Weekly GitHub Report for Tensorflow: January 17, 2025 - January 24, 2025
Weekly GitHub Report for Tensorflow
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Table of Contents
I. News
1.1 Recent Version Releases:
The current version of this repository is v2.18.0
1.2 Version Information:
The TensorFlow 2.18.0 release, created on October 21, 2024, introduces several key updates, including the addition of a fourth parameter to the TfLiteOperatorCreate function for a cleaner API, the disabling of TensorRT support in CUDA builds, and the implementation of Hermetic CUDA for more reproducible builds. Notably, TensorFlow now supports NumPy 2.0 by default, with changes in type promotion rules, and introduces enhancements in tf.lite such as support for TensorType_INT4 and TensorType_INT16 in various operations.
II. Issues
2.1 Top 5 Active Issues:
We consider active issues to be issues that that have been commented on most frequently within the last week. Bot comments are omitted.
-
Cannot use GpuDelegate - java.lang.IllegalArgumentException: Internal error: Cannot create interpreter: This issue involves a user encountering a "java.lang.IllegalArgumentException: Internal error: Cannot create interpreter" error when attempting to use the GpuDelegate in a TensorFlow Lite project on an Android device. The user has provided a repository to replicate the issue and is seeking assistance to resolve the problem, as previous related issues appear to have been abandoned.
- The comments discuss potential configuration issues with the GpuDelegate, suggesting testing with CPU-only execution, which works fine. Suggestions include configuring GpuDelegateFactory.Options for better control and checking compatibility documentation. The user clarifies the use of GpuDelegateFactory for mobile devices and expresses doubt about the helper's ability to resolve the issue, hoping for input from a more experienced contributor.
- Number of comments this week: 10
-
TFnode on TensorflowonSpark 2.2.5: This issue involves a user experiencing an error with TFNode while running code on Microsoft Fabric, using TensorFlow version 2.12 and TensorflowOnSpark version 2.2.5, with the intention of printing a "hello world" message to verify cluster operations. The problem appears to be related to compatibility issues, as TensorflowOnSpark 2.2.5 is designed for TensorFlow 1.x, and the user is seeking advice on resolving this issue without downgrading TensorFlow due to platform constraints.
- The user initially requests help due to frustration, and a responder asks for a minimal code snippet to assist with debugging. Another commenter identifies the issue as a compatibility problem between TensorFlowOnSpark and TensorFlow versions, suggesting using TensorFlow 1.15. The user responds that downgrading is not possible on Microsoft Fabric, seeking alternative solutions.
- Number of comments this week: 5
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It doesn't support on python3.13: This issue is about the inability to install TensorFlow version 2.17 on Python 3.13, as the installation process fails to find a compatible version of TensorFlow for this Python version. The problem is particularly significant because Python 3.13 is the default version in the latest Fedora 41 release, and users are unable to use TensorFlow on this major distribution without downgrading their Python version.
- The comments discuss the recurring issue of TensorFlow's delayed support for new Python versions, with users expressing frustration over the lack of compatibility with Python 3.13. Some suggest using older Python versions as a workaround, while others criticize the TensorFlow team's release process and call for better synchronization with Python's release schedule. There is also a discussion about the technical and logistical challenges involved in supporting new Python versions, including dependencies and build processes.
- Number of comments this week: 4
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Unable to install TensorFlow: No matching distribution found for TensorFlow!: This issue is about a user who is unable to install TensorFlow due to a "No matching distribution found" error, which occurs because they are attempting to use an unsupported Python version (3.13) for TensorFlow 2.8. The user is seeking assistance to resolve this installation problem, as they have also unsuccessfully tried using older Python versions like 2.7 and 2.6.
- The comments clarify that TensorFlow 2.8 does not support Python 3.13, and suggest using a compatible Python version, such as 3.8 to 3.11. The user initially misunderstood the required Python version, trying older versions that are also incompatible.
- Number of comments this week: 4
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Tensorflow 2.14.0 installation/run on C++ in visual studio code: This issue involves a user attempting to set up TensorFlow 2.14.0 for C++ development in Visual Studio Code on macOS 14.4, but encountering errors related to the target
//tensorflow/tools/pip_package:pip_packagenot being declared. The user is seeking guidance on how to properly install and configure TensorFlow for C++ use, as they are currently facing build failures when trying to compile the necessary libraries.- The comments provide guidance on setting up TensorFlow for C++ development, clarifying that the target mentioned is for Python packages. Instructions are given for installing dependencies and building the C++ library, but the user encounters a build failure, prompting further troubleshooting.
- Number of comments this week: 4
2.2 Top 5 Stale Issues:
We consider stale issues to be issues that has had no activity within the last 30 days. The team should work together to get these issues resolved and closed as soon as possible.
As of our latest update, there are no stale issues for the project this week.
2.3 Open Issues
This section lists, groups, and then summarizes issues that were created within the last week in the repository.
Issues Opened This Week: 24
Summarized Issues:
- Compatibility Issues with TensorFlow and TensorFlowOnSpark: Users are experiencing compatibility problems when running TensorFlowOnSpark 2.2.5 with TensorFlow 2.12 on Microsoft Fabric, as TensorFlowOnSpark is designed for TensorFlow 1.x. This leads to errors with the TFNode function, and downgrading TensorFlow is not feasible in their environment.
- Segmentation Faults and Crashes in TensorFlow Operations: Several issues report segmentation faults and crashes in TensorFlow operations like
RaggedTensorToTensor,tf.raw_ops.RaggedGather, andtf.nn.conv3d_transposedue to data type mismatches and invalid argument errors. These problems occur on specific inputs and have been reproduced on various TensorFlow versions, including Nightly builds.
- Crashes in TensorFlow Pooling and Convolution Functions: TensorFlow functions like
tensorflow.nn.max_pool1d,tensorflow.nn.max_pool3d, andtensorflow.keras.backend.conv2dare crashing due to invalid input parameters, such as excessively largeksizevalues and deprecated APIs. These issues highlight the need for input validation and updating to the latest APIs for better performance.
- Invalid Argument Errors in TensorFlow Operations: Bugs in TensorFlow operations like
tensorflow.raw_ops.RecordInputandtensorflow.compat.v1.nn.depthwise_conv2d_nativeare causing crashes due to invalid argument errors, such as negative batch sizes anddilationsexceeding 32-bit integer ranges. These issues require careful input validation to prevent such errors.
- Dimension Mismatch and Index Errors in TensorFlow: TensorFlow operations like
tensorflow.raw_ops.ScatterNdNonAliasingAddandtensorflow.nn.depthwise_conv2d_backprop_filterare crashing due to dimension mismatches and invalid index errors. These issues are demonstrated with specific inputs and require adjustments to input dimensions and indices.
- Installation and Configuration Challenges with TensorFlow: Users face difficulties installing TensorFlow on various systems, such as Windows 10 with Python 3.13 and macOS using Poetry, due to incompatible distributions and missing installation candidates. These issues highlight the need for using supported Python versions and resolving dependency conflicts.
- Feature Requests for TensorFlow Enhancements: Users request new features in TensorFlow, such as a logging mechanism for GPU memory allocation and dynamic setting of
LD_LIBRARY_PATHfor CUDA libraries. These enhancements aim to improve diagnostic capabilities and ease of use in specific environments.
- Errors in TensorFlow Lite and Tutorials: Users encounter errors with TensorFlow Lite on Android devices and in TensorFlow tutorials, such as "Cannot create interpreter" and CUDA device detection failures. These issues require configuration adjustments and updates to tutorial instructions for successful execution.
- Compilation and Build Issues with TensorFlow: Users face challenges compiling and building TensorFlow on systems like Debian 12 and macOS, encountering errors related to compiler flags and undeclared inclusion errors. These issues necessitate guidance on correct installation and configuration procedures.
- Warnings and Performance Delays in TensorFlow: Users report warnings related to
cuFFT,cuDNN, andcuBLASregistration, as well as significant delays in model training due to JIT compilation. These issues suggest the need for optimized builds and performance improvements in TensorFlow.
2.4 Closed Issues
This section lists, groups, and then summarizes issues that were closed within the last week in the repository. This section also links the associated pull requests if applicable.
Issues Closed This Week: 21
Summarized Issues:
- Segmentation Faults in TensorFlow Operations: Segmentation faults have been reported in TensorFlow operations, particularly with
tf.raw_ops.TensorArrayV3on Linux Ubuntu 20.04 when thesizeparameter exceeds system memory limits. These faults cause crashes, highlighting the need for better memory management in TensorFlow operations.
- Discrepancies in Activation Function Outputs: Significant discrepancies have been observed in TensorFlow when using the exponential activation function, with differences in output values between CPU and GPU computations. These discrepancies result in large gradient differences, affecting model performance and consistency across different hardware.
- Crashes in TensorFlow Lite Conversions: Crashes have been reported in TensorFlow Lite conversions, particularly when quantizing GRU layers on Apple M1 Pro and using YOLO11n models on Android. These issues suggest potential compatibility problems or bugs in the TFLite Android library and Keras 3.0 integration.
- Compilation and Installation Errors: Users have encountered various compilation and installation errors with TensorFlow, including issues with cross-compiling for RISC systems, compiling the C++ interface on Linux, and installing on Windows and Alpine Docker images. These errors often relate to compatibility issues with specific system configurations or dependencies.
- TensorFlow Model and Layer Issues: Issues have been reported with TensorFlow models and layers, including a bug with the XLA compiler failing to compile
tf.keras.layers.Conv2Dwithpadding='valid'. Additionally, initializing models with dropout layers under MirroredStrategy fails due to distributed variable errors.
- Requests for Feature Additions and Updates: There have been requests for feature additions and updates in TensorFlow, such as adding int8 support to Unsorted_Segment_X operators for better mobile deployment and publishing TensorFlow Lite version 2.18.0 on Maven Central and CocoaPods. These requests aim to enhance TensorFlow's functionality and accessibility.
- Documentation and Compatibility Concerns: Concerns have been raised about the absence of a compatibility table for TensorFlow 2.18 with CUDA and cuDNN in Spanish documentation. This absence may lead to confusion among users relying on non-English resources, highlighting the need for consistent documentation across languages.
- Miscellaneous Issues: Various other issues include spam content removal, errors in loading models in Google Colab, and difficulties with pip installations. These issues reflect a range of challenges users face when working with TensorFlow in different environments.
2.5 Issue Discussion Insights
This section will analyze the tone and sentiment of discussions within this project's open and closed issues that occurred within the past week. It aims to identify potentially heated exchanges and to maintain a constructive project environment.
Based on our analysis, there are no instances of toxic discussions in the project's open or closed issues from the past week.
III. Pull Requests
3.1 Open Pull Requests
This section provides a summary of pull requests that were opened in the repository over the past week. The top three pull requests with the highest number of commits are highlighted as 'key' pull requests. All other pull requests are grouped based on similar characteristics for easier analysis.
Pull Requests Opened This Week: 5
Key Open Pull Requests
1. [mlir][tosa] Update Tensorflow to match TOSA v1.0 specification: This pull request aims to update TensorFlow's TOSA (Tensor Operator Set Architecture) implementation to align with the TOSA v1.0 specification by incorporating changes from upstream LLVM patches, specifically addressing updates to convolution operators' accumulation types, TileOp, PadOp, and ensuring equalized ranks for certain operations.
- URL: pull/85608
- Merged: No
2. numpy copy fix: This pull request addresses an issue in the TensorFlow project by modifying the use of the astype() function to prevent unnecessary data copying by propagating copy=None instead of the default copy=True, thereby allowing the function to only perform a copy when explicitly specified.
- URL: pull/85408
- Merged: No
- Associated Commits: 35115
3. Fix typos in documentation strings: This pull request addresses and corrects several typographical errors found in the documentation strings of the TensorFlow project, as indicated by the commit with SHA 4dafefa8ac5304c113ad6dc47d5424363002615e, and is currently open for review.
- URL: pull/85639
- Merged: No
- Associated Commits: 4dafe
Other Open Pull Requests
- QNN Types and Compiler Integration in TensorFlow: The pull requests focus on introducing basic wrappers for QNN types and replacing the compiler component with Qualcomm implementations in the TensorFlow project. The first pull request manages dynamic resources and handles various parameters and tensor dimensions, while the second one includes commits from related pull requests for easier review and provides a test command to verify changes, despite some models being disabled and a test failing due to a bug.
3.2 Closed Pull Requests
This section provides a summary of pull requests that were closed in the repository over the past week. The top three pull requests with the highest number of commits are highlighted as 'key' pull requests. All other pull requests are grouped based on similar characteristics for easier analysis.
Pull Requests Closed This Week: 12
Key Closed Pull Requests
1. [RFC] rocprof insights for rocprof data: This pull request proposes the development of a Python package named "rocprof insights" to enhance the analysis and visualization of AMD hardware performance data collected by ROCm/HIP profiling tools, offering functionalities such as data loading, statistical analysis, and visualization through various plots, with potential features like overlaying latency on computational graphs and tracing input/output values for accuracy checks.
- URL: pull/85559
- Merged: No
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ccced549a7b60da1bcd5569cd2dc061baf66f1df, a382c8e9a523feda55f6a5a2d4178708adbf916c, 532dfdeb899cffbfadb5aa57ea40f0c2cde08bc1, b854f31923763910d15a9dd9402f028dc228ecfe, 58d805ca1bc742193a6126d58b630712af42aeb2, f47708cbe4347ff05571212619f50343f084165d, 3c370295b4b9d1563ec96cf835eadf598f27a636, e5dd7eb37004d803ae2d1bd119455263478b42ee, 9f051a44d361077ca21c4c4c0983000181455720, 1afb6631d9dcbe98907806d42ab17e26e4069042, f854dd21d5c3d28e441c6f4e737f52ce54ebb6aa, 554f81b65da81779ae201cc1f3065d0eb0b829d9, 68c43a91bb998ab325c2eadc7da8ba47238c8d18, 2baacd6e596f909cebdde101303f8e0c455efa50, 33a418c300e57c7328de785280b534c02fd3eb5a, e554cb020fdec2ef240c29d91e796597522de775, 1c5d7d423cd47077bbdc624e950e616a56b64564, 4d45349a22df8e301b19a02b392e416e13069529, d7f29c053000a7773b37b81d0cb5113f0bf914c8, 4ca780c0f70258625a445d7d21cc742411f354c6, 3ac1ead6a39b2d14e06c0385d4f8ce98c8e31567, ed3e40f2a2bac873463b40268e2b39e1b552b11c, 5788ee90cfe4186f7d4dae5b17025fe22e2bdb3d, 7550346acacab61dd426ad364356fbe6b122a04e, 04c3e86051e2cb88f200b40b2eeec2dccc44bea2, e2acb525ee73cf7d56daeb94c9c5d4c456dc5dd7, 188d115eb0b24dfcace3a0057fb82887d668cd10, 1d38891bea46814b162886f9b008f8dbcdcd7a88, 87930b53ddf5522be76dbf476b9fd10becf6d060, c13754e769d1bce52e01ed415351606ca89fd80a, 66ca76eda8072c75aafbd43d419e3eac860eb51b, b0d3319786ccaed5fdf9a5e370db9db1c08d9721, 713b6c2e16b106cdd49e59fc8295120916ba66cd, 09053aa3c5c01b6bc9bf7a04b3b5b05324477e12, b6971fc8f4c9b60aa9a49366a0113505fa82a761, d91630ae06a45d835288d2e62a1c436c4942b767, 76f247df86f77898ef40c7d5de44332c8d71aad5, 252a956e19d2305e07e9790bf930e2d0e4d8f355, 7ec4c63d2f2797c950df462266723742eeff6ca5, 2464a3fbc6255ba1dfac5deb08d9719d4566c517, 8aedd805b932084523b8e27ea27b69984f49fe7e, 071eec5941ed3dc715b3b5f885d84f9fa946308f, 3a90ef0b374d470721179514282c14e10798bcbe, 67d8f1616566242fd54f17c6d33c8933bf00df39, ac4cbe0e50ca6dcea7eb563f92f8a9afb55a2f4b, 02686eac78ca8fed9073475d30d2ac4a5db1db23, 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2. Branch for Pack and DUS op: This pull request involves multiple updates to the TensorFlow project, including the addition of LiteRt Qualcomm wrappers, modifications to the TensorWrapper to return data sizes in bytes, implementation of a TensorPool for managing TensorWrappers, support for cloning static tensors with different data types, the introduction of op builders, and support for the Pack and DUS operations, although it was ultimately not merged.
- URL: pull/85635
- Merged: No
- Associated Commits: 509ecd09f428e064ffec3dee20d5f6672bf04812, 7564faa2b27f5b825343a57a4f0fc6a4e3cbaec1, 5a3f286bf702c195be7dfc28508c2a7f4618b7b7, 451fbd85cd0be867b668198ed5f6421c4222f2d5, f82a59839a13153160d20e05d7528288e4a62a7a, c01f63262e9534552ede08650f3f84ef6ba687be, 94763eaa1b585abbf998620f1aaa8b200cf59205, 4400f747fb98e1bf9d5edcfeb299f2f238d14727, 21bfff421d92999c94ad1b21107c1b680d7cb5fb, cbc17bf4cf7bac4f4014416685050b2ab94bc079, 10fcc96b4d2327a7dccc936c7c63032171c96757, a90ee6c7b8394a06677633190ffd2327921db569
3. Extend tensorflow restore kernel op to support type casting upon reading.: This pull request aims to enhance the TensorFlow restore kernel operation by adding support for type casting during the reading process, as indicated by the series of commits that include extending the TFRecord dataset, implementing custom protocol buffers, conducting unit tests and optimizations, and specifically extending the restore function to support tensor casting.
- URL: pull/85263
- Merged: No
- Associated Commits: 685d73b9279b3bacfb9377ead786154a100e8b86, 30e40a1baa05d7eb626e203f35e4fb9c0ea0273c, 724bcdfbcaacb73e51795cacf7c3d1e79f295a7d, f65cd2e960f54fa71e3f328238dc41d742d522f9, c69e7f40711cddeea86e44abb27ad6a9833aeaf4
Other Closed Pull Requests
- Layer Utilities Optimization: This pull request focuses on optimizing the layer utilities module by enhancing runtime performance, memory efficiency, and code readability through various improvements such as introducing constants for validation, combining validation and conversion steps, improving error handling, and implementing lazy evaluation, although it was ultimately not merged.
- TensorFlow 2.14.0 C++ Installation Fix: This pull request addresses issue #85385 by making necessary updates to the BUILD file, setup script, and manifest to fix the installation and running of TensorFlow 2.14.0 on C++ in Visual Studio Code, although it was not merged.
- Typographical Error Corrections: This pull request addresses and corrects typographical errors across multiple files in the TensorFlow project, as evidenced by the commits updating files such as
full_type_util.handtensorflow/core/protobuf/config.proto, and has been successfully merged into the main codebase.
- Profiler Trace Viewer Thread ID Display Issue: This pull request addresses a display issue with thread IDs in the profiler trace viewer for the TensorFlow project, as referenced in issue #79128, and includes a commit that resolves the problem, although it was not merged into the main codebase.
- GPU Tracing Bug Fix: This pull request addresses a bug in TensorFlow's GPU tracing by ensuring that relative timestamps in
XSpaceare converted to absolute timestamps during theConvertGpuXSpaceToStepStatsprocess, thereby maintaining compatibility withRunMetadataand correcting issues in analysis tools liketimeline.Timeline.
- Parallel Matrix Multiplication Fix: This pull request addresses the incorrect implementation of parallel matrix multiplication in TensorFlow by fixing the misuse of the adjoint parameter for transpose, which previously led to incorrect results when using
matmulwithinvectorized_mapfor complex data types, as commonly encountered in quantum computing simulations.
- Broken Links in Config.proto: This pull request addresses the issue of broken links in the
config.protofile within the TensorFlow project, as indicated by its title, and has been successfully merged, with the changes encapsulated in a single commit that updates the file.
- Support for Quint8 Type in Quantize Operations: This pull request adds support for the quint8 type to the uniform_quantize and uniform_dequantize operations, updates the TF2XLA bridge for proper conversion, and involves moving certain operation definitions to comply with existing code comments, with contributions from @mahmoud-abuzaina and unit tests added by @nhatleSummer22.
- Tosa Conv Ops Enhancement: This pull request aims to enhance the Tosa Conv Ops in the TensorFlow project by adding an
acc_typeand adjusting the TileOp to accommodate multiples as input, although it was not merged.
3.3 Pull Request Discussion Insights
This section will analyze the tone and sentiment of discussions within this project's open and closed pull requests that occurred within the past week. It aims to identify potentially heated exchanges and to maintain a constructive project environment.
Based on our analysis, there are no instances of toxic discussions in the project's open or closed pull requests from the past week.
IV. Contributors
4.1 Contributors
Active Contributors:
We consider an active contributor in this project to be any contributor who has made at least 1 commit, opened at least 1 issue, created at least 1 pull request, or made more than 2 comments in the last month.
If there are more than 10 active contributors, the list is truncated to the top 10 based on contribution metrics for better clarity.
| Contributor | Commits | Pull Requests | Issues | Comments |
|---|---|---|---|---|
| mihaimaruseac | 6 | 0 | 0 | 78 |
| Venkat6871 | 2 | 2 | 0 | 36 |
| gaikwadrahul8 | 2 | 2 | 0 | 29 |
| tilakrayal | 3 | 2 | 0 | 21 |
| codinglover222 | 7 | 3 | 2 | 4 |
| weilhuan-quic | 11 | 3 | 0 | 0 |
| arzoo0511 | 0 | 0 | 0 | 14 |
| LongZE666 | 0 | 0 | 12 | 1 |
| dnmaster1 | 0 | 0 | 2 | 10 |
| cj401-amd | 9 | 1 | 0 | 0 |