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Releases · tensorflow/tensorflow · GitHub

• Updates curl to 8.11.0 to handle CVE-2024-2004, CVE-2024-2379, CVE-2024-2398, CVE-2024-2466, CVE-2024-6197, CVE-2024-7264, CVE-2024-8096 and CVE-2024-9681.

• Loosen ml_dtypes upperbound to < 1.0.0 to reduce conflicts when installed with other ML ecosystem components.

• tf.lite
  • Interpreter:
    • tf.lite.Interpreter gives warning of future deletion and a redirection notice to its new location at ai_edge_litert.interpreter. See the migration guide for details.
• Tensorflow-tpu for this patch is skipped due to some sparsecore related bugs. We suggest to upgrade to 2.19.0 instead.

• 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.)
  • Python API:
    • 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 for details.

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

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

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

• 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 for details.

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

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

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

• 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.

• 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.

• TensorFlow now supports and is compiled with NumPy 2.0 by default. Please see the NumPy 2 release notes and the NumPy 2 migration guide.
  • Note that NumPy's type promotion rules have been changed(See NEP 50for 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.
• tf.lite:
  • The LiteRT repo is live (see announcement), 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.

• 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 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.

• tf.tensor_scatter_update, tf.tensor_scatter_add and of other reduce types.

  • Support bad_indices_policy.

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

• 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.

• 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.
  • SignatureRunner is now supported for models with no signatures.

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

• 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.

• TensorFlow now supports and is compiled with NumPy 2.0 by default. Please see the NumPy 2 release notes and the NumPy 2 migration guide.
  • Note that NumPy's type promotion rules have been changed(See NEP 50 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.
• tf.lite:
  • The LiteRT repo is live (see announcement), 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.

• 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 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.

• tf.tensor_scatter_update, tf.tensor_scatter_add and of other reduce types.

  • Support bad_indices_policy.

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

• 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.
  • SignatureRunner is now supported for models with no signatures.

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

• 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.

• TensorFlow now supports and is compiled with NumPy 2.0 by default. Please see the NumPy 2 release notes and the NumPy 2 migration guide.
  • Note that NumPy's type promotion rules have been changed(See NEP 50for 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.
• tf.lite:
  • The LiteRT repo is live (see announcement), 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.

• 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 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.

• tf.tensor_scatter_update, tf.tensor_scatter_add and of other reduce types.

  • Support bad_indices_policy.

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

• 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.
  • SignatureRunner is now supported for models with no signatures.

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

• 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.

• TensorFlow now supports and is compiled with NumPy 2.0 by default. Please see the NumPy 2 release notes and the NumPy 2 migration guide.
  • Note that NumPy's type promotion rules have been changed(See NEP 50for 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.
• tf.lite:
  • The LiteRT repo is live (see announcement), 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.

• 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 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.

• tf.tensor_scatter_update, tf.tensor_scatter_add and of other reduce types.

  • Support bad_indices_policy.

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

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

• 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.

• 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.

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

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

• 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.

• 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.

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

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