Tensorflow

TensorFlow

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Breaking Changes

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Known Caveats

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Major Features and Improvements

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Bug Fixes and Other Changes

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Keras

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Breaking Changes

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Known Caveats

* <CAVEATS REGARDING THE RELEASE (BUT NOT BREAKING CHANGES).>
* <ADDING/BUMPING DEPENDENCIES SHOULD GO HERE>
* <KNOWN LACK OF SUPPORT ON SOME PLATFORM, SHOULD GO HERE>

Major Features and Improvements

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* <IF RELEASE CONTAINS MULTIPLE FEATURES FROM SAME AREA, GROUP THEM TOGETHER>

Bug Fixes and Other Changes

* <SIMILAR TO ABOVE SECTION, BUT FOR OTHER IMPORTANT CHANGES / BUG FIXES>
* <IF A CHANGE CLOSES A GITHUB ISSUE, IT SHOULD BE DOCUMENTED HERE>
* <NOTES SHOULD BE GROUPED PER AREA>

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

<INSERT>, <NAME>, <HERE>, <USING>, <GITHUB>, <HANDLE>

TensorFlow

Breaking Changes

* `LiteRT`, a.k.a. `tf.lite`:
* C++ API:
* The public constants `tflite::Interpreter:kTensorsReservedCapacity`
and `tflite::Interpreter:kTensorsCapacityHeadroom` are now const
references, rather than `constexpr` compile-time constants.
(This is to enable better API compatibility for TFLite in Play services
while preserving the implementation flexibility to change the values of
these constants in the future.)
* Interpreter:
* `tf.lite.Interpreter` gives deprecation warning redirecting to its new location at `ai_edge_litert.interpreter`, as the API `tf.lite.Interpreter` will be deleted in TF 2.20. See the [migration guide](https://ai.google.dev/edge/litert/migration) for details.

Known Caveats

Major Features and Improvements

* `tf.lite`
* `tfl.Cast` op is now supporting `bfloat16` in runtime kernel.

Bug Fixes and Other Changes

* We have stopped publishing `libtensorflow` packages but it can still be unpacked from the PyPI package.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

Akhil Goel, akhilgoe, Alain Flaischer, Alex, Alexander Pivovarov, Alexander Shadchin, Alexis Praga, Amrinfathima-Mcw, Andrey Pikas, Andrey Portnoy, Ankur Singh, Ashiq Imran, Assoap, c8ef, charleshofer, Chase Riley Roberts, Chenhao Jiang, Chongyun Lee, Claudio Desouza, Corentin Godeau, Crefeda Rodrigues, Danny Burrow, dependabot[bot], Dimitris Vardoulakis, Dragan Mladjenovic, Elfie Guo, Emmanuel Ferdman, fiberflow, flyingcat, Gary Yi-Hung Chen, Georg Stefan Schmid, Gerwout Van Der Veen, Harsha H S, Harshit Monish, Hugo Mano, i.Pear, Ilia Sergachev, Jane Liu, Jaroslav Sevcik, Jc (Jonathan Chen), Jerry Ge, Jian Li, johndoknjas, Johnny, Jonathan Albrecht, Kaixi Hou, Kanvi Khanna, keerthanakadiri, Kevin Ji, Kiran Sai Ramineni, kwoncy2020, LakshmiKalaKadali, Lee, Jun Seok, Mahmoud Abuzaina, Matt Bahr, mayuyuace, Melissa Weber Mendonça, misterBart, Mkarpushin-Enhancelab, Mmakevic-Amd, mraunak, nallave, Nayana Thorat, Nayana-Ibm, nick.camarena, Nicolas Castet, Om Thakkar, oyzh, Parsa Homayouni, Patrick Toulme, Pavel Emeliyanenko, Pavithra Eswaramoorthy, Pearu Peterson, pemeliya, Philipp Hack, Ravi Kumar Soni, redwrasse, Ruturaj Vaidya, Sallenkey-Wei, Sandeep Gupta, Sergey Kozub, Sevin Fide Varoglu, Shanbin Ke, Shaogang Wang, Shixin Zhang, Shraiysh, Shu Wang, Silvio Traversaro, snadampal, Sunita Nadampalli, Tai Ly, Tatwai Chong, tchatow, tdanyluk, Terry Sun, Tilak, Tj Xu, Trevor Morris, Twice, vfdev, Vladimir Silyaev, Weisser, Pascal, wokron, Won Jeon, Xuefei Jiang, Zentrik, Zoranjovanovic-Ns

TensorFlow

Breaking Changes

* `tf.lite`
* C API:
* An optional, fourth parameter was added `TfLiteOperatorCreate` as a step forward towards a cleaner API for `TfLiteOperator`. Function `TfLiteOperatorCreate` was added recently, in TensorFlow Lite version 2.17.0, released on 7/11/2024, and we do not expect there will be much code using this function yet. Any code breakages can be easily resolved by passing nullptr as the new, 4th parameter.

* TensorRT support is disabled in CUDA builds for code health improvement.

* TensorFlow now supports and is compiled with NumPy 2.0 by default. Please see the [NumPy 2 release notes](https://numpy.org/doc/stable/release/2.0.0-notes.html) and the [NumPy 2 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html#numpy-2-migration-guide).
* Note that NumPy's type promotion rules have been changed(See [NEP 50](https://numpy.org/neps/nep-0050-scalar-promotion.html#nep50)for details). This may change the precision at which computations happen, leading either to type errors or to numerical changes to results.
* Tensorflow will continue to support NumPy 1.26 until 2025, aligning with community standard deprecation timeline [here](https://scientific-python.org/specs/spec-0000/).

* Hermetic CUDA support is added.

Hermetic CUDA uses a specific downloadable version of CUDA instead of the user’s locally installed CUDA. Bazel will download CUDA, CUDNN and NCCL distributions, and then use CUDA libraries and tools as dependencies in various Bazel targets. This enables more reproducible builds for Google ML projects and supported CUDA versions.

* Remove the `EnumNamesXNNPackFlags` function in `tensorflow/lite/acceleration/configuration/configuration_generated.h`.

This change is a bug fix in the automatically generated code. This change is automatically generated by the new flatbuffer generator. The flatbuffers library is updated to 24.3.25 in https://github.com/tensorflow/tensorflow/commit/c17d64df85a83c1bd0fd7dcc0b1230812b0d3d48. The new flatbuffers library includes the following change https://github.com/google/flatbuffers/pull/7813 which fixed a underlying flatbuffer code generator bug.


Known Caveats

Major Features and Improvements

* `tf.lite`:
* The LiteRT [repo](https://github.com/google-ai-edge/LiteRT) is live (see [announcement](https://developers.googleblog.com/en/tensorflow-lite-is-now-litert/)), which means that in the coming months there will be changes to the development experience for TFLite. The TF Lite Runtime source will be moved later this year, and sometime after that we will start accepting contributions through that repo.
* SignatureRunner is now supported for models with no signatures.

Bug Fixes and Other Changes

* `tf.data`
* Add optional `synchronous` argument to `map`, to specify that the `map` should run synchronously, as opposed to be parallelizable when `options.experimental_optimization.map_parallelization=True`. This saves memory compared to setting `num_parallel_calls=1`.
* Add optional `use_unbounded_threadpool` argument to `map`, to specify that the `map` should use an unbounded threadpool instead of the default pool that is based on the number of cores on the machine. This can improve throughput for map functions which perform IO or otherwise release the CPU.
* Add [`tf.data.experimental.get_model_proto`](https://www.tensorflow.org/api_docs/python/tf/data/experimental/get_model_proto) to allow users to peek into the analytical model inside of a dataset iterator.

* `tf.lite`
* `Dequantize` op supports `TensorType_INT4`.
* This change includes per-channel dequantization.
* Add support for `stablehlo.composite`.
* `EmbeddingLookup` op supports per-channel quantization and `TensorType_INT4` values.
* `FullyConnected` op supports `TensorType_INT16` activation and `TensorType_Int4` weight per-channel quantization.
* Enable per-tensor quantization support in dynamic range quantization of `TRANSPOSE_CONV` layer. Fixes TFLite converter [bug](https://github.com/tensorflow/tensorflow/issues/76624).

* `tf.tensor_scatter_update`, `tf.tensor_scatter_add` and of other reduce types.
* Support `bad_indices_policy`.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

Akhil Goel, akhilgoe, Alexander Pivovarov, Amir Samani, Andrew Goodbody, Andrey Portnoy, Anthony Platanios, bernardoArcari, Brett Taylor, buptzyb, Chao, Christian Clauss, Cocoa, Daniil Kutz, Darya Parygina, dependabot[bot], Dimitris Vardoulakis, Dragan Mladjenovic, Elfie Guo, eukub, Faijul Amin, flyingcat, Frédéric Bastien, ganyu.08, Georg Stefan Schmid, Grigory Reznikov, Harsha H S, Harshit Monish, Heiner, Ilia Sergachev, Jan, Jane Liu, Jaroslav Sevcik, Kaixi Hou, Kanvi Khanna, Kristof Maar, Kristóf Maár, LakshmiKalaKadali, Lbertho-Gpsw, lingzhi98, MarcoFalke, Masahiro Hiramori, Mmakevic-Amd, mraunak, Nobuo Tsukamoto, Notheisz57, Olli Lupton, Pearu Peterson, pemeliya, Peyara Nando, Philipp Hack, Phuong Nguyen, Pol Dellaiera, Rahul Batra, Ruturaj Vaidya, sachinmuradi, Sergey Kozub, Shanbin Ke, Sheng Yang, shengyu, Shraiysh, Shu Wang, Surya, sushreebarsa, Swatheesh-Mcw, syzygial, Tai Ly, terryysun, tilakrayal, Tj Xu, Trevor Morris, Tzung-Han Juang, wenchenvincent, wondertx, Xuefei Jiang, Ye Huang, Yimei Sun, Yunlong Liu, Zahid Iqbal, Zhan Lu, Zoranjovanovic-Ns, Zuri Obozuwa

Bug Fixes and Other Changes

* Add necessary header files in the aar library. These are needed if developers build apps with header files unpacked from tflite aar files from maven.
* Implement Name() for GCSWritableFile to fix the profiler trace viewer cache file generation.
* Fix `cstring.h` missing file issue with the Libtensorflow archive.

TensorFlow

Breaking Changes

* GPU
* Support for NVIDIA GPUs with compute capability 5.x (Maxwell generation) has been removed from TF binary distributions (Python wheels).

Major Features and Improvements

* Add `is_cpu_target_available`, which indicates whether or not TensorFlow was built with support for a given CPU target. This can be useful for skipping target-specific tests if a target is not supported.

* `tf.data`
* Support `data.experimental.distribued_save`. `distribued_save` uses tf.data service (https://www.tensorflow.org/api_docs/python/tf/data/experimental/service) to write distributed dataset snapshots. The call is non-blocking and returns without waiting for the snapshot to finish. Setting `wait=True` to `tf.data.Dataset.load` allows the snapshots to be read while they are being written.

Bug Fixes and Other Changes

* GPU
* Support for NVIDIA GPUs with compute capability 8.9 (e.g. L4 & L40) has been added to TF binary distributions (Python wheels).
* Replace `DebuggerOptions` of TensorFlow Quantizer, and migrate to `DebuggerConfig` of StableHLO Quantizer.
* Add TensorFlow to StableHLO converter to TensorFlow pip package.
* TensorRT support: this is the last release supporting TensorRT. It will be removed in the next release.
* NumPy 2.0 support: TensorFlow is going to support NumPy 2.0 in the next release. It may break some edge cases of TensorFlow API usage.

* `tf.lite`
* Quantization for `FullyConnected` layer is switched from per-tensor to per-channel scales for dynamic range quantization use case (`float32` inputs / outputs and `int8` weights). The change enables new quantization schema globally in the converter and inference engine. The new behaviour can be disabled via experimental flag `converter._experimental_disable_per_channel_quantization_for_dense_layers = True`.
* C API:
* The experimental `TfLiteRegistrationExternal` type has been renamed as `TfLiteOperator`, and likewise for the corresponding API functions.
* The Python TF Lite Interpreter bindings now have an option `experimental_default_delegate_latest_features` to enable all default delegate features.
* Flatbuffer version update:
* `GetTemporaryPointer()` bug fixed.

* `tf.data`
* Add `wait` to `tf.data.Dataset.load`. If `True`, for snapshots written with `distributed_save`, it reads the snapshot while it is being written. For snapshots written with regular `save`, it waits for the snapshot until it's finished. The default is `False` for backward compatibility. Users of `distributed_save` are recommended to set it to `True`.

* `tf.tpu.experimental.embedding.TPUEmbeddingV2`
* Add `compute_sparse_core_stats` for sparse core users to profile the data with this API to get the `max_ids` and `max_unique_ids`. These numbers will be needed to configure the sparse core embedding mid level api.
* Remove the `preprocess_features` method since that's no longer needed.

Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

Abdulaziz Aloqeely, Ahmad-M-Al-Khateeb, Akhil Goel, akhilgoe, Alexander Pivovarov, Amir Samani, Andrew Goodbody, Andrey Portnoy, Ashiq Imran, Ben Olson, Chao, Chase Riley Roberts, Clemens Giuliani, dependabot[bot], Dimitris Vardoulakis, Dragan Mladjenovic, ekuznetsov139, Elfie Guo, Faijul Amin, Gauri1 Deshpande, Georg Stefan Schmid, guozhong.zhuang, Hao Wu, Haoyu (Daniel), Harsha H S, Harsha Hs, Harshit Monish, Ilia Sergachev, Jane Liu, Jaroslav Sevcik, Jinzhe Zeng, Justin Dhillon, Kaixi Hou, Kanvi Khanna, LakshmiKalaKadali, Learning-To-Play, lingzhi98, Lu Teng, Matt Bahr, Max Ren, Meekail Zain, Mmakevic-Amd, mraunak, neverlva, nhatle, Nicola Ferralis, Olli Lupton, Om Thakkar, orangekame3, ourfor, pateldeev, Pearu Peterson, pemeliya, Peng Sun, Philipp Hack, Pratik Joshi, prrathi, rahulbatra85, Raunak, redwrasse, Robert Kalmar, Robin Zhang, RoboSchmied, Ruturaj Vaidya, sachinmuradi, Shawn Wang, Sheng Yang, Surya, Thibaut Goetghebuer-Planchon, Thomas Preud'Homme, tilakrayal, Tj Xu, Trevor Morris, wenchenvincent, Yimei Sun, zahiqbal, Zhu Jianjiang, Zoranjovanovic-Ns

Bug Fixes and Other Changes

* Fixed: Incorrect dependency metadata in TensorFlow Python packages causing installation failures with certain package managers such as Poetry.