Weekly GitHub Report for Pytorch: June 09, 2025 - June 16, 2025 (12:06:37)
Weekly GitHub Report for Pytorch
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Table of Contents
I. News
1.1 Recent Version Releases:
The current version of this repository is v2.6.0
1.2 Version Information:
The PyTorch 2.6 release, created on January 29, 2025, introduces significant updates including support for torch.compile with Python 3.13, a new performance-related feature torch.compiler.set_stance, and enhancements to AOTInductor. Notable changes include the deprecation of PyTorch's official Anaconda channel, the introduction of FP16 support on X86 CPUs, and a backward compatibility-breaking change in the default behavior of torch.load.
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.
-
[MPS] Performance regression and visual bug with ComfyUI Flux dev since nightly 20250510: This issue reports a performance regression and visual artifact in the ComfyUI denoising preview when using nightly builds of torch from May 11, 2025, onwards, on a 64GB M3 Max MacBook Pro. The problem is reproducible and appears to be linked to torch, as reverting to the May 10, 2025, nightly build resolves the issue, suggesting a change in torch is the cause.
- The comments discuss attempts to reproduce the issue, with requests for workflow files and download links for checkpoint files. Some users report not experiencing the performance regression or artifacts, while others identify a specific pull request as the source of the visual artifact. It is noted that the performance difference is observed only when the preview is enabled, and disabling it stabilizes performance across versions.
- Number of comments this week: 12
-
High-performance LLM quantization on X86 CPU with native PyTorch: This issue discusses the implementation of high-performance quantization for large language models (LLMs) on X86 CPUs using native PyTorch, aiming to enhance storage efficiency, reduce memory usage, and decrease inference latency. The feature supports various configurations and aims to provide performance comparable to popular LLM serving frameworks, allowing PyTorch users to achieve efficient quantization with a native experience.
- The comments discuss adding and removing pull requests related to the feature, with some concerns about potential confusion in the description regarding quantization types. There is ongoing communication about the status of certain pull requests, with requests for links and updates on their progress. Additionally, a request for a review of the release feature is made.
- Number of comments this week: 9
-
Reproducibility of results without AVX512 by setting ATEN_CPU_CAPABILITY=avx2: This issue highlights a discrepancy in the reproducibility of results when running a PyTorch script on machines with and without AVX-512 support, even when the environment variable
ATEN_CPU_CAPABILITYis set toavx2. The user reports that despite attempts to disable AVX-512 in both ATEN and MKL, different results are observed, suggesting that AVX-512 might still be utilized elsewhere in the system.- The comments discuss potential reasons for the discrepancy, including the role of MKL and the limitations of testing in virtual machines. Suggestions are made to verify results on physical machines, and it is noted that the issue might be related to the specific machine rather than AVX2/AVX512 capabilities. The user clarifies that the discrepancy is observed on multiple physical machines, indicating a broader issue with AVX-512 usage beyond ATEN settings.
- Number of comments this week: 8
-
nn.RNN(...).to('cuda')fails withcuDNN error: CUDNN_STATUS_BAD_PARAMon GPU, but works on CPU: This issue describes a problem where a simplenn.RNNmodel in PyTorch runs correctly on a CPU but fails on a GPU with acuDNN_STATUS_BAD_PARAMerror when attempting to transfer the model to CUDA. The error appears to be related to the initialization of cuDNN parameters during theflatten_parameters()process, which occurs when the model is moved to the GPU.- The comments reveal that the issue is reproducible on certain GPUs with cuDNN version 8.9, but not on others with newer versions. It is suggested that upgrading to cuDNN 9.3.0 resolves the problem, indicating a compatibility issue with older cuDNN versions. The discussion highlights the need for PyTorch to provide clearer guidance on supported cuDNN versions to prevent similar issues.
- Number of comments this week: 8
-
Hangs on torch.tril on Intel GPU: This issue reports a bug where the
torch.triloperation hangs when executed on an Intel GPU, specifically when running a script that involves creating a large boolean tensor and applying thetrilfunction. The problem persists even after updating to a newer version of PyTorch, and it is suspected to be related to an integer index overflow in thetriloperation.- The comments involve assigning the issue to specific team members, labeling it under the appropriate module, and discussing the inability to reproduce the issue on some systems. Additional details about the system environment are requested, and it is noted that the issue might be related to an integer index overflow, as the operation hangs indefinitely on the device side.
- Number of comments this week: 7
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.
- ImportError: cannot import name 'triton_key' from 'triton.compiler.compiler': This issue involves an ImportError encountered when attempting to import 'triton_key' from 'triton.compiler.compiler', which is causing a backend compiler failure in a PyTorch environment. The error occurs within a script that utilizes the OotdPipeline and involves compiling components with Torch's compile function, specifically affecting the 'inductor' backend due to the missing import.
- Alternate algorithm for computing MaxPool2D under specific condition.: This issue proposes an alternative algorithm for computing MaxPool2D in PyTorch when the stride is equal to 1, suggesting that a kernel size of 5 can be represented by two MaxPool2D operations with a kernel size of 3, and similarly, a kernel size of 7 can be represented by three such operations. The motivation behind this approach is to reduce computational costs on the CPU by modifying the MaxPool2D layer directly, as demonstrated by testing code that shows a significant speedup in execution time compared to the traditional method.
- cuda_utils.so: failed to map segment from shared object: This issue involves a problem encountered when running a PyTorch model within a Docker container, where the execution of a cached shared object file,
cuda_utils.so, fails due to a missing execution permission despite being run as the root user. The error occurs in a setup with atmpfsdirectory having permissions set to1777, and the problem is specifically related to the inability to map a segment from the shared object, which is crucial for the model's execution. - Enable UFMT on all files in PyTorch: This issue involves enabling uniform formatting (UFMT) across all files in the PyTorch codebase, as currently, approximately 1,500 files are not formatted according to the UFMT standards. The process requires removing file names from the
exclude_patternsin theUFMTsection of the.lintrunner.tomlfile and running a specific command to apply the formatting, with additional preparatory work needed to resolve known issues such as import cycles and misplaced annotations before the UFMT changes can be committed. - [JIT archive] Add a flag to not include debug files: This issue proposes the addition of a flag to the
torch.jit.save()function in PyTorch to exclude.debug_pklfiles, which are primarily used for debugging purposes and can significantly increase the file size of JIT archives. The motivation behind this feature request is to reduce the size of model files, particularly for deployment on mobile devices, where storage space is limited, as demonstrated by the user's experience of reducing a model's file size from 6.7MB to 5.6MB by manually removing these debug files.
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: 86
Summarized Issues:
- PyTorch Compilation and Tracing Issues: These issues involve various problems with PyTorch's compilation and tracing functionalities, such as bugs in
torch.compileand Dynamo, which lead to errors likeTypeErrorandArgsMismatchError. These problems often arise due to incorrect handling of method call information, unexpected argument types, or conflicts with backend optimizations.
- Distributed Training and Device Compatibility: These issues highlight challenges in distributed training environments and device compatibility, such as errors in distributed data parallel (DDP) setups and inconsistencies across different hardware. Problems include assertion errors due to missing
DeviceMeshand discrepancies in results on older GPU models.
- PyTorch Documentation and API Enhancements: These issues propose improvements to PyTorch's documentation and APIs, aiming to clarify existing functionalities and introduce new features. Suggestions include better documentation for shape-checking methods and the addition of new communication manipulation functions.
- Bugs in PyTorch's Distributed and Quantization Modules: These issues address bugs in PyTorch's distributed and quantization modules, such as security risks in the
torch.distributedmodule and performance issues with quantization on X86 CPUs. Solutions involve modifying socket behavior and optimizing quantized GEMM patterns.
- PyTorch's Inductor and AOTAutograd Enhancements: These issues focus on enhancing PyTorch's Inductor and AOTAutograd functionalities, addressing challenges like efficient parameter handling and runtime overhead. Proposed solutions include storing parameters in cache entries and adding profiler events.
- PyTorch's Backend and Device Integration: These issues involve integrating new backends and devices into PyTorch, such as migrating to the PrivateUse1/openReg mechanism and implementing Vulkan-PyTorch interoperability. These efforts aim to streamline device integration and enable zero-copy data transfer.
- PyTorch's Memory Management and Performance: These issues describe memory management problems and performance regressions in PyTorch, such as out-of-memory errors due to reference cycles and slowdowns at high thread counts on AArch64 architecture. Solutions include updating libraries and addressing reference cycles.
- PyTorch's Test Failures and Flakiness: These issues highlight test failures and flakiness in PyTorch's test suites, often due to platform-specific problems or recent updates. Disabled tests and flaky behavior are common, requiring investigation and resolution by contributors.
- PyTorch's ONNX Export and Model Checkpointing: These issues involve bugs in PyTorch's ONNX export process and model checkpointing, such as incorrect parameter settings and incomplete model checkpoint files. Solutions include fixing parameter settings and ensuring complete model states are saved.
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: 13
Summarized Issues:
- Numerical Discrepancies in Neural Network Operations: This issue highlights a discrepancy in the results of a simple neural network operation executed on computers with and without AVX-512 support. The user observes a minor numerical difference in outputs despite using deterministic algorithms and setting
ATEN_CPU_CAPABILITY=avx2, raising concerns about reproducibility and the potential impact of hardware-specific optimizations on floating-point precision in PyTorch.
- Compilation and Tracing Bugs in PyTorch: Several issues involve bugs related to PyTorch's compilation and tracing functionalities. One issue involves a bug encountered when attempting to compile a ResNet50 model using PyTorch's
aot_compile, where the error "torch._dynamo.exc.Unsupported: Failed to trace builtin operator" occurs. Another issue describes a bug where themake_fxfunction fails with an error in the latest nightly build, potentially due to a regression introduced by a recent pull request.
- Discrepancies in PyTorch Functionality Across Devices: This issue highlights a discrepancy in the behavior of the
torch.eye()function when executed on CPU versus GPU. The function produces inconsistent results due to the inability to resize tensors borrowed from NumPy, as indicated by a runtime error encountered during the execution of a provided Python script.
- CUDA Graph Capture and Dynamic Indexing: A bug in the PyTorch library where the
torch.cuda.CUDAGraphAPI fails to capture CUDA graphs when using dynamic indexing is described. This results in aRuntimeError, while the capture succeeds when using dynamic slicing, highlighting a discrepancy in behavior between these two operations during graph capture.
- Type Annotations and Compatibility Issues: This issue addresses incorrect type annotations for boolean operators in
torch.Tensor, which result in type-checking errors when using boolean operations with tensors and other data types. It suggests a need to simplify the overload definitions to improve compatibility and accuracy.
- Configuration and Compatibility in PyTorch: Two issues involve configuration and compatibility concerns in PyTorch. One issue involves adding a configuration option to the Cutlass backend to allow users to control which operations are subjected to Cutlass lowerings. Another issue is about a user inquiring whether there is support for PyTorch version 2.7.1 with CUDA 12.4, as they are unable to find the necessary weights on the specified PyTorch download page.
- Accuracy Problems in PyTorch Tests on AMD GPUs: This issue pertains to accuracy problems encountered in the
inductor/test_torchinductor_opinfotests specifically on AMD GPUs. It affects five tests related to operations like division and remainder with float16 precision, and was resolved following a confirmation from a continuous integration run.
- GitHub Outage Impact on PyTorch CI: This issue pertains to a GitHub outage that caused PyTorch continuous integration (CI) jobs to fail during the checkout step due to a remote error from GitLab. The outage resulted in failed cloning of submodules and necessitated the creation of this issue for potential follow-up actions despite the incident's effects having subsided.
- Test Failures on ROCm Platform: Two issues pertain to the disabling of tests in the
TestForeachCUDAsuite on the ROCm platform due to their failure on the main branch. The teststest_parity__foreach_ceil_fastpath_inplace_cuda_complex128andtest_parity__foreach_ceil_fastpath_inplace_cuda_complex64involve several contributors and ROCm support for resolution.
- Compatibility with Newer GPU Models: This issue highlights a compatibility problem where the current PyTorch version used in the project is outdated and does not support the NVIDIA GeForce RTX 5090 Laptop GPU. It prompts a request to upgrade the PyTorch dependency to version 2.7.1 or higher to ensure compatibility with newer GPU models.
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. Other pull requests are grouped based on similar characteristics for easier analysis. Up to 25 pull requests are displayed in this section, while any remaining pull requests beyond this limit are omitted for brevity.
Pull Requests Opened This Week: 222
Key Open Pull Requests
1. Convert sparse rst to md: This pull request involves converting the sparse.rst documentation file in the PyTorch project to MyST markdown format, addressing issue #155033, and ensuring that the documentation tests pass successfully, with additional related file updates handled in a separate pull request #155430.
- URL: pull/155438
- Merged: No
- Associated Commits: ba326, 07441, e3283, f526c, a7751, 3aa1c, 77494, 34399, 8932f, 5fa0d, 7eb1f, 31529, 9411f, 16f0c, 12072, dcce7, bb5da, 55e44, 7e0e4, 04ccc, be98a, 80606, da2e2, d864a, cc36b, 583fc, 2c0e5, f6b27, a358f, 21a24, a0da4, d2555, 150fc, 3d897, 4f93b
2. [DRAFT][cuDNN][SDPA] Introduce TORCH_CUDNN_SDPA_AVOID_RECOMPILE=1: This pull request introduces the TORCH_CUDNN_SDPA_AVOID_RECOMPILE=1 option to the PyTorch project, allowing users to opt-in to use the variable-sequence length/ragged path for the common BSHD layout case to avoid recompiling for different sequence lengths, and is built on top of a previous pull request (#149282).
- URL: pull/155958
- Merged: No
- Associated Commits: 06902, af389, 475c9, 3bae6, cd825, 1e008, fceda, 82f22, f10fc, 0ff1b, c9cac, c7aca, 94745, 4d676, 576bf, a1b66, 26e0e, a3f55, f62f5, 69ffb, b0aed, ec6e8, deac0, 33281, 9fabe, fa93a, 61e31, 645a7, 00cda, fee69, 73253, 67099, 3a29d, 36383
3. [ca] default on in CI, with fallback for tests in test/compiled_autograd_skips/: This pull request aims to enable compiled autograd by default in continuous integration (CI) for tests run with the environment variable PYTORCH_TEST_WITH_DYNAMO=1, while providing a fallback mechanism for tests located in the test/compiled_autograd_skips/ directory if they are skipped.
- URL: pull/155480
- Merged: No
- Associated Commits: 2ecf2, 49a0d, 15bf9, d8f39, b2c44, 469c5, 33a2d, 2157a, d6265, 1a317, 89634, d1ecb, c8b52
Other Open Pull Requests
- Gradient Accumulation Enhancements: This topic involves enhancing the compiled autograd's gradient accumulation process by introducing a
call_accumulate_gradfunction. The function performs gradient mutations in Python bytecode, allowing for better integration with Dynamo and fixing several tests related to sparse tensors.
- Contiguity Checks: A new C++ function,
definitely_contiguous_fast, is introduced to handle dynamic shapes more gracefully. It potentially returns false instead of throwing errors for tensors with unbacked sizes or strides, providing alternative paths where contiguity checks are not critical.
- Documentation Format Conversion: Several pull requests focus on converting documentation files from reStructuredText (.rst) to Markdown (.md) format. These changes address various files and ensure proper references and formatting are maintained across the updated files.
- Runtime Profiling and Testing Enhancements: Enhancements include introducing runtime profiler information for the AOTDispatcher prologue and enabling tests in
test_aot_inductor.py. These changes involve multiple commits and updates to improve testing and profiling capabilities.
- Issue Fixes and Improvements: Several pull requests address various issues such as discrepancies in tensor operations, TypeError issues, and argument validation. These fixes ensure consistent results and proper error handling across different scenarios.
- Workflow and Script Enhancements: Enhancements include removing Conda from the Windows CI process, integrating workflows with
pull.ymlandtest.sh, and converting batch scripts to PowerShell. These changes aim to streamline processes and improve script handling.
- API and Functionality Updates: Updates include introducing a new XPU API, implementing guard collectives in distributed jobs, and refining alignment checks for matrix multiplications. These changes enhance the project's capabilities and address specific issues.
- Dynamo and Storage Refactoring: Refactoring efforts focus on the DynamoStore component and the automatic functionalization of operations. These changes involve implementing separate storage solutions and multiple updates from various collaborators.
- Host and Device-Side API Support: Work-in-progress efforts aim to add support for a new host-side TMA API and introduce device-side TMA tests. These changes involve multiple commits and collaboration with several contributors.
- Miscellaneous Enhancements: Other enhancements include testing worker pool quiescence, refining CrossEntropyLoss documentation, and replacing scripts with PowerShell alternatives. These changes involve multiple updates and contributions from various collaborators.
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. Other pull requests are grouped based on similar characteristics for easier analysis. Up to 25 pull requests are displayed in this section, while any remaining pull requests beyond this limit are omitted for brevity.
Pull Requests Closed This Week: 256
Key Closed Pull Requests
1. [Release/2.6] upgrade numpy: This pull request aims to upgrade the numpy library in the PyTorch project, as indicated by the title '[Release/2.6] upgrade numpy', and involves multiple commits addressing various improvements, bug fixes, and enhancements across different components of the project, although the pull request itself has not been merged.
- URL: pull/155461
- Merged: No
- Associated Commits: c69ea, af92b, aad1c, f3c08, 5363f, 5fbc4, 2b84d, c92f6, 1d3ff, f9e99, 46f55, 0cdf8, 6628b, c953e, 22775, 9b688, 4b9b7, f61bf, 31b52, b1a10, 5eb54, d9eed, 41811, 23e39, f01a6, 929ef, 4e418, 478a9, 7d329, 3a3de, f35ab, 7092d, 8c034, be126, e1858, d155d, 4d9de, a99cc, 51829, 983ea, 47f4e, eb304, 6e304, e2067, 57421, a61b5, 4658a, e19c1, 232eb, 1d2c2, a2639, cd15d, 9c34a, 8d4b8, dcb8a, 7be6b, ca3c3, 32070, 2236d, 1eba9, 93864, 88b97, bbd00, 1f32b, d33dd, f1481, ea546, ac7d6, ed487, 66dfe, 3783d, 8adc1, 5c4fa, 639ee, b445b, 8d72c, 374e5, 6a3b5, e607b, aafc7, d5947, 1b753, ba1ba, 70f30, 3398f, 8354d, 737cf, 4202f, 7c27e, 2e2c7, 3a818, 53ad2, 8eb5d, dbe8c, fcdff, 92b55, f6789, 2e1ed, 13339, 82ac2, 3608e, bfb23, 86b0a, 03714, 34caa, ac032, 5dd61, 7d528, d9a03, 7c072, 73dd0, b08d9, d70a9, 7ad5a, 2fd46, ed8c6, bf084, 20ad8, 2fb0a, 8cfa9, 50a04, 45896, 9d0a4, 1a808, 6fe84, a3632, 68180, fb24f, e53a9, 2cda1, 9d566, a7044, c7ba8, cbd7b, c3733, faf90, a87c9, ce6b7, dc41a, 469ce, 8ccfc, 1290e, 93693, 75628, f4c96, 9cf15, 5c42a, 1a150, 1ded2, 2ff80, b6e5f, 50924, 4642c, 95d7f, 22d88, 882f3, 8fe3c, 7c63c, cce16, e4e68, 2045a, bbf4b, 0ad73, a8545, ce580, 8ce8b
2. Convert fx.rst to fx.md: This pull request involves converting the documentation file for the 'fx' module from reStructuredText format (.rst) to Markdown format (.md) as part of a larger documentation update effort, and includes multiple commits for updates and syntax corrections, with contributions from Svetlana Karslioglu.
- URL: pull/155482
- Merged: No
- Associated Commits: dad66, 2ef7a, 20bc8, 4621f, 7c575, 99d08, 6b4d7, fe27a, 6ae40, 484f2, 8a7d9, 78090, 13e9d, b4988, b0475, 42eab, 0df57, 90b71, 751a7, eae2e, e4dcb, 57c9a
3. convert: rst to myst (1/2): This pull request involves converting two documentation files, torch.compiler_dynamo_overview.rst and torch.compiler_fake_tensor.rst, from reStructuredText (rst) format to MyST Markdown format as part of a larger effort to address issue #155038, with the changes being split into two separate pull requests for clarity and review purposes.
- URL: pull/155833
- Merged: No
- Associated Commits: 91cbc, c2646, c375e, 87760, 035db, 60de7, c65cc, d5b58, c0e86, 3fb71, 245f4, 7cce9, 04ddb, b2617, 18b74
Other Closed Pull Requests
- Documentation Conversion to Markdown: Several pull requests focused on converting PyTorch documentation files from reStructuredText (.rst) to Markdown (.md) format. These changes were part of efforts to address specific issues and ensure compliance with project standards, involving files related to named_tensor, nested, and various nn.attention modules, among others.
- Metal Kernel Migration: Multiple pull requests aimed to migrate various functions, such as hardsigmoid, hardswish, leaky_relu, and softshrink, to utilize Metal kernels within the PyTorch project. These migrations were part of a series of stacked changes to enhance performance on Metal-supported devices.
- CUDA and CI/CD Pipeline Updates: Several pull requests focused on updating the CUDA versions and improving the CI/CD pipeline in the PyTorch project. These updates included removing older CUDA builds, replacing them with newer versions, and optimizing the continuous integration process by reusing old wheel files.
- Error Handling and Optimization Improvements: Various pull requests addressed error handling and optimization improvements in the PyTorch project. These included enhancing error messaging for unsupported subclasses, improving error reporting consistency, and optimizing memory usage by making APIs more generic.
- Triton and NVSHMEM Integration: A pull request introduced an experimental feature enabling Triton kernels to utilize NVSHMEM device functions. This integration allows for the initialization of NVSHMEM in Triton and the use of specific NVSHMEM functions within Triton kernels.
- Compiler and Build Fixes: Some pull requests focused on fixing compiler errors and build issues in the PyTorch project. These included addressing symbolic integer casting issues and resolving sccache configuration problems for nvcc on specific distributions.
- Testing and Refactoring: A few pull requests involved testing and refactoring efforts within the PyTorch project. These included testing fixes related to the Git CLI, refactoring tests for new and old APIs, and adding tests for integration with Helion.
- DDPOptimizer and Metadata Propagation: A pull request addressed an issue in the DDPOptimizer related to static tensor indices by ensuring correct metadata and attribute propagation. This change prevents repeated cudagraph re-recording, which could appear as a hang to users.
- Configuration and Compilation Time Optimization: A pull request introduced a new configuration option,
cutlass_enabled_ops, to control which operations utilize CUTLASS lowerings. This aims to optimize compilation time while maintaining backward compatibility.
- Schema and Return Parsing Improvements: A pull request improved the handling of single-element tuples in the return parsing of the
infer_schemafunction. This involved modifying the implementation and adding tests to ensure correct functionality.
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.
-
[CD] Move build_magma.bat to build_magma.py
- Toxicity Score: 0.55 (Defensive responses,Repeated questioning,Frustration expressed)
- This GitHub conversation involves username1 proposing a change to a script, which is met with skepticism by username2, who questions the necessity of the change. Username1 responds defensively, leading to a tense exchange where username2 reiterates their concerns, and username1 expresses frustration over the lack of understanding. The tone becomes increasingly strained as both parties fail to reach a consensus.
-
remove allow-untyped-defs from quantization.py
- Toxicity Score: 0.55 (Defensive responses, perceived criticism, escalating tension)
- This GitHub conversation involves a series of interactions where username1 initiates a pull request, and username2 provides feedback that is perceived as critical. Username1 responds defensively, indicating frustration with the feedback. Username3 attempts to mediate by offering constructive suggestions, but the tone remains tense as username1 feels misunderstood. The conversation shows signs of escalating tension, with username1 and username2 exchanging increasingly terse comments.
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 |
|---|---|---|---|---|
| malfet | 193 | 35 | 10 | 158 |
| bobrenjc93 | 269 | 45 | 7 | 7 |
| svekars | 64 | 5 | 30 | 181 |
| Skylion007 | 60 | 21 | 0 | 168 |
| laithsakka | 97 | 27 | 3 | 34 |
| davidberard98 | 105 | 16 | 8 | 20 |
| guilhermeleobas | 108 | 29 | 2 | 1 |
| nirajkamal | 71 | 5 | 0 | 39 |
| clee2000 | 92 | 14 | 4 | 3 |
| guangyey | 63 | 5 | 0 | 43 |