.keras.Model):
model_for_variables = keras_model
else:
model_for_variables = keras_model()
current_model_weights = tf.nest.map_structure(
lambda v: v.read_value().numpy(), model_for_variables.variables
)
with tf.compat.v1.Session(graph=g) as sess:
sess.run(tf.compat.v1.initializers.variables(captured_variables))
sess.run(
fetches=assign_ops,
feed_dict=dict(zip(placeholders, current_model_weights)),
)
initial_weights = sess.run(
fetches=(trainable_variables, non_trainable_variables)
)
@tf.function
def predict_on_batch(
model_weights: ModelWeights, x: Any, training: bool = True
) -> Any:
with tf.init_scope():
if tf.executing_eagerly():
raise KerasFunctionalModelError(
'tf.keras.Model used as a FunctionalModel is only usable inside a '
'tff.tf_computation decorated callable or a graph context.'
)
# Make a copy of the weights container; can't mutate Python containers
# inside a tf.function.
trainable, non_trainable = (list(w) for w in model_weights)
# Here were intercept variable creation requests during the
# `tf.keras.models.clone_model()` call.
#
# Instead of forwarding the variable request to TF core and getting a
# `tf.Variable` back, we skip that and return only the `tf.Tensor` that
# corresponds to the `tf.Variable` recreation request (avoiding any variable
# creation). This works because TF operations that accept `tf.Variable`
# inputs automatically call `variable.read_value()` and then operate on that
# resulting tensor. We're relying on shortcutting that and providing the
# tensor straight away.
#
# For example, `tf.matmul` doesn't notice its input is `tf.Variable` or
# `tf.Tensor`:
#
# v = tf.Variable([[1], [2], [3]])
# tf.matmul(v, [[4, 5, 6]])
#
# and
#
# v = tf.constant([[1], [2], [3]])
# tf.matmul(v, [[4, 5, 6]])
#
# both result in:
#
# <tf.Tensor: shape=(3, 3), dtype=int32, numpy=
# array([[ 4, 5, 6],
# [ 8, 10, 12],
# [12, 15, 18]], dtype=int32)>
def swap_tensor_parameter_for_variable(_, **kwargs):
if kwargs.get('trainable', True):
return trainable.pop(0)
else:
return non_trainable.pop(0)
with tf.variable_creator_scope(swap_tensor_parameter_for_variable):
if isinstance(keras_model, tf.keras.Model):
variableless_model = tf.keras.models.clone_model(keras_model)
else:
variableless_model = keras_model()
return variableless_model(x, training)
def loss(
output: Any, label: Any, sample_weight: Optional[Any] = None
) -> float:
return loss_fn(y_true=label, y_pred=output, sample_weight=sample_weight)
if metrics_constructor is not None:
metrics_fns = keras_utils.create_functional_metric_fns(metrics_constructor)
else:
metrics_fns = (
empty_metrics_state,
noop_update_metrics,
noop_finalize_metrics,
)
return FunctionalModel(
initial_weights=initial_weights,
predict_on_batch_fn=predict_on_batch,
loss_fn=loss,
metrics_fns=metrics_fns,
input_spec=input_spec,
)
def keras_model_from_functional_weights(
*, model_weights: ModelWeights, keras_model: tf.keras.Model
) -> tf.keras.Model:
"""Creates a new Keras model using the model weights from a `FunctionalModel`.
This method is effectively the reverse of `functional_model_from_keras`. Since
the trained weights are external to the model, this method expects a nested
structure of tensors that was used to train a `FunctionalModel`
IMPORTANT: this method must be run in a graph context (e.g. inside a
`tf.Graph` context, or a `tff.tf_computation` decorated callable), otherwise
the Keras model construction will differ from how the `FunctionalModel` was
originally created.
Args:
model_weights: A nested structure of tensors matching the structure of
`tff.learning.models.FunctionalModel.initial_weights` for a model
constructed using `functional_model_from_keras`.
keras_model: A Keras model to use for cloning a new model but with the input
weights.
Returns:
A newly constructed `tf.keras.Model` that matches the architecture of
the input `keras_model` argument but with the weight values from
`model_weights.
"""
if tf.compat.v1.executing_eagerly_outside_functions():
raise ValueError(
'`keras_model_from_functional_weights()` can only be called from within'
' a graph context.'
)
# Convert to mutable lists that we can `pop` weights off of.
trainable_weights, non_trainable_weights = [list(w) for w in model_weights]
def variable_creator_with_weights(next_creator_fn, **kwargs):
try:
if kwargs.get('trainable', True):
weight = trainable_weights.pop(0)
else:
weight = non_trainable_weights.pop(0)
except IndexError:
raise ValueError(
'`model_weights` contains fewer weights than `keras_model` uses, '
'check that the weights argument is matching the model type.'
) from None
if 'initial_value' in kwargs:
kwargs['initial_value'] = weight
elif 'initializer' in kwargs:
kwargs['initializer'] = tf.compat.v1.constant_initializer(value=weight)
else:
raise ValueError(
"Can't set weights to a Keras model that creates variables without "
'initial values.'
)
return next_creator_fn(**kwargs)
with tf.variable_creator_scope(variable_creator_with_weights):
new_model = tf.keras.models.clone_model(keras_model)
if trainable_weights or non_trainable_weights:
raise ValueError(
'`model_weights` contained more variables than `keras_model` uses, '
'check that the weights argument is matching the model type.'
)
return new_model