Tensorflow-federated

Major Features and Improvements

* Added `tff.federated_min` and `tff.federated_max` intrinsics.
* Added a `get_value()` method to `tff.program.SavedModelFileReleaseManager,`
for retrieving values that were previously released.
* Added `tff.program.PeriodicReleaseManager` to release values at regular
intervals.
* Learning program logic now saves next evaluation time so that it can be
loaded upon computation restarts.
* `DistributeAggregateForm` now skips normalizing the all_equal bit.
* Added parallelism to Vizier program logic.
* Enabled building protos with certain Bazel versions.

Breaking Changes

* Updated the version of `attrs` to `23.1`.
* Updated the version of `cachetools` to `~=5.3`.
* Updated the version of `dp-accounting` to `0.4.3`.
* Updated the version of `google-vizier` to `0.1.11`.
* Updated the version of `jax` to `0.4.14`.
* Updated the version of `portpicker` to `~=1.6`.
* Updated the version of `tensorflow` to `2.13.0`.
* Updated the version of `tensorflow-model-optimization` to `0.7.5`.
* Updated the version of `tensorflow-privacy` to `0.8.11`.
* Updated the version of `typing-extensions` to `~=4.5.0`.
* Increased `TF_CUDA_VERSION` to `12`.
* Removed the `tff.program.Capabilities` enum from the iterator.
* Deleted Python executors.
* Removed the deprecated `is_{foo}` functions from `tff.Type`s. Users should
use `isinstance` checks instead.
* Deleted go-related BUILD targets for TFF proto files.

Bug Fixes

* Removed non-existent GCP doc from TFF guides.
* Cleaned up exceptions in the `tff.program` package for consistency and
clarity.
* Fixed various pytype errors.
* Fixed various `undefined-variable` lint errors.
* Fixed a `UnicodeDecodeError` in the FedRecon tutorial.
* Fixed Python 3.11 related errors.

Breaking Changes

* Removed `context` argument from
`tff.learning.algorithms.build_personalization_eval_computation`. Now a
personalization function only takes a model, a train dataset, and a test
dataset as arguments.

Bug Fixes

* Fix a rare infinite loop issue caused by very small float values when using
`tff.learning.ddp_secure_aggregator`.

Major Features and Improvements

* Updated the type annotation for the `dtype` parameter to `tff.TensorType`.
* Added adaptive tuning function to `ThresholdSampling` class.
* Added
`tff.learning.models.ReconstructionModel.from_keras_model_and_variables`,
which provides a way to get a `ReconstructionModel` from a Keras model and
lists of global and local trainable/non_trainable *variables*.
* Switched `tff.learning.algorithms.build_fed_recon_eval` to use a stateful
metrics aggregator.

Breaking Changes

* Removed `tff.learning.models.ReconstructionModel.from_keras_model`, which
has been replaced by
`tff.learning.models.ReconstructionModel.from_keras_model_and_layers`.
* Removed the following functions from the py_typecheck module: `check_len`,
`check_callable`, `is_dataclass`,`is_attrs`, `check_subclass` and
`check_not_none`. They are unused or can be replaced by Python type
annotations.

Bug Fixes

* Fixed a small bug in the TFF SGDM optimizer, to only track momentum as a
hparam if it is both specified and nonzero.

Major Features and Improvements

* DTensor TF executor is now integrated with the default TFF C++ worker.
* Added federated program documentation and guidelines.
* Removed the `pytype` dependency from TFF.
* `tff.learning.algorithms.build_fed_recon_eval` now supports TFF optimizers.

Breaking Changes

* Updated `tff.types.deserialize_type` to not accept/return `None`.
* Removed the `tff.framework.ComputationBuildingBlock.is_foo` methods.
* Renamed `tff.learning.algorithms.build_personalization_eval` to
`tff.learning.algorithms.build_personalization_eval_computation`
* `tff.learning.models.ReconstructionModel.from_keras_model` will now check
that global and local variables are disjoint, raise ValueError if they are
not.

Bug Fixes

* Fixed `tff.learning.models.ReconstructionModel.has_only_global_variables`
(it was returning incorrect value).

Major Features and Improvements

* Removed compression for `worker_binary`.
* Allowed tensor and numpy float-like objects in optimizer hyperparameters.
* Improved API/filtering logic in `FilteringReleaseManager`.

Breaking Changes

* Renamed `build_personalization_eval` to
`build_personalization_eval_computation`.
* Updated `tff.to_type` implementation and type annotation to not
accept/return `None`.

Bug Fixes

* Fixed and documented pytype errors in the `program` package.
* Fixed bug in how `tff.program.NativeFederatedContext` handles arguments of
various types.

Major Features and Improvements

* Updated algorithms built from `tff.learning.models.FunctionalModel` to allow
nested outputs.
* Added the `PrefetchingDataSource` back to the `tff.program` API now that the
flakiness has been fixed.

Bug Fixes

* Changed return type of
`tff.simulation.compose_dataset_computation_with_learning_process` to be a
`tff.learning.templates.LearningProcess`.
* Fixed flaky tests in `prefetching_data_source_test`.
* Fixed type annotations and lint errors.
* Cleaned up error messages and typing information in
`tff.learning.optimizers`.