Weekly GitHub Report for Pytorch: August 25, 2025 - September 01, 2025 (12:01:45)

Weekly GitHub Report for Pytorch

Thank you for subscribing to our weekly newsletter! Each week, we deliver a comprehensive summary of your GitHub project's latest activity right to your inbox, including an overview of your project's issues, pull requests, contributors, and commit activity.

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Table of Contents

• I. News
  • 1.1. Recent Version Releases
  • 1.2. Other Noteworthy Updates
• II. Issues
  • 2.1. Top 5 Active Issues
  • 2.2. Top 5 Stale Issues
  • 2.3. Open Issues
  • 2.4. Closed Issues
  • 2.5. Issue Discussion Insights

• III. Pull Requests
  • 3.1. Open Pull Requests
  • 3.2. Closed Pull Requests
  • 3.3. Pull Request Discussion Insights

• IV. Contributors
  • 4.1. Contributors

I. News

1.1 Recent Version Releases:

The current version of this repository is v2.6.0

1.2 Version Information:

Released on January 29, 2025, PyTorch 2.6 introduces significant enhancements including torch.compile support for Python 3.13, a new dynamic compilation control API torch.compiler.set_stance, and improved AOTInductor packaging and ABI compatibility. Notable highlights also include FP16 support on X86 CPUs, expanded Intel GPU support with simplified installation, and a backward-incompatible security improvement flipping the default of torch.load to weights_only=True, alongside numerous performance optimizations, bug fixes, and deprecations such as the discontinuation of official Conda packages.

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.

1. Memory Throughput on B200 for copy_: This issue discusses the unexpectedly low memory throughput observed on the B200 GPU for elementwise operations such as copy_ and cat in PyTorch, despite expectations of higher performance based on vectorized memory operations. It includes benchmarking scripts and detailed analysis of how tensor contiguity, data types, and vectorization impact throughput, along with suggestions for improving performance by enhancing vectorization in TensorIterator and special-casing common patterns.

   • The comments explore the causes of slow memory throughput, noting that copy_ calls cudaMemcpyAsync which can be slower than vectorized operations, and that small benchmark sizes may skew results. They discuss CPU overhead, the impact of data types on throughput, and the challenges of vectorizing .contiguous() calls for arbitrary tensor layouts. Suggestions include increasing iteration counts for more accurate benchmarks, using wider dtypes to improve throughput, and future improvements via vectorized TensorIterator and specialized handling of common cases, with a linked pull request aiming to enhance cat performance for narrow data types.
   • Number of comments this week: 9

2. aten.slice adds guard on backed symint which causes inconsistent behavior between eager and trace modes: This issue reports an inconsistency in behavior between eager and trace modes in PyTorch when using aten.slice with backed symbolic integers (symints) for dynamic slicing indices. Specifically, the guards added during tracing cause errors for input shapes that are accepted in eager mode, leading to questions about whether these guards should be removed or if alternative semantics should be used to support flexible slice bounds consistently.

   • The discussion clarifies that the observed inconsistency is expected due to the way the named Dims API and slice decomposition handle dynamic shapes and guards, which specialize based on the tracing sample input and require consistent guards across the min/max range. Commenters suggest workarounds such as restricting the dynamic shape range to ensure consistent output size or using unbacked symints to allow more flexible slicing semantics, acknowledging that fully supporting conditional slice output sizes is a complex ongoing effort.
   • Number of comments this week: 8

3. CUDA 13 -- sm_120 -- Nvidia 5090 -- ptxas warning : Value of threads per SM for entry .... is out of range. .minnctapersm will be ignored: This issue reports a compilation warning encountered when building PyTorch with CUDA 13.0 targeting the sm_120 architecture (Nvidia 5090), where the value of threads per SM is out of range and the .minnctapersm setting is ignored. The user suggests that the compilation parameters for sm_120 need adjustment and shares references to previous related fixes and documentation, along with some proposed code changes to update the maximum threads per SM for this new architecture.

   • In the comments, contributors investigate the correct CUDA parameters for sm_120 by running tests to determine hardware limits and suggest specific launch_bounds values. A proposed fix involves updating the CUDA_MAX_THREADS_PER_SM constant in the PyTorch source to 1536 for architecture 1200, and the issue is relabeled for proper triage while users confirm the warning persists in CI jobs.
   • Number of comments this week: 6

4. mobilenetv3_large_100 aot_eager accuracy failure: This issue reports an accuracy failure in the mobilenetv3_large_100 model when using the aot_eager compilation mode, which cannot be reproduced locally by the reporter. The discussion centers around the model's known flakiness, potential causes related to small tensor multipliers, and suggestions for increasing tolerance or adding logging to better understand the failure, with plans to skip the test temporarily while further investigation continues.

   • The comments reveal attempts to reproduce the failure locally were unsuccessful, and historical flakiness of the model was noted. Suggestions included investigating commit history, enhancing CI logging, and adjusting tolerance levels, especially after a CUDA upgrade; meanwhile, the test will be skipped to allow ongoing investigation and coordination with relevant teams.
   • Number of comments this week: 6

5. remove a newly added cpp_ext warning: This issue addresses a newly introduced warning in PyTorch version 2.8.0 related to the TORCH_CUDA_ARCH_LIST environment variable, which is currently printed on all ranks during multi-GPU distributed training. The reporter suggests that the warning should be made aware of torch.dist to only print on rank 0, suppressed unless the logging level is DEBUG/INFO, and improved to inform users about the specific architectures being set automatically.

   • The comments clarify that the warning was added earlier in the year and changed from a print statement to a warning, with discussion about whether it was previously suppressed. Contributors plan to adjust the logic so the warning only appears if no GPUs are detected, and confirm that there is no CMake involvement in JIT CPP/CUDA extensions, indicating custom handling is required.
   • Number of comments this week: 5

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.

1. ImportError: cannot import name 'triton_key' from 'triton.compiler.compiler': This issue reports an ImportError encountered when attempting to import the name 'triton_key' from the 'triton.compiler.compiler' module, which causes the PyTorch backend compiler 'inductor' to fail during model compilation. The user provides detailed environment information and code snippets showing that the error arises while compiling specific pipeline components with torch.compile, indicating a potential compatibility or version mismatch problem between PyTorch, Triton, and their dependencies.
2. Alternate algorithm for computing MaxPool2D under specific condition.: This issue proposes an alternate algorithm for computing MaxPool2D when the stride is equal to 1, by representing a larger kernel size (e.g., 5 or 7) as multiple smaller MaxPool2D operations with kernel size 3. This method aims to reduce computational cost on the CPU by decreasing the number of operations per cell and suggests modifying the MaxPool2D layer directly to avoid additional overhead during backpropagation.
3. cuda_utils.so: failed to map segment from shared object: This issue describes a problem encountered when running a PyTorch model inside a Docker container with a tmpfs mounted at /tmp having permissions set to 1777. Although the model compiles successfully, execution fails with an error indicating that the shared object cuda_utils.so cannot map a segment due to missing execute permissions on the file, despite the script running as root and directory permissions being correct.
4. Enable UFMT on all files in PyTorch: This issue addresses the task of enabling uniform formatting (UFMT) across all files in the PyTorch codebase, specifically targeting around 1,500 files that are currently excluded from UFMT enforcement. It outlines the process for removing files from the exclusion list in the .lintrunner.toml configuration, running the formatter, and managing known formatting conflicts, while also providing a detailed worklist organized by directory to coordinate incremental formatting efforts.
5. [JIT archive] Add a flag to not include debug files: This issue proposes adding a flag to the torch.jit.save() function that allows users to exclude debug files, specifically .debug_pkl files, from the JIT archive to reduce the overall file size. The motivation stems from observations that these debug files, which are only used for debugging purposes, can occupy a significant portion of the model size, especially for small or quantized models, and removing them manually does not affect model correctness.

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: 80

Summarized Issues:

• torch.compile regressions and performance issues: PyTorch 2.8.0 introduced regressions in torch.compile where previously working models now fail to compile due to tensor size mismatches and recompilation errors on macOS arm64. Additionally, torch.compile with the inductor backend produces incorrect results for certain tensor operations and can cause significant slowdowns, such as a threefold decrease in token processing speed on Intel XPU devices and unexpectedly longer execution times in max-autotune mode.
• [issues/161372, issues/161743, issues/161763, issues/161764]

• CUDA and GPU architecture support problems: Compilation warnings occur when building for the sm_120 architecture on Nvidia RTX 5090 GPUs due to outdated parameters, and CUDA 13 binaries are requested for aarch64 platforms like Spark and Thor. Symmetric memory functionality is broken for AMD GPUs in the latest nightly builds, causing runtime errors, and a RuntimeError arises on Intel Arc 140V GPUs due to unsupported free memory queries.
• [issues/161376, issues/161377, issues/161722, issues/161403]

• Memory reporting and leaks on specialized devices: The torch.xpu.mem_get_info() function inaccurately reports free memory on XPU devices, and a memory leak occurs when in-place operations are applied to non-leaf tensors with requires_grad=True, causing unreleased references and growing memory usage.
• [issues/161381, issues/161391]

• Test failures and CI issues on specific platforms: Multiple tests are disabled or failing consistently on ROCm and XPU platforms, including autotune search space tests and background thread tests, complicating validation and regression detection. Inductor periodic CI suffers from sequential test execution where failures block subsequent checks, reducing test coverage visibility.
• [issues/161418, issues/161463, issues/161483, issues/161697, issues/161698, issues/161525]

• Accuracy and dtype annotation bugs in compilation backends: Intermittent accuracy failures occur in models like mobilenetv3_large_100 under aot_eager compilation mode, and dtype annotation bugs in AOT graph nodes cause mismatches between expected and actual tensor types. Additionally, speculate_subgraph fails on subclass inputs due to strict type checks, and bfloat16 precision causes accuracy issues in meta-llama models with the inductor backend.
• [issues/161419, issues/161425, issues/161456, issues/161457]

• Documentation and link errors: The PyTorch 2.8.0 stable documentation contains malformed links causing navigation problems, and outdated installation instructions in C++ debugging docs require updating to modern pip commands. Clarifications are also requested for configuration options like activation_memory_budget due to inconsistent references.
• [issues/161375, issues/161509, issues/161650]

• Runtime errors and crashes due to tensor operations and graph handling: Creating tensors with invalid large negative dimensions causes runtime errors, and slicing operations in bucketize lowering crash due to missing stride information. Modifying ExportedProgram graphs prevents saving/loading, and concatenating jagged nested tensors triggers unexpected argument errors.
• [issues/161490, issues/161609, issues/161671, issues/161812]

• DataLoader and memory usage inefficiencies: Using DataLoader with CocoDetection on Windows results in high first-batch latency due to pickling overhead, and certain tensor operations like mask.sum(dim=[-2, -1]) consume excessive VRAM regardless of block size, prompting requests for more efficient solutions.
• [issues/161492, issues/161494]

• Permission and build environment issues: PermissionErrors occur during XPU header installation in CI/CD processes, and Windows 2019 AMI git settings cause CI regressions due to case sensitivity problems, requiring configuration changes to prevent errors.
• [issues/161498, issues/161815]

• NaN and numerical stability problems in modules: The nn.TransformerEncoder produces NaNs when using float-type attention and padding masks in eval mode with no_grad, likely due to sparsity fast path interactions, and logsumexp backward passes produce NaNs when input contains -inf values, even if those values do not affect the output.
• [issues/161500, issues/161638]

• Compatibility and attribute errors in model export and compilation: Exporting certain models with torch.export fails due to unregistered proxy dispatch modes, and dynamically added attributes to NamedTuple subclasses do not persist after torch.compile with the eager backend, causing AttributeErrors.
• [issues/161563, issues/161610]

• Backend-specific performance and correctness discrepancies: Flex attention is significantly slower than scaled dot product attention despite similar memory usage, and the CPU and MPS backends differ in index_add behavior with boolean tensors, leading to inconsistent results.
• [issues/161473, issues/161524]

• CI and workflow automation failures: The GitHub Actions workflow for checking PR mergeability fails with symbolic ref errors but incorrectly reports success, and force merging PRs without CI signals causes trunk failures requiring reverts.
• [issues/161566, issues/161632]

• Type annotation and API signature improvements: Proposals exist to improve type signatures of publicly exported PyTorch APIs to enhance type checking coverage, and specific type hint corrections are needed for parameters like input_layouts to avoid confusion and linter errors.
• [issues/161646, issues/161713]

• Compilation graph break and logging issues: Certain graph breaks cause excessive warning logs that disrupt users, especially with experimental capture_scalar_outputs enabled, and fullgraph=True mode should error or warn when skipping frames due to unsupported opcodes instead of silently falling back to eager execution.
• [issues/161790, issues/161796]

• Autotuning and CUDA kernel errors: Exhaustive autotuning on specific matrix multiplication shapes causes illegal memory access errors on NVIDIA H100 GPUs, and CUDA kernels for functions like torch.histc produce invalid shared memory read errors leading to out-of-bounds accesses and crashes.
• [issues/161842, issues/161760]

• Requests for new features and attributes: Users request adding torch.dtype.kind similar to NumPy for dtype classification, protobuf support in torch.compile Dynamo to avoid graph breaks, and a new package for numerical algorithms to support various techniques.
• [issues/161765, issues/161774, issues/161623]

• Build and packaging gaps: Missing Linux s390x and Windows arm64 binary wheels for newer Python versions are noted, raising questions about whether to produce these builds, and CUDA 13 Linux wheel builds for PyTorch audio are missing expected artifacts.
• [issues/161515, issues/161516, issues/161719]

• Miscellaneous bugs and improvements: CPU overhead for CUDA matmuls increased due to repeated string operations, warnings in cpp_extension during multi-GPU training are noisy and should be limited, and flash decoding optimizations for FlexAttention on x86 CPUs are proposed to improve LLM inference.
• [issues/161822, issues/161629, issues/161757]

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: 58

Summarized Issues:

• Export and Serialization Issues: Multiple issues report failures and bugs related to PyTorch's export and serialization functionalities. Problems include strict export not capturing side effects, inability to export models with bfloat16 constants or complex tensors, assertion errors during export involving FakeTensor and BatchedTensor, and function schema mismatches causing TypeErrors during export operations.
• [issues/159787, issues/159928, issues/160196, issues/160749, issues/161076]

• Triton and GPU Numerical Accuracy Bugs: Several issues highlight numerical inaccuracies and runtime errors in Triton-based kernels and GPU backends, especially on NVIDIA H100 GPUs. These include incorrect results in scaled matrix multiplication, flex attention test failures with float16/bfloat16, and 64-bit indexing not supported in Triton templates causing NotImplementedErrors.
• [issues/159940, issues/160409, issues/160955]

• Inductor Backend and Compiler Failures: The PyTorch inductor backend and compiler face multiple bugs causing crashes and incorrect code generation. These include runtime errors when adding complex tensors with empty inputs, assertion errors due to tensor stride mismatches, data-dependent slicing errors, and pickling errors during asynchronous kernel compilation.
• [issues/160495, issues/161244, issues/161318, issues/161618]

• Test Failures and Disabling on XPU and Other Platforms: Numerous tests are consistently failing on the xpu platform and other environments, leading to their disabling. These include autotune, AOTFxirTestCase, TestDraftExport, and CondTests suite tests, often complicated by invalid platform designations and causing broad test disablement discussions.
• [issues/160951, issues/160969, issues/160970, issues/161384, issues/161482, issues/161484, issues/161682, issues/161691, issues/161696, issues/161701, issues/161702, issues/161714, issues/161758]

• CI and Build System Issues: Several issues report CI pipeline failures, timeouts, and build errors. Problems include MacOS test timeouts due to test distribution changes, nightly CI failures from corrupted artifacts or dirty git states, build failures from missing files caused by stale caches, and platform-specific dependency errors.
• [issues/160498, issues/161422, issues/161428, issues/161510, issues/161608, issues/161791]

• Runtime and API Warnings and Errors: Various runtime warnings and errors occur in optimizer usage, distributed tests, and tensor operations. Examples include persistent UserWarnings from LBFGS optimizer closures, backend type association errors in distributed tests, and runtime errors from setting requires_grad in inference mode.
• [issues/160197, issues/161154, issues/161191]

• MPS Backend Bugs on Apple Silicon: The Metal Performance Shaders backend on Apple Silicon GPUs exhibits multiple bugs, including failure to learn in simple models due to incorrect parameter updates, incorrect results for linear operations on non-contiguous tensors, and index_add failures with certain dtypes and shapes.
• [issues/161361, issues/161446, issues/161640]

• Documentation and Code Quality Improvements: Some issues propose documentation corrections and code quality improvements, such as fixing typos in variable names, updating type alias definitions, and aligning pip installation instructions with best practices.
• [issues/160834, issues/160854, issues/161282, issues/161480, issues/161611]

• Transformer and Model Compilation Failures: Transformer-related tests and model compilations fail on newer PyTorch nightly builds due to assertion errors and compatibility issues, causing crashes and aborted runs that do not occur on stable releases.
• [issues/161505, issues/161506]

• Tensor Memory Layout and Consistency Issues: There are inconsistencies in tensor memory layouts and results across devices and functions, including torch.einsum producing transposed layouts, torch.rand_like not preserving strides, and torch.linalg.matrix_rank and torch.median yielding different results on CPU versus GPU.
• [issues/161415, issues/161729, issues/161769, issues/161841]

• Security Vulnerabilities and Mitigation Plans: A prioritized security analysis identifies critical vulnerabilities in PyTorch such as JIT code injection and pickle deserialization flaws, proposing detailed mitigation strategies and a phased roadmap to improve project security.
• [issues/161327]

• Performance and Latency Concerns: Performance issues are reported related to cross-process tensor sharing latency in torch.multiprocessing, impacting time-sensitive applications like RLHF.
• [issues/161481]

• Variable Naming and Bytecode Transformation Bugs: A code generation error occurs in Dynamo when a variable name is used in both local and cell scopes, causing a KeyError during bytecode transformation due to incorrect variable treatment.
• [issues/161542]

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.

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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: 201

Key Open Pull Requests

1. [ROCm] Bump AOTriton to 0.11b: This pull request updates the AOTriton library to version 0.11b for ROCm, introducing significant new features and optimizations for SDPA operators on AMD systems, including high-performance AITER Assembly kernels for specific GPUs, alignment with CUDA's natural logsumexp behavior, support for a new causal variant, and a revamped build system with selective GPU image packaging, while also addressing kernel bugs and known issues with certain GPU targets.

• URL: pull/161754

• Merged: No

• Associated Commits: 85386, 69342, c194d, bd319, 543da, f7db9, 01f1c, 287cf, 2fb59, 407d6, 38463, 829c0, 11913, 0be57, d161c, 2c261, 92ebb, 4384a, 0b7d8, b0ce2, 0f524, 37b79, 88e10, fbe1c, 61d8d, 8550a, d82aa, 6c8fe, 6afe8, d574e, a4d97, f7a9b

2. [WIP] [3/N] Enable 6 fsdp test on Intel GPU: This pull request aims to port and enable six Fully Sharded Data Parallel (FSDP) distributed test cases to run on Intel GPUs by modifying test files under test/distributed/fsdp, incorporating support for the XPU backend, and maintaining original code styles while addressing compatibility and backend-specific issues.

• URL: pull/161601

• Merged: No

• Associated Commits: 06f62, 40c25, 1e511, bc567, 66b7b, 686b4, fe428, 41faa, 38162, 42202, 82dae, e8eeb, f3caf, daf38, 2fcc0, b9fea, c01c9, 44b99, 6154d, 0a1e0, 789b6, 1a993, f0929, e4f77, c29e0, e22af

3. [2/2]Add summary report for vllm build and test : This pull request adds a summary report feature for the vllm build and test process in the PyTorch project.

• URL: pull/161585

• Merged: No

• Associated Commits: 7811d, 4765b, 645f0, b15fa, c3cc5, 99cfd, f8faa, e4d08, b8ea3, 3df99, 8041d, cc597, 97ec9, 0ae79, 5230f, 8b981, ad476, c8ba2

Other Open Pull Requests

• Intel GPU support in distributed and composite tests: Multiple pull requests focus on porting distributed tensor and shared test cases to support Intel GPU by enabling detection of the accelerator backend with torch.accelerator.current_accelerator(), skipping unsupported tests, and maintaining original code style. These changes also include enabling TestCompositeCompliance, TestMathBits, and TestFakeTensor test classes on Intel GPU while addressing lint and driver issues.
  • pull/161771, pull/161703, pull/161604, pull/161397

• Inductor backend improvements and caching mechanisms: Several pull requests improve the Inductor backend by optimizing kernel implementations such as fusing RoPE kernels, introducing a _custom_ops bucketing mode to reduce kernel generation overhead, and adding unique keys and src_hash properties to kernel inputs and templates to enable caching and filtering. These changes also include runtime estimation for communication optimizations and proper handling of addmm operation in get_mm_configs.
  • pull/161420, pull/161499, pull/161468, pull/161469, pull/161534, pull/161547

• Flex attention and paged attention mask enhancements: Pull requests introduce multiple improvements and bug fixes to flex attention and paged attention mask generation, including adding upper mask conditions, passing kv_len as an argument, fixing C++ buffer generation bugs, adding tests, and supporting maximum post-modification scores as auxiliary outputs with new API structures.
  • pull/161551, pull/161667

• Bug fixes in padding and TorchInductor scalar indexing: Bug fixes address negative padding handling to correctly include zero-sized dimensions and prevent exceptions, as well as fixing cross-device scalar indexing failures in TorchInductor to ensure consistent behavior with eager mode.
  • pull/161639, [pull/161447](https://github.com/pull/161447]

• ONNX and internal verification cleanup: Pull requests remove unused logic from the internal ONNX verification module and clean up the torch.onnx namespace by removing imports of private functions to streamline the public API.
  • pull/161449, pull/161546

• XPU device properties and API enhancements: A pull request adds a UUID property to XPU device properties using Intel SYCL device info extension, while another introduces the get_remote_tensor API for symmetric tensor retrieval and refactors buffer and signal pad implementations to unify backend functionality.
  • pull/161392, pull/161533

• Dynamo and symbolic integer (symint) development: One pull request introduces a Local Map HOP feature to the Dynamo component, and another is a work-in-progress attempt to implement and test a new symbolic integer wrapper in PyTorch.
  • pull/161458, pull/161828

• MIOpen integration improvements in ROCm backend: A pull request revamps MIOpen integration by updating source files to follow best practices, specifically avoiding reshape_ calls inside backward operations.
  • pull/161687

• Custom Triton kernel registration for FLOP counting: A new API is introduced for registering custom Triton kernels to enable accurate FLOP counting of decomposed Torch operators within FlopCounterMode, including example usage and unit tests.
  • pull/161547

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: 255

Key Closed Pull Requests

1. [MPS] sparse add unary funcs + add for sparse tensors: This pull request proposes adding several unary functions and an addition operation for sparse tensors on the MPS backend, along with enabling partial tests for these unary functions in the sparse test suite to facilitate a gradual migration to testing SparseMPS with test_sparse.py.

• URL: pull/160839

• Merged: No

• Associated Commits: 3798c, eb658, c4a07, 410de, e4a6c, 95c0c, 33396, e30a5, a32a4, d740c, 906d0, 9f345, f7092

2. [dynamo][vllm] Support typing.get_type_hints: This pull request proposes adding support for the Python function typing.get_type_hints within the dynamo and vllm components of the PyTorch project.

• URL: pull/161362

• Merged: No

• Associated Commits: 29750, ad793, b5c33, 11ec2, 49726, 801f5, 249f8, 6acdf, b5a55, b45f6, dd9e4, 52e6d, 87161

3. [dynamo] Refactor convert_frame.compile_frame to be self contained function. [5/n]: This pull request refactors the convert_frame.compile_frame function to be self-contained by changing its signature to directly accept frame information instead of a callback transform function, thereby simplifying the building of a fullgraph capture API on top of it.

• URL: pull/160900

• Merged: No

• Associated Commits: e36d7, 39bc0, c1b8b, 4aaf5, 78bea, dbcb5, c1467, 930bd, 6e899, 75360, 02286, d4611

Other Closed Pull Requests

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
yangw-dev	647	16	3	23
malfet	114	6	13	151
coconutruben	149	33	0	6
guangyey	93	23	0	61
anijain2305	106	10	2	27
swolchok	87	36	0	6
guilhermeleobas	91	22	1	5
etaf	66	16	28	7
ezyang	52	13	1	48
ydwu4	65	15	5	20