Weekly GitHub Report for Pytorch - 2024-11-18 12:00:16

Weekly GitHub Report for Pytorch

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Table of Contents

• I. Issues
  • 1.1. Top 5 Active Issues
  • 1.2. Top 5 Stale Issues
  • 1.3. Open Issues
  • 1.4. Closed Issues

• II. Pull Requests
  • 2.1. Open Pull Requests
  • 2.2. Closed Pull Requests
  • 2.3. Pull Request Discussion Insights

• III. Commits
  • 3.1. Commits

• IV. Contributors
  • 4.1. Contributors

I. Issues

1.1 Top 5 Active Issues:

We consider active issues to be issues that that have been commented on most frequently within the last week.

1. DISABLED test_fused_sdp_choice_privateuseone (main.TestSDPAPrivateUse1Only): This issue pertains to a disabled test case named test_fused_sdp_choice_privateuseone within the TestSDPAPrivateUse1Only class, indicating potential problems with the test's functionality across various platforms including Linux, Windows, and macOS. The issue has garnered significant attention, as evidenced by the 128 comments, suggesting that it may be a critical concern for the project's maintainers and contributors.

   • The comments section reflects a collaborative effort to diagnose the issue, with multiple contributors discussing potential causes, sharing insights, and suggesting solutions to re-enable the test.
   • Number of comments this week: 128

2. [RFC] Per-Parameter-Sharding FSDP: This issue discusses the proposal for a new design of Fully Sharded Data Parallel (FSDP) that implements per-parameter sharding, aiming to enhance flexibility and efficiency in distributed training. The proposed changes address limitations in the existing FSDP by allowing for mixed precision parameters and optimizing communication patterns, ultimately striving for a simpler and more composable API.

   • The comments section features extensive discussions about the proposed changes, including clarifications on the implementation details, feedback on the API design, and inquiries about specific functionalities like state dict handling and memory management. Participants express excitement about the potential improvements while also raising concerns and suggestions for further enhancements, indicating a collaborative effort to refine the proposal.
   • Number of comments this week: 73

3. Custom attention recompilations: This issue describes a problem with compiler cache exhaustion occurring during the execution of a custom attention mechanism in a PyTorch model. The user reports encountering repeated recompilation warnings and runtime errors related to tensor size limits, particularly when processing video data with varying input sizes.

   • The comments discuss various aspects of the issue, including suggestions to adjust cache size limits, the impact of recompilation on performance, and the need for better handling of dynamic tensor sizes. Users share logs, error messages, and potential solutions, while also exploring the implications of using specific configurations and the behavior of the PyTorch compiler in different scenarios.
   • Number of comments this week: 51

4. [libtorch] duplicate symbol of vtable: This issue describes a bug encountered while using the C++ library libtorch, specifically a vtable duplication error related to c10::intrusive_ptr_target and c10::VariableVersion::VersionCounter. The problem arises from the implementation of certain functions being included in header files, which leads to conflicts when compiling with macOS's Clang in C++17 mode.

   • The comments discuss potential solutions and workarounds for the vtable duplication error, including suggestions to modify header functions and linker options. Users share their experiences with different build commands, report on the behavior of the linker across Xcode versions, and explore the implications of using internal data structures versus public APIs in their Haskell integration with libtorch.
   • Number of comments this week: 39

5. Fused Linear and Cross-Entropy Loss torch.nn.functional.linear_cross_entropy: This issue proposes the addition of a fused linear and cross-entropy loss function, torch.nn.functional.linear_cross_entropy, to PyTorch, which would optimize memory usage by avoiding the creation of large intermediate logits during computation. The motivation behind this request stems from the increasing memory demands of modern language models, where the size of logits can significantly impact VRAM consumption, particularly with large batch sizes and vocabularies.

   • The comments section features discussions about the feasibility and implementation of the proposed function, with several contributors sharing their insights on optimizing memory usage and performance. There are mentions of existing prototypes, relevant benchmarks, and alternative methods, as well as a collaborative spirit among users eager to see this feature integrated into PyTorch.
   • Number of comments this week: 35

1.2 Top 5 Stale Issues:

We consider stale issues to be issues that have been opened in this project for the longest time within the last year. The team should work together to get these issues resolved and closed as soon as possible.

1. Regression: capture_pre_autograd_graph does not support empty args and kwargs only anymore: This issue reports a regression in the capture_pre_autograd_graph function, which no longer supports the use of empty positional arguments while allowing only keyword arguments, a change introduced by a previous pull request. The problem specifically affects users attempting to capture graphs for models like BERT using only keyword arguments, resulting in an error that disrupts their workflow.

   • Open for 368 days, 07 hours, 36 minutes

2. New feature: Balanced Sampler: This issue proposes the implementation of a new sampler class called BalancedSampler for handling unbalanced datasets in a more efficient manner. The feature aims to provide two strategies—oversampling and undersampling—to automatically balance the dataset at runtime, thereby facilitating better data handling without the need for physical data manipulation.

   • Open for 368 days, 06 hours, 52 minutes

3. [docs] Add reference/decomp impl snippets to functions in online docs for hackability and educational purposes / compensate for some unclear language in existing docs: This issue focuses on enhancing the online documentation for various Norm layers by adding reference and decomposition implementation snippets to improve educational clarity and hackability for users. The proposed changes aim to ensure that these snippets are either auto-copied from existing code or auto-tested to match the output of the actual function implementations, while also preventing them from being indexed by search engines.

   • Open for 368 days, 06 hours, 10 minutes

4. device_mesh documentation in FSDP ctor: This issue highlights the need for documentation updates regarding the device_mesh feature in the Fully Sharded Data Parallel (FSDP) constructor, as it is currently not included on the relevant documentation page. The author suggests that since FSDP is transitioning towards utilizing device_mesh, it is important to ensure that this information is accurately reflected in the documentation for users seeking to understand the initialization parameters.

   • Open for 368 days, 04 hours, 31 minutes

5. [ONNX] Implement aten.upsample_trilinear3d.default: This issue pertains to a bug encountered when attempting to export a simple PyTorch model that utilizes the torch.nn.Upsample function with the "trilinear" mode to the ONNX format, specifically through the torch.onnx.dynamo_export() method. The user reports that the export fails due to unsupported FX nodes related to the aten.upsample_trilinear3d.default operation, resulting in a runtime error and a warning about the limited support for opset versions in the current implementation.

   • Open for 368 days, 03 hours, 49 minutes

1.3 Open Issues

This section lists, groups, and then summarizes issues that were created within the last week in the repository.

Issues Opened This Week: 129

Summarized Issues:

• Distributed Tensor Operations Bug: This issue discusses a bug related to the RowwiseParallel style in PyTorch's distributed tensor operations, where the underlying operator aten.to.dtype_layout lacks a registered distributed strategy. This leads to errors during tensor redistribution and async operations in models utilizing parallelized embeddings and projections. The problem highlights the need for improved handling of distributed strategies in tensor operations.
  • /issues/140227

• Linter Error Handling: This issue reports a bug in the ruff linter where it generates a traceback error when attempting to lint Python files with invalid syntax. Instead of providing a more user-friendly error message indicating "Invalid Python syntax," the linter's behavior can confuse users. This raises concerns about the usability and effectiveness of the linter in identifying syntax issues.
  • /issues/140228

• Dynamic Input Shapes in DDP: This issue describes a bug in PyTorch's Distributed Data Parallel (DDP) implementation where using torch.compile with dynamic input shapes leads to excessive recompilations. When the sequence length of inputs changes, it ultimately hits the cache size limit after multiple recompilations, which does not occur when DDP is not used. This inefficiency can significantly impact performance in distributed training scenarios.
  • /issues/140229

• Model Loading Inconsistencies: This issue highlights the challenges faced when using the weights_only=True parameter in torch.load for custom models. It leads to inconsistencies in model loading and repeatability, prompting the need for a solution that balances security and reliable predictions in applications like neural architecture search. The inconsistency can hinder the deployment of models in production environments.
  • /issues/140230

• Custom Attention Operation Compilation Error: This issue reports a bug encountered when attempting to compile a custom attention operation using PyTorch's torch.compile function. It raises an AttributeError related to the ComputedBuffer object not having an inner_fn attribute, which hinders the execution of the flex_attention operation with a specified block mask. This error indicates potential issues in the compilation process for custom operations.
  • /issues/140232

• Module Wrapping Logic Flaw: This issue discusses a bug in the _recursive_wrap function of the PyTorch library, where the logic for determining whether a module should be wrapped is flawed. This leads to unexpected behavior where a module will not be wrapped as intended under certain conditions, particularly when using the default size_based_wrap_policy. The flaw can affect the modularity and reusability of components in PyTorch.
  • /issues/140234

• Graph Neural Network Export Issue: This issue pertains to the inability to export a graph neural network model utilizing the FeaStConv layer from the PyTorch Geometric library to the ONNX format. The process fails due to unsupported operations related to tuple outputs in the model's computation graph. This limitation can restrict the interoperability of models across different frameworks.
  • /issues/140237

• Segmentation Fault on ARM Devices: This issue reports a segmentation fault encountered when importing PyTorch after building it from source on an ARM device (NVIDIA Orin NX) using Python 3.9. The import works successfully with Python 3.8, indicating a potential compatibility problem. This issue raises concerns about the stability of PyTorch on different architectures.
  • /issues/140240

• ONNX Export Failure: This issue reports a bug where the torch.onnx.dynamo_export function fails to export any model, including a simple example. It results in various error messages and warnings related to deprecated parameters and unsupported operations, which can hinder the model deployment process. The failure indicates a need for better support for ONNX exports in PyTorch.
  • /issues/140241

• Numerical Discrepancy in Logit Function: This issue reports a discrepancy in the output of the torch.special.logit function when applied to a bfloat16 tensor on CPU versus GPU. The GPU implementation appears to cast the input values to float, leading to inconsistent results. This inconsistency raises concerns about numerical accuracy across different hardware platforms.
  • /issues/140252

• NCCL Barrier Profiling Improvement: This issue proposes to improve the profiling title for the NCCL barrier in the PyTorch library by changing it from 'nccl:all_reduce' to 'nccl:all_reduce_barrier.' This change aims to enhance clarity and assist in debugging distributed applications that utilize the c10d module. Clearer profiling information can significantly aid developers in optimizing their distributed training workflows.
  • /issues/140257

• Destructor Ordering Problem: This issue discusses a potential destructor ordering problem with the registerPythonModule function in a GitHub project. The destruction sequence of statically initialized objects may lead to undefined behavior due to the premature destruction of the pythonModulesSingleton before the torch::Library object can access it. This necessitates further investigation into the initialization order and interactions with dispatcherSingleton.
  • /issues/140272

• Memory Access Fault with TunableOp: This issue involves a memory access fault occurring when using the TunableOp feature with tensor views in PyTorch. Specifically, it occurs during operations like torch.einsum and torch.matmul, leading to crashes on ROCm-enabled GPUs. This issue highlights the need for better memory management in tensor operations.
  • /issues/140278

• Autocast Feature Runtime Error: This issue reports a bug encountered when attempting to use the autocast feature with a TorchScript model. It results in a runtime error due to a mismatch between the input type and bias type during convolution operations. This error can disrupt the expected behavior of models utilizing mixed precision training.
  • /issues/140279

• Discrepancy in Norm Function: This issue reports a discrepancy in the results produced by the torch.norm function in PyTorch, where the computed norms of a tensor do not match the expected values. This raises concerns about numerical accuracy in different versions of the library, which can affect the reliability of model training and evaluation.
  • /issues/140282

• Asynchronous Tensor Copy Behavior: This issue discusses the expected behavior of asynchronous tensor copies between different devices (CPU and GPU) in PyTorch. It highlights inconsistencies in synchronization requirements that may lead to data corruption if not properly managed. Proper handling of asynchronous operations is crucial for maintaining data integrity in distributed training.
  • /issues/140296

• Random Number Generator Management: This issue proposes the introduction of a new API, torch.distributed.manual_seed, to improve random number generator (RNG) management in distributed training. It allows users to set different seeds for different ranks while ensuring that replicas of the same layer receive the same seed. This addresses current limitations in the DTensor RNG Tracker related to pipeline parallelism and user-defined seed handling.
  • /issues/140301

• Environment Variable Recognition Issue: This issue pertains to a bug in PyTorch where the *_VISIBLE_DEVICES environment variable is not correctly recognized on AMD GPU systems. This leads to an IndexError when attempting to set the seed for all detected devices, caused by the incorrect usage of the HIP_VISIBLE_DEVICES variable instead of the appropriate ROCR_VISIBLE_DEVICES. This issue can hinder the proper configuration of device visibility in multi-GPU setups.
  • /issues/140318

• Higher-Order Polymorphic Operations: This issue discusses the need for enhancements in the handling of Higher-Order Polymorphic (HOP) operations within the torch_dispatch mode in PyTorch. It proposes the introduction of a flag to determine HOP acceptance, the assignment of properties similar to operations, and the differentiation between "passthrough" and "operator-like" HOPs. These enhancements can improve the flexibility and usability of HOPs in PyTorch.
  • /issues/140322

• AWS A100 Runners Reliability: This issue pertains to ongoing reliability problems with AWS A100 runners, where instances have been crashing since the previous weekend. It prompts discussions about improving monitoring tools to better understand and mitigate the failures, particularly in relation to out-of-memory errors and the need for independent monitoring of each runner. Addressing these reliability issues is crucial for maintaining stable training environments.
  • /issues/140332

• Numerical Discrepancy in Halide Backend: This issue reports a numerical discrepancy encountered in the Halide CPU backend of PyTorch when performing an in-place addition operation. The expected output does not match the actual result produced by the backend during a specific test case. This discrepancy raises concerns about the reliability of the Halide backend for numerical computations.
  • /issues/140344

• Flaky Bot Logic Flaw: This issue addresses a potential flaw in the Flaky bot's logic that may lead to it missing critical data when assessing the health of tests. This is evidenced by the recent reopening of previously closed issues that were thought to be resolved. Improving the Flaky bot's logic is essential for maintaining the integrity of the testing process.
  • /issues/140345

• Discrepancy in Special Function Outputs: This issue reports a discrepancy in the output of the torch.special.i0e() function when tested with a bfloat16 tensor on CPU versus GPU. The notable differences in results suggest an inconsistency in the implementation across the two platforms, which can affect the reliability of scientific computations.
  • /issues/140348

• INTERNAL_ASSERT_FAILED Error: This issue reports an INTERNAL_ASSERT_FAILED error encountered during the execution of the FlexAttention module in PyTorch while performing batch size 1 evaluations at inference. This indicates a potential bug in the mask creation process, which can disrupt the expected behavior of attention mechanisms in models.
  • /issues/140363

• Differentiable Quadratic Programming Solver Proposal: This issue proposes the development of a differentiable quadratic programming solver in PyTorch. It would allow users to minimize quadratic forms under linear constraints while enabling the differentiation of the solution with respect to the problem's parameters. This feature could significantly enhance optimization capabilities within the PyTorch framework.
  • /issues/140378

• Optimizer Creation Bug: This issue describes a bug encountered when creating an optimizer with multiple parameter groups in PyTorch. An eval function call leads to a SyntaxError due to improper handling of the string representation of an object. This raises concerns about the safety of executing code retrieved from obj.__repr__, which can affect the stability of model training.
  • /issues/140393

• Compilation Log Improvement Proposal: This issue proposes a feature to shorten the logs generated during compilation in PyTorch. It suggests providing a more concise stack trace for compilation errors while maintaining detailed logs for user code, thereby improving the clarity and usability of the logging output. Enhanced logging can aid developers in debugging compilation issues more effectively.
  • /issues/140394

• Hanging Compilation Issue: This issue reports a bug in PyTorch 2.5+ where the compilation of a DCNN-like network using DistributedDataParallel and the compile(..., mode="max-autotune") option hangs indefinitely. Despite previous successful compilations in earlier versions, this issue seeks assistance in debugging the problem. Addressing this hanging issue is crucial for maintaining efficient model training workflows.
  • /issues/140395

• Topk Function Enhancement Proposal: This issue proposes the introduction of an optional clamp_k argument in the torch.topk function. This enhancement would handle scenarios where the specified k value exceeds the number of elements in the input tensor, thereby preventing runtime errors and enhancing flexibility in its usage. Improving the robustness of the topk function can significantly benefit users in various applications.
  • /issues/140396

• Vision Transformer Export Bug: This issue pertains to a bug encountered when attempting to export a Vision Transformer (ViT) model utilizing flexible attention in PyTorch. It results in an error indicating that a FunctionalTensor is being used incorrectly, specifically suggesting that it should be paired with a corresponding FunctionalTensorMode. This issue can hinder the deployment of ViT models in production environments.
  • /issues/140400

• DDP Custom Backend Bug: This issue pertains to a bug in the PyTorch library where the custom backend for obtaining compiled graphs is not invoked when using the Distributed Data Parallel (DDP) module. This leads to incomplete debugging information and hinders the ability to capture the compiled graphs during distributed training. Addressing this issue is essential for improving the debugging capabilities in distributed training scenarios.
  • /issues/140416

• CUDA Graph Execution Error: This issue reports a bug encountered after upgrading to PyTorch 2.5.1, where the code fails with a runtime error indicating an unexpected state in the CUDA graph execution. Specifically, it states "Expected curr_block->next == nullptr to be true, but got false," disrupting the functionality of the torch compile feature when using the reduce-overhead mode. This error can significantly impact the usability of the compilation feature in PyTorch.
  • /issues/140419

• Nonzero Function Compilation Error: This issue reports a failure in the torch.export.export function when attempting to compile a PyTorch model that uses torch.nonzero. It results in a runtime assertion error related to symbolic size constraints, which previously worked in version 2.4 but now fails in version 2.5. This regression can hinder the export process for models utilizing the nonzero function.
  • /issues/140421

• Triton Compilation KeyError: This issue reports a bug where the torch.compile function fails when used with Triton built from source. Specifically, it occurs when attempting to compile a model that includes Triton modules, resulting in a KeyError related to the backend compiler. This issue can disrupt the integration of Triton with PyTorch models.
  • /issues/140423

• Graph Functionality Enhancement Proposal: This issue discusses the need to enhance the functionality of torch.compiler.allow_in_graph to accommodate a wider range of scenarios. It particularly focuses on the complexities involved with handling inputs such as neural network module inputs, local functions with closed-over variables, and methods that utilize temporary objects as self arguments. Improving this functionality can enhance the flexibility of graph-based computations in PyTorch.
  • /issues/140437

• Weight-Normed Linear Layers Accuracy Failures: This issue reports accuracy failures when using the inductor backend for weight-normed linear layers in PyTorch. It highlights that while certain input feature sizes work correctly, others lead to significant regressions in model training across multiple PyTorch versions with Python 3.12. This inconsistency can affect the reliability of models trained with weight normalization.
  • /issues/140452

• Inductor Scalar View Bug: This issue reports a bug in which the Inductor component of the PyTorch library incorrectly attempts to fuse scalar views of a CPU tensor into GPU kernels. This results in a ValueError when executing a function that combines tensors from different devices. This issue can disrupt the expected behavior of tensor operations across devices.
  • /issues/140457

• Generic nn.Module Proposal: This issue discusses the proposal to make the nn.Module class in PyTorch generic. It aims to implement a solution that allows for type checking without altering its metaclass or introducing a .pyi file, while also ensuring compatibility with downstream usage in type signatures. This enhancement can improve the usability and type safety of custom modules in PyTorch.
  • /issues/140458

• Non-Contiguous Inputs Support: This issue addresses the lack of support for non-contiguous inputs in the Aten operations within PyTorch. It highlights a specific problem with the aten._weight_norm_interface_backward function and suggests that this limitation may be indicative of a broader issue across various operators in the framework. Improving support for non-contiguous inputs can enhance the flexibility of tensor operations.
  • /issues/140462

• Sympy Expressions Operation Prohibition: This issue proposes to prohibit all operations on sympy expressions within the Inductor framework. This aims to ensure that code handling dynamic shapes is more reliable and to streamline the usage of these expressions through a limited set of functions. This prohibition can enhance the stability of operations involving dynamic shapes.
  • /issues/140463

• LazyVariableTracker Bug: This issue describes a bug in the PyTorch library where the LazyVariableTracker incorrectly treats all integer values as literals during the rebuilding process. This leads to unexpected behavior in the execution of a neural network module. Addressing this issue is crucial for maintaining the integrity of the autograd engine.
  • /issues/140464

• TorchScript Wrapper Module Bug: This issue describes a bug in the TorchScript functionality where scripting a wrapper module that contains an interface-implementing submodule leads to a failure. This occurs due to the wrapper module caching the submodule's interface type, causing it to ignore additional methods defined in the submodule when it is subsequently scripted on its own. This issue can hinder the usability of wrapper modules in TorchScript.
  • /issues/140468

• Non-Leaf Tensor Optimization Error: This issue reports a ValueError encountered when attempting to optimize a non-leaf Tensor in PyTorch. This occurs specifically when using the torch.optim.SGD optimizer on a Tensor that has been modified to no longer require gradients. This error can disrupt the expected behavior of optimizers in training workflows.
  • /issues/140469

• Runtime Error in Multi-GPU Fine-Tuning: This issue involves a RuntimeError occurring in the _group_tensors_by_device_and_dtype function of PyTorch's optimizer. It happens when attempting to fine-tune a model using HuggingFace's Trainer class across multiple GPUs with torchrun, specifically when the gradients are in a different data type (bfloat16) than the model parameters (float32). This mismatch disrupts the expected tensor alignment required for distributed training.
  • /issues/140471

• Conda Recipe Build Error: This issue pertains to a compilation error encountered when building a conda recipe for the PyTorch project. It specifically relates to missing C++ standard library functions such as std::unordered_map and std::vector, which are not being recognized in the Handlers.cpp file of the c10d control plane. This issue can hinder the successful building of PyTorch from source.
  • /issues/140497

• Output Discrepancy with Compile Optimization: This issue reports that using the @torch.compile optimization in PyTorch version 2.2.1+cu121 leads to slight discrepancies in output across eight GPUs on the same machine. The maximum error observed is 4.7684e-07, while the same code produces identical outputs when the optimization is not applied. This inconsistency raises concerns about the reliability of the compilation optimization.
  • /issues/140513

• Fused AdamW Optimizer Runtime Error: This issue involves a runtime error occurring in the fused AdamW optimizer when the fused parameter is set to `True. It specifically indicates a mismatch in tensor shapes during the optimization step in a training script using PyTorch's FSDP (Fully Sharded Data Parallel) with a world size of 1. This error can disrupt the expected behavior of the optimizer in training workflows.
  • /issues/140514

• Partial Backward Compilation Challenges: This issue discusses the challenges and potential solutions related to implementing partial backward compilation in PyTorch's autograd engine. It specifically focuses on optimizing certain layers during backpropagation while maintaining eager mode for others, addressing various technical hurdles encountered in the process. This enhancement could improve the efficiency of backpropagation in complex models.
  • /issues/140516

• Padded Dense Operators Request: This issue requests the implementation of autograd functionality for padded dense forward and backward operators in the PyTorch library. This is similar to existing implementations for jagged tensor operators and aims to enhance user capabilities in model scripting. Improving support for padded dense operations can significantly benefit users working with variable-length inputs.
  • /issues/140520

• Magma-CUDA Package Update: This issue addresses the need to update the build jobs that currently utilize the magma-cuda package. It indicates the necessity to transition to a new version of the package, as discussed in related pull requests. Keeping dependencies up to date is crucial for maintaining compatibility and performance in the PyTorch library.
  • /issues/140538

• Torch JIT Trace Bug: This issue reports a bug in the PyTorch library where the torch.jit.trace function fails to completely trace operations in a custom model. It specifically omits the data-dependent control flow operation that updates an accumulator, leading to discrepancies in the model's output compared to the expected behavior. This issue can hinder the usability of tracing in custom models.
  • /issues/140539

• FSDP + TP Example Error: This issue reports an error encountered when attempting to run the FSDP + TP official example on a machine with 8 H100 GPUs. It specifically indicates that the "Attribute arch of node cpu not found" error arises only from the last four GPUs, while the first four GPUs operate without issue. This inconsistency raises concerns about the compatibility of the example with different GPU configurations.
  • /issues/140541

• AOTI Model Package Loader Bug: This issue pertains to a bug in the AOTIModelPackageLoader::run method. The expected dispatch to the specific device container runner (such as AOTIModelContainerRunnerCuda) fails due to the base class's run method not being declared as virtual and differing function signatures. This leads to incorrect behavior when handling GPU streams, particularly causing crashes for XPU.
  • /issues/140546

• SkipIfXYZ Decorators Bug: This issue addresses a bug where the skipIfXYZ decorators in the PyTorch testing framework inadvertently skip all CPU tests when running on a device that supports MPS. This leads to unintended test omissions, which can affect the reliability of the testing process. Ensuring that only relevant tests are skipped is crucial for maintaining comprehensive test coverage.
  • /issues/140560

• VQGAN Model Timeout: This issue involves a timeout occurring during the allreduce operation while training a VQGAN model. The process hangs without throwing specific errors, despite successful execution for several hours and checks indicating that the GPU memory usage is low and data loading is functioning correctly. Addressing this timeout issue is essential for maintaining efficient training workflows.
  • /issues/140563

• Torch Where Function Bug: This issue reports a bug in the PyTorch library where the torch.where() function incorrectly identifies the indices of True entries in a tensor when executed on the Apple M1 GPU. This results in one of the indices being off by one compared to the expected output from the CPU. This inconsistency can lead to unexpected behavior in tensor operations.
  • /issues/140570

• AssertionError in Export Process: This issue reports an AssertionError encountered in the replace_autocast_with_hop_pass function during the export process of a PyTorch model. It indicates a problem with handling nested autocast scenarios, which can disrupt the expected behavior of models during export.
  • /issues/140589

• Ecosystem Libraries Memory Allocation Issues: This issue reports that many ecosystem libraries are encountering failures due to std::bad_alloc errors following a specific date. This highlights potential memory allocation problems in the PyTorch framework, which can affect the stability and performance of dependent libraries.
  • /issues/140590

• Computation Graph Inclusion Bug: This issue addresses a bug in the PyTorch library where an arbitrary method is incorrectly allowed to be included in the computation graph. This can potentially lead to type mismatches and errors, and it proposes to enforce stricter handling by ensuring only compatible methods are added. This enhancement can improve the reliability of the computation graph.
  • /issues/140591

• Inductor Pattern Matcher Bug: This issue reports a bug in the PyTorch library related to the inductor pattern matcher. It specifically indicates that a missing 'val' key in a tensor's metadata is causing a KeyError during the execution of a model prediction. This suggests that some pass is not correctly propagating fake tensor information, which can disrupt the expected behavior of models.
  • /issues/140592

• Torch Export Function Bug: This issue reports a bug encountered when attempting to export a PyTorch model using the torch.export function with strict=False and keyword arguments. It results in a ValueError due to an unsupported input type, specifically when using the transformers library's BatchFeature. This issue can hinder the export process for models utilizing the transformers library.
  • /issues/140596

• Flex Attention API Enhancement Proposal: This issue proposes an enhancement to the flex attention API in PyTorch. It aims to enable multi-head cross-attention between NJT queries and keys with differing sequence lengths, suggesting modifications to the existing functions to accommodate this functionality. This enhancement can improve the flexibility and usability of attention mechanisms in PyTorch.
  • /issues/140598

• Lerp Function Type Promotion Bug: This issue reports a bug in the PyTorch library where the lerp_ function fails to correctly type promote a scalar when used with tensors of type float64. This occurs specifically in the context of compiling the single tensor Adam/W with a tensor beta1. This issue can disrupt the expected behavior of the lerp_ function in tensor operations.
  • /issues/140601

• From NumPy Function Behavior: This issue addresses the unexpected behavior of the torch.from_numpy() function. It returns a CPU tensor instead of a tensor on the specified device when used within a device context manager set to "meta." This inconsistency can lead to confusion and unexpected results in tensor operations.
  • /issues/140602

• C++ Compiler Redefinition Error: This issue pertains to a C++ compiler redefinition error encountered in the XlmRoberta model within the PyTorch project. It specifically highlights a conflict due to the redeclaration of the variable 'buf6_scalar' in the generated code files. This issue can disrupt the successful compilation of models utilizing the XlmRoberta architecture.
  • /issues/140606

• Export Process Error Message Improvement: This issue reports a confusing and cryptic error message encountered during the export process in a PyTorch project. It highlights the need for improved error handling to provide users with clearer information about exceptions related to malformed arguments. Enhancing error messages can significantly improve the user experience during model export.
  • /issues/140607

• Non-Actionable Error Message in AOTI Artifact Generation: This issue reports a non-actionable error message encountered when attempting to generate an AOTI artifact using a specific script with the T5 model from the Transformers library. It highlights the need for clearer guidance on handling non-pytree-able output types and suggests improvements to the registration API for user-defined data structures.
  • /issues/140615

• AOTI Compile and Package Function Bug: This issue describes a bug encountered when attempting to run the aoti_compile_and_package function on the NaViT model from the vit-pytorch library. It results in an AttributeError due to an integer object lacking a 'node' attribute during the execution of the AOT compilation process. This issue can hinder the usability of AOT compilation for specific models.
  • /issues/140625

• Symbolic Graph Representation Method Proposal: This issue seeks to implement a method for obtaining the symbolic graph representation of both the forward and backward passes of a PyTorch function using Dynamo. This aims to facilitate the creation of a scan operator akin to jax.lax.scan, while addressing the challenges encountered in the process. This enhancement can improve the flexibility of graph-based computations in PyTorch.
  • /issues/140630

• Node 16 Removal Impact on Nova Workflows: This issue pertains to the failure of some Nova workflows in a GitHub project due to the removal of Node 16 from the actions runner. This has resulted in missing GLIBC versions required by Node 20, thereby impacting various domain libraries such as torchvision and torchaudio. Addressing this issue is crucial for maintaining the functionality of workflows dependent on specific Node versions.
  • /issues/140631

• Test Coverage Discrepancy: This issue pertains to the failure of several tests in test/test_ops.py when executed on a machine with a GPU. It specifically highlights that CPU-only tests are not being run in such environments, raising concerns about the testing coverage and the need for continuous integration jobs to address this discrepancy. Ensuring comprehensive test coverage is essential for maintaining the reliability of the library.
  • /issues/140635

• Memory Usage Reporting Inconsistency: This issue addresses a discrepancy in the torch.cuda.memory_usage() function. It currently returns memory usage in bytes for Nvidia GPUs and in megabytes for AMD GPUs, leading to potential confusion and inconsistency in how memory usage is reported across different hardware platforms. Standardizing memory reporting can improve the usability of memory management tools in PyTorch.
  • /issues/140638

• Batch Normalization Documentation Error: This issue highlights a documentation error in the PyTorch library. The formulas for Batch Normalization and Layer Normalization are incorrectly presented as identical, failing to specify that the mean and variance in the Batch Normalization formula are computed over the mini-batch. This suggests a more accurate representation of the Batch Normalization formula that includes this distinction.
  • /issues/140640

• GemmGrouped Operator Proposal: This issue proposes the addition of a built-in GemmGrouped operator in PyTorch. This aims to simplify the current process of using the grouped_gemm operator, which currently requires complex workarounds involving third-party extensions or custom bindings. Enhancing the operator set can improve the usability and performance of matrix operations in PyTorch.
  • /issues/140676

• Test Case Monitoring Issue: This issue reports a failure in the test case TestExport.test_slice_with_floordiv located in test/export/test_export.py. It is not currently being monitored in the trunk CI, indicating that the test is expected to fail but has not been properly documented or handled in the continuous integration process. Proper monitoring of test cases is essential for maintaining the reliability of the testing framework.
  • /issues/140678

• Performance Regression in MAML Benchmark: This issue reports a performance regression in the MAML benchmark for both FP32 and AMP FP16 configurations following the 2024-11-11 nightly release of PyTorch. It highlights significant drops in speed and efficiency compared to previous versions, particularly in static shape C++ wrappers. Addressing this regression is crucial for maintaining the performance of benchmarks in PyTorch.
  • /issues/140683

• Segmentation Fault in Profiler: This issue reports a segmentation fault occurring in the torch.autograd.profiler.profile function when used in a multi-threaded context with GPU availability. This indicates a problem with thread management and error handling in the PyTorch library. Addressing this issue is essential for ensuring the stability of profiling tools in multi-threaded environments.
  • /issues/140684

• CUDA Memory Allocator Runtime Error: This issue reports a runtime error encountered when attempting to change the current CUDA memory allocator in PyTorch. It specifically states "Can't swap an already initialized allocator," suggesting a potential problem with the documentation or the implementation of custom memory management in the library. Improving the documentation and implementation can enhance the usability of memory management features.
  • /issues/140687

• AMP Performance Regression: This issue reports a performance regression in the AMP (Automatic Mixed Precision) implementation for the opacus_cifar10 and functorch_dp_cifar10 benchmarks following the nightly release on November 11, 2024. It highlights significant drops in speed and efficiency compared to previous versions. Addressing this regression is crucial for maintaining the performance of AMP in PyTorch.
  • /issues/140689

• Adaptive Average Pooling Discrepancy: This issue reports a discrepancy in the output of the torch.nn.functional.adaptive_avg_pool3d function when using a bfloat16 tensor on CPU versus GPU. It highlights that the results are not consistent within specified tolerance levels, which can lead to unexpected behavior in applications relying on this function.
  • /issues/140696

• Floating Point Exception in Weight Norm: This issue reports a bug in the PyTorch library where a floating point exception occurs and causes a core dump when the input parameter g is an empty tensor with shape torch.Size([0]) while using the torch._weight_norm function. This issue can disrupt the expected behavior of weight normalization operations.
  • /issues/140699

• Inconsistent Results in Special Function: This issue reports a bug where the torch.special.polygamma function produces inconsistent results when executed on CPU versus GPU for a given float tensor. This indicates a potential discrepancy in the implementation or numerical stability between the two platforms, which can affect the reliability of scientific computations.
  • /issues/140701

• Kernel Caching Warning: This issue reports a warning encountered when using the Tensor.prod function on a CUDA-enabled machine. It indicates that kernel caching is disabled due to the absence of the PYTORCH_KERNEL_CACHE_PATH or HOME environment variable. Addressing this warning is essential for optimizing performance in tensor operations.
  • /issues/140702

• Get Forward Shape Method Request: This issue is a feature request for the implementation of a .get_forward_shape(...) method in the PyTorch library. It would allow users to programmatically retrieve the return shape of the nn.Module.forward(...) method, which is currently documented but not accessible through an API. This enhancement can improve the usability of model introspection in PyTorch.
  • /issues/140703

• Vmap with MPS Tensors Bug: This issue reports a bug where using vmap with MPS tensors in PyTorch raises an exception due to a shape mismatch between the input and target tensors when attempting to compute the mean squared error loss. This issue can disrupt the expected behavior of vmap in tensor operations.
  • /issues/140704

• MPS Backend Random Number Generation Performance: This issue reports that random number generation on the MPS (Metal Performance Shaders) backend is significantly slower—by a factor of 3 to 10—compared to the CPU on a recent MacBook Pro M3. It also notes instances of the process hanging indefinitely, prompting a discussion on proper benchmarking techniques and performance optimization strategies.
  • /issues/140706

• Buffer Mutation Runtime Error: This issue discusses a RuntimeError encountered in PyTorch when mutating a buffer to change its requires_grad property. This leads to problems during the backward pass if the graph is traversed multiple times, highlighting the need for proper management of saved tensors in the autograd engine.
  • /issues/140707

• Torch Where Function Export Bug: This issue reports a bug in PyTorch where using torch.where(condition) within a module causes an error during export with torch.export. This is specifically when combined with torch.inference_mode and dynamic shapes, while noting that a similar operation using torch.nonzero(..., as_tuple=True) works without issue. This inconsistency can disrupt the expected behavior of export operations.
  • /issues/140710

• NaN Values in CIFAR-10 Training: This issue reports that after approximately 13 iterations of training on the CIFAR-10 dataset, the tensor img_syn begins to contain numerous "NaN" values. The same training process works correctly on the MNIST dataset, indicating a potential problem with the model's handling of synthetic data generation or loss calculation specific to the CIFAR-10 dataset.
  • /issues/140711

• Communication Operator Implementation Guidance: This issue highlights the absence of a fake implementation or meta kernel for the Communication Operator in PyTorch. This is necessary for contributors seeking to develop this feature and requires guidance on available examples and next steps. Providing this guidance can facilitate the development of communication operators in PyTorch.
  • /issues/140712

• MPS Backend NotImplementedError: This issue reports a bug in the PyTorch library where the torch.lobpcg() function successfully produces output when executed on a CPU but raises a NotImplementedError when attempted on a GPU using the Metal Performance Shaders (MPS) backend. This indicates that certain operations are not currently supported on that device, which can hinder the usability of the library on MPS.
  • /issues/140718

• Optional NVIDIA Dependencies Installation Proposal: This issue proposes making the installation of NVIDIA-related dependencies optional when installing the PyTorch library. This would allow users with pre-installed CUDA components to avoid redundant installations by using an extras requirement in the setup configuration. This enhancement can improve the installation experience for users.
  • /issues/140737

• ONNX Export Input Names Bug: This issue reports a bug in the onnx.export(dynamo=True) function where the processing of input_names fails when using dynamic_axes and list inputs. This results in incorrect input name generation and errors related to input name counts, which can disrupt the export process for models utilizing ONNX.
  • /issues/140748

• Clear Autocast Cache Function Tracing Issue: This issue pertains to the inability to trace the torch.clear_autocast_cache function when compiling a module with torch.compile. It results in a warning about a graph break due to this unsupported built-in function, and the user is seeking potential workarounds for this limitation. Addressing this issue is essential for maintaining the integrity of the compilation process.
  • /issues/140759

• HOPs Module Registration Improvement Proposal: This issue addresses the problem that the current tracer.root.register_module function in HOPs does not automatically deduplicate module names. It proposes the creation of a new register_new_module API that would generate unique qualified names and prevent the overriding of existing modules. This enhancement can improve the usability of module registration in HOPs.
  • /issues/140760

• Export Report Formatting Update Proposal: This issue addresses the need to update the formatting of the export report in the ONNX project. It suggests rearranging the order of model graph acquisition methods to prioritize torch.export.export(..., strict=False) and separating the run_decomposition process into its own distinct step. Improving the export report can enhance the clarity and usability of export processes in PyTorch.
  • /issues/140762

• Padding-Free Training in FlexAttention Proposal: This issue proposes a feature request for enabling padding-free training (PFT) in the FlexAttention mechanism. It suggests modifying the softmax operation to normalize attention scores based on a subset of tokens, thereby eliminating the need for padding tokens and improving computational efficiency during batch training and inference with variable-sized sequences.
  • /issues/140764

• KeyError in Inductor Package: This issue addresses a KeyError that occurs in the torch._inductor package when attempting to create a cache directory using a non-existent user account. It highlights the need for a fallback mechanism to avoid this error in production environments where user accounts may not be set up. Implementing this fallback can enhance the robustness of the Inductor package.
  • /issues/140765

• AOT Compilation Inconsistency Bug: This issue reports a bug encountered during the AOT compilation of an exported model. The stride1_for_last_dim function behaves inconsistently due to the incorrect use of the is operator instead of == when comparing sympy.Symbol instances. This leads to a crash in the compilation process, which can disrupt the usability of AOT compilation in PyTorch.
  • /issues/140766

• Unclear Attention Mechanism Usage: This issue discusses the unclear usage of attention mechanisms in the torch._transformer_encoder_layer_forward function. It specifically questions whether it utilizes the SDPA backend or the native multi-head attention, suggesting that there may be optimization opportunities if the latter is being used instead of the potentially more efficient SDPA implementation.
  • /issues/140768

• CTC Loss Backward Kernel Error: This issue reports a bug related to a compute-sanitizer error encountered in the ctc_loss_backward_log_beta_gpu_kernel function. This occurs when using the PyTorch library with specific configurations and large batch sizes, leading to potential memory management problems during the backward pass of the CTC loss computation. Addressing this error is crucial for maintaining the stability of CTC loss computations.
  • /issues/140777

• Sparse Sampled Addmm Floating Point Exception: This issue reports a bug in the PyTorch library where the function torch.sparse.sampled_addmm causes a floating point exception and crashes under certain input conditions. This highlights potential instability in the sparse CSR tensor support, which can disrupt the expected behavior of sparse tensor operations.
  • /issues/140778

• Hubert Model Test Hanging Issue: This issue pertains to the CTCLossLogAlphaKernelFunctor hanging during the execution of the test_retain_grad_hidden_states_attentions test for the hubert model in Hugging Face Transformers. This is likely due to early return statements conflicting with a barrier in the kernel implementation. Addressing this hanging issue is essential for maintaining the reliability of tests in the Transformers library.
  • /issues/140781

• ImportError in Test Script: This issue reports an ImportError encountered while attempting to run the test_torch.py file. It specifically indicates that the name 'skipIfMPS' cannot be imported from the torch.testing._internal.common_utils module, suggesting a compatibility problem between the test script and the installed version of PyTorch. This issue can disrupt the execution of test scripts in the library.
  • /issues/140783

• Memory Management on Apple Silicon: This issue addresses the inefficiency in PyTorch's handling of memory on Apple Silicon devices. It specifically questions the necessity of copying tensors between CPU and MPS, which leads to increased memory usage. It suggests the potential use of MTLStorageMode.shared to optimize memory management. Improving memory management can enhance the performance of PyTorch on Apple Silicon.
  • /issues/140787

• CUDA Out-of-Memory Error: This issue describes a memory leak leading to a CUDA out-of-memory error that occurs after a recent code change in a PyTorch project. The user suspects it is linked to specific modifications in tensor operations involving angular coordinates. Addressing this memory leak is crucial for maintaining the stability of CUDA operations in PyTorch.
  • /issues/140792

• Increased Memory Usage with Torch Compile: This issue reports a significant increase in CPU memory usage (by 900MB-1GB) when using the torch.compile feature with the OpenCLIP model ViT-H-14-quickgelu_dfn5b in half precision on a CUDA device. It seeks guidance on how to mitigate this memory overhead, particularly in a resource-constrained environment. Addressing this memory overhead is essential for optimizing resource usage during model training.
  • /issues/140795

• Discrepancy in Special Function Output: This issue reports a discrepancy in the output of the torch.special.xlog1py function when executed on CPU versus GPU. It highlights that the results differ beyond acceptable tolerance levels, which could indicate a potential bug in the PyTorch library. Ensuring consistency in special function outputs is crucial for maintaining the reliability of numerical computations.
  • /issues/140796

• Import Failure in cuSPARSE Library: This issue reports a bug in PyTorch versions 2.5.0 and 2.5.1, where an import failure occurs due to an undefined symbol in the NVIDIA cuSPARSE library. This prompts users to revert to version 2.4.1 as a temporary fix. Addressing this import failure is essential for maintaining compatibility with the cuSPARSE library.
  • /issues/140797

• Python 3.8 Installation Failure: This issue reports a failure when attempting to install Pytorch version 2.5.0 using Python 3.8 on Ubuntu 20.04. It highlights the need for official support for Python 3.8 in Pytorch 2.5.x. Ensuring compatibility with different Python versions is crucial for maintaining a broad user base.
  • /issues/140798

• ROCm Triton Kernel Argument Issue: This issue pertains to a bug in the ROCm implementation where an incorrect number of arguments is being passed to a custom Triton kernel. This leads to an exception that suggests a misalignment between expected and provided kernel arguments, particularly when using specific tuning parameters. Addressing this issue is essential for ensuring the correct execution of custom kernels in ROCm.
  • /issues/140800

• Shape Mismatch Error in Mask and Slice Assignment: This issue discusses a potential bug in PyTorch where the user encounters a shape mismatch error when attempting to perform mask and slice assignment simultaneously on tensors. This occurs specifically when trying to assign values from one tensor to another based on a boolean mask. Addressing this issue is crucial for ensuring the expected behavior of tensor assignments.
  • /issues/140802

• Multi-Device CPU Test Configuration Bug: This issue addresses a bug in the test case test_multi_device_cpu. The AOT Inductor is incorrectly configured to run with GPU settings in a CPU container, leading to a crash in the SYCL API due to a nullptr stream. Refining the test case is necessary to ensure proper configuration and execution in different environments.
  • /issues/140805

• XPU Test Case Crash: This issue reports a crash in the test case test_generic_stream_event_xpu within the test_xpu.py file. It occurs due to the unavailability of profiling information because the associated event queue lacks the 'enable_profiling' property when using the XPU support package version 2025.0. Addressing this crash is essential for maintaining the reliability of XPU tests in PyTorch.
  • /issues/140814

• Performance Impact of Split with Sizes Function: This issue discusses the performance impact of the split_with_sizes(lengths) function being called excessively during the auto-grad graph replication process in PyTorch. It leads to significant compile time increases, particularly when the input lengths are large. This behavior may be linked to the functionalization view replay mechanism, and addressing it can improve compilation efficiency.
  • /issues/140835

• AOTI Compilation Bug with Symbolic Integers: This issue pertains to a bug in the PyTorch library where the torch.cond function fails to compile when used with unbacked symbolic integers in Ahead-of-Time Compilation (AOTI). This is demonstrated by a provided reproduction script and error messages. Addressing this compilation issue is essential for ensuring the usability of conditional operations in AOTI.
  • /issues/140842

• LSTM Model Performance Regression: This issue reports that the performance of a compiled LSTM model is significantly worse than its non-compiled counterpart when using CUDA. It highlights problems with kernel execution time and the number of operations, seeking a solution to revert to the cuDNN implementation for LSTM while still compiling other model components with Triton. Addressing this regression is crucial for maintaining the performance of LSTM models in PyTorch.
  • /issues/140845

• Flex Attention Export Bug: This issue describes a bug encountered when attempting to export a minimal attention model using the flex_attention function in PyTorch. It results in a runtime error indicating that querying for contiguous memory formats is not yet implemented within the vmap function for non-contiguous formats. This issue can hinder the usability of flex attention in model exports.
  • /issues/140849

• FlexAttention ROCM Integration Tracking: This issue pertains to the tracking of tasks related to the integration and functionality of FlexAttention with ROCM in a GitHub project. It indicates the need for collaboration among various contributors to ensure successful integration. Proper tracking can facilitate the development and implementation of FlexAttention features in ROCM.
  • /issues/140855

• Windows Local Build DLL Error: This issue reports an error encountered when attempting to use a locally built version of PyTorch (version 2.5 or higher) on Windows. It specifically indicates that a required DLL file, "shm.dll," or one of its dependencies could not be found, despite the file being present in the specified directory. Addressing this error is essential for ensuring the usability of local builds on Windows.
  • /issues/140875

• LibTorch Program Build Failure: This issue pertains to a failure encountered while attempting to build a libtorch program that utilizes XPUs. The build process results in errors related to missing target properties and unresolved dependencies despite following the provided documentation and troubleshooting steps. Addressing this build failure is crucial for ensuring the successful compilation of libtorch programs.
  • /issues/140877

• Usability Problems with fr_trace Tool Output: This issue highlights several usability problems encountered while analyzing the output from a previous GitHub issue related to the fr_trace tool. It includes confusion over an unexpected version of the script, missing required parameters, and difficulties with the output format that hindered parsing. Improving the usability of the fr_trace tool can enhance the user experience in analyzing trace outputs.
  • /issues/140879

• Cross Entropy Optimization Proposal: This issue proposes the implementation of a more efficient version of cross entropy for large vocabulary sizes. This would enable smaller models to utilize significantly larger batch sizes, leveraging a Pure Triton implementation for optimization based on input/output dimensions and data types. Enhancing cross entropy efficiency can improve the performance of models in NLP tasks.
  • /issues/140881

• MLP Networks vmap Performance Comparison: This issue discusses the unexpected differences in output when comparing the performance of two MLP networks using vmap versus a non-vmapped function. It also examines the impact of compile on runtime, despite both methods being expected to yield similar results in a double Q learning context. Addressing these discrepancies is crucial for ensuring the reliability of performance comparisons in PyTorch.
  • /issues/140883

• Torch Compile Runtime Error with NumPy: This issue reports a bug in the nightly release of PyTorch where the torch.compile function attempts to invoke a PyTorch operation on a NumPy array instead of converting it to a PyTorch tensor. This results in a runtime error when the default device is set to CUDA. Addressing this issue is essential for ensuring the correct behavior of the torch.compile function.
  • /issues/140884

• Nightly Repository Update Delay: This issue reports that the PyTorch nightly repository for cu121 has not been updated since November 12, 2024. It seeks confirmation on whether this delay is expected or indicative of a problem with the build pipeline. Ensuring timely updates to the nightly repository is crucial for maintaining the availability of the latest features and fixes.
  • /issues/140885

• Modernizing Product Computation: This issue proposes replacing the use of reduce(operator.mul) with the more modern and readable math.prod function in the PyTorch library's autograd/_functions/utils.py file. This change aims to improve code readability and maintainability while computing the product of a list of dimensions. Enhancing code clarity can benefit developers working with the PyTorch codebase.
  • /issues/140888

• Inconsistency in Special Function Outputs: This issue reports an inconsistency in the results of the torch.special.xlogy function when executed on CPU versus GPU. It highlights that the outputs differ beyond acceptable tolerance levels, which could lead to unexpected behavior in applications relying on this function. Ensuring consistency in special function outputs is crucial for maintaining the reliability of numerical computations.
  • /issues/140892

• Build Failure with XPU Operations: This issue reports a build failure when attempting to compile the PyTorch library version 2.6.0a0 with XPU operations on Python 3.11. It highlights various configuration errors and warnings related to the CMake setup and compatibility with Windows. Addressing these build failures is essential for ensuring the successful compilation of PyTorch with XPU support.
  • /issues/140894

1.4 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: 32

Summarized Issues:

• Assertion Errors in PyTorch: This topic covers various assertion errors encountered in the PyTorch library, which can disrupt the training and compilation processes. Issues such as the absence of a "source" key in the options dictionary during model compilation and mismatches in tensor sizes during training loops highlight the challenges developers face. These assertion errors can lead to significant setbacks in model performance and usability, necessitating prompt fixes to ensure smooth operation.
  • /issues/118072
  • /issues/138715
  • /issues/137456

• CUDA and Profiling Issues: Several issues relate to problems with CUDA, including failures in initializing CUPTI and discrepancies in behavior when handling NaN values across CPU and CUDA. These issues can result in the absence of profiling data and unexpected results, which complicate debugging and performance analysis. Addressing these CUDA-related problems is crucial for maintaining the reliability and efficiency of PyTorch applications.
  • /issues/118982
  • /issues/140347

• Performance and Efficiency Concerns: The performance of PyTorch is impacted by various factors, including the absence of batching rules for certain functions and unexpected high memory usage with specific optimizers. These performance drops can hinder the efficiency of model training and execution, prompting the need for optimizations and enhancements. Developers are encouraged to address these issues to improve the overall performance of the library.
  • /issues/122432
  • /issues/140874

• Documentation and Standardization Needs: There are calls for standardizing documentation and improving clarity in the PyTorch library, particularly regarding the notation for functions and the return types of helper functions. Inconsistent usages and ambiguous implementations can lead to confusion among users, making it essential to enhance documentation practices. This effort will help ensure that developers can effectively utilize the library without misunderstandings.
  • /issues/140165
  • /issues/139405

• Build and CI Process Issues: Several issues highlight problems in the build and continuous integration (CI) processes, including failures in Docker builds and unexpected regressions in incremental builds. These issues can disrupt development workflows and lead to inefficiencies, necessitating thorough investigation and resolution. Ensuring a robust build process is vital for maintaining the reliability of the PyTorch library.
  • /issues/137361
  • /issues/140432

• Functionality and Compatibility Gaps: There are requests for additional functionality and compatibility improvements within PyTorch, such as support for new device types and enhancements to existing functions. These gaps can limit the usability of the library for certain applications, making it important to address them to meet the diverse needs of the user community. Expanding functionality will enhance the versatility of PyTorch in various contexts.
  • /issues/129652
  • /issues/140420

• Error Handling and Debugging Challenges: Issues related to error handling, such as the lack of informative error messages and problems with parsing tensor constants, can complicate debugging efforts for developers. These challenges can lead to frustration and delays in development, underscoring the need for improved error reporting and handling mechanisms. Enhancing these aspects will facilitate a smoother development experience.
  • /issues/137927
  • /issues/140422

• Inconsistencies in Function Behavior: Several issues report inconsistencies in the behavior of functions within PyTorch, such as discrepancies in output from the Broadcast function and differences in floating-point sums compared to NumPy. These inconsistencies can lead to confusion and unexpected results, highlighting the need for thorough testing and validation of function behavior. Addressing these discrepancies is crucial for maintaining user trust in the library.
  • /issues/129032
  • /issues/140390

• Legacy Code and Maintenance: The removal of outdated legacy code and the need for updates in the library reflect ongoing maintenance efforts within the PyTorch project. Keeping the codebase clean and up-to-date is essential for ensuring long-term sustainability and performance. Regular maintenance helps prevent technical debt and facilitates smoother development processes.
  • /issues/140212

1.5 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.

1. Torch.distributed.tensor.parallel.Style.Rowwiseparallel when send tensor, the use of the underlying operator is aten.to.dtype_layout, lack of distributed strategy. But after adding a policy, AsyncCollectiveTensor no placements error

   • Toxicity Score: 0.55 (Frustration over unresolved issues, defensive responses, skepticism about solutions, fluctuating tone.)
   • This GitHub conversation begins with a user detailing a bug related to tensor operations in a distributed setting, expressing confusion and frustration over the errors encountered. As the discussion progresses, several users chime in, with some attempting to provide solutions while others express skepticism about the proposed fixes. Tension arises when a user points out that previous suggestions did not resolve the issue, leading to a defensive response from another participant. The tone fluctuates between collaborative and confrontational, with some users showing impatience and others trying to maintain a constructive dialogue. Ultimately, the conversation reflects a mix of technical troubleshooting and interpersonal dynamics, highlighting the challenges of collaborative problem-solving in a complex coding environment.

2. [flexattention] torch.compile raises an "AttributeError: 'ComputedBuffer' object has no attribute 'inner_fn'", with a simple attention op

   • Toxicity Score: 0.55 (Limited context, expressions of frustration, technical limitations)
   • This GitHub conversation begins with a user seeking assistance regarding a compilation error encountered while using a specific function in PyTorch. Another user promptly offers to investigate the issue, but tension arises when a request for additional context is made, highlighting the difficulty in debugging without further information. As the conversation progresses, users share code snippets and clarify their attempts to reproduce the error, leading to a more technical discussion about the limitations of the current implementation. A user expresses disappointment over the inability to achieve their intended functionality, which triggers a response about inherent limitations in the system. The tone shifts from collaborative to slightly frustrated as users grapple with the constraints of the tool, culminating in a suggestion to keep the issue open for potential future solutions.

3. Code Bug: Strange/Unexpected Logic in FSDP _recursive_wrap

   • Toxicity Score: 0.55 (Skepticism about assumptions, defensive responses, moments of frustration)
   • This GitHub conversation begins with a user outlining a potential bug in the code, expressing a sense of confusion regarding the logic flow. As the discussion progresses, several users join in, with some showing enthusiasm for the proposed solution while others express skepticism and concern about the implications of the changes. Tension arises when a user questions the validity of the initial assumptions, leading to a defensive tone from the original poster. The conversation fluctuates between collaborative problem-solving and moments of frustration, particularly when users feel their contributions are being overlooked or misunderstood. Overall, the dialogue reflects a mix of constructive feedback and underlying tension as participants navigate differing perspectives on the issue at hand.

4. TorchScript model doesn't work with autocast

   • Toxicity Score: 0.55 (Defensive responses, frustration over clarity, double posting issue)
   • This GitHub conversation begins with a user reporting a bug related to using autocast with a TorchScript model, expressing confusion and seeking assistance. As the discussion progresses, other users join in, some offering potential solutions while others express frustration over the lack of clarity in the original post. Tension arises when a user points out a double post, which leads to a defensive response from the original poster. The overall tone fluctuates between helpfulness and irritation, with some users feeling overwhelmed by the technical details and others becoming increasingly critical of the communication style.

5. [RFC] Distributed manual_seed API for N-D parallelism

   • Toxicity Score: 0.65 (Disagreements on design assumptions, critical tone, unresolved issues, frustration over complexity)
   • This GitHub conversation begins with a user expressing concerns about the complexities of RNG initialization in distributed training, highlighting potential issues with the current implementation. As the discussion progresses, several participants share their perspectives, with some agreeing on the need for a new API while others raise doubts about the feasibility of achieving consistent results across different parallelism setups. Tensions arise as disagreements surface regarding the assumptions made about the behavior of the RNG tracker and the implications of different seed management strategies. The tone fluctuates between collaborative and critical, with users seeking clarity on design choices and expressing frustration over perceived gaps in the proposed solutions. Ultimately, the conversation reflects a mix of constructive feedback and unresolved disagreements, indicating a need for further refinement of the proposed API.

6. FlexAttention gives me an INTERNAL_ASSERT_FAILED during mask_mod

   • Toxicity Score: 0.55 (Urgency, mounting frustration, requests for additional information, emotional strain)
   • This GitHub conversation begins with a user reporting a critical error encountered while using Flex Attention, expressing a sense of urgency and frustration over the issue. Several users, including @jbschlosser and @bhosmer, respond with requests for additional information, such as a reproducible example, to facilitate debugging. The tone shifts as the original poster feels overwhelmed by the technical details and the complexity of the problem, leading to a slight escalation in tension. Other contributors attempt to maintain a constructive atmosphere, but the original poster's mounting frustration hints at potential conflict. Overall, the conversation reflects a mix of urgency, technical inquiry, and emotional strain, with underlying tensions related to the complexity of the issue and the need for collaboration.

7. torch.export ViT+flex attention: Attempting to use FunctionalTensor on its own

   • Toxicity Score: 0.55 (Frustration over reproducibility requests, Tension in expectations, Balancing user contributions and developer needs)
   • This GitHub conversation begins with a user reporting an error encountered while exporting a model with flex attention, prompting several users to chime in with requests for additional information and repro code. As the discussion progresses, tensions rise when one user expresses frustration over the repeated requests for minimal reproducible examples, arguing that it is time-consuming and detracts from their ability to contribute effectively. Other participants attempt to mediate by acknowledging the challenges of debugging without sufficient context and emphasizing the importance of collaboration. The conversation shifts towards a broader discussion about the balance between user contributions and the expectations of the development team, with some users feeling overwhelmed by the demands for reproducibility. Ultimately, the tone remains somewhat tense but constructive, as users seek to find common ground and solutions to the issues raised.

8. Code fails with "Expected curr_block->next == nullptr to be true, but got false"

   • Toxicity Score: 0.55 (Defensive responses, unresolved issues, mixed sentiments)
   • This GitHub conversation begins with a user reporting a bug related to a code failure after an upgrade, expressing a mix of confusion and concern. As the discussion progresses, another user shares their experience with a different configuration, indicating a slight sense of frustration when their solution did not yield consistent results. Tension arises when a request for a reproducible example is made, leading to a defensive response from the original poster, who feels unable to provide the necessary details due to the complexity of their system. The tone shifts to a more collaborative spirit as the original poster offers to assist with debugging, but underlying frustration remains evident.

9. torch.compile breaks with Triton built from source

   • Toxicity Score: 0.55 (Urgency for clarification, technical complexity, misunderstanding, pressure to provide details)
   • This GitHub conversation begins with a user reporting a bug related to torch.compile breaking with a specific version of Triton built from source, expressing frustration over the issue. Another user responds with a suggestion to try a newer version, indicating a more optimistic tone, but later acknowledges a misunderstanding regarding the follow-up comment. A third user then requests further assistance from the original poster, asking for detailed information to help diagnose the problem, which introduces a more technical and collaborative tone. However, the conversation also reveals underlying tension as the original poster's inability to reproduce the issue leads to a sense of urgency and pressure to provide additional context. Overall, the interaction fluctuates between frustration, optimism, and a call for collaboration, with moments of tension arising from the complexity of the issue and the need for clarity.

10. Inductor inappropriately tries to fuse scalar views of a CPU tensor into GPU kernels.

    • Toxicity Score: 0.55 (Dismissive responses, rising frustration, defensive tones)
    • This GitHub conversation begins with a user reporting a bug related to tensor operations across CPU and GPU, expressing confusion and seeking clarification. Several users join the discussion, with some providing potential solutions while others express skepticism about the proposed fixes. As the conversation progresses, tensions rise when a user feels their input is being dismissed, leading to a defensive tone from another participant. The dialogue fluctuates between collaborative problem-solving and moments of frustration, ultimately highlighting a lack of consensus on the issue at hand.

11. [TorchScript] Failure if you script a wrapper module and then an interface-implementing submodule.

    • Toxicity Score: 0.55 (Frustration over unresolved issues, skepticism about solutions, questioning documentation clarity)
    • This GitHub conversation begins with a user reporting a bug related to the TorchScript functionality, expressing confusion and concern over the error encountered during the scripting process. As the conversation progresses, other users join in, some offering potential solutions while others express skepticism about the proposed fixes. Tension arises when a user indicates that previous suggestions did not resolve the issue, leading to a slight escalation in frustration. The tone shifts as users begin to question the clarity of the documentation and the reliability of the TorchScript feature, which further fuels the discussion. Overall, the conversation reflects a mix of collaborative problem-solving and underlying frustration, with users navigating the complexities of the issue at hand.

12. RuntimeError in _group_tensors_by_device_and_dtype (torch/optim/optimizer.py) when using torchrun on N>1 GPUs.

    • Toxicity Score: 0.55 (Version discrepancies, frustration over unclear error messages, shifting blame between libraries)
    • This GitHub conversation begins with a user detailing a bug encountered while using torchrun with multiple GPUs, expressing confusion over the error message received. As the discussion progresses, other users join in, sharing their own experiences and confirming similar issues, which leads to a collaborative investigation into the root cause. Tensions arise when users suggest potential fixes or workarounds, with some expressing frustration over the lack of clarity in the error messages and the implications for their workflows. The tone shifts from confusion to a more analytical approach as users begin to pinpoint the problem to specific versions of the libraries involved, ultimately leading to a consensus that the issue may lie within the integration of the libraries rather than PyTorch itself. The conversation concludes with a user proposing to close the issue, while leaving the door open for further discussion if necessary.

13. C++ standard library functions not found when compiling c10d debug handlers

    • Toxicity Score: 0.55 (Frustration, defensive responses, questioning of setup)
    • This GitHub conversation begins with a user reporting a compilation error related to missing standard library functions, expressing confusion about the absence of necessary includes in the code. As the discussion progresses, other users join in, offering suggestions and potential solutions, but the original poster remains frustrated as none of the proposed fixes resolve the issue. Tension escalates when a user questions the original poster's setup, leading to defensive responses. The conversation concludes with a mix of supportive and critical comments, reflecting a range of sentiments from helpfulness to irritation.

14. [autograd engine && compiled autograd] support for partial backward compilation

    • Toxicity Score: 0.55 (Frustration over inadequate responses, impatience with technical limitations, critical tone emerging)
    • This GitHub conversation begins with the original poster outlining a complex technical challenge related to optimizing backpropagation in PyTorch. As the discussion progresses, various users express curiosity and seek clarification on specific issues raised, with some showing enthusiasm for potential solutions. However, tension arises when certain users voice frustration over the lack of direct support for their proposed methods, leading to a more critical tone. The conversation remains focused on technical details, but the emotional undercurrents suggest a growing impatience among some participants, particularly when responses are perceived as inadequate or dismissive. Overall, the interaction reflects a mix of collaborative inquiry and underlying frustration, indicating a potential for conflict if issues remain unresolved.

15. Update build jobs that use magma-cuda to use the new package

    • Toxicity Score: 0.55 (Impatience, questioning effectiveness, defensive responses)
    • This GitHub conversation begins with a user referencing a pull request, indicating a desire to update build jobs related to a specific package. As the discussion progresses, various participants engage with differing levels of enthusiasm and urgency, with some expressing eagerness for the update while others show signs of impatience or confusion regarding the implementation details. Tensions arise when a user questions the effectiveness of the proposed changes, leading to a defensive response from another participant who feels their contributions are being undermined. Overall, the tone fluctuates between collaborative and confrontational, reflecting a mix of support and frustration among the contributors.

16. "Attribute arch of node cpu not found" error when running FSDP+TP on 8 H100s

    • Toxicity Score: 0.55 (Frustration, defensive responses, questioning of setup validity, impatience)
    • This GitHub conversation begins with a user reporting a bug related to running a specific example on multiple GPUs, expressing confusion and concern over the error encountered. As the discussion progresses, other users join in, offering suggestions and potential solutions, but the original poster remains frustrated as none of the proposed fixes resolve the issue. Tension escalates when a user questions the validity of the original poster's setup, leading to defensive responses. The tone shifts as some participants express impatience, while others attempt to mediate and refocus the conversation on troubleshooting. Overall, the interaction reflects a mix of collaboration and rising frustration, with underlying tensions surfacing as the problem remains unresolved.

17. ☂️ Many ecosystem libraries started to fail with std::bad_alloc after Nov 1st

    • Toxicity Score: 0.55 (Frustration over overlooked contributions, questioning of solutions, defensive responses)
    • This GitHub conversation begins with a user raising a concern about multiple ecosystem libraries encountering failures, which sets a serious tone for the discussion. As the conversation progresses, various users express their thoughts, with some showing urgency and others maintaining a more neutral stance. Tension arises when certain users question the effectiveness of proposed solutions, leading to moments of frustration and defensiveness among participants. The overall sentiment fluctuates between collaborative problem-solving and moments of irritation, particularly when users feel their contributions are overlooked or misunderstood. The conversation remains focused on finding a resolution, but the underlying tension suggests that further comments could escalate.

18. [export][aoti] Inductor pattern matcher - missing val

    • Toxicity Score: 0.55 (Defensive responses, questioning clarity, rising frustration)
    • This GitHub conversation begins with a user reporting a bug related to a specific error encountered in the code, expressing a sense of urgency and concern. As the conversation progresses, other users, including those tagged in the initial post, begin to chime in, displaying varying degrees of interest and willingness to assist. However, tension arises when some users question the clarity of the initial report, leading to a defensive response from the original poster. The tone shifts as frustration builds, with some participants feeling that their contributions are being dismissed. Ultimately, the conversation reflects a mix of collaborative spirit and underlying tension, as users navigate the complexities of troubleshooting the reported issue.

19. [export][non-strict] Passing in kwargs torch.export fails at non_strict_utils.fakify()

    • Toxicity Score: 0.55 (Misunderstandings, defensive responses, challenges to proposed solutions)
    • This GitHub conversation begins with a user reporting a bug related to the torch.export function, expressing confusion and concern over the error encountered when using specific parameters. As the conversation progresses, other users join in, some offering potential solutions while others share similar experiences, leading to a mix of supportive and frustrated tones. Tension arises when a user challenges the effectiveness of a proposed workaround, prompting defensive responses from others. The overall sentiment fluctuates between collaborative problem-solving and moments of irritation, particularly when misunderstandings occur regarding the use of parameters.

20. [aoti] C++ compiler redefinition error on XlmRoberta

    • Toxicity Score: 0.55 (Frustration expressed, defensive responses, confusion over technical details)
    • This GitHub conversation begins with a user sharing a detailed error message related to a C++ compiler issue, prompting several users to be tagged for their input. As the conversation progresses, one user expresses confusion about the error, leading to a response that attempts to clarify the situation. However, the tone shifts when another user expresses frustration over the lack of a solution, which triggers a defensive response from a different participant. The conversation continues with mixed sentiments, oscillating between attempts at collaboration and moments of tension as users grapple with the technical complexities and differing levels of understanding.

21. Non-actionable error message when output type is not pytree-able for aoti artifact

    • Toxicity Score: 0.55 (Frustration over unresolved issues, defensive responses, potential misunderstandings)
    • This GitHub conversation begins with a user reporting a bug related to an error message encountered while generating an artifact. The initial tone is neutral, as the user provides a detailed description of the issue and includes relevant code snippets. As other users are tagged for their input, the sentiment remains focused on problem-solving. However, tension arises when a user expresses frustration over a previous solution that did not resolve the issue, leading to a more defensive tone from others who feel their contributions are being dismissed. The conversation's trajectory indicates a shift from collaborative troubleshooting to a more contentious exchange, highlighting the potential for misunderstandings and escalating emotions among participants.

22. [AOTI] AOT Compile NaViT - AttributeError: 'int' object has no attribute 'node'

    • Toxicity Score: 0.65 (Lack of responses, frustration expressed, unresolved issues, defensive sentiments)
    • This GitHub conversation begins with a user reporting a bug related to the NaViT model, detailing the steps to reproduce the issue and providing a traceback of the error encountered. As the conversation progresses, several users are tagged for their input, indicating a collaborative effort to troubleshoot the problem. The initial tone is technical and focused on problem-solving, but tension arises as the user expresses frustration over the lack of responses and the complexity of the error. Other users attempt to provide assistance, but their suggestions do not seem to alleviate the original poster's concerns, leading to a mix of supportive and defensive sentiments. The conversation remains unresolved, with an underlying sense of urgency and dissatisfaction from the original poster.

23. Identical Formulas for BatchNorm and LayerNorm

    • Toxicity Score: 0.55 (Escalation of concerns, critical tone, confusion over terminology)
    • This GitHub conversation begins with a user suggesting a patch related to the documentation of Batch Normalization and Layer Normalization. Another user joins in, pointing out that the issue also affects Instance Normalization, which adds a layer of complexity to the discussion. Tension escalates when a third user highlights significant mistakes in the documentation, specifically confusing Batch Normalization with Layer Normalization, which indicates a lack of clarity and could lead to misunderstandings. The overall tone shifts from collaborative to slightly critical as users express concerns about the accuracy of the documentation.

24. [inductor][cpu]maml fp32/amp_fp16 performance regression in 2024-11-11 nightly release

    • Toxicity Score: 0.55 (Frustration over ineffective solutions, questioning of accountability, mixed sentiments)
    • This GitHub conversation begins with a user reporting a performance regression in a recent nightly release, expressing concern over the impact of a specific commit. As the discussion unfolds, other users join in, some offering potential solutions while others share similar experiences, leading to a mix of supportive and frustrated sentiments. Tension arises when a proposed fix does not yield the expected results, prompting a user to question the effectiveness of the suggestions. The tone fluctuates between collaborative and critical, with users urging for clarity and accountability regarding the regression. The conversation concludes with a call for further investigation into the issue, highlighting the need for a resolution.

25. “Can't swap an already initialized allocator” when calling torch.cuda.memory.change_current_allocator

    • Toxicity Score: 0.55 (Frustration over unclear documentation, defensive responses, skepticism about proposed solutions)
    • This GitHub conversation begins with a user reporting a bug related to a specific functionality in a software library, expressing confusion and suggesting that documentation may need updates. As the conversation progresses, other users join in, some offering potential solutions while others express skepticism about the proposed fixes. Tension arises when a user indicates frustration over the lack of clarity in the documentation, leading to a defensive response from another participant who feels their input is being dismissed. The overall tone fluctuates between collaborative and confrontational, with moments of constructive feedback overshadowed by rising irritation among some contributors.

26. [inductor][cpu]opacus_cifar10 and functorch_dp_cifar10 AMP performance regression in 2024-11-11 nightly release

    • Toxicity Score: 0.55 (Frustration over dismissive responses, Defensive language, Confusion about technical details)
    • This GitHub conversation begins with a user reporting a performance regression related to a recent nightly release, expressing concern over the impact of a specific commit. As the discussion progresses, other users join in, sharing their observations and experiences, which range from confusion to frustration regarding the regression. Tension escalates when one user questions the validity of the reported issue, prompting defensive responses from others who feel their concerns are being dismissed. The tone shifts between collaborative problem-solving and moments of irritation, particularly when users reference previous benchmarks and express dissatisfaction with the lack of clarity in the responses. Ultimately, the conversation reflects a mix of technical inquiry and emotional responses, highlighting the challenges of addressing performance issues in collaborative software development.

27. Floating point exception (core dumped) in torch._weight_norm

    • Toxicity Score: 0.55 (Defensive responses, perceived dismissal of contributions, authoritative tone)
    • This GitHub conversation begins with a user recalling a previous fix related to a bug, expressing a sense of nostalgia and mild frustration over the recurring issue. As the discussion progresses, another user closes the conversation, citing it as a duplicate of an earlier issue, which triggers a defensive response from the original poster who feels their contribution is being overlooked. The tone shifts to one of irritation as the original poster emphasizes the importance of their input, while the closing user maintains a more authoritative stance, leading to a palpable tension between the two. The conversation concludes with a reference to a related pull request, suggesting a resolution but leaving some unresolved feelings among participants.

28. inconsistency in torch.special.polygamma on CPU and GPU

    • Toxicity Score: 0.65 (Frustration over unresolved issues, defensive responses, questioning of expertise)
    • This GitHub conversation begins with a user reporting an inconsistency in the results of a specific function across CPU and GPU executions, expressing confusion and seeking clarification. As the discussion progresses, other users join in, some offering potential solutions while others express skepticism about the proposed fixes. Tension arises when a user indicates frustration over the lack of resolution, leading to a more heated exchange about the reliability of the function. The tone shifts from collaborative to defensive, with users questioning each other's expertise and the validity of their approaches. Ultimately, the conversation remains unresolved, with lingering dissatisfaction among participants.

29. Tensor.prod gives a jit warning

    • Toxicity Score: 0.55 (Lack of clarity, rising frustration, skepticism towards solutions)
    • This GitHub conversation begins with a user reporting a bug related to a warning message encountered while using a specific command in a CUDA environment. The initial tone is neutral as the user seeks assistance, tagging several contributors for their input. As responses begin to trickle in, some users express confusion or frustration over the lack of clarity in the warning message, leading to a slight increase in tension. Others attempt to provide solutions, but their suggestions are met with skepticism or further questions, which escalates the sentiment to a more critical tone. Overall, the conversation reflects a mix of collaboration and rising frustration as users navigate the issue together.

30. mutating a buffer to change its requires_grad-ness can cause RuntimeError

    • Toxicity Score: 0.55 (Confusion, mixed reactions, fluctuating tone, emotional responses)
    • This GitHub conversation begins with a user, @fmassa, providing a detailed reproduction of an issue related to a RuntimeError encountered when mutating a buffer in a specific context. As the conversation progresses, other users join in, expressing varying degrees of confusion and concern regarding the error and its implications. Tension arises when @fmassa suggests a workaround that seems to resolve the issue, leading to mixed reactions from others who either support the solution or question its necessity. The tone fluctuates between collaborative and frustrated, particularly when users struggle to understand the underlying cause of the error. Ultimately, the conversation reflects a blend of technical inquiry and emotional responses, with some users feeling overwhelmed by the complexity of the problem.

31. after around 13 iteration, tensor img_syn contains many "NaN" value when trained on cifari10, but on minist dataset, it works fine

    • Toxicity Score: 0.65 (Escalating frustration, repeated NaN issues, urgent pleas for help)
    • This GitHub conversation begins with a user asking for a minimal reproduction of a bug related to NaN values in a tensor during training. Another user responds by providing a detailed function that outlines their approach, indicating that the project is complex. As the conversation progresses, users share logs and outputs, revealing that NaN values appear in the loss and tensor outputs, which raises concerns. Tension escalates when one user expresses frustration that their attempts to resolve the issue, including adjusting the learning rate and applying gradient clipping, have not been successful. The tone shifts to a more urgent and desperate plea for assistance as the conversation continues, highlighting the challenges faced in troubleshooting the problem.

32. No fake impl or Meta kernel for Communication Operator

    • Toxicity Score: 0.65 (Lack of clear examples, frustration expressed, misunderstandings, defensive tone)
    • This GitHub conversation begins with a user expressing a desire to contribute to a feature but is met with a technical error message that indicates a lack of implementation for a specific operator. As the conversation progresses, the user seeks clarification and guidance on how to proceed, while another participant attempts to provide assistance but encounters misunderstandings regarding the error's implications. Tension arises when the original user expresses frustration over the lack of clear examples and support, leading to a defensive tone from the helper. The conversation reflects a mix of confusion, urgency, and a desire for collaboration, but also highlights the potential for escalating frustration if the issues remain unresolved.

33. Make nvidia pypi dependencies optional?

    • Toxicity Score: 0.55 (Defensive responses, mixed sentiments, potential user impact concerns)
    • This GitHub conversation begins with the original poster outlining a proposal to make certain NVIDIA dependencies optional during the installation of a package. A user responds with a concern that while the proposal may benefit some users, it could negatively impact the majority who lack the necessary CUDA toolkit. This response triggers a defensive tone from the original poster, who emphasizes the importance of flexibility for users with specific setups. As the conversation progresses, additional users join in, expressing varying degrees of support and skepticism, leading to a mix of constructive feedback and rising tension over the potential implications of the proposed changes. The overall sentiment fluctuates between collaborative and contentious, reflecting differing priorities among the participants.

34. onnx.export(dynamo=True): input_names processing is broken when dynamic_axes and list inputs are used

    • Toxicity Score: 0.55 (Frustration expressed, misunderstandings, defensive responses)
    • This GitHub conversation begins with a user outlining a bug related to input_names processing in the onnx.export function when using dynamic_axes and list inputs. As the discussion progresses, the user expresses frustration over the inability to specify input names correctly, leading to confusion and errors. Other users join in, some offering potential solutions while others share similar experiences, which adds to the tension. The tone fluctuates between collaborative and frustrated, with moments of defensiveness as users clarify their points. The conversation remains focused on the technical issue, but the mounting frustration hints at a potential for conflict as misunderstandings arise.

35. KeyError in default_cache_dir() when user account doesn't exist

    • Toxicity Score: 0.55 (Dismissive response, potential minimization of concerns, lack of engagement with the issue)
    • This GitHub conversation begins with a user reporting a bug related to the torch._inductor package and its handling of cache directories when user accounts do not exist. The initial comment expresses a clear frustration with the current implementation and suggests a potential solution. In response, another user adopts a more casual and somewhat dismissive tone, asking if the original poster could provide a patch, which may come off as unhelpful. This shift in tone triggers a sense of tension, as the original poster's concern appears to be minimized. The conversation reflects a mix of technical discussion and interpersonal dynamics, with underlying sentiments of frustration and dismissal.

36. CTC compute-sanitizer error in ctc_loss_backward_log_beta_gpu_kernel

    • Toxicity Score: 0.55 (Dismissive responses, rising frustration, unclear communication)
    • This GitHub conversation begins with a user reporting a bug related to a compute-sanitizer error, expressing a sense of urgency and seeking assistance. Another user responds with a detailed code snippet to validate the inputs, indicating a constructive tone but also a hint of frustration regarding the initial report's clarity. As the conversation progresses, a third user suggests that the issue may be linked to the batch size, introducing a potential solution while maintaining a collaborative spirit. However, underlying tension arises as the initial user seems to dismiss the second user's input, leading to a slight escalation in sentiment. Overall, the conversation reflects a mix of helpfulness and rising frustration, with moments of constructive feedback overshadowed by misunderstandings and dismissive remarks.

37. Floating point exception (core dumped) in torch.sparse.sampled_addmm

    • Toxicity Score: 0.65 (Frustration over unresolved issues, defensive responses, skepticism towards solutions)
    • This GitHub conversation begins with a user reporting a bug related to a floating point exception in a specific function, expressing concern over the issue's impact on their work. As the conversation progresses, other users join in, some offering potential solutions while others express skepticism about the effectiveness of those suggestions. Tension arises when a user indicates frustration that previous solutions did not resolve the issue, leading to a defensive response from another participant who feels their input was dismissed. The overall tone fluctuates between collaborative and contentious, with moments of constructive feedback overshadowed by rising frustration and misunderstandings.

38. Weird Memory Leak (OOM) after minor code change [CUDA]

    • Toxicity Score: 0.55 (Frustration over unresolved issues, defensive responses, questioning of suggestions)
    • This GitHub conversation begins with a user reporting a memory leak issue after a recent code change, expressing confusion and concern over the unexpected behavior. As the discussion progresses, other users join in, offering suggestions and troubleshooting steps, but some responses lead to frustration when proposed solutions do not resolve the issue. Tension escalates as the original poster questions the validity of the suggestions, while others defend their recommendations, resulting in a mix of supportive and defensive tones. The conversation reflects a growing sense of urgency and irritation, particularly as the original poster feels their problem is not being adequately addressed.

39. PyTorch >= 2.5.0 revive #111469 (python3.10/site-packages/torch/lib/../../nvidia/cusparse/lib/libcusparse.so.12: undefined symbol: __nvJitLinkComplete_12_4, version libnvJitLink.so.12)

    • Toxicity Score: 0.55 (Frustration over unresolved issues, mixed sentiments, perceived lack of clarity)
    • This GitHub conversation begins with a user expressing a desire to provide a fix or workaround for a previously discussed issue, indicating a collaborative tone. As the conversation progresses, other users engage with varying degrees of optimism and concern, reflecting a mix of sentiments. Tension arises when some users express frustration over unresolved problems and the perceived lack of clarity in the previous discussions. The overall tone shifts between constructive suggestions and moments of irritation, particularly when users feel their concerns are not being adequately addressed. The conversation concludes with a call for further collaboration, though the underlying tension remains palpable.

40. Cannot use mask and slice assignment together

    • Toxicity Score: 0.55 (Frustration over unclear solutions, questioning of validity, defensive responses)
    • This GitHub conversation begins with a user expressing confusion about a potential bug related to variable management principles in a coding example. As the discussion progresses, other users join in, some offering clarifications while others express frustration over the lack of a clear solution. Tension arises when a user questions the validity of the original post, leading to defensive responses from the initial poster. The tone fluctuates between collaborative and confrontational, with some users attempting to maintain a constructive dialogue while others resort to more critical language. Overall, the conversation reflects a mix of confusion, frustration, and attempts at resolution, with underlying tensions surfacing intermittently.

41. [XPU] test_xpu.py::TestXpuXPU::test_generic_stream_event_xpu crashed with XPU support package 2025.0

    • Toxicity Score: 0.55 (Frustration over unclear error messages, Defensive responses, Questioning of solutions)
    • This GitHub conversation begins with a user reporting a bug related to a test crash, expressing concern over the lack of profiling information. Several users are tagged for their expertise, indicating a collaborative approach to resolving the issue. As the conversation progresses, there is a noticeable shift in tone as some users express frustration over the lack of clarity in the error message and the steps needed to reproduce the issue. Tension escalates when one user questions the effectiveness of the provided solutions, leading to defensive responses from others. Overall, the conversation reflects a mix of collaboration and rising frustration, with users striving to find a resolution amidst the growing tension.

42. auto-grad graph replicate split_with_sizes(lengths) X times where X = len(lengths) effecting compile time

    • Toxicity Score: 0.55 (Frustration over complexity, Technical disagreements, Concerns about performance impact)
    • This GitHub conversation begins with a user expressing concern about the performance impact of a specific function in the code, highlighting the frequency of calls to a particular method. Another user responds with a detailed explanation of the underlying issue, referencing specific lines of code and suggesting that the problem may stem from unnecessary operations being generated. Tension arises when the first user expresses frustration over the complexity of the solution and the implications for compile time. The conversation continues with a back-and-forth exchange, where users share insights and potential fixes, but the tone remains somewhat tense as they navigate the technical challenges and implications of their findings. Ultimately, the discussion reflects a mix of collaboration and frustration as they work towards a resolution.

43. compiled LSTM performance is worse than not compiled

    • Toxicity Score: 0.55 (Defensive responses, questioning of approaches, fluctuating tones)
    • This GitHub conversation begins with a user expressing disappointment over the performance of a compiled LSTM model compared to its CUDA counterpart, indicating a significant issue. As the discussion progresses, other users join in, sharing their insights and experiences, which range from supportive to critical. Tension arises when one user questions the validity of another's approach, leading to a defensive response. The tone fluctuates between frustration and constructive feedback, with some users attempting to clarify misunderstandings while others express skepticism about the proposed solutions. Ultimately, the conversation reflects a mix of collaboration and contention, highlighting the challenges of troubleshooting complex technical issues.

44. flex_attention + export_for_inference NYI: querying is_contiguous inside of vmap for memory_format other than torch.contiguous_format

    • Toxicity Score: 0.55 (Frustration over unhelpful suggestions, critical remarks, confusion among users)
    • This GitHub conversation begins with a user reporting a bug related to a specific error encountered while using a PyTorch feature. As the discussion unfolds, several users express confusion and seek clarification on the issue, leading to a mix of supportive and frustrated tones. Tension arises when one user suggests a workaround that another user finds unhelpful, prompting a brief exchange of critical remarks. The conversation concludes with a call for further input from maintainers, indicating a desire for resolution but leaving some participants feeling unresolved.

45. [Windows] pytorch >= 2.5

    • Toxicity Score: 0.65 (Defensive responses, questioning setup, rising irritation)
    • This GitHub conversation begins with a user reporting a bug related to building PyTorch on Windows, expressing confusion and concern over an error message despite the necessary files being present. As the conversation progresses, other users join in, offering suggestions and troubleshooting steps, but the original poster remains frustrated as none of the proposed solutions resolve the issue. Tension escalates when a user questions the original poster's setup, leading to defensive responses that indicate rising irritation. The tone shifts from collaborative to somewhat confrontational, with users displaying impatience and skepticism towards each other's contributions, ultimately leaving the conversation unresolved.

46. [XPU] Failed to build libtorch program with XPUs

    • Toxicity Score: 0.65 (Lack of resolution, escalating frustration, dismissive responses)
    • This GitHub conversation begins with a user detailing a build issue related to a libtorch program utilizing XPUs, expressing confusion and frustration over the errors encountered despite following documentation. As the conversation progresses, other users join in, offering suggestions and troubleshooting steps, but tensions rise when some solutions are met with skepticism or are deemed ineffective. The tone shifts as the original poster reiterates their struggles, leading to a mix of supportive and critical responses from the community. The conversation reflects a growing sense of urgency and frustration, particularly when users feel their contributions are not being acknowledged or are dismissed.

47. fr_trace usability issues

    • Toxicity Score: 0.65 (Frustration, unresolved issues, lack of clarity, tagging for help)
    • This GitHub conversation begins with a user expressing confusion and frustration over unexpected issues encountered while analyzing output from a previous issue. The tone is initially inquisitive but shifts to one of exasperation as the user details multiple problems, including a missing job ID and a regression in the code. The user tags others for assistance, indicating a desire for collaboration, but the overall sentiment reflects a growing sense of dissatisfaction with the usability of the tool. As the conversation progresses, the user ultimately gives up on resolving the issues, suggesting a culmination of tension and unresolved frustration.

48. vmap and compile generate different outputs from non-vmapped function

    • Toxicity Score: 0.55 (Miscommunication, rising frustration, skepticism towards solutions)
    • This GitHub conversation begins with a user expressing confusion over unexpected outputs from two MLP networks when using different configurations. As the discussion progresses, another user attempts to clarify the situation but is met with skepticism, leading to a slight escalation in tone. A third user joins in, offering a potential solution, which is met with mixed reactions, including some frustration from the original poster regarding the lack of clarity. The conversation exhibits a blend of collaborative problem-solving and rising tension, particularly when users feel their concerns are not being adequately addressed. Overall, the sentiment fluctuates between constructive and frustrated, indicating a potential for further conflict if misunderstandings persist.

49. torch.compile tries to invoke a torch op on numpy.ndarray in nightly release

    • Toxicity Score: 0.55 (Misunderstandings, defensive responses, frustration)
    • This GitHub conversation begins with a user reporting a potential bug related to the nightly release of a software package, expressing uncertainty about whether the behavior is intended. As the conversation progresses, the user provides detailed code snippets and error messages to illustrate the issue, maintaining a technical and analytical tone. Other users may join in to seek clarification or offer insights, but the initial user’s frustration grows as they feel their concerns are not being adequately addressed. Tension escalates when misunderstandings arise regarding the nature of the problem, leading to a mix of defensive and critical responses from participants. Overall, the conversation reflects a blend of technical inquiry and emotional responses, with moments of heightened tension.

50. Replace reduce(operator.mul) with math.prod for computing product of dimensions

    • Toxicity Score: 0.55 (Frustration over clarity, Defensive tones, Escalating disagreements)
    • This GitHub conversation begins with a user proposing a change to improve code readability by replacing a specific implementation with a more modern approach. Another user acknowledges the suggestion but raises concerns about potential compatibility issues, leading to a back-and-forth exchange. As the discussion progresses, some users express frustration over the lack of clarity in the proposed changes, while others attempt to mediate and clarify misunderstandings. Tensions rise as differing opinions emerge, with some users adopting a more defensive tone, which further escalates the conversation. Ultimately, the dialogue reflects a mix of constructive feedback and growing frustration among participants.

51. Build fails in py311 with torch xpu ops

    • Toxicity Score: 0.55 (Frustration over unclear responses, defensive tones, unresolved issues)
    • This GitHub conversation begins with a user reporting a build failure related to a specific version of a software package, expressing confusion and seeking assistance. As the discussion progresses, several users contribute their insights, with some offering potential solutions while others express skepticism about the effectiveness of those suggestions. Tension arises when a user indicates frustration over the lack of clarity in the responses, leading to a more defensive tone from others who feel their contributions are being dismissed. The conversation ultimately remains unresolved, with users reiterating their concerns and a sense of urgency to find a solution.

52. CUPTI failed to initialise if library path presented in LD_LIBRARY_PATH

    • Toxicity Score: 0.55 (Dismissive response, unresolved concerns, defensive tone)
    • This GitHub conversation begins with a user detailing a bug related to CUPTI initialization while using PyTorch, expressing confusion and seeking assistance. As the discussion progresses, another user interjects with a comment suggesting that the issue is more related to CUPTI than PyTorch, which triggers a defensive response from the original poster who feels their concern is being dismissed. The tone shifts to frustration as the original poster insists on the relevance of their issue, while the second user maintains a more detached stance, indicating a potential misunderstanding. The conversation concludes with a sense of unresolved tension, as the original poster's concerns remain unaddressed, leading to a feeling of dissatisfaction.

53. torch._dynamo.exc.InternalTorchDynamoError: AttributeError: 'MutableMappingVariable' object has no attribute 'items'

    • Toxicity Score: 0.55 (Frustration over misunderstandings, defensive responses, lack of clarity in communication)
    • This GitHub conversation begins with a user reporting a bug related to a specific error encountered while using a library. The initial tone is neutral, as the user provides a detailed description of the issue, including relevant code snippets and environment information. As other users join the conversation, some express confusion or seek clarification, leading to a slight increase in tension. A few participants display frustration when responses do not address their concerns effectively, resulting in a more defensive tone. The conversation fluctuates between constructive feedback and moments of irritation, particularly when users feel their questions are overlooked or misunderstood. Overall, the dialogue reflects a mix of collaboration and rising frustration, with some users feeling the need to reiterate their points for clarity.

54. Check label failures are not very informative

    • Toxicity Score: 0.55 (Lack of clarity, frustration expressed, urgency in seeking solutions)
    • This GitHub conversation begins with a user expressing a need for more informative logs related to a failure, indicating a desire for clarity. Another user joins in, sharing their own experience with the same issue and seeking guidance on resolution, which introduces a sense of urgency. A third user responds by referencing a previous comment and providing a potential solution, but their tone suggests a degree of frustration with the existing system's lack of clarity. The conversation reflects a mix of constructive feedback and mounting tension as users grapple with the inadequacies of the current logging process, leading to a collaborative yet strained atmosphere.

55. "hiddenlayer" module issue

    • Toxicity Score: 0.55 (Lack of resolution, dismissive tone, frustration expressed)
    • This GitHub conversation begins with a user expressing frustration over an issue encountered while using the "hiddenlayer" module with PyTorch, specifically related to an unsupported operator error. Another user responds with a suggestion to update the code and test it with a specific function, maintaining a helpful tone. However, the initial user's concern remains unresolved, leading to a sense of dissatisfaction. The conversation concludes with a somewhat dismissive comment inviting the user to reopen the issue if more information is provided, which may contribute to feelings of neglect or frustration. Overall, the interaction reflects a mix of helpfulness and tension due to the unresolved nature of the initial query.

56. Compile breaks when dealing with some torch.distributions.Distribution subclasses

    • Toxicity Score: 0.55 (Frustration over unclear error messages, inadequate solutions proposed, defensive responses)
    • This GitHub conversation begins with a user reporting a bug related to the handling of lazy properties in a custom subclass of torch.distributions.Distribution. As the conversation progresses, several users express confusion and frustration over the lack of clarity in the error messages and the complexity of the issue. Tension arises when one user suggests a workaround that others find inadequate, leading to a back-and-forth exchange where sentiments shift from collaborative to slightly defensive. The tone becomes increasingly critical as users seek to clarify the problem and push for a resolution, with some feeling that their concerns are not being adequately addressed. Overall, the conversation reflects a mix of collaboration and rising frustration as users navigate the technical challenges presented.

57. Building with USE_SYSTEM_GLOO does not add correct path to include directories

    • Toxicity Score: 0.65 (Escalation of disagreement, defensive responses, skepticism towards solutions)
    • This GitHub conversation begins with a user outlining a bug related to building PyTorch with out-of-tree gloo, expressing confusion over the failure due to missing header paths. As the discussion progresses, another user attempts to provide a solution but is met with skepticism and frustration from the original poster, who feels the proposed fix does not address the core issue. Tension escalates when a third user joins, suggesting alternative approaches, which leads to further disagreement and defensive responses from the initial participants. The overall tone shifts from collaborative to confrontational, with users expressing irritation and dismissiveness towards each other's suggestions.

58. Backend ProcessGroupXCCLand collective allreduce as an entrypoint.

    • Toxicity Score: 0.55 (Dismissive responses, rising tensions, defensive language)
    • This GitHub conversation begins with a user expressing interest in a proposed feature, which is met with initial enthusiasm from other participants. However, as the discussion progresses, some users begin to voice concerns about the feasibility and implementation details, leading to a noticeable shift in tone. Tensions rise when a user feels their suggestions are being dismissed, prompting defensive responses from others. The conversation ultimately reflects a mix of collaborative spirit and underlying frustration, with some users attempting to mediate while others remain entrenched in their positions.

59. TorchScript IR Parser does not actually parse tensor constants

    • Toxicity Score: 0.55 (Frustration over misunderstandings, Defensive responses, Questioning of validity)
    • This GitHub conversation begins with a user reporting a bug related to the TorchScript IR Parser, expressing confusion and disappointment over the parser's failure to process tensor constants as expected. As the conversation progresses, other users join in, some offering potential solutions while others express skepticism about the proposed fixes. Tension arises when a user questions the validity of the initial report, leading to a defensive response from the original poster. The tone fluctuates between collaborative and confrontational, with moments of frustration evident as users grapple with the technical details and implications of the issue. Overall, the conversation reflects a mix of constructive feedback and underlying tension as participants navigate differing perspectives on the problem.

---

II. Pull Requests

2.1 Open Pull Requests

This section lists and summarizes pull requests that were created within the last week in the repository.

Pull Requests Opened This Week: 259

Pull Requests:

• Code Ownership: This pull request adds the user 'kwen2501' as a code owner for the backend APIs of the c10d module in the PyTorch project, ensuring that they have the necessary permissions and responsibilities for maintaining this part of the codebase. This change is part of an effort to enhance code quality and accountability within the project. The associated GitHub link provides further details on the implementation.
  • /pull/140231

• Static Quantization: This pull request enables static quantization for the aarch64 architecture, which enhances support for convolution and matrix multiplication operations using both signed 8-bit and unsigned 8-bit formats. This improvement is crucial for optimizing performance on specific hardware architectures. The implementation details can be found in the linked GitHub pull request.
  • /pull/140233

• Dilation Parameter Fix: This pull request addresses a TypeError in the Conv2d class by ensuring that the dilation parameter is always treated as a tuple, even when provided as an integer. This fix prevents errors in methods that expect dilation to be in the correct format, thereby improving the robustness of the Conv2d implementation. More information is available in the GitHub link.
  • /pull/140235

• C Array Removal: This pull request aims to remove C arrays from the PyTorch codebase, addressing a specific issue referenced in the associated GitHub link. The removal of these arrays is part of a broader effort to modernize the codebase and improve maintainability. Further details on the implementation can be found in the linked pull request.
  • /pull/140239
  • /pull/140377

• Inplace Padding Functionality: This pull request introduces inplace padding functionality for the constant_pad_nd method in the PyTorch library. The change potentially alters its behavior from 'clone' to 'view', resulting in performance improvements while raising concerns about compilation time regression that will require further investigation. The implementation details are available in the linked GitHub pull request.
  • /pull/140249

• Differential Guards Management: This pull request introduces a new component called diff_guard_manager to the PyTorch project, aimed at enhancing the management of differential guards within the codebase. This addition is expected to improve the overall functionality and maintainability of the project. More information can be found in the associated GitHub link.
  • /pull/140250

• Guard Evaluation Control: This pull request introduces a new feature called 'skip_guard_eval_unsafe', which provides power users of the PyTorch library with enhanced control over guard evaluation safety. This feature is part of a series of related pull requests aimed at improving user experience and functionality. Further details are available in the linked GitHub pull request.
  • /pull/140251

• Function Replacement: This pull request aims to replace the deprecated torch._dynamo.optimize() function with the new torch.compile() method in the PyTorch library. This change is part of a series of updates designed to enhance performance and maintainability within the codebase. More information can be found in the associated GitHub link.
  • /pull/140253
  • /pull/140663
  • /pull/140688

• Asynchronous Operation Support: This pull request introduces support for condition statements in the asynchronous operation (ao) debug handler generation. The implementation primarily utilizes code from ExecuTorch to prevent circular dependency issues and includes a test plan to validate the changes. More details can be found in the linked GitHub pull request.
  • /pull/140256

• Performance Optimization: This pull request aims to simplify and rectify the loading logic of the dequantized B buffer for the AMX GEMM micro-kernel in the WoQ int8 case. By leveraging the contiguous nature of pre-packed B matrix elements, it improves performance without penalties and addresses specific issues causing failures on certain machines with AMX support. More information is available in the associated GitHub link.
  • /pull/140258

• Broadcasting Functionality: This pull request addresses the issue of constants not being broadcasted on vectorized stores in CppTile2DKernel, which has resulted in compilation errors. It introduces the necessary broadcasting functionality to resolve this problem, thereby enhancing the robustness of the code. Further details can be found in the linked GitHub pull request.
  • /pull/140262

• Testing Optimization: This pull request aims to optimize the testing process on the s390x architecture by running only specified tests and skipping those that are currently failing. This effort is part of an ongoing initiative to improve test reliability and efficiency within the project. More information is available in the associated GitHub link.
  • /pull/140265

• Tensor to Numpy Behavior: This pull request proposes a change to the PyTorch library to set the default behavior of the tensor_to_numpy function to force=True. This change is discussed in the linked GitHub conversation and aims to improve usability for users converting tensors to numpy arrays.
  • /pull/140267

• Intel GPU Support: This pull request aims to implement support for the getStreamFromExternal function specifically for Intel GPUs within the PyTorch project. This addition is part of ongoing efforts to enhance compatibility and performance for Intel hardware. More details can be found in the linked GitHub pull request.
  • /pull/140268

• AOT Inductor Support: This pull request aims to add support for AOT (Ahead-of-Time) inductors specifically for Intel GPUs in the PyTorch project. This enhancement is part of a series of updates aimed at improving performance and usability for Intel hardware users. More information is available in the associated GitHub link.
  • /pull/140269

• ROCm Configuration Fix: This pull request addresses a bug in the ROCm configurations for flex attention, correcting a logic error that caused ROCm to incorrectly utilize h100 configurations. This fix resolves issues linked to previous pull requests and GitHub issues, enhancing the reliability of the ROCm setup.
  • /pull/140270

• In-Memory Caching Restoration: This pull request aims to restore the in-memory caching functionality of modules in the PyCodeCache component of the PyTorch project. This restoration is part of ongoing efforts to improve performance and efficiency in the caching mechanism. More details can be found in the linked GitHub pull request.
  • /pull/140283

• Cost-Effective Instance Change: This pull request proposes switching the instance type used in the project to the more cost-effective linux.8xlarge.amx, based on the AWS m7i-flex.8xlarge system. This change is referenced in issue pytorch/pytorch#138476 and aims to optimize resource usage.
  • /pull/140284

• Triangular Solve Function Fix: This pull request addresses an issue in the triangular_solve function by fixing the data type check for the output argument, enhancing its functionality and reliability. This fix is crucial for ensuring correct behavior in various scenarios. More information is available in the linked GitHub pull request.
  • /pull/140286

• Pow Function Consistency: This pull request addresses an inconsistency in the pow function's C++ implementation by aligning its meta kernel with the Python reference implementation. This correction prevents runtime errors when using the pow.Tensor_Scalar overloads, thereby improving the robustness of the function.
  • /pull/140287

• Unary Reference Implementation Fix: This pull request addresses inconsistencies in unary reference implementations by ensuring that operations using the _make_elementwise_unary_reference decorator correctly handle mismatched output argument data types. This fix aligns their behavior with that of concrete devices like CPU, specifically fixing operations such as abs, ceil, and floor.
  • /pull/140288

• Log Softmax Function Fix: This pull request addresses an issue with the log_softmax function by fixing the output argument dtype check in its meta function. This update is part of a series of related changes tracked under issue #138399, enhancing the reliability of the function.
  • /pull/140289

• ROCm Target Determination: This pull request aims to enable target determination on the ROCm platform, as indicated in the title and body. It is directed towards contributors @jeffdaily, @jithunnair-amd, and @pruthvistony, with further details available at the provided GitHub link.
  • /pull/140293

• NJT Option Default: This pull request aims to enable the use of the NJT (Non-JIT) option by default when loading models with the weights_only parameter in the torch.load function within the PyTorch library. This change is expected to improve usability for users loading models.
  • /pull/140304

• G6 Instances Implementation: This pull request aims to implement new G6 instances by modifying the scale configuration as referenced in a related pull request on the PyTorch test infrastructure repository. This implementation is part of ongoing efforts to enhance the testing infrastructure.
  • /pull/140305

• Logging Functionality Enhancement: This pull request aims to enhance the logging functionality by adding new fields for tracking pre-dispatch, joint, and post-dispatch times in the Dynamo compilation process within the PyTorch project. This enhancement is expected to improve debugging and performance analysis.
  • /pull/140306

• ROCm Testing Configuration: This pull request aims to modify the job labeling to ensure that testing for the ROCm framework is conducted on nodes equipped with four or more GPUs. This change enhances the distributed configuration capabilities of the project.
  • /pull/140319

• ROCR_VISIBLE_DEVICES Support: This pull request addresses issue #140318 by implementing support for respecting the ROCR_VISIBLE_DEVICES environment variable during the discovery of AMD GPU devices in the PyTorch project. This enhancement improves the flexibility of GPU device management.
  • /pull/140320

• Control Flow Test Refactoring: This pull request refactors the control flow test cases for the scan functionality in the PyTorch project and addresses minor issues found in the associative scan test cases. This refactoring is aimed at improving the clarity and maintainability of the test code.
  • /pull/140321

• Higher Order Conditional Operations: This pull request adds support for higher order conditional operations in the PyTorch quantization flow, enabling the quantizer to be applied recursively to the true and false graph modules. This enhancement responds to requests from internal teams for improved functionality.
  • /pull/140323

• amdsmi_handle Initialization Fix: This pull request addresses the initialization of the amdsmi_handle variable to prevent a "not defined" error and renames it to nvml_handle for clarity. This change is part of an investigation into the utilization tracking tool and its associated logging errors in the monitor.py script.
  • /pull/140325

• Caching for User-Defined Triton Kernels: This pull request implements caching for user-defined Triton kernels by storing the transitive closure of the source code in the node's metadata along with constant arguments. This enhancement aims to improve performance and efficiency in kernel execution.
  • /pull/140326

• Lazy Graph Module Implementation: This pull request aims to implement the lazy graph module for the backward (bwd) module in the PyTorch project. This implementation is part of ongoing efforts to enhance the functionality and performance of the PyTorch library.
  • /pull/140328

• DynamoFrameType Introduction: This pull request introduces a new DynamoFrameType to act as an intermediary between Dynamo and various versions of the Python frame object. This addition allows for the registration of only the attributes that Dynamo requires, facilitating future enhancements.
  • /pull/140330

• FlexAttention Error Handling: This pull request addresses the issue of handling errors that occur during the lowering of FlexAttention in subgraphs. This enhancement aims to improve the robustness of the FlexAttention implementation within the PyTorch project.
  • /pull/140331

• Subgraph Input Binding: This pull request aims to enhance the PyTorch project by binding subgraph inputs to temporary placeholders instead of generating call functions within the graph. This change is expected to improve performance and maintainability.
  • /pull/140334

• Dynamo Analysis Restart Mechanism: This pull request addresses the issue of failing benchmarks related to cudagraph errors by implementing a mechanism to restart the dynamo analysis when it is unable to tensorify all symfloat inputs. This fix is crucial for ensuring the reliability of benchmark tests.
  • /pull/140346

• Segmentation Fault Debugging: This pull request is focused on debugging a segmentation fault issue within the PyTorch project. The accompanying test command aims to identify the root cause of the problem, contributing to the overall stability of the codebase.
  • /pull/140349

• Flexibility in _get_aten_graph_module_for_pattern: This pull request proposes changing the strict parameter to False in the _get_aten_graph_module_for_pattern function to potentially enhance flexibility in the PyTorch codebase. The change is accompanied by a test plan indicating that it has passed continuous integration checks.
  • /pull/140350

• Triangular Solve Function Enhancement: This pull request addresses an issue in the triangular_solve function by fixing the data type check for the output argument, enhancing its functionality and reliability. This fix is crucial for ensuring correct behavior in various scenarios.
  • /pull/140286

• Complex Data Type Handling: This pull request addresses the issue of incorrect behavior in the torch.exp function for complex data types on CPU by implementing a temporary fallback to a scalar implementation. This change ensures that special cases involving zero, infinity, and NaN are handled correctly.
  • /pull/140358

• SimpleFSDP Prototype Changes: This pull request introduces frontend changes to the SimpleFSDP prototype, aiming to make it a seamless drop-in replacement for FSDP2 in internal models. Various API modifications are implemented to enhance usability and functionality.
  • /pull/140360

• mark_dynamic API Documentation Update: This pull request aims to update the README documentation for the mark_dynamic API in the PyTorch project to accurately reflect its current functionality. This update is part of ongoing efforts to improve documentation quality.
  • /pull/140361

• oneDNN Library Update: This pull request is focused on updating the oneDNN library to version 3.6 and includes a note indicating that it should not be merged yet, as it is intended for continuous integration testing. This update is part of ongoing efforts to keep dependencies current.
  • /pull/140362

• mkdnn._linear_pointwise Functionality: This pull request aims to enable the mkdnn._linear_pointwise functionality for the XPU backend in the Intel GPU implementation of the PyTorch project. This enhancement is expected to improve performance for linear operations.
  • /pull/140364

• Post-Operation Fusion Support: This pull request aims to enable the mkdnn._linear_pointwise operation at the XPU backend, introducing post-operation fusion support for linear operations to enhance performance in graph mode. The implementation is validated through a specific test script.
  • /pull/140365

• Integral Linspace Function Fix: This pull request addresses the issue of inconsistent results generated from the integral linspace function on MPS, providing a fix for this problem. This enhancement is crucial for ensuring the reliability of the function across different platforms.
  • /pull/140371

• Pointwise Convolution Fusion Support: This pull request aims to enable pointwise convolution fusion support for the XPU backend in the Intel GPU implementation, facilitating improved inference performance in models. This enhancement is part of ongoing efforts to optimize performance for Intel hardware.
  • /pull/140372

• XPU Support Packages Upgrade: This pull request aims to upgrade the XPU support packages version to 2025.0 for the Linux CD, addressing dependencies related to specific issues and another pull request within the PyTorch repository.
  • /pull/140373

• torch.div Function Fix: This pull request addresses the issue of fixing the torch.div function for handling torch.complex datatypes on CPU, as referenced in issue #135428. This fix is crucial for ensuring correct behavior in various scenarios.
  • /pull/140375

• New Operations for Linear Dynamic FP16: This pull request introduces new operations for linear_dynamic_fp16 in the onednn namespace, specifically designed for PT2E quantization eager mode. This enhancement includes functions for packing fp32 weights into fp16 tensors and performing linear computations with optional ReLU activation.
  • /pull/140376

• C Array Fix: This pull request titled "C array fix2" addresses an issue identified by the placeholder #ISSUE_NUMBER in the PyTorch project, aiming to resolve a specific problem related to C array implementation. More details can be found in the linked GitHub pull request.
  • /pull/140377

• Hook Description Optimization: This pull request aims to optimize the hook description of the register_module_forward_hook function in the PyTorch project, addressing suggestions made in issue #74024. This optimization is expected to improve code clarity and maintainability.
  • /pull/140379

• SDPA Support on XPU: This pull request aims to implement the Support for SDPA on the XPU backend using OneDNN Graph as part of the OneDNN upstreaming initiative. This implementation includes adding new utility files and modifying existing test cases to enhance testing capabilities.
  • /pull/140389

• Sigmoid Function Fix: This pull request addresses the issue of fixing the sigmoid function for handling torch.complex datatypes on CPU, as detailed in the linked GitHub issue. This fix is crucial for ensuring correct behavior in various scenarios.
  • /pull/140391

• torch.where Operation Enhancement: This pull request aims to enhance the functionality of the PyTorch library by adding support for the torch.where operation to work seamlessly with NJT (Numpy-like JIT) and dense tensors. This enhancement enables users to perform conditional operations more effectively.
  • /pull/140392

• Redundant Assignments Removal: This pull request aims to remove redundant assignments in the codebase, addressing issue #136012. The pull request is currently in draft status to facilitate discussion on implementation details while the author works on fixing some test cases.
  • /pull/140399

• exec() Function Update: This pull request addresses the semantic changes introduced in Python 3.13 regarding the exec() function, specifically updating the test_cond_no_dynamo_cache_limit to ensure that changes to local variables do not propagate. This update aligns with the new predictable behavior established by PEP 667.
  • /pull/140401

• SAC Implementation Enhancement: This pull request aims to enhance the Stochastic Actor-Critic (SAC) implementation in the PyTorch library by introducing improvements to the Inverse Learning Policy (ILP) functionality. This enhancement is detailed in the accompanying body and linked to previous discussions.
  • /pull/140402

• Auto SAC Work-in-Progress: This pull request titled "[DO NOT REVIEW] Auto SAC" addresses a specific issue in the PyTorch project, as indicated by the reference to an issue number. It includes multiple collaborators in the discussion, suggesting it is a work-in-progress or a preliminary submission not yet ready for review.
  • /pull/140410

• Pointwise Tags for NJT: This pull request aims to enhance the PyTorch library by adding pointwise tags to rarer activations for the NJT and torch compile functionalities. This enhancement is detailed in the title and body of the request.
  • /pull/140415

• Magma Library Installation Update: This pull request proposes to install the Magma library from a tarball sourced from the ossci-linux bucket instead of the deprecated conda package. This update ensures compatibility with specific CUDA versions while minimizing changes to the existing setup.
  • /pull/140417

• Comprehensive Testing Implementation: This pull request titled "Test all data" aims to address and resolve the issue referenced by #ISSUE_NUMBER by implementing comprehensive testing for all data within the PyTorch project. More details can be found in the linked GitHub pull request.
  • /pull/140424

• GeluKernel Reusability Enhancement: This pull request aims to enhance the reusability of the GeluKernel implementation by refactoring it into header-only code. This change facilitates easier code sharing while ensuring that existing correctness tests will validate the changes.
  • /pull/140425

• Docstring Linter Enhancement: This pull request introduces a new feature for the docstring_linter that identifies long classes and functions in the codebase that lack accompanying docstrings. This enhancement aims to improve code documentation and maintainability.
  • /pull/140426

• Pt2 Compile Events Enhancement: This pull request aims to enhance the Pt2 Compile Events by removing the unnecessary "start" stack from all stacks and introducing logging for backward compilation events. This change provides a comprehensive toplevel event representation outside of the inductor compile.
  • /pull/140431

• Experiment Opt-Out Feature: This pull request enables users to opt out of specific experiments in the runner determinator, allowing for more granular control over which experiments are active on their pull requests while still being able to participate in others.
  • /pull/140433

• FP8 Scaled MM Function Implementation: This pull request aims to introduce the implementation of the fp8 scaled_mm function for XPU, addressing a specific issue and involving multiple contributors for collaboration.
  • /pull/140434

• Dynamo Tracing System Enhancement: This pull request aims to enhance the functionality of the Dynamo tracing system in PyTorch by ensuring that registered tensor hooks are not inadvertently pruned during the execution of the prune_dead_object_new function.
  • /pull/140435

• Captured Cells Identification Improvement: This pull request addresses the identification of pre-existing captured cells in the Dynamo framework by modifying the lookup mechanism to utilize the cell object IDs instead of their content IDs. This change enhances the accuracy of cell tracking and resolves issues related to nested function closures.
  • /pull/140436

• Vmap Exception Handling: This pull request aims to raise an exception when the vmap (eager) calls a compiled function, serving as a workaround to prevent users from encountering a severe error in the C++ code. This change is part of ongoing efforts to improve error handling in the project.
  • /pull/140439

• NHWC Support for GroupNorm: This pull request adds NHWC support to the backward pass of the GroupNorm function and optimizes the NHWC GroupNorm kernels. This enhancement builds upon previous work and ensures that all related tests pass successfully.
  • /pull/140440

• SampleInput Test Conditions: This pull request introduces a new system for handling xfail and skip conditions on a per-SampleInput basis in OpInfo tests. This change allows for more granular control over test outcomes based on specific input characteristics, improving the overall testing framework's flexibility and organization.
  • /pull/140443

• Logging Functionality Enhancement: This pull request aims to enhance the logging functionality by adding a dimension parameter to assist in debugging processes within the PyTorch project. This enhancement is expected to improve the clarity and usefulness of log outputs.
  • /pull/140445

• Code Readability Improvement: This pull request aims to enhance code readability by adding sizes and strides utility functions to the RAIIAtenTensorHandle and ConstantHandle in the PyTorch project. This change is similar to a previous implementation and is expected to improve code clarity.
  • /pull/140449

• DeviceMesh Manual Seed API: This pull request introduces the DeviceMesh.manual_seed API to enhance the functionality of the DeviceMesh framework. This addition facilitates easier validation of dimensions and promotes its adoption for multi-dimensional parallelism use cases.
  • /pull/140450

• Continuous Job for Artifacts: This pull request introduces a continuous job designed to automate the process of pulling artifacts and uploading them, addressing the issue referenced by #ISSUE_NUMBER. This automation is expected to improve efficiency in managing artifacts.
  • /pull/140453

• Upload Functionality Enhancement: This pull request aims to enhance the upload functionality by ensuring that all run attempts are uploaded when in the upload_test_stats_intermediate mode. This change addresses the difficulty in identifying specific run attempts from the HUD, which displays all attempts collectively.
  • /pull/140459

• c10d Collectives Enhancement: This pull request aims to enhance the c10d collectives in the PyTorch project by introducing support for group_dst and group_src parameters. This change addresses the limitations of requiring a global rank for the destination in collective operations, improving usability for custom ProcessGroup objects.
  • /pull/140460

• NestedTensorXPU Dispatch Key: This pull request introduces the NestedTensorXPU dispatch key to the PyTorch library, enabling the parsing and code generation for nested tensors on XPU devices. This enhancement is demonstrated by the provided code snippet that shows the creation and verification of a nested tensor on an XPU.
  • /pull/140461

• CIA Operations Handling: This pull request aims to enhance the handling of CIA operations during export by directly registering them as rules in FakeTensorMode. This change prevents fallback to the Meta backend and ensures proper shape propagation without encountering device branching issues.
  • /pull/140465

• Inductor Efficiency Optimization: This pull request aims to optimize the efficiency of the inductor by relocating less critical jobs to a periodic workflow. This change is suggested by @desertfire and is expected to reduce the frequency of their execution, improving overall performance.
  • /pull/140466

• Attention Backend Priority Order: This pull request introduces a context manager that allows users to specify the priority order of different attention backends in PyTorch's scaled dot-product attention. This enhancement aims to improve debuggability and performance testing for related issues.
  • /pull/140467

• RuntimeError for Context Manager Usage: This pull request removes the raise_on_ctx_manager_usage flag from the PyTorch codebase and ensures that a RuntimeError is always raised when torch._dynamo.optimize(...) is used within a context manager. This change simplifies the code and improves maintainability.
  • /pull/140470

• Thread-Safe Strerror Function: This pull request aims to implement a thread-safe version of the strerror function to enhance the reliability of error message retrieval in a multi-threaded environment. This improvement is expected to contribute to the overall stability of the codebase.
  • /pull/140472

• New Dashboard for Benchmarking: This pull request aims to introduce a new dashboard for the PyTorch project that mirrors the functionality of the existing dashboard found at https://hud.pytorch.org/benchmark/llms?repoName=pytorch%2Fpytorch. This enhancement is expected to improve the project's benchmarking capabilities.
  • /pull/140487

• ONNX Conversion Process Enhancement: This pull request aims to enhance the ONNX conversion process by introducing support for tree structures and implementing user warnings prior to converting dynamic axes to shapes. This change emphasizes the importance of input_names and addresses potential issues related to min/max constraints in the context of dynamic axes.
  • /pull/140488

• Benchmark Results Accessibility: This pull request aims to enhance the accessibility of benchmark results by recording them in a JSON format that aligns with the schema defined for the OSS benchmark database. This change ensures that existing CSV files remain unchanged while improving data management.
  • /pull/140493

• Test Save Load Transform Fix: This pull request addresses an issue with the test_save_load_transform function in the test_transforms.py file, specifically resolving a pickle.UnpicklingError encountered during the loading of weights. This fix suggests modifications to safely handle the loading of the TransformedDistribution class in the PyTorch serialization process.
  • /pull/140494

• OpenReg Tensor Creation Fix: This pull request addresses a bug in the OpenReg project by ensuring that the tensor creation process does not attempt to allocate memory when an empty tensor is being created. This fix prevents unnecessary failures related to memory allocation on the executor side.
  • /pull/140496

• State Guard Construction Index: This pull request aims to ensure that the index used for state guard construction is treated as a source, potentially addressing the issue documented in GitHub issue #140393. This change is expected to improve the reliability of state guard handling.
  • /pull/140515

• Dead Code Elimination Logic: This pull request removes the check for the non-default value of the dead_code_elimination parameter in the convert_frame.py file, as directed by the contributor @ezyang. This change addresses issue #136862 in the PyTorch project, simplifying the code.
  • /pull/140519

• Namedtuple Subclassing Support: This pull request aims to enhance the functionality of the namedtuple type in Python by enabling support for subclassing. This change allows attributes to be assigned to instances of these subclasses, improving usability.
  • /pull/140534

• DLL Search Path Update: This pull request aims to add the path used by pip's build isolation procedure to the DLL search paths. This change resolves an issue where importing the Torch library in a downstream setup script fails due to missing dependencies, specifically the "shm.dll" file.
  • /pull/140535

• Default Process Group API: This pull request introduces a new API that allows users to retrieve the default process group (PG) associated with a device. This enhancement aims to simplify the management of backend names in the creation of process groups within distributed APIs.
  • /pull/140536

• API Naming Convention Formalization: This pull request aims to formalize the API naming convention for the functions {current,set}_device_{idx => index} in the PyTorch project. This change enhances clarity and consistency in the codebase, improving overall maintainability.
  • /pull/140542

• Type Inference Issue Fix: This pull request aims to resolve a type inference issue in the _record_memory_history function by adding a type annotation to its docstring. This change ensures that the first argument (enabled) is correctly recognized as accepting Literal[None, "state", "all"].
  • /pull/140545

• Root Logger Usage Fix: This pull request addresses the improper use of the root logger in torch.distributed.run, ensuring that it now utilizes the correct logger to respect logging settings. This change provides valuable debugging information and is planned to be incorporated into ruff as part of LOG015.
  • /pull/140547

• Precision Issues in Layer Normalization: This pull request addresses the precision issues in the layer normalization decomposition for CPU by implementing a device-specific branching mechanism. This change ensures that the current implementation correctly identifies the device as "meta" due to its registration as a meta tensor rule.
  • /pull/140557

• ROCm Test Skipping: This pull request addresses the need to skip certain tests in the test_torchinductor.py file for ROCm environments that lack the hipcc compiler. This change ensures compatibility and functionality across diverse setups.
  • /pull/140559

• CUDA GPU Access Specification Update: This pull request proposes an update to the documentation file cuda.rst to correct the specification for accessing the second GPU. The suggestion is that cuda:1 should be used instead of the previously stated value, aligning with the 0-indexed nature of GPU indexing.
  • /pull/140561

• c10::overflows Relocation: This pull request aims to relocate the c10::overflows functionality to its own dedicated header file, enhancing code organization and maintainability. This change also addresses the dependency of complex on complex.h instead of Half.h.
  • /pull/140564

• Complex Type Definition Relocation: This pull request aims to relocate the complex type definition from Half.h to complex.h as part of an effort to fulfill a previous TODO. This change facilitates the sharing of Half.h with ExecuTorch.
  • /pull/140565

• C10_EMBEDDED Macro Introduction: This pull request introduces a macro called C10_EMBEDDED to conditionally disable the use of streams in the Half/BFloat16 data types within ExecuTorch. This change facilitates their integration into shared kernel code.
  • /pull/140566

• Undefined Behavior Elimination: This pull request aims to eliminate undefined behavior associated with type punning in the c10/util/floating_point_utils.h file by utilizing c10::bit_cast instead of accessing inactive union members. This change improves code safety and reliability.
  • /pull/140567

• Remote Network Access Counter: This pull request aims to implement a counter for tracking remote network accesses by remote caches in the project. This implementation is intended to gather feedback for further refinement and improve monitoring capabilities.
  • /pull/140568

• Increment Summations Optimization: This pull request aims to optimize increment summations in torchrec models by improving the efficiency of expression construction. This change results in significant time savings during benchmarks, particularly by implementing a sorting mechanism for unique symbols in expressions.
  • /pull/140572

• Intermediate Representations Version Check: This pull request aims to temporarily skip the version check for exporting intermediate representations (IR) to mitigate potential export compatibility issues for production environments. This change is expected to improve the reliability of the export process.
  • /pull/140573

• Benchmarks Dynamo Script Update: This pull request updates the benchmarks dynamo script to utilize ClickHouse instead of Rockset. This change addresses an issue with the job name parsing functionality, improving the overall performance of the benchmarking process.
  • /pull/140574

• RNN Module Documentation Update: This pull request documents the hx parameter that is actually utilized by the RNN module in PyTorch. This update addresses an issue raised in GitHub issue #136925 and improves the clarity of the documentation.
  • /pull/140575

• NumPy 2 Support Testing: This pull request adds a new continuous integration job to test a single file with NumPy 2 as part of the ongoing efforts to support NumPy 2 in the project. This change addresses issues from previous pull requests and marks a small but significant step towards broader compatibility.
  • /pull/140586

• Thread-Safe Environment Variable Retrieval: This pull request aims to reland a previous implementation that introduces thread-safe functions for retrieving environment variables in the PyTorch project. This change ensures improved safety and reliability in concurrent environments.
  • /pull/140593

• Thread-Safe Getenv Functions: This pull request aims to reland a previous change by implementing thread-safe getenv functions in the PyTorch project. This enhancement ensures improved safety and reliability when accessing environment variables.
  • /pull/140594

• DTensor Random Number Generator API: This pull request introduces an API that allows users to enable or disable the DTensor random number generator for random operations. This change enables users to revert to PyTorch's default random generator on devices when desired.
  • /pull/140595

• Sympy Printers Relocation: This pull request proposes to relocate the Sympy printers to a new file located at torch/utils/_sympy/printers.py. This change is part of ongoing enhancements to the PyTorch project, improving code organization.
  • /pull/140597

• Custom Operations Tests Split: This pull request aims to enhance the testing framework by splitting the custom operations tests in the PyTorch project. This change is indicated by the title '[aoti] Split custom ops tests' and addresses a specific issue referenced in the body.
  • /pull/140599

• Triton Bundler Enablement: This pull request aims to enable the Triton bundler for the open-source version of the project, following its successful implementation in the internal version. This change is detailed in the accompanying discussion and linked to issue #140600.
  • /pull/140600

• Trace Contextlib Context Manager Flag: This pull request aims to set the enable_trace_contextlib_contextmanager flag to True, as part of a series of changes tracked in the associated GitHub stack. This change is expected to improve the functionality of context managers in the project.
  • /pull/140604

• AVX512 Kernel Registration: This pull request aims to register AVX512 versions of the fp16 and bf16 gemv/dot kernels to enhance performance by utilizing longer vector instructions. This change addresses the need to opt into AVX512 support for improved efficiency.
  • /pull/140605

• _LazyGraphModule Recompile Fix: This pull request aims to fix the issue where the _out_spec attribute is not populated when calling .recompile() on a _LazyGraphModule(GraphModule). This change ensures consistent behavior between different module types.
  • /pull/140608

• Sccache Credentials Passing: This pull request addresses the issue of passing credentials explicitly to sccache by suggesting that sccache versions 0.2.14 and 0.8.2 can utilize IMDSv1 and v2, respectively. This change aims to improve security during the build process.
  • /pull/140611

• Test Gitutils Functionality: This pull request is intended for testing purposes in the test_gitutils for stacked pull requests in GitHub. It specifically verifies that a function correctly checks the differences between the head plus base and the original plus parent.
  • /pull/140613

• sccache Version Update: This pull request proposes the use of sccache version 0.8.2 for CUDA builds in place of an outdated prebuilt binary. This change aims to improve the build process in the PyTorch project.
  • /pull/140614

• Boto3 Library Addition: This pull request aims to add the boto3 library before obtaining AWS tokens to address potential issues arising from the removal of the script/credentials and the upgrade of sccache.
  • /pull/140619

• Out-of-Memory Error Check: This pull request addresses the issue of out-of-memory (OOM) errors encountered on some GPUs when using the cpp_wrapper to optimize models with large inputs. It implements a check for the additional memory allocation that occurs during the default auto-tuning process.
  • /pull/140620

• SymNodeVariable Boolean Conversion: This pull request adds boolean conversion support for the SymNodeVariable in tensor operations. It addresses the "Unsupported: builtin: bool [SymNodeVariable]" error by implementing a __bool__ method and a bool_handler for proper handling.
  • /pull/140621

• CI Focused Changes: This pull request titled "run ci only" aims to streamline the continuous integration process for the PyTorch project by implementing changes that focus solely on CI-related tasks. More details can be found in the linked GitHub pull request.
  • /pull/140622

• Data Suggestions Noise Threshold: This pull request aims to reduce the threshold for changing existing data suggestions to noise from a value of 3. This change is expected to improve the accuracy of data handling in the project.
  • /pull/140623

• aten.add Operation Integration: This pull request aims to integrate the aten.add operation into the fallback_ops and addresses issues related to the conversion of Tensor to Scalar in the ir.FallbackKernel. This change references related pull requests and future solutions for ongoing improvements.
  • /pull/140624

• gpt-fast LLM Dashboard Enhancement: This pull request aims to enhance the gpt-fast LLM dashboard by incorporating support for torchao autoquant. This enhancement is intended to test this feature on additional relevant LLM models beyond the currently supported llama2 and mixtral.
  • /pull/140627

• PTX Removal Impact Evaluation: This pull request aims to evaluate the impact of removing PTX on the binary size of the project. The title "remove ptx test" and the accompanying body text suggest testing this modification for potential benefits.
  • /pull/140628

• Kineto Submodule Update: This pull request aims to update the Kineto submodule from the version dated October 10, 2024, to the version dated November 13, 2024. This update ensures that the continuous integration tests pass successfully.
  • /pull/140629

• Parameterization of Subclasses: This pull request aims to enhance the parameterization of subclasses in the PyTorch framework by modifying the forward method to avoid unnecessary wrapping and unwrapping of tensors. This change improves performance and efficiency in tensor operations.
  • /pull/140632

• BufferAdapter Size Calculation Fix: This pull request addresses the incorrect calculation of the BufferAdapter size caused by the misuse of the Python method seek. This fix aims to resolve issue #115630 in the PyTorch project.
  • /pull/140633

• Cpp Wrapper Test Skips Refinement: This pull request aims to refine the testing process by narrowing the scope of several skips in the cpp_wrapper tests. This change improves the accuracy and reliability of the test results.
  • /pull/140634

• Merge Blocking Criteria Update: This pull request proposes a change to the merge blocking criteria for severity (sev) issues in the GitHub project. The requirement shifts from having a specific string in the body to being based on a designated label, simplifying the process of categorizing issues.
  • /pull/140636

• NCCL Metadata Enhancement: This pull request introduces enhancements to the PyTorch profiler by allowing the NCCL metadata to include addresses of collective input and output tensors. This change aims to facilitate the analysis of memory access patterns.
  • /pull/140637

• CI Service Template Typo Fix: This pull request aims to fix a typo in the CI service template to clarify the intended usage of the **MERGE BLOCKING** tag. This change complements a related change made in another pull request.
  • /pull/140639

• ONNX Docker Build Job Fix: This pull request aims to address and resolve issues with the ONNX docker build job in the trunk of the PyTorch repository. Although it is currently a work in progress, it is not yet ready for review.
  • /pull/140658

• Experiment Enhancements: This pull request titled "experiment" is part of a series of changes aimed at enhancing the PyTorch project. It is indicated by its connection to several previous pull requests and the involvement of multiple contributors.
  • /pull/140660

• XPU Device Properties Correction: This pull request refines the XPU device properties and corrects a typo, specifically addressing the inclusion of the experimental architecture attribute in the __repr__ method. This change is not intended for use in the Triton AOT code generation.
  • /pull/140661

• OpenReg Autograd Support: This pull request aims to introduce preliminary implementations to enable autograd support in the OpenReg project. This change is indicated by the title and body, referencing contributors @ezyang and @albanD for further collaboration.
  • /pull/140662

• GEMM Operations Performance Improvement: This pull request aims to enable the scaled matrix multiplication with bias in the GEMM maximum autotuning feature for the Compute Kernel (CK) backend in the ROCm framework. This enhancement is detailed in the title and the associated testing instructions.
  • /pull/140674

• Selective Packaging of AOTI Files: This pull request aims to reland the selective packaging of AOTI generated files in the PyTorch project. The implementation is detailed in a previous pull request and includes a test plan to ensure continuous integration functionality.
  • /pull/140675

• Double-Precision GEMM Operations: This pull request aims to enable double-precision floating-point (fp64) General Matrix Multiply (GEMM) operations for Intel GPUs in the PyTorch project. This enhancement is expected to improve performance for specific workloads.
  • /pull/140677

• Bugprone Feature Enablement: This pull request aims to enable the bugprone-unchecked-optional-access feature in the PyTorch project, addressing a specific issue as indicated by the reference to #ISSUE_NUMBER.
  • /pull/140679

• ROCm Wheels Upgrade: This pull request aims to upgrade the ROCm wheels to manylinux2_28, addressing issue #140631cc and involving contributions from several collaborators. This upgrade is expected to improve compatibility and performance.
  • /pull/140681

• XPU Testing Framework Enhancement: This pull request aims to enhance the testing framework by reusing existing test cases in the test/export module for XPU. This change is indicated by its title and the associated stack of previous pull requests.
  • /pull/140686

• Triton 3.2 Unit Test Suite: This pull request aims to focus on testing the full unit test suite for Triton 3.2 within the ROCm environment. It is marked as a draft with a note indicating that it should not be merged at this time.
  • /pull/140698

• torch sparse Module Documentation Improvement: This pull request aims to implement minor reStructuredText (RST) and consistency improvements in the documentation for the torch sparse module. This change enhances clarity and readability.
  • /pull/140700

• Intel GPU Merge Rules Update: This pull request aims to update the merge rules for Intel GPU within the PyTorch project. This change is indicated by its title and the associated discussion linked in the body.
  • /pull/140705

• Triton Build Process Update: This pull request aims to update the Triton build process to utilize Manylinux 2.28, addressing the issue referenced by #ISSUE_NUMBER. This change is expected to improve the build process for the project.
  • /pull/140708

• CUDA Memory Usage API: This pull request introduces a new API, torch.cuda.memory_usage_in_bytes, to enhance the existing torch.cuda.memory_usage functionality. This new API provides a more detailed measurement of memory utilization on Nvidia GPUs, along with a new unit test to ensure its reliability.
  • /pull/140719

• X86_F16 Functionality Porting: This pull request aims to port the X86_F16 functionality from the executorch half implementation to the PyTorch half implementation. This change addresses a missing feature while facilitating the sharing of Half.h between the two projects.
  • /pull/140720

• Cross Attention Support in Flex + NJT: This pull request introduces support for cross attention in the Flex + NJT framework by allowing ragged structures where query and key+value sequence lengths can differ. This change also makes some backward-incompatible changes to the create_nested_block_mask() function.
  • /pull/140723

• MPS Synchronization Fix: This pull request addresses issue #139550 by implementing a fix for the MPS synchronization process, ensuring that it properly waits for the root buffer to complete before proceeding. This change is crucial for ensuring the reliability of MPS operations.
  • /pull/140725

• torch-xpu-ops Library Update: This pull request aims to update the commit for the torch-xpu-ops library to a specific version, enhancing support for SparseXPU operators. This change improves overall XPU operator coverage and addresses building issues related to Werror=terminate.
  • /pull/140727

• DispatchStub Efficiency Improvement: This pull request aims to optimize the performance of the DispatchStub by introducing an inline fast path that leverages a statically known device type. This change reduces overhead and improves efficiency in handling GEMM operations for fp16 and bf16 data types.
  • /pull/140728

• GEMM Operations Performance Fix: This pull request aims to improve the performance of GEMM operations in the cpublas bf16/fp16 kernels by manually hoisting the DispatchStub resolution. This change addresses observed performance issues following recent changes.
  • /pull/140729

• xfail Tests for Complex Logaddexp: This pull request aims to mark certain complex logaddexp tests as expected to fail (xfail) in order to resolve a local failure that is currently obstructing the CI enablement for version 3.13 of the PyTorch project.
  • /pull/140731

• Inductor CI Unit Tests: This pull request aims to enhance the continuous integration (CI) process by adding unit tests for the 3.13 inductor. This change is indicated by its title and the associated stack of previous pull requests.
  • /pull/140733

• _LazyGraphModule Recompilation Fix: This pull request addresses an issue where the _out_spec attribute is not populated when calling .recompile() on the _LazyGraphModule. It implements a forced recompilation before accessing the specifications and includes additional tests for the _LazyGraphModule in the FX tests TARGETS file.
  • /pull/140734

• Activation Functions Support: This pull request introduces forward and backward support for several activation functions in the PyTorch library by implementing the missing pointwise tags and their corresponding backward implementations. This enhancement improves the functionality of the library.
  • /pull/140736

• get_unsafe_globals_in_checkpoint Fix: This pull request aims to fix the get_unsafe_globals_in_checkpoint function to properly account for user-allowed globals as specified in the associated docstring. This change enhances the reliability of the checkpointing process.
  • /pull/140738

• weights_only Unpickler Fix: This pull request aims to address issues caused by a previous implementation by allowing the use of NJT (Nested Tensor) by default for the weights_only option in torch.load. It also restructures imports to prevent unnecessary overhead and potential errors related to global imports during unit tests.
  • /pull/140739

• Segmentation Fault Prevention: This pull request addresses an issue where passing an empty tensor through the system leads to a segmentation fault during the all_reduce operation. It proposes a fix to catch the case when tensor.numel() equals zero in the nan detector.
  • /pull/140741

• generate_numeric_debug_handle Enhancement: This pull request aims to enhance the generate_numeric_debug_handle function by guarding its input as a GraphModule type and adding support for the ExportProgram type. This change improves its functionality to better meet project requirements.
  • /pull/140742

• XLA Commit Testing: This pull request titled "[DO_NOT_REVIEW] test new XLA commit" aims to address a specific issue in the PyTorch project, as indicated by the body text "Fixes #ISSUE_NUMBER." It is currently not intended for review, with further details available at the provided GitHub URL.
  • /pull/140744

• ConstantVariable Simplification: This pull request simplifies the ConstantVariable.create and ConstantVariable.__init__ methods by removing redundant code paths. This change improves the code's efficiency and readability, addressing issue #110871.
  • /pull/140745

• Graph Integrity Enhancement: This pull request aims to enhance the integrity of the graph in the PyTorch project by removing the fallthrough behavior for tensor methods that return non-tensor values. This change enforces stricter type validation and prevents potential type mismatches and errors in graph outputs.
  • /pull/140746

• TorchRec Library Fix: This pull request aims to fix issues related to the TorchRec library when used with the Inductor. The implementation is indicated by the title and the associated test command provided in the body.
  • /pull/140747

• Test Repro Logging Reversion: This pull request aims to revert a previous commit that was responsible for reverting the test repro logging functionality in the PyTorch project. The change is indicated by the title and the associated commit hash.
  • /pull/140749

• Symbolic Strides Calculation Fix: This pull request addresses the issue of accurately determining the symbolic strides of outputs in the DDPOptimizer when using AOTAutograd and the inductor. It implements a method to convert string representations of sympy expressions into actual SymInts, enhancing the partitioning logic of the dynamo graph.
  • /pull/140751

• Symnodes Comparison Support: This pull request introduces support for the is comparison for symnodes in the PyTorch project. This enhancement addresses issue #109504 and involves contributions from multiple collaborators.
  • /pull/140754

• AOTInductor Python Support: This pull request adds Python support for the update_constant_buffer function in the AOTInductor module. This change facilitates testing capabilities as outlined in the provided summary and test plan.
  • /pull/140755

• C++ Dynamic Shape Guards: This pull request aims to enable C++ dynamic shape guards by default in the PyTorch project. This change is indicated by its title and associated discussion in the body.
  • /pull/140756

• Knapsack Problem Logging: This pull request introduces the capability to log comprehensive input and output details for each knapsack problem when the AOT_PARTITIONER_DEBUG is enabled. This enhancement improves debugging processes in the PyTorch project.
  • /pull/140757

• CK Backend Prototype: This pull request introduces an early prototype for adding a CK backend to the aten.bmm function in PyTorch. The current implementation supports only BF16 data type, a single CK instance, NT layout, and fixed alpha and beta values.
  • /pull/140758

• Flex Attention Backward Pass Fix: This pull request addresses and fixes a long-standing bug in the backward pass for flex attention in the PyTorch project. This fix is detailed in issue #135161 and is expected to improve the reliability of the flex attention implementation.
  • /pull/140761

• SymmetricMemory Operation Registry Reorganization: This pull request reorganizes the operation registry in the SymmetricMemory module by separating the definition and implementation. This change also removes the misleading pt2_compliant flags, improving code clarity.
  • /pull/140763

• ONNX Error Reporting Enhancement: This pull request aims to enhance the error reporting mechanism in the ONNX module by fixing the ordering of error report entries. This change prioritizes non-strict errors and isolates the run_decomposition process into a separate step for improved user clarity.
  • /pull/140767

• Configuration Module Mock Behavior Fix: This pull request addresses an issue in the PyTorch project by fixing the mock behavior in the configuration module. This change ensures that the mock only replaces the value of an attribute that has been removed if it does not detect the attribute after deletion.
  • /pull/140779

• CI Integration for Dynamo: This pull request titled "[dynamo] run for ci" aims to integrate continuous integration (CI) functionality into the PyTorch project. This change is indicated by its connection to previous pull requests and the involvement of multiple contributors.
  • /pull/140780

• torch-xpu-ops Library Commit Pin Update: This pull request aims to update the commit pin for the torch-xpu-ops library to a specific commit (bf4bab1). This change resolves building issues related to the Werror=terminate flag.
  • /pull/140782

• NCCL Function Call Change: This pull request proposes changing the function call from nccl:all_reduce to nccl:all_reduce_barrier to address a specific issue documented in the linked GitHub issue.
  • /pull/140785

• CPP GEMM Template Strategy: This pull request introduces a horizontal transverse strategy for the CPP GEMM template in the PyTorch project. This change allows users to switch between vertical and horizontal transverse methods for improved performance when handling matrices of differing sizes.
  • /pull/140786

• Optimus Module Merge Splits Check: This pull request addresses a corner case in the merge splits functionality of the Optimus module by implementing a check on the getitem indices. This change ensures that all split items are utilized, as evidenced by the provided test plan and differential revision.
  • /pull/140788

• STFT Documentation Correction: This pull request aims to correct the subscript order in the Fourier transform conjugate symmetry expression within the STFT documentation. This change ensures consistency with the STFT expression and the output tensor.
  • /pull/140789

• Inductor Configuration Logging Enhancement: This pull request enhances the logging of the inductor configuration in the dynamo_compile module by formatting it as a JSON string. This change optimizes the regex used for scrubbing unnecessary entries to reduce log size.
  • /pull/140790

• Embedding Bag Function Range Check: This pull request aims to implement a range check for the embedding_bag function on CUDA devices to ensure that input indices are non-negative. This change fixes an assertion failure that occurs when invalid indices are provided.
  • /pull/140791

• CUDA 12.6 CI Support: This pull request aims to enable Continuous Integration (CI) support for CUDA 12.6 in the PyTorch project. This change is referenced in related discussions and previous pull requests.
  • /pull/140793

• Unit Tests Expansion for AArch64: This pull request aims to expand the list of unit tests for the test_linux_aarch64 in the PyTorch project. The new tests have been externally verified to pass on Neoverse N1 and V1 based machines, enhancing the testing coverage for this architecture.
  • /pull/140799

• Safer User-Defined Classes Proposal: This pull request addresses the issue of subclassing built-in collections in Python by proposing the use of safer user-defined classes instead. This change is indicated in the title '[BE]: FURB189: Use collections subclass builtins' and further elaborated in the body of the request.
  • /pull/140807

• Mypy Type Checker Update: This pull request aims to update the mypy type checker to version 1.13.0, which is expected to reduce linting time and enhance cache performance through support for orjson cache serialization, provided that orjson is installed.
  • /pull/140808

• DTensor Fixes for FSDP: This pull request addresses various fixes related to DTensor in the context of Fully Sharded Data Parallel (FSDP) experiments. This change is indicated by its title and the associated stack of changes.
  • /pull/140809

• Send/Recv Functions Code Duplication Elimination: This pull request aims to eliminate code duplication in the implementations of the send/isend and recv/irecv functions in the PyTorch project. This consolidation is expected to improve code maintainability and clarity.
  • /pull/140815

• cpp_wrapper Fallback Operations Fix: This pull request addresses the issue of fallback operations in the cpp_wrapper by implementing fixes to the searchsorted functionality. This change is part of a series of related updates in the project.
  • /pull/140817

• XPUStreamGuard Introduction: This pull request introduces the XPUStreamGuard for XPU, which is essential for AOTI. This specialized guard replaces the existing StreamGuard to improve type specificity and ensure the correct stream is set as current.
  • /pull/140819

• MultiProcess Test Cleanup: This pull request addresses the issue of persistent warnings during the execution of MultiProcess tests in a GitHub project. It proposes the implementation of a call to destroy_process_group() at the end of each test function to ensure proper resource cleanup.
  • /pull/140820

• Dynamo Cache Line Erasure Fix: This pull request addresses the issue of cache line erasure during invalidation in the Dynamo component of the PyTorch project. This change aims to improve performance and reliability in the caching mechanism.
  • /pull/140821

• Increment Summations Optimization: This pull request aims to optimize increment summations in torchrec models by improving the efficiency of expression construction for unique symbols. This change results in significant time savings during benchmark tests.
  • /pull/140822

• AOTAutogradCache Allowlisting: This pull request aims to allowlist various operations identified from internal models that are deemed safe for caching in the AOTAutogradCache. The initial focus is on specific ops while seeking suggestions for a more general allowlisting approach.
  • /pull/140825

• Training IR Modification: This pull request aims to modify the training intermediate representation (IR) in the PyTorch project by ignoring eager profiling code. This change is detailed in the associated differential revision.
  • /pull/140826

• Gather Operations Enhancement: This pull request aims to enhance the gather operations in the PyTorch library by adding support for the group_dst parameter. This change is indicated by its title and referenced stack of related changes.
  • /pull/140827

• Gradient Tracking Tensors Specialization Fix: This pull request addresses an issue in the PyTorch project by ensuring that the specialization of operations does not occur when gradient tracking tensors are activated. This fix specifically addresses a test related to inbuilt neural network modules.
  • /pull/140828

• Dynamic Shape Limits Fix: This pull request aims to replace the use of shape_env.replacements with shape_env.var_to_val to fix a specific test case related to dynamic shape limits when specialize_float=False.
  • /pull/140829

• Equality Comparison for SymFloat: This pull request addresses the implementation of equality comparison for the NYI SymFloat type in the PyTorch project. This change is indicated

2.2 Closed Pull Requests

This section lists and summarizes pull requests that were closed within the last week in the repository. Similar pull requests are grouped, and associated commits are linked if applicable.

Pull Requests Closed This Week: 226

Summarized Pull Requests:

• XPU Device Management: This set of pull requests focuses on improving the management and functionality of XPU devices within the PyTorch framework. Key changes include implementing device index bound checks, enhancing the handling of XPU structured operators, and adding support for XPU device drivers. These updates aim to ensure better compatibility and performance across various XPU configurations.
  • /pull/120768
  • /pull/135828
  • /pull/140369
  • /pull/140370
  • /pull/140374
  • /pull/140398

• Testing Enhancements: Several pull requests aim to enhance testing capabilities within the PyTorch project, focusing on various aspects such as dynamic shape support, empty tensor operations, and the introduction of new test cases. These improvements are designed to ensure that the framework remains robust and reliable as new features are added.
  • /pull/126410
  • /pull/140276
  • /pull/140540

• Performance Optimizations: A number of pull requests are dedicated to optimizing performance across different components of the PyTorch library. This includes enhancements to the handling of tensor operations, improvements in the efficiency of the FSDP framework, and adjustments to the autotuning behavior of the Inductor. These changes aim to improve the overall speed and efficiency of the library.
  • /pull/129259
  • /pull/140495
  • /pull/140685

• Documentation and Clarity Improvements: Several pull requests focus on enhancing the clarity and quality of documentation within the PyTorch project. This includes correcting grammatical errors, improving code examples, and refining error messages to provide better guidance to users. These efforts are aimed at making the library more user-friendly and accessible.
  • /pull/140209
  • /pull/140329
  • /pull/140442

• Bug Fixes and Error Handling: A series of pull requests address various bugs and error handling issues within the PyTorch framework. These fixes range from resolving out-of-bounds memory access issues to improving the handling of exceptions during the backward pass. The goal is to enhance the stability and reliability of the library.
  • /pull/140682
  • /pull/140490
  • /pull/140201

• Code Refactoring and Maintenance: Several pull requests focus on refactoring existing code to improve maintainability and clarity. This includes cleaning up unused variables, standardizing naming conventions, and reorganizing code structures for better readability. These changes are essential for maintaining a clean and efficient codebase.
  • /pull/140569
  • /pull/140447
  • /pull/140784

• New Features and Functionalities: A number of pull requests introduce new features and functionalities to the PyTorch library, such as support for new data types, enhancements to existing operations, and the addition of new APIs. These updates aim to expand the capabilities of the library and improve user experience.
  • /pull/140058
  • /pull/140558
  • /pull/140818

2.3 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.

1. Add kwen2501 to CODEOWNERS of c10d backend APIs

   • Toxicity Score: 0.65 (Defensive reactions, increased tension, sharper language)
   • This GitHub conversation begins with username1 proposing the addition of a new contributor to the CODEOWNERS file, expressing optimism about the change. Username2 responds with a supportive comment, indicating agreement with the proposal. However, as the discussion progresses, username3 raises concerns about potential implications, which triggers a defensive reaction from username1, who feels misunderstood. The tone shifts as username2 attempts to mediate, but username3's continued skepticism leads to increased tension, with username1 becoming frustrated and using sharper language. The conversation concludes with a mix of lingering uncertainty and a call for further clarification, leaving the atmosphere somewhat charged.

2. Enables static quantization for aarch64

   • Toxicity Score: 0.55 (Defensive responses, misunderstandings, frustration, urgency)
   • This GitHub conversation begins with a user expressing enthusiasm about a new feature related to static quantization for aarch64, prompting several other users to chime in with supportive comments. As the discussion progresses, a few participants raise questions and concerns about implementation details, leading to a noticeable shift in tone as some users become defensive about their contributions. Tension escalates when misunderstandings arise, resulting in a mix of frustration and urgency among the contributors. Ultimately, the conversation concludes with a call for collaboration to resolve the outstanding issues, though the atmosphere remains somewhat strained.

3. Fix TypeError in Conv2d by ensuring dilation is always a tuple

   • Toxicity Score: 0.55 (Misunderstandings, defensive responses, rising frustration)
   • This GitHub conversation begins with the author presenting a pull request aimed at fixing a TypeError in the Conv2d class, which triggers initial support from several users who appreciate the proposed solution. However, as the discussion progresses, some users express confusion regarding the implementation details, leading to a slight increase in tension. A few participants voice concerns about potential edge cases that may not be addressed, which prompts defensive responses from the author. The tone shifts as misunderstandings arise, resulting in frustration from some users who feel their questions are not being adequately addressed. Ultimately, the conversation reflects a mix of constructive feedback and rising tension, with a few users becoming increasingly critical of the proposed changes.

4. Remove C arrays

   • Toxicity Score: 0.65 (Defensive responses, critical interjections, escalating tension)
   • This GitHub conversation begins with username1 proposing a change to remove C arrays, which is met with initial support from username2, who expresses optimism about the potential benefits. However, as the discussion progresses, username3 raises concerns about the implications of the change, leading to a defensive response from username1. Tension escalates when username4 interjects with a critical perspective, prompting username2 to counter with a more assertive tone. The conversation ultimately reflects a mix of collaboration and frustration, with underlying tensions surfacing as differing opinions clash.

5. [Inductor] inplace padding

   • Toxicity Score: 0.55 (Defensive responses, questioning of validity, frustration over misunderstandings)
   • This GitHub conversation begins with a user sharing performance test results related to a proposed change in functionality, expressing a generally positive sentiment about the outcomes. As the discussion progresses, other users engage with varying degrees of enthusiasm, some offering supportive feedback while others raise concerns about potential regressions. Tension arises when a user questions the validity of the performance metrics, leading to a defensive response from the original poster. The tone shifts as some participants express frustration over perceived misunderstandings, while others attempt to mediate the situation. Ultimately, the conversation reflects a mix of collaboration and conflict, with underlying tensions related to differing interpretations of the data presented.

6. [dynamo][guards] Introduce a diff_guard_manager

   • Toxicity Score: 0.65 (Defensive replies, challenges to feasibility, feelings of being unheard)
   • This GitHub conversation begins with a user expressing enthusiasm about a proposed feature, prompting a series of responses that range from supportive to critical. As the discussion progresses, some participants share their insights, while others voice concerns about implementation details, leading to a noticeable shift in tone. Tensions rise when a user challenges the feasibility of the suggestions, resulting in defensive replies from others. The conversation ultimately reflects a mix of collaboration and frustration, with some users feeling unheard or dismissed, which further escalates the emotional stakes of the dialogue.

7. [dynamo] skip_guard_eval_unsafe stance for power users

   • Toxicity Score: 0.65 (Defensive replies, questioning rationale, feelings of dismissal)
   • This GitHub conversation begins with a user sharing a pull request and tagging several collaborators for their input. As the discussion progresses, some users express confusion about the proposed changes, leading to a mix of supportive and critical responses. Tension arises when a user questions the rationale behind certain decisions, prompting defensive replies from others. The tone shifts as frustrations mount, with some participants feeling dismissed or misunderstood, while others attempt to mediate the situation. Ultimately, the conversation reflects a blend of collaboration and conflict, highlighting the challenges of reaching consensus in a technical discussion.

8. [Dynamo] Replace torch._dynamo.optimize() with torch.compile() [4/N]

   • Toxicity Score: 0.65 (Defensive responses, skepticism, frustration over misunderstandings)
   • This GitHub conversation begins with a collaborative tone as multiple users, including @ezyang and @chauhang, engage in discussing a proposed change. However, as the conversation progresses, @penguinwu expresses confusion about the implementation details, which triggers a slight shift in sentiment. @voznesenskym attempts to clarify but is met with skepticism from @EikanWang, leading to a more defensive tone. The discussion escalates when @jgong5 raises concerns about potential impacts, prompting @Guobing-Chen to respond with frustration over perceived misunderstandings. Overall, the conversation reflects a mix of collaboration and rising tension, culminating in a more critical atmosphere as users grapple with differing perspectives.

9. support condition branch in ao debug handler

   • Toxicity Score: 0.65 (Defensive responses, questioning effectiveness, skepticism, frustration)
   • This GitHub conversation begins with username1 presenting a new feature related to the ao debug handler, which is met with initial support from username2, who acknowledges the contribution positively. However, as the discussion progresses, username1 expresses concern over potential issues with the implementation, leading to a defensive response from username2. Tension escalates when username1 questions the effectiveness of the proposed solution, prompting username2 to react with frustration. The conversation concludes with username1 suggesting further revisions, while username2 remains skeptical about the necessity of those changes, indicating a lingering discord between the two participants.

10. Simplify & rectify dequantized B buffer loading for AMX GEMM micro-kernel for WoQ int8 case

    • Toxicity Score: 0.55 (Frustration, defensive responses, questioning of implications, misunderstandings)
    • This GitHub conversation begins with a technical proposal from the author, who outlines a simplification in the loading of a dequantized B buffer for a specific micro-kernel case. Several users, including @leslie-fang-intel, engage with the proposal, expressing varying degrees of support and skepticism. As the discussion progresses, tensions arise when some contributors question the implications of the changes on performance and compatibility, leading to moments of frustration and defensive responses. The tone shifts as users attempt to clarify misunderstandings, but the conversation remains charged with a mix of constructive feedback and underlying irritation, particularly when addressing potential issues with specific hardware support.

11. Permute test

    • Toxicity Score: 0.67 (Defensive responses, skepticism, unresolved disagreements, aggressive language)
    • This GitHub conversation begins with username1 proposing a solution to an issue, which is met with initial support from username2. However, as the discussion progresses, username3 expresses skepticism about the effectiveness of the proposed fix, leading to a defensive response from username1. Tensions rise when username4 joins the conversation, questioning the validity of the previous comments and prompting username2 to defend their stance more aggressively. The tone shifts as frustration becomes evident, with username1 feeling undermined and username3 becoming increasingly dismissive. The conversation concludes with unresolved disagreements, leaving a sense of lingering tension among the participants.

12. Broadcast constants on vectorised stores in CppTile2DKernel

    • Toxicity Score: 0.55 (Frustration over lack of clarity, Confusion among participants, Escalating tensions)
    • This GitHub conversation begins with a user highlighting a technical issue related to broadcasting constants in a specific kernel, prompting a series of responses from various contributors. As the discussion unfolds, some users express confusion and seek clarification, while others provide potential solutions or workarounds. Tensions arise when a few participants exhibit frustration over the lack of progress or clarity, leading to a more heated exchange. Overall, the tone fluctuates between collaborative and contentious, with some users feeling dismissed or misunderstood, which further escalates the conversation.

13. Run only listed tests on s390x

    • Toxicity Score: 0.55 (Misunderstandings, defensive responses, frustration)
    • This GitHub conversation begins with the original poster outlining a proposal to run only specific tests on the s390x architecture, referencing a previous pull request. Several users, including @voznesenskym and @penguinwu, respond with varying degrees of support and questions, leading to a mix of constructive feedback and some confusion. As the discussion progresses, tensions rise when a user expresses dissatisfaction with the clarity of the proposal, prompting defensive responses from others. The tone shifts as some participants become frustrated with perceived misunderstandings, while others attempt to mediate and clarify the intent behind the changes. Overall, the conversation reflects a blend of collaboration and conflict, with moments of heightened emotion and a need for clearer communication among the contributors.

14. [RFC] Default tensor_to_numpy force=True

    • Toxicity Score: 0.67 (Escalation of disagreement, defensive responses, critical remarks)
    • This GitHub conversation begins with a user expressing a suggestion for a change, which is met with initial support from others. However, as the discussion progresses, some participants begin to voice concerns about the implications of the proposed changes, leading to a noticeable shift in tone. Tensions rise when a user challenges the feasibility of the suggestion, prompting defensive responses from others. The conversation ultimately becomes more heated, with users exchanging critical remarks and frustrations about misunderstandings, indicating a breakdown in collaborative dialogue.

15. [Intel GPU] Support getStreamFromExternel for XPU.

    • Toxicity Score: 0.65 (Frustration expressed, defensive reactions, misunderstandings, dismissive comments)
    • This GitHub conversation begins with a user seeking support for a specific feature, prompting responses from multiple contributors. As the discussion progresses, some users express confusion and frustration over the proposed solutions, leading to a noticeable increase in tension. A few participants exhibit impatience, while others attempt to clarify misunderstandings, resulting in a mix of constructive feedback and defensive reactions. The tone shifts as disagreements arise, with some users feeling dismissed, which further escalates the emotional stakes of the conversation.

16. [AOIT] Support AOT Inductor for Intel GPU.

    • Toxicity Score: 0.55 (Misunderstandings, frustration, defensive tones, lack of clarity)
    • This GitHub conversation begins with a user seeking support for a technical issue, prompting responses from multiple contributors. As the discussion progresses, some users express confusion and frustration over the clarity of previous explanations, leading to a noticeable increase in tension. Several participants attempt to clarify their points, but misunderstandings persist, resulting in a mix of supportive and defensive tones. Ultimately, the conversation reflects a struggle to reach consensus, with some users feeling overwhelmed by the complexity of the topic, while others remain optimistic about finding a solution.

17. [ROCm] Bug fix for flex attention configs avoiding ROCm path

    • Toxicity Score: 0.55 (Misunderstandings, rising frustration, defensive responses)
    • This GitHub conversation begins with a user presenting a follow-up fix to a previous issue, indicating a collaborative effort to resolve a logic error. Several users are tagged, suggesting a collective interest in the topic. As the discussion progresses, some users express confusion or frustration regarding the implementation details, leading to a slight increase in tension. Others attempt to clarify the situation, but the tone shifts as misunderstandings arise, resulting in defensive responses. Overall, the conversation reflects a mix of collaboration and rising frustration, with users navigating through technical challenges and communication barriers.

18. [inductor] PyCodeCache: restore in-memory caching of modules

    • Toxicity Score: 0.65 (Frustration over misunderstandings, defensive responses, challenges to feasibility)
    • This GitHub conversation begins with a user sharing a proposal for restoring in-memory caching of modules, which prompts a series of responses from various contributors. Initially, the tone is collaborative, with users expressing interest and asking clarifying questions. However, as the discussion progresses, some contributors begin to express frustration over misunderstandings and perceived lack of clarity in the proposal. Tension escalates when certain users challenge the feasibility of the suggested approach, leading to defensive responses from others. The conversation ultimately reflects a mix of constructive feedback and rising irritation, indicating a shift from a cooperative atmosphere to one marked by contention.

19. Switch to linux.8xlarge.amx

    • Toxicity Score: 0.67 (Frustration, critical tone, defensive responses, breakdown in communication)
    • This GitHub conversation begins with username1 proposing a change to utilize a more cost-effective instance type, expressing optimism about the potential benefits. Username2 responds with enthusiasm, agreeing with the suggestion and providing additional context. However, as the discussion progresses, username1 becomes frustrated with username2's lack of understanding regarding the technical details, leading to a tense exchange. Username2 attempts to clarify their position, but the tone shifts as username1's comments become increasingly critical, suggesting a breakdown in communication. The conversation concludes with username2 feeling defensive, indicating a lingering tension that could affect future interactions.

20. triangular_solve: fix meta function output argument dtype check.

    • Toxicity Score: 0.65 (Frustration expressed, misunderstandings, critical interjections, defensive responses)
    • This GitHub conversation begins with username1 proposing a fix for a meta function output argument dtype check, which is met with initial support from username2. However, as the discussion progresses, username1 expresses frustration over the lack of clarity in the implementation details provided by username2. Tension escalates when username3 interjects with a critical perspective, prompting username1 to defend their approach more vigorously. The tone shifts as username2 attempts to clarify their position, but misunderstandings lead to further disagreements, resulting in a somewhat heated exchange among the participants. Overall, the conversation reflects a mix of collaboration and conflict, with underlying frustrations surfacing as the dialogue unfolds.

21. pow: fix meta function output argument dtype check.

    • Toxicity Score: 0.65 (Defensive reactions, critiques of understanding, impatience)
    • This GitHub conversation begins with a user expressing confusion over a technical issue related to the pow function's implementation, prompting a response from another user who attempts to clarify the situation. As the discussion progresses, tensions rise when a third user points out inconsistencies in the proposed solution, leading to a defensive reaction from the original poster. The tone shifts to frustration as users exchange critiques of each other's understanding and approaches, with some comments reflecting impatience and a sense of urgency to resolve the issue. Ultimately, the conversation concludes with a tentative agreement on the next steps, though underlying tensions remain evident.

22. Fix unary references' out dtype check.

    • Toxicity Score: 0.55 (Misunderstandings, defensive responses, emotional stakes)
    • This GitHub conversation begins with a user presenting a pull request aimed at fixing specific reference implementations, which prompts a mix of supportive and critical responses from other users. As the discussion progresses, some participants express confusion over the proposed changes, leading to a slight increase in tension. A few users voice their concerns about the implications of the modifications, while others defend the approach, resulting in a back-and-forth exchange that oscillates between constructive feedback and frustration. The tone shifts as misunderstandings arise, with some users feeling dismissed, which further escalates the emotional stakes of the conversation. Ultimately, the dialogue reflects a blend of collaboration and contention, highlighting the challenges of technical discussions in a public forum.

23. log_softmax: fix meta function output argument dtype check.

    • Toxicity Score: 0.65 (Frustration over clarity, Defensive replies, Sarcastic remarks)
    • This GitHub conversation begins with a user expressing confusion over a recent change, prompting a response from another user who attempts to clarify the issue. As the discussion progresses, tensions rise when a third user voices frustration over the lack of clarity in the documentation, leading to defensive replies from the original poster. The tone shifts as users begin to question each other's understanding of the problem, resulting in a mix of constructive feedback and sarcastic remarks. Ultimately, the conversation reflects a struggle to reach consensus, with underlying frustrations surfacing intermittently.

24. Enable Target Determination

    • Toxicity Score: 0.65 (Defensive responses, critical comments, misunderstandings)
    • This GitHub conversation begins with username1 introducing a pull request aimed at enabling target determination, tagging several collaborators for their input. As the discussion progresses, username2 raises concerns about the implementation details, leading to a defensive response from username1 who feels their work is being undermined. Tension escalates when username3 interjects with a critical comment, prompting username1 to express frustration over the perceived lack of support. The tone shifts as username2 attempts to clarify their position, but misunderstandings persist, resulting in a back-and-forth that highlights underlying frustrations among the participants. Overall, the conversation reflects a mix of collaboration and conflict, with moments of defensiveness and critique.

25. Allow NJT by default for weights_only torch.load

    • Toxicity Score: 0.65 (Defensive responses, questioning expertise, unresolved disagreements)
    • This GitHub conversation begins with a user expressing a suggestion for a feature enhancement, which is met with initial support from another participant. However, as the discussion progresses, differing opinions emerge, leading to a noticeable shift in tone as some users become defensive about their viewpoints. Tension escalates when a user questions the feasibility of the proposed changes, prompting a frustrated response from another contributor who feels their expertise is being undermined. The conversation concludes with a mix of conciliatory remarks and lingering disagreements, indicating unresolved issues among the participants.

26. Try using new G6 instances

    • Toxicity Score: 0.65 (Frustration expressed, defensive responses, questioning feasibility, assertive language)
    • This GitHub conversation begins with username1 suggesting the use of new G6 instances, which is met with initial curiosity from username2. As the discussion progresses, username1 expresses frustration over a lack of clarity in the implementation details, prompting username2 to defend their position. Tension escalates when username3 joins the conversation, questioning the feasibility of the proposed changes, leading to a back-and-forth exchange that reveals underlying disagreements. The tone shifts from collaborative to defensive, with participants increasingly using assertive language, indicating a growing sense of urgency and irritation among the contributors.

27. [logging] Add dynamo_compile fields for pre-dispatch/joint/post-dispatch times

    • Toxicity Score: 0.65 (Defensive replies, questioning feasibility, feelings of dismissal)
    • This GitHub conversation begins with a user expressing anticipation for the upcoming internal testing of a proposed feature, tagging multiple collaborators for their input. As the discussion progresses, some users show enthusiasm for the changes, while others raise concerns about potential issues, leading to a mix of supportive and critical responses. Tension arises when a user questions the feasibility of the implementation, prompting defensive replies from others who feel their contributions are being undermined. The tone shifts as frustration becomes evident, with some users feeling dismissed, which further escalates the conversation. Ultimately, the dialogue reflects a blend of collaboration and conflict, highlighting the challenges of group dynamics in technical discussions.

28. ROCm: Enable 4 gpu tests for distributed config

    • Toxicity Score: 0.55 (Confusion, defensive responses, frustration, questioning of feasibility)
    • This GitHub conversation begins with a user proposing a change to ensure jobs are allocated to nodes with at least four GPUs, tagging several collaborators for their input. As the discussion unfolds, some users express confusion about the implementation details, leading to a mix of supportive and critical responses. Tension arises when a user questions the feasibility of the proposed solution, prompting defensive replies from others who feel their contributions are being undermined. The tone shifts as some participants become frustrated with the lack of clarity, while others attempt to mediate and refocus the conversation on constructive feedback. Overall, the dialogue reflects a blend of collaboration and conflict, with underlying sentiments of urgency and concern about the project's direction.

29. Respect ROCR_VISIBLE_DEVICES on AMD GPU device discovery

    • Toxicity Score: 0.65 (Escalation of disagreement, critical responses, unresolved issues)
    • This GitHub conversation begins with username1 proposing a fix related to device discovery on AMD GPUs, expressing optimism about its potential impact. Username2 responds with a mix of curiosity and skepticism, prompting further clarification. As the discussion progresses, username3 interjects with a critical perspective, leading to a noticeable shift in tone as tensions rise. Username1 defends their position, while username2 attempts to mediate, but the atmosphere becomes increasingly charged. Ultimately, the conversation concludes with unresolved issues and lingering frustration among participants, indicating a potential for future conflict.

30. Refactored scan testcases

    • Toxicity Score: 0.65 (Frustration over feedback, defensive responses, questioning of necessity)
    • This GitHub conversation begins with username1 presenting a pull request aimed at refactoring test cases, which is met with initial support from username2. However, as the discussion progresses, username1 expresses frustration over unclear feedback and perceived lack of engagement from other contributors. Tensions rise when username3 questions the necessity of the changes, prompting username1 to defend their approach more assertively. The tone shifts as username2 attempts to mediate, but the conversation remains charged, with underlying frustrations surfacing intermittently. Ultimately, the dialogue reflects a mix of collaboration and conflict, highlighting the challenges of consensus-building in technical discussions.

31. [pt2e][quant] Add simple cond support for annotate, prepare and convert

    • Toxicity Score: 0.65 (Defensive responses, critical interjections, underlying irritation)
    • This GitHub conversation begins with a user expressing enthusiasm about the new feature being proposed, highlighting its potential benefits. As the discussion progresses, another user raises concerns about the implementation details, leading to a defensive response from the original poster. Tension escalates when a third user interjects with a critical comment, prompting a back-and-forth exchange that includes some frustration and dismissive remarks. The tone shifts as users attempt to clarify misunderstandings, but underlying irritation remains evident, suggesting a struggle to reach consensus. Ultimately, the conversation reflects a mix of constructive feedback and emotional responses, indicating a challenging dynamic among participants.

32. [Testing][Invesitation]

    • Toxicity Score: 0.65 (Escalating frustration, defensive responses, skepticism towards solutions)
    • This GitHub conversation begins with a user expressing confusion over an error related to an undefined variable, prompting a request for clarification from another user. As the discussion progresses, tensions rise when one user indicates frustration with the lack of a solution, leading to a defensive response from another participant who feels their contributions are being dismissed. The tone shifts as users begin to exchange more pointed remarks, with some suggesting alternative approaches while others remain skeptical. The conversation concludes with a mix of constructive feedback and lingering dissatisfaction, highlighting the challenges of collaboration in troubleshooting technical issues.

33. [RFC] Implement caching for user defined triton kernels

    • Toxicity Score: 0.58 (Defensive responses, questioning design choices, underlying frustrations)
    • This GitHub conversation begins with a user presenting a pull request that introduces a caching mechanism for user-defined Triton kernels, which is met with initial interest from other contributors. As the discussion progresses, some users express concerns about the implementation's complexity and potential drawbacks, leading to a mix of constructive feedback and frustration. Tensions rise when a user questions the necessity of certain design choices, prompting defensive responses from the original poster. The tone shifts as contributors attempt to clarify misunderstandings, but underlying frustrations remain evident, suggesting a struggle to reach consensus. Overall, the conversation reflects a blend of collaboration and conflict, with varying degrees of sentiment among participants.

34. Use lazy graph module for bwd module

    • Toxicity Score: 0.55 (Impatience, defensive remarks, skepticism, perceived dismissiveness)
    • This GitHub conversation begins with a user expressing a need for clarification on the use of a specific module, prompting responses from several contributors. As the discussion progresses, some users exhibit impatience and frustration over perceived delays in responses, while others attempt to provide assistance and solutions. Tensions rise when a proposed solution is met with skepticism, leading to a back-and-forth exchange that includes defensive remarks and a slight escalation in tone. Ultimately, the conversation reflects a mix of collaborative efforts and underlying frustration, with some users feeling dismissed or misunderstood.

35. [dynamo] Add a DynamoFrameType type above Python frame object

    • Toxicity Score: 0.65 (Defensive responses, questioning feasibility, feelings of dismissal)
    • This GitHub conversation begins with a user expressing enthusiasm about a new feature, prompting a series of responses from various contributors. As the discussion progresses, some users share their insights and suggestions, while others raise concerns about implementation details. Tension arises when a user questions the feasibility of a proposed solution, leading to a defensive response from another contributor. The tone shifts as frustrations surface, with some users feeling dismissed or misunderstood. Ultimately, the conversation concludes with a mix of agreement and lingering uncertainty about the next steps, reflecting a collaborative yet strained atmosphere.

36. Made FlexAttention error on subgraph lowering failure

    • Toxicity Score: 0.65 (Frustration over unclear solutions, mixed reactions, emotional escalation)
    • This GitHub conversation begins with a user expressing confusion over an error related to subgraph lowering, prompting several contributors to chime in with their insights. As the discussion progresses, some users display frustration over the lack of clarity in previous solutions, leading to a noticeable increase in tension. A few participants attempt to clarify misunderstandings, but their efforts are met with mixed reactions, resulting in a back-and-forth that escalates the emotional tone. Ultimately, the conversation reflects a blend of collaboration and irritation, with users feeling both supported and challenged by the ongoing dialogue.

37. [hop] bind subgraph inputs to temporary phs instead of creating call_functions in graph.

    • Toxicity Score: 0.65 (Escalation of frustration, defensive responses, assertive language)
    • This GitHub conversation begins with a user expressing a technical concern regarding the implementation of a feature, prompting responses from multiple contributors. As the discussion progresses, some users display a sense of urgency and frustration over the lack of clarity in previous solutions, leading to a noticeable increase in tension. Several participants attempt to clarify misunderstandings, but their efforts are met with mixed reactions, including defensiveness from some users. The tone shifts as disagreements arise, with a few contributors resorting to more assertive language, which further escalates the situation. Ultimately, the conversation reflects a blend of collaboration and conflict, highlighting the challenges of technical discussions in a public forum.

38. Restart dynamo analysis when we fail to tensorify away all symfloat inputs

    • Toxicity Score: 0.65 (Frustration over unresolved issues, critical tone, defensive reactions)
    • This GitHub conversation begins with a user expressing a technical issue related to a specific analysis process, prompting a series of responses from other users who attempt to provide solutions. As the discussion progresses, some users exhibit frustration when their suggestions do not resolve the problem, leading to a noticeable increase in tension. A few participants adopt a more critical tone, questioning the effectiveness of previous contributions and expressing dissatisfaction with the overall progress. The conversation culminates in a mix of constructive feedback and defensive reactions, indicating a potential for further conflict among the contributors.

39. [for ci][dont merge] Debugging a segfault

    • Toxicity Score: 0.55 (Frustration expressed, Confusion over solutions, Defensive responses)
    • This GitHub conversation begins with a user seeking assistance regarding a debugging issue, prompting several contributors to chime in with their insights. As the discussion progresses, some users express confusion and frustration over the proposed solutions, leading to a noticeable increase in tension. A few participants display impatience, while others attempt to mediate and clarify misunderstandings. The overall tone fluctuates between collaborative and contentious, with certain comments triggering defensive responses from others, indicating a growing divide in perspectives.

40. Change strict to False in _get_aten_graph_module_for_pattern

    • Toxicity Score: 0.65 (Defensive responses, confrontational tone, unresolved concerns)
    • This GitHub conversation begins with a user proposing a change to a codebase, which is met with initial support from other contributors. However, as the discussion progresses, some users express confusion and disagreement regarding the implications of the proposed change. Tensions rise when one user criticizes another's understanding of the issue, leading to defensive responses. The tone shifts from collaborative to confrontational, with users using more assertive language. Ultimately, the conversation concludes with a mix of unresolved concerns and a call for further clarification, leaving the atmosphere somewhat strained.

41. Fix the check for can_use_expanded_index_path

    • Toxicity Score: 0.67 (Defensive responses, critical feedback, escalating frustration)
    • This GitHub conversation begins with username1 proposing a fix for an issue, expressing optimism about the solution's effectiveness. As the discussion progresses, username2 raises concerns about the implementation, leading to a defensive response from username1. Tension escalates when username3 joins, echoing username2's concerns, which prompts username1 to become increasingly frustrated. The tone shifts as username1 feels attacked, while username2 and username3 maintain a critical stance, resulting in a back-and-forth exchange that highlights underlying disagreements. The conversation concludes with username1 attempting to clarify their position, but the atmosphere remains charged.

42. fix torch.exp for torch.complex datatypes on CPU

    • Toxicity Score: 0.55 (Misunderstandings, defensive responses, rising frustration)
    • This GitHub conversation begins with a user presenting a fix for a specific issue related to complex datatypes in PyTorch, which is met with initial support from other contributors. As the discussion progresses, some users express confusion regarding the implementation details, leading to a slight increase in tension. A few contributors voice their concerns about the potential implications of the fix, which prompts defensive responses from the original poster. The tone shifts as misunderstandings arise, resulting in a mix of frustration and urgency among participants. Ultimately, the conversation reflects a collaborative yet strained effort to address the technical challenges at hand.

43. [WIP] SimpleFSDP prototype frontend changes

    • Toxicity Score: 0.55 (Defensive responses, raised concerns, fluctuating tones)
    • This GitHub conversation begins with the author outlining the enhancements made to the SimpleFSDP prototype, acknowledging the contributions of other users. As the discussion progresses, several participants express their thoughts on the proposed changes, with some showing enthusiasm while others raise concerns about specific implementations. Tension arises when a user questions the practicality of certain modifications, leading to a defensive response from the original poster. The tone fluctuates between collaborative and confrontational, with some users attempting to mediate the discussion while others become increasingly frustrated. Ultimately, the conversation reflects a mix of constructive feedback and underlying tension regarding the direction of the project.

44. [Do not merge][CI test] Update oneDNN v3.6

    • Toxicity Score: 0.65 (Frustration over communication, Defensive replies, Escalation of disagreements)
    • This GitHub conversation begins with a user expressing a desire for clarity regarding a recent update, prompting responses from multiple participants who share varying degrees of understanding and support. As the discussion progresses, some users exhibit frustration over perceived lack of communication, leading to a noticeable shift in tone as disagreements arise. Tensions escalate when a user challenges another's approach, resulting in defensive replies that further complicate the dialogue. Ultimately, the conversation reflects a mix of collaborative intent and underlying frustration, with several users attempting to mediate while others remain entrenched in their positions.

45. [Intel GPU] Enable mkdnn._linear_pointwise at XPU backend

    • Toxicity Score: 0.55 (Confusion, frustration, defensive responses, questioning feasibility)
    • This GitHub conversation begins with a user tagging several contributors to draw attention to a proposed change, indicating a collaborative tone. As the discussion progresses, some users express confusion about the implementation details, leading to a slight increase in frustration. Tension escalates when one user questions the feasibility of the proposed solution, prompting defensive responses from others. The conversation ultimately reflects a mix of constructive feedback and underlying irritation, suggesting that while collaboration is present, there are also signs of growing discontent among participants.

46. [Intel GPU] Enable mkdnn._linear_pointwise at XPU backend

    • Toxicity Score: 0.55 (Misunderstandings, defensive responses, frustration, concerns about oversight)
    • This GitHub conversation begins with a user introducing a pull request aimed at enhancing functionality, which is met with initial interest from several contributors. As the discussion progresses, some users express confusion regarding implementation details, leading to a slight increase in tension. A few contributors voice their concerns about the proposed changes, which prompts defensive responses from others. The tone shifts as misunderstandings arise, resulting in frustration from some users who feel their points are being overlooked. Ultimately, the conversation reflects a mix of collaborative spirit and underlying tension, with contributors striving to clarify their positions while navigating differing opinions.

47. Fix inconsistent results from integral linspace on MPS

    • Toxicity Score: 0.65 (Defensive responses, accusations of misunderstanding, escalating frustration)
    • This GitHub conversation begins with username1 proposing a fix for an issue, expressing optimism about the potential improvements. As the discussion progresses, username2 raises concerns about the implementation, leading to a defensive response from username1, who feels misunderstood. Tension escalates when username3 joins, echoing username2's concerns, which prompts username1 to become increasingly frustrated. The tone shifts as username1 accuses the others of not appreciating the effort put into the solution, while username2 attempts to clarify their position, resulting in a back-and-forth that highlights underlying frustrations. The conversation concludes with a tentative agreement to revisit the proposed changes, but the atmosphere remains charged.

48. [Intel GPU] Enable mkldnn::_convolution.pointwise at XPU backend

    • Toxicity Score: 0.65 (Frustration expressed, defensive replies, challenges to feasibility)
    • This GitHub conversation begins with a user expressing enthusiasm about a proposed feature, prompting a series of responses that range from supportive to critical. As the discussion progresses, some participants voice concerns about implementation details, leading to a noticeable shift in tone as frustration emerges. Tensions escalate when a user challenges the feasibility of the suggestions, resulting in defensive replies from others. The conversation concludes with a mix of acknowledgment and lingering disagreements, highlighting a divide in perspectives among the contributors.

49. [CD] Upgrade XPU support packages version to 2025.0 for Linux CD

    • Toxicity Score: 0.67 (Defensive responses, escalating frustration, lack of consensus)
    • This GitHub conversation begins with username1 proposing an upgrade to the XPU support packages, which is met with initial support from username2, who expresses optimism about the potential improvements. However, as the discussion progresses, username3 raises concerns about compatibility issues, leading to a defensive response from username1. Tension escalates when username4 questions the feasibility of the proposed changes, prompting username1 to become increasingly frustrated and dismissive. The tone shifts as username2 attempts to mediate, but the conversation remains charged, with underlying disagreements surfacing and a lack of consensus evident among the participants.

50. fix torch.div for torch.complex datatypes on CPU

    • Toxicity Score: 0.65 (Frustration over unclear solutions, misunderstandings, defensive reactions)
    • This GitHub conversation begins with a user expressing a technical issue related to a specific function, prompting responses from multiple contributors. As the discussion progresses, some users display frustration over the lack of clarity in previous solutions, leading to a noticeable increase in tension. Several participants attempt to clarify their positions, but misunderstandings arise, resulting in a mix of constructive feedback and defensive reactions. The tone shifts from collaborative to somewhat confrontational as users challenge each other's approaches, indicating a growing sense of urgency to resolve the issue. Ultimately, the conversation reflects a struggle to maintain a productive dialogue amidst rising frustrations.

51. [Quant][Onednn] add linear_dynamic_fp16 ops

    • Toxicity Score: 0.65 (Defensive responses, questioning of correctness, rising frustrations)
    • This GitHub conversation begins with a user presenting a pull request that introduces new operations for linear_dynamic_fp16 in the onednn namespace, aiming to enhance quantization in eager mode. As the discussion unfolds, several users express varying degrees of enthusiasm and skepticism regarding the implementation details and performance implications. Tensions arise when some contributors question the correctness and efficiency of the proposed changes, leading to defensive responses from the original poster. The tone shifts as frustrations mount, with some users feeling that their concerns are not being adequately addressed, resulting in a more confrontational atmosphere. Ultimately, the conversation reflects a mix of collaborative spirit and underlying tension as participants navigate technical disagreements.

52. C array fix2

    • Toxicity Score: 0.67 (Defensive responses, critical remarks, escalating tensions)
    • This GitHub conversation begins with username1 proposing a solution to an issue, which is met with initial enthusiasm from username2. However, as the discussion progresses, username3 raises concerns about the effectiveness of the proposed fix, leading to a defensive response from username1. Tensions escalate when username2 expresses disappointment over the lack of clarity in the explanations provided, prompting username1 to react with frustration. The tone shifts further as username3 and username2 exchange critical remarks, indicating a growing sense of dissatisfaction and urgency to resolve the matter. Overall, the conversation reflects a mix of collaboration and conflict, with underlying tensions surfacing as participants seek resolution.

53. Optimize hook description of register_module_forward_hook

    • Toxicity Score: 0.55 (Confusion, misunderstandings, defensiveness)
    • This GitHub conversation begins with a user proposing an optimization to the hook description, which is met with initial support from other contributors. However, as the discussion progresses, some users express confusion regarding the proposed changes, leading to a slight increase in tension. A few contributors voice their concerns about the clarity of the modifications, while others defend the approach, resulting in a mix of supportive and critical sentiments. The tone shifts as misunderstandings arise, prompting a call for clarification from one user, which further escalates the discussion. Overall, the conversation reflects a blend of collaboration and frustration, with moments of defensiveness emerging as contributors navigate differing perspectives.

54. [WIP] [Intel GPU] Add SDPA on XPU with OneDNN Graph

    • Toxicity Score: 0.58 (Lack of clarity, Frustration over dependencies, Defensive responses)
    • This GitHub conversation begins with the author outlining the motivation and technical details of a pull request, which is met with initial support from several contributors. As the discussion progresses, some users express concerns about the readiness of the proposed changes, leading to a slight increase in tension. A few contributors voice frustration over the lack of clarity regarding dependencies and testing requirements, which prompts defensive responses from others. The tone shifts as misunderstandings arise, resulting in a mix of constructive feedback and pointed critiques. Ultimately, the conversation reflects a blend of collaboration and underlying tension, with contributors navigating both technical challenges and interpersonal dynamics.

55. fix sigmoid for torch.complex datatypes on CPU

    • Toxicity Score: 0.55 (Mixed sentiments, resistance to suggestions, expressions of frustration)
    • This GitHub conversation begins with a user expressing a need for a fix related to complex datatypes in PyTorch, prompting responses from multiple contributors. As the discussion progresses, some users show enthusiasm for the proposed solutions, while others express skepticism or confusion, leading to a mix of supportive and critical tones. Tension arises when certain suggestions are met with resistance, and frustrations are voiced regarding the clarity and effectiveness of the solutions offered. The conversation reflects a collaborative yet occasionally contentious atmosphere, with users navigating differing opinions and technical challenges.

56. Support torch.where with NJT and dense tensor

    • Toxicity Score: 0.65 (Defensive responses, critical interjections, unresolved tension)
    • This GitHub conversation begins with username1 proposing a feature enhancement, expressing optimism about its potential benefits. Username2 responds with enthusiasm, suggesting additional use cases, which initially fosters a collaborative atmosphere. However, as the discussion progresses, username1 raises concerns about implementation challenges, leading to a shift in tone as username2 becomes defensive. Tension escalates when username3 interjects with a critical perspective, prompting username1 to express frustration over perceived dismissiveness. The conversation concludes with username2 attempting to clarify their position, but the underlying tension remains palpable, indicating unresolved issues among the participants.

57. remove redundant assign

    • Toxicity Score: 0.55 (Lack of clarity, rising frustration, critical tone)
    • This GitHub conversation begins with a user sharing a draft pull request and inviting feedback from multiple collaborators. As the discussion unfolds, some participants express confusion about certain implementation details, leading to a mix of constructive suggestions and moments of frustration. Tension arises when a few users feel their concerns are not being adequately addressed, resulting in a shift towards a more critical tone. Despite the initial collaborative spirit, the conversation hints at underlying disagreements that could escalate if not managed carefully.

58. [3.13] deal with exec() semantic change in test_cond_no_dynamo_cache_limit

    • Toxicity Score: 0.67 (Defensive responses, escalating tensions, pointed remarks)
    • This GitHub conversation begins with a user expressing confusion over a recent change in semantics related to a specific function, prompting a response from another user who attempts to clarify the issue. As the discussion progresses, tensions rise when a third user challenges the clarity of the initial explanation, leading to a defensive tone from the first user. The conversation takes a more heated turn as disagreements about the implications of the change surface, with users exchanging pointed remarks and frustrations about the lack of documentation. Ultimately, the dialogue reflects a mix of constructive feedback and escalating frustration, indicating a potential for further conflict.

59. improve SAC ILP

    • Toxicity Score: 0.67 (Misunderstandings, frustration, dismissive responses, heated exchanges)
    • This GitHub conversation begins with a user seeking improvements on a specific issue, prompting responses from multiple contributors. As the discussion progresses, some users express confusion and frustration over the proposed solutions, leading to a noticeable increase in tension. Several participants attempt to clarify their positions, but misunderstandings persist, resulting in a mix of supportive and critical tones. The conversation culminates in a more heated exchange, with some users feeling dismissed or undervalued, which further escalates the emotional stakes of the dialogue.

60. [DO NOT REVIEW] Auto SAC

    • Toxicity Score: 0.65 (Defensive responses, questioning effectiveness, feelings of dismissal)
    • This GitHub conversation begins with a user expressing a desire to address a specific issue, tagging multiple contributors for their input. As the discussion progresses, some users show enthusiasm for the proposed changes, while others raise concerns about potential implications. Tensions arise when a user questions the effectiveness of a suggested solution, leading to defensive responses from others. The tone shifts as frustration builds, with some participants feeling dismissed, which further escalates the conversation. Ultimately, the dialogue reflects a mix of collaboration and conflict, highlighting the challenges of consensus-building in a technical environment.

61. [BE]: Add pointwise tags to activations for NJT and torch compile

    • Toxicity Score: 0.65 (Defensive responses, skepticism, frustration, lack of support)
    • This GitHub conversation begins with username1 proposing a feature enhancement, which is met with initial enthusiasm from username2. However, as the discussion progresses, username3 raises concerns about the implementation details, leading to a defensive response from username1. Tension escalates when username4 questions the feasibility of the proposed changes, prompting username2 to express frustration over the lack of support for the idea. The tone shifts as username1 attempts to clarify misunderstandings, but the conversation remains charged, with several participants expressing skepticism about the project's direction. Overall, the dialogue reflects a mix of collaboration and conflict, with underlying frustrations surfacing as the conversation unfolds.

62. Install magma from a tarball

    • Toxicity Score: 0.65 (Defensive responses, technical jargon, critiques of explanations, misunderstandings)
    • This GitHub conversation begins with a user expressing confusion about the installation process for magma, prompting a response from another user who provides clarification but inadvertently introduces technical jargon that further complicates the discussion. As the conversation progresses, a third user interjects with a critique of the initial explanation, leading to a defensive tone from the first user. Tension escalates when a fourth user points out inconsistencies in the provided instructions, causing frustration among the participants. The conversation concludes with a mix of constructive feedback and lingering misunderstandings, highlighting a need for clearer communication.

63. Test all data

    • Toxicity Score: 0.67 (Defensive responses, escalation of frustration, impatience from participants)
    • This GitHub conversation begins with username1 proposing a solution to an issue, which is met with initial enthusiasm from username2. However, as the discussion progresses, username1 expresses frustration that username2's solution did not work as intended, leading to a shift in tone. Username2 responds defensively, which triggers further tension and disagreement among other participants. The conversation escalates as more users join in, with some expressing confusion and others showing impatience, ultimately creating a charged atmosphere that hints at unresolved issues and potential conflict.

64. Extract reusable portions of GeluKernel into header

    • Toxicity Score: 0.55 (Defensive replies, questioning feasibility, fluctuating tones)
    • This GitHub conversation begins with a user expressing a desire to improve code reusability, prompting a series of responses from multiple participants. As the discussion progresses, some users show enthusiasm for the proposed changes, while others raise concerns about potential implications. Tension arises when a user questions the feasibility of the implementation, leading to defensive replies from those advocating for the changes. The tone fluctuates between collaborative and confrontational, with moments of frustration evident as users seek clarity and resolution. Ultimately, the conversation reflects a mix of constructive feedback and underlying disagreements, indicating a complex dynamic among the contributors.

65. docstring_linter finds long classes and functions without docstrings

    • Toxicity Score: 0.67 (Frustration, defensive reactions, curt responses, unresolved conflict)
    • This GitHub conversation begins with username1 seeking clarification on a specific issue, expressing mild confusion. Username2 responds with a detailed explanation, but username1's follow-up indicates that the solution did not resolve the problem, leading to a tone of frustration. As the discussion progresses, username3 joins in, attempting to mediate but inadvertently escalating tensions by suggesting a different approach. Username1 reacts defensively, feeling dismissed, while username2 becomes increasingly curt in their responses. The conversation concludes with a sense of unresolved conflict, as participants express dissatisfaction with the lack of progress.

66. Remove start stack, log backward compile to empty stack

    • Toxicity Score: 0.65 (Defensive responses, critical interjections, escalating frustration)
    • This GitHub conversation begins with username1 presenting a proposed change, which is met with initial support from username2, who expresses enthusiasm about the potential improvements. However, as the discussion progresses, username3 raises concerns about the implementation details, leading to a defensive response from username1. Tension escalates when username4 interjects with a critical perspective, prompting username2 to side with username1, resulting in a back-and-forth exchange that becomes increasingly heated. The overall tone shifts from collaborative to confrontational, with several participants feeling frustrated and misunderstood, ultimately leading to a call for clarification from username5 to diffuse the situation.

67. Enable opting out of experiments even when they're being rolled out

    • Toxicity Score: 0.65 (Defensive responses, questioning feasibility, mixed sentiments)
    • This GitHub conversation begins with username1 presenting a feature request, which is met with initial support from username2, who expresses enthusiasm about the potential benefits. However, as the discussion progresses, username3 raises concerns about the implementation details, leading to a defensive response from username1. Tension escalates when username4 questions the feasibility of the proposed changes, prompting username2 to counter with a more assertive tone. The conversation concludes with username1 attempting to clarify misunderstandings, but the overall sentiment remains mixed, with lingering frustration evident among some participants.

68. add fp8 scaled_mm for XPU

    • Toxicity Score: 0.65 (Frustration over overlooked suggestions, defensive responses, disagreement on implementation details)
    • This GitHub conversation begins with a user proposing a new feature, which is met with initial enthusiasm from several contributors. However, as the discussion progresses, some users express confusion and seek clarification, leading to a slight increase in tension. A few contributors become frustrated when their suggestions are overlooked, resulting in a more defensive tone from the original poster. The conversation takes a turn when a disagreement arises over implementation details, prompting a few users to adopt a more critical stance. Ultimately, the dialogue reflects a mix of collaboration and rising frustration, with some contributors feeling their input is undervalued.

69. [dynamo] Track from registered tensor hooks in prune_dead_object_new

    • Toxicity Score: 0.55 (Defensive responses, rising tension, mixed sentiments)
    • This GitHub conversation begins with a technical proposal from the author, who outlines the need for a specific feature related to tensor hooks. Several contributors, including @voznesenskym and @penguinwu, express their support and provide constructive feedback, maintaining a collaborative tone. However, as the discussion progresses, some participants, such as @EikanWang, raise concerns about potential implications, leading to a slight increase in tension. The atmosphere shifts as @jgong5 responds defensively to critiques, which escalates the sentiment among the group. Ultimately, the conversation reflects a mix of constructive dialogue and underlying frustration, indicating a need for careful navigation of differing opinions.

70. [dynamo] Identify pre-existing captured cells by cell id rather than content id

    • Toxicity Score: 0.65 (Defensive responses, misunderstandings, rising frustrations)
    • This GitHub conversation begins with a user outlining a technical issue and proposing a patch to address it. Several participants, including usernames like @voznesenskym and @penguinwu, engage with varying degrees of enthusiasm and skepticism, with some expressing confusion over the proposed changes. As the discussion progresses, tensions rise when a user points out potential flaws in the implementation, leading to defensive responses from others. The tone shifts as frustrations surface, particularly when misunderstandings about the patch's functionality arise. Ultimately, the conversation reflects a mix of collaboration and conflict, with users striving to clarify their positions while navigating the complexities of the technical details.

71. Raise exception if vmap (eager) calls compiled function

    • Toxicity Score: 0.65 (Frustration over unresolved issues, Defensive reactions, Disagreements on effectiveness)
    • This GitHub conversation begins with a user expressing a concern about an issue related to a previous fix, prompting responses from multiple contributors. As the discussion progresses, some users display frustration over the lack of a proper solution, while others attempt to clarify misunderstandings. Tensions rise as disagreements emerge regarding the effectiveness of proposed changes, leading to a mix of constructive feedback and defensive reactions. The tone fluctuates between collaborative and confrontational, with certain comments triggering further disputes among participants. Overall, the conversation reflects a struggle to reach consensus amidst varying perspectives and emotional responses.

72. add NHWC support to GroupNorm backward pass and optimize NHWC GroupNorm kernels

    • Toxicity Score: 0.65 (Defensive reactions, critical tone, miscommunication)
    • This GitHub conversation begins with username1 expressing enthusiasm about the addition of NHWC support to the GroupNorm backward pass, highlighting the successful passing of related tests. Username2 follows up with a request for clarification on implementation details, which username1 responds to positively, fostering a collaborative atmosphere. However, tension arises when username3 raises concerns about potential performance issues, prompting a defensive reaction from username1, who feels their work is being undermined. The conversation shifts as username2 attempts to mediate, but the tone becomes increasingly critical, leading to a back-and-forth exchange that hints at frustration and miscommunication among the participants.

73. General per-SampleInput xfail / skip system

    • Toxicity Score: 0.55 (Confusion, overlooked suggestions, defensive tone)
    • This GitHub conversation begins with a user expressing enthusiasm about the new functionality being introduced, which prompts a series of clarifying questions from other participants. As the discussion progresses, some users express confusion regarding specific implementation details, leading to a slight increase in tension. A few participants display frustration when their suggestions are overlooked or misunderstood, resulting in a more defensive tone from others. Ultimately, the conversation concludes with a mix of appreciation for the contributions made and lingering concerns about the clarity of the proposed changes, indicating a need for further discussion.

74. Add dim to logging to help debug

    • Toxicity Score: 0.67 (Defensive responses, misunderstandings, perceived disrespect)
    • This GitHub conversation begins with username1 suggesting an enhancement to the logging system for better debugging. Username2 responds positively, expressing appreciation for the idea but requests further clarification on implementation details. Tension arises when username3 questions the necessity of the proposed change, leading to a defensive response from username1, who feels their suggestion is being dismissed. The tone shifts as username2 attempts to mediate, but the discussion becomes increasingly heated, with multiple users expressing frustration over misunderstandings and perceived lack of respect for contributions. Ultimately, the conversation concludes with a mix of unresolved issues and a call for more constructive dialogue.

75. [AOTI][refactor] Add sizes and strides util functions

    • Toxicity Score: 0.55 (Dismissive responses, frustration over clarity, underlying disagreements)
    • This GitHub conversation begins with a user expressing enthusiasm about the proposed changes, prompting a series of supportive comments from other participants. As the discussion progresses, a few users raise questions about implementation details, leading to a slight increase in tension as some responses come off as dismissive. A user then expresses frustration over a lack of clarity in the explanations provided, which triggers defensive reactions from others. The tone shifts as some participants attempt to mediate the situation, but underlying disagreements persist, resulting in a mix of constructive feedback and lingering dissatisfaction among the contributors.

76. [DeviceMesh] Add DeviceMesh.manual_seed API

    • Toxicity Score: 0.55 (Concerns about implementation, Frustration over misunderstandings, Defensive responses)
    • This GitHub conversation begins with a user proposing a new API feature, which is met with initial support from several contributors. As the discussion progresses, some users express concerns about the implementation details, leading to a slight increase in tension. A few contributors provide constructive feedback, while others display frustration over perceived misunderstandings or lack of clarity in the proposal. The tone shifts as disagreements arise, with some users feeling defensive about their viewpoints. Ultimately, the conversation reflects a mix of collaboration and conflict, highlighting the challenges of reaching consensus in a technical discussion.

77. Continuous job for pulling artifacts and doing upload

    • Toxicity Score: 0.65 (Defensive responses, escalating frustration, lack of clarity, persistent disagreements)
    • This GitHub conversation begins with username1 proposing a solution to a problem, which is met with initial enthusiasm from username2. However, as the discussion progresses, username3 raises concerns about the feasibility of the proposed solution, leading to a defensive response from username1. Tension escalates when username2 expresses disappointment over the lack of clarity in the explanations provided, prompting username1 to become increasingly frustrated. The tone shifts as username4 attempts to mediate, but the underlying disagreements persist, resulting in a somewhat contentious atmosphere among the participants.

78. Upload all run attempts when in upload_test_stats_intermediate

    • Toxicity Score: 0.55 (Misunderstandings, escalating frustration, lack of clarity)
    • This GitHub conversation begins with username1 suggesting an enhancement to the upload process, emphasizing the difficulty in identifying specific run attempts. Username2 responds with a positive tone, acknowledging the suggestion and expressing appreciation for the input. However, as the discussion progresses, username1 becomes increasingly frustrated with the lack of clarity in the responses, leading to a noticeable shift in sentiment. Tension escalates when username2 misinterprets username1's concerns, prompting username1 to clarify their position more assertively. The conversation concludes with a mix of constructive feedback and lingering misunderstandings, leaving the atmosphere somewhat strained.

79. [C10D] Support group_dst/group_src in c10d collectives

    • Toxicity Score: 0.55 (Skepticism, slight escalation, frustration expressed)
    • This GitHub conversation begins with a user expressing annoyance over a technical limitation regarding the use of global ranks in a specific context. As the discussion progresses, another user attempts to clarify the reasoning behind the design choice, but their response is met with skepticism, leading to a slight escalation in tone. A third participant joins in, echoing the initial frustration and suggesting alternative approaches, which further intensifies the dialogue. The overall sentiment shifts from constructive to somewhat contentious as users grapple with the implications of the design decision, indicating a growing tension among the participants.

80. [Intel GPU] Add NestedTensorXPU to parseDispatchKey and codegen

    • Toxicity Score: 0.65 (Defensive responses, unresolved issues, skepticism, escalating tensions)
    • This GitHub conversation begins with username1 proposing the addition of a new dispatch key for NestedTensorXPU, presenting a code snippet to illustrate its functionality. Username2 responds positively, expressing enthusiasm for the contribution and suggesting minor adjustments for clarity. However, username3 interjects with concerns about potential compatibility issues, which leads to a defensive response from username1, who feels misunderstood. The tone shifts as username2 attempts to mediate, but username3's continued skepticism escalates tensions, prompting username1 to express frustration over the lack of support for the proposed changes. The conversation concludes with a mix of unresolved issues and lingering dissatisfaction among the participants.

81. Register CIA ops to FakeTensorMode directly in export

    • Toxicity Score: 0.65 (Frustration over complexity, Defensive replies, Criticism of initial approach)
    • This GitHub conversation begins with a user presenting a technical issue related to CIA operations and their handling during export, which prompts a response from another user who offers a potential solution. As the discussion progresses, some users express confusion and seek clarification, while others show frustration over the complexity of the proposed changes. Tension escalates when a user criticizes the initial approach, leading to defensive replies from the original poster. The tone shifts from collaborative to somewhat confrontational, with users increasingly questioning each other's understanding and intentions. Ultimately, the conversation reflects a mix of technical discourse and interpersonal dynamics, highlighting the challenges of communication in a collaborative environment.

82. [Inductor efficiency] Move less critical Inductor jobs to periodic

    • Toxicity Score: 0.67 (Frustration over overlooked suggestions, Defensive responses, Pointed remarks)
    • This GitHub conversation begins with @desertfire proposing a change to improve efficiency by moving less critical inductor jobs to a periodic workflow. Initially, the response is positive, with several users expressing support for the idea. However, as the discussion progresses, some users raise concerns about potential impacts on performance, leading to a shift in tone as frustration emerges. @username1 expresses disappointment that their previous suggestions were overlooked, which triggers a defensive response from @username2. The conversation becomes increasingly tense, with users exchanging pointed remarks and questioning each other's understanding of the issue, indicating a growing divide in opinions.

83. [SDPA] Allow user-specified priority order with context manager

    • Toxicity Score: 0.55 (Defensive responses, misunderstandings, challenges to feasibility)
    • This GitHub conversation begins with a user proposing a feature enhancement and seeking feedback from the community. Several participants engage with varying degrees of enthusiasm, with some expressing support while others raise concerns about implementation details. As the discussion progresses, tensions arise when a user challenges the feasibility of the proposed solution, leading to defensive responses from the original poster. The tone shifts as misunderstandings occur, prompting clarifications and further debate. Ultimately, the conversation reflects a mix of collaboration and frustration, with users navigating differing perspectives on the proposal's merits.

84. Add and use thread-safe strerror

    • Toxicity Score: 0.67 (Defensive responses, questioning validity, escalating tensions)
    • This GitHub conversation begins with username1 proposing a solution to an issue, which is met with initial support from username2. However, as the discussion progresses, username3 raises concerns about the implementation, leading to a defensive response from username1. Tensions escalate when username4 questions the validity of the proposed changes, prompting username1 to express frustration. The tone shifts as username2 attempts to mediate, but the conversation remains charged, with several participants expressing dissatisfaction and confusion over the direction of the discussion. Ultimately, the conversation reflects a mix of collaboration and conflict, with underlying tensions surfacing as different viewpoints clash.

85. Add a dashboard for torchao

    • Toxicity Score: 0.55 (Lack of clarity, defensive responses, questioning feasibility)
    • This GitHub conversation begins with a user expressing enthusiasm about adding a new dashboard feature, which is met with initial support from other contributors. However, as the discussion progresses, some users raise concerns about the implementation details, leading to a shift in tone as frustration emerges over perceived lack of clarity. Tension escalates when one user questions the feasibility of the proposed changes, prompting defensive responses from others. The conversation concludes with a mix of optimism and lingering uncertainty, as participants attempt to reconcile differing viewpoints while maintaining a collaborative spirit.

86. [ONNX] Improve the conversion of from dynamic axes to shapes

    • Toxicity Score: 0.67 (Frustration, skepticism, heated exchange, dismissal of suggestions)
    • This GitHub conversation begins with a user expressing a desire for improvements in the conversion process related to dynamic axes and shapes. As the discussion progresses, another user raises concerns about the feasibility of the proposed features, leading to a slight increase in tension. A third participant attempts to mediate by suggesting alternative approaches, but their input is met with skepticism from the original poster, who feels their suggestions are being dismissed. The tone shifts as frustration builds, culminating in a heated exchange where users question each other's understanding of the technical details involved. Overall, the conversation reflects a mix of collaboration and conflict, with underlying tensions surfacing as participants navigate differing perspectives.

87. Record PR time benchmark results in JSON format

    • Toxicity Score: 0.65 (Frustration over technical details, disagreements, inadequate responses to concerns)
    • This GitHub conversation begins with a user outlining a proposal to record benchmark results in a specific JSON format, aiming to enhance accessibility for the open-source community. Several users, including @voznesenskym and @penguinwu, engage with the proposal, expressing varying degrees of support and suggestions for improvement. As the discussion progresses, some participants exhibit frustration over technical details and the clarity of the implementation, leading to a noticeable increase in tension. The tone shifts as disagreements arise, with some users feeling that their concerns are not being adequately addressed, while others attempt to mediate and refocus the conversation on constructive solutions. Ultimately, the conversation reflects a mix of collaboration and conflict, highlighting the challenges of consensus-building in technical discussions.

88. fix test_save_load_transform.

    • Toxicity Score: 0.65 (Dismissive tone, escalating frustration, critiques of suggestions)
    • This GitHub conversation begins with a user reporting an issue related to a test function, expressing confusion and concern over an error message encountered during execution. Another user responds with a suggestion, but their tone is perceived as dismissive, leading to a slight escalation in frustration from the original poster. As the discussion progresses, additional users join in, some offering alternative solutions while others critique the initial suggestions, which further heightens the tension. The conversation ultimately reflects a mix of collaborative problem-solving and underlying frustration, with users occasionally resorting to sharper language as misunderstandings arise.

89. OpenReg: Fix issue when creating empty tensor

    • Toxicity Score: 0.65 (Defensive replies, rising tensions, frustration expressed)
    • This GitHub conversation begins with the author outlining a technical issue related to tensor creation, prompting a response from another user who expresses confusion about the proposed solution. As the discussion progresses, tensions rise when a third user critiques the initial suggestion, leading to defensive replies from the original poster. The tone shifts from collaborative to confrontational, with users exhibiting frustration and impatience. Ultimately, the conversation reflects a struggle to reach consensus, with underlying sentiments of irritation becoming more pronounced as the dialogue continues.

90. Ensure index for state guard construction is a source

    • Toxicity Score: 0.65 (Defensive responses, questioning effectiveness, perceived dismissiveness)
    • This GitHub conversation begins with a user proposing a potential fix for an existing issue, tagging several collaborators for their input. As the discussion unfolds, some users express appreciation for the effort, while others raise concerns about the implementation details. Tension arises when a user questions the effectiveness of the proposed solution, leading to defensive responses from the original poster. The tone shifts as some participants become frustrated with perceived dismissiveness, resulting in a back-and-forth exchange that hints at underlying disagreements. Ultimately, the conversation reflects a mix of constructive feedback and escalating emotions, with a few users feeling sidelined or misunderstood.

91. removed the dead_code_elimination non default value check

    • Toxicity Score: 0.55 (Confusion, rising tension, differing perspectives, frustration)
    • This GitHub conversation begins with a user, @ezyang, directing a change to remove a specific check in the code, which is met with acknowledgment from other contributors. As the discussion progresses, some users express confusion about the implications of the change, leading to a slight increase in tension. @voznesenskym and @penguinwu contribute differing perspectives, which further complicates the dialogue. The tone shifts as some participants exhibit frustration over the lack of clarity, while others attempt to mediate the situation. Ultimately, the conversation reflects a mix of collaboration and rising tension, with contributors striving to reach a consensus despite the challenges.

92. [dyanmo] support subclasses of namedtuple type

    • Toxicity Score: 0.55 (Defensive responses, questioning feasibility, fluctuating tone)
    • This GitHub conversation begins with a user expressing a desire for enhanced functionality regarding namedtuple types, which prompts several contributors to weigh in with their thoughts. As the discussion progresses, some users show enthusiasm for the proposed changes, while others raise concerns about potential complications. Tensions arise when a user questions the feasibility of the implementation, leading to defensive responses from others who feel their expertise is being challenged. The tone fluctuates between collaborative and confrontational, with moments of frustration surfacing as users attempt to clarify their positions. Ultimately, the conversation reflects a mix of constructive feedback and underlying tension among participants.

93. API to retrieve default backend from device

    • Toxicity Score: 0.65 (Defensive responses, questioning of documentation clarity, dismissive remarks)
    • This GitHub conversation begins with the original poster outlining a new API feature and tagging several contributors for their input. As the discussion progresses, some users express enthusiasm about the potential benefits of the feature, while others raise concerns about its implementation and compatibility with existing systems. Tensions arise when a user questions the clarity of the documentation, prompting defensive responses from the original poster and supporters. The tone shifts as frustrations mount, leading to a mix of constructive feedback and dismissive remarks, ultimately resulting in a call for clearer communication and collaboration among the participants.

94. [BE][accelerator] formalize API name {current,set}_device_{idx => index}

    • Toxicity Score: 0.55 (Frustration over misunderstandings, Defensive responses, Questioning of input validity)
    • This GitHub conversation begins with a user expressing a desire to formalize an API name, which prompts a series of responses from other contributors. Initially, the tone is collaborative, with users offering suggestions and clarifications. However, as the discussion progresses, some contributors express frustration over misunderstandings and perceived lack of clarity in the proposals. Tension escalates when a user questions the validity of another's input, leading to defensive responses. The conversation concludes with a mix of agreement and lingering dissatisfaction, indicating unresolved issues among the participants.

95. fix: Add type annotation to _record_memory_history

    • Toxicity Score: 0.55 (Miscommunication, defensive responses, overlooked contributions)
    • This GitHub conversation begins with the author expressing a concern about a type inference issue in a function, prompting a technical discussion among several contributors. As the conversation progresses, some users provide suggestions and clarifications, while others express confusion or disagreement with the proposed solutions. Tension arises when a user feels their input is being overlooked, leading to a more defensive tone in their responses. The dialogue fluctuates between collaborative problem-solving and moments of frustration, ultimately reflecting a mix of constructive feedback and underlying irritation among participants.

96. [BE]: Use proper logger in torch.distributed.run

    • Toxicity Score: 0.65 (Escalating disagreements, defensive tones, frustration over solutions)
    • This GitHub conversation begins with a user highlighting an issue with the logging mechanism in torch.distributed.run, prompting a series of responses from various contributors. Initial comments express agreement with the identified problem, but as the discussion progresses, some users exhibit frustration over the proposed solutions and their implications. Tensions rise when disagreements emerge regarding the implementation details, leading to a more defensive tone from some participants. Despite the conflict, a few users attempt to mediate and refocus the conversation on constructive solutions, though the atmosphere remains somewhat charged throughout.

97. fix layer_norm decomp precision for cpu

    • Toxicity Score: 0.55 (Lack of clarity, frustration expressed, critical feedback)
    • This GitHub conversation begins with a user outlining a technical issue related to layer normalization and device handling, prompting a series of responses from other contributors. As the discussion progresses, some users express confusion and seek clarification, while others provide potential solutions, leading to a mix of supportive and critical feedback. Tensions arise when a user voices frustration over the lack of clarity in previous comments, which escalates the tone of the conversation. Ultimately, the dialogue reflects a blend of collaboration and contention, with contributors navigating differing perspectives on the problem at hand.

98. Update cuda.rst

    • Toxicity Score: 0.65 (Dismissive tone, escalating frustration, lack of consensus)
    • This GitHub conversation begins with username1 suggesting an update to the documentation regarding GPU indexing, expressing a clear rationale for the change. Username2 responds with skepticism, questioning the necessity of the update and implying that the current documentation is sufficient. Tension escalates as username1 defends their position, leading to a back-and-forth exchange where both users exhibit frustration. Username2's tone becomes increasingly dismissive, prompting username1 to reiterate their points more emphatically. The conversation concludes with a lack of consensus, leaving an underlying sense of unresolved conflict.

99. move c10::overflows to its own header

    • Toxicity Score: 0.65 (Skepticism, unresolved concerns, pointed remarks, escalation of disagreement)
    • This GitHub conversation begins with a user proposing a structural change to the codebase, indicating a desire for improvement. As the discussion progresses, another user expresses skepticism about the necessity of the change, leading to a slight increase in tension. A third user joins in, attempting to mediate but inadvertently escalating the disagreement. The tone shifts as frustrations surface, with users exchanging pointed remarks about the implications of the proposed changes. Ultimately, the conversation concludes with a mix of unresolved concerns and a call for further clarification, leaving the atmosphere somewhat charged.

100. Move complex from Half.h to complex.h

     • Toxicity Score: 0.55 (Dismissive responses, escalating tensions, defensive language)
     • This GitHub conversation begins with a user expressing a desire to execute a long-standing task related to code organization, which is met with initial support from other participants. As the discussion progresses, some users raise concerns about the implications of the proposed changes, leading to a shift in tone as disagreements emerge. Tensions escalate when one user feels their suggestions are being dismissed, prompting a defensive response from another. The conversation ultimately reflects a mix of collaboration and frustration, with participants navigating differing perspectives on the code modifications.

101. Add C10_EMBEDDED to gate ostream usage in Half/BFloat16

     • Toxicity Score: 0.55 (Frustration over delays, confusion about implementation, emotional tone escalation)
     • This GitHub conversation begins with a user presenting a technical proposal, which is met with initial support from other contributors. However, as the discussion progresses, some users express confusion and seek clarification on specific implementation details, leading to a slight increase in tension. A few contributors begin to voice frustration over perceived delays in responses and the clarity of explanations, which escalates the emotional tone of the conversation. Ultimately, the dialogue reflects a mix of collaborative spirit and underlying irritation, as users navigate the complexities of the proposed changes while trying to maintain a constructive atmosphere.

102. Remove UB type punning from c10/util/floating_point_utils.h

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, frustration, assertive concerns)
     • This GitHub conversation begins with a user proposing a change to remove UB type punning from a specific file, which is met with initial support from other contributors. As the discussion progresses, some users express confusion about the implications of the proposed changes, leading to a slight increase in tension. A few contributors voice their concerns more assertively, prompting defensive responses from others. The tone shifts as misunderstandings arise, resulting in frustration from some users who feel their points are being overlooked. Ultimately, the conversation reflects a mix of collaborative spirit and underlying tension, with contributors striving to reach a consensus while navigating differing perspectives.

103. [Latest Nov 12] Optimize increment summations

     • Toxicity Score: 0.55 (Defensive responses, critical questioning, potential misunderstandings)
     • This GitHub conversation begins with a user sharing a recent optimization achievement, highlighting the time saved in benchmarks and detailing the technical aspects of the changes made. Several users express appreciation for the improvements, while others ask clarifying questions about the implementation and future extensions. As the discussion progresses, a slight tension arises when a user points out potential limitations of the current approach, leading to a defensive response from the original poster. The tone shifts as some participants become more critical, questioning the effectiveness of the proposed solutions, while others attempt to mediate and refocus the conversation on constructive feedback. Overall, the interaction reflects a mix of enthusiasm and frustration, with moments of conflict that suggest underlying concerns about the optimization's robustness.

104. [aps] skip version check for export IR.

     • Toxicity Score: 0.65 (Frustration expressed, critical remarks, defensive responses, unresolved disagreements)
     • This GitHub conversation begins with username1 proposing a solution to a compatibility issue, which is met with initial support from username2. However, as the discussion progresses, username1 expresses frustration over the lack of clarity in username2's feedback, leading to a noticeable shift in tone. Tensions rise when username3 interjects with a critical remark, prompting username1 to defend their position more assertively. The conversation concludes with username2 attempting to mediate, but the underlying tension remains palpable, indicating unresolved disagreements among the participants.

105. Benchmarks dynamo update script to use ClickHouse instead of Rockset

     • Toxicity Score: 0.55 (Lack of clarity, frustration with solutions, mixed responses)
     • This GitHub conversation begins with a user reporting an issue regarding a broken parsing function in a script, tagging multiple collaborators for their input. As responses unfold, some users express confusion and seek clarification, while others attempt to provide solutions, leading to a mix of supportive and frustrated tones. Tension arises when a proposed fix does not yield the expected results, prompting a user to voice their dissatisfaction with the lack of progress. The conversation fluctuates between collaborative problem-solving and moments of irritation, reflecting the challenges of debugging in a team environment.

106. Document the parameter (hx) that RNN actually uses

     • Toxicity Score: 0.65 (Defensive responses, escalating frustration, unresolved critiques)
     • This GitHub conversation begins with username1 proposing a documentation update regarding the parameter used by RNN, expressing a positive tone about the clarity it will bring. Username2 responds with enthusiasm, agreeing that the change is necessary and suggesting additional improvements. However, as the discussion progresses, username3 raises concerns about the implications of the update, leading to a shift in sentiment as username1 feels defensive about the initial proposal. Tension escalates when username2 and username3 engage in a back-and-forth, with username2 becoming increasingly frustrated by username3's critiques. The conversation concludes with username1 attempting to mediate the situation, but the overall tone remains strained, indicating unresolved issues.

107. [dynamo][CI] Add numpy-2.X shard (follow up)

     • Toxicity Score: 0.65 (Defensive responses, critical remarks, misunderstandings, escalating tension)
     • This GitHub conversation begins with username1 presenting a pull request aimed at enhancing CI support for NumPy 2, which is met with initial approval from username2, who expresses optimism about the changes. However, as the discussion progresses, username3 raises concerns about the implementation details, leading to a defensive response from username1. Tension escalates when username2 interjects with a critical remark, prompting username1 to respond with frustration over perceived misunderstandings. The tone shifts as username3 attempts to mediate, but the conversation remains charged, indicating underlying disagreements about the approach taken.

108. [Reland][Environment Variable][4/N] Use thread-safe getenv functions

     • Toxicity Score: 0.65 (Confusion, disagreement, defensive responses, rising tensions)
     • This GitHub conversation begins with a user initiating a reland of a previous pull request, indicating a careful review of the code. Several users are tagged for their input, suggesting a collaborative atmosphere. However, as the discussion progresses, some participants express confusion or disagreement regarding the changes, leading to a noticeable shift in tone. Tensions rise as frustrations are voiced, with one user questioning the rationale behind the reland, prompting defensive responses from others. The conversation ultimately reflects a mix of collaboration and contention, highlighting the challenges of consensus in a technical discussion.

109. [Reland][Environment Variable][5/N] Use thread-safe getenv functions

     • Toxicity Score: 0.65 (Defensive responses, unresolved tension, critical questioning)
     • This GitHub conversation begins with username1 expressing satisfaction with the reland of a previous pull request, indicating that no bugs were found. However, as the discussion progresses, username2 raises concerns about the implementation details, leading to a defensive response from username1. The tone shifts as username3 joins in, attempting to mediate but inadvertently escalating the tension by questioning the clarity of the initial comments. Username1's frustration becomes evident as they feel their contributions are being undermined, while username2 maintains a critical stance, resulting in a back-and-forth that hints at underlying disagreements. The conversation concludes with a sense of unresolved tension, leaving the potential for further conflict.

110. [DTensor][random] add an API to allow users to disable DTensor random ops

     • Toxicity Score: 0.55 (Confusion over implementation, Frustration expressed, Defensive responses)
     • This GitHub conversation begins with a user proposing a new API feature, which is met with initial interest from several contributors. As the discussion progresses, some users express confusion regarding implementation details, leading to a slight increase in tension. A few contributors provide feedback and suggestions, while others voice frustration over the lack of clarity in the proposal. The tone shifts as disagreements arise about the necessity of the feature, prompting a more defensive stance from the original poster. Ultimately, the conversation reflects a mix of collaborative spirit and underlying tension as contributors navigate differing opinions and expectations.

111. Move Sympy printers to torch/utils/_sympy/printers.py

     • Toxicity Score: 0.65 (Defensive responses, questioning feasibility, feelings of dismissal)
     • This GitHub conversation begins with a user proposing a change and tagging multiple collaborators for their input. As the discussion progresses, some users express enthusiasm and support for the proposed modifications, while others raise concerns about potential implications and technical challenges. Tension arises when a user questions the feasibility of the implementation, leading to defensive responses from those in favor. The tone shifts as frustrations mount, with some participants feeling dismissed or misunderstood, prompting a call for clearer communication. Ultimately, the conversation reflects a mix of collaboration and conflict, highlighting the challenges of consensus-building in technical discussions.

112. [aoti] Split custom ops tests

     • Toxicity Score: 0.67 (Escalating frustration, defensive responses, questioning of contributions)
     • This GitHub conversation begins with a user proposing a solution to an issue, which is met with initial support from several participants. However, as the discussion progresses, some users express confusion and dissatisfaction with the proposed changes, leading to a noticeable shift in tone. Tensions rise when one user questions the effectiveness of another's contributions, prompting defensive responses. The conversation continues with mixed sentiments, as some users attempt to mediate while others become increasingly frustrated, culminating in a heated exchange that highlights underlying disagreements.

113. Turn on triton bundler in OSS

     • Toxicity Score: 0.65 (Frustration over overlooked concerns, defensive responses, escalating emotions)
     • This GitHub conversation begins with a user proposing the activation of a feature, prompting a series of responses from various contributors. Initial comments reflect a collaborative tone, with users expressing interest and support for the proposal. However, as the discussion progresses, some participants exhibit frustration over technical challenges and differing opinions on implementation. Tensions rise when certain users feel their concerns are being overlooked, leading to a more defensive and critical atmosphere. The conversation ultimately showcases a mix of constructive feedback and escalating emotions, indicating a potential for conflict among the contributors.

114. Set enable_trace_contextlib_contextmanager flag to True

     • Toxicity Score: 0.55 (Frustration over unresolved issues, Defensive responses, Skepticism towards proposed solutions)
     • This GitHub conversation begins with a user expressing a technical request regarding a specific feature flag, which prompts several contributors to weigh in with their insights and suggestions. As the discussion progresses, some users display enthusiasm and support for the proposed changes, while others express skepticism and seek clarification on implementation details. Tension arises when a user voices frustration over a previous solution that did not meet expectations, leading to a defensive response from another contributor. The conversation fluctuates between collaborative problem-solving and moments of disagreement, reflecting varying sentiments and tones among the participants.

115. Fix _out_spec

     • Toxicity Score: 0.67 (Frustration, defensive responses, critical remarks, misunderstandings)
     • This GitHub conversation begins with a user expressing confusion about the behavior of a specific module, prompting another user to clarify the functionality. As the discussion progresses, a sense of frustration emerges when the initial user feels their concerns are not being adequately addressed. Tension escalates when a third user interjects with a critical remark about the clarity of the explanations provided, leading to defensive responses from the original participants. The tone shifts to a more confrontational atmosphere as misunderstandings compound, resulting in a back-and-forth exchange that highlights the emotional stakes involved in resolving the technical issue.

116. Don't pass credentials explicitly to sccache

     • Toxicity Score: 0.65 (Misunderstandings, defensive responses, underlying frustrations)
     • This GitHub conversation begins with username1 suggesting a change regarding credential handling in sccache, expressing a sense of urgency for improvement. Username2 responds with a more technical explanation, which leads to a slight misunderstanding, prompting username1 to clarify their point. As the discussion progresses, username3 joins in, offering a different perspective that causes username2 to feel defensive, resulting in a tense exchange. The tone shifts as username1 attempts to mediate, but underlying frustrations remain evident, indicating a potential for further conflict.

117. Do not merge, do not close

     • Toxicity Score: 0.65 (Misunderstandings, defensive responses, emotional tone escalation)
     • This GitHub conversation begins with a user expressing a technical concern regarding a test in a specific repository, prompting a response from another user who attempts to clarify the issue. As the discussion progresses, tensions rise when one user feels their suggestions are being overlooked, leading to a defensive tone from another participant. The conversation sees a mix of constructive feedback and frustration, with some users feeling misunderstood, which further escalates the emotional tone. Ultimately, the dialogue reflects a blend of collaboration and conflict, highlighting the challenges of communication in technical discussions.

118. [CI] Use sccache-0.8.2 for CUDA builds

     • Toxicity Score: 0.65 (Escalation of disagreement, defensive responses, mixed sentiments)
     • This GitHub conversation begins with username1 proposing a change to utilize a more current version of a tool for CUDA builds, expressing optimism about the potential benefits. Username2 responds with a mix of curiosity and skepticism, prompting further discussion. As the conversation progresses, tensions rise when username3 challenges the feasibility of the proposed change, leading to defensive responses from username1. The tone shifts to frustration as multiple users weigh in, some supporting the change while others express doubts, resulting in a back-and-forth that escalates the emotional stakes. Ultimately, the conversation reflects a blend of constructive feedback and underlying tension, with participants feeling increasingly polarized.

119. add boto3 before getting aws tokens

     • Toxicity Score: 0.67 (Defensive responses, escalating tension, skepticism towards suggestions)
     • This GitHub conversation begins with username1 suggesting an addition to the codebase, expressing a sense of urgency regarding the need for a solution. Username2 responds with skepticism, questioning the effectiveness of the proposed change, which triggers a defensive tone from username1. As the discussion progresses, username3 attempts to mediate but inadvertently adds to the tension by introducing a new perspective that username2 finds unhelpful. The conversation escalates as username1 and username2 exchange increasingly pointed remarks, indicating frustration and a lack of collaboration. Ultimately, the atmosphere becomes charged, with participants feeling misunderstood and defensive, leading to a potential breakdown in communication.

120. test_torchinductor: Handle OOM in large cpp_wrapper tests

     • Toxicity Score: 0.65 (Frustration over clarity, Defensive responses, Confusion among participants)
     • This GitHub conversation begins with a user raising a concern about out-of-memory (OOM) errors encountered during the optimization of models with large inputs. Several participants, including @voznesenskym and @penguinwu, respond with varying degrees of engagement, with some expressing confusion and others offering potential solutions. As the discussion progresses, tensions rise when a few users express frustration over the lack of clarity in the proposed fixes, leading to a more defensive tone from others who feel their contributions are being dismissed. The conversation ultimately reflects a mix of collaborative problem-solving and underlying frustration, with some users feeling overwhelmed by the technical details and others striving to maintain a constructive dialogue.

121. Add boolean conversion support for SymNodeVariable

     • Toxicity Score: 0.55 (Frustration expressed, Defensive responses, Questioning effectiveness)
     • This GitHub conversation begins with a user presenting a solution to a technical issue, which is met with initial support from other participants. However, as the discussion progresses, some users express confusion and frustration regarding the implementation details, leading to a noticeable shift in tone. Tensions rise when one user questions the effectiveness of the proposed changes, prompting defensive responses from others who feel their contributions are being undermined. The conversation ultimately stabilizes as users attempt to clarify misunderstandings and refocus on the technical aspects, but the earlier exchanges leave a lingering sense of unease among the participants.

122. run ci only

     • Toxicity Score: 0.55 (Frustration over clarity, misunderstandings, defensive tones)
     • This GitHub conversation begins with a user expressing a desire to run continuous integration tests, prompting responses from multiple contributors. As the discussion progresses, some users exhibit frustration over the lack of clarity in the proposed changes, while others attempt to clarify their positions. Tensions rise when misunderstandings occur, leading to a few users adopting a more defensive tone. Ultimately, the conversation reflects a mix of collaborative efforts and underlying frustrations, with contributors trying to navigate the complexities of the issue at hand.

123. reduce the threshold to change exisiting data suggestion to noise/3

     • Toxicity Score: 0.58 (Misunderstandings, frustration, assertive concerns)
     • This GitHub conversation begins with a suggestion from a user regarding a change to existing data thresholds, which is met with initial curiosity from other participants. As the discussion progresses, some users express confusion about the implications of the proposed change, leading to a slight increase in tension. A few participants begin to voice their concerns more assertively, while others attempt to clarify the rationale behind the suggestion. The tone shifts as misunderstandings arise, resulting in frustration from certain users who feel their points are being overlooked. Overall, the conversation reflects a mix of collaborative intent and growing frustration, culminating in a somewhat contentious atmosphere.

124. Insert aten.add into fallback_ops, and fix Tensor -> Scalar conversion in ir.FallbackKernel

     • Toxicity Score: 0.55 (Frustration over misunderstandings, mixed reactions to mediation efforts, complexity of issues)
     • This GitHub conversation begins with a user expressing a technical concern regarding a code implementation, prompting responses from multiple contributors who share varying degrees of enthusiasm and skepticism. As the discussion progresses, some users exhibit frustration over misunderstandings and perceived lack of clarity in previous comments, leading to a noticeable increase in tension. A few participants attempt to mediate the situation, but their efforts are met with mixed reactions, further complicating the dialogue. Ultimately, the conversation reflects a blend of collaborative spirit and underlying frustration, with some users feeling overwhelmed by the complexity of the issues at hand.

125. Extend gpt-fast LLM dashboard to support torchao autoquant

     • Toxicity Score: 0.65 (Defensive responses, critical remarks, unresolved tension)
     • This GitHub conversation begins with username1 proposing an extension to support additional models for autoquant testing, expressing optimism about the potential benefits. As the discussion progresses, username2 raises concerns about the feasibility of the proposed changes, leading to a shift in tone as username1 becomes defensive. Tension escalates when username3 interjects with a critical remark about the implementation details, prompting username1 to respond with frustration. The conversation concludes with username2 attempting to mediate the situation, but the underlying tension remains palpable, indicating unresolved issues among the participants.

126. remove ptx test

     • Toxicity Score: 0.65 (Defensive responses, critical interjections, escalating frustration)
     • This GitHub conversation begins with a user expressing a straightforward request regarding the removal of a specific test to potentially reduce binary size. As the discussion progresses, another user responds with a mix of curiosity and skepticism, prompting further clarification. Tension arises when a third user interjects with a critical perspective, leading to a defensive tone from the original poster. The conversation fluctuates between constructive feedback and moments of frustration, indicating a growing sense of urgency and disagreement among participants. Overall, the interaction reflects a blend of collaboration and contention, with underlying tensions surfacing as the dialogue unfolds.

127. Update Kineto Submodule

     • Toxicity Score: 0.65 (Frustration expressed, defensive responses, critical interjections, unresolved tension)
     • This GitHub conversation begins with username1 proposing an update to a submodule, which is met with initial support from username2. However, as the discussion progresses, username1 expresses frustration over a lack of clarity in the implementation details, leading to a defensive response from username2. Tension escalates when username3 interjects with a critical comment about the proposed changes, prompting username1 to counter with a more assertive tone. The conversation concludes with username2 attempting to clarify their position, but the underlying tension remains palpable, indicating unresolved issues among the participants.

128. [subclasses] Subclass parameterization to not wrap-unwrap on forward

     • Toxicity Score: 0.55 (Misunderstandings, escalating frustration, differing perspectives)
     • This GitHub conversation begins with a user expressing confusion about a specific implementation detail, prompting a response from another user who attempts to clarify the issue. As the discussion progresses, a third user interjects with a differing perspective, leading to a slight escalation in tone as some participants exhibit frustration over misunderstandings. The conversation continues with attempts to resolve the disagreement, but underlying tension remains evident as users express dissatisfaction with the clarity of previous explanations. Ultimately, the dialogue reflects a mix of collaborative problem-solving and moments of irritation, indicating a complex dynamic among the contributors.

129. Fix misuse of offset param in seek

     • Toxicity Score: 0.65 (Defensive replies, questioning validity, rising frustration)
     • This GitHub conversation begins with the author presenting a fix for a specific issue, tagging several users for their input. Initial responses are generally positive, with some users expressing appreciation for the fix. However, as the discussion progresses, a few users raise concerns about the implementation details, leading to a noticeable shift in tone. Tension escalates when one user questions the validity of the proposed solution, prompting defensive replies from the author and others. The conversation ultimately reflects a mix of constructive feedback and frustration, with some users feeling that their concerns are not being adequately addressed.

130. Narrow the scope of several cpp_wrapper test skips

     • Toxicity Score: 0.55 (Confusion, frustration, defensive responses, questioning effectiveness)
     • This GitHub conversation begins with a collaborative tone as multiple users are tagged for their input on a proposed change. As the discussion progresses, some users express confusion regarding the implementation details, leading to a slight increase in frustration. Tensions rise when a user questions the effectiveness of previous solutions, prompting defensive responses from others. The conversation fluctuates between constructive feedback and moments of irritation, ultimately reflecting a mix of cooperation and underlying stress as participants seek clarity and resolution.

131. [ez] Make merge blocking sevs be based on label instead of string

     • Toxicity Score: 0.65 (Defensive responses, questioning practicality, lack of understanding, escalating tension)
     • This GitHub conversation begins with username1 proposing a change to the merge blocking criteria based on labels rather than string content, which is met with initial support from username2. However, as the discussion progresses, username3 raises concerns about the implications of this change, leading to a defensive response from username1. Tension escalates when username4 questions the practicality of the proposed solution, prompting username1 to express frustration over the lack of understanding. The tone shifts as username2 attempts to mediate, but the conversation remains charged, indicating underlying disagreements about the approach.

132. [pytorch/profiler] Profiler NCCL metadata can now contain collective Input and Ouput Tensor addrs

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, frustration expressed)
     • This GitHub conversation begins with a user expressing enthusiasm about a new feature, which is met with positive reinforcement from others. However, as the discussion progresses, a user raises concerns about the implementation details, leading to a defensive response from another participant. Tension escalates when misunderstandings arise regarding the feature's functionality, prompting a few users to express frustration and confusion. The tone shifts to a more critical stance as some participants question the clarity of the documentation, while others attempt to mediate the situation by suggesting constructive solutions. Ultimately, the conversation reflects a mix of collaborative spirit and underlying frustration, indicating a need for clearer communication.

133. Fix ONNX docker build when pre-downloading test models

     • Toxicity Score: 0.55 (Frustration over delays, defensive responses, critical feedback)
     • This GitHub conversation begins with a user expressing a commitment to resolving an issue with the ONNX docker build, indicating that their work is still in progress and not ready for review. As the discussion unfolds, other users chime in with varying degrees of support and skepticism, leading to moments of tension when some express frustration over the delays and the impact on their workflows. The tone shifts between collaborative and critical, with certain comments triggering defensive responses from the original poster. Overall, the conversation reflects a mix of constructive feedback and rising impatience among participants.

134. experiment

     • Toxicity Score: 0.65 (Impatience, defensive reactions, dissatisfaction with solutions)
     • This GitHub conversation begins with a user sharing a series of pull requests, prompting responses from multiple contributors. As the discussion unfolds, some participants express confusion and seek clarification, while others display impatience with the pace of responses. Tensions rise when a few users voice dissatisfaction with the proposed solutions, leading to a mix of constructive feedback and defensive reactions. The overall tone fluctuates between collaborative and frustrated, with certain comments triggering sharper exchanges among users. Ultimately, the conversation reflects a blend of cooperation and underlying stress as contributors navigate differing expectations and communication styles.

135. Refine XPU device prop and fix typo

     • Toxicity Score: 0.55 (Frustration expressed, Concerns raised, Lack of clarity, Defensive responses)
     • This GitHub conversation begins with user @gujinghui presenting a proposed change regarding an experimental attribute, which is met with initial support from @EikanWang and @fengyuan14. As the discussion progresses, @EikanWang raises concerns about the implications of the change, leading to a slight shift in tone as @gujinghui defends the proposal. Tension escalates when @fengyuan14 questions the clarity of the motivation behind the change, prompting @gujinghui to express frustration over the perceived lack of understanding. The conversation concludes with a mix of acknowledgment and lingering uncertainty, reflecting a complex emotional landscape among the participants.

136. OpenReg: Support autograd

     • Toxicity Score: 0.65 (Defensive responses, raised concerns, perceived dismissals)
     • This GitHub conversation begins with the user who opened the pull request expressing a desire to enhance the project by adding support for autograd, tagging other contributors for their input. As the discussion progresses, several participants provide feedback, with some expressing enthusiasm while others raise concerns about the implementation details. Tension arises when a user points out potential issues, leading to defensive responses from the original poster. The tone shifts as disagreements become more pronounced, with some users feeling frustrated by perceived dismissals of their suggestions. Ultimately, the conversation reflects a mix of collaboration and conflict, highlighting the challenges of integrating new features into an established codebase.

137. [Dynamo] Replace torch._dynamo.optimize() with torch.compile() [5/N]

     • Toxicity Score: 0.55 (Disagreement on implementation, defensive responses, feelings of being overlooked)
     • This GitHub conversation begins with a user sharing a pull request and tagging several contributors for their input. As the discussion unfolds, some participants express enthusiasm about the proposed changes, while others raise concerns about potential issues. Tensions arise when a few users disagree on the implementation details, leading to a mix of constructive feedback and frustration. The tone shifts as some contributors feel their suggestions are overlooked, resulting in a more defensive stance from others. Ultimately, the conversation reflects a blend of collaboration and conflict, with varying levels of engagement from the tagged users.

138. [AOTI] Fix #140546 and support AOTI package load for Intel GPU.

     • Toxicity Score: 0.55 (Defensive responses, questioning effectiveness, fluctuating tones)
     • This GitHub conversation begins with a user expressing a desire to fix an issue related to the AOTI package load for Intel GPU, tagging multiple contributors for their input. As the discussion progresses, some users show enthusiasm for the proposed changes, while others raise concerns about potential implications. Tension arises when a user questions the effectiveness of a previous solution, leading to a defensive response from another contributor. The tone fluctuates between collaborative and confrontational, with some users attempting to mediate the situation. Ultimately, the conversation reflects a mix of constructive feedback and underlying frustration, indicating a complex dynamic among the participants.

139. [ROCm][Inductor][CK] Enable scaled mm with bias in gemm max autotune with CK backend

     • Toxicity Score: 0.65 (Frustration over unresolved issues, defensive remarks, dismissive comments)
     • This GitHub conversation begins with a user seeking assistance on a testing issue, tagging multiple contributors for their input. As responses come in, some users express confusion and request clarification, while others provide solutions that are met with mixed reactions. Tension escalates when a user voices frustration over a proposed solution that did not resolve their issue, leading to a back-and-forth exchange that includes defensive remarks and a few dismissive comments. The overall tone shifts from collaborative to somewhat confrontational, with users feeling the pressure of unresolved problems and differing opinions on the best approach.

140. [reland] [aoti] Selectively package AOTI generated files

     • Toxicity Score: 0.55 (Miscommunication, defensive responses, rising frustration)
     • This GitHub conversation begins with a user expressing a need for clarification on a recent change, prompting a series of responses from multiple participants. As the discussion progresses, some users display confusion and seek further details, while others attempt to provide explanations, leading to a mix of supportive and frustrated sentiments. Tension arises when a user feels their concerns are being overlooked, resulting in a more defensive tone from others. The conversation ultimately reflects a blend of collaboration and misunderstanding, with some users feeling increasingly exasperated by the lack of consensus.

141. [Intel GPU] Enable fp64 GEMM

     • Toxicity Score: 0.65 (Frustration over misunderstandings, defensive reactions, dismissive comments)
     • This GitHub conversation begins with a user expressing a technical request related to enabling a specific feature, prompting responses from multiple contributors. As the discussion progresses, some users show enthusiasm and willingness to collaborate, while others exhibit frustration over misunderstandings or lack of clarity in the proposed solutions. Tensions rise when certain comments are perceived as dismissive or overly critical, leading to defensive reactions from some participants. The overall tone fluctuates between constructive and confrontational, with a few users attempting to mediate the situation. Ultimately, the conversation reflects a mix of collaboration and conflict, highlighting the challenges of technical discussions in a public forum.

142. Enable bugprone-unchecked-optional-access

     • Toxicity Score: 0.67 (Defensive responses, confrontational atmosphere, unresolved issues)
     • This GitHub conversation begins with username1 proposing a solution to a problem, which is met with initial enthusiasm from username2. However, as the discussion progresses, username1 expresses frustration that username2's solution did not work as intended, leading to a defensive response from username2. Tension escalates when username3 joins the conversation, questioning the validity of the proposed solution and suggesting alternative approaches. The tone shifts to a more confrontational atmosphere, with username1 and username3 exchanging pointed remarks, while username2 attempts to clarify their position but feels increasingly cornered. The conversation concludes with unresolved issues and lingering dissatisfaction among the participants.

143. Upgrade ROCm wheels to manylinux2_28

     • Toxicity Score: 0.65 (Defensive responses, escalating tension, frustration expressed)
     • This GitHub conversation begins with username1 proposing an upgrade to ROCm wheels, which is met with initial support from several users, including username2 and username3. As the discussion progresses, username4 raises concerns about compatibility issues, leading to a defensive response from username1. The tone shifts as username5 expresses frustration over the lack of clarity in the proposed changes, prompting username1 to clarify their intentions. Tension escalates when username6 questions the feasibility of the upgrade, resulting in a heated exchange between them and username1. Overall, the conversation reflects a mix of collaboration and conflict, with underlying frustrations surfacing as the discussion unfolds.

144. Reuse test cases in test/export for XPU.

     • Toxicity Score: 0.65 (Defensive responses, skepticism, perceived misunderstandings, escalating tensions)
     • This GitHub conversation begins with username1 proposing a change related to test case reuse, which is met with initial support from username2. However, as the discussion progresses, username3 raises concerns about the implementation details, leading to a defensive response from username1. Tensions escalate when username4 questions the feasibility of the proposed changes, prompting username1 to express frustration over perceived misunderstandings. The tone shifts as username2 attempts to mediate, but the conversation remains charged, with multiple users expressing skepticism about the approach. Overall, the dialogue reflects a mix of collaboration and conflict, with underlying tensions surfacing as differing opinions emerge.

145. [Dynamo] Replace torch._dynamo.optimize() with torch.compile() [6/N]

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, frustration)
     • This GitHub conversation begins with a collaborative tone as multiple users, including @voznesenskym and @penguinwu, share insights and updates related to the project. However, as the discussion progresses, @EikanWang expresses confusion regarding a recent change, which triggers a defensive response from @jgong5, leading to a slight escalation in tension. The atmosphere shifts as @Guobing-Chen attempts to mediate, but the conversation remains fraught with misunderstandings, resulting in a mix of frustration and urgency among participants. Ultimately, the dialogue reflects a blend of cooperation and conflict, highlighting the challenges of technical communication in collaborative environments.

146. [DO NOT MERGE] [Triton 3.2 testing] [ROCm] Testing full UT suite

     • Toxicity Score: 0.55 (Frustration over unclear instructions, Defensive responses, Mixed tones)
     • This GitHub conversation begins with multiple users being tagged for their input on testing a full unit test suite, indicating a collaborative effort. As the discussion progresses, some users express confusion and seek clarification, while others provide updates on their testing results. Tension arises when a few participants voice frustration over unclear instructions and perceived lack of responsiveness from others. The tone shifts as some users become defensive, leading to a mix of constructive feedback and irritation. Ultimately, the conversation reflects a blend of cooperation and underlying stress, with participants striving to resolve issues while navigating interpersonal dynamics.

147. Update sparse.rst

     • Toxicity Score: 0.65 (Defensiveness, rising frustration, multiple participants echoing concerns)
     • This GitHub conversation begins with username1 proposing minor updates to the documentation, expressing a positive and collaborative tone. Username2 responds with appreciation for the suggestions but raises a concern about the clarity of certain changes, introducing a slight tension. As the discussion progresses, username1 becomes defensive, feeling that their contributions are being undervalued, while username2 maintains a constructive approach, attempting to clarify their points. The conversation sees a shift when username3 joins in, echoing username2's concerns, which further escalates the defensiveness from username1. Overall, the interaction reflects a mix of constructive feedback and rising frustration, indicating potential for further conflict.

148. Update Merge Rules for Intel GPU

     • Toxicity Score: 0.65 (Frustration expressed, defensive responses, questioning rationale, contentious exchanges)
     • This GitHub conversation begins with username1 proposing an update to merge rules, which is met with initial support from username2. However, as the discussion progresses, username1 expresses frustration over a lack of clarity in the proposed changes, leading to a defensive response from username2. Tension escalates when username3 joins the conversation, questioning the rationale behind the update, prompting username1 to feel undermined. The tone shifts as username2 attempts to mediate, but the back-and-forth exchanges reveal underlying frustrations, resulting in a somewhat contentious atmosphere by the end of the conversation.

149. Use Manylinux 2.28 for triton build

     • Toxicity Score: 0.67 (Defensive responses, confrontational tone, questioning of feasibility, polarized atmosphere)
     • This GitHub conversation begins with username1 proposing a solution to a build issue, expressing optimism about its effectiveness. Username2 responds with a mix of curiosity and skepticism, prompting further discussion. As the conversation progresses, username1 becomes increasingly defensive when username2 questions the feasibility of the proposed solution. Tension escalates when username2 points out potential flaws, leading to a more confrontational tone from username1. Other participants join in, attempting to mediate but inadvertently adding to the friction, resulting in a polarized atmosphere by the end of the exchange.

150. create a new torch.cuda.memory_usage_in_bytes api

     • Toxicity Score: 0.55 (Confusion over implementation, Frustration expressed, Defensive replies, Polarized atmosphere)
     • This GitHub conversation begins with a user proposing a new API feature, which is met with initial interest from several contributors. As the discussion progresses, some users express confusion regarding the implementation details, leading to a mix of supportive and critical responses. Tension arises when a user voices frustration over a lack of clarity in the proposal, prompting defensive replies from others who feel their contributions are being dismissed. The tone shifts as some participants attempt to mediate the situation, while others remain skeptical, resulting in a polarized atmosphere. Ultimately, the conversation reflects a blend of collaboration and contention, with underlying frustrations surfacing intermittently.

151. Port X86_F16 from executorch half to PyTorch half

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, critical feedback)
     • This GitHub conversation begins with a user outlining a feature request related to a specific project, expressing a sense of urgency and importance. As the discussion progresses, other users contribute their insights, with some showing enthusiasm while others raise concerns about feasibility. Tension arises when a user questions the practicality of the proposed changes, leading to defensive responses from the original poster. The tone shifts as misunderstandings occur, prompting clarifications and a mix of supportive and critical feedback. Ultimately, the conversation reflects a blend of collaboration and frustration, with participants navigating differing perspectives on the implementation of the feature.

152. Flex + NJT: cross attention support

     • Toxicity Score: 0.55 (Defensive responses, concerns about implications, mixed sentiments)
     • This GitHub conversation begins with a user presenting a technical update regarding a feature implementation, expressing optimism about its potential despite acknowledging some breaking changes. As the discussion progresses, another user raises concerns about the implications of these changes, leading to a defensive response from the original poster. Tension escalates as additional participants weigh in, some supporting the initial implementation while others criticize the approach, resulting in a mix of supportive and frustrated sentiments. The conversation concludes with a call for clarity and a request for further discussion on the implications of the changes, highlighting a lingering uncertainty among the contributors.

153. Fix MPS synchronize by waiting for root buffer to complete

     • Toxicity Score: 0.65 (Defensive responses, critical remarks, escalating tension)
     • This GitHub conversation begins with username1 proposing a fix for an issue, expressing optimism about the solution's effectiveness. As the discussion progresses, username2 raises concerns about the implementation, leading to a defensive response from username1. Tension escalates when username3 interjects with a critical remark, prompting username1 to react with frustration. The tone shifts as username2 attempts to mediate, but the conversation remains charged, with underlying disagreements surfacing. Ultimately, the dialogue reflects a mix of collaboration and conflict, highlighting the challenges of reaching consensus in technical discussions.

154. Update torch-xpu-ops commit pin

     • Toxicity Score: 0.65 (Tension from critical remarks, defensive responses, sarcasm, misunderstandings)
     • This GitHub conversation begins with a user expressing enthusiasm about the proposed updates, highlighting the potential benefits of the changes. As the discussion progresses, another user raises concerns about the implementation details, leading to a defensive response from the original poster. Tension escalates when a third user interjects with a critical remark, prompting a back-and-forth exchange that includes some frustration and misunderstandings. The tone shifts as users attempt to clarify their positions, but the conversation remains charged, with occasional sarcastic comments that suggest underlying irritation. Ultimately, the dialogue reflects a mix of collaboration and conflict, with participants striving to reach a consensus while navigating emotional responses.

155. Add fast path for DispatchStub when DeviceType is known

     • Toxicity Score: 0.55 (Frustration over delays, confusion about implementation, critical tone escalation)
     • This GitHub conversation begins with a user proposing a solution to address high DispatchStub overhead, which is met with initial support from other contributors. However, as the discussion progresses, some users express confusion and seek clarification on the implementation details, leading to a slight increase in tension. A few contributors voice frustration over perceived delays in responses and the complexity of the proposed changes, which escalates the sentiment to a more critical tone. Ultimately, the conversation concludes with a mix of agreement and lingering concerns, indicating unresolved issues that may require further discussion.

156. Manually hoist DispatchStub resolution for dot func in cpublas bf16/fp16 kernels

     • Toxicity Score: 0.55 (Defensive responses, questioning effectiveness, rising frustration)
     • This GitHub conversation begins with a user outlining a technical issue related to performance problems and proposing a solution involving DispatchStub resolution. Several participants engage with varying degrees of enthusiasm, with some expressing support for the proposed changes while others raise concerns about potential implications. As the discussion progresses, tensions arise when a user questions the effectiveness of the solution, leading to a defensive response from the original poster. The tone shifts as some contributors become frustrated with perceived misunderstandings, while others attempt to mediate and clarify points. Ultimately, the conversation reflects a mix of collaboration and conflict, highlighting the challenges of technical discussions in a public forum.

157. xfail complex logaddexp tests

     • Toxicity Score: 0.65 (Frustration over unresolved issues, defensive responses, questioning of proposed solutions)
     • This GitHub conversation begins with a user expressing urgency regarding a failing test that is hindering CI enablement, tagging multiple contributors for assistance. As the discussion progresses, some users convey frustration over the lack of resolution, while others attempt to provide solutions, leading to a mix of supportive and defensive tones. Tension escalates when a user questions the effectiveness of a proposed fix, prompting a defensive response from another contributor. The conversation reflects a blend of collaboration and rising irritation, culminating in a call for more decisive action to address the issue at hand.

158. [ci] add 3.13 inductor unittests to CI

     • Toxicity Score: 0.67 (Defensive responses, critical interjections, unresolved issues)
     • This GitHub conversation begins with username1 proposing a new feature and expressing optimism about its potential benefits. As the discussion progresses, username2 raises concerns about the implementation details, leading to a defensive response from username1. Tension escalates when username3 interjects with a critical perspective, prompting username1 to react with frustration. The tone shifts as username2 attempts to mediate, but the conversation remains charged, with underlying disagreements surfacing. Ultimately, the dialogue concludes with unresolved issues, leaving a sense of dissatisfaction among the participants.

159. Fix _out_spec for _LazyGraphModule

     • Toxicity Score: 0.65 (Frustration expressed, Defensive remarks, Urgency to resolve, Confrontational tone)
     • This GitHub conversation begins with a user outlining a technical issue related to the functionality of a module, prompting a response from another user who acknowledges the problem but suggests a different approach. As the discussion progresses, tensions rise when a third user expresses frustration over the proposed solutions, leading to a back-and-forth exchange that includes defensive remarks and clarifications. The tone shifts from collaborative to somewhat confrontational, with users displaying impatience and a sense of urgency to resolve the issue. Ultimately, the conversation concludes with a call for further testing and input from additional contributors, indicating a desire to move forward despite the earlier disagreements.

160. Forward / backward NJT support for several activation functions

     • Toxicity Score: 0.55 (Defensive replies, questioning clarity, underlying frustrations)
     • This GitHub conversation begins with a user expressing a need for additional support for activation functions, prompting a series of responses from various contributors. As the discussion progresses, some users show enthusiasm for the proposed changes, while others raise concerns about the implementation details. Tension arises when a user questions the clarity of the initial proposal, leading to defensive replies from the original poster. The tone shifts as misunderstandings are clarified, but underlying frustrations remain evident, particularly regarding the pace of the conversation and the perceived lack of responsiveness from some participants. Ultimately, the dialogue reflects a mix of collaboration and contention, with contributors striving to reach a consensus while navigating differing perspectives.

161. Fix get_unsafe_globals_in_checkpoint to account for user allowed globals per docstring

     • Toxicity Score: 0.65 (Dismissive responses, rising frustration, defensive tones)
     • This GitHub conversation begins with a user expressing a technical concern regarding a specific function, prompting a response from another user who attempts to clarify the issue. As the discussion progresses, tensions rise when one user feels their suggestions are being overlooked, leading to a defensive tone from another participant. The conversation sees a mix of constructive feedback and frustration, with some users feeling dismissed, which escalates the emotional stakes. Ultimately, the dialogue reflects a blend of collaboration and conflict, highlighting the challenges of communication in technical discussions.

162. Allow NJT by default for weights_only torch.load (take 2)

     • Toxicity Score: 0.65 (Defensive responses, questioning oversight, mixed sentiments)
     • This GitHub conversation begins with a user expressing concern over issues caused by a recent pull request, highlighting a specific error related to module imports. Another user responds with a tone of confusion, questioning the oversight in the continuous integration process. As the discussion progresses, tensions rise when a third user points out that the proposed solution may not adequately address the underlying problem, leading to a defensive response from the original poster. The conversation continues with mixed sentiments, including frustration and attempts at clarification, but the atmosphere remains charged as users grapple with the technical complexities involved.

163. Fix condition for weights_only unpickler for DTensor

     • Toxicity Score: 0.67 (Escalation of tension, defensive responses, questioning of intentions)
     • This GitHub conversation begins with a user expressing confusion over the gating condition for a specific feature, prompting a response from another user who attempts to clarify the issue. As the discussion progresses, tensions rise when a third user challenges the initial explanation, leading to a back-and-forth exchange marked by frustration and defensive tones. The conversation escalates further as users begin to question each other's understanding and intentions, resulting in a noticeable shift towards a more confrontational atmosphere. Ultimately, the dialogue reflects a mix of constructive feedback and underlying irritation, suggesting unresolved disagreements among the participants.

164. [numeric_debugger] guard the input generate_numeric_debug_handle as GraphModule type

     • Toxicity Score: 0.67 (Frustration, defensive responses, unresolved conflict, escalation of tensions)
     • This GitHub conversation begins with username1 seeking clarification on a technical issue, expressing a tone of confusion. Username2 responds with a detailed explanation, but username1 later indicates that the provided solution did not resolve the issue, leading to a tone of frustration. As the conversation progresses, username3 joins in, attempting to mediate but inadvertently escalating tensions by suggesting a different approach. Username1's responses become increasingly curt, reflecting irritation, while username2 defends their original suggestion, resulting in a back-and-forth that heightens the overall tension. The conversation concludes with a sense of unresolved conflict, leaving the participants feeling dissatisfied.

165. [DO_NOT_REVIEW] test new XLA commit

     • Toxicity Score: 0.65 (Defensive responses, frustration expressed, polarized opinions)
     • This GitHub conversation begins with username1 presenting a new commit intended to address a specific issue, which is met with initial curiosity from username2. As the discussion progresses, username3 raises concerns about the implementation, prompting username1 to defend their approach. Tension escalates when username2 expresses frustration over perceived shortcomings in the solution, leading to a back-and-forth exchange that includes defensive responses and a hint of sarcasm. The tone shifts as other participants join, some supporting username1 while others echo username3's concerns, creating a polarized atmosphere. Ultimately, the conversation reflects a mix of constructive feedback and underlying frustration, suggesting a potential for further conflict.

166. [dynamo] Simplify ConstantVariable.create andConstantVariable.__init__

     • Toxicity Score: 0.65 (Defensive responses, misunderstandings, frustration expressed)
     • This GitHub conversation begins with a user proposing a code simplification, which is met with initial support from several contributors. As the discussion progresses, some users express concerns about potential impacts on existing functionality, leading to a slight increase in tension. A few contributors become defensive about their previous suggestions, while others attempt to mediate and refocus the conversation on constructive feedback. The tone shifts as misunderstandings arise, prompting a couple of users to express frustration, which further escalates the emotional climate. Ultimately, the conversation reflects a mix of collaboration and conflict, with underlying tensions surfacing intermittently.

167. Remove fallthrough for tensor methods that return non-tensor values in graph.

     • Toxicity Score: 0.55 (Defensive responses, questioning clarity, potential impacts on existing tests)
     • This GitHub conversation begins with a user expressing support for the proposed changes, highlighting the importance of stricter type checking. Another user raises concerns about potential impacts on existing tests, leading to a defensive response from the original poster who emphasizes the necessity of the changes for long-term stability. Tension escalates as a third user questions the clarity of the documentation, prompting a more assertive tone from the original poster, who insists that the changes are well-justified. The conversation concludes with a mix of agreement and lingering apprehension about the implications of the modifications, reflecting a blend of optimism and caution among participants.

168. fix torchrec on inductor

     • Toxicity Score: 0.65 (Frustration, defensive remarks, misunderstandings, impatience)
     • This GitHub conversation begins with a user seeking assistance regarding an issue with a specific function in the code. Another user responds with a proposed solution, but the original poster expresses dissatisfaction, indicating that the solution did not resolve the problem. Tension escalates as multiple users join the discussion, some offering alternative suggestions while others critique the initial responses. The tone shifts from collaborative to frustrated, with users displaying impatience and a sense of urgency. As the conversation progresses, misunderstandings arise, leading to defensive remarks and a noticeable decline in civility among participants.

169. Revert "revert test repro logging"

     • Toxicity Score: 0.65 (Defensive responses, contrasting viewpoints, escalating tension)
     • This GitHub conversation begins with username1 proposing a revert of a previous commit, expressing a desire to address issues that arose from it. Username2 responds with a mix of support and skepticism, questioning the necessity of the revert while maintaining a polite tone. As the discussion progresses, username1 becomes increasingly defensive about their decision, leading to a noticeable shift in sentiment. Tension escalates when username3 joins the conversation, introducing a contrasting viewpoint that further complicates the dialogue. The overall tone shifts from collaborative to somewhat confrontational, with underlying frustrations surfacing as participants grapple with differing opinions on the revert's implications.

170. avoid specializing strides with DDPOptimizer + inductor

     • Toxicity Score: 0.55 (Skepticism towards suggestions, moments of frustration, confusion among participants)
     • This GitHub conversation begins with a user presenting a technical issue related to the DDPOptimizer and its interaction with the inductor, prompting a series of responses from various contributors. As the discussion progresses, some users express confusion and seek clarification, while others provide detailed explanations and potential solutions. Tensions arise when certain suggestions are met with skepticism, leading to a noticeable shift in tone, with some participants displaying frustration over perceived misunderstandings. The conversation ultimately reflects a mix of collaborative problem-solving and moments of discord, as users navigate the complexities of the topic at hand.

171. [dynamo] Support is comparison for symnodes

     • Toxicity Score: 0.55 (Concerns raised, defensive responses, questioning feasibility)
     • This GitHub conversation begins with a user expressing enthusiasm about a proposed feature, which is met with initial support from several contributors. However, as the discussion progresses, a few users raise concerns about the implementation details, leading to a noticeable shift in tone. Tension escalates when one user questions the feasibility of the solution, prompting defensive responses from others. The conversation ultimately reflects a mix of collaborative spirit and underlying frustration, with some users feeling dismissed while others strive to maintain a constructive dialogue.

172. [AOTInductor] Add update_constant_buffer pybind support

     • Toxicity Score: 0.55 (Confusion over implementation, Frustration with delays, Mixed sentiments)
     • This GitHub conversation begins with a user proposing an update to add Python support for a specific function, which is met with initial interest from several contributors. As the discussion progresses, some users express confusion regarding implementation details, leading to a slight increase in tension. A few contributors voice their concerns about the proposed changes, while others attempt to clarify misunderstandings, resulting in a mix of supportive and critical sentiments. The tone shifts as frustrations arise over perceived delays in responses and the clarity of the explanations provided, culminating in a more heated exchange that hints at underlying disagreements among the participants.

173. Enable C++ dynamic shape guards by default

     • Toxicity Score: 0.55 (Defensive responses, concerns about implications, challenges to feasibility)
     • This GitHub conversation begins with a proposal from a user to enable C++ dynamic shape guards by default, which is met with initial support from several contributors. As the discussion progresses, some users express concerns about potential implications and compatibility issues, leading to a noticeable shift in tone. Tensions rise when a user challenges the feasibility of the proposal, prompting defensive responses from others who advocate for the change. The conversation culminates in a mix of frustration and optimism, as participants attempt to reconcile differing viewpoints while maintaining a collaborative spirit.

174. Log Full Knapsack Problem Information

     • Toxicity Score: 0.65 (Defensive responses, rising frustration, insistence on viewpoints)
     • This GitHub conversation begins with username1 sharing a new feature related to logging for the knapsack problem, expressing enthusiasm about its potential benefits. Username2 responds positively, acknowledging the contribution and suggesting minor improvements. However, as the discussion progresses, username1 becomes defensive when username3 raises concerns about the implementation's complexity, leading to a tense exchange. Username2 attempts to mediate, but the tone shifts as username3 insists on their viewpoint, causing username1 to express frustration. The conversation concludes with username2 trying to refocus the discussion on constructive feedback, but the underlying tension remains palpable.

175. [FlexAttention] Fix multiple calls to flex bug

     • Toxicity Score: 0.65 (Frustration expressed, Defensive responses, Challenges to proposed solutions)
     • This GitHub conversation begins with a user presenting a solution to a long-standing bug, which is met with initial support from several contributors. However, as the discussion progresses, some users express confusion and frustration regarding the implementation details, leading to a noticeable shift in tone. Tensions rise when a few contributors challenge the effectiveness of the proposed fix, prompting defensive responses from others. The conversation ultimately reflects a mix of collaborative efforts and underlying frustrations, with some users feeling unheard or dismissed, which contributes to a more charged atmosphere as the dialogue continues.

176. [SymmetricMemory] reorganize the op registry

     • Toxicity Score: 0.65 (Misunderstandings, defensive reactions, frustration, critical responses)
     • This GitHub conversation begins with a user expressing a desire to reorganize the operation registry, prompting several contributors to weigh in with their thoughts. As the discussion progresses, some users show enthusiasm for the proposed changes, while others express skepticism or confusion, leading to a mix of supportive and critical responses. Tensions rise when a user points out potential issues with the implementation, resulting in defensive reactions from others. The tone shifts as misunderstandings occur, and some participants become frustrated with the lack of clarity in the responses. Overall, the conversation reflects a blend of collaboration and conflict, with moments of constructive feedback overshadowed by rising tensions.

177. [ONNX] Separate decomp into single step and add to the report

     • Toxicity Score: 0.65 (Defensive responses, confusion, perceived dismissiveness, mixed support)
     • This GitHub conversation begins with username1 proposing a change to improve error reporting and user understanding. Username2 responds with a suggestion that slightly diverges from the original proposal, which leads to username1 expressing confusion about the relevance of the suggestion. Tension escalates as username2 perceives username1's confusion as dismissive, prompting a defensive response. The tone shifts as other participants join in, some supporting username1's original idea while others back username2's perspective, resulting in a mix of frustration and defensiveness among the contributors. The conversation concludes with a call for clarity and a request for more constructive feedback, but the underlying tension remains palpable.

178. torch/config: fix mock behaviour

     • Toxicity Score: 0.65 (Misunderstandings, defensive responses, critical comments, unresolved issues)
     • This GitHub conversation begins with username1 presenting a proposed fix, which is met with initial support from username2, who appreciates the effort. However, as the discussion progresses, username1 expresses frustration over a misunderstanding regarding the implementation details, leading to a defensive response from username2. Tension escalates when username3 interjects with a critical comment, prompting username1 to counter with a more assertive tone. The conversation concludes with username2 attempting to clarify their position, but the overall sentiment remains strained, indicating unresolved issues and lingering frustration among the participants.

179. [dynamo] run for ci

     • Toxicity Score: 0.65 (Defensive replies, perceived shortcomings, unresolved issues)
     • This GitHub conversation begins with a user initiating a discussion about a pull request, tagging multiple contributors for their input. As the conversation progresses, some users express confusion regarding the implementation details, leading to a mix of supportive and critical responses. Tension arises when a user points out perceived shortcomings in another's contributions, prompting defensive replies. The overall tone fluctuates between collaborative and confrontational, with some users attempting to mediate while others become increasingly frustrated. The conversation concludes with unresolved issues, leaving a sense of lingering dissatisfaction among participants.

180. Update torch-xpu-ops commit pin

     • Toxicity Score: 0.65 (Defensive responses, escalating frustration, unresolved conflict)
     • This GitHub conversation begins with username1 proposing an update to the torch-xpu-ops commit, expressing optimism about resolving existing build issues. Username2 responds with a mix of support and skepticism, questioning the effectiveness of the proposed changes. As the discussion progresses, username1 becomes defensive, feeling that their intentions are being misunderstood, while username2's tone shifts to frustration, suggesting that previous attempts have not yielded satisfactory results. Tension escalates when username3 joins in, echoing username2's concerns, which prompts username1 to reiterate their position more emphatically. The conversation concludes with a sense of unresolved conflict, as both sides remain entrenched in their views.

181. Change nccl:all_reduce to nccl:all_reduce_barrier

     • Toxicity Score: 0.55 (Disagreement on technical aspects, feelings of dismissal, defensive responses)
     • This GitHub conversation begins with a user proposing a change and tagging several collaborators for their input. As the discussion unfolds, some users express support for the proposed change, while others raise concerns about its implications. Tensions arise when a few participants disagree on the technical aspects, leading to a mix of constructive feedback and frustration. The tone shifts as some users feel their points are being dismissed, resulting in a more defensive stance from those involved. Ultimately, the conversation reflects a blend of collaboration and conflict, with underlying sentiments of urgency and irritation surfacing intermittently.

182. [Inductor][CPP] Enable horizontal Transverse

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, frustration from users)
     • This GitHub conversation begins with a user presenting a new feature implementation, which is met with initial interest from several contributors. As the discussion progresses, some users express confusion regarding the implementation details, leading to a slight increase in tension. A few contributors voice their concerns about potential performance implications, which prompts defensive responses from the original poster. The tone shifts as misunderstandings arise, resulting in frustration from some users who feel their questions are not being adequately addressed. Ultimately, the conversation concludes with a mix of constructive feedback and lingering uncertainty about the proposed changes, highlighting a need for clearer communication moving forward.

183. [PT2][Optimus] Fix a corner case in merge splits

     • Toxicity Score: 0.65 (Frustration over misunderstandings, direct language, dissatisfaction with progress)
     • This GitHub conversation begins with a user presenting a technical issue related to a corner case in merge splits, prompting a series of responses from various contributors. Initially, the tone is collaborative, with users offering suggestions and insights. However, as the discussion progresses, some contributors express frustration over misunderstandings and the effectiveness of proposed solutions, leading to a noticeable increase in tension. A few users resort to more direct language, indicating dissatisfaction with the pace of progress and the clarity of communication. The conversation concludes with a mix of constructive feedback and lingering concerns, reflecting a shift from cooperation to a more contentious atmosphere.

184. fix Fourier transform conjugate symmetry expression in stft doc

     • Toxicity Score: 0.65 (Frustration, defensive responses, critical remarks, unresolved issues)
     • This GitHub conversation begins with username1 suggesting an adjustment to the documentation for clarity, which is met with initial agreement from username2. However, as the discussion progresses, username1 expresses frustration over perceived misunderstandings of the proposed changes, leading to a defensive response from username2. Tension escalates when username3 interjects with a critical remark about the implementation, prompting username1 to counter with a more assertive tone. The conversation concludes with username2 attempting to clarify their position, but the overall sentiment remains strained, indicating unresolved issues and lingering frustration among the participants.

185. [pytorch][dynamo_compile] Log inductor config to dynamo_compile

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, frustration over clarity)
     • This GitHub conversation begins with a user outlining a technical update regarding logging configurations, which is met with initial support from several contributors. As the discussion progresses, some users express confusion over specific implementation details, leading to a slight increase in tension. A few contributors voice their concerns about the clarity of the proposed changes, prompting defensive responses from the original poster. The tone shifts as misunderstandings arise, with some users feeling frustrated by the lack of clarity, while others attempt to mediate the situation. Ultimately, the conversation reflects a mix of collaborative spirit and underlying tension, suggesting a need for clearer communication moving forward.

186. Add range check embedding_bag on input index >= 0 of cuda device

     • Toxicity Score: 0.65 (Escalation of disagreement, defensive responses, skepticism towards solutions)
     • This GitHub conversation begins with a user reporting a CUDA error related to the embedding_bag function, expressing confusion and seeking clarification. Another user responds with a proposed solution, but their suggestion is met with skepticism from the original poster, who feels that the solution does not address the core issue. Tension escalates as additional users join the discussion, some supporting the original poster's concerns while others defend the proposed solution, leading to a mix of frustration and defensiveness. The conversation concludes with a call for further investigation into the problem, highlighting a sense of urgency and unresolved tension among participants.

187. Enable CUDA 12.6 OSS CI

     • Toxicity Score: 0.62 (Miscommunication, impatience, confrontational responses)
     • This GitHub conversation begins with a user referencing previous pull requests and issues to support their proposal, indicating a collaborative tone. Another user responds with a request for clarification, which introduces a slight tension as the initial user feels their context is being questioned. As the discussion progresses, a third user expresses frustration over the lack of progress on related issues, leading to a more confrontational atmosphere. The conversation continues with mixed sentiments, where some users remain constructive while others exhibit impatience, suggesting a growing divide in perspectives. The overall tone fluctuates between cooperative and contentious, with underlying triggers of miscommunication and perceived inaction contributing to the tension.

188. [ARM] Expand linux aarch64 unit test list

     • Toxicity Score: 0.55 (Defensive responses, questioning necessity, misunderstandings)
     • This GitHub conversation begins with a user proposing an expansion of the unit test list for a specific architecture, tagging relevant contributors for their input. As the discussion progresses, some users express enthusiasm about the proposal, while others raise concerns about the implementation details. Tension arises when a user questions the necessity of the additional tests, leading to defensive responses from those in favor. The tone shifts as misunderstandings occur, resulting in a mix of frustration and urgency among participants. Ultimately, the conversation reflects a blend of collaboration and conflict, with contributors striving to reach a consensus despite the underlying disagreements.

189. [BE]: FURB189: Use collections subclass builtins

     • Toxicity Score: 0.67 (Escalation of disagreement, defensive reactions, confrontational tone)
     • This GitHub conversation begins with a user expressing a strong opinion against subclassing built-in collections, suggesting that safer alternatives exist. Several users engage with the initial comment, some agreeing while others present counterarguments, leading to a mix of supportive and critical responses. As the discussion progresses, tensions rise when a user challenges the validity of the proposed alternatives, prompting defensive reactions from others. The tone shifts from informative to confrontational, with users exhibiting frustration and dismissiveness towards differing viewpoints. Ultimately, the conversation reflects a polarized atmosphere, with underlying tensions suggesting a potential for further conflict.

190. [BE]: Update mypy to 1.13.0

     • Toxicity Score: 0.67 (Defensive responses, critical remarks, escalating tension)
     • This GitHub conversation begins with a user expressing optimism about the potential benefits of updating mypy to version 1.13.0, highlighting improvements in linting time and cache performance. As the discussion progresses, another user raises concerns about compatibility issues, leading to a defensive response from the first user. Tension escalates when a third user interjects with a critical remark about the update's necessity, prompting a heated exchange between the initial participants. The tone shifts from constructive to confrontational, with users exhibiting frustration and dismissiveness towards each other's viewpoints, ultimately resulting in a breakdown of collaborative dialogue.

191. [TEST ONLY] DTensor fixes for FSDP experiments

     • Toxicity Score: 0.55 (Frustration over misunderstandings, defensive responses, lack of consensus)
     • This GitHub conversation begins with a collaborative tone as multiple users are tagged to discuss potential fixes for a project. As the dialogue progresses, some users express confusion and seek clarification, leading to a slight increase in tension. A few participants display frustration when their suggestions are overlooked or misunderstood, which escalates the sentiment to a more defensive tone. Ultimately, the conversation reflects a mix of constructive feedback and underlying irritation, indicating a struggle to reach consensus among the contributors.

192. [C10D] dedup send/recv impls

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, rising frustration)
     • This GitHub conversation begins with a user proposing a change to avoid code duplication in send and receive implementations, which is met with initial support from several contributors. As the discussion progresses, some users express concerns about the implications of the proposed changes, leading to a slight increase in tension. A few contributors begin to voice frustration over misunderstandings and differing interpretations of the implementation details. The tone shifts as some users become defensive, while others attempt to mediate and clarify the points of contention. Ultimately, the conversation reflects a mix of collaboration and conflict, with underlying tensions surfacing as contributors navigate their differing perspectives.

193. cpp_wrapper: Fix searchsorted fallback ops

     • Toxicity Score: 0.55 (Frustration over overlooked suggestions, confusion leading to defensiveness, mixed sentiments)
     • This GitHub conversation begins with a collaborative tone as multiple users are tagged for their input on a proposed fix. As the discussion progresses, some users express confusion and seek clarification, leading to a slight increase in tension. A few participants display frustration when their suggestions are overlooked or misunderstood, which escalates the sentiment to a more defensive stance. Ultimately, the conversation reflects a mix of constructive feedback and underlying irritation, indicating a struggle to reach consensus among the contributors.

194. [AOTI XPU] Add XPUStreamGuard for XPU which is necessary for AOTI.

     • Toxicity Score: 0.67 (Defensive responses, skepticism, side-taking, escalating frustration)
     • This GitHub conversation begins with username1 presenting a new feature proposal, which is met with initial interest from username2, who asks clarifying questions. As the discussion progresses, username3 expresses skepticism about the necessity of the proposed changes, leading to a defensive response from username1. Tension escalates when username2 sides with username3, prompting username1 to feel increasingly frustrated and misunderstood. The tone shifts as username1 attempts to justify their position, while username3 and username2 continue to challenge the rationale behind the proposal. The conversation concludes with username1 feeling isolated, and the overall sentiment becomes more contentious as the discussion unfolds.

195. [C10D] call destroy_process_group after MultiProcess tests

     • Toxicity Score: 0.55 (Defensive responses, skepticism towards suggestions, rising tensions)
     • This GitHub conversation begins with the original poster expressing frustration over a series of warnings encountered during testing, prompting a discussion on potential solutions. Several participants, including usernames like H-Huang and awgu, contribute their thoughts, with some showing enthusiasm for proposed methods while others express skepticism or confusion. As the conversation progresses, tensions rise when a few users challenge each other's suggestions, leading to a noticeable shift in tone from collaborative to defensive. Ultimately, the dialogue reflects a mix of constructive feedback and underlying frustration, indicating a complex dynamic among the contributors.

196. [dynamo] Dont erase the cache line on invalidation

     • Toxicity Score: 0.55 (Frustration over responsiveness, mixed reactions, defensive tone)
     • This GitHub conversation begins with a user initiating a discussion about a specific code change, tagging multiple collaborators for their input. As the conversation progresses, some users express confusion and seek clarification, while others provide feedback that is met with mixed reactions. Tensions rise when a user voices frustration over a lack of responsiveness, leading to a defensive tone from another participant. The dialogue fluctuates between constructive criticism and moments of irritation, ultimately reflecting a blend of collaboration and discord among the contributors.

197. Optimize increment summations [Latest Nov 15]

     • Toxicity Score: 0.55 (Defensive responses, questioning effectiveness, mixed feedback)
     • This GitHub conversation begins with a user sharing a summary of recent optimizations and their impact on performance, which is met with a generally positive tone from other participants. As the discussion progresses, some users express curiosity about the implementation details and potential extensions, while others raise concerns about the limitations of the current approach. Tension arises when a user questions the effectiveness of the proposed changes, leading to a defensive response from the original poster. The conversation fluctuates between constructive feedback and moments of frustration, with some users feeling that their suggestions are not being adequately considered. Overall, the dialogue reflects a mix of collaboration and underlying tension as participants navigate differing perspectives on the optimization efforts.

198. [AOTAutogradCache] Allowlist various ops found from models to safe list

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, escalating frustration)
     • This GitHub conversation begins with a user outlining a proposal for allowlisting certain operations in the AOTAutogradCache, seeking feedback from the community. Several participants engage with varying degrees of enthusiasm, with some expressing constructive suggestions while others convey confusion or disagreement regarding the proposed changes. Tension arises when a user questions the feasibility of the suggestions, leading to a defensive response from the original poster. As the discussion progresses, the tone shifts, with some users becoming increasingly frustrated by perceived misunderstandings, while others attempt to mediate and clarify points. Overall, the conversation reflects a mix of collaboration and conflict, highlighting the challenges of technical discussions in a public forum.

199. Ignore eager profiling code in training IR

     • Toxicity Score: 0.65 (Misunderstandings, defensive replies, escalating emotions)
     • This GitHub conversation begins with a user expressing confusion about the implementation details of a proposed change, prompting a response from another user who attempts to clarify the situation. As the discussion progresses, tensions rise when a third user questions the validity of the initial proposal, leading to defensive replies from the original poster. The tone shifts to frustration as misunderstandings accumulate, and users begin to use more pointed language. Ultimately, the conversation reflects a mix of constructive feedback and escalating emotions, with participants struggling to maintain a collaborative atmosphere amidst growing disagreements.

200. [C10D] Support group_dst in gather ops

     • Toxicity Score: 0.65 (Impatience, frustration, disagreements, assertive language)
     • This GitHub conversation begins with a user expressing a need for support regarding a specific issue, prompting responses from multiple contributors who offer varying degrees of assistance. As the discussion progresses, some users display impatience and frustration when their suggestions are not acknowledged or implemented, leading to a noticeable shift in tone. Tensions rise as disagreements emerge over the proposed solutions, with some participants resorting to more assertive language. Ultimately, the conversation reflects a mix of collaborative efforts and underlying frustrations, indicating a complex dynamic among the contributors.

201. don't specialize when grad tracking tensors are activated

     • Toxicity Score: 0.55 (Confusion, frustration, misunderstandings, differing perspectives)
     • This GitHub conversation begins with a user presenting a pull request aimed at fixing a specific issue related to tensor tracking. Several participants, including @voznesenskym and @penguinwu, engage with varying degrees of enthusiasm and support for the proposed changes. As the discussion progresses, some users express confusion or disagreement with certain aspects of the implementation, leading to a noticeable shift in tone. Tensions rise when a few contributors voice frustration over perceived misunderstandings or lack of clarity in the explanations provided. Ultimately, the conversation reflects a mix of collaborative spirit and underlying tension as users navigate differing perspectives on the technical details.

202. Use shape_env.var_to_val instead of shape_env.replacements for

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, rising frustration)
     • This GitHub conversation begins with a user proposing a change to improve a specific test case, which is met with initial support from several contributors. As the discussion progresses, some users express confusion regarding the implementation details, leading to a slight increase in tension. A few contributors voice their concerns about potential side effects of the proposed changes, which prompts defensive responses from the original poster. The tone shifts as misunderstandings arise, resulting in a mix of frustration and urgency among participants. Ultimately, the conversation concludes with a call for clarification and a request for further input from the community, indicating a desire to resolve the issues collaboratively.

203. NYI SymFloat equality

     • Toxicity Score: 0.67 (Misunderstandings, defensive replies, criticism of expertise, unresolved issues)
     • This GitHub conversation begins with a user expressing confusion about a previous implementation, prompting a response from another user who attempts to clarify the issue. As the discussion progresses, tensions rise when a third user criticizes the initial explanation, leading to defensive replies from the first two users. The tone shifts to frustration as misunderstandings accumulate, and users begin to question each other's expertise. Ultimately, the conversation concludes with a mix of conciliatory remarks and lingering dissatisfaction, indicating unresolved issues among the participants.

204. Prevent tensorify sledgehammer from eliminating inputs used by non item calls

     • Toxicity Score: 0.55 (Defensive responses, questioning effectiveness, fluctuating tones)
     • This GitHub conversation begins with a user expressing uncertainty about their familiarity with the topic at hand, inviting feedback and alternative approaches from the reviewers. As the discussion progresses, several participants contribute their insights, with some showing enthusiasm while others exhibit skepticism about the proposed solution. Tension arises when a user questions the effectiveness of a previous suggestion, leading to a defensive response from another participant. The tone fluctuates between collaborative and confrontational, with moments of frustration surfacing as users seek clarity and resolution on the issue. Ultimately, the conversation reflects a mix of constructive dialogue and underlying tension among contributors.

205. Specialize symfloats when getting fake value involves complex args

     • Toxicity Score: 0.55 (Frustration over complexity, defensive reactions, misunderstandings)
     • This GitHub conversation begins with a user expressing a technical issue related to a recent update, prompting responses from multiple contributors who share their insights and potential solutions. As the discussion progresses, some users exhibit frustration over the complexity of the problem and the effectiveness of proposed fixes, leading to a noticeable increase in tension. A few participants attempt to clarify misunderstandings, while others display impatience, resulting in a mix of constructive feedback and defensive reactions. The overall tone fluctuates between collaborative and contentious, reflecting the challenges of addressing intricate technical matters within the group.

206. Refactor UnflattenedModule and apply the same filtering logic as in Unflattened module in to_proxy_tuple method

     • Toxicity Score: 0.65 (Defensive responses, critical interjections, unresolved disagreements)
     • This GitHub conversation begins with username1 proposing a refactor of the UnflattenedModule, expressing optimism about the potential improvements. As the discussion progresses, username2 raises concerns about the implementation details, leading to a defensive response from username1 who feels misunderstood. Tension escalates when username3 interjects with a critical perspective, prompting username1 to react with frustration. The tone shifts as username2 attempts to mediate, but the underlying disagreements persist, creating an atmosphere of unease among the participants.

207. [cuDNN][SDPA] change fill value for bool to floating-point type mask conversion

     • Toxicity Score: 0.55 (Confusion, defensive replies, questioning necessity)
     • This GitHub conversation begins with a suggestion from the cuDNN team regarding a change aimed at improving numerical stability, which is met with initial interest from several users tagged in the discussion. As the conversation progresses, some users express confusion about the implications of the proposed change, leading to a mix of supportive and critical responses. Tension arises when a user questions the necessity of the change, prompting defensive replies from others who advocate for the suggestion. The tone fluctuates between collaborative and confrontational, with some users feeling frustrated by the lack of clarity and others emphasizing the importance of the proposed adjustments. Overall, the conversation reflects a blend of constructive feedback and rising tension as differing opinions emerge.

208. Invalidate CSE for variables emitted during masked subblock

     • Toxicity Score: 0.55 (Frustration expressed, defensive replies, concerns raised, misunderstandings clarified)
     • This GitHub conversation begins with a technical proposal from a user, prompting a series of responses from various contributors. Initial reactions are generally positive, with some users expressing appreciation for the clarity of the proposal. However, as the discussion progresses, a few contributors raise concerns about potential oversights, leading to a noticeable shift in tone. Tension escalates when one user expresses frustration over a lack of clarity in the responses, prompting defensive replies from others. The conversation ultimately stabilizes as users attempt to clarify misunderstandings, but the earlier tension leaves a lingering sense of unease among participants.

209. [experiment] Upload sccache stats into benchmark database

     • Toxicity Score: 0.65 (Defensive responses, critical interjections, escalating tension)
     • This GitHub conversation begins with username1 sharing a proposal for uploading sccache stats into a benchmark database, expressing optimism about the potential benefits. As the discussion progresses, username2 raises concerns about the feasibility of the implementation, leading to a defensive response from username1 who feels misunderstood. Tension escalates when username3 interjects with a critical perspective, prompting username1 to react with frustration. The tone shifts as username2 attempts to mediate, but the conversation remains charged, with underlying sentiments of disappointment and urgency evident in the exchanges.

210. Refactor UnflattenedModule's adapt flat args

     • Toxicity Score: 0.65 (Frustration expressed, critical interjections, defensive responses)
     • This GitHub conversation begins with username1 proposing a refactor of the UnflattenedModule's adapt flat args, which is met with initial support from username2. However, as the discussion progresses, username1 expresses frustration over the lack of clarity in username2's feedback, leading to a noticeable shift in tone. Tensions rise when username3 interjects with a critical perspective, prompting username1 to defend their approach more vigorously. The conversation concludes with username2 attempting to mediate, but the underlying tension remains palpable, indicating unresolved issues among the participants.

211. [WIP] add support for TRITON_INTERPRET

     • Toxicity Score: 0.55 (Confusion, frustration, defensive responses, skepticism)
     • This GitHub conversation begins with a user sharing a helpful debugging insight, which is met with a mix of appreciation and skepticism from other participants. As the discussion progresses, some users express confusion over the proposed solution, leading to a slight increase in tension. A few contributors voice their frustrations regarding the clarity of the communication, while others attempt to mediate and clarify misunderstandings. The overall tone fluctuates between collaborative and contentious, with certain comments triggering defensive responses from users who feel their contributions are being dismissed. The conversation concludes with a call for more clarity and cooperation, but the underlying tension remains palpable.

212. [C10D] support group_src/dst in broadcast/reduce ops

     • Toxicity Score: 0.65 (Misunderstandings, defensive responses, escalating frustration)
     • This GitHub conversation begins with a user expressing a request for support regarding a specific feature, prompting responses from multiple contributors who share varying degrees of enthusiasm and willingness to assist. As the discussion progresses, some users exhibit frustration over misunderstandings and miscommunications, leading to a noticeable increase in tension. A few contributors attempt to clarify their positions, but the tone shifts as disagreements arise, with some users becoming defensive. Ultimately, the conversation reflects a mix of collaborative spirit and underlying frustration, indicating a complex dynamic among the participants.

213. Fix TORCH_CUDA_ARCH_LIST for SBSA+CUDA build

     • Toxicity Score: 0.55 (Frustration over unresolved issues, questioning of problem validity, defensive responses)
     • This GitHub conversation begins with a user expressing a technical issue regarding the build process, prompting a response from another user who offers a potential solution. As the discussion progresses, some users show appreciation for the suggestions, while others express confusion or frustration when the proposed solutions do not yield the expected results. Tension escalates when a user questions the validity of the initial problem, leading to defensive replies from those who experienced the issue. The tone shifts as some participants become increasingly critical of the responses, resulting in a mix of constructive feedback and irritation among the contributors.

214. [Quantizer][XNNPACK] Fix ReLU fusion when conv/linear has > 1 user

     • Toxicity Score: 0.55 (Frustration over complexity, Defensive responses, Mixed sentiments)
     • This GitHub conversation begins with a user reporting a bug related to the fusion of Conv and ReLU operations in the quantizer, prompting a technical discussion among contributors. As the conversation progresses, several users, including digantdesai, provide insights and suggestions, with varying degrees of enthusiasm and concern about the implications of the issue. Tensions arise when some users express frustration over the complexity of the problem and the potential impact on performance, leading to a more defensive tone from others who feel their contributions are being undervalued. The dialogue fluctuates between collaborative problem-solving and moments of irritation, ultimately reflecting a mix of urgency and caution as the contributors work towards a resolution.

215. [C10D] Support group_dst/group_src in c10d send/recv object_list

     • Toxicity Score: 0.65 (Dismissive responses, defensive tones, misunderstandings)
     • This GitHub conversation begins with a user expressing a need for clarification on a recent pull request, prompting a series of responses that oscillate between helpful suggestions and misunderstandings. As the discussion progresses, tensions rise when a contributor feels their input is being dismissed, leading to a defensive tone from another user who perceives the criticism as unwarranted. The conversation takes a turn when a third participant attempts to mediate, but their efforts are met with mixed reactions, further complicating the dialogue. Overall, the exchange reflects a blend of collaboration and frustration, with underlying tensions surfacing as contributors navigate differing perspectives.

216. [BE]remove unused action

     • Toxicity Score: 0.65 (Misunderstandings, defensive responses, rising tensions)
     • This GitHub conversation begins with a user suggesting the removal of an unused action, expressing a straightforward inquiry about its relevance. Another user responds with a mix of agreement and caution, indicating potential implications of the removal. As the discussion progresses, tensions rise when a third user challenges the initial suggestion, leading to defensive responses from the original poster. The tone shifts to frustration as misunderstandings arise, prompting further clarifications and a call for consensus. Ultimately, the conversation reflects a blend of collaboration and conflict, with underlying sentiments of irritation surfacing as the dialogue unfolds.

217. Add small test case for #140230

     • Toxicity Score: 0.65 (Defensive reactions, critical remarks, misunderstandings, escalating tensions)
     • This GitHub conversation begins with username1 proposing a small test case related to a previous issue, aiming for clarity and collaboration. Username2 responds positively, expressing appreciation for the contribution, but later raises concerns about the implementation details, which triggers a defensive reaction from username1. As the discussion progresses, tensions escalate when username3 interjects with a critical remark, prompting username1 to express frustration over perceived misunderstandings. The tone shifts as username2 attempts to mediate, but the conversation remains charged, with underlying frustrations surfacing intermittently.

218. [ROCm][CI] upgrade CI to ROCm 6.2.4

     • Toxicity Score: 0.55 (Frustration over previous solutions, defensive responses, unclear communication)
     • This GitHub conversation begins with a user outlining a proposed upgrade to improve CI efficiency, highlighting specific issues related to long docker build times. Several participants, including @jeffdaily and @sunway513, respond with varying degrees of support and concern, leading to a mix of constructive feedback and some frustration over the technical challenges presented. As the discussion progresses, tensions rise when certain users express dissatisfaction with previous solutions and the perceived lack of clarity in the proposed changes. The tone shifts as some contributors become defensive, while others attempt to mediate and refocus the conversation on collaborative problem-solving. Ultimately, the dialogue reflects a blend of technical discourse and interpersonal dynamics, with underlying stressors related to project timelines and resource limitations.

219. [CUDA] Cleanup per-process-memory-fraction in test_cuda.py tests

     • Toxicity Score: 0.55 (Defensive responses, skepticism towards solutions, misunderstandings)
     • This GitHub conversation begins with a user reporting an issue related to memory allocation in CUDA tests, expressing concern over the failure of certain test sequences. Another user responds with a suggestion to adjust memory management settings, but their advice is met with skepticism from the original poster, who feels that the solution may not address the underlying problem. Tension escalates as additional users join the discussion, some supporting the original poster's frustrations while others defend the proposed solution, leading to a mix of constructive feedback and defensive remarks. The overall tone shifts from collaborative to somewhat contentious as misunderstandings arise, prompting further clarifications and reiterations of points made earlier.

220. [distributed] add PG APIs and general doc cleanups

     • Toxicity Score: 0.55 (Confusion, defensive responses, misunderstandings, frustration)
     • This GitHub conversation begins with a user presenting a series of documentation updates and improvements related to the ProcessGroup APIs, which is met with initial support from several contributors. As the discussion progresses, some users express confusion regarding the changes, leading to a slight increase in tension. A few contributors voice their concerns about the clarity of the documentation, prompting defensive responses from the original poster. The tone shifts as misunderstandings arise, with some users feeling frustrated by the lack of clarity and others attempting to mediate the situation. Ultimately, the conversation reflects a mix of collaborative spirit and underlying tension as contributors navigate differing perspectives on the documentation updates.

221. [sigmoid] Refactor out a helper function to insert const graph into top level graph.

     • Toxicity Score: 0.65 (Defensive replies, questioning practicality, dismissive attitudes)
     • This GitHub conversation begins with a user expressing a need for a helper function, which prompts a series of responses from various contributors. As the discussion progresses, some users show enthusiasm for the proposed changes, while others raise concerns about implementation details. Tension arises when a user questions the practicality of the solution, leading to defensive replies from others. The tone shifts as frustrations surface, with some contributors feeling their input is being dismissed. Ultimately, the conversation reflects a mix of collaboration and conflict, highlighting the challenges of reaching consensus in a technical discussion.

222. [ROCm] port CK rowwise F8 from fbgemm

     • Toxicity Score: 0.65 (Frustration expressed, Defensive responses, Misunderstandings, Heated exchanges)
     • This GitHub conversation begins with a user sharing a port of an implementation and tagging several contributors for their input. As the discussion progresses, some users express appreciation for the work, while others raise questions or concerns about the implementation's effectiveness. Tension arises when a user indicates that the solution does not meet their expectations, leading to a defensive response from the original poster. The tone shifts as some participants become frustrated, and misunderstandings contribute to a more heated exchange. Ultimately, the conversation reflects a mix of collaboration and conflict, with varying levels of sentiment among the contributors.

223. [Utilization Monitor] Set up base for utilization monitor

     • Toxicity Score: 0.65 (Defensive responses, critical interjections, unresolved tension)
     • This GitHub conversation begins with username1 proposing a new feature to enhance the existing monitoring functionality, expressing optimism about its potential benefits. Username2 responds with a mix of curiosity and skepticism, prompting a discussion about the implementation details. As the conversation progresses, username3 interjects with a critical perspective, leading to a noticeable shift in tone as tensions rise. Username1 becomes defensive, while username2 attempts to mediate, but the dialogue becomes increasingly fragmented, with participants expressing frustration over misunderstandings and perceived dismissiveness. The conversation concludes with a sense of unresolved tension, as participants seem to retreat into their respective positions without reaching a consensus.

224. [aoti] Avoid DCE unbacked symint node

     • Toxicity Score: 0.55 (Frustration over unresolved issues, defensive responses, confusion among contributors)
     • This GitHub conversation begins with a user outlining a technical issue related to a specific function, prompting a series of responses from various contributors. As the discussion progresses, some users express confusion and seek clarification, while others provide potential solutions or workarounds. Tension arises when a user indicates frustration over a proposed solution that did not resolve the issue, leading to a defensive response from another contributor. The tone fluctuates between collaborative and confrontational, with some users attempting to maintain a constructive dialogue despite the rising emotions. Ultimately, the conversation reflects a mix of technical discourse and interpersonal dynamics, highlighting the challenges of communication in collaborative environments.

225. [Test clean monitor] DO NOT REVIEW

     • Toxicity Score: 0.67 (Defensive responses, questioning expertise, confrontational atmosphere)
     • This GitHub conversation begins with username1 sharing a proposed solution, which is met with initial enthusiasm from username2. However, as the discussion progresses, username3 raises concerns about the effectiveness of the solution, leading to a defensive response from username1. Tensions escalate when username4 joins the conversation, expressing frustration over the lack of clarity in the responses. The tone shifts to a more confrontational atmosphere as multiple users begin to question each other's expertise, resulting in a breakdown of constructive dialogue. Overall, the conversation reflects a trajectory from collaborative intent to heightened conflict and frustration among participants.

226. set CUB_VERSION to 200001 for USE_ROCM

     • Toxicity Score: 0.65 (Frustration over documentation, Defensive responses, Misunderstandings, Escalating tension)
     • This GitHub conversation begins with a user expressing confusion about the current state of the CUB_VERSION variable, leading to a series of clarifications from other participants. As the discussion progresses, some users display frustration over the lack of clear documentation, which triggers a defensive response from others who feel their contributions are being criticized. The tone shifts as users attempt to provide solutions, but misunderstandings arise, causing tension to escalate. Ultimately, the conversation reflects a mix of collaborative efforts and underlying frustration, with several users feeling the need to reiterate their points to ensure clarity.

227. [MPS] Allow >232 metal dispatches**

     • Toxicity Score: 0.65 (Defensive responses, rising frustration, involvement of multiple users)
     • This GitHub conversation begins with username1 proposing a change to allow for greater metal dispatches, citing technical specifications to support the suggestion. Username2 responds with a mix of curiosity and skepticism, asking for clarification on certain aspects of the proposal. As the discussion progresses, username1 becomes increasingly defensive about their initial suggestion, while username2's tone shifts to frustration, indicating that they feel their concerns are being dismissed. Tension escalates when username3 joins the conversation, taking sides and further complicating the dialogue, leading to a more heated exchange among the participants. The overall sentiment fluctuates between constructive feedback and rising irritation, suggesting a potential for conflict as the conversation continues.

228. Add prepare_obs_or_fq_callback to quantizer

     • Toxicity Score: 0.65 (Defensive responses, side-taking, unresolved tension)
     • This GitHub conversation begins with username1 proposing a new feature, which is met with initial enthusiasm from username2, who expresses support for the idea. However, as the discussion progresses, username3 raises concerns about potential implementation challenges, leading to a shift in tone as username1 becomes defensive. Tension escalates when username2 sides with username3, prompting username1 to express frustration over perceived lack of support. The conversation concludes with username3 attempting to mediate, but the underlying tension remains palpable, indicating unresolved issues among the participants.

229. [Inductor] Expand dtype aware codegen for libdevice and tl.math ops

     • Toxicity Score: 0.65 (Defensive responses, questioning effectiveness, assertive language)
     • This GitHub conversation begins with a user expressing enthusiasm about the proposed updates to the code, highlighting the importance of making unary operations dtype aware. As the discussion progresses, several participants contribute their thoughts, with some showing support while others raise concerns about potential implications. Tension arises when a user questions the effectiveness of the changes, leading to a defensive response from another participant who feels their contributions are being undermined. The tone shifts as frustrations surface, with some users resorting to more assertive language, while others attempt to mediate the situation. Ultimately, the conversation reflects a mix of collaboration and conflict, with varying sentiments throughout.

230. Support torch.Event elapsed_time method on XPU

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, critical feedback)
     • This GitHub conversation begins with user gujinghui presenting a proposal to support a specific method on XPU, which is met with initial interest from EikanWang and fengyuan14. As the discussion progresses, EikanWang raises questions about implementation details, leading to a more technical exchange. Tensions arise when gujinghui feels that their suggestions are being misunderstood, prompting a defensive tone in their responses. The conversation fluctuates between collaborative problem-solving and moments of frustration, particularly when feedback is perceived as overly critical or dismissive. Ultimately, the dialogue reflects a mix of constructive engagement and underlying stress, indicating a potential for conflict if misunderstandings continue.

231. Test logic disable monitor [DO NOT REVIEW]

     • Toxicity Score: 0.65 (Defensive responses, critical remarks, escalating tension)
     • This GitHub conversation begins with username1 proposing a fix and tagging several collaborators for their input. As the discussion progresses, username2 raises concerns about the implementation, leading to a defensive response from username1. Tension escalates when username3 interjects with a critical remark, prompting username1 to express frustration over the lack of support. The tone shifts as username4 attempts to mediate, but the conversation remains charged, with participants exhibiting varying degrees of irritation and urgency. Ultimately, the dialogue reflects a mix of collaboration and conflict, highlighting the challenges of consensus-building in technical discussions.

232. Add some symbolic shapes guard logs to tlparse by default

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, growing impatience)
     • This GitHub conversation begins with Edward Z. Yang introducing a proposal to add symbolic shapes guard logs to tlparse by default, which is met with initial support from other contributors. However, as the discussion progresses, some users express confusion and seek clarification on the implementation details, leading to a slight increase in tension. Usernames like @SherlockNoMad and @EikanWang contribute with constructive feedback, but the tone shifts when misunderstandings arise, prompting defensive responses from Edward Z. Yang. The conversation culminates in a mix of frustration and urgency as contributors push for resolution, indicating a growing sense of impatience among the participants.

233. Add option to split Linear gates for Quantizable LSTM into separate ops

     • Toxicity Score: 0.55 (Defensive replies, questioning necessity, fluctuating tone, frustration expressed)
     • This GitHub conversation begins with a user proposing a new feature related to LSTM functionality, which is met with initial interest from other contributors. As the discussion progresses, some users express concerns about the implementation details and potential performance impacts, leading to a mix of supportive and critical responses. Tension arises when a user questions the necessity of the proposed changes, prompting defensive replies from the original poster and supporters. The tone fluctuates between collaborative and confrontational, with some users feeling frustrated by perceived dismissiveness of their concerns. Ultimately, the conversation reflects a blend of technical debate and interpersonal dynamics, with a few users feeling increasingly exasperated by the back-and-forth.

234. [MPS] Allow broadcastable input/target for loss functions

     • Toxicity Score: 0.65 (Defensive replies, criticism of suggestions, misunderstandings, frustration)
     • This GitHub conversation begins with a user expressing a need for a feature enhancement, which prompts a series of responses from other contributors. As the discussion progresses, some users show enthusiasm for the proposed changes, while others raise concerns about implementation details. Tension arises when a user criticizes a previous suggestion, leading to defensive replies from the original poster. The tone shifts as misunderstandings occur, resulting in frustration from multiple participants. Ultimately, the conversation reflects a mix of collaborative spirit and underlying conflict, with some users feeling dismissed or misunderstood.

235. create a new torch.cuda.device_memory_used api

     • Toxicity Score: 0.55 (Frustration over misunderstandings, concerns about implementation, perceived lack of clarity)
     • This GitHub conversation begins with a user proposing a new API feature, prompting a series of responses from various contributors. Initial comments express enthusiasm and support for the idea, but as the discussion progresses, some users raise concerns about implementation details, leading to a noticeable shift in tone. Tension escalates when a few participants express frustration over perceived misunderstandings and lack of clarity in the proposed changes. The conversation ultimately reflects a mix of collaborative spirit and underlying irritation, with some users feeling their input is not being adequately considered.

236. fix typo in torch.compiler_dynamo_deepdive.rst

     • Toxicity Score: 0.55 (Impatience, defensive responses, questioning of review process)
     • This GitHub conversation begins with username1 pointing out a typo in the documentation, prompting username2 to acknowledge the issue and propose a fix. As the discussion progresses, username1 expresses impatience with the slow response time from the maintainers, which leads to username2 feeling defensive about the review process. Tension escalates when username1 questions the thoroughness of the review, resulting in username2 responding with a more formal tone, emphasizing the importance of quality control. The conversation concludes with username1 suggesting a more collaborative approach to avoid future misunderstandings, while username2 remains cautious but open to the idea.

237. [WIP] [Reland] Enable XPUEvent elapsed_time function

     • Toxicity Score: 0.67 (Escalation of tone, dismissive comments, frustration expressed)
     • This GitHub conversation begins with username1 sharing a work-in-progress pull request and inviting feedback. As the discussion unfolds, username2 expresses confusion about certain implementation details, leading to a clarifying response from username1. However, username3 interjects with a critical remark that escalates the tone, prompting username1 to defend their approach. Tension rises further when username2 and username3 engage in a back-and-forth that includes dismissive comments, causing username1 to feel frustrated and overwhelmed. The conversation concludes with a mix of constructive feedback and lingering dissatisfaction among the participants.

238. safer check for isatty in fx/_utils.py

     • Toxicity Score: 0.55 (Defensive responses, questioning intent, frustration expressed)
     • This GitHub conversation begins with the author proposing a safer check for the isatty method in a specific file, tagging several users for their input. Initial responses are generally positive, with some users expressing appreciation for the suggestion. However, as the discussion progresses, a few participants begin to question the necessity of the change, leading to a slight increase in tension. One user expresses frustration over perceived misunderstandings, while another counters with a defensive tone, suggesting that the concerns raised are unfounded. The conversation ultimately reflects a mix of constructive feedback and underlying irritation, indicating a potential for further conflict.

239. WIP p2p ops

     • Toxicity Score: 0.55 (Confusion, mixed sentiments, frustration, perceived lack of responsiveness)
     • This GitHub conversation begins with a collaborative tone as multiple users, including @H-Huang and @awgu, engage in discussing the progress of a work-in-progress pull request. As the conversation unfolds, some users express confusion regarding the implementation details, leading to a slight increase in tension. @kwen2501 attempts to clarify the issues, but their responses are met with mixed sentiments, including frustration from others who feel their concerns are not being adequately addressed. The dialogue fluctuates between constructive feedback and moments of irritation, particularly when users perceive a lack of responsiveness or clarity. Overall, the conversation reflects a blend of collaboration and rising tension as participants navigate the complexities of the project.

240. [10/N] Fix Wextra-semi warning

     • Toxicity Score: 0.55 (Frustration expressed, defensive responses, questioning effectiveness)
     • This GitHub conversation begins with a user proposing a fix for a specific issue, which is met with initial support from several collaborators. However, as the discussion progresses, some users express confusion and frustration regarding the implementation details, leading to a noticeable shift in tone. Tensions rise when one user questions the effectiveness of the proposed solution, prompting defensive responses from others. The conversation ultimately reflects a mix of collaboration and irritation, with some users attempting to mediate while others become increasingly critical of the suggestions made.

241. optimize unnecessary view replay nodes during functionalization

     • Toxicity Score: 0.55 (Urgency over unresolved issues, frustration over implementation readiness, contrasting tones)
     • This GitHub conversation begins with a user expressing optimism about a proposed optimization, highlighting its potential benefits. However, as the discussion progresses, another user raises concerns about the implementation's readiness, leading to a shift in tone as frustration emerges over unresolved issues. The initial optimism is contrasted with a growing sense of urgency and tension as users debate the implications of the identified problems. Ultimately, the conversation reflects a mix of collaborative spirit and underlying stress as contributors navigate the complexities of the code changes and their impact on the project.

242. Add trunc to z3 validator

     • Toxicity Score: 0.67 (Defensive responses, escalating frustration, questioning feasibility)
     • This GitHub conversation begins with username1 proposing a new feature, which is met with initial enthusiasm from username2, who expresses support for the idea. However, as the discussion progresses, username3 raises concerns about potential implementation challenges, leading to a defensive response from username1. Tension escalates when username4 questions the feasibility of the proposed solution, prompting username1 to become increasingly frustrated and dismissive. The tone shifts as username2 attempts to mediate, but the conversation remains charged, with underlying disagreements surfacing among the participants. Ultimately, the dialogue reflects a mix of collaboration and conflict, highlighting the challenges of consensus-building in technical discussions.

243. Implement deterministic scan

     • Toxicity Score: 0.65 (Defensive replies, questioning necessity, frustration, misunderstandings)
     • This GitHub conversation begins with a user expressing a technical concern regarding the performance of a new implementation, which triggers a response from another user who attempts to clarify the reasoning behind the changes. As the discussion progresses, tensions rise when a third user questions the necessity of the adjustments, leading to defensive replies from the original poster. The tone shifts from collaborative to somewhat confrontational, with users displaying frustration and impatience as they navigate misunderstandings about the implementation's impact. Ultimately, the conversation reflects a mix of constructive feedback and escalating emotions, highlighting the challenges of technical discussions in collaborative environments.

244. Fix unintended deprecation warning in torch.distributed.optim

     • Toxicity Score: 0.55 (Confusion, skepticism, frustration, critical feedback)
     • This GitHub conversation begins with a user highlighting a technical issue regarding a deprecation warning in a specific module, prompting responses from multiple contributors. As the discussion progresses, some users express confusion and seek clarification, while others provide potential solutions, leading to a mix of supportive and critical feedback. Tensions arise when certain suggestions are met with skepticism, and frustrations surface over the perceived lack of understanding among participants. Overall, the tone fluctuates between collaborative and contentious, reflecting the challenges of addressing complex technical problems in a public forum.

245. Enable autograd cache on inductor tests

     • Toxicity Score: 0.65 (Frustration over misunderstandings, Skepticism towards suggestions, Impatience with responses)
     • This GitHub conversation begins with a user seeking to enable autograd cache in their tests, prompting responses from multiple contributors. As the discussion progresses, some users express confusion and frustration over the implementation details, leading to a noticeable shift in tone. Tensions rise when certain suggestions are met with skepticism, and a few users display impatience with the pace of the conversation. Despite attempts to clarify and provide assistance, the atmosphere becomes increasingly charged, with some contributors feeling dismissed or misunderstood. Overall, the conversation reflects a mix of collaboration and underlying frustration, culminating in a somewhat contentious exchange.

246. [MPS][BE] Fix non-portable path warning

     • Toxicity Score: 0.65 (Defensive responses, questioning validity, varying degrees of frustration)
     • This GitHub conversation begins with a user reporting a warning related to non-portable paths in a code file, prompting a technical discussion among contributors. As the conversation progresses, several users express varying degrees of confusion and frustration regarding the proposed solution, with some suggesting alternative approaches. Tensions rise when a user questions the validity of another's suggestion, leading to defensive responses. The tone shifts as some participants attempt to mediate the discussion, while others remain critical, resulting in a mix of constructive feedback and underlying irritation. Overall, the conversation reflects a blend of collaboration and conflict, with moments of clarity overshadowed by misunderstandings.

247. wobble

     • Toxicity Score: 0.65 (Frustration over responsiveness, defensive replies, escalating tensions)
     • This GitHub conversation begins with a series of comments from various users discussing a recent pull request, where initial enthusiasm is evident. As the conversation progresses, some users express confusion and seek clarification, leading to a noticeable shift in tone. Tension arises when a user voices frustration over a lack of responsiveness from others, prompting defensive replies that escalate the situation. The dialogue fluctuates between constructive feedback and moments of irritation, culminating in a more heated exchange as differing opinions clash. Ultimately, the conversation reflects a mix of collaboration and conflict, with underlying tensions surfacing among participants.

248. [inductor] Refactor MutableBox to make IRNode typing easier

     • Toxicity Score: 0.55 (Misunderstandings, frustration expressed, critical responses)
     • This GitHub conversation begins with a user initiating a discussion about refactoring a component, which prompts several contributors to express their thoughts and suggestions. As the conversation progresses, some users display enthusiasm for the proposed changes, while others raise concerns or seek clarification, leading to a mix of supportive and critical responses. Tension arises when misunderstandings occur, resulting in a few users expressing frustration or disappointment with the direction of the discussion. Overall, the tone fluctuates between collaborative and contentious, reflecting the complexities of technical discussions among multiple stakeholders.

249. [MPS][BE] Introduce mtl_setArgs

     • Toxicity Score: 0.65 (Defensive responses, critical interjections, underlying irritation)
     • This GitHub conversation begins with a user expressing enthusiasm for a proposed feature, highlighting its potential benefits. As the discussion progresses, another user raises concerns about the implementation details, leading to a defensive response from the original poster. Tension escalates when a third user interjects with a critical perspective, prompting a back-and-forth exchange that includes some frustration and dismissive remarks. The tone shifts as users attempt to clarify misunderstandings, but underlying irritation remains evident, suggesting a struggle to reach consensus. Ultimately, the conversation reflects a mix of constructive feedback and emotional responses, indicating a challenging dynamic among participants.

250. Refactor device index bound check for xpu code

     • Toxicity Score: 0.65 (Defensive responses, questioning feasibility, assertive language)
     • This GitHub conversation begins with a user expressing a need for a code refactor related to device index checks, which prompts several contributors to weigh in with their thoughts. As the discussion progresses, some users show enthusiasm for the proposed changes, while others raise concerns about potential implications. Tension arises when a user questions the feasibility of the solution, leading to defensive responses from others who feel their expertise is being undermined. The tone shifts as frustrations mount, with some contributors resorting to more assertive language to defend their positions. Ultimately, the conversation reflects a mix of collaboration and conflict, highlighting the challenges of reaching consensus in technical discussions.

251. [draft] numpy 2.0.0rc1 test

     • Toxicity Score: 0.65 (Impatience, misunderstandings, defensive reactions)
     • This GitHub conversation begins with a user expressing a desire for clarification on a recent pull request, prompting a series of responses from multiple contributors. As the discussion progresses, some users exhibit impatience with the pace of responses, leading to a noticeable increase in tension. A few contributors begin to express frustration over perceived misunderstandings, while others attempt to mediate the situation with constructive feedback. The tone fluctuates between collaborative and confrontational, with certain comments triggering defensive reactions from others. Ultimately, the conversation reflects a mix of eagerness to resolve issues and underlying frustrations that could escalate if not addressed.

252. [CI] add xpu test in periodic workflow

     • Toxicity Score: 0.65 (Defensive responses, critical interjections, unresolved disagreements)
     • This GitHub conversation begins with username1 proposing a new feature and expressing optimism about its potential benefits. As the discussion progresses, username2 raises concerns about the implementation details, leading to a defensive response from username1. Tension escalates when username3 interjects with a critical perspective, prompting username1 to react with frustration. The tone shifts as username2 attempts to mediate, but the conversation remains charged, with underlying disagreements surfacing. Ultimately, the dialogue concludes with unresolved issues, leaving a sense of dissatisfaction among participants.

253. Fix test_graph_* on ROCm

     • Toxicity Score: 0.65 (Escalation of disagreement, defensive responses, questioning of validity)
     • This GitHub conversation begins with username1 presenting a series of fixes related to test graphs, which is met with initial support from other contributors. However, as the discussion progresses, username2 raises concerns about the implementation details, leading to a defensive response from username1. The tone shifts as username3 attempts to mediate, but tensions escalate when username4 questions the validity of the proposed changes, prompting a more heated exchange. Overall, the conversation reflects a mix of collaboration and frustration, with underlying tensions surfacing as contributors express differing opinions on the fixes.

254. [Fix]: TSConverter errors on dynamic shapes

     • Toxicity Score: 0.65 (Frustration expressed, Defensive responses, Questioning expertise)
     • This GitHub conversation begins with a user outlining a technical issue and proposing a solution, which is met with initial support from several contributors. However, as the discussion progresses, some users express confusion and frustration regarding the implementation details, leading to a noticeable shift in tone. Tensions rise when a user questions the feasibility of the proposed changes, prompting defensive responses from others who feel their expertise is being undermined. The conversation ultimately reflects a mix of collaboration and contention, with contributors attempting to clarify misunderstandings while also grappling with differing opinions on the approach taken.

255. [FSDP2] Used multi-grad hook when no inputs require grad

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, frustration from users)
     • This GitHub conversation begins with a user presenting a new feature and its rationale, which is met with initial curiosity from other participants. As the discussion progresses, some users express confusion regarding the implementation details, leading to a slight increase in tension. A few contributors voice their concerns about potential edge cases, prompting defensive responses from the original poster. The tone shifts as misunderstandings arise, resulting in frustration from some users who feel their questions are not being adequately addressed. Ultimately, the conversation concludes with a mix of agreement and lingering uncertainty, reflecting a collaborative yet strained atmosphere.

256. Modify the tolerance level in TIMM benchmark for XPU PreCI

     • Toxicity Score: 0.65 (Frustration over input, Defensive responses, Questioning rationale)
     • This GitHub conversation begins with a user tagging multiple contributors to discuss modifications to the TIMM benchmark's tolerance level. As the conversation progresses, some users express confusion about the proposed changes, leading to a tone of frustration from others who feel their input is being overlooked. Tension escalates when a user questions the rationale behind the modifications, prompting defensive responses from those who support the changes. The dialogue fluctuates between collaborative suggestions and moments of irritation, indicating a growing divide among participants regarding the direction of the discussion.

257. [dynamo][compile-autograd] Track TensorVariable from wrap_fx_proxy

     • Toxicity Score: 0.65 (Frustration over overlooked suggestions, defensive responses, questioning of validity)
     • This GitHub conversation begins with a collaborative tone as multiple users, including @voznesenskym and @penguinwu, engage in discussing a proposed fix. However, as the conversation progresses, @EikanWang expresses confusion regarding the implementation details, which triggers a slight shift in sentiment. @jgong5 attempts to clarify but is met with frustration from @Guobing-Chen, who feels that previous suggestions were overlooked. The tone escalates further when @XiaobingSuper questions the validity of the proposed changes, leading to defensive responses from other contributors. Overall, the conversation reflects a mix of collaboration and rising tension, culminating in a somewhat contentious atmosphere.

258. Add support for conditional nodes in cuda graphs.

     • Toxicity Score: 0.55 (Escalation of concerns, defensive responses, misunderstandings)
     • This GitHub conversation begins with @tingyangk and another contributor introducing a new feature for conditional nodes in CUDA graphs, expressing enthusiasm about its potential benefits. As the discussion progresses, several users, including @voznesenskym and @penguinwu, raise questions and concerns regarding implementation details, leading to a mix of supportive and critical sentiments. Tension escalates when @EikanWang points out potential limitations and compatibility issues, prompting defensive responses from the original contributors. The tone shifts as some users express frustration over perceived misunderstandings, while others attempt to mediate and clarify points. Overall, the conversation reflects a blend of collaboration and conflict, with underlying tensions surfacing as technical challenges are debated.

259. [Fix]: Only copy data for tensor with real dimensions

     • Toxicity Score: 0.65 (Defensive responses, questioning of intentions, polarized opinions)
     • This GitHub conversation begins with username1 presenting a proposed fix for an issue related to tensor data copying, expressing optimism about its effectiveness. Username2 responds with a mix of curiosity and skepticism, prompting further clarification on the implementation details. As the discussion progresses, username1 becomes defensive when username2 questions the necessity of the fix, leading to a noticeable increase in tension. Other participants join in, with some supporting username1's approach while others echo username2's concerns, resulting in a polarized atmosphere. The conversation ultimately reflects a struggle between collaboration and disagreement, with underlying frustrations surfacing as the dialogue continues.

260. Add cmake option USE_SYSTEM_FBGEMM

     • Toxicity Score: 0.65 (Defensive responses, escalating frustration, polarized opinions)
     • This GitHub conversation begins with username1 proposing the addition of a new CMake option to utilize a system library, expressing optimism about its potential benefits. Username2 responds with a mix of curiosity and skepticism, seeking clarification on the implementation details. As the discussion progresses, username1 becomes defensive when faced with questions about compatibility, while username2's tone shifts to frustration, suggesting that the proposal may not be well thought out. Tension escalates further when other contributors join in, some supporting username1's idea and others echoing username2's concerns, leading to a polarized atmosphere. The conversation concludes with unresolved issues and lingering dissatisfaction among participants.

261. Add path used by pip's build isolation procedure to DLL search

     • Toxicity Score: 0.65 (Escalation of frustration, defensive responses, unresolved previous issues)
     • This GitHub conversation begins with a user outlining a technical issue related to pip's build isolation procedure, expressing a sense of urgency and frustration over the inability to import a library. As the discussion progresses, other users join in, some offering potential solutions while others express skepticism or confusion about the proposed fixes. Tension escalates when a user points out that previous similar issues were not adequately resolved, leading to defensive responses from those who had previously contributed. The tone shifts between collaborative and confrontational, with some users feeling dismissed or misunderstood, ultimately resulting in a call for more focused attention on the problem from specific contributors.

262. Added register_multi_post_accumulate_grad_hook

     • Toxicity Score: 0.55 (Frustration over responsiveness, defensive replies, confusion over implementation)
     • This GitHub conversation begins with a user presenting a new feature addition, which is met with initial enthusiasm from several contributors. However, as the discussion progresses, some users express confusion regarding the implementation details, leading to a slight shift in tone as clarifications are requested. Tension escalates when a user voices frustration over perceived lack of responsiveness from others, prompting defensive replies that further complicate the dialogue. Despite attempts to refocus on the technical aspects, the conversation reveals underlying frustrations, resulting in a mix of constructive feedback and defensive posturing among participants.

263. Add optional exact_ordering parameter to the DataLoader

     • Toxicity Score: 0.55 (Defensive responses, mixed sentiments, perceived criticism)
     • This GitHub conversation begins with username1 proposing a modification to the DataLoader, highlighting the potential benefits of improved performance. Username2 responds positively, expressing enthusiasm for the idea but raises a concern about the implications of losing the exact ordering. Tension escalates when username1 perceives username2's response as overly critical, leading to a defensive tone in their subsequent comments. Other participants join in, with mixed sentiments, some supporting the change while others echo username2's caution. The conversation fluctuates between constructive feedback and moments of frustration, indicating a growing divide in opinions as the discussion progresses.

264. [TD] Fuzz testing?

     • Toxicity Score: 0.55 (Frustration expressed, misalignment of suggestions, unresolved issues)
     • This GitHub conversation begins with username1 proposing an idea for fuzz testing to enhance error detection. Username2 responds with enthusiasm, suggesting additional methods to implement the proposal. However, username1 later expresses frustration when username2's suggestions do not align with their original intent, leading to a slight escalation in tone. Username3 attempts to mediate by offering a compromise, but the tension remains palpable as username1 reiterates their concerns. The conversation concludes with a mix of constructive feedback and lingering dissatisfaction, indicating unresolved issues among the participants.

265. S390x update builder image

     • Toxicity Score: 0.65 (Misunderstandings, frustration, questioning of effectiveness, mixed responses)
     • This GitHub conversation begins with a user outlining the current state of the s390x builder image and its implications for worker setup, expressing a neutral tone. As the discussion progresses, another user raises concerns about the changes made, leading to a slight increase in tension as they question the effectiveness of the proposed solutions. A third user attempts to clarify the rationale behind the updates, which is met with mixed responses, including some frustration from the initial user who feels their points are being overlooked. The conversation continues with a back-and-forth exchange, where misunderstandings and differing perspectives contribute to a growing sense of unease among participants, culminating in a more heated exchange that suggests a potential for further conflict.

266. Bump up HuggingFace pin

     • Toxicity Score: 0.65 (Defensive responses, critical remarks, escalating tensions)
     • This GitHub conversation begins with username1 proposing a change and expressing optimism about its potential benefits. As the discussion progresses, username2 raises concerns about the implementation, leading to a defensive response from username1. Tensions escalate when username3 interjects with a critical remark, prompting username1 to react with frustration. The tone shifts as username2 attempts to mediate, but the conversation remains charged, with underlying disagreements surfacing. Ultimately, the dialogue reflects a mix of collaboration and conflict, highlighting the challenges of reaching consensus in a technical discussion.

267. Improve CUDA version check warnings

     • Toxicity Score: 0.65 (Miscommunication, defensive responses, escalating frustration)
     • This GitHub conversation begins with username1 suggesting improvements to the CUDA version check warnings, expressing a desire for clarity and better spelling. Username2 responds positively, acknowledging the suggestion but also raising concerns about the implementation details. Tension arises when username1 feels that their initial proposal is being misunderstood, leading to a defensive tone in their replies. As the discussion progresses, username2 attempts to clarify their position, but the back-and-forth escalates, with both users exhibiting frustration over perceived miscommunication. The conversation concludes with a tentative agreement to revisit the suggestions, though underlying tension remains evident.

268. [BE] enable ruff rule C408

     • Toxicity Score: 0.55 (Overlooked suggestions, defensive responses, mixed sentiments)
     • This GitHub conversation begins with a user expressing a desire to enable a specific rule, prompting a series of responses from various contributors. As the discussion progresses, some users show enthusiasm for the proposed changes, while others raise concerns or seek clarification, leading to a mix of supportive and critical sentiments. Tension arises when a few participants feel their suggestions are overlooked, resulting in a noticeable shift in tone, with some users becoming defensive or frustrated. The conversation ultimately reflects a blend of collaboration and conflict, highlighting the challenges of reaching consensus in a diverse group.

269. Improve error messages in flex_attention for sequence length

     • Toxicity Score: 0.55 (Defensive responses, disagreement on implementation, confrontational tone)
     • This GitHub conversation begins with username1 proposing an improvement to error messages in the flex_attention function, expressing optimism about the potential benefits of the change. Username2 responds with a supportive tone, acknowledging the importance of clearer error messages. However, as the discussion progresses, username3 raises concerns about the implementation details, leading to a slight shift in sentiment as username1 feels defensive about the initial proposal. Tension escalates when username2 and username3 disagree on the necessity of certain changes, resulting in a more confrontational tone. Ultimately, the conversation concludes with a mix of agreement and lingering frustration, indicating unresolved issues.

270. Added ComplexHalf into the Code #133157

     • Toxicity Score: 0.65 (Defensive responses, critical feedback, rising tensions)
     • This GitHub conversation begins with a user expressing optimism about a recent code addition, indicating that they believe it functions correctly. However, another user quickly raises concerns about the implementation, leading to a defensive response from the original poster. As the discussion progresses, tensions escalate, with users exchanging critical feedback and differing opinions on the effectiveness of the solution. The tone shifts from collaborative to confrontational, highlighting frustrations and misunderstandings, which further complicates the dialogue. Ultimately, the conversation reflects a mix of constructive criticism and rising irritation among participants.

271. [AMD][EMU1.4] Add TORCHINDUCTOR_DISABLE_EPILOGUE_FUSION env

     • Toxicity Score: 0.55 (Confusion over solutions, defensive responses, questioning effectiveness)
     • This GitHub conversation begins with a user outlining a bug related to the default configuration of epilogue_fusion, prompting a technical discussion among several contributors. As the conversation progresses, some users express confusion over the proposed solution, leading to a mix of supportive and critical responses. Tensions rise when a user questions the effectiveness of the suggested fix, resulting in defensive replies from others who feel their contributions are being undermined. The tone fluctuates between collaborative and frustrated, with some users attempting to clarify misunderstandings while others become increasingly terse. Ultimately, the conversation reflects a blend of constructive feedback and underlying frustration regarding the issue at hand.

272. [dtensor] placement import forward fix

     • Toxicity Score: 0.55 (Confusion over implementation, Frustration expressed, Defensive replies)
     • This GitHub conversation begins with a user presenting a fix for an import issue, tagging several collaborators for their input. As the discussion progresses, some users express confusion about the implementation details, leading to a mix of supportive and critical responses. Tension arises when a few participants voice frustration over the lack of clarity in the proposed solution, prompting defensive replies from others. The tone fluctuates between collaborative and confrontational, with some users attempting to mediate while others escalate their concerns. Ultimately, the conversation reflects a blend of constructive feedback and underlying frustration, indicating a need for clearer communication among the contributors.

273. Pianpwk/test test auto dyn

     • Toxicity Score: 0.65 (Defensive responses, questioning effectiveness, feelings of dismissal)
     • This GitHub conversation begins with a user expressing a desire to address a specific issue, tagging multiple collaborators for their input. As the discussion progresses, some users show enthusiasm for the proposed changes, while others raise concerns about potential implications. Tensions rise when a user questions the effectiveness of a previous solution, leading to defensive responses from others. The tone shifts as frustration becomes evident, with some participants feeling dismissed or misunderstood. Ultimately, the conversation reflects a mix of collaboration and conflict, highlighting the challenges of group dynamics in technical discussions.

274. Fix SDPA flop counter for meta tensors

     • Toxicity Score: 0.65 (Misinterpretations, defensive replies, escalating tensions)
     • This GitHub conversation begins with a user expressing a technical issue regarding the flop counter for SDPA when using meta tensors, prompting a response from another user who acknowledges the problem but suggests a different approach. As the discussion progresses, tensions rise when a third user questions the effectiveness of the proposed solution, leading to defensive replies from the original poster. The tone shifts to frustration as users begin to misinterpret each other's intentions, resulting in a back-and-forth that escalates the emotional stakes. Ultimately, the conversation reflects a mix of collaboration and conflict, with users struggling to maintain a constructive dialogue amidst growing misunderstandings.

275. test NVPL docker image

     • Toxicity Score: 0.67 (Critical tone, unresolved tension, dismissive language)
     • This GitHub conversation begins with username1 sharing a link to a test related to a Docker image, prompting username2 to express confusion about the relevance of the test. As the discussion progresses, username1 attempts to clarify their point, but username2's responses become increasingly critical, suggesting that the initial test was poorly executed. Tension escalates when username1 perceives username2's tone as dismissive, leading to a back-and-forth exchange where both users exhibit frustration. Ultimately, the conversation concludes with username1 feeling unheard and username2 remaining unconvinced, leaving an unresolved tension in their interaction.

276. [TESTING] triton 2.2.x

     • Toxicity Score: 0.65 (Frustration over responsiveness, mixed reactions, misunderstandings, impatience)
     • This GitHub conversation begins with a user initiating a discussion about a specific pull request, tagging multiple contributors for their input. As the conversation progresses, some users express confusion and seek clarification, while others provide feedback that is met with mixed reactions. Tensions rise when a user voices frustration over a lack of responsiveness, leading to defensive replies from others. The overall tone fluctuates between collaborative and contentious, with moments of agreement overshadowed by misunderstandings and impatience among participants.

277. Only include aotriton if it is needed

     • Toxicity Score: 0.55 (Defensive tone, challenges to suggestions, moments of frustration)
     • This GitHub conversation begins with aotriton expressing a concern about compatibility issues with Fedora, prompting aotriton to suggest checking before including it to avoid failures. As the discussion progresses, other users join in, with some expressing agreement while others raise questions about the implications of the proposed changes. Tensions arise when a user challenges the initial suggestion, leading to a defensive tone from aotriton, who reiterates the importance of the issue. The conversation fluctuates between collaborative problem-solving and moments of frustration, ultimately reflecting a mix of constructive feedback and underlying tension among participants.

278. [Don't merge] export strict optimize for Windows.

     • Toxicity Score: 0.65 (Frustration, lack of resolution, unhelpful suggestions, growing impatience)
     • This GitHub conversation begins with user @Xuhan reporting a failure in a test case on Windows, expressing confusion and seeking assistance from @jerryzh168 and @leslie-fang-intel. The tone is initially neutral, but as @Xuhan details the errors encountered, a sense of frustration emerges due to the lack of clarity in the error messages. @jerryzh168 responds with a suggestion that appears to be unhelpful, which triggers further tension as @Xuhan reiterates the issue without resolution. The conversation escalates as @leslie-fang-intel joins, attempting to clarify the situation, but the back-and-forth reveals a growing impatience from @Xuhan, who feels their concerns are not being adequately addressed. The overall sentiment shifts from collaborative to increasingly strained as the users struggle to find a solution.

279. [dynamo] remove contextlib from skipfiles

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, frustration)
     • This GitHub conversation begins with a user proposing a change, which is met with initial support from several contributors. As the discussion progresses, some users express confusion regarding the implementation details, leading to a slight increase in tension. A few contributors voice their concerns more assertively, prompting defensive responses from others. The tone shifts as misunderstandings arise, resulting in frustration from some participants. Ultimately, the conversation reflects a mix of collaborative spirit and underlying tension, with contributors navigating differing perspectives and expectations.

280. Add DispatchKey of MTIA in TensorImpl.h

     • Toxicity Score: 0.67 (Defensive responses, critical tone shifts, polarized opinions)
     • This GitHub conversation begins with username1 proposing a change to the codebase, expressing optimism about its potential benefits. Username2 responds with a mix of curiosity and skepticism, prompting a discussion that reveals differing perspectives on the implementation. As the conversation progresses, tensions rise when username1 feels misunderstood and defensive about their approach, while username2's tone shifts to a more critical stance. Other participants join in, some supporting username1 and others siding with username2, leading to a polarized atmosphere. The exchange culminates in a heated debate, with both sides becoming increasingly frustrated, indicating a breakdown in constructive dialogue.

281. [inductor] Re-add reduction loop reordering

     • Toxicity Score: 0.65 (Frustration over overlooked concerns, defensive responses, confusion among contributors)
     • This GitHub conversation begins with a user initiating a discussion about re-adding a feature, prompting responses from multiple contributors. As the conversation progresses, some users express confusion or disagreement with the proposed changes, leading to a noticeable shift in tone. Tensions rise when certain participants feel their concerns are being overlooked, resulting in frustration and defensive responses. The dialogue fluctuates between collaborative suggestions and moments of irritation, indicating a struggle to reach consensus among the contributors. Ultimately, the conversation reflects a mix of constructive feedback and underlying tension, suggesting that the contributors are navigating both technical issues and interpersonal dynamics.

282. [inductor] Alternate reduction loop orders

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, rising frustration)
     • This GitHub conversation begins with multiple users acknowledging the previous contributions and referencing a series of related pull requests. As the discussion progresses, some users express confusion regarding the implementation details, leading to a slight increase in tension. Usernames indicate a mix of collaborative and frustrated sentiments, with some participants seeking clarification while others feel their concerns are being overlooked. The tone shifts as misunderstandings arise, prompting a few users to respond defensively, which escalates the conversation further. Overall, the interaction reflects a blend of cooperation and rising frustration, culminating in a need for clearer communication among the contributors.

283. Add ScalarList overload to _foreach_lerp

     • Toxicity Score: 0.58 (Frustration over delays, defensive responses, confusion among contributors)
     • This GitHub conversation begins with a user proposing a new feature, which is met with initial support from several contributors. However, as the discussion progresses, some users express confusion and seek clarification, leading to a slight increase in tension. A few contributors display frustration over perceived delays in responses, while others attempt to mediate and refocus the conversation on constructive feedback. The tone shifts as disagreements arise regarding implementation details, with some users becoming defensive about their suggestions. Ultimately, the conversation reflects a mix of collaboration and underlying tension, indicating a need for clearer communication among participants.

284. implement torch._foreach_rsqrt

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, frustration, concerns about feasibility)
     • This GitHub conversation begins with a user proposing the implementation of a new feature, which is met with initial enthusiasm from several contributors. As the discussion progresses, some users express confusion regarding the proposed changes, leading to a slight increase in tension. A few contributors voice their concerns about the feasibility of the implementation, prompting defensive responses from the original poster. The tone shifts as misunderstandings arise, resulting in frustration from some participants who feel their input is being overlooked. Ultimately, the conversation concludes with a mix of agreement and lingering uncertainty about the next steps, reflecting a complex emotional landscape among the contributors.

285. Enable XPUEvent elapsed_time function

     • Toxicity Score: 0.65 (Defensive responses, questioning feasibility, fluctuating tone)
     • This GitHub conversation begins with the author expressing a clear motivation for their pull request, seeking input from specific collaborators. As the discussion progresses, various users contribute their thoughts, with some showing enthusiasm while others express concerns about dependencies and compatibility issues. Tension arises when a user questions the feasibility of the proposed changes, leading to defensive responses from the original poster. The tone fluctuates between collaborative and confrontational, with moments of frustration evident as users grapple with technical challenges and differing opinions. Ultimately, the conversation reflects a mix of constructive feedback and underlying stress over the project's direction.

286. Support dlpack for privateuse1

     • Toxicity Score: 0.65 (Frustration, critical remarks, defensive responses, unresolved issues)
     • This GitHub conversation begins with username1 proposing a solution to support a feature, which is met with initial enthusiasm from username2. However, as the discussion progresses, username1 expresses frustration over the lack of clarity in username2's feedback, leading to a noticeable shift in tone. Tension escalates when username3 interjects with a critical remark, prompting username1 to defend their position more assertively. The conversation concludes with username2 attempting to mediate, but the underlying tension remains palpable, indicating unresolved issues among the participants.

287. Tighten type hints for tensor arithmetic

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, frustration, concerns about impact)
     • This GitHub conversation begins with a user proposing a change to tighten type hints for tensor arithmetic, which is met with initial support from several contributors. As the discussion progresses, some users express confusion regarding the implementation details, leading to a slight increase in tension. A few contributors voice their concerns about potential impacts on existing code, which prompts defensive responses from others who advocate for the change. The tone shifts as misunderstandings arise, resulting in frustration from some users who feel their points are being overlooked. Ultimately, the conversation reflects a mix of collaboration and contention, with underlying tensions surfacing as contributors navigate differing perspectives.

288. Implement unfold_backward on MPS

     • Toxicity Score: 0.55 (Defensive responses, critiques of implementation, fluctuating support and frustration)
     • This GitHub conversation begins with the author presenting a pull request that introduces a new implementation of a feature, highlighting its technical details and the demand for it in the community. As the discussion progresses, several users express their support and appreciation for the contribution, while others raise questions and concerns about the implementation's efficiency and compatibility. Tension arises when a user critiques the approach taken, leading to defensive responses from the author and supporters, who emphasize the thoroughness of the work. The conversation fluctuates between constructive feedback and moments of frustration, ultimately reflecting a mix of collaborative spirit and underlying stress over differing opinions.

289. [AOTI] Refactor CppWrapperCpu.codegen_reinterpret_view

     • Toxicity Score: 0.65 (Frustration over misunderstandings, perceived lack of clarity, dismissive responses)
     • This GitHub conversation begins with a technical proposal from a user, prompting responses from multiple contributors who express varying degrees of support and concern. As the discussion progresses, some users highlight potential issues with the implementation, leading to a noticeable increase in tension. A few participants exhibit frustration over misunderstandings and perceived lack of clarity in the proposed changes, while others attempt to mediate and clarify points of confusion. The tone shifts between collaborative and confrontational, with some users feeling dismissed or misunderstood, which further escalates the emotional stakes of the conversation. Ultimately, the dialogue reflects a mix of constructive feedback and underlying frustration, indicating a complex dynamic among the contributors.

290. [XPU] Add driver and icpx version information for XPU

     • Toxicity Score: 0.65 (Escalating frustration, defensive responses, unclear documentation)
     • This GitHub conversation begins with a user presenting a new feature addition, which is met with initial enthusiasm from several participants. However, as the discussion progresses, some users express confusion regarding implementation details, leading to a slight shift in tone towards frustration. A few contributors raise concerns about the clarity of the documentation, which triggers a defensive response from the original poster. The conversation sees a mix of constructive feedback and escalating tension, with some users feeling dismissed, while others attempt to mediate the situation. Ultimately, the dialogue reflects a blend of collaboration and conflict, highlighting the challenges of technical communication in a community setting.

291. [CI]Move CPU inductor test runners and cases to save cost

     • Toxicity Score: 0.65 (Frustration expressed, defensive responses, critical interjections, unresolved issues)
     • This GitHub conversation begins with username1 proposing a cost-saving measure related to CPU inductor test runners, which is met with initial support from username2. However, as the discussion progresses, username1 expresses frustration over a lack of clarity in username2's response, leading to a more defensive tone from username2. Tension escalates when username3 interjects with a critical remark about the feasibility of the proposal, prompting username1 to counter with a more assertive stance. The conversation concludes with username2 attempting to clarify their position, but the overall sentiment remains strained, indicating unresolved issues and potential misunderstandings among the participants.

292. [AOTI][refactor] Clean up call chain in wrapper codegen

     • Toxicity Score: 0.65 (Defensive responses, questioning feasibility, rising frustration)
     • This GitHub conversation begins with a user expressing a desire for clarity regarding the proposed changes, prompting a series of responses that range from supportive to critical. As the discussion progresses, tensions rise when a user questions the feasibility of the suggested refactor, leading to defensive replies from others who feel their contributions are being undermined. The tone shifts as some participants become increasingly frustrated, while others attempt to mediate the situation by reiterating the importance of collaboration. Ultimately, the conversation reflects a mix of constructive feedback and underlying frustration, highlighting the challenges of maintaining a positive dialogue in technical discussions.

293. [cpu] Modify inductor opt flag --- ftree-loop-vectorize

     • Toxicity Score: 0.65 (Escalating disagreements, defensive responses, frustration expressed)
     • This GitHub conversation begins with a user proposing modifications to optimization flags, which triggers a series of responses from various contributors. Initially, the tone is collaborative, with users expressing interest in the proposed changes. However, as the discussion progresses, some users begin to voice concerns about the implications of the modifications, leading to a noticeable shift in sentiment. Tensions rise when disagreements arise over the effectiveness of the proposed solutions, with some users expressing frustration and others defending their positions. The conversation culminates in a mix of constructive feedback and defensive retorts, indicating a potential for further conflict.

294. Use device-agnostic runtime API in distributed DDP/FSDP instead of cuda device specific.

     • Toxicity Score: 0.55 (Defensive responses, critical remarks, concerns about feasibility)
     • This GitHub conversation begins with @jgong5 introducing a proposal for a device-agnostic runtime API, which is met with initial support from @gujinghui and @EikanWang, who express enthusiasm for the potential improvements. However, as the discussion progresses, @fengyuan14 raises concerns about implementation challenges, leading to a shift in tone as @guangyey responds defensively, feeling that the concerns are unwarranted. Tension escalates when @EikanWang interjects with a critical remark about the feasibility of the proposal, prompting @jgong5 to clarify their position. The conversation concludes with a mix of cautious optimism and lingering skepticism, as participants weigh the benefits against the potential complications, indicating a complex emotional landscape throughout the exchange.

295. [Intel GPU] Avoid atomic add for XPU device in satter_add by deterministic mode

     • Toxicity Score: 0.67 (Escalation of frustration, defensive responses, misunderstandings)
     • This GitHub conversation begins with a user expressing a technical concern regarding the implementation of a specific operation in the XPU device, highlighting the absence of a deterministic mode. Another user responds with a suggestion, but their solution is met with skepticism, leading to a back-and-forth exchange where frustrations surface. As the discussion progresses, tensions rise due to perceived misunderstandings and differing expectations about the implementation timeline. The tone shifts from collaborative to defensive, with users increasingly emphasizing their viewpoints, which further escalates the emotional stakes of the conversation. Ultimately, the dialogue reflects a mix of urgency and irritation, indicating a challenging collaborative environment.

296. CD Enable Python 3.13 on windows

     • Toxicity Score: 0.65 (Defensive responses, questioning clarity, underlying frustrations)
     • This GitHub conversation begins with a user expressing enthusiasm about the integration of Python 3.13 on Windows, highlighting the recent progress made in the project. As the discussion unfolds, another user raises concerns about potential issues that could arise from the changes, leading to a defensive response from the initial user. Tension escalates when a third participant questions the clarity of the implementation details, prompting a back-and-forth exchange that reveals underlying frustrations. The tone shifts from collaborative to somewhat confrontational, as users begin to express dissatisfaction with each other's contributions and the perceived lack of communication. Ultimately, the conversation reflects a mix of optimism and growing unease among the participants.

297. fix test_float_to_int_conversion_nonfinite for NumPy 2

     • Toxicity Score: 0.65 (Defensive responses, questioning validity, fluctuating tones)
     • This GitHub conversation begins with a user outlining a problem related to a test failure after an upgrade, expressing concern over undefined behaviors in NumPy. As the discussion progresses, other users join in, some offering potential solutions while others express skepticism about the proposed fixes. Tension arises when a user questions the validity of the initial approach, leading to a defensive response from the original poster. The tone fluctuates between collaborative and confrontational, with moments of frustration evident as users grapple with the complexities of the issue. Ultimately, the conversation reflects a mix of constructive feedback and underlying tension as participants navigate differing opinions and technical challenges.

298. fix trace nn.parameters()

     • Toxicity Score: 0.65 (Disagreements, overlooked feedback, rising frustrations)
     • This GitHub conversation begins with a user proposing a fix and tagging multiple contributors for their input. As the discussion unfolds, some users express appreciation for the proposed changes, while others raise concerns about potential issues. Tensions arise when a few participants disagree on the implementation details, leading to a mix of supportive and critical responses. The tone shifts as frustrations surface, with some users feeling that their feedback is being overlooked. Ultimately, the conversation reflects a blend of collaboration and conflict, highlighting the challenges of reaching consensus in a technical discussion.

299. Add architecture to XPU device property

     • Toxicity Score: 0.55 (Defensive responses, questioning feasibility, mixed sentiments)
     • This GitHub conversation begins with a user expressing enthusiasm about a proposed feature enhancement, prompting a series of supportive comments from other contributors. However, as the discussion progresses, a few users raise concerns about potential implementation challenges, leading to a noticeable shift in tone. Tension escalates when one user questions the feasibility of the proposed changes, resulting in defensive responses from others who feel their contributions are being undermined. The conversation ultimately concludes with a mix of cautious optimism and lingering doubts, as participants attempt to reconcile differing viewpoints while maintaining a collaborative spirit.

300. [Dynamo] Allow filter() to handle infinite iterator

     • Toxicity Score: 0.55 (Frustration expressed, defensive responses, underlying disagreements)
     • This GitHub conversation begins with a user presenting a proposed solution to a technical issue, which is met with initial curiosity from other participants. As the discussion progresses, some users express confusion and seek clarification, while others provide feedback that is both constructive and critical. Tensions rise when a user voices frustration over the lack of clarity in the proposed solution, leading to a defensive response from the original poster. The tone shifts as some participants attempt to mediate the situation, but underlying disagreements persist, resulting in a mix of supportive and critical comments. Overall, the conversation reflects a blend of collaboration and contention, with moments of heightened emotion.

301. [logging] Use aot_graph_name for the metrics key for backward compilation.

     • Toxicity Score: 0.65 (Defensive responses, critical interjections, escalating frustration)
     • This GitHub conversation begins with username1 proposing a change to improve logging metrics during backward compilation, expressing optimism about the potential benefits. As the discussion progresses, username2 raises concerns about the implementation details, leading to a defensive response from username1, who feels misunderstood. Tension escalates when username3 interjects with a critical comment, prompting username1 to react with frustration. The conversation continues with mixed sentiments, as some users support the change while others remain skeptical, resulting in a back-and-forth exchange that highlights differing perspectives and a growing sense of urgency to resolve the issues raised.

302. NJT OpInfo tests v2

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, concerns about efficiency)
     • This GitHub conversation begins with a user presenting a pull request that outlines updates to OpInfo-based tests for non-contiguous NJTs, which is met with initial interest and acknowledgment from other participants. As the discussion progresses, some users express confusion regarding specific implementation details, leading to a slight increase in tension. A few participants voice their concerns about the efficiency of the proposed changes, which triggers defensive responses from the original poster. The tone shifts as misunderstandings arise, resulting in a mix of frustration and clarification attempts. Ultimately, the conversation concludes with a tentative agreement on the next steps, though some underlying tensions remain unresolved.

303. ILP for auto FSDP wrapping

     • Toxicity Score: 0.55 (Confusion over implementation, Frustration with documentation, Defensive replies)
     • This GitHub conversation begins with a user presenting a new pull request and outlining its purpose, which is met with initial interest from other participants. As the discussion progresses, some users express confusion regarding specific implementation details, leading to a mix of supportive and critical responses. Tension arises when a user voices frustration over a lack of clarity in the documentation, prompting defensive replies from others who feel the initial explanations were sufficient. The tone fluctuates between collaborative and confrontational, with some users attempting to mediate while others become increasingly agitated. Ultimately, the conversation reflects a blend of constructive feedback and rising discontent among participants.

304. Use Manylinux2_28 for wheel builds

     • Toxicity Score: 0.67 (Defensive responses, critical interjections, questioning expertise)
     • This GitHub conversation begins with a user expressing enthusiasm about the proposed changes, highlighting the potential benefits for future builds. Another user joins in, showing support but also raising a concern about compatibility issues that might arise. As the discussion progresses, a third user interjects with a critical perspective, suggesting that the proposed solution may not address all underlying problems, which triggers a defensive response from the original poster. The tone shifts as frustrations mount, leading to a more heated exchange where users begin to question each other's expertise. Ultimately, the conversation concludes with a mix of agreement on the need for further testing and lingering doubts about the implementation's effectiveness.

305. [ONNX] Use TracedONNXFunction op signature to promote inputs to tensors

     • Toxicity Score: 0.65 (Defensive responses, critical interjections, unresolved disagreements)
     • This GitHub conversation begins with username1 presenting a proposed change and expressing optimism about its potential benefits. As the discussion progresses, username2 raises concerns about the implementation details, leading to a defensive response from username1. Tension escalates when username3 interjects with a critical perspective, prompting username1 to react with frustration. The tone shifts as username2 attempts to mediate, but the conversation remains charged, with underlying disagreements surfacing. Ultimately, the dialogue reflects a mix of collaboration and conflict, highlighting the challenges of reaching consensus in technical discussions.

306. [dynamo] add SymNode bitwise and/or

     • Toxicity Score: 0.55 (Lack of clarity, defensive responses, frustration expressed)
     • This GitHub conversation begins with a user presenting a pull request and tagging multiple collaborators for input. As the discussion progresses, some users express confusion regarding the implementation details, leading to a mix of supportive and critical feedback. Tensions rise when a few participants voice frustration over perceived lack of clarity and responsiveness from others, resulting in a more defensive tone from those being questioned. The conversation ultimately reflects a blend of collaborative effort and underlying stress, as contributors navigate differing expectations and communication styles.

307. [Intel GPU] Add device guard for XPU structured operator in torchgen

     • Toxicity Score: 0.55 (Frustration, critical feedback, defensive responses, questioning effectiveness)
     • This GitHub conversation begins with the author presenting a pull request (PR) that builds upon a previous submission, aiming to address a specific issue with structured operators. Several users, including @voznesenskym and @penguinwu, engage with the PR, expressing varying degrees of support and skepticism regarding the proposed changes. As the discussion progresses, tensions arise when some contributors question the effectiveness of the solution, leading to moments of frustration and defensiveness from the original author. The tone shifts as users provide feedback, with some comments becoming more critical, while others attempt to mediate the discussion. Ultimately, the conversation reflects a mix of collaboration and contention, highlighting the challenges of technical consensus in open-source development.

308. dynamo: guard on FSDP module parameters

     • Toxicity Score: 0.55 (Disagreements on safety, expressions of frustration, defensive responses)
     • This GitHub conversation begins with a user outlining a technical issue related to the FSDP module parameters, prompting a series of responses from various contributors. As the discussion progresses, some users express concern over the implications of the proposed changes, while others provide clarifications and suggestions. Tensions arise when disagreements about the safety and necessity of certain guards are voiced, leading to a mix of frustration and defensiveness among participants. Overall, the tone fluctuates between collaborative and contentious, reflecting the complexity of the technical challenges being addressed.

309. (wip) [CUTLASS] pull out static shapes as kernel args

     • Toxicity Score: 0.65 (Frustration over unclear explanations, defensive responses, disagreements about proposed changes)
     • This GitHub conversation begins with a collaborative tone as multiple users are tagged to discuss a pull request. As the dialogue progresses, some participants express confusion regarding the implementation details, leading to a slight increase in tension. Usernames indicate a mix of supportive and critical feedback, with a few contributors showing frustration over unclear explanations. The conversation takes a more serious turn when disagreements arise about the proposed changes, prompting defensive responses from some users. Overall, the atmosphere fluctuates between constructive criticism and rising irritation, suggesting a potential for further conflict.

310. Add some error messages for flexattention

     • Toxicity Score: 0.55 (Frustration over unclear communication, Overlooked suggestions, Rising tensions)
     • This GitHub conversation begins with a user seeking assistance regarding error messages related to flexattention, prompting responses from multiple contributors. As the discussion progresses, some users express confusion and frustration over the lack of clarity in the initial request, leading to a noticeable shift in tone. Tensions rise as certain contributors feel their suggestions are being overlooked, while others attempt to mediate the situation by reiterating the need for clearer communication. The conversation ultimately reflects a mix of collaborative efforts and underlying frustration, with several users feeling the pressure of unresolved issues.

311. Add CUDA 12.6 Linux Builds to Binaries Matrix

     • Toxicity Score: 0.55 (Frustration expressed, Defensive replies, Concerns about clarity)
     • This GitHub conversation begins with a user proposing the addition of CUDA 12.6 Linux builds to the binaries matrix, tagging several collaborators for their input. Initial responses are generally positive, with some users expressing enthusiasm for the update. However, as the discussion progresses, a few participants raise concerns about compatibility and implementation details, leading to a noticeable shift in tone. Tension escalates when one user expresses frustration over a lack of clarity in the proposed changes, prompting defensive replies from others. The conversation concludes with a mix of constructive feedback and lingering uncertainty, indicating that while some users are supportive, others remain skeptical about the feasibility of the changes.

312. Allow PyTorch to be built with USE_DISTRIBUTED=0

     • Toxicity Score: 0.65 (Misunderstandings, defensive replies, frustration, questioning feasibility)
     • This GitHub conversation begins with a user expressing a need for a specific feature related to building PyTorch, which prompts a response from another user who acknowledges the request but raises concerns about potential implications. As the discussion progresses, tensions arise when a third user questions the feasibility of the proposed changes, leading to defensive replies from the original poster. The tone shifts as misunderstandings occur, resulting in frustration from multiple participants. Ultimately, the conversation reflects a mix of collaboration and conflict, with users attempting to clarify their positions while navigating the growing tension.

313. [dynamo] "skip_guard_eval_unsafe" API for power users

     • Toxicity Score: 0.55 (Concerns about assumptions, frustration over complexity, defensive responses)
     • This GitHub conversation begins with the author presenting a new API proposal aimed at reducing guard overhead for power users, which is met with initial interest from several contributors. As the discussion progresses, some users express concerns about the assumptions underlying the proposal, leading to a slight increase in tension. A few contributors voice frustration over the complexity of the implementation and the potential for user error, which escalates the tone of the conversation. Despite the disagreements, others attempt to clarify points and provide constructive feedback, but the overall sentiment remains mixed, with some users feeling defensive about their contributions. The conversation concludes with a call for further testing and validation of the proposed changes, indicating a willingness to collaborate despite the earlier tensions.

314. enable concat linear with mkldnn linear by flag

     • Toxicity Score: 0.55 (Confusion, defensive responses, frustration, misunderstandings)
     • This GitHub conversation begins with a user outlining a technical change aimed at improving functionality, which is met with initial support from several contributors. As the discussion progresses, some users express confusion regarding the implementation details, leading to a slight increase in tension. A few contributors voice concerns about potential drawbacks, prompting defensive responses from others who advocate for the change. The tone shifts as misunderstandings arise, resulting in frustration from some users who feel their points are being overlooked. Ultimately, the conversation reflects a mix of collaborative spirit and underlying tension, with contributors navigating differing perspectives on the proposed changes.

315. [Intel GPU] Support RegisterSparseXPU.cpp codegen.

     • Toxicity Score: 0.65 (Defensive responses, questioning feasibility, perceived misunderstandings)
     • This GitHub conversation begins with username1 presenting a pull request aimed at enhancing code generation for sparse operations on Intel GPUs, which is met with initial enthusiasm from username2. However, as the discussion progresses, username3 raises concerns about the implementation details, leading to a defensive response from username1. Tension escalates when username4 questions the feasibility of the proposed changes, prompting username1 to express frustration over perceived misunderstandings. The tone shifts as username2 attempts to mediate, but the conversation remains charged, with underlying frustrations surfacing among the participants.

316. cpp_wrapper_cpu: Ensure reinterpret_view results in RAIIAtenTensorHandle

     • Toxicity Score: 0.65 (Frustration over unresolved issues, defensive responses, mixed reactions to proposed solutions)
     • This GitHub conversation begins with a user presenting a technical issue related to segfaults, prompting responses from multiple contributors. As the discussion progresses, some users express confusion and seek clarification, while others provide solutions that are met with mixed reactions. Tensions rise when a user voices frustration over a proposed fix that did not resolve the issue, leading to a back-and-forth exchange characterized by defensive tones. The conversation concludes with a few users attempting to mediate and refocus the discussion on constructive solutions, but the underlying tension remains palpable.

317. Enable all fixed cpp_wrapper tests

     • Toxicity Score: 0.55 (Misunderstandings, frustration over overlooked suggestions, defensive responses)
     • This GitHub conversation begins with a collaborative tone as multiple users are tagged for their input on enabling fixed cpp_wrapper tests. As the discussion progresses, some users express confusion and seek clarification, leading to a slight increase in tension. A few participants display frustration when their suggestions are overlooked or misunderstood, which escalates the sentiment to a more defensive tone. Ultimately, the conversation reflects a mix of constructive feedback and underlying irritation, indicating a potential for further conflict if misunderstandings persist.

318. Fix broken AOTInductor node and kernel counts

     • Toxicity Score: 0.55 (Frustration over overlooked suggestions, confusion leading to defensive responses, rising tension among contributors)
     • This GitHub conversation begins with a collaborative tone as multiple users are tagged to discuss a technical issue. As the dialogue progresses, some participants express confusion and seek clarification, leading to a slight increase in tension. A few users display frustration when their suggestions are overlooked or misunderstood, which escalates the sentiment to a more defensive stance. Ultimately, the conversation reflects a mix of constructive feedback and rising irritation, indicating a potential for conflict among contributors.

319. fix shared submodule module call signature

     • Toxicity Score: 0.65 (Escalation of tension, frustration expressed, skepticism towards solutions)
     • This GitHub conversation begins with a user expressing a technical issue related to a shared submodule, prompting a response from another user who offers a potential solution. As the discussion progresses, the tone shifts as some users express skepticism about the proposed fix, leading to a back-and-forth exchange that escalates in tension. A few participants display frustration over the lack of clarity in the explanations provided, while others attempt to mediate and clarify misunderstandings. Ultimately, the conversation reflects a mix of collaborative efforts and rising irritation, with some users feeling their concerns are not being adequately addressed.

320. dispatcher module for multiple graphs

     • Toxicity Score: 0.65 (Defensive responses, questioning practicality, emotional reactions)
     • This GitHub conversation begins with username1 presenting a new feature related to a dispatcher module, which is met with initial curiosity from username2. As the discussion progresses, username3 raises concerns about the implementation details, leading to a defensive response from username1. Tension escalates when username4 questions the practicality of the proposed changes, prompting username1 to express frustration over the lack of understanding. The tone shifts as username2 attempts to mediate, suggesting a collaborative approach, but the conversation remains charged with underlying disagreements. Ultimately, the dialogue reflects a mix of constructive feedback and emotional responses, indicating a complex dynamic among the participants.

321. [Intel GPU] fix xpu build libm issue.

     • Toxicity Score: 0.55 (Frustration over unresolved issues, Defensive replies, Questioning effectiveness of solutions)
     • This GitHub conversation begins with a user expressing a technical issue related to a build problem, prompting responses from multiple contributors. As the discussion progresses, some users show enthusiasm for potential solutions, while others convey frustration over unresolved issues. Tensions rise when a user questions the effectiveness of a proposed fix, leading to defensive replies from others. The tone fluctuates between collaborative and confrontational, with some participants feeling dismissed or misunderstood. Ultimately, the conversation reflects a mix of constructive feedback and underlying frustration, indicating a complex dynamic among the contributors.

322. [Intel GPU] Extract common utils for conv&qconv

     • Toxicity Score: 0.65 (Defensive responses, critical remarks, unresolved issues, escalating tension)
     • This GitHub conversation begins with a user expressing enthusiasm for the proposed changes, highlighting their potential benefits. As the discussion progresses, another user raises concerns about the implementation details, leading to a defensive response from the original poster. Tension escalates when a third user interjects with a critical remark, prompting a back-and-forth exchange that includes some frustration and misunderstandings. Ultimately, the conversation concludes with a tentative agreement to revisit the concerns raised, but the tone remains somewhat strained, indicating unresolved issues among the participants.

323. [aotinductor] fix a few issues in bandwidth profiler

     • Toxicity Score: 0.55 (Frustration over benchmarks, questioning of validity, defensive responses)
     • This GitHub conversation begins with a user outlining several issues encountered with the bandwidth profiler and presenting a proposed solution. As the discussion progresses, various participants express their thoughts, with some showing optimism about the fixes while others raise concerns about the effectiveness of the changes. Tension arises when a user questions the validity of the benchmarks, leading to a defensive response from the original poster. The tone fluctuates between collaborative and critical, with some users feeling frustrated by the lack of clarity in the results. Ultimately, the conversation reflects a mix of constructive feedback and underlying tension regarding the proposed changes and their implications.

324. Split int4wo weight packing

     • Toxicity Score: 0.55 (Misunderstandings, frustration over responsiveness, varying perspectives)
     • This GitHub conversation begins with a user presenting a pull request aimed at addressing a specific issue, which is met with initial support from several contributors. As the discussion progresses, some users express confusion regarding the implementation details, leading to a slight increase in tension. A few contributors voice their concerns about the clarity of the proposed changes, while others attempt to clarify the intentions behind the code. The tone shifts as misunderstandings arise, prompting a couple of users to exhibit frustration over perceived lack of responsiveness. Ultimately, the conversation reflects a mix of collaborative spirit and underlying tension, with contributors striving to reach a consensus while navigating differing perspectives.

325. [DTensorTestbase] Fix @with_comms inactive problem

     • Toxicity Score: 0.55 (Defensive replies, questioning effectiveness, fluctuating tones)
     • This GitHub conversation begins with a user expressing a technical issue related to the functionality of a decorator, prompting a series of responses from various contributors. As the discussion progresses, some users show enthusiasm for the proposed solution, while others raise concerns about its implementation and potential implications. Tension arises when a user questions the effectiveness of the solution, leading to defensive replies from others who feel their contributions are being undermined. The tone fluctuates between collaborative and confrontational, with some users attempting to mediate the discussion while others become increasingly frustrated. Ultimately, the conversation reflects a mix of constructive feedback and underlying tension among participants.

326. [Inductor] make decompose_mm_pass support cpu case

     • Toxicity Score: 0.55 (Concerns raised, defensive responses, questioning implementation)
     • This GitHub conversation begins with a user expressing enthusiasm about a new feature that enhances functionality, prompting a series of supportive comments from other users. However, as the discussion progresses, a few participants raise concerns about potential issues, leading to a noticeable shift in tone. Tension escalates when one user questions the implementation details, which triggers defensive responses from others. The conversation ultimately concludes with a mix of appreciation for the contributions and lingering doubts about the changes, reflecting a complex emotional landscape among the participants.

327. [Dynamo] Replace torch._dynamo.optimize() with torch.compile() [1/N]

     • Toxicity Score: 0.55 (Frustration over communication, Defensive responses, Confusion among participants)
     • This GitHub conversation begins with a proposal from a user regarding a code change, which is met with initial support from several contributors. However, as the discussion progresses, some users express confusion and seek clarification, leading to a slight increase in tension. A few participants display frustration over the lack of clear communication, while others attempt to mediate and refocus the conversation on the technical aspects. The tone fluctuates between collaborative and defensive, with some users feeling dismissed, which further escalates the emotional stakes. Ultimately, the conversation reflects a mix of constructive feedback and underlying tension, suggesting a need for improved communication among the contributors.

328. [Intel GPU] format XPU oneDNN integration codes

     • Toxicity Score: 0.65 (Frustration over feedback, Defensive responses, Questioning feasibility)
     • This GitHub conversation begins with a user expressing enthusiasm about the proposed integration of XPU oneDNN codes, prompting a series of supportive comments from other users who appreciate the initiative. However, as the discussion progresses, some users raise concerns about the implementation details, leading to a noticeable shift in tone as frustration emerges from those who feel their feedback is being overlooked. Tension escalates when a user directly questions the feasibility of the changes, resulting in defensive responses from others who advocate for the proposal. The conversation concludes with a mix of cautious optimism and lingering doubts, reflecting a complex emotional landscape among the participants.

329. [inductor] Log how compile_threads is set

     • Toxicity Score: 0.55 (Frustration expressed, defensive reactions, confusion over clarity)
     • This GitHub conversation begins with a user tagging multiple contributors to draw attention to a specific issue, indicating a collaborative tone. As the discussion progresses, some users express confusion and seek clarification, while others provide insights or suggestions, leading to a mix of supportive and critical responses. Tension arises when a user expresses frustration over a lack of clarity in previous comments, prompting defensive reactions from others. The conversation fluctuates between constructive feedback and moments of irritation, reflecting varying sentiments among the participants.

330. [3/x][fx minimizer] Support all_outputs in minimizer

     • Toxicity Score: 0.55 (Frustration over overlooked suggestions, Defensive responses, Assertive advocacy for viewpoints)
     • This GitHub conversation begins with a user expressing a technical suggestion regarding output nodes in a minimizer, which is met with initial interest from other participants. As the discussion progresses, some users share their insights and experiences, while others raise questions that lead to a slight increase in tension. A few participants exhibit frustration when their suggestions are overlooked or misunderstood, prompting defensive responses. The tone shifts as some users become more assertive in advocating for their viewpoints, leading to a mix of constructive feedback and underlying irritation. Overall, the conversation reflects a blend of collaboration and rising tension as differing opinions emerge.

331. Logging Refactor - Remove Print Statements

     • Toxicity Score: 0.65 (Defensive responses, questioning of necessity, unresolved issues)
     • This GitHub conversation begins with a user expressing optimism about the proposed logging refactor, highlighting its potential benefits for model control. As the discussion progresses, another user raises concerns about the implementation details, leading to a defensive response from the original poster. Tension escalates when a third user questions the necessity of the changes, prompting a more frustrated tone from the first user, who feels their efforts are being undermined. The conversation concludes with a mix of acknowledgment and lingering skepticism, indicating unresolved issues and a potential for further conflict.

332. [pytorch/profiler] Profiler NCCL metadata can now contain collective Input and Ouput Tensor addrs

     • Toxicity Score: 0.55 (Defensive responses, concerns raised, slight escalation in tone)
     • This GitHub conversation begins with a technical update from the original poster, who shares new features related to memory access patterns. Several users, including @robieta and @chaekit, express interest and appreciation for the update, contributing positively to the discussion. However, as the conversation progresses, a few users raise concerns about the implications of the changes, leading to a slight shift in tone. Tension escalates when @guotuofeng questions the clarity of the implementation, prompting defensive responses from others. The conversation ultimately stabilizes, but the earlier disagreements leave a lingering sense of unease among participants.

333. [WIP] Update torch-xpu-ops pin commit

     • Toxicity Score: 0.65 (Defensive responses, critical interjections, escalating tension)
     • This GitHub conversation begins with a user expressing enthusiasm about the proposed updates, highlighting the potential benefits for the community. As the discussion progresses, another user raises concerns about the implications of the changes, leading to a defensive response from the first user. Tension escalates when a third participant interjects with a critical perspective, prompting a back-and-forth exchange that reveals underlying frustrations. The tone shifts from collaborative to confrontational, with users using more assertive language as they defend their positions. Ultimately, the conversation concludes with a tentative agreement to revisit the issues raised, but not without lingering discomfort among the participants.

334. [FR] Enable best effort parital analysis and verbose mode for trace printing

     • Toxicity Score: 0.55 (Confusion, defensive responses, misunderstandings, frustration)
     • This GitHub conversation begins with a user outlining a proposal for enhancements to a script, which is met with initial support from several contributors. As the discussion progresses, some users express confusion regarding the implementation details, leading to a slight increase in tension. A few contributors voice their concerns about the feasibility of the proposed changes, which prompts defensive responses from others who advocate for the suggestions. The tone shifts as misunderstandings arise, resulting in frustration from some participants. Ultimately, the conversation reflects a mix of collaborative spirit and underlying tension, with contributors navigating differing perspectives on the proposed enhancements.

335. Optimize incremental summation of unique symbols using CustomAdd sympy expression.

     • Toxicity Score: 0.55 (Frustration over clarity, Defensive responses, Questioning effectiveness)
     • This GitHub conversation begins with a user expressing enthusiasm about the performance improvements achieved through a recent optimization. As the discussion progresses, another user raises a concern regarding the implementation details, which leads to a slight shift in tone as some participants begin to question the effectiveness of the proposed changes. Tension escalates when a user expresses frustration over a lack of clarity in the explanations provided, prompting defensive responses from others who feel their contributions are being undermined. The conversation ultimately settles into a more collaborative tone as users work towards clarifying misunderstandings and refining the proposed solution, though some underlying tension remains evident.

336. Recover non-standard bool test for msort

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, questioning of validity)
     • This GitHub conversation begins with a user expressing curiosity about the behavior of a non-standard boolean value in the context of a specific function. As the discussion progresses, another user provides insights that lead to a deeper exploration of the issue, but this also triggers some confusion and frustration among participants regarding the technical details. Tensions rise when a user questions the validity of the proposed solution, prompting defensive responses from others. The tone fluctuates between collaborative and confrontational, with some users feeling misunderstood or dismissed. Ultimately, the conversation reflects a mix of constructive feedback and underlying frustration as participants navigate the complexities of the topic.

337. Add base class for single-subgraph inductor HOPs

     • Toxicity Score: 0.55 (Defensive tone, critical feedback, questioning necessity)
     • This GitHub conversation begins with the author presenting a new pull request aimed at adding a base class for single-subgraph inductor HOPs, which is met with initial interest from several contributors. As the discussion progresses, some users express enthusiasm about the potential benefits of the proposed changes, while others raise concerns regarding implementation details and future migration plans. Tension arises when a few contributors question the necessity of the changes, leading to a defensive tone from the author. The conversation fluctuates between collaborative suggestions and critical feedback, with some users feeling frustrated by perceived lack of clarity in the proposal. Ultimately, the dialogue reflects a mix of constructive criticism and support, with underlying tensions surfacing as contributors navigate differing perspectives on the implementation.

338. Remove most rockset references

     • Toxicity Score: 0.55 (Defensive responses, questioning expertise, mixed sentiments)
     • This GitHub conversation begins with username1 proposing the removal of references to a specific database technology, expressing a sense of urgency and clarity in their request. Username2 responds with a supportive tone, agreeing with the need for changes but seeking clarification on certain aspects of the proposal. Tension arises when username3 questions the necessity of the changes, leading to a defensive response from username1, who feels their expertise is being undermined. The conversation continues with mixed sentiments, as some users express frustration while others attempt to mediate, ultimately resulting in a more collaborative atmosphere as they work towards a consensus on the proposed modifications.

339. Fix compile mode limitation in associative_scan tests

     • Toxicity Score: 0.65 (Misunderstandings, escalating frustration, defensive responses)
     • This GitHub conversation begins with a user addressing a limitation in the associative_scan tests, indicating a mistake in the compile mode. Another user, @ydwu4, is tagged for their input, suggesting a collaborative tone. However, as the discussion progresses, some users express confusion and frustration over the initial implementation, leading to a noticeable shift in sentiment. Tensions rise as misunderstandings about the proposed changes emerge, prompting defensive responses from some participants. The conversation ultimately reflects a mix of constructive feedback and escalating frustration, highlighting the challenges of collaboration in technical discussions.

340. [wip] debug cpu/gpu backprop mismatch

     • Toxicity Score: 0.65 (Frustration, miscommunication, escalating tension)
     • This GitHub conversation begins with username1 seeking clarification on a technical issue related to backpropagation discrepancies between CPU and GPU. Username2 responds with a potential solution, but username1 expresses frustration that the proposed fix did not resolve the issue. Tension escalates as username3 joins the discussion, offering a different perspective that further complicates the conversation. Username1's tone shifts to exasperation, while username2 attempts to maintain a constructive approach, leading to a back-and-forth that highlights misunderstandings and miscommunications. The overall sentiment fluctuates between collaborative and contentious, indicating a growing sense of urgency to resolve the problem.

341. [SymmetricMemory] improve the API for stream_write_value32

     • Toxicity Score: 0.55 (Confusion, misunderstandings, frustration, heated exchanges)
     • This GitHub conversation begins with a user presenting a pull request aimed at improving an API, which is met with initial support from several contributors. As the discussion progresses, some users express confusion regarding the changes, leading to a slight increase in tension. A few contributors voice their concerns about the implications of the modifications, while others defend the proposed updates, resulting in a mix of supportive and critical sentiments. The tone shifts as misunderstandings arise, prompting a more heated exchange between users, with some feeling frustrated by the lack of clarity. Ultimately, the conversation reflects a blend of collaboration and contention, highlighting the challenges of technical discussions in a community setting.

342. [pt2] Add meta for _add_relu

     • Toxicity Score: 0.67 (Defensive responses, escalating frustration, lack of understanding)
     • This GitHub conversation begins with username1 raising a concern about an error encountered in the logs related to a missing meta function. Username2 responds with a suggestion that seems to alleviate some of the initial tension, but username1 expresses skepticism about the effectiveness of the proposed solution. As the discussion progresses, username3 joins in, offering additional insights that lead to a slight increase in frustration from username1, who feels their issue is not being fully understood. Username2 attempts to clarify their position, but the tone becomes more defensive, indicating a growing sense of irritation. The conversation concludes with username1 reiterating their dissatisfaction, leaving the atmosphere somewhat charged and unresolved.

343. [pipelining] add type checking to _backward functions

     • Toxicity Score: 0.65 (Defensive reactions, raised concerns, feelings of dismissal, heated exchanges)
     • This GitHub conversation begins with a user expressing a desire to enhance the functionality of a specific feature, prompting a series of responses from various contributors. As the discussion progresses, some users show enthusiasm for the proposed changes, while others raise concerns about potential issues. Tensions arise when a user points out flaws in the implementation, leading to defensive reactions from others. The tone shifts as frustrations mount, with some contributors feeling dismissed or misunderstood, ultimately resulting in a more heated exchange. The conversation concludes with a mix of agreement and lingering dissatisfaction among participants.

344. [dynamo] Remove dead code path for capturing __class__ in UserFunctionVariable

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, frustration, concerns about bugs)
     • This GitHub conversation begins with a user presenting a patch aimed at removing unnecessary code, which is met with initial support from several contributors. As the discussion progresses, some users express confusion regarding the implications of the changes, leading to a slight increase in tension. A few contributors voice their concerns about potential bugs introduced by the patch, prompting defensive responses from the original poster. The tone shifts as misunderstandings arise, resulting in frustration from some users who feel their points are being overlooked. Ultimately, the conversation concludes with a mix of agreement and lingering doubts about the implementation, reflecting a complex emotional landscape among the participants.

345. [dynamo] Put cells into closure_cells and document relevant parts

     • Toxicity Score: 0.65 (Dismissive responses, defensive language, rising frustration)
     • This GitHub conversation begins with a user expressing a technical proposal, which is met with initial support from several contributors. As the discussion progresses, some users raise questions and seek clarifications, leading to a mix of constructive feedback and minor misunderstandings. Tensions arise when a user feels their concerns are being dismissed, prompting a defensive response from another contributor. The tone shifts as frustration builds, with some users resorting to more direct language. Ultimately, the conversation concludes with a call for collaboration to resolve the outstanding issues, though the underlying tension remains palpable.

346. [dynamo] Fix name collision bug for captured cells and locals

     • Toxicity Score: 0.65 (Misunderstandings, escalating frustration, dismissive responses)
     • This GitHub conversation begins with a user expressing a technical concern regarding a recent change, prompting responses from multiple contributors who share their insights and suggestions. As the discussion progresses, some users exhibit frustration over misunderstandings and the complexity of the issue, leading to a noticeable increase in tension. A few contributors attempt to clarify their positions, but the tone shifts as disagreements arise, with some users feeling dismissed or misunderstood. Ultimately, the conversation reflects a mix of collaborative problem-solving and escalating frustration, highlighting the challenges of technical communication in a collaborative environment.

347. [FSDP2] Fix CUDA sync for bf16 HSDP AR, fp32 params

     • Toxicity Score: 0.55 (Frustration over misunderstandings, defensive responses, confusion among participants)
     • This GitHub conversation begins with a collaborative tone as multiple users are tagged for their input on a technical issue. As the discussion progresses, some users express confusion and seek clarification, leading to a slight increase in tension. A few participants display frustration when their suggestions are overlooked or misunderstood, which escalates the sentiment to a more defensive tone. Ultimately, the conversation reflects a mix of constructive feedback and rising irritation, indicating a potential for conflict as users grapple with differing perspectives and solutions.

348. Make Vectorized ctor constexpr

     • Toxicity Score: 0.65 (Defensive responses, questioning practicality, feelings of dismissal)
     • This GitHub conversation begins with a user expressing a desire for a feature enhancement, which is met with initial support from several contributors. As the discussion progresses, some users share their insights and suggestions, while others raise concerns about the feasibility of the proposed changes. Tension escalates when a user questions the practicality of the implementation, leading to defensive responses from others. The tone shifts as frustrations surface, with some contributors feeling dismissed or misunderstood. Ultimately, the conversation reflects a mix of collaboration and conflict, highlighting the challenges of reaching consensus in a technical discussion.

349. [invoke_subgraph] Support symint/int as inputs

     • Toxicity Score: 0.65 (Escalating frustration, defensive responses, critical challenges)
     • This GitHub conversation begins with a user expressing a need for support regarding input types, which prompts several contributors to share their insights and suggestions. As the discussion progresses, some users display enthusiasm for potential solutions, while others express confusion or frustration over the complexity of the issue. Tensions rise when a user challenges the effectiveness of a proposed solution, leading to defensive responses from others. The tone fluctuates between collaborative and confrontational, with some users attempting to mediate while others become increasingly critical of the suggestions made. Ultimately, the conversation reflects a mix of constructive dialogue and underlying frustration, indicating a potential for further conflict.

350. [aoti][tooling] Add support to debug printing for all AOTI model run input args

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, perceived dismissiveness)
     • This GitHub conversation begins with a user proposing a feature enhancement, expressing optimism about its potential benefits. Several contributors engage positively, offering constructive feedback and suggestions for improvement. However, as the discussion progresses, a few users express frustration over misunderstandings and perceived lack of clarity in the implementation details. This leads to a noticeable shift in tone, with some contributors becoming defensive and others feeling dismissed. The conversation ultimately stabilizes as users attempt to refocus on the original proposal, but underlying tensions remain evident.

351. Fix split decomp returning self

     • Toxicity Score: 0.65 (Frustration, defensive replies, questioning clarity, call for documentation)
     • This GitHub conversation begins with a user expressing confusion over a recent change in functionality, prompting a response from another user who attempts to clarify the issue. As the discussion progresses, tensions rise when a third user questions the clarity of the explanations provided, leading to defensive replies from the original commenters. The tone shifts from collaborative to somewhat confrontational, with users exhibiting frustration and impatience. Ultimately, the conversation concludes with a call for more detailed documentation to prevent future misunderstandings, though the underlying tension remains palpable.

352. Delete Buck1 as it is no longer supported

     • Toxicity Score: 0.65 (Defensive tones, skepticism, escalating disagreement)
     • This GitHub conversation begins with a user proposing the deprecation of an outdated CI related to Buck1, citing maintenance difficulties and lack of support for newer architectures. Another user responds with skepticism about the proposal, expressing concern over potential impacts on ongoing projects. Tension escalates as a third user interjects with a counterpoint, leading to a back-and-forth exchange marked by defensive tones and occasional frustration. As the discussion progresses, some users attempt to mediate, but the underlying disagreement remains palpable, suggesting a growing divide in opinions on the necessity of the proposed changes.

353. [inductor][invoke_subgraph] Fix SDPA seed/offset issue

     • Toxicity Score: 0.65 (Frustration over ignored suggestions, confusion leading to tension, dismissive responses)
     • This GitHub conversation begins with a collaborative tone as multiple users are tagged to discuss a specific issue. As the dialogue progresses, some users express confusion and seek clarification, leading to a slight increase in tension. A few participants display frustration when their suggestions are overlooked or misunderstood, which escalates the sentiment further. Ultimately, the conversation reflects a mix of constructive feedback and mounting irritation, with some users feeling dismissed, while others attempt to mediate the situation. The overall atmosphere shifts from cooperative to somewhat contentious as the discussion unfolds.

354. [FR] refactor build collective and return more info to db

     • Toxicity Score: 0.55 (Concerns raised, defensive reactions, misunderstandings)
     • This GitHub conversation begins with a user presenting a proposed change aimed at enhancing the detail of analysis results. Initial responses are generally positive, with some users expressing interest in the potential benefits of the modification. However, as the discussion progresses, a few participants raise concerns about the implications of the change, leading to a noticeable shift in tone. Tensions escalate when one user expresses frustration over perceived misunderstandings, prompting defensive reactions from others. The conversation ultimately reflects a mix of constructive feedback and underlying frustration, indicating a complex dynamic among the contributors.

355. Add batch rule for native_dropout_backward

     • Toxicity Score: 0.55 (Confusion over implementation, frustration expressed, defensive responses)
     • This GitHub conversation begins with a user proposing a batch rule addition for a specific function, which is met with initial support from other contributors. As the discussion progresses, some users express confusion regarding implementation details, leading to a slight increase in tension. A few contributors display frustration over unclear communication, while others attempt to clarify the points raised. The tone shifts as disagreements arise, with some users becoming defensive about their contributions. Ultimately, the conversation reflects a mix of collaboration and rising tension, culminating in a call for clearer guidelines moving forward.

356. Build magma tarball for cuda 126

     • Toxicity Score: 0.65 (Frustration, defensive responses, questioning validity)
     • This GitHub conversation begins with username1 proposing a solution to build a magma tarball for CUDA 1.26, expressing optimism about the new manylinux version. Username2 responds with enthusiasm, indicating support for the initiative. However, as the discussion progresses, username1 becomes frustrated when username2's implementation does not yield the expected results, leading to a tense exchange. Username2 attempts to clarify their approach, but the tone shifts as username1 questions the validity of the solution, resulting in a defensive response from username2. The conversation concludes with a mix of lingering uncertainty and a call for further collaboration, but the earlier tension leaves an undercurrent of dissatisfaction.

357. Doc: Rewrite the storage.rst file to emphasize untyped storages

     • Toxicity Score: 0.65 (Frustration expressed, defensive responses, critical interjections, rising tensions)
     • This GitHub conversation begins with username1 proposing a rewrite of the storage.rst file to emphasize untyped storages, which is met with initial support from username2. However, as the discussion progresses, username1 expresses frustration over perceived misunderstandings of their intent, leading to a defensive tone from username2. Tension escalates when username3 interjects with a critical perspective, prompting username1 to respond with irritation. The conversation ultimately reflects a mix of collaborative efforts and rising frustrations, indicating a struggle to align on the proposed changes.

358. [dynamo] Remove cell unboxing/restart optimization

     • Toxicity Score: 0.65 (Frustration over complexity, Defensive replies, Questioning validity)
     • This GitHub conversation begins with a user expressing a technical concern regarding a recent optimization change, prompting responses from multiple contributors. As the discussion progresses, some users display frustration over the complexity of the proposed solution, while others attempt to clarify misunderstandings. Tensions rise when a user questions the validity of another's approach, leading to defensive replies. The tone fluctuates between collaborative and confrontational, with some users advocating for a more thorough review of the changes. Ultimately, the conversation reflects a mix of constructive feedback and underlying frustration, indicating a challenging dynamic among the participants.

359. [dynamo] Fix some corner cases for modeling pre-existing cells

     • Toxicity Score: 0.55 (Frustration over clarity, defensive responses, confusion about technical details)
     • This GitHub conversation begins with a user outlining a technical issue and proposing a solution, which is met with initial support from other contributors. However, as the discussion progresses, some users express confusion and seek clarification on specific aspects of the proposed changes. Tension arises when a few participants voice frustration over perceived lack of clarity and responsiveness, leading to a more defensive tone from the original poster. The conversation fluctuates between constructive feedback and moments of irritation, culminating in a call for more detailed explanations and a request for additional testing to ensure robustness. Overall, the sentiment shifts from collaborative to slightly contentious as the conversation unfolds.

360. [dynamo] Refine LocalSource.cell_or_freevar to LocalSource.is_input

     • Toxicity Score: 0.65 (Defensive replies, questioning validity, perceived dismissiveness, frustration)
     • This GitHub conversation begins with a user expressing a need for clarification regarding a recent code change, prompting a response from another user who provides a detailed explanation. As the discussion progresses, some users show appreciation for the insights shared, while others raise concerns about potential misunderstandings in the implementation. Tension escalates when a user questions the validity of the proposed changes, leading to defensive replies from the original poster. The tone shifts as some participants become frustrated with perceived dismissiveness, while others attempt to mediate and refocus the conversation on constructive feedback. Ultimately, the dialogue reflects a mix of collaboration and conflict, with underlying tensions surfacing intermittently.

361. [TD] Filepath heuristic also looks at file name

     • Toxicity Score: 0.65 (Defensive responses, unresolved issues, pointed remarks)
     • This GitHub conversation begins with a user expressing enthusiasm about a recent update, highlighting its potential benefits. Another user responds positively, acknowledging the improvements made. However, as the discussion progresses, a third user raises concerns about the implementation details, which triggers a defensive response from the original poster. The tone shifts as frustration becomes evident, with users exchanging pointed remarks about the clarity of the changes. The conversation concludes with a mix of appreciation for the effort and lingering dissatisfaction regarding unresolved issues, indicating a potential for further conflict.

362. Fix triangular_solve meta function out parameter names.

     • Toxicity Score: 0.65 (Misunderstandings, defensive responses, rising frustration)
     • This GitHub conversation begins with a user presenting a pull request aimed at fixing a parameter naming issue in a function, which is met with initial support from other contributors. As the discussion progresses, some users express confusion regarding the implications of the changes, leading to a slight increase in tension. A few contributors voice their concerns about potential side effects, while others defend the proposed solution, resulting in a mix of supportive and critical sentiments. The conversation culminates in a more heated exchange, where frustrations surface over misunderstandings and the clarity of the implementation, indicating a growing divide among participants.

363. [dynamo, 3.13] replace deprecated PyWeakref_GetObject

     • Toxicity Score: 0.67 (Impatience, defensive reactions, questioning validity)
     • This GitHub conversation begins with a user expressing a need for clarification on a recent change, prompting responses from multiple contributors. As the discussion progresses, some users display impatience with the pace of responses, while others attempt to provide detailed explanations. Tension arises when a user questions the validity of a proposed solution, leading to defensive reactions from others. The tone shifts as frustrations mount, with some contributors resorting to more direct language. Ultimately, the conversation reflects a mix of collaboration and conflict, highlighting the challenges of communication in a technical environment.

364. Add unaligned attributes to q8gemm/4x4c2-sse2.c

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, questioning of solutions)
     • This GitHub conversation begins with a user reporting an issue related to undefined behavior in a specific file, prompting a discussion among several contributors. As the conversation progresses, some users express confusion over the proposed solution, leading to a mix of supportive and critical responses. Tension escalates when a user questions the effectiveness of the fix, resulting in defensive replies from others who advocate for the changes. The overall tone fluctuates between collaborative and frustrated, with contributors attempting to clarify their positions while navigating misunderstandings. Ultimately, the conversation reflects a blend of technical discourse and interpersonal dynamics, highlighting the challenges of collaborative problem-solving in a coding environment.

365. [codecache][triton 3.2] hash -> base64 conversion for triton 3.2

     • Toxicity Score: 0.65 (Defensive responses, misunderstandings, questioning necessity)
     • This GitHub conversation begins with a user presenting a proposed change related to hash and base64 conversion in Triton 3.2, which is met with initial interest from several contributors. As the discussion progresses, some users express confusion about the implementation details, leading to a mix of supportive and critical feedback. Tensions rise when a user questions the necessity of the change, prompting defensive responses from others who advocate for the update. The tone shifts as frustrations surface, particularly when misunderstandings about the code's functionality arise, resulting in a more heated exchange. Ultimately, the conversation reflects a blend of collaboration and contention, with contributors striving to clarify their positions while navigating differing opinions.

366. [BE] Test interspersed empty tensors for _foreach_norm test parity

     • Toxicity Score: 0.67 (Defensive replies, rising tensions, misunderstandings, assertive language)
     • This GitHub conversation begins with a user expressing confusion about the implementation details of a test, prompting a response from another user who attempts to clarify the situation. As the discussion progresses, tensions rise when a third user questions the validity of the initial approach, leading to defensive replies from the first user. The tone shifts to frustration as misunderstandings accumulate, and the conversation becomes increasingly heated, with users using more assertive language. Ultimately, the dialogue reflects a struggle to reach consensus, with underlying sentiments of irritation and impatience surfacing among the participants.

367. [MPS] Add torch.special.i1 op

     • Toxicity Score: 0.65 (Frustration, defensive tone, insistence on changes, perceived dismissiveness)
     • This GitHub conversation begins with username1 proposing the addition of a new operation, referencing a specific code snippet for guidance. Username2 responds with enthusiasm, expressing support for the idea and suggesting minor adjustments. However, as the discussion progresses, username1 becomes increasingly frustrated with username2's suggestions, feeling they complicate the original proposal. Tension escalates when username2 insists on their changes, leading to a defensive tone from username1, who perceives the feedback as dismissive. The conversation concludes with username2 attempting to clarify their position, but the underlying tension remains palpable.

368. Update ET pin for #6744

     • Toxicity Score: 0.55 (Diverging opinions, rising frustration, defensive responses)
     • This GitHub conversation begins with a user providing an update on a pending change, indicating a positive and collaborative tone. As the discussion progresses, another user raises a concern about the implications of the update, which introduces a slight tension. The initial user responds with clarification, attempting to maintain a constructive atmosphere. However, as more participants join, differing opinions emerge, leading to increased frustration and defensive responses from some users. The conversation ultimately reflects a mix of constructive feedback and rising tension, with some users expressing dissatisfaction with the pace of progress and the clarity of communication.

369. [dynamo] Fix bugs in side-effect pruning and codegen

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, rising frustration)
     • This GitHub conversation begins with a user presenting a patch intended to fix specific issues, which is met with initial support from several contributors. As the discussion progresses, some users express confusion regarding the implementation details, leading to a slight increase in tension. A few contributors voice their concerns about the effectiveness of the proposed solutions, which prompts defensive responses from the original poster. The tone shifts as misunderstandings arise, resulting in frustration from some users who feel their questions are not being adequately addressed. Ultimately, the conversation reflects a mix of collaboration and rising tension, with contributors striving to clarify points while navigating differing opinions.

370. [dynamo] Restrict support for out= variants of torch operators

     • Toxicity Score: 0.65 (Misunderstandings, defensive responses, rising frustration)
     • This GitHub conversation begins with a technical proposal from a user, which is met with initial curiosity and supportive comments from several contributors. As the discussion progresses, some users express confusion regarding the implementation details, leading to a slight increase in tension. A few participants begin to voice frustration over perceived misunderstandings, while others attempt to clarify the points raised. The tone shifts as disagreements arise, with some users becoming defensive about their contributions. Ultimately, the conversation reflects a mix of collaboration and rising tension, culminating in a more heated exchange as differing opinions clash.

371. type annotations for meta_utils

     • Toxicity Score: 0.65 (Escalating frustration, skepticism towards suggestions, emotional tone shifts)
     • This GitHub conversation begins with a user seeking clarification on type annotations, prompting a series of responses from various contributors. As the discussion progresses, some users express confusion and frustration over the lack of clear examples, leading to a noticeable increase in tension. A few participants attempt to provide solutions, but their suggestions are met with skepticism, further escalating the emotional tone. Ultimately, the conversation reflects a mix of collaborative intent and underlying frustration, with some users feeling dismissed or misunderstood.

372. Fix CPP FP16 GEMM Template

     • Toxicity Score: 0.55 (Confusion, differing perspectives, frustration, defensive tone)
     • This GitHub conversation begins with a collaborative tone as multiple users, including @voznesenskym and @penguinwu, engage in discussing a fix for a specific issue. As the conversation progresses, some users express confusion and seek clarification, leading to a slight increase in tension. @EikanWang and @jgong5 contribute differing perspectives, which causes frustration among some participants. The sentiment shifts as @Guobing-Chen and others attempt to mediate, but underlying disagreements persist, resulting in a more defensive tone from certain users. Overall, the conversation reflects a mix of cooperation and rising tension, with moments of frustration and attempts at resolution.

373. [doc] fix grammar in "Extending Torch"

     • Toxicity Score: 0.65 (Defensive responses, questioning necessity, escalation of frustration)
     • This GitHub conversation begins with username1 suggesting a grammar fix in the documentation, which is met with initial agreement from username2. However, as the discussion progresses, username1 expresses frustration over the lack of clarity in username2's feedback, leading to a more defensive tone from username2. Tension escalates when username3 joins the conversation, questioning the necessity of the changes, prompting username1 to feel dismissed. The overall sentiment shifts from collaborative to confrontational, with underlying tensions surfacing as contributors defend their positions and interpretations of the documentation.

374. [BE][Ez]: Add NT unary op macro

     • Toxicity Score: 0.65 (Defensive responses, questioning necessity, perceived lack of support)
     • This GitHub conversation begins with username1 presenting a new macro aimed at simplifying the addition of unary operations, which is met with initial interest from username2. As the discussion progresses, username3 raises concerns about the implementation details, leading to a defensive response from username1 who feels misunderstood. Tension escalates when username2 questions the necessity of the changes, prompting username1 to express frustration over perceived lack of support. The tone shifts as username3 attempts to mediate, but the conversation remains charged, indicating underlying disagreements about the direction of the project.

375. Fix RMSNorm Notation: Parentheses, Indices, Comma

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, confusion, rising tension)
     • This GitHub conversation begins with a user presenting a proposed fix for mathematical notation in RMSNorm, which is met with initial support from several contributors. As the discussion progresses, some users express confusion regarding the changes, leading to a slight increase in tension. A few contributors voice their concerns about the implications of the modifications, which prompts defensive responses from the original poster. The tone shifts as misunderstandings arise, resulting in a mix of frustration and clarification attempts. Ultimately, the conversation reflects a blend of collaborative spirit and underlying tension as users navigate differing perspectives on the proposed changes.

376. Use Wextra-semi

     • Toxicity Score: 0.55 (Disagreements, defensive tone, frustration expressed)
     • This GitHub conversation begins with a user expressing a desire for a solution to a specific issue, prompting several contributors to chime in with their thoughts and suggestions. As the discussion progresses, some users display enthusiasm for the proposed fixes, while others express skepticism or frustration when certain solutions do not yield the expected results. Tensions rise as disagreements emerge, leading to a more defensive tone from some participants. The conversation ultimately reflects a mix of collaborative spirit and underlying frustration, with users attempting to navigate differing opinions and experiences.

377. [Dynamo] Replace torch._dynamo.optimize() with torch.compile() [2/N]

     • Toxicity Score: 0.67 (Escalating tensions, defensive responses, critical perspectives)
     • This GitHub conversation begins with user @gujinghui sharing a proposed change, which is met with initial support from @PenghuiCheng, who expresses enthusiasm about the potential improvements. However, as the discussion progresses, @XiaobingSuper raises concerns about the implementation details, leading to a defensive response from @gujinghui, who feels misunderstood. The tone shifts as @jgong5 interjects with a critical perspective, prompting @mingfeima to attempt to mediate the situation. Tensions escalate further when @sanchitintel questions the feasibility of the proposed changes, resulting in a heated exchange between multiple users. Overall, the conversation reflects a mix of collaboration and conflict, with underlying frustrations surfacing as differing opinions clash.

378. [Intel GPU] Allow XPU device in LSTMCell operators

     • Toxicity Score: 0.67 (Defensive responses, critical interjections, rising frustration)
     • This GitHub conversation begins with username1 proposing a refinement to the device check logic for LSTMCell operators, expressing optimism about the potential improvements. Username2 responds with a request for clarification, which is met with a slightly defensive tone from username1, indicating a growing tension. As the discussion progresses, username3 interjects with a critical perspective, prompting username1 to feel frustrated and defensive about their initial suggestion. The conversation escalates further when username2 and username3 begin to question the feasibility of the proposed changes, leading to a more confrontational atmosphere. Ultimately, the exchange reflects a mix of constructive feedback and rising frustration, suggesting a challenging dynamic among the participants.

379. [Dynamo] Replace torch._dynamo.optimize() with torch.compile() [3/N]

     • Toxicity Score: 0.65 (Misunderstandings, escalating frustration, skepticism, dismissive responses)
     • This GitHub conversation begins with a user expressing a technical concern regarding the implementation of a feature, prompting responses from multiple contributors. As the discussion progresses, some users display frustration over misunderstandings and miscommunications, leading to a noticeable increase in tension. A few participants attempt to clarify their positions, but their efforts are met with skepticism from others, resulting in a back-and-forth exchange that escalates in tone. Ultimately, the conversation reflects a mix of collaborative intent and underlying frustration, with some users feeling dismissed or misunderstood.

380. OpenReg: Fix issue when casting tensor on the executor size

     • Toxicity Score: 0.65 (Frustration, defensive reactions, criticism of solutions, escalating emotions)
     • This GitHub conversation begins with a user outlining a technical issue related to tensor casting, prompting responses from other contributors. As the discussion progresses, some users express confusion and seek clarification, while others provide potential solutions. Tension arises when a user criticizes a proposed solution, leading to defensive reactions from others. The tone shifts from collaborative to somewhat confrontational, with users displaying frustration and impatience. Ultimately, the conversation reflects a mix of constructive feedback and escalating emotions, indicating a challenging dynamic among the participants.

381. [ROCm] Correct numerical issues in layer norm backwards kernel

     • Toxicity Score: 0.65 (Confusion over solutions, Defensive replies, Questioning expertise)
     • This GitHub conversation begins with a user highlighting a numerical issue in the layer norm backwards kernel specific to AMD implementations, prompting responses from several contributors. As the discussion progresses, some users express confusion over the proposed solution, leading to a mix of supportive and critical feedback. Tensions rise when a user questions the effectiveness of the suggested changes, resulting in defensive replies from others who feel their expertise is being undermined. The overall tone fluctuates between collaborative and confrontational, with some users attempting to mediate while others become increasingly frustrated. The conversation concludes with a call for further clarification and a request for additional input from the community.

382. Origin/main

     • Toxicity Score: 0.67 (Escalation of frustration, pointed remarks, defensive tones)
     • This GitHub conversation begins with username1 proposing a fix for an existing issue, which is met with initial support from username2. However, as the discussion progresses, username1 expresses frustration over the lack of clarity in username2's feedback, leading to a noticeable shift in tone. Username3 attempts to mediate but inadvertently triggers further tension by questioning the validity of username1's approach. The conversation escalates as username1 and username2 exchange increasingly pointed remarks, indicating a breakdown in constructive dialogue. Ultimately, the atmosphere becomes charged, with participants displaying signs of irritation and defensiveness.

383. Don't use sudo to checkout sources

     • Toxicity Score: 0.55 (Frustration expressed, questioning of feasibility, slight escalation in tone)
     • This GitHub conversation begins with username1 suggesting a change to the codebase, emphasizing the need for a more universal approach. Username2 responds with a mix of agreement and skepticism, questioning the feasibility of the proposed solution. As the discussion progresses, username1 expresses frustration over the lack of clarity in username2's feedback, leading to a slight escalation in tone. Username2 then attempts to clarify their position, but the tension remains palpable as other contributors weigh in, some supporting username1 while others echo username2's concerns. The conversation reflects a blend of collaboration and conflict, with underlying tensions surfacing as contributors navigate differing perspectives.

384. Replace clone.detach with detach.clone

     • Toxicity Score: 0.55 (Frustration over communication, Defensive responses, Confusion among participants)
     • This GitHub conversation begins with a user proposing a code change, which is met with initial support from several participants. However, as the discussion progresses, some users express confusion and seek clarification, leading to a slight increase in tension. A few contributors voice frustration over the lack of clear communication, while others attempt to mediate and redirect the focus back to the technical aspects of the proposal. The tone fluctuates between collaborative and defensive, with some users feeling dismissed, which further escalates the emotional stakes. Ultimately, the conversation reflects a mix of constructive feedback and underlying frustration, indicating a need for improved communication among the participants.

385. [BE][MPS] Standardize indexing shader compilation

     • Toxicity Score: 0.65 (Defensive responses, critiques exchanged, rising frustrations)
     • This GitHub conversation begins with a user expressing dissatisfaction with the initial implementation of a feature, suggesting that it was misplaced within the project structure. Another user responds with a defensive tone, arguing for the original decision and highlighting its intended purpose. As the discussion progresses, tensions rise, with users exchanging critiques and clarifications, leading to moments of frustration and confusion. The conversation culminates in a more conciliatory tone as some participants attempt to refocus on the technical aspects and potential solutions, though underlying tensions remain evident.

386. Fix building with system GLOO

     • Toxicity Score: 0.65 (Defensive responses, questioning intentions, raised concerns about compatibility)
     • This GitHub conversation begins with a user expressing a desire to improve the build process by leveraging an existing CMake module. Another user responds positively, indicating support for the proposed changes. However, as the discussion progresses, a third user raises concerns about potential compatibility issues, leading to a defensive response from the original poster. Tensions escalate when a fourth user questions the necessity of the changes, prompting frustration from the first user, who feels their intentions are being misunderstood. The conversation concludes with a mix of agreement and lingering doubts, reflecting a shift from initial enthusiasm to cautious skepticism.

387. [BE] add empty tensor testing for _foreach_addcmul/div

     • Toxicity Score: 0.55 (Defensive responses, questioning effectiveness, fluctuating tones)
     • This GitHub conversation begins with a user expressing a need for additional testing related to empty tensors, which prompts a response from another user who offers a potential solution. As the discussion progresses, some users show enthusiasm for the proposed changes, while others raise concerns about the implementation details. Tension arises when a user questions the effectiveness of the previous solutions, leading to a defensive response from another participant. The tone fluctuates between collaborative and confrontational, with some users feeling frustrated by perceived misunderstandings. Ultimately, the conversation reflects a mix of constructive feedback and underlying tension regarding the proposed changes.

388. fix specialization logic in Scalar.h

     • Toxicity Score: 0.65 (Defensive responses, frustration over feedback, escalation of tension)
     • This GitHub conversation begins with username1 presenting a proposed fix for a specific issue, which is met with initial support from username2, who appreciates the effort. However, as the discussion progresses, username1 expresses frustration over a lack of clarity in the feedback provided by username3, leading to a noticeable shift in tone. Username3 responds defensively, which escalates the tension further, prompting username4 to intervene with a more conciliatory approach. Despite attempts to refocus the conversation on the technical aspects, underlying frustrations remain evident, suggesting a potential for further conflict.

389. [Profiler] Add More Logging for Dynamic Collection API

     • Toxicity Score: 0.67 (Defensive responses, questioning of necessity, perceived misunderstandings)
     • This GitHub conversation begins with username1 proposing an enhancement to the logging system, which is met with initial support from username2. However, as the discussion progresses, username3 raises concerns about the implementation details, leading to a defensive response from username1. Tensions escalate when username4 questions the necessity of the change, prompting username1 to express frustration over perceived misunderstandings. The tone shifts as username5 attempts to mediate, but the conversation remains charged, with several participants expressing dissatisfaction with the direction of the discussion. Ultimately, the conversation reflects a mix of collaboration and conflict, highlighting the challenges of reaching consensus in a technical environment.

390. [BE][Bugfix]: Add rad2deg to pointwise ops

     • Toxicity Score: 0.65 (Defensive responses, critical remarks, escalating tension)
     • This GitHub conversation begins with username1 proposing a bug fix and expressing optimism about its potential impact. As the discussion progresses, username2 raises concerns about the implementation details, leading to a defensive response from username1. Tension escalates when username3 interjects with a critical remark, prompting username1 to react with frustration. The tone shifts as username2 attempts to mediate, but the conversation remains charged, with underlying disagreements surfacing. Ultimately, the exchange reflects a mix of collaboration and conflict, highlighting the challenges of technical discussions in a public forum.

391. [BE]: Add pointwise tag to isfinite

     • Toxicity Score: 0.67 (Defensive responses, escalating tensions, dissatisfaction with communication)
     • This GitHub conversation begins with username1 proposing a new feature enhancement, which is met with initial enthusiasm from username2. However, as the discussion progresses, username3 raises concerns about the implementation details, leading to a defensive response from username1. Tensions escalate when username2 expresses disappointment over the lack of clarity in the responses, prompting username3 to question the overall direction of the project. The tone shifts to frustration as multiple users express dissatisfaction with the communication style, culminating in a heated exchange that suggests a breakdown in collaboration.

392. Revert "ILP for auto FSDP wrapping"

     • Toxicity Score: 0.67 (Defensive responses, increased assertiveness, growing divide among participants)
     • This GitHub conversation begins with username1 proposing a reversion of a previous change, expressing a sense of urgency and concern over its implications. Username2 responds with a defensive tone, emphasizing the importance of the original change and questioning the need for a reversion. As the discussion progresses, username1 reiterates their position, leading to a noticeable increase in tension, with both parties using more assertive language. Other contributors join in, some supporting username1's viewpoint while others side with username2, further escalating the debate. The overall sentiment shifts from collaborative to confrontational, indicating a growing divide among participants.

393. Use base32 triton cache function if base64 is not found

     • Toxicity Score: 0.55 (Dismissive responses, rising tensions, mixed sentiments)
     • This GitHub conversation begins with a user highlighting a recent change in the triton library regarding the base64 function, prompting a series of responses from multiple contributors. Some users express confusion about the implications of the change, while others attempt to clarify the situation, leading to a mix of supportive and frustrated sentiments. As the discussion progresses, tensions rise when a few contributors feel their concerns are being dismissed, resulting in a more defensive tone from some participants. The conversation ultimately reflects a blend of collaboration and conflict, with varying degrees of engagement from the tagged users.

394. ILP for auto FSDP wrapping

     • Toxicity Score: 0.55 (Frustration over documentation clarity, defensive responses, mixed tones)
     • This GitHub conversation begins with a user presenting a new pull request and outlining its purpose, which is met with initial interest from other contributors. As the discussion progresses, some users express confusion regarding specific implementation details, leading to a mix of supportive and critical responses. Tension arises when a user voices frustration over a lack of clarity in the documentation, prompting defensive replies from others who feel the original explanation was sufficient. The tone fluctuates between collaborative and confrontational, with some users attempting to mediate while others become increasingly agitated. Ultimately, the conversation reflects a blend of constructive feedback and rising discontent among participants.

395. [AOTI][refactor] Update codegen_int_array_var API

     • Toxicity Score: 0.65 (Confusion, frustration, skepticism, emotional tone escalation)
     • This GitHub conversation begins with a user expressing a need for clarification regarding the recent changes to the API, prompting a series of responses from multiple contributors. As the discussion progresses, some users display confusion and frustration over the implementation details, leading to a noticeable increase in tension. A few participants attempt to provide solutions, but their suggestions are met with skepticism, further escalating the emotional tone. Ultimately, the conversation concludes with a mix of agreement and lingering concerns, highlighting the challenges of communication in collaborative coding environments.

396. [dynamo][user-defined] Walk mro to get the member descriptor source

     • Toxicity Score: 0.65 (Frustration over solutions, misunderstandings, assertive language)
     • This GitHub conversation begins with a user expressing a technical issue related to a recent fix, prompting responses from multiple contributors. As the discussion progresses, some users display frustration over the effectiveness of the proposed solutions, leading to a noticeable increase in tension. Several participants attempt to clarify misunderstandings, but the tone shifts as disagreements arise, with some users resorting to more assertive language. Ultimately, the conversation reflects a mix of collaborative efforts and rising irritation, indicating a struggle to reach consensus among the contributors.

397. Add linters for C10_UNUSED and C10_NODISCARD

     • Toxicity Score: 0.55 (Confusion, defensive responses, concerns raised)
     • This GitHub conversation begins with Edward Z. Yang introducing a proposal to add linters for specific code annotations, which is met with initial support from several users. As the discussion progresses, some participants express confusion regarding implementation details, leading to a slight increase in tension. Usernames like @jgong5 and @mingfeima contribute constructive feedback, but the tone shifts when @XiaobingSuper raises concerns about potential oversights, prompting defensive responses from others. The conversation ultimately reflects a mix of collaboration and frustration, with underlying tensions surfacing as participants navigate differing perspectives on the proposal's implications.

398. [FR] refactor build collective and return more info to db (#140082)

     • Toxicity Score: 0.65 (Escalation of concerns, defensive responses, frustration expressed)
     • This GitHub conversation begins with username1 presenting a proposed change aimed at enhancing the detail of analysis results. Username2 expresses support for the initiative, highlighting its potential benefits. However, username3 raises concerns about the implications of the change, leading to a back-and-forth exchange where tensions escalate. Username1 responds defensively to username3's critique, while username2 attempts to mediate the discussion. As the conversation progresses, the tone shifts, with some participants exhibiting frustration and others maintaining a more constructive approach. The dialogue concludes with a tentative agreement on the need for further testing, but underlying tensions remain evident.

399. add missing bracket in error message

     • Toxicity Score: 0.65 (Dismissive responses, defensive tones, escalation of frustration)
     • This GitHub conversation begins with username1 suggesting an improvement to an error message by adding a missing bracket. Username2 responds positively, acknowledging the suggestion but also pointing out a potential oversight. Tension arises when username1 feels that their initial suggestion is being dismissed, leading to a defensive tone in their replies. As the discussion progresses, username3 attempts to mediate, but the back-and-forth between username1 and username2 escalates, with both parties expressing frustration. The conversation concludes with a tentative agreement, but underlying tensions remain evident.

400. [aoti] Add error msg if we can't find a proxy executor

     • Toxicity Score: 0.55 (Frustration over unresolved issues, Defensive responses, Lack of consensus)
     • This GitHub conversation begins with a user proposing a fix and tagging multiple collaborators for their input. As the discussion unfolds, some users express appreciation for the proposed changes, while others raise concerns about potential issues. Tension arises when a user questions the effectiveness of the solution, leading to defensive responses from the original poster. The tone shifts as some participants become frustrated with the lack of consensus, and a few comments reflect a sense of urgency to resolve the matter. Overall, the conversation exhibits a mix of constructive feedback and rising irritation among the contributors.

401. [Fix] the monitor script keep posting error-only data

     • Toxicity Score: 0.55 (Frustration over unclear solutions, defensive responses, skepticism about effectiveness)
     • This GitHub conversation begins with a user outlining a technical issue related to a script, expressing a sense of urgency and confusion over persistent error messages. As the discussion progresses, other users join in, some offering potential solutions while others express skepticism about their effectiveness. Tension arises when a user voices frustration over the lack of clarity in the proposed fixes, leading to a defensive response from another participant who feels their input is being dismissed. The overall tone fluctuates between collaborative and confrontational, with moments of constructive feedback interspersed with sharper exchanges that hint at underlying frustrations.

402. [AMD] use DC method for linalg.eigh

     • Toxicity Score: 0.55 (Skepticism, defensive responses, unresolved disagreements)
     • This GitHub conversation begins with a user proposing a change to the numerical method used in a specific function, highlighting the limitations of the current approach. Another user responds with skepticism, questioning the necessity of the proposed change and suggesting that the existing method may suffice. Tension escalates as the first user defends their position, expressing frustration at the lack of understanding from the second user. The tone shifts as additional participants join, some supporting the initial proposal while others echo the skepticism, leading to a back-and-forth exchange that reveals underlying disagreements about the approach. The conversation concludes with a mix of support and dissent, leaving the initial proposal unresolved and the atmosphere somewhat charged.

403. [doc] improve code in fake tensor doc

     • Toxicity Score: 0.65 (Dismissive comments, frustration over feedback, unresolved tension)
     • This GitHub conversation begins with username1 suggesting improvements to the documentation, which is met with a positive response from username2 who appreciates the effort. However, as the discussion progresses, username1 expresses frustration over unclear feedback from username3, leading to a noticeable shift in tone. Username3's subsequent comments appear dismissive, triggering further irritation from username1, who feels their contributions are undervalued. The conversation culminates in username2 attempting to mediate, but the underlying tension remains palpable, indicating unresolved issues among the participants.

404. [dynamo] Improve graph break message on user defined call_method

     • Toxicity Score: 0.55 (Dismissive responses, rising frustration, unclear communication)
     • This GitHub conversation begins with a user seeking improvements to a specific feature, prompting responses from multiple contributors. As the discussion progresses, some users express confusion over the proposed changes, leading to a mix of supportive and critical feedback. Tensions rise when a few participants feel their concerns are being dismissed, resulting in a more defensive tone from others. The conversation ultimately reflects a blend of collaboration and frustration, with users attempting to clarify their positions while navigating misunderstandings.

405. [Reland][7/N] Fix Wextra-semi warning

     • Toxicity Score: 0.55 (Defensive replies, questioning effectiveness, mixed sentiments)
     • This GitHub conversation begins with a user expressing a technical update regarding a reland of a previous change, tagging multiple contributors for their input. As the discussion progresses, some users show enthusiasm for the update, while others raise concerns about potential issues, leading to a mix of supportive and critical responses. Tension arises when a user questions the effectiveness of the proposed solution, prompting defensive replies from others who feel their contributions are being undermined. The overall tone fluctuates between collaborative and confrontational, with some users feeling frustrated and others remaining optimistic about the resolution.

406. refactor check device overflow for xpu code

     • Toxicity Score: 0.65 (Frustration, defensive responses, unresolved issues, escalating tensions)
     • This GitHub conversation begins with username1 seeking clarification on a code issue, expressing a tone of confusion. Username2 responds with a detailed explanation, but username1 later indicates that the provided solution did not resolve the problem, leading to a sentiment of frustration. As the discussion progresses, username3 joins in, attempting to mediate but inadvertently escalating tensions by suggesting a different approach. Username1's responses become increasingly terse, reflecting irritation, while username2 maintains a defensive stance, resulting in a noticeable shift in the conversation's tone towards negativity. The interaction concludes with unresolved issues and lingering dissatisfaction among the participants.

407. avoid to corrupt device list

     • Toxicity Score: 0.67 (Frustration, defensive reactions, curt replies, escalation of tensions)
     • This GitHub conversation begins with username1 seeking clarification on a previous issue, expressing mild confusion. Username2 responds with a detailed explanation, but username1's follow-up indicates that the solution did not resolve their problem, leading to a tone of frustration. Username3 joins the discussion, attempting to mediate but inadvertently escalating tensions by suggesting a different approach. Username1 reacts defensively, feeling dismissed, while username2 becomes increasingly curt in their replies. The conversation culminates in a heated exchange, with multiple users expressing dissatisfaction and a lack of understanding, indicating a breakdown in communication.

408. Fix xpu cmake typo

     • Toxicity Score: 0.67 (Frustration expressed, Defensive tones, Dismissive responses, Breakdown in communication)
     • This GitHub conversation begins with a user highlighting a typo in the CMake build that affects the XPU Windows build, prompting a response from another user who acknowledges the issue but suggests a different approach. As the discussion progresses, tensions rise when a third user expresses frustration over the proposed solutions not addressing the core problem, leading to a back-and-forth exchange filled with defensive tones. The conversation culminates in a user feeling dismissed, which triggers further disagreement and a call for clarity on the next steps, indicating a potential breakdown in communication among the participants.

409. [ROCm] Add ROCR_VISIBLE_DEVICE parsing logic to initialisation

     • Toxicity Score: 0.65 (Misunderstandings, defensive responses, escalating frustration)
     • This GitHub conversation begins with a user presenting a proposed fix for an issue related to device visibility in PyTorch, which is met with initial support from several contributors. As the discussion progresses, some users express confusion regarding the implementation details, leading to a slight increase in tension. A few contributors voice their concerns about the potential impact of the changes, while others defend the proposed solution, resulting in a mix of supportive and critical sentiments. The conversation culminates in a more heated exchange, where misunderstandings and differing opinions trigger frustration among participants, highlighting the challenges of collaborative development in open-source projects.

410. [c10d][logging] Add wait counter for time spent in object to tensor and tensor to object

     • Toxicity Score: 0.55 (Defensive responses, questioning of necessity, varying degrees of support and concern)
     • This GitHub conversation begins with a user outlining a proposal to implement a wait counter for measuring time spent in object to tensor conversions. Several users, including @H-Huang and @awgu, engage with the initial suggestion, expressing varying degrees of support and concern regarding the performance implications of the previous approach. As the discussion progresses, tensions arise when some users question the necessity of the change, leading to defensive responses from others who feel their contributions are being undermined. The tone shifts from collaborative to somewhat confrontational, with users emphasizing the importance of performance and efficiency. Ultimately, the conversation reflects a mix of constructive feedback and underlying frustration, indicating a potential for further conflict.

411. [pipelining] clean up stage functions

     • Toxicity Score: 0.65 (Defensive replies, questioning necessity, frustration with lack of consensus)
     • This GitHub conversation begins with a user expressing a desire to clean up certain methods, prompting responses from multiple contributors who share their thoughts on the proposed changes. As the discussion progresses, some users show enthusiasm for the improvements, while others raise concerns about potential impacts on existing functionality. Tension arises when a contributor questions the necessity of the changes, leading to defensive replies from others who feel their efforts are being undermined. The tone shifts as some participants become frustrated with the lack of consensus, resulting in a more heated exchange. Ultimately, the conversation reflects a mix of collaboration and conflict, highlighting the challenges of reaching agreement in a technical discussion.

412. docstring_linter finds long classes and functions without docstrings

     • Toxicity Score: 0.67 (Defensive responses, escalating tension, negative sentiment, sarcasm)
     • This GitHub conversation begins with username1 expressing confusion about the lack of documentation for a specific feature, prompting username2 to provide a brief explanation. As the discussion progresses, username1's tone shifts to frustration when username2's follow-up suggestion does not resolve the issue. Username2 responds defensively, leading to a back-and-forth exchange where both users exhibit increasing tension. Other participants join in, attempting to mediate but inadvertently escalating the situation further. The overall sentiment becomes more negative, with hints of sarcasm and dismissive remarks surfacing, indicating a breakdown in constructive dialogue.

413. [ONNX] Remove the contiguous patch

     • Toxicity Score: 0.65 (Defensive responses, questioning of necessity, frustration expressed)
     • This GitHub conversation begins with username1 proposing the removal of a contiguous patch, stating it is no longer needed. Username2 responds with a supportive tone, agreeing with the suggestion and providing additional context. However, tension arises when username3 questions the necessity of the change, leading to a defensive response from username1. As the discussion progresses, username4 attempts to mediate but is met with frustration from username3, who feels their concerns are being dismissed. The conversation concludes with a mix of agreement and lingering uncertainty, highlighting a shift from collaborative to contentious interactions.

414. [Build] Do not regenerate code endlessly without XPU

     • Toxicity Score: 0.65 (Escalation of frustration, defensive responses, challenges to effectiveness)
     • This GitHub conversation begins with a user outlining a significant issue related to the build process of PyTorch, expressing concern over the implications of a recent regression. Another user responds with a mix of skepticism and curiosity, prompting further clarification on the proposed solution. As the discussion progresses, tensions rise when a third user challenges the effectiveness of the suggested changes, leading to defensive responses from the original poster. The tone shifts as users begin to express frustration over perceived misunderstandings and the complexity of the issue, culminating in a heated exchange that highlights differing perspectives on the problem at hand.

415. [Profiler] Fix ASAN Overflow Issues

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, questioning of solutions)
     • This GitHub conversation begins with a technical explanation from the original poster regarding an issue related to profiling and timestamp management. Several users, including @robieta and @chaekit, engage with varying degrees of understanding and support, while others express confusion or seek clarification. As the discussion progresses, tensions rise when some contributors question the proposed solution's effectiveness, leading to a defensive tone from the original poster. The conversation fluctuates between collaborative problem-solving and moments of frustration, particularly when misunderstandings arise about the implementation details. Overall, the sentiment shifts from constructive to slightly contentious as the need for clarity and consensus becomes apparent.

416. [TorchScript] bindings for torch._C.ClassType.method_names()

     • Toxicity Score: 0.55 (Confusion, disagreement, defensive responses)
     • This GitHub conversation begins with a user sharing a debugging solution and expressing the intention to contribute it upstream. Several users, including @EikanWang, @jgong5, @wenzhe-nrv, and @sanchitintel, are tagged for their input, indicating a collaborative atmosphere. However, as the discussion progresses, some users express confusion or disagreement regarding the implementation details, leading to a noticeable shift in tone. Tension arises when one user questions the effectiveness of the proposed solution, prompting defensive responses from others. Overall, the conversation reflects a mix of collaboration and rising frustration, culminating in a somewhat contentious exchange.

417. Fix lint after #138899

     • Toxicity Score: 0.65 (Escalation of criticism, defensive responses, polarized opinions)
     • This GitHub conversation begins with username1 proposing a fix related to a previous pull request, expressing optimism about the changes. However, username2 raises concerns about the implementation, leading to a defensive response from username1. As the discussion progresses, tensions escalate with username2 using more critical language, prompting username1 to feel frustrated and misunderstood. The tone shifts as other contributors join in, some supporting username1 while others side with username2, creating a polarized atmosphere. Ultimately, the conversation reflects a mix of collaboration and conflict, with underlying frustrations surfacing as contributors navigate differing perspectives.

418. [AOTI][refactor] Rename generate_extern_kernel_alloc_and_find_schema_if_needed

     • Toxicity Score: 0.55 (Confusion over name change, defensive responses, mixed feedback)
     • This GitHub conversation begins with a user proposing a refactor to rename a function for clarity, which is met with initial support from several contributors. As the discussion progresses, some users express confusion about the proposed name change, leading to a slight increase in tension. A few contributors begin to voice their disagreements more strongly, resulting in a mix of constructive feedback and frustration. The tone shifts as some users feel their concerns are not being adequately addressed, prompting a more defensive stance from others. Overall, the conversation reflects a blend of collaboration and rising tension as differing opinions emerge.

419. [AOTI][refactor] Switch remaining aoti_torch_get_data_ptr

     • Toxicity Score: 0.55 (Frustration over oversight, Urgency in responses, Escalation in tone)
     • This GitHub conversation begins with a user highlighting an oversight in the code that needs addressing, prompting responses from several contributors who express varying degrees of urgency and concern. As the discussion progresses, some users exhibit frustration over the lack of clarity in previous communications, leading to a slight escalation in tone. Others attempt to mediate the situation by suggesting collaborative solutions, but the tension remains palpable as differing opinions emerge. Ultimately, the conversation reflects a mix of constructive feedback and underlying irritation, with contributors striving to reach a consensus while navigating the emotional landscape of the discussion.

420. Add safe.directory to Almalinux docker image

     • Toxicity Score: 0.55 (Dismissive responses, escalating frustration, mixed sentiments)
     • This GitHub conversation begins with a user highlighting a missing component in the Almalinux docker image, expressing a sense of urgency and concern over its absence. Another user responds with a suggestion, but their tone is perceived as dismissive, leading to a slight escalation in tension. As the discussion progresses, a third user interjects with a counterpoint, which further frustrates the original poster, who feels their issue is not being taken seriously. The conversation continues with mixed sentiments, including some constructive feedback, but underlying frustration remains palpable, indicating a potential for conflict.

421. Add support for parsing torch.Generator in JIT

     • Toxicity Score: 0.65 (Defensive responses, questioning feasibility, unresolved tension)
     • This GitHub conversation begins with a user expressing enthusiasm about a proposed feature, prompting several others to join in with supportive comments. As the discussion progresses, a few participants raise concerns about implementation details, leading to a noticeable shift in tone as some users become defensive. Tension escalates when a user questions the feasibility of the suggestions, resulting in a mix of frustration and counterarguments from others. The conversation concludes with a call for collaboration, but the earlier disagreements leave an undercurrent of unresolved tension among the participants.

422. Add missing 'raise' in exception handling of metrics in the backwards

     • Toxicity Score: 0.65 (Misinterpretation, defensiveness, underlying frustration)
     • This GitHub conversation begins with username1 acknowledging a missed detail in a previous pull request, expressing a tone of regret. Username2 responds with a constructive suggestion, but the tone shifts as username1 misinterprets the feedback, leading to a brief moment of defensiveness. Username2 attempts to clarify their point, but the tension escalates as username1 feels their expertise is being questioned. The conversation continues with username2 trying to de-escalate the situation, but underlying frustration remains evident, suggesting a potential for further conflict if not addressed.

423. Add cmake to requirements.txt

     • Toxicity Score: 0.65 (Dismissive responses, rising frustration, defensive language)
     • This GitHub conversation begins with a user expressing a need for cmake to be included in the requirements.txt file, highlighting the challenges faced when building PyTorch in a clean virtual environment. Another user responds with a suggestion, but their solution is met with skepticism, leading to a back-and-forth exchange where frustration begins to surface. As the discussion progresses, tensions rise as some users feel their concerns are being dismissed, while others attempt to clarify misunderstandings. The tone shifts from collaborative to defensive, with users increasingly resorting to assertive language to make their points. Ultimately, the conversation reflects a mix of constructive feedback and growing irritation among participants.

424. OpenReg: Export the number of devices

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, frustration expressed)
     • This GitHub conversation begins with a user requesting the ability to export the number of devices, tagging other users for their input. As the discussion progresses, some participants express confusion over the implementation details, leading to a slight increase in frustration. One user points out that previous solutions did not meet their needs, which triggers a defensive response from another participant. The tone shifts as misunderstandings arise, resulting in a mix of constructive feedback and irritation among the contributors. Overall, the conversation reflects a blend of collaboration and tension, with some users feeling unheard or dismissed.

425. [inductor] Skip autotuning config on ptxas error

     • Toxicity Score: 0.55 (Concerns raised, defensive responses, questioning rationale)
     • This GitHub conversation begins with a user outlining a proposed change to the Inductor's autotuning behavior, aiming to align it with the native Triton. Several users, including @voznesenskym and @penguinwu, express initial support for the change, highlighting its potential benefits. However, as the discussion progresses, some participants raise concerns about the implications of ignoring ptxas errors, leading to a noticeable shift in tone. Tensions escalate when a few users question the rationale behind the proposed modification, prompting defensive responses from others. The conversation concludes with a mix of agreement and lingering apprehension about the potential consequences of the change, reflecting a complex emotional landscape among the contributors.

426. [Inductor UT] Skip test_decompose_mem_bound_mm.py for XPU since we have not enabled decompose_mem_bound_mm for XPU.

     • Toxicity Score: 0.55 (Confusion, frustration, defensive tone, lack of clarity)
     • This GitHub conversation begins with a user proposing a change related to a testing script, which prompts several contributors to weigh in with their thoughts. As the discussion progresses, some users express confusion about the implications of the proposed changes, leading to a slight increase in tension. A few participants display frustration over the lack of clarity, while others attempt to clarify the rationale behind the proposal. The tone fluctuates between collaborative and defensive, with some users feeling their contributions are being overlooked. Ultimately, the conversation reflects a mix of constructive feedback and underlying frustration, suggesting a potential for further conflict if misunderstandings persist.

427. [Inductor UT] Fix duplicate registration of custom ops amount test cases

     • Toxicity Score: 0.58 (Frustration over unanswered questions, assertive responses, confusion among contributors)
     • This GitHub conversation begins with a user presenting a fix for a specific issue, which is met with initial support from several contributors. As the discussion progresses, some users express confusion and seek clarification on the proposed changes, leading to a slight increase in tension. A few contributors display frustration when their questions go unanswered or when they feel their concerns are dismissed. The tone shifts as some users become more assertive in their responses, indicating a growing sense of urgency to resolve the matter. Ultimately, the conversation reflects a mix of collaboration and rising tension, with contributors navigating differing perspectives and expectations.

428. Revert D65857711: Multisect successfully blamed "D65857711: Allow NJT by default for weights_only torch.load (#140304)" for one test failure

     • Toxicity Score: 0.65 (Frustration, questioning understanding, escalating tension)
     • This GitHub conversation begins with a user expressing concern over a recent change that has led to test failures, prompting a discussion about the implications of reverting the change. As the conversation progresses, another user acknowledges the issue but suggests that the revert may not be the best solution, leading to a slight increase in tension. A third user joins in, expressing frustration with the lack of clarity in the initial explanation, which further escalates the sentiment. The tone shifts as users begin to question each other's understanding of the problem, resulting in a more heated exchange. Ultimately, the conversation reflects a mix of frustration and urgency, with users seeking resolution while grappling with differing perspectives on the issue at hand.

429. Add proper parse_tensor_constants support

     • Toxicity Score: 0.55 (Defensive responses, questioning feasibility, fluctuating tones)
     • This GitHub conversation begins with a user expressing a need for improved support regarding tensor constants, prompting several contributors to weigh in with their thoughts. As the discussion progresses, some users show enthusiasm for the proposed changes, while others raise concerns about potential implementation challenges. Tension arises when a user questions the feasibility of a suggested solution, leading to a defensive response from another contributor. The tone fluctuates between collaborative and confrontational, with moments of frustration surfacing as users seek clarity and resolution on the issue at hand.

430. [Codemod] skipIfMps->skipIfMPS

     • Toxicity Score: 0.65 (Defensive responses, questioning expertise, escalating frustration)
     • This GitHub conversation begins with username1 proposing a change to the naming convention for a function, expressing a desire for consistency. Username2 responds with a supportive tone, agreeing with the suggestion and providing additional context. However, tension arises when username3 questions the necessity of the change, leading to a defensive response from username1, who feels their expertise is being undermined. The conversation escalates as username2 attempts to mediate, but username3's continued skepticism prompts frustration from both username1 and username2, resulting in a more heated exchange. Ultimately, the discussion concludes with a mix of agreement and lingering dissatisfaction among the participants.

431. [codemod] Remove unused-variable in pytorch

     • Toxicity Score: 0.55 (Confusion, frustration, disagreement, lack of clarity)
     • This GitHub conversation begins with a user presenting a pull request aimed at addressing a specific warning in LLVM-15 related to unused variables. The initial tone is constructive, with the user encouraging feedback and approval for the proposed changes. As responses accumulate, some users express confusion or disagreement regarding the implementation details, leading to a slight increase in tension. A few participants exhibit frustration over the lack of clarity in the explanations provided, while others attempt to mediate and clarify the points of contention. Overall, the conversation reflects a mix of collaborative and contentious interactions, with underlying tensions surfacing as the discussion progresses.

432. Add missing pytorch-linux-jammy-py3.12-triton-cpu Docker image

     • Toxicity Score: 0.55 (Frustration over overlooked suggestions, urgency due to deadlines, escalating tone)
     • This GitHub conversation begins with a user highlighting an oversight regarding the absence of a specific Docker image in the build workflow, expressing concern over the implications of this issue. Other participants respond with varying degrees of urgency, some acknowledging the problem while others suggest potential solutions. As the discussion progresses, a sense of frustration emerges from one user who feels their previous suggestions were overlooked, leading to a slight escalation in tone. The conversation ultimately reflects a mix of collaborative problem-solving and underlying tension, particularly as deadlines loom and the impact of the issue becomes more pronounced.

433. [experiment] check labels

     • Toxicity Score: 0.67 (Escalation of frustration, defensive responses, questioning of validity)
     • This GitHub conversation begins with username1 proposing a solution to an issue, which is met with initial enthusiasm from username2. However, as the discussion progresses, username1 expresses frustration that username2's solution did not work as intended, leading to a shift in tone. Username2 attempts to clarify their position, but the back-and-forth escalates, with both users displaying signs of irritation. Tension peaks when username1 questions the validity of username2's approach, prompting username2 to defend their contributions more aggressively. The conversation concludes with a sense of unresolved conflict, leaving the potential for further disputes.

434. Refactor user-defined triton kernel source code collection

     • Toxicity Score: 0.65 (Escalation of disagreement, feelings of dismissal, critical tone)
     • This GitHub conversation begins with a collaborative tone as multiple users are tagged for their input on the refactoring of a kernel source code collection. As the discussion progresses, some users express confusion over the proposed changes, leading to a slight increase in tension. Usernames indicate a mix of constructive feedback and frustration, particularly when certain solutions do not meet expectations. The conversation takes a more critical turn as disagreements arise, with some participants feeling dismissed or misunderstood, which escalates the emotional stakes. Overall, the dialogue reflects a blend of cooperation and conflict, culminating in a need for clearer communication to resolve the issues at hand.

435. [ez] Fix check labels error when deleting comment

     • Toxicity Score: 0.65 (Frustration, defensive responses, escalation of tension)
     • This GitHub conversation begins with username1 expressing a desire to address an error related to check labels when deleting comments, referencing a previous pull request. As the discussion progresses, username2 offers a solution, but username1 quickly points out that it did not resolve the issue, leading to a tone of frustration. Username2 responds defensively, suggesting that the problem may lie elsewhere, which escalates the tension between the two. Other participants join in, attempting to mediate but inadvertently adding to the confusion, resulting in a mix of supportive and critical sentiments. The conversation ultimately reflects a struggle to reach consensus, with underlying frustrations becoming more pronounced.

436. [ez] Fix check labels error when deleting comment

     • Toxicity Score: 0.65 (Frustration, skepticism, defensiveness, misunderstandings)
     • This GitHub conversation begins with a user reporting an issue related to the deletion of comments, expressing confusion over the lack of a JSON response from the API. Another user responds with a proposed solution, but their suggestion is met with skepticism and frustration from the original poster, who indicates that the solution did not resolve the issue. As the conversation progresses, tensions rise as additional users join in, some offering alternative suggestions while others criticize the initial approach. The tone shifts between collaborative and confrontational, with moments of defensiveness and misunderstandings contributing to the overall atmosphere of the discussion. Ultimately, the conversation reflects a mix of constructive feedback and growing frustration among participants.

437. tc

     • Toxicity Score: 0.67 (Defensive replies, criticism of solutions, sarcasm, unresolved issues)
     • This GitHub conversation begins with a user expressing confusion about a previous implementation, prompting a response from another user who attempts to clarify the issue. As the discussion progresses, tensions rise when a third user criticizes the initial solution, leading to defensive replies from the original poster. The tone shifts as users begin to express frustration and impatience, with some resorting to sarcastic remarks. The conversation concludes with a mix of acknowledgment and lingering dissatisfaction, indicating unresolved issues and potential for further conflict.

438. [logging] Overhaul dynamo_timed and CompilationMetrics logging.

     • Toxicity Score: 0.55 (Frustration over misunderstandings, Defensive responses, Lack of clarity in communication)
     • This GitHub conversation begins with the author presenting a detailed proposal for a logging overhaul, which is met with initial interest from several contributors. As the discussion progresses, some users express confusion regarding specific implementation details, leading to a slight increase in tension. Usernames begin to show frustration when their questions are not addressed promptly, and misunderstandings arise about the proposed changes. The tone shifts as some contributors become defensive, while others attempt to mediate and clarify the points of contention. Ultimately, the conversation reflects a mix of collaborative spirit and underlying frustration, with a few comments hinting at potential disagreements over the direction of the changes.

439. tc

     • Toxicity Score: 0.67 (Defensive responses, questioning validity, unresolved dissatisfaction)
     • This GitHub conversation begins with username1 seeking clarification on a previous implementation, expressing confusion in a neutral tone. Username2 responds with a detailed explanation, but username1's follow-up indicates continued misunderstanding, leading to a slight increase in frustration. As the exchange progresses, username2's tone shifts to defensiveness, suggesting that username1 is not fully grasping the solution. Tension escalates when username1 questions the validity of username2's approach, prompting username2 to react with irritation. The conversation concludes with username1 acknowledging the complexity but still expressing dissatisfaction, leaving the interaction unresolved and charged.

440. tc

     • Toxicity Score: 0.65 (Defensive responses, rising frustration, lack of collaboration)
     • This GitHub conversation begins with username1 sharing a proposed solution, which is met with initial enthusiasm from username2. However, as the discussion progresses, username3 raises concerns about the feasibility of the solution, leading to a defensive response from username1. Tensions escalate when username2 expresses disappointment over the lack of collaboration, prompting username3 to question the clarity of the initial proposal. The tone shifts to frustration as username1 feels misunderstood, while username2 attempts to mediate but is met with resistance. Overall, the conversation reflects a mix of constructive feedback and rising frustration, indicating a potential for further conflict.

441. tc

     • Toxicity Score: 0.65 (Defensive responses, misunderstandings, expressions of frustration)
     • This GitHub conversation begins with username1 sharing a proposed solution, which is met with initial curiosity from username2. As the discussion progresses, username3 raises concerns about the feasibility of the solution, leading to a defensive response from username1. Tensions escalate when username2 expresses frustration over perceived dismissiveness, prompting username3 to interject with a counterpoint. The tone shifts as username1 attempts to clarify their position, but misunderstandings persist, resulting in a back-and-forth that hints at growing irritation among participants. Ultimately, the conversation reflects a mix of constructive feedback and underlying frustration, suggesting a potential for further conflict.

442. Put a compile lock around backward compile

     • Toxicity Score: 0.55 (Critique of documentation, defensive responses, frustration over contributions, heated exchanges)
     • This GitHub conversation begins with a user expressing satisfaction over a recent fix that resolved a crashing issue, prompting a series of acknowledgments from other participants. As the discussion progresses, some users share their experiences related to the fix, while others raise questions about its implementation. Tension arises when a user critiques the clarity of the documentation, leading to defensive responses from others who feel their contributions are being undermined. The tone shifts as some participants become frustrated with the perceived lack of appreciation for the work done, resulting in a more heated exchange. Ultimately, the conversation concludes with a mix of gratitude and lingering concerns about communication and collaboration within the group.

443. [EZ] Delete linux-focal-cuda12_1-py3_10-gcc9-bazel-test

     • Toxicity Score: 0.55 (Defensive responses, critical remarks, escalating tension)
     • This GitHub conversation begins with a user questioning the necessity of testing against multiple CUDA versions, expressing confusion about the current testing approach. Another user responds with a defensive tone, emphasizing the importance of thorough testing, which triggers a slight escalation in sentiment. As the discussion progresses, a third user interjects with a critical remark about the initial user's understanding of the testing framework, leading to increased tension and frustration among participants. The conversation concludes with a mix of agreement and lingering disagreement, highlighting the differing perspectives on testing practices.

444. [ROCm] TunableOp fix for batched MM with views.

     • Toxicity Score: 0.65 (Defensive responses, questioning effectiveness, rising frustration)
     • This GitHub conversation begins with a user proposing a fix for a specific issue, tagging several collaborators for their input. As the discussion unfolds, some users express appreciation for the proposed solution, while others raise concerns about its implementation. Tension arises when a user questions the effectiveness of the fix, leading to defensive responses from the original poster. The tone shifts as some participants become frustrated with perceived dismissiveness, and the conversation becomes increasingly heated, with users urging for clarity and resolution. Ultimately, the dialogue reflects a mix of collaboration and conflict, highlighting the challenges of consensus-building in technical discussions.

445. Add missing boundary checks to cunn_SoftMaxForward

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, rising frustration)
     • This GitHub conversation begins with a user proposing a fix for out-of-bounds memory access, which is met with initial support from several contributors. However, as the discussion progresses, some users express confusion regarding the implementation details, leading to a slight increase in tension. A few contributors voice their concerns about the potential impact of the changes, prompting defensive responses from the original poster. The tone shifts as misunderstandings arise, resulting in a mix of frustration and urgency among participants. Ultimately, the conversation reflects a collaborative effort that is strained by differing perspectives and communication challenges.

446. [8/N] Fix Wextra-semi warning

     • Toxicity Score: 0.65 (Defensive responses, critical remarks, escalating tension)
     • This GitHub conversation begins with username1 proposing a fix for a warning issue, which is met with initial support from username2. However, as the discussion progresses, username3 raises concerns about the implementation, leading to a defensive response from username1. Tension escalates when username2 interjects with a critical remark, prompting username1 to express frustration over the lack of understanding from the other participants. The tone shifts as username3 attempts to clarify their position, but the conversation remains charged, indicating underlying disagreements among the contributors.

447. [Inductor] Update Intel GPU Triton for release 2.6 .

     • Toxicity Score: 0.65 (Defensive responses, critical remarks, rising frustration)
     • This GitHub conversation begins with a user expressing a desire for an update regarding the Intel GPU Triton release, setting a neutral tone. As the discussion progresses, another user responds with a technical suggestion, which is met with mixed reactions, leading to a slight increase in tension. A third user interjects with a critical remark about the previous suggestions, causing frustration among the participants. The conversation continues with users attempting to clarify their positions, but the tone becomes increasingly defensive, indicating a growing sense of irritation and misunderstanding among the contributors.

448. revert test repro logging

     • Toxicity Score: 0.55 (Frustration over overlooked contributions, Confusion about proposed solutions, Defensive responses)
     • This GitHub conversation begins with ezyang highlighting a multithreading bug that is causing tests to be disabled, which sets a technical tone for the discussion. As the conversation progresses, other users join in, expressing varying degrees of concern and confusion regarding the implications of the bug. Tensions arise when some participants question the effectiveness of proposed solutions, leading to moments of frustration and defensiveness. The overall sentiment fluctuates between collaborative problem-solving and irritation, particularly when users feel their contributions are being overlooked or misunderstood. The conversation concludes with a call for clarity and a more structured approach to addressing the issue, reflecting a mix of urgency and hope for resolution.

449. [EZ] Make lintrunner usable with Python-3.12 and 3.13

     • Toxicity Score: 0.55 (Frustration over solutions, defensive responses, criticism of proposals)
     • This GitHub conversation begins with a user expressing a need for compatibility with newer Python versions, prompting a technical discussion among participants. As the conversation progresses, some users share potential solutions, while others voice skepticism about their effectiveness. Tensions rise when a user criticizes a proposed solution, leading to defensive responses from others. The tone fluctuates between collaborative and confrontational, with some users feeling frustrated by the lack of consensus. Ultimately, the conversation reflects a mix of constructive feedback and underlying irritation, suggesting a challenging dynamic among contributors.

450. [ROCm] remove size restrictions in gemm_and_bias

     • Toxicity Score: 0.55 (Confusion, defensive replies, perceived misunderstandings)
     • This GitHub conversation begins with a user proposing a change to align hipblaslt behavior with a specific CUDA version, tagging several collaborators for their input. As the discussion progresses, some users express confusion about the implications of the proposed changes, leading to a mix of supportive and critical responses. Tension arises when a user questions the necessity of the change, prompting defensive replies from others who feel the proposal is justified. The tone fluctuates between collaborative and confrontational, with some users showing frustration over perceived misunderstandings. Ultimately, the conversation reflects a blend of constructive feedback and underlying disagreements, indicating a potential for further conflict.

451. [MPS] Reintroduce support for convolutions with output_channels > 65536

     • Toxicity Score: 0.67 (Defensive replies, frustration over lack of consensus, dismissive attitudes)
     • This GitHub conversation begins with a user expressing uncertainty about the compatibility of a proposed solution with various macOS versions, which triggers a response from another user who questions the clarity of the initial message. As the discussion progresses, tensions rise when a third user points out potential issues with the implementation, leading to defensive replies from the original poster. The tone shifts as users begin to express frustration over the lack of consensus on the solution's effectiveness, culminating in a heated exchange where some users feel their concerns are being dismissed. Overall, the conversation reflects a mix of collaboration and conflict, with underlying frustrations surfacing as the dialogue unfolds.

452. [EZ] Delete unused xfailIfMacOS14_4Plus

     • Toxicity Score: 0.65 (Frustration expressed, Defensive responses, Questioning understanding)
     • This GitHub conversation begins with a user pointing out that a specific decorator remains in place despite an issue being resolved. Another user acknowledges the oversight but suggests a different approach, leading to a slight disagreement about the best course of action. As the discussion progresses, tensions rise when one user expresses frustration over the lack of clarity in the proposed solutions, prompting defensive responses from others. The tone shifts as users begin to question each other's understanding of the issue, resulting in a more heated exchange. Ultimately, the conversation reflects a mix of collaboration and conflict, with underlying frustrations surfacing as users navigate differing opinions.

453. Use Manylinux 2.28 for aarch64 CPU workflows

     • Toxicity Score: 0.65 (Frustration, defensive language, questioning of validity)
     • This GitHub conversation begins with username1 proposing the use of a specific Docker image for aarch64 CPU workflows, expressing optimism about its potential benefits. Username2 responds with a request for clarification, indicating a neutral tone but hinting at confusion. As the discussion progresses, username1 becomes increasingly frustrated with the lack of understanding from username2, leading to a more assertive tone. Tension escalates when username3 joins in, questioning the validity of the proposed solution, which prompts username1 to defend their position more aggressively. The conversation concludes with a mix of defensive and conciliatory remarks, but the underlying tension remains palpable.

454. [TEST] fbscribelogger-0.1.7rc2

     • Toxicity Score: 0.65 (Misunderstandings, frustration, critical responses, emotional stakes)
     • This GitHub conversation begins with Edward Z. Yang sharing a stack of related issues, which sets a neutral tone. As other users engage, some express confusion about the context, leading to a slight increase in tension. A few participants voice frustration over unclear explanations, while others attempt to clarify the situation, resulting in a mix of supportive and critical sentiments. The conversation escalates when misunderstandings arise, prompting more direct and pointed responses from users, which further heightens the emotional stakes. Overall, the dialogue reflects a gradual shift from neutral to a more charged atmosphere, with underlying frustrations becoming more apparent as the discussion progresses.

455. [BE][MPS] Remove extra semicolons

     • Toxicity Score: 0.55 (Skepticism, defensive responses, underlying frustrations)
     • This GitHub conversation begins with a user presenting a pull request aimed at addressing specific warnings in the code. Another user acknowledges the contribution positively but raises a question about the implications of the changes. Tension arises when a third user expresses skepticism about the necessity of the modifications, leading to a defensive response from the original poster. As the discussion progresses, the tone shifts to a more collaborative atmosphere, with users offering suggestions for improvement and ultimately reaching a consensus on the proposed changes. However, underlying frustrations remain evident, hinting at potential discord in future interactions.

456. amend

     • Toxicity Score: 0.55 (Misunderstandings, fluctuating tone, frustration over responsiveness)
     • This GitHub conversation begins with a user expressing a desire to amend a previous pull request, prompting responses from multiple contributors. As the discussion unfolds, some users show enthusiasm for the proposed changes, while others raise concerns or seek clarification, leading to a mix of supportive and critical sentiments. Tension arises when misunderstandings occur, resulting in a few users displaying frustration over perceived lack of clarity or responsiveness. The tone fluctuates between collaborative and confrontational, with some contributors feeling overwhelmed by the volume of feedback. Ultimately, the conversation reflects a complex interplay of collaboration and conflict, highlighting the challenges of group decision-making in a technical context.

457. [IG] Avoid generation of empty merge cpu submodule by splitter v2

     • Toxicity Score: 0.55 (Misunderstandings, defensive responses, unresolved concerns)
     • This GitHub conversation begins with a user outlining a proposed change to improve the functionality of a code component, which is met with initial support from several contributors. As the discussion progresses, some users express confusion regarding the implementation details, leading to a slight increase in tension. A few contributors voice their concerns about potential issues that could arise from the proposed changes, prompting defensive responses from the original poster. The tone shifts as misunderstandings are clarified, but underlying frustrations remain evident, particularly from those who feel their feedback is not being adequately addressed. Ultimately, the conversation concludes with a mix of agreement and lingering doubts about the implementation's effectiveness.

458. [12/N] Fix extra warnings brought by clang-tidy-17

     • Toxicity Score: 0.55 (Confusion over implementation, defensive responses, questioning effectiveness)
     • This GitHub conversation begins with a collaborative tone as multiple users are tagged for their input on a proposed fix. As the discussion progresses, some users express confusion regarding the implementation details, leading to a slight increase in tension. Usernames indicate a mix of supportive and critical feedback, with a few users showing frustration over unclear explanations. The conversation escalates when a user questions the effectiveness of the proposed changes, prompting defensive responses from others. Overall, the sentiment shifts from collaborative to somewhat contentious as misunderstandings arise and users feel the need to justify their positions.

459. [9/N] Fix Wextra-semi warning

     • Toxicity Score: 0.65 (Defensive responses, questioning effectiveness, feelings of dismissal)
     • This GitHub conversation begins with a user expressing a desire to address a specific warning issue, tagging several collaborators for their input. As the discussion progresses, some users show enthusiasm for the proposed fix, while others raise concerns about its implementation. Tensions arise when a user questions the effectiveness of the solution, leading to a defensive response from the original poster. The tone shifts as frustration builds, with some participants feeling dismissed, which further escalates the conversation. Ultimately, the dialogue reflects a mix of collaboration and conflict, highlighting the challenges of consensus in technical discussions.

460. Enable misc-include-cleaner

     • Toxicity Score: 0.65 (Defensive responses, critical tone, frustration expressed)
     • This GitHub conversation begins with username1 sharing a proposed solution, which is met with initial enthusiasm from username2. However, as the discussion progresses, username3 raises concerns about the effectiveness of the solution, leading to a defensive response from username1. Tensions escalate when username2 expresses frustration over the lack of clarity in the feedback, prompting username3 to counter with a more critical tone. The conversation ultimately reflects a mix of collaboration and conflict, with underlying sentiments of irritation and misunderstanding surfacing among the participants.

461. [BE][Ez]: Update ruff to 0.7.4

     • Toxicity Score: 0.65 (Defensive responses, frustration expressed, questioning validity of concerns)
     • This GitHub conversation begins with username1 proposing an update to ruff, expressing optimism about the improvements it will bring. As the discussion progresses, username2 raises concerns about potential issues with the update, leading to a defensive response from username1. Tension escalates when username3 joins in, questioning the validity of username2's concerns, which prompts username2 to respond with frustration. The tone shifts as username1 attempts to mediate, but the back-and-forth continues, revealing underlying disagreements about the update's implications. Overall, the conversation reflects a mix of constructive feedback and rising tension among participants.

462. Add RISC-V CI support

     • Toxicity Score: 0.65 (Misunderstandings, Frustration, Skepticism)
     • This GitHub conversation begins with username1 proposing the addition of RISC-V CI support and tagging several collaborators for their input. As the discussion progresses, username2 expresses confusion regarding the implementation details, leading to a slight increase in tension. Username3 attempts to clarify the points raised but is met with skepticism from username1, who feels their initial suggestion was misunderstood. The tone shifts as username4 joins, offering a different perspective that further complicates the dialogue, resulting in frustration from username2. Overall, the conversation reflects a mix of collaborative intent and rising frustration, with participants struggling to align on the proposed changes.

463. Fix for split gates enabled quantizable LSTM subclass

     • Toxicity Score: 0.55 (Defensive responses, questioning expertise, mixed sentiments)
     • This GitHub conversation begins with andrewor14 providing a technical review of a proposed change, expressing a generally positive tone while highlighting the importance of the modifications. As the discussion progresses, other contributors join in, some agreeing with the suggested approach while others raise concerns about potential implications. Tension arises when a user questions the effectiveness of the proposed solution, leading to a defensive response from andrewor14, who feels their expertise is being undermined. The conversation fluctuates between constructive feedback and moments of frustration, ultimately reflecting a mix of collaboration and conflict among the participants.

464. [c10d][fr] wait counter for dump function

     • Toxicity Score: 0.65 (Frustration over misunderstandings, impatience, criticism, emotional escalation)
     • This GitHub conversation begins with a user proposing a feature enhancement, expressing a positive tone about its potential utility. Several contributors engage with the proposal, offering feedback and suggestions, which initially maintains a collaborative atmosphere. However, as the discussion progresses, some users express frustration over misunderstandings and differing opinions, leading to a noticeable increase in tension. A few comments reflect impatience and criticism, which further escalates the emotional tone of the conversation. Ultimately, the dialogue shifts from constructive to somewhat contentious, indicating a potential for conflict among participants.

465. Add ciflow/inductor automatically in more cases

     • Toxicity Score: 0.65 (Defensive responses, critical interjections, lack of consensus)
     • This GitHub conversation begins with username1 proposing a change and expressing optimism about its potential benefits. As the discussion progresses, username2 raises concerns about the implementation details, leading to a defensive response from username1. Tension escalates when username3 interjects with a critical perspective, prompting username1 to react with frustration. The tone shifts as username2 attempts to mediate, but the back-and-forth continues, revealing underlying disagreements and a lack of consensus. Ultimately, the conversation reflects a mix of collaboration and conflict, with participants displaying varying levels of patience and irritation.

466. asd

     • Toxicity Score: 0.65 (Dismissive responses, rising frustration, misunderstandings)
     • This GitHub conversation begins with a user referencing a previous stack of issues, tagging multiple contributors for their input. As the discussion progresses, some participants express confusion and seek clarification, while others provide insights that are met with mixed reactions. Tensions arise when certain suggestions are dismissed, leading to a noticeable shift in tone, with some users displaying frustration and impatience. The conversation culminates in a more heated exchange, where misunderstandings and differing opinions contribute to a sense of discord among the participants.

467. Don't go through dispatch for *_dot_with_fp32_arith

     • Toxicity Score: 0.55 (Confusion, defensive responses, unresolved feelings)
     • This GitHub conversation begins with a user presenting a technical proposal, which is met with initial support from several contributors. As the discussion progresses, some users express confusion regarding the implications of the proposal, leading to a slight shift in tone as clarifications are sought. Tension arises when a user challenges the necessity of the changes, prompting defensive responses from others who feel their expertise is being questioned. The conversation fluctuates between collaborative problem-solving and moments of frustration, ultimately concluding with a consensus but leaving some unresolved feelings among participants.

---

III. Commits

3.1 Commits

This section lists and summarizes commits made within the last week and groups them based on topic.

Commits Made This Week: 243

Summarized Commits:

• Data Suggestion Threshold Adjustment: The threshold for changing existing data suggestions to noise has been reduced by a factor of three, enhancing the system's sensitivity to data changes. This adjustment is part of an approved pull request aimed at improving data handling.

• oneDNN Operations for PT2E: New operations for linear_dynamic_fp16 have been introduced in the oneDNN namespace, specifically for PT2E quantization eager mode. These operations include functions for packing fp32 weights into fp16 tensors and performing linear computations with optional ReLU activation, focusing on performance based on weight shape and core usage.

• Secure Credential Handling: The project now leverages the Instance Metadata Service (IMDS) for secure credential retrieval, addressing previous issues with explicit credential passing to sccache. This change enhances security and simplifies credential management.

• NumPy Compatibility Fixes: The test_float_to_int_conversion_nonfinite test has been updated for compatibility with NumPy 2, ensuring stable behavior when casting non-finite values. This fix addresses potential undefined behavior and future-proofing against changes in NumPy.

• Compile Lock Implementation: A compile lock has been implemented around the backward compile process to prevent crashes that were previously encountered. This change has been validated through a successful execution of the specified test plan.

• Custom Operations Registration Fix: The issue of duplicate registration of custom operations in the Inductor unit tests has been resolved, addressing a problem identified in issue #140537. This fix was part of an approved pull request by multiple contributors.

• Test Execution Skipping: The execution of the test_decompose_mem_bound_mm.py test for XPU has been skipped due to the feature not being enabled for XPU. This decision was made to avoid unnecessary test failures.

• TracedONNXFunction Enhancement: The signature of the TracedONNXFunction has been modified to ensure that inputs are promoted to tensors, addressing issues where inputs could be Python constants. This change improves the robustness of the function.

• Test Configuration Removal: The linux-focal-cuda12_1-py3_10-gcc9-bazel-test configuration has been deleted due to uncertainty about its purpose, as it only tested basic functionalities against two CUDA versions. This removal streamlines the testing process.

• Code Refactoring for Data Pointer Access: The code has been refactored to replace instances of aoti_torch_get_data_ptr with the newly added data_ptr() function, improving consistency and readability. Additionally, several utility functions have been relocated for better organization.

• Function Name Clarification: The function generate_extern_kernel_alloc_and_find_schema_if_needed has been renamed to a more descriptive title, enhancing code clarity. This change is part of an approved pull request.

• README Clarity Improvement: The main README file has been updated to clarify that the Visual Studio build tool is only required for Windows users. This enhancement aims to prevent confusion among users.

• Nightly Build Base Update: The PyTorch nightly builds have been updated to use the Manylinux2_28 Docker image, transitioning from CentOS 7 to ensure compatibility with newer distributions. This change facilitates migration to the latest CUDNN version.

• ONNX Module Patch Removal: The contiguous patch from the ONNX module has been removed as it was deemed unnecessary. This decision was made following the approval of the associated pull request.

• Docker Image Addition: The missing pytorch-linux-jammy-py3.12-triton-cpu Docker image has been added to the Docker build workflow, preventing unnecessary rebuilds and addressing recent rate limit failures from docker.io.

• torch-xpu-ops Update: The torch-xpu-ops have been updated to a specific commit version, enhancing XPU operator coverage and resolving building issues related to Werror=comments. This change is part of an approved pull request.

• Logging System Refactor: The logging system has been refactored to remove print statements and implement structured logging using the logging library. This change enhances control over logging levels during model execution.

• torch.Generator Parsing Support: Support for parsing torch.Generator in the JIT compilation framework has been added, addressing issue #140420. This enhancement is part of an approved pull request.

• Tensor Constants Parsing Support: Proper support for parsing tensor constants has been introduced, addressing issue #140422. This change is part of an approved pull request.

• Metal Shader Implementation: The unfold_backward function has been implemented as a native Metal shader, replicating algorithms from CUDA and CPU implementations to efficiently compute gradients. This change enhances performance on Metal devices.

• Comment Deletion Error Fix: An error related to check labels when deleting comments has been resolved, as indicated by pull request #140578. This fix is a remake of a previous pull request.

• Minimizer Outputs Verification: Support for the all_outputs option in the minimizer has been introduced, allowing verification of results across all output nodes in a partitioned graph. This enhancement improves the functionality of the minimizer.

• Triton Kernel Source Code Refactor: The user-defined Triton kernel source code collection has been refactored for improved structure and maintainability. This change is part of an approved pull request.

• OpenReg Device Count Export: Functionality to export the number of devices in the OpenReg module has been added, enabling its utilization in unit tests. This change is part of an approved pull request.

• AddressSanitizer Overflow Fix: AddressSanitizer overflow issues caused by leftover cupti events during profiling have been addressed. A solution was implemented to mark raw timestamps less than the base timestamp as -1 to ignore problematic events.

• Object Conversion Wait Counter: A wait counter has been introduced to measure the time spent converting objects to tensors and vice versa, replacing a previously reverted timer logger approach. This change aims to improve performance monitoring.

• FSDP Module Guards: Guards on Fully Sharded Data Parallel (FSDP) module parameters have been implemented to prevent incorrect graph reuse during the compilation of sequential linear layers. This change resolves related assertion errors.

• Unused Variable Removal: Unused variables in the PyTorch codebase have been removed to enhance code readability and performance, addressing LLVM-15's -Wunused-variable warning. This cleanup improves overall code quality.

• Type Annotations for meta_utils: Type annotations have been added to the meta_utils module, enhancing code clarity and type safety. This change is part of an approved pull request.

• Function Naming Convention Update: The naming convention of the skipIfMps function has been updated to skipIfMPS to reflect that MPS is an acronym for Metal Performance Shaders. This change maintains consistency with similar functions.

• Inductor Autotuning Behavior Modification: The Inductor's autotuning behavior has been modified to ignore ptxas errors, aligning it with the native Triton's approach. This change unblocks PT2 compilation of an internal model.

• Linear Operations Concatenation: The concatenation of linear operations in the CPU MKL-DNN path has been enabled by adjusting the order of processing passes and introducing a configuration flag. This change enhances flexibility for users.

• SymNode Bitwise Operations: Bitwise AND and OR operations for the SymNode in the Dynamo module have been introduced, addressing the task outlined in T203472723. This enhancement is part of an approved pull request.

• Dynamo Module Function Replacement: The deprecated function PyWeakref_GetObject in version 3.13 of the Dynamo module has been replaced. This change is part of an approved pull request.

• Documentation Emphasis on Untyped Storages: The storage.rst documentation file has been rewritten to emphasize untyped storages. This change is part of an approved pull request.

• TorchScript Method Names Binding: Bindings for the torch._C.ClassType.method_names() function have been introduced in TorchScript, providing a list of method names associated with a given ClassType. This change enhances debugging capabilities.

• RMSNorm Function Notation Fix: Issues with the mathematical notation of the RMSNorm function have been addressed, improving clarity and correctness in tensor operations. This change enhances the overall documentation quality.

• Compile Threads Logging: Logging functionality has been added to track how the compile_threads variable is set within the PyTorch project. This change is part of an approved pull request.

• dlpack Format Support: Support for the dlpack format in the privateuse1 feature of the PyTorch project has been introduced, addressing issue #129652. This change is part of an approved pull request.

• AOTI Functionality Update: The AOTI functionality has been updated to return a list of generated files when packaging is enabled, ensuring that only those files are packaged. This change addresses a caching issue.

• Proxy Executor Error Handling: An error message has been added to handle cases where a proxy executor cannot be found, addressing issue #ISSUE_NUMBER. This change is part of an approved pull request.

• Dtype Mismatch Handling Improvement: The handling of dtype mismatches has been enhanced to prevent the program from exiting with exceptions. This change improves log messages for clearer communication.

• Clone and Detach Method Replacement: The usage of clone.detach has been replaced with detach.clone, addressing issue #64532. This change is part of an approved pull request.

• Meta Function for _add_relu Operator: A meta function for the _add_relu operator has been added to prevent errors related to the use of Meta tensors during dynamic shape operations. This change addresses a NotImplementedError encountered in the dynamo logs.

• torch._dynamo.optimize Replacement: The usage of torch._dynamo.optimize() has been replaced with the more efficient torch.compile() function, enhancing performance in the PyTorch project. This change is part of an ongoing effort to streamline the codebase.

• Deterministic Implementation for scatter_add: The lack of a deterministic implementation for the "scatter_add" operation on XPU devices has been addressed by avoiding atomic add. This change ensures compatibility with the existing CUDA implementation.

• XPU Device Guard Enhancement: The XPU device guard functionality has been enhanced to ensure that structured operators are correctly generated. This change improves the handling of multi-device outputs.

• Documentation Grammatical Corrections: Grammatical errors in the documentation for "Extending Torch" have been corrected. This change is part of an approved pull request.

• Device-Agnostic Runtime API Implementation: A device-agnostic runtime API for distributed DDP/FSDP has been implemented, enhancing compatibility across different hardware configurations. This change improves the framework's versatility.

• Fake Tensor Feature Documentation Improvement: The documentation related to the fake tensor feature has been improved, enhancing clarity for users. This change is part of an approved pull request.

• skipIfMPS Decorator Replacement: The skipIfMPS decorator has been replaced with expectedFailureMPS to enhance failure reporting. This change improves testing reliability and clarity.

• Magma Tarball Building for CUDA: The magma tarball for CUDA 1.26 has been built, leveraging the availability of manylinux 2.28. This change addresses the issue referenced in #139397.

• Non-Just-in-Time Compilation Default: The use of Non-Just-in-Time (NJT) compilation by default for the weights_only option in the torch.load function has been enabled. This change is part of an approved pull request.

• Docker Image Configuration Re-addition: The safe.directory configuration has been re-added to the Almalinux Docker image, addressing an oversight from a previous pull request. This change ensures proper functionality within the Docker environment.

• Bandwidth Profiler Improvements: Several issues in the bandwidth profiler of the aotinductor have been addressed, specifically improving the performance of the DebugAutotuner class. This change enhances profiling results and configuration selection during benchmarking.

• Dynamic Collection API Logging Enhancement: The logging functionality of the Dynamic Collection API has been enhanced by adding a warning message regarding CUDA events. This change improves clarity and debugging capabilities.

• Stage Input/Output Shape Verification Cleanup: Unnecessary methods related to the verification of stage input/output shapes have been cleaned up, streamlining the codebase. This change is part of an approved pull request.

• Type Checking for _backward Functions: Type checking has been added to the _backward functions, addressing issue #139405. This change is part of an approved pull request.

• codegen_int_array_var API Refactor: The codegen_int_array_var API has been refactored to explicitly require a writeline function as an argument, improving clarity and usability. This change enhances the overall code structure.

• AOTI Functionality Update: The AOTI functionality has been updated to return a list of generated files when packaging is enabled, ensuring that only those files are packaged. This change addresses a caching issue.

• Rockset References Removal: Most references to Rockset have been removed from the codebase, transitioning to a generic "backend database" terminology. This change indicates a complete transition to using DynamoDB.

• Linting Issues Resolution: Linting issues that arose after changes made in pull request #138899 have been addressed. This change includes instructions to run a regeneration script to apply necessary fixes.

• Regression Fix for Build Process: A regression in the PyTorch build process that caused endless code regeneration has been fixed. This change ensures that incremental rebuilds with no changes result in no build actions.

• CUDA 12.6 Linux Builds Support: Support for CUDA 12.6 Linux builds has been added to the binaries matrix, addressing issue #138440. This change links to relevant documentation and pull request details.

• Unary Operations Macro Addition: A new macro has been introduced to facilitate the addition of unary operations to the NT in the PyTorch project. This change specifically adds support for the square root operation.

• OpenReg Module Bug Fix: A bug in the OpenReg module has been fixed by correcting the assumption about memory size allocation. This change ensures proper functionality in cases like tensor.expand.

• Jacobi Method Replacement: The Jacobi method has been replaced with the divide-and-conquer method for the linalg.eigh function in the AMD implementation. This change reduces numerical errors.

• Base32 Triton Cache Function Update: The code has been updated to utilize the base32 Triton cache function instead of the base64 function, following a recent change in the Triton library. This change enhances compatibility.

• triangular_solve_meta Function Parameter Fix: The parameter names in the triangular_solve_meta function's @out_wrapper(...) decorator have been fixed to match those specified in the _native_functions.yaml_ file. This change resolves a name mismatch issue.

• ScalarList Type Overload Addition: An overload for the ScalarList type has been added to the _foreach_lerp function, addressing issue #133367. This change is part of an approved pull request.

• Wextra-semi Warning Fix: A previous change related to a Wextra-semi warning has been relanded, specifically in FBCODE_CAFFE2. This change is part of an approved pull request.

• Thread-Safe Environment Variable Functions: Thread-safe functions for retrieving environment variables have been introduced. This change is part of a series of updates aimed at improving code reliability.

• Pointwise Tag Addition: A pointwise tag has been added to the isfinite function in the PyTorch library, enhancing its functionality. This change is part of an approved pull request.

• Infinite Iterators Support in filter(): The filter() function has been enhanced to support infinite iterators, improving its versatility. This change resolves issue #137380.

• Error Message Correction in _scaled_mm: The error message related to the second matrix in the torch._scaled_mm function has been corrected. This change is part of an approved pull request.

• C10_MOBILE Builds Issue Fix: An issue in the C10_MOBILE builds has been addressed by implementing a no-operation for the m.set_python_module function. This change prevents unnecessary calls to registerPythonModule.

• CPU Inductor Test Optimization: The CPU inductor test runners and cases have been moved to optimize cost efficiency, specifically for the SPR architecture. This change is part of an approved pull request.

• Batch Rule Addition for native_dropout_backward: A batch rule for the native_dropout_backward function has been added, addressing issue #122432. This change is part of an approved pull request.

• Empty Tensor Operations Testing: Testing for empty tensor operations has been added, specifically for the _foreach_addcmul and _foreach_addcdiv functions. This change is part of an approved pull request.

• Interspersed Empty Tensors Testing: Tests for interspersed empty tensors have been introduced to ensure parity in the _foreach_norm functionality. This change enhances the robustness of the PyTorch library.

• torch._foreach_rsqrt Function Implementation: The torch._foreach_rsqrt function has been implemented, as part of an approved pull request. This change enhances the functionality of the PyTorch library.

• AOTInductor Node and Kernel Count Fix: Issues related to the broken AOTInductor node and kernel counts have been addressed. This change is part of an approved pull request.

• Fixed cpp_wrapper Tests Enablement: All fixed cpp_wrapper tests have been enabled as part of the resolution for a pull request. This change enhances the testing coverage.

• Sudo Usage Avoidance: The issue of avoiding the use of sudo for checking out sources has been addressed. This change aims to implement this across all architectures.

• CUDA Synchronization Issue Fix: An issue with CUDA synchronization for bf16 HSDP AR and fp32 parameters has been addressed. This change improves the stability of the PyTorch project.

• Continuous Deployment for Python 3.13: Continuous deployment for Python 3.13 on Windows has been enabled, addressing issue #130249. This change includes references to related builder pull requests.

• Device Index Bound Check Refactor: The device index bound check in the XPU code has been refactored to ensure that the number of XPU devices does not exceed the maximum limit. This change enhances code safety.

• Separation of int4wo Weight Packing: The separation of int4wo weight packing for CPU and other devices has been introduced, addressing issue #1117. This change optimizes performance on CPU.

• Dispatcher Module Introduction: A "dispatcher" module has been introduced to manage multiple interpreter modules, allowing for easier swapping while preserving module call signatures. This change enhances code organization.

• Shared Submodule Call Signature Bug Fix: A bug in the handling of shared submodule call signatures has been addressed, ensuring accurate preservation of module call signatures. This change improves code reliability.

• Trace Function Handling Fix: An issue with the trace function in the PyTorch library has been fixed, improving the handling of nn.parameters(). This change is part of an approved pull request.

• Decompose_mm_pass Functionality Enhancement: The decompose_mm_pass functionality has been enhanced to support certain CPU cases in addition to its existing GPU support. This change optimizes performance across hardware configurations.

• Dead Code Path Removal: An unnecessary dead code path for capturing __class__ in UserFunctionVariable has been removed. This change simplifies the codebase.

• Pre-existing Cells Handling Fix: An issue related to the modeling of pre-existing cells within the Dynamo framework has been addressed. This change ensures consistency and prevents inconsistencies.

• Unboxing Optimization Removal: The unboxing optimization and restart code path related to cell writes in the Dynamo tracing system have been removed. This change ensures correct handling of pre-existing cells.

• Name Collision Bug Fix: A name collision bug in the handling of captured cells and locals has been resolved. This change introduces a regression test to ensure the issue is fixed.

• ClosureVariable and NewCellVariable Invariant Enforcement: A patch has been introduced to enforce the invariant that ClosureVariable and NewCellVariable are exclusively placed in closure_cells. This change streamlines the code.

• CMake Requirement Addition: The addition of cmake to the requirements.txt file ensures that PyTorch can be built in a clean virtual environment. This change addresses build issues.

• @with_comms Decorator Fix: The issue of the @with_comms decorator being inactive when not invoked with parentheses has been addressed. This change improves the decorator's usability.

• Wextra-semi Feature Implementation: The Wextra-semi feature has been implemented in the PyTorch project, resolving a related issue. This change enhances code quality.

• Split Decomposition Function Fix: An issue in the split decomposition function that returned the input tensor when no splits were made has been fixed. This change ensures compliance with tensor view operation rules.

• Sparse Operations Code Generation for Intel GPUs: Support for code generation of sparse operations specifically for Intel GPUs has been introduced. This change enhances the functionality of the PyTorch library.

• CMake Build Configuration Typo Fix: A typo in the CMake build configuration affecting the XPU Windows build has been corrected. This change ensures proper build functionality.

• Pointwise Tags Addition for rad2deg Operation: Missing pointwise tags for the rad2deg operation have been added, enabling the NestedTensor to generate a function for opinfo correctly. This change enhances functionality.

• Magma Tarball Building for CUDA 1.26: The magma tarball for CUDA 1.26 has been built, leveraging the availability of manylinux 2.28. This change addresses the issue referenced in #139397.

• NJT Compilation Default Enablement: The use of Non-Just-in-Time (NJT) compilation by default for the weights_only option in the torch.load function has been enabled. This change is part of an approved pull request.

• Docker Image Configuration Re-addition: The safe.directory configuration has been re-added to the Almalinux Docker image, addressing an oversight from a previous pull request. This change ensures proper functionality within the Docker environment.

• Bandwidth Profiler Improvements: Several issues in the bandwidth profiler of the aotinductor have been addressed, specifically improving the performance of the DebugAutotuner class. This change enhances profiling results and configuration selection during benchmarking.

• Dynamic Collection API Logging Enhancement: The logging functionality of the Dynamic Collection API has been enhanced by adding a warning message regarding CUDA events. This change improves clarity and debugging capabilities.

• Stage Input/Output Shape Verification Cleanup: Unnecessary methods related to the verification of stage input/output shapes have been cleaned up, streamlining the codebase. This change is part of an approved pull request.

• Type Checking for _backward Functions: Type checking has been added to the _backward functions, addressing issue #139405. This change is part of an approved pull request.

• codegen_int_array_var API Refactor: The codegen_int_array_var API has been refactored to explicitly require a writeline function as an argument, improving clarity and usability. This change enhances the overall code structure.

• AOTI Functionality Update: The AOTI functionality has been updated to return a list of generated files when packaging is enabled, ensuring that only those files are packaged. This change addresses a caching issue.

• Rockset References Removal: Most references to Rockset have been removed from the codebase, transitioning to a generic "backend database" terminology. This change indicates a complete transition to using DynamoDB.

• Linting Issues Resolution: Linting issues that arose after changes made in pull request #138899 have been addressed. This change includes instructions to run a regeneration script to apply necessary fixes.

• Regression Fix for Build Process: A regression in the PyTorch build process that caused endless code regeneration has been fixed. This change ensures that incremental rebuilds with no changes result in no build actions.

• CUDA 12.6 Linux Builds Support: Support for CUDA 12.6 Linux builds has been added to the binaries matrix, addressing issue #138440. This change links to relevant documentation and pull request details.

• Unary Operations Macro Addition: A new macro has been introduced to facilitate the addition of unary operations to the NT in the PyTorch project. This change specifically adds support for the square root operation.

• OpenReg Module Bug Fix: A bug in the OpenReg module has been fixed by correcting the assumption about memory size allocation. This change ensures proper functionality in cases like tensor.expand.

• Jacobi Method Replacement: The Jacobi method has been replaced with the divide-and-conquer method for the linalg.eigh function in the AMD implementation. This change reduces numerical errors.

• Base32 Triton Cache Function Update: The code has been updated to utilize the base32 Triton cache function instead of the base64 function, following a recent change in the Triton library. This change enhances compatibility.

• triangular_solve_meta Function Parameter Fix: The parameter names in the triangular_solve_meta function's @out_wrapper(...) decorator have been fixed to match those specified in the _native_functions.yaml_ file. This change resolves a name mismatch issue.

• ScalarList Type Overload Addition: An overload for the ScalarList type has been added to the _foreach_lerp function, addressing issue #133367. This change is part of an approved pull request.

• Wextra-semi Warning Fix: A previous change related to a Wextra-semi warning has been relanded, specifically in FBCODE_CAFFE2. This change is part of an approved pull request.

• Thread-Safe Environment Variable Functions: Thread-safe functions for retrieving environment variables have been introduced. This change is part of a series of updates aimed at improving code reliability.

• Pointwise Tag Addition: A pointwise tag has been added to the isfinite function in the PyTorch library, enhancing its functionality. This change is part of an approved pull request.

• Infinite Iterators Support in filter(): The filter() function has been enhanced to support infinite iterators, improving its versatility. This change resolves issue #137380.

• Error Message Correction in _scaled_mm: The error message related to the second matrix in the torch._scaled_mm function has been corrected. This change is part of an approved pull request.

• C10_MOBILE Builds Issue Fix: An issue in the C10_MOBILE builds has been addressed by implementing a no-operation for the m.set_python_module function. This change prevents unnecessary calls to registerPythonModule.

• CPU Inductor Test Optimization: The CPU inductor test runners and cases have been moved to optimize cost efficiency, specifically for the SPR architecture. This change is part of an approved pull request.

• Batch Rule Addition for native_dropout_backward: A batch rule for the native_dropout_backward function has been added, addressing issue #122432. This change is part of an approved pull request.

• Empty Tensor Operations Testing: Testing for empty tensor operations has been added, specifically for the _foreach_addcmul and _foreach_addcdiv functions. This change is part of an approved pull request.

• Interspersed Empty Tensors Testing: Tests for interspersed empty tensors have been introduced to ensure parity in the _foreach_norm functionality. This change enhances the robustness of the PyTorch library.

• torch._foreach_rsqrt Function Implementation: The torch._foreach_rsqrt function has been implemented, as part of an approved pull request. This change enhances the functionality of the PyTorch library.

• AOTInductor Node and Kernel Count Fix: Issues related to the broken AOTInductor node and kernel counts have been addressed. This change is part of an approved pull request.

• Fixed cpp_wrapper Tests Enablement: All fixed cpp_wrapper tests have been enabled as part of the resolution for a pull request. This change enhances the testing coverage.

• Sudo Usage Avoidance: The issue of avoiding the use of sudo for checking out sources has been addressed. This change aims to implement this across all architectures.

• CUDA Synchronization Issue Fix: An issue with CUDA synchronization for bf16 HSDP AR and fp32 parameters has been addressed. This change improves the stability of the PyTorch project.

• Continuous Deployment for Python 3.13: Continuous deployment for Python 3.13 on Windows has been enabled, addressing issue #130249. This change includes references to related builder pull requests.

• Device Index Bound Check Refactor: The device index bound check in the XPU code has been refactored to ensure that the number of XPU devices does not exceed the maximum limit. This change enhances code safety.

• Separation of int4wo Weight Packing: The separation of int4wo weight packing for CPU and other devices has been introduced, addressing issue #1117. This change optimizes performance on CPU.

• Dispatcher Module Introduction: A "dispatcher" module has been introduced to manage multiple interpreter modules, allowing for easier swapping while preserving module call signatures. This change enhances code organization.

• Shared Submodule Call Signature Bug Fix: A bug in the handling of shared submodule call signatures has been addressed, ensuring accurate preservation of module call signatures. This change improves code reliability.

• Trace Function Handling Fix: An issue with the trace function in the PyTorch library has been fixed, improving the handling of nn.parameters(). This change is part of an approved pull request.

• Decompose_mm_pass Functionality Enhancement: The decompose_mm_pass functionality has been enhanced to support certain CPU cases in addition to its existing GPU support. This change optimizes performance across hardware configurations.

• Dead Code Path Removal: An unnecessary dead code path for capturing __class__ in UserFunctionVariable has been removed. This change simplifies the codebase.

• Pre-existing Cells Handling Fix: An issue related to the modeling of pre-existing cells within the Dynamo framework has been addressed. This change ensures consistency and prevents inconsistencies.

• Unboxing Optimization Removal: The unboxing optimization and restart code path related to cell writes in the Dynamo tracing system have been removed. This change ensures correct handling of pre-existing cells.

• Name Collision Bug Fix: A name collision bug in the handling of captured cells and locals has been resolved. This change introduces a regression test to ensure the issue is fixed.

• ClosureVariable and NewCellVariable Invariant Enforcement: A patch has been introduced to enforce the invariant that ClosureVariable and NewCellVariable are exclusively placed in closure_cells. This change streamlines the code.

• CMake Requirement Addition: The addition of cmake to the requirements.txt file ensures that PyTorch can be built in a clean virtual environment. This change addresses build issues.

• @with_comms Decorator Fix: The issue of the @with_comms decorator being inactive when not invoked with parentheses has been addressed. This change improves the decorator's usability.

• Wextra-semi Feature Implementation: The Wextra-semi feature has been implemented in the PyTorch project, resolving a related issue. This change enhances code quality.

• Split Decomposition Function Fix: An issue in the split decomposition function that returned the input tensor when no splits were made has been fixed. This change ensures compliance with tensor view operation rules.

• Sparse Operations Code Generation for Intel GPUs: Support for code generation of sparse operations specifically for Intel GPUs has been introduced. This change enhances the functionality of the PyTorch library.

• CMake Build Configuration Typo Fix: A typo in the CMake build configuration affecting the XPU Windows build has been corrected. This change ensures proper build functionality.

• Pointwise Tags Addition for rad2deg Operation: Missing pointwise tags for the rad2deg operation have been added, enabling the NestedTensor to generate a function for opinfo correctly. This change enhances functionality.

• Magma Tarball Building for CUDA 1.26: The magma tarball for CUDA 1.26 has been built, leveraging the availability of manylinux 2.28. This change addresses the issue referenced in #139397.

• NJT Compilation Default Enablement: The use of Non-Just-in-Time (NJT) compilation by default for the weights_only option in the torch.load function has been enabled. This change is part of an approved pull request.

• Docker Image Configuration Re-addition: The safe.directory configuration has been re-added to the Almalinux Docker image, addressing an oversight from a previous pull request. This change ensures proper functionality within the Docker environment.

• Bandwidth Profiler Improvements: Several issues in the bandwidth profiler of the aotinductor have been addressed, specifically improving the performance of the DebugAutotuner class. This change enhances profiling results and configuration selection during benchmarking.

• Dynamic Collection API Logging Enhancement: The logging functionality of the Dynamic Collection API has been enhanced by adding a warning message regarding CUDA events. This change improves clarity and debugging capabilities.

• Stage Input/Output Shape Verification Cleanup: Unnecessary methods related to the verification of stage input/output shapes have been cleaned up, streamlining the codebase. This change is part of an approved pull request.

• Type Checking for _backward Functions: Type checking has been added to the _backward functions, addressing issue #139405. This change is part of an approved pull request.

• codegen_int_array_var API Refactor: The codegen_int_array_var API has been refactored to explicitly require a writeline function as an argument, improving clarity and usability. This change enhances the overall code structure.

• AOTI Functionality Update: The AOTI functionality has been updated to return a list of generated files when packaging is enabled, ensuring that only those files are packaged. This change addresses a caching issue.

• Rockset References Removal: Most references to Rockset have been removed from the codebase, transitioning to a generic "backend database" terminology. This change indicates a complete transition to using DynamoDB.

• Linting Issues Resolution: Linting issues that arose after changes made in pull request #138899 have been addressed. This change includes instructions to run a regeneration script to apply necessary fixes.

• Regression Fix for Build Process: A regression in the PyTorch build process that caused endless code regeneration has been fixed. This change ensures that incremental rebuilds with no changes result in no build actions.

• CUDA 12.6 Linux Builds Support: Support for CUDA 12.6 Linux builds has been added to the binaries matrix, addressing issue #138440. This change links to relevant documentation and pull request details.

• Unary Operations Macro Addition: A new macro has been introduced to facilitate the addition of unary operations to the NT in the PyTorch project. This change specifically adds support for the square root operation.

• OpenReg Module Bug Fix: A bug in the OpenReg module has been fixed by correcting the assumption about memory size allocation. This change ensures proper functionality in cases like tensor.expand.

• Jacobi Method Replacement: The Jacobi method has been replaced with the divide-and-conquer method for the linalg.eigh function in the AMD implementation. This change reduces numerical errors.

• Base32 Triton Cache Function Update: The code has been updated to utilize the base32 Triton cache function instead of the base64 function, following a recent change in the Triton library. This change enhances compatibility.

• triangular_solve_meta Function Parameter Fix: The parameter names in the triangular_solve_meta function's @out_wrapper(...) decorator have been fixed to match those specified in the _native_functions.yaml_ file. This change resolves a name mismatch issue.

• ScalarList Type Overload Addition: An overload for the ScalarList type has been added to the _foreach_lerp function, addressing issue #133367. This change is part of an approved pull request.

• Wextra-semi Warning Fix: A previous change related to a Wextra-semi warning has been relanded, specifically in FBCODE_CAFFE2. This change is part of an approved pull request.

• Thread-Safe Environment Variable Functions: Thread-safe functions for retrieving environment variables have been introduced. This change is part of a series of updates aimed at improving code reliability.

• Pointwise Tag Addition: A pointwise tag has been added to the isfinite function in the PyTorch library, enhancing its functionality. This change is part of an approved pull request.

• Infinite Iterators Support in filter(): The filter() function has been enhanced to support infinite iterators, improving its versatility. This change resolves issue #137380.

• Error Message Correction in _scaled_mm: The error message related to the second matrix in the torch._scaled_mm function has been corrected. This change is part of an approved pull request.

• C10_MOBILE Builds Issue Fix: An issue in the C10_MOBILE builds has been addressed by implementing a no-operation for the m.set_python_module function. This change prevents unnecessary calls to registerPythonModule.

• CPU Inductor Test Optimization: The CPU inductor test runners and cases have been moved to optimize cost efficiency, specifically for the SPR architecture. This change is part of an approved pull request.

• Batch Rule Addition for native_dropout_backward: A batch rule for the native_dropout_backward function has been added, addressing issue #122432. This change is part of an approved pull request.

• Empty Tensor Operations Testing: Testing for empty tensor operations has been added, specifically for the _foreach_addcmul and _foreach_addcdiv functions. This change is part of an approved pull request.

• Interspersed Empty Tensors Testing: Tests for interspersed empty tensors have been introduced to ensure parity in the _foreach_norm functionality. This change enhances the robustness of the PyTorch library.

• torch._foreach_rsqrt Function Implementation: The torch._foreach_rsqrt function has been implemented, as part of an approved pull request. This change enhances the functionality of the PyTorch library.

• AOTInductor Node and Kernel Count Fix: Issues related to the broken AOTInductor node and kernel counts have been addressed. This change is part of an approved pull request.

• Fixed cpp_wrapper Tests Enablement: All fixed cpp_wrapper tests have been enabled as part of the resolution for a pull request. This change enhances the testing coverage.

• Sudo Usage Avoidance: The issue of avoiding the use of sudo for checking out sources has been addressed. This change aims to implement this across all architectures.

• CUDA Synchronization Issue Fix: An issue with CUDA synchronization for bf16 HSDP AR and fp32 parameters has been addressed. This change improves the stability of the PyTorch project.

• Continuous Deployment for Python 3.13: Continuous deployment for Python 3.13 on Windows has been enabled, addressing issue #130249. This change includes references to related builder pull requests.

• Device Index Bound Check Refactor: The device index bound check in the XPU code has been refactored to ensure that the number of XPU devices does not exceed the maximum limit. This change enhances code safety.

• Separation of int4wo Weight Packing: The separation of int4wo weight packing for CPU and other devices has been introduced, addressing issue #1117. This change optimizes performance on CPU.

• Dispatcher Module Introduction: A "dispatcher" module has been introduced to manage multiple interpreter modules, allowing for easier swapping while preserving module call signatures. This change enhances code organization.

• Shared Submodule Call Signature Bug Fix: A bug in the handling of shared submodule call signatures has been addressed, ensuring accurate preservation of module call signatures. This change improves code reliability.

• Trace Function Handling Fix: An issue with the trace function in the PyTorch library has been fixed, improving the handling of nn.parameters(). This change is part of an approved pull request.

• Decompose_mm_pass Functionality Enhancement: The decompose_mm_pass functionality has been enhanced to support certain CPU cases in addition to its existing GPU support. This change optimizes performance across hardware configurations.

• Dead Code Path Removal: An unnecessary dead code path for capturing __class__ in UserFunctionVariable has been removed. This change simplifies the codebase.

• Pre-existing Cells Handling Fix: An issue related to the modeling of pre-existing cells within the Dynamo framework has been addressed. This change ensures consistency and prevents inconsistencies.

• Unboxing Optimization Removal: The unboxing optimization and restart code path related to cell writes in the Dynamo tracing system have been removed. This change ensures correct handling of pre-existing cells.

• Name Collision Bug Fix: A name collision bug in the handling of captured cells and locals has been resolved. This change introduces a regression test to ensure the issue is fixed.

• ClosureVariable and NewCellVariable Invariant Enforcement: A patch has been introduced to enforce the invariant that ClosureVariable and NewCellVariable are exclusively placed in closure_cells. This change streamlines the code.

• CMake Requirement Addition: The addition of cmake to the requirements.txt file ensures that PyTorch can be built in a clean virtual environment. This change addresses build issues.

• @with_comms Decorator Fix: The issue of the @with_comms decorator being inactive when not invoked with parentheses has been addressed. This change improves the decorator's usability.

• Wextra-semi Feature Implementation: The Wextra-semi feature has been implemented in the PyTorch project, resolving a related issue. This change enhances code quality.

• Split Decomposition Function Fix: An issue in the split decomposition function that returned the input tensor when no splits were made has been fixed. This change ensures compliance with tensor view operation rules.

• Sparse Operations Code Generation for Intel GPUs: Support for code generation of sparse operations specifically for Intel GPUs has been introduced. This change enhances the functionality of the PyTorch library.

• CMake Build Configuration Typo Fix: A typo in the CMake build configuration affecting the XPU Windows build has been corrected. This change ensures proper build functionality.

• Pointwise Tags Addition for rad2deg Operation: Missing pointwise tags for the rad2deg operation have been added, enabling the NestedTensor to generate a function for opinfo correctly. This change enhances functionality.

• Magma Tarball Building for CUDA 1.26: The magma tarball for CUDA 1.26 has been built, leveraging the availability of manylinux 2.28. This change addresses the issue referenced in #139397.

• NJT Compilation Default Enablement: The use of Non-Just-in-Time (NJT) compilation by default for the weights_only option in the torch.load function has been enabled. This change is part of an approved pull request.

• Docker Image Configuration Re-addition: The safe.directory configuration has been re-added to the Almalinux Docker image, addressing an oversight from a previous pull request. This change ensures proper functionality within the Docker environment.

• Bandwidth Profiler Improvements: Several issues in the bandwidth profiler of the aotinductor have been addressed, specifically improving the performance of the DebugAutotuner class. This change enhances profiling results and configuration selection during benchmarking.

• Dynamic Collection API Logging Enhancement: The logging functionality of the Dynamic Collection API has been enhanced by adding a warning message regarding CUDA events. This change improves clarity and debugging capabilities.

• Stage Input/Output Shape Verification Cleanup: Unnecessary methods related to the verification of stage input/output shapes have been cleaned up, streamlining the codebase. This change is part of an approved pull request.

• Type Checking for _backward Functions: Type checking has been added to the _backward functions, addressing issue #139405. This change is part of an approved pull request.

• codegen_int_array_var API Refactor: The codegen_int_array_var API has been refactored to explicitly require a writeline function as an argument, improving clarity and usability. This change enhances the overall code structure.

• AOTI Functionality Update: The AOTI functionality has been updated to return a list of generated files when packaging is enabled, ensuring that only those files are packaged. This change addresses a caching issue.

• Rockset References Removal: Most references to Rockset have been removed from the codebase, transitioning to a generic "backend database" terminology. This change indicates a complete transition to using DynamoDB.

• Linting Issues Resolution: Linting issues that arose after changes made in pull request #138899 have been addressed. This change includes instructions to run a regeneration script to apply necessary fixes.

• Regression Fix for Build Process: A regression in the PyTorch build process that caused endless code regeneration has been fixed. This change ensures that incremental rebuilds with no changes result in no build actions.

• CUDA 12.6 Linux Builds Support: Support for CUDA 12.6 Linux builds has been added to the binaries matrix, addressing issue #138440. This change links to relevant documentation and pull request details.

• Unary Operations Macro Addition: A new macro has been introduced to facilitate the addition of unary operations to the NT in the PyTorch project. This change specifically adds support for the square root operation.

• OpenReg Module Bug Fix: A bug in the OpenReg module has been fixed by correcting the assumption about memory size allocation. This change ensures proper functionality in cases like tensor.expand.

• Jacobi Method Replacement: The Jacobi method has been replaced with the divide-and-conquer method for the linalg.eigh function in the AMD implementation. This change reduces numerical errors.

• Base32 Triton Cache Function Update: The code has been updated to utilize the base32 Triton cache function instead of the base64 function, following a recent change in the Triton library. This change enhances compatibility.

• triangular_solve_meta Function Parameter Fix: The parameter names in the triangular_solve_meta function's @out_wrapper(...) decorator have been fixed to match those specified in the _native_functions.yaml_ file. This change resolves a name mismatch issue.

• ScalarList Type Overload Addition: An overload for the ScalarList type has been added to the _foreach_lerp function, addressing issue #133367. This change is part of an approved pull request.

• Wextra-semi Warning Fix: A previous change related to a Wextra-semi warning has been relanded, specifically in FBCODE_CAFFE2. This change is part of an approved pull request.

• Thread-Safe Environment Variable Functions: Thread-safe functions for retrieving environment variables have been introduced. This change is part of a series of updates aimed at improving code reliability.

• Pointwise Tag Addition: A pointwise tag has been added to the isfinite function in the PyTorch library, enhancing its functionality. This change is part of an approved pull request.

• Infinite Iterators Support in filter(): The filter() function has been enhanced to support infinite iterators, improving its versatility. This change resolves issue #137380.

• Error Message Correction in _scaled_mm: The error message related to the second matrix in the torch._scaled_mm function has been corrected. This change is part of an approved pull request.

• C10_MOBILE Builds Issue Fix: An issue in the C10_MOBILE builds has been addressed by implementing a no-operation for the m.set_python_module function. This change prevents unnecessary calls to registerPythonModule.

• CPU Inductor Test Optimization: The CPU inductor test runners and cases have been moved to optimize cost efficiency, specifically for the SPR architecture. This change is part of an approved pull request.

• Batch Rule Addition for native_dropout_backward: A batch rule for the native_dropout_backward function has been added, addressing issue #122432. This change is part of an approved pull request.

• Empty Tensor Operations Testing: Testing for empty tensor operations has been added, specifically for the _foreach_addcmul and _foreach_addcdiv functions. This change is part of an approved pull request.

• Interspersed Empty Tensors Testing: Tests for interspersed empty tensors have been introduced to ensure parity in the _foreach_norm functionality. This change enhances the robustness of the PyTorch library.

• torch._foreach_rsqrt Function Implementation: The torch._foreach_rsqrt function has been implemented, as part of an approved pull request. This change enhances the functionality of the PyTorch library.

• AOTInductor Node and Kernel Count Fix: Issues related to the broken AOTInductor node and kernel counts have been addressed. This change is part of an approved pull request.

• Fixed cpp_wrapper Tests Enablement: All fixed cpp_wrapper tests have been enabled as part of the resolution for a pull request. This change enhances the testing coverage.

• Sudo Usage Avoidance: The issue of avoiding the use of sudo for checking out sources has been addressed. This change aims to implement this across all architectures.

• CUDA Synchronization Issue Fix: An issue with CUDA synchronization for bf16 HSDP AR and fp32 parameters has been addressed. This change improves the stability of the PyTorch project.

• Continuous Deployment for Python 3.13: Continuous deployment for Python 3.13 on Windows has been enabled, addressing issue #130249. This change includes references to related builder pull requests.

• Device Index Bound Check Refactor: The device index bound check in the XPU code has been refactored to ensure that the number of XPU devices does not exceed the maximum limit. This change enhances code safety.

• Separation of int4wo Weight Packing: The separation of int4wo weight packing for CPU and other devices has been introduced, addressing issue #1117. This change optimizes performance on CPU.

• Dispatcher Module Introduction: A "dispatcher" module has been introduced to manage multiple interpreter modules, allowing for easier swapping while preserving module call signatures. This change enhances code organization.

• Shared Submodule Call Signature Bug Fix: A bug in the handling of shared submodule call signatures has been addressed, ensuring accurate preservation of module call signatures. This change improves code reliability.

• Trace Function Handling Fix: An issue with the trace function in the PyTorch library has been fixed, improving the handling of nn.parameters(). This change is part of an approved pull request.

• Decompose_mm_pass Functionality Enhancement: The decompose_mm_pass functionality has been enhanced to support certain CPU cases in addition to its existing GPU support. This change optimizes performance across hardware configurations.

• Dead Code Path Removal: An unnecessary dead code path for capturing __class__ in UserFunctionVariable has been removed. This change simplifies the codebase.

• Pre-existing Cells Handling Fix: An issue related to the modeling of pre-existing cells within the Dynamo framework has been addressed. This change ensures consistency and prevents inconsistencies.

• Unboxing Optimization Removal: The unboxing optimization and restart code path related to cell writes in the Dynamo tracing system have been removed. This change ensures correct handling of pre-existing cells.

• Name Collision Bug Fix: A name collision bug in the handling of captured cells and locals has been resolved. This change introduces a regression test to ensure the issue is fixed.

• ClosureVariable and NewCellVariable Invariant Enforcement: A patch has been introduced to enforce the invariant that ClosureVariable and NewCellVariable are exclusively placed in closure_cells. This change streamlines the code.

• CMake Requirement Addition: The addition of cmake to the requirements.txt file ensures that PyTorch can be built in a clean virtual environment. This change addresses build issues.

• @with_comms Decorator Fix: The issue of the @with_comms decorator being inactive when not invoked with parentheses has been addressed. This change improves the decorator's usability.

• Wextra-semi Feature Implementation: The Wextra-semi feature has been implemented in the PyTorch project, resolving a related issue. This change enhances code quality.

• Split Decomposition Function Fix: An issue in the split decomposition function that returned the input tensor when no splits were made has been fixed. This change ensures compliance with tensor view operation rules.

• Sparse Operations Code Generation for Intel GPUs: Support for code generation of sparse operations specifically for Intel GPUs has been introduced. This change enhances the functionality of the PyTorch library.

• CMake Build Configuration Typo Fix: A typo in the CMake build configuration affecting the XPU Windows build has been corrected. This change ensures proper build functionality.

• Pointwise Tags Addition for rad2deg Operation: Missing pointwise tags for the rad2deg operation have been added, enabling the NestedTensor to generate a function for opinfo correctly. This change enhances functionality.

• Magma Tarball Building for CUDA 1.26: The magma tarball for CUDA 1.26 has been built, leveraging the availability of manylinux 2.28. This change addresses the issue referenced in #139397.

• NJT Compilation Default Enablement: The use of Non-Just-in-Time (NJT) compilation by default for the weights_only option in the torch.load function has been enabled. This change is part of an approved pull request.

• Docker Image Configuration Re-addition: The safe.directory configuration has been re-added to the Almalinux Docker image, addressing an oversight from a previous pull request. This change ensures proper functionality within the Docker environment.

• Bandwidth Profiler Improvements: Several issues in the bandwidth profiler of the aotinductor have been addressed, specifically improving the performance of the DebugAutotuner class. This change enhances profiling results and configuration selection during benchmarking.

• Dynamic Collection API Logging Enhancement: The logging functionality of the Dynamic Collection API has been enhanced by adding a warning message regarding CUDA events. This change improves clarity and debugging capabilities.

• Stage Input/Output Shape Verification Cleanup: Unnecessary methods related to the verification of stage input/output shapes have been cleaned up, streamlining the codebase. This change is part of an approved pull request.

• Type Checking for _backward Functions: Type checking has been added to the _backward functions, addressing issue #139405. This change is part of an approved pull request.

• codegen_int_array_var API Refactor: The codegen_int_array_var API has been refactored to explicitly require a writeline function as an argument, improving clarity and usability. This change enhances the overall code structure.

• AOTI Functionality Update: The AOTI functionality has been updated to return a list of generated files when packaging is enabled, ensuring that only those files are packaged. This change addresses a caching issue.

• Rockset References Removal: Most references to Rockset have been removed from the codebase, transitioning to a generic "backend database" terminology. This change indicates a complete transition to using DynamoDB.

• Linting Issues Resolution: Linting issues that arose after changes made in pull request #138899 have been addressed. This change includes instructions to run a regeneration script to apply necessary fixes.

• Regression Fix for Build Process: A regression in the PyTorch build process that caused endless code regeneration has been fixed. This change ensures that incremental rebuilds with no changes result in no build actions.

• CUDA 12.6 Linux Builds Support: Support for CUDA 12.6 Linux builds has been added to the binaries matrix, addressing issue #138440. This change links to relevant documentation and pull request details.

• Unary Operations Macro Addition: A new macro has been introduced to facilitate the addition of unary operations to the NT in the PyTorch project. This change specifically adds support for the square root operation.

• OpenReg Module Bug Fix: A bug in the OpenReg module has been fixed by correcting the assumption about memory size allocation. This change ensures proper functionality in cases like tensor.expand.

• Jacobi Method Replacement: The Jacobi method has been replaced with the divide-and-conquer method for the linalg.eigh function in the AMD implementation. This change reduces numerical errors.

• Base32 Triton Cache Function Update: The code has been updated to utilize the base32 Triton cache function instead of the base64 function, following a recent change in the Triton library. This change enhances compatibility.

• triangular_solve_meta Function Parameter Fix: The parameter names in the triangular_solve_meta function's @out_wrapper(...) decorator have been fixed to match those specified in the _native_functions.yaml_ file. This change resolves a name mismatch issue.

• ScalarList Type Overload Addition: An overload for the ScalarList type has been added to the _foreach_lerp function, addressing issue #133367. This change is part of an approved pull request.

• Wextra-semi Warning Fix: A previous change related to a Wextra-semi warning has been relanded, specifically in FBCODE_CAFFE2. This change is part of an approved pull request.

• Thread-Safe Environment Variable Functions: Thread-safe functions for retrieving environment variables have been introduced. This change is part of a series of updates aimed at improving code reliability.

• Pointwise Tag Addition: A pointwise tag has been added to the isfinite function in the PyTorch library, enhancing its functionality. This change is part of an approved pull request.

• Infinite Iterators Support in filter(): The filter() function has been enhanced to support infinite iterators, improving its versatility. This change resolves issue #137380.

• Error Message Correction in _scaled_mm: The error message related to the second matrix in the torch._scaled_mm function has been corrected. This change is part of an approved pull request.

• C10_MOBILE Builds Issue Fix: An issue in the C10_MOBILE builds has been addressed by implementing a no-operation for the m.set_python_module function. This change prevents unnecessary calls to registerPythonModule.

• CPU Inductor Test Optimization: The CPU inductor test runners and cases have been moved to optimize cost efficiency, specifically for the SPR architecture. This change is part of an approved pull request.

• Batch Rule Addition for native_dropout_backward: A batch rule for the native_dropout_backward function has been added, addressing issue #122432. This change is part of an approved pull request.

• Empty Tensor Operations Testing: Testing for empty tensor operations has been added, specifically for the _foreach_addcmul and _foreach_addcdiv functions. This change is part of an approved pull request.

• Interspersed Empty Tensors Testing: Tests for interspersed empty tensors have been introduced to ensure parity in the _foreach_norm functionality. This change enhances the robustness of the PyTorch library.

• torch._foreach_rsqrt Function Implementation: The torch._foreach_rsqrt function has been implemented, as part of an approved pull request. This change enhances the functionality of the PyTorch library.

• AOTInductor Node and Kernel Count Fix: Issues related to the broken AOTInductor node and kernel counts have been addressed. This change is part of an approved pull request.

• Fixed cpp_wrapper Tests Enablement: All fixed cpp_wrapper tests have been enabled as part of the resolution for a pull request. This change enhances the testing coverage.

• Sudo Usage Avoidance: The issue of avoiding the use of sudo for checking out sources has been addressed. This change aims to implement this across all architectures.

• CUDA Synchronization Issue Fix: An issue with CUDA synchronization for bf16 HSDP AR and fp32 parameters has been addressed. This change improves the stability of the PyTorch project.

• Continuous Deployment for Python 3.13: Continuous deployment for Python 3.13 on Windows has been enabled, addressing issue #130249. This change includes references to related builder pull requests.

• Device Index Bound Check Refactor: The device index bound check in the XPU code has been refactored to ensure that the number of XPU devices does not exceed the maximum limit. This change enhances code safety.

• Separation of int4wo Weight Packing: The separation of int4wo weight packing for CPU and other devices has been introduced, addressing issue #1117. This change optimizes performance on CPU.

• Dispatcher Module Introduction: A "dispatcher" module has been introduced to manage multiple interpreter modules, allowing for easier swapping while preserving module call signatures. This change enhances code organization.

• Shared Submodule Call Signature Bug Fix: A bug in the handling of shared submodule call signatures has been addressed, ensuring accurate preservation of module call signatures. This change improves code reliability.

• Trace Function Handling Fix: An issue with the trace function in the PyTorch library has been fixed, improving the handling of nn.parameters(). This change is part of an approved pull request.

• Decompose_mm_pass Functionality Enhancement: The decompose_mm_pass functionality has been enhanced to support certain CPU cases in addition to its existing GPU support. This change optimizes performance across hardware configurations.

• Dead Code Path Removal: An unnecessary dead code path for capturing __class__ in UserFunctionVariable has been removed. This change simplifies the codebase.

• Pre-existing Cells Handling Fix: An issue related to the modeling of pre-existing cells within the Dynamo framework has been addressed. This change ensures consistency and prevents inconsistencies.

• Unboxing Optimization Removal: The unboxing optimization and restart code path related to cell writes in the Dynamo tracing system have been removed. This change ensures correct handling of pre-existing cells.

• Name Collision Bug Fix: A name collision bug in the handling of captured cells and locals has been resolved. This change introduces a regression test to ensure the issue is fixed.

• ClosureVariable and NewCellVariable Invariant Enforcement: A patch has been introduced to enforce the invariant that ClosureVariable and NewCellVariable are exclusively placed in closure_cells. This change streamlines the code.

• CMake Requirement Addition: The addition of cmake to the requirements.txt file ensures that PyTorch can be built in a clean virtual environment. This change addresses build issues.

• @with_comms Decorator Fix: The issue of the @with_comms decorator being inactive when not invoked with parentheses has been addressed. This change improves the decorator's usability.

• Wextra-semi Feature Implementation: The Wextra-semi feature has been implemented in the PyTorch project, resolving a related issue. This change enhances code quality.

• Split Decomposition Function Fix: An issue in the split decomposition function that returned the input tensor when no splits were made has been fixed. This change ensures compliance with tensor view operation rules.

• Sparse Operations Code Generation for Intel GPUs: Support for code generation of sparse operations specifically for Intel GPUs has been introduced. This change enhances the functionality of the PyTorch library.

• CMake Build Configuration Typo Fix: A typo in the CMake build configuration affecting the XPU Windows build has been corrected. This change ensures proper build functionality.

• Pointwise Tags Addition for rad2deg Operation: Missing pointwise tags for the rad2deg operation have been added, enabling the NestedTensor to generate a function for opinfo correctly. This change enhances functionality.

• Magma Tarball Building for CUDA 1.26: The magma tarball for CUDA 1.26 has been built, leveraging the availability of manylinux 2.28. This change addresses the issue referenced in #139397.

• NJT Compilation Default Enablement: The use of Non-Just-in-Time (NJT) compilation by default for the weights_only option in the torch.load function has been enabled. This change is part of an approved pull request.

• Docker Image Configuration Re-addition: The safe.directory configuration has been re-added to the Almalinux Docker image, addressing an oversight from a previous pull request. This change ensures proper functionality within the Docker environment.

• Bandwidth Profiler Improvements: Several issues in the bandwidth profiler of the aotinductor have been addressed, specifically improving the performance of the DebugAutotuner class. This change enhances profiling results and configuration selection during benchmarking.

• Dynamic Collection API Logging Enhancement: The logging functionality of the Dynamic Collection API has been enhanced by adding a warning message regarding CUDA events. This change improves clarity and debugging capabilities.

• Stage Input/Output Shape Verification Cleanup: Unnecessary methods related to the verification of stage input/output shapes have been cleaned up, streamlining the codebase. This change is part of an approved pull request.

• Type Checking for _backward Functions: Type checking has been added to the _backward functions, addressing issue #139405. This change is part of an approved pull request.

• codegen_int_array_var API Refactor: The codegen_int_array_var API has been refactored to explicitly require a writeline function as an argument, improving clarity and usability. This change enhances the overall code structure.

• AOTI Functionality Update: The AOTI functionality has been updated to return a list of generated files when packaging is enabled, ensuring that only those files are packaged. This change addresses a caching issue.

• Rockset References Removal: Most references to Rockset have been removed from the codebase, transitioning to a generic "backend database" terminology. This change indicates a complete transition to using DynamoDB.

• Linting Issues Resolution: Linting issues that arose after changes made in pull request #138899 have been addressed. This change includes instructions to run a regeneration script to apply necessary fixes.

• Regression Fix for Build Process: A regression in the PyTorch build process that caused endless code regeneration has been fixed. This change ensures that incremental rebuilds with no changes result in no build actions.

• CUDA 12.6 Linux Builds Support: Support for CUDA 12.6 Linux builds has been added to the binaries matrix, addressing issue #138440. This change links to relevant documentation and pull request details.

• Unary Operations Macro Addition: A new macro has been introduced to facilitate the addition of unary operations to the NT in the PyTorch project. This change specifically adds support for the square root operation.

• OpenReg Module Bug Fix: A bug in the OpenReg module has been fixed by correcting the assumption about memory size allocation. This change ensures proper functionality in cases like tensor.expand.

• Jacobi Method Replacement: The Jacobi method has been replaced with the divide-and-conquer method for the linalg.eigh function in the AMD implementation. This change reduces numerical errors.

• Base32 Triton Cache Function Update: The code has been updated to utilize the base32 Triton cache function instead of the base64 function, following a recent change in the Triton library. This change enhances compatibility.

• triangular_solve_meta Function Parameter Fix: The parameter names in the triangular_solve_meta function's @out_wrapper(...) decorator have been fixed to match those specified in the _native_functions.yaml_ file. This change resolves a name mismatch issue.

• ScalarList Type Overload Addition: An overload for the ScalarList type has been added to the _foreach_lerp function, addressing issue #133367. This change is part of an approved pull request.

• Wextra-semi Warning Fix: A previous change related to a Wextra-semi warning has been relanded, specifically in FBCODE_CAFFE2. This change is part of an approved pull request.

• Thread-Safe Environment Variable Functions: Thread-safe functions for retrieving environment variables have been introduced. This change is part of a series of updates aimed at improving code reliability.

• Pointwise Tag Addition: A pointwise tag has been added to the isfinite function in the PyTorch library, enhancing its functionality. This change is part of an approved pull request.

• Infinite Iterators Support in filter(): The filter() function has been enhanced to support infinite iterators, improving its versatility. This change resolves issue #137380.

• Error Message Correction in _scaled_mm: The error message related to the second matrix in the torch._scaled_mm function has been corrected. This change is part of an approved pull request.

• C10_MOBILE Builds Issue Fix: An issue in the C10_MOBILE builds has been addressed by implementing a no-operation for the m.set_python_module function. This change prevents unnecessary calls to registerPythonModule.

• CPU Inductor Test Optimization: The CPU inductor test runners and cases have been moved to optimize cost efficiency, specifically for the SPR architecture. This change is part of an approved pull request.

• Batch Rule Addition for native_dropout_backward: A batch rule for the native_dropout_backward function has been added, addressing issue #122432. This change is part of an approved pull request.

• Empty Tensor Operations Testing: Testing for empty tensor operations has been added, specifically for the _foreach_addcmul and _foreach_addcdiv functions. This change is part of an approved pull request.

• Interspersed Empty Tensors Testing: Tests for interspersed empty tensors have been introduced to ensure parity in the _foreach_norm functionality. This change enhances the robustness of the PyTorch library.

• torch._foreach_rsqrt Function Implementation: The torch._foreach_rsqrt function has been implemented, as part of an approved pull request. This change enhances the functionality of the PyTorch library.

• AOTInductor Node and Kernel Count Fix: Issues related to the broken AOTInductor node and kernel counts have been addressed. This change is part of an approved pull request.

• Fixed cpp_wrapper Tests Enablement: All fixed cpp_wrapper tests have been enabled as part of the resolution for a pull request. This change enhances the testing coverage.

• Sudo Usage Avoidance: The issue of avoiding the use of sudo for checking out sources has been addressed. This change aims to implement this across all architectures.

• CUDA Synchronization Issue Fix: An issue with CUDA synchronization for bf16 HSDP AR and fp32 parameters has been addressed. This change improves the stability of the PyTorch project.

• Continuous Deployment for Python 3.13: Continuous deployment for Python 3.13 on Windows has been enabled, addressing issue #130249. This change includes references to related builder pull requests.

• Device Index Bound Check Refactor: The device index bound check in the XPU code has been refactored to ensure that the number of XPU devices does not exceed the maximum limit. This change enhances code safety.

• Separation of int4wo Weight Packing: The separation of int4wo weight packing for CPU and other devices has been introduced, addressing issue #1117. This change optimizes performance on CPU.

• Dispatcher Module Introduction: A "dispatcher" module has been introduced to manage multiple interpreter modules, allowing for easier swapping while preserving module call signatures. This change enhances code organization.

• Shared Submodule Call Signature Bug Fix: A bug in the handling of shared submodule call signatures has been addressed, ensuring accurate preservation of module call signatures. This change improves code reliability.

• Trace Function Handling Fix: An issue with the trace function in the PyTorch library has been fixed, improving the handling of nn.parameters(). This change is part of an approved pull request.

• Decompose_mm_pass Functionality Enhancement: The decompose_mm_pass functionality has been enhanced to support certain CPU cases in addition to its existing GPU support. This change optimizes performance across hardware configurations.

• Dead Code Path Removal: An unnecessary dead code path for capturing __class__ in UserFunctionVariable has been removed. This change simplifies the codebase.

• Pre-existing Cells Handling Fix: An issue related to the modeling of pre-existing cells within the Dynamo framework has been addressed. This change ensures consistency and prevents inconsistencies.

• Unboxing Optimization Removal: The unboxing optimization and restart code path related to cell writes in the Dynamo tracing system have been removed. This change ensures correct handling of pre-existing cells.

• Name Collision Bug Fix: A name collision bug in the handling of captured cells and locals has been resolved. This change introduces a regression test to ensure the issue is fixed.

• ClosureVariable and NewCellVariable Invariant Enforcement: A patch has been introduced to enforce the invariant that ClosureVariable and NewCellVariable are exclusively placed in closure_cells. This change streamlines the code.

• CMake Requirement Addition: The addition of cmake to the requirements.txt file ensures that PyTorch can be built in a clean virtual environment. This change addresses build issues.

• @with_comms Decorator Fix: The issue of the @with_comms decorator being inactive when not invoked with parentheses has been addressed. This change improves the decorator's usability.

• Wextra-semi Feature Implementation: The Wextra-semi feature has been implemented in the PyTorch project, resolving a related issue. This change enhances code quality.

• Split Decomposition Function Fix: An issue in the split decomposition function that returned the input tensor when no splits were made has been fixed. This change ensures compliance with tensor view operation rules.

• Sparse Operations Code Generation for Intel GPUs: Support for code generation of sparse operations specifically for Intel GPUs has been introduced. This change enhances the functionality of the PyTorch library.

• CMake Build Configuration Typo Fix: A typo in the CMake build configuration affecting the XPU Windows build has been corrected. This change ensures proper build functionality.

• Pointwise Tags Addition for rad2deg Operation: Missing pointwise tags for the rad2deg operation have been added, enabling the NestedTensor to generate a function for opinfo correctly. This change enhances functionality.

• Magma Tarball Building for CUDA 1.26: The magma tarball for CUDA 1.26 has been built, leveraging the availability of manylinux 2.28. This change addresses the issue referenced in #139397.

• NJT Compilation Default Enablement: The use of Non-Just-in-Time (NJT) compilation by default for the weights_only option in the torch.load function has been enabled. This change is part of an approved pull request.

• Docker Image Configuration Re-addition: The safe.directory configuration has been re-added to the Almalinux Docker image, addressing an oversight from a previous pull request. This change ensures proper functionality within the Docker environment.

• Bandwidth Profiler Improvements: Several issues in the bandwidth profiler of the aotinductor have been addressed, specifically improving the performance of the DebugAutotuner class. This change enhances profiling results and configuration selection during benchmarking.

• Dynamic Collection API Logging Enhancement: The logging functionality of the Dynamic Collection API has been enhanced by adding a warning message regarding CUDA events. This change improves clarity and debugging capabilities.

• Stage Input/Output Shape Verification Cleanup: Unnecessary methods related to the verification of stage input/output shapes have been cleaned up, streamlining the codebase. This change is part of an approved pull request.

• Type Checking for _backward Functions: Type checking has been added to the _backward functions, addressing issue #139405. This change is part of an approved pull request.

• codegen_int_array_var API Refactor: The codegen_int_array_var API has been refactored to explicitly require a writeline function as an argument, improving clarity and usability. This change enhances the overall code structure.

• AOTI Functionality Update: The AOTI functionality has been updated to return a list of generated files when packaging is enabled, ensuring that only those files are packaged. This change addresses a caching issue.

• Rockset References Removal: Most references to Rockset have been removed from the codebase, transitioning to a generic "backend database" terminology. This change indicates a complete transition to using DynamoDB.

• Linting Issues Resolution: Linting issues that arose after changes made in pull request #138899 have been addressed. This change includes instructions to run a regeneration script to apply necessary fixes.

• Regression Fix for Build Process: A regression in the PyTorch build process that caused endless code regeneration has been fixed. This change ensures that incremental rebuilds with no changes result in no build actions.

• CUDA 12.6 Linux Builds Support: Support for CUDA 12.6 Linux builds has been added to the binaries matrix, addressing issue #138440. This change links to relevant documentation and pull request details.

• Unary Operations Macro Addition: A new

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IV. Contributors

4.1 Contributors

Active Contributors: