Weekly GitHub Report for Tensorflow - 2025-01-20 12:01:20
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.
-
TypeError: true_fn and false_fn arguments to tf.cond must have the same number, type, and overall structure of return values: This issue involves a TypeError encountered when using TensorFlow 2.17.0 with Python 3.12.4, where the
tf.condfunction requirestrue_fnandfalse_fnarguments to have the same number, type, and overall structure of return values, which was not a problem in previous versions. The user initially faced a ValueError due to unsupportedclass_weightfor Python generator inputs and attempted to resolve it by using a TensorFlow dataset, but encountered further issues with type consistency in thetf.condfunction.- The comments discuss attempts to resolve the issue by using
sample_weightinstead ofclass_weight, which worked but led to different training results compared to previous TensorFlow versions. Users express concerns about the behavior ofsample_weightversusclass_weightand note that the issue persists in newer TensorFlow versions, with some opting to revert to older versions where the code functions correctly. - Number of comments this week: 7
- The comments discuss attempts to resolve the issue by using
-
Failed to build
tensorflow_ccin Windows when linking: This issue involves a failure to build thetensorflow_cclibrary on Windows using LLVM/Clang, where the linking process fails due to missing symbols such asSessionandSavedModelBundleInterface. The problem persists across different versions of LLVM/Clang, and the user suspects it might be related to incomplete exported symbol definitions and missing dependencies in the build configuration.- The comments discuss potential causes and solutions, including missing dependencies and configuration issues in the build files. Users express frustration over the complexity of manually fixing dependencies and the lack of official support for building
tensorflow_ccon Windows. Some suggest using Linux or WSL as alternatives, while others inquire about submitting pull requests to address the issue. The TensorFlow team acknowledges the problem and is working on a fix, but notes thattensorflow_ccis not typically supported on Windows. - Number of comments this week: 4
- The comments discuss potential causes and solutions, including missing dependencies and configuration issues in the build files. Users express frustration over the complexity of manually fixing dependencies and the lack of official support for building
-
Tensorflow not supported on Windows + ARM CPUs: This issue highlights a problem with TensorFlow not being supported on Windows systems with ARM CPUs, specifically when attempting to import TensorFlow on a Windows 11 machine with a Snapdragon processor. The user reports successful installation but encounters an ImportError related to DLL load failure, indicating a lack of support for the ARM architecture on Windows.
- The comments discuss the issue being initially marked as a duplicate of an older issue related to unsupported CPU architectures, but it is later clarified that this is a different problem due to the ARM architecture. The conversation reveals that TensorFlow currently lacks support for Windows on ARM CPUs, with suggestions to try using WSL or compiling from source. The discussion also touches on the absence of ARM wheels for Windows and the possibility of future support, with a recommendation to use Colab as an alternative.
- Number of comments this week: 4
-
What is preventing TF to use GPU when used in native windows?: This issue discusses the lack of GPU support for TensorFlow on native Windows environments for versions greater than 2.10, questioning the reasons behind this decision and seeking guidance on how to implement such support independently. The user is also inquiring about the necessary Python package requirements and compiler specifications needed to achieve GPU support on Windows.
- The comments reveal that the TensorFlow team has limited resources, leading to a focus on Linux environments and WSL2 for GPU support, with Windows support being deprioritized. Contributors discuss the challenges of maintaining Windows GPU support, the shift in TensorFlow's development priorities, and the potential for using alternative platforms like JAX or PyTorch. There is also a discussion about the technical difficulties in compiling TensorFlow with GPU support on Windows, including missing files and build script complexities.
- Number of comments this week: 3
-
Memory Allocation Issues: This issue involves a memory allocation problem when training a neural network using TensorFlow 2.18.0 on an Ubuntu 24.04.1 LTS system with WSL2, where the user has set a virtual GPU memory limit of 10GB, but the training process exceeds this limit and results in an out-of-memory error. The user provides a detailed code snippet to reproduce the issue and expects the training to stay within the specified memory limit, but instead, it fails with multiple memory allocation errors.
- Multiple users report similar memory allocation issues across different systems and TensorFlow versions, with one user noting that the errors affect execution time and prevent code completion. Another user expresses frustration over the persistent issue and mentions attempts to mitigate it by adjusting memory settings and batch sizes. A TensorFlow team member suggests running the code on Colab and disabling XLA to potentially reduce memory usage, asking for feedback on their attempt to replicate the issue.
- Number of comments this week: 3
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: 26
Summarized Issues:
- Model Deployment on Android: Deploying a Yolov8-seg.pt segmentation model on an Android device presents challenges, including the need to specify NormalizationOptions metadata for preprocessing input images due to the input tensor type kTfLiteFloat32. Additionally, an error arises because the model's output is expected to have only one output but returns two, causing initialization issues.
- Segmentation Faults and Crashes: Users report segmentation faults and crashes in various scenarios, such as iterating over a dataset created from a data service using TensorFlow 2.18.0 on Linux Ubuntu 22.04, and using the
RaggedTensorToTensoroperation in TensorFlow 2.17 on Ubuntu 20.04. These issues lead to execution crashes, highlighting potential bugs in the TensorFlow operations.
- Library and Build Errors: Several issues involve errors related to library loading and build processes, such as a
java.lang.UnsatisfiedLinkErrorwhen initializing a TensorFlow Lite interpreter on a Samsung S20 device, and a build failure for the target//tensorflow/compiler/mlir/lite:tensorflow_lite_quantizeon a Linux Debian system. These errors suggest missing libraries or ambiguous constructor calls in the build process.
- TensorFlow Version and Compatibility Issues: Users encounter compatibility issues with TensorFlow versions, such as errors with TensorFlow 2.17 and 2.18 on WSL2 due to pre-existing registrations of cuDNN, cuFFT, and cuBLAS factories, and difficulties compiling the TensorFlow C API on a JETSON AGX Xavier. These issues affect the build or installation process and require specific configurations to resolve.
- Unexpected File Size Increase: A significant increase in the size of the libtensorflow.dll file for Windows is reported, growing from 238MB in version 2.16.2 to 931MB in version 2.18.0, while Linux versions remain unchanged. This unexpected size disparity raises concerns about the lack of notice in the release notes.
- Model Conversion and Inference Issues: Issues arise with model conversion and inference, such as two distinct models producing identical inference results when converted to TensorRT for inference on a Jetson AGX Xavier. This suggests potential problems with the model conversion or inference process in TensorRT.
- Compilation and Configuration Challenges: Users face challenges compiling TensorFlowLite for Swift without Bitcode due to Apple's discontinuation of Bitcode, and seek guidance on compiling TensorFlow C API on a JETSON AGX Xavier. These issues highlight the need for updated compilation instructions and support for native ARM architectures.
- Memory and Device Configuration Bugs: Bugs are reported in TensorFlow's device configuration functions, such as
tf.config.LogicalDeviceConfiguration()failing to set a memory limit on a GPU in version 2.17.0, while the deprecatedtf.config.experimental.VirtualDeviceConfiguration()works. These issues indicate inconsistencies in the device configuration APIs.
- Training and Compatibility Issues: Users experience difficulties training models due to compatibility issues with TensorFlow and related packages, particularly with the
tensorflow-iopackage not supporting Python 3.12 on Windows. These issues lead to errors during the training process and require compatibility updates.
- Depthwise Convolution and Stride Support: Questions arise about TensorFlow's
tf.nn.depthwise_conv2dsupport for unequal stride dimensions, and guidance is sought on converting pretrained weights with unequal strides. These inquiries highlight the need for alternative operations to accommodate specific model requirements.
- Sample Weights in Custom Loss Functions: A feature request for TensorFlow 2.13.1 involves the inability to pass sample weights to a customized loss function during model training, resulting in a ValueError. This issue suggests a need for enhanced support for sample weights in custom loss functions.
- TFNode and Microsoft Fabric Errors: Users encounter errors with TFNode while running a simple "hello world" script on Microsoft Fabric using TensorFlow version 2.12 and TensorflowonSpark version 2.2.5. Assistance is sought to understand the cause of these errors, indicating potential compatibility issues with the platform.
- Operation Crashes and Invalid Arguments: Multiple issues report crashes in TensorFlow operations due to invalid arguments, such as
tf.nn.conv3d_transposeandtensorflow.nn.max_pool1dfunctions crashing with specific input values. These bugs highlight the need for robust error handling and validation in TensorFlow operations.
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: 57
Summarized Issues:
- TensorFlow Import Errors on Windows: Users have reported ImportErrors when attempting to load TensorFlow on Windows platforms, often due to DLL load failures related to the
_pywrap_tensorflow_internalmodule. These issues suggest compatibility problems or missing dependencies, with recommendations to check version compatibility and install necessary redistributables.
- TensorFlow GPU and CPU Discrepancies: There is a significant discrepancy between CPU and GPU outputs when using the exponential activation function in TensorFlow, with a maximum difference of 65536.0. This issue has been suggested to be more relevant to the Keras repository.
- TensorFlow Compilation and Build Errors: Users have encountered various compilation and build errors in TensorFlow, including linker errors in Visual Studio, ambiguous call errors in Bazel, and path handling issues on Windows. These problems often require specific configurations or modifications to resolve.
- TensorFlow Version and Environment Issues: Users have faced issues with TensorFlow version 2.18.0, including installation failures on Python 3.10 and GPU detection problems on Windows 11. These issues highlight the challenges of maintaining compatibility across different environments and hardware configurations.
- TensorFlow Functionality Changes and Errors: Changes in TensorFlow functionality, such as the
tf.math.bincountfunction no longer broadcasting over weights, have caused errors for users. These issues often require adjustments in code to accommodate new behavior.
- TensorFlow Lite and XNN Pack Support: Users have requested support for TensorFlow Lite and XNN Pack to enhance model performance on specific hardware configurations, but have encountered difficulties due to lack of support or pending updates.
- Spam and Irrelevant Issues: The TensorFlow GitHub repository has been subject to spam issues, including irrelevant inquiries about customer support for various financial services. These issues are often closed as spam and do not pertain to the TensorFlow project.
- issues/84743, issues/84747, issues/84748, issues/84750, issues/84755, issues/84757, issues/84760, issues/84880, issues/85101, issues/85102, issues/85103, issues/85104, issues/85106, issues/85107, issues/85132, issues/85134, issues/85135, issues/85139, issues/85143, issues/85146, issues/85148, issues/85149, issues/85150, issues/85151, issues/85152, issues/85153, issues/85154, issues/85155, issues/85156, issues/85157, issues/85158, issues/85159, issues/85160, issues/85162, issues/85163, issues/85164, issues/85221, issues/85222, issues/85241, issues/85244, issues/85247, issues/85259
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.
- 4080 RTX not detected on windows 11 24H2
- Toxicity Score: 0.55 (Frustration expressed, Lack of resolution, Minimal engagement.)
- This GitHub conversation involves a user expressing frustration over their GPU not being detected, despite trying different Python versions. Another user responds by marking the issue as a duplicate and referencing an unresolved ticket, which further frustrates the original user, who requests an ETA or explanation. The tone is tense, with the original user feeling dismissed and the responder providing minimal engagement.
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: 4
Key Open Pull Requests
1. 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 evidenced by a series of commits that include extending the TFRecord dataset, implementing custom protocol buffers, conducting unit tests and optimizations, fixing issues, and specifically extending the restore function to accommodate tensor casting.
- URL: pull/85263
- Merged: No
- Associated Commits: 685d73b9279b3bacfb9377ead786154a100e8b86, 30e40a1baa05d7eb626e203f35e4fb9c0ea0273c, 724bcdfbcaacb73e51795cacf7c3d1e79f295a7d, f65cd2e960f54fa71e3f328238dc41d742d522f9, c69e7f40711cddeea86e44abb27ad6a9833aeaf4
2. [oneDNN] Add Infer after last allow to be added to Allow list: This pull request addresses an issue in the auto-mixed precision pass for FP16_CPU by adding a sub-pass that allows an infer node to be included in the allow set if its direct upstream node is already in the allow set, thereby preventing unnecessary cast nodes from being inserted between fuse-able nodes and ensuring consistent optimizations for both BF16 and FP16_CPU on the CPU.
- URL: pull/84874
- Merged: No
- Associated Commits: a8e59aa91a1fd8391e839b0fda6736b3276a6b89
3. Qualcomm AI Engine Direct - Add dispatch options for QC: This pull request introduces new dispatch options for Qualcomm AI Engine Direct by adding htp runtime options, log level settings, and fixing the event BUILD in the TensorFlow project.
- URL: pull/84932
- Merged: No
- Associated Commits: c992580ca61287ced7789f794face7d58dc0df5d
Other Open Pull Requests
- Update aarch64-compatible versions of oneDNN and ACL: This pull request updates the aarch64-compatible versions of oneDNN to 3.7 and ACL to 24.12 in the TensorFlow project. The update aims to enhance performance, improve memory management, and address numerous bugs from the outdated versions.
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: 10
Key Closed Pull Requests
1. Develop upstream sync 20250113: This pull request involves a weekly upstream synchronization for the TensorFlow project, specifically addressing various merge conflicts and updates across multiple files such as .bazelrc, gpu_device_test.cc, and matmul_op_fused.cc, among others, while also incorporating numerous commits that include fixes for build errors, updates to test configurations, and adjustments for compatibility with ROCm and CUDA environments.
- URL: pull/84741
- Merged: No
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2. Fix ImportError for TensorFlow Lite 2.17.0 on ARM Architecture: This pull request addresses an ImportError issue in TensorFlow Lite 2.17.0 for the ARMv7 architecture by cleaning the build environment, adjusting the build process for Python 3.10, verifying the generated wheel, and conducting import tests, while also seeking feedback on the build configuration and test results.
- URL: pull/78206
- Merged: No
- Associated Commits: 6fd7a9069ffa4a55191cb3e66fc53d8476bd8176, c9ca68d9636269aa9eefe81353667e934fef90d0
3. enhancement: add sonargit pr metrics: This pull request introduces a GitHub workflow utilizing the SonarGit Action to gather and log pull request metrics such as open times, merge rates, and change failure rates, aiming to enhance the development workflow by providing actionable insights, although it was not merged.
- URL: pull/84696
- Merged: No
- Associated Commits: fb8573e60c6cbaab18852a7de0b592a2a8c25875, b8f1ed55ef43f4b820489d3993dbf4c77c43547a
Other Closed Pull Requests
- TypeError in TensorFlow's tf.function: This pull request addresses a TypeError in TensorFlow where the
output_signatureargument of thetf.functionwas incorrectly specified as a list instead of atf.TypeSpecobject. It provides a step-by-step solution to replace the list with a validtf.TypeSpecor a nested structure oftf.TypeSpecobjects to resolve the issue.
- Profiler Module Documentation and Display Issues: Two pull requests address issues in the TensorFlow project's profiler module. One removes non-standard
- Build Configuration Updates: This pull request updates the build configuration in the .bazelrc file of the TensorFlow project. It replaces a non-existent 'opt' configuration with an existing value, thereby preventing build failures.
- Qualcomm AI Engine Direct Enhancements: Two pull requests aim to enhance the Qualcomm AI Engine Direct by introducing dispatch options for QC, adding htp runtime options, log level settings, and fixing the event BUILD. However, neither was merged into the main project.
- ml-dtypes Dependency Upper Bound Update: This pull request addresses the issue of restrictive upper bounds on the ml-dtypes dependency in the TensorFlow project. It updates the upper bound to allow for greater compatibility, resolving installation conflicts with other machine learning ecosystem components.
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.
- LiteRt Qualcomm wrappers
- Toxicity Score: 0.55 (Defensive responses, unresolved dissatisfaction, mediation attempts.)
- This GitHub conversation involves username1 expressing frustration over username2's implementation, which did not meet expectations. Username2 responds defensively, leading to a tense exchange. Username3 attempts to mediate by suggesting a compromise, but username1 remains dissatisfied, escalating the tension.
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 | 5 | 0 | 0 | 69 |
| Venkat6871 | 1 | 1 | 0 | 43 |
| gaikwadrahul8 | 2 | 2 | 0 | 29 |
| codinglover222 | 7 | 3 | 2 | 3 |
| tilakrayal | 0 | 0 | 0 | 14 |
| elicewalter156 | 0 | 0 | 13 | 0 |
| LongZE666 | 0 | 0 | 12 | 1 |
| dnmaster1 | 0 | 0 | 2 | 10 |
| jackkyy79 | 0 | 0 | 10 | 0 |
| muayyad-alsadi | 0 | 0 | 0 | 9 |