Weekly GitHub Report for Pytorch

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Table of Contents

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

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

• IV. Contributors
  • 4.1. Contributors

I. News

1.1 Recent Version Releases:

The current version of this repository is v2.6.0

1.2 Version Information:

Released on January 29, 2025, PyTorch 2.6 introduces significant enhancements including torch.compile support for Python 3.13, a new dynamic compilation control API torch.compiler.set_stance, and improved AOTInductor packaging and ABI compatibility. Notable highlights also include FP16 support on X86 CPUs, expanded Intel GPU support, FlexAttention for X86 CPUs targeting LLMs, and a backward-incompatible security improvement flipping the default of torch.load to weights_only=True, alongside the deprecation of official Conda packages.

II. Issues

2.1 Top 5 Active Issues:

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

1. [ONCALL: DISTRIBUTED] [TRIAGED] [MODULE: DTENSOR] [BOT-TRIAGED] [ONCALL: DISTRIBUTED PARALLELISMS] Rowwise MXFP8 linear + TP=2 produces wrong placement: This issue describes a bug in the DTensor sharding strategies for the torch._scaled_mm operation when running a rowwise MXFP8 linear layer with tensor parallelism set to 2, which results in an incorrect output placement error due to the lack of a valid strategy for 1D block scales sharded on the contracting dimension. The problem causes the output DTensor to be sharded instead of partially summed as expected, leading to failures in distributed training setups that rely on this operation, and the root cause lies in DTensor rejecting the shard placement for 1D scales rather than mapping it correctly.

   • The comments confirm the bug and discuss potential fixes, including adding support in DTensor for 1D scale shards or using alternative approaches like local_map with explicit allreduce or switching to 2D scales; workarounds such as using the spmd_types backend or modifying scale tensor shapes are suggested, and there is interest in repro cases to assist with debugging.
   • Number of comments this week: 6

2. [TRIAGE REVIEW] [MODULE: DOUBLE BACKWARDS] [MODULE: LINEAR ALGEBRA] [MODULE: CORRECTNESS (SILENT)] [MODULE: FORWARD AD] [MODULE: FUNCTORCH] [BOT-TRIAGED] Differentiating the forward-mode (JVP) rule of linalg.det / slogdet / linalg.solve silently returns a wrong second derivative: This issue reports that the forward-mode automatic differentiation (AD) rules for certain linear algebra operations (linalg.det, linalg.slogdet, and linalg.solve) in PyTorch produce silently incorrect second derivatives when using nested forward-mode differentiation such as jvp(jvp). The problem arises because the tangent propagation through the intermediate matrix inverse step is not properly differentiable, causing the second-order derivatives to be wrong while first-order derivatives remain correct, and a fix involving recomputing the inverse with differentiable operations under nested differentiation is proposed and underway.

   • The comments confirm the root cause as the non-differentiability of the saved LU factorization path in forward-mode AD, discuss a detailed fix plan including recomputing the inverse for higher-order derivatives, and announce a PR that addresses both this issue and a related one, with regression tests validating the fix to machine precision.
   • Number of comments this week: 5

3. [TRIAGE REVIEW] [MODULE: RANDOM] [MODULE: CORRECTNESS (SILENT)] [MODULE: MPS] [BOT-TRIAGED] [MPS] multinomial(p, 1) intermittently returns zero-probability indices — exponential_() emits exact -0.0, Gumbel fast path turns it into a NaN that argmax selects: This issue describes a bug in the MPS backend of PyTorch where torch.multinomial(p, 1) intermittently returns indices corresponding to zero-probability entries due to the exponential_() function emitting exact -0.0 values, which the Gumbel fast path then converts into NaNs that cause argmax to select invalid indices. This results in silent incorrect token emissions during sampling on Apple Silicon devices, with a suggested fix involving clamping the exponential output before division to prevent zero values, and a current workaround of performing sampling on the CPU.

   • The comments confirm reproducibility of the bug and discuss a ready fix involving clamping the exponential kernel output similar to CUDA; contributors request the issue be marked actionable to submit a PR, which is acknowledged and supported by maintainers given the real-world impact on LLM inference.
   • Number of comments this week: 5

4. torch.export(strict=False) stack_trace only keeps frames named forward, dropping helper methods (e.g. forward_impl): This issue reports that when using torch.export with strict=False, the stack trace metadata only retains frames named exactly "forward," causing helper methods like forward_impl to be omitted and resulting in all operations appearing to originate from the same call site. This behavior is a regression from the previous default strict=True mode, which preserved the full call stack, and the user suggests that the current name-based filtering should be replaced with a location-based filter to correctly capture user-defined helper methods without including internal tracer frames.

   • The comments include a request for clarification on the original intent of the filter, an offer to review pull requests addressing the issue, and a volunteer expressing interest in working on a fix, which was acknowledged positively.
   • Number of comments this week: 4

5. [MODULE: BUILD] [MODULE: ROCM] [TRIAGED] [MODULE: SDPA] [BOT-TRIAGED] hip ck backend fmha_fwd_args mismatch: This issue reports a type mismatch bug in the HIP CK backend functions related to fmha_fwd_args, causing build errors in PyTorch versions 2.12.0 and 2.12.1 on AMD ROCm. The errors involve incorrect initialization of pointer members with integer values and narrowing conversions from float to integer types, leading to source code build failures.

   • The comments clarify the affected PyTorch versions as 2.12.0 and 2.12.1, correct an initial version typo, and discuss whether the issue has been fixed in more recent versions, with the original reporter indicating plans to test building the latest version.
   • Number of comments this week: 4

2.2 Top 5 Stale Issues:

We consider stale issues to be issues that has had no activity within the last 30 days. The team should work together to get these issues resolved and closed as soon as possible.

As of our latest update, there are no stale issues for the project this week.

2.3 Open Issues

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

Issues Opened This Week: 84

Summarized Issues:

• Embedding Bag Inconsistencies and Memory Errors: The embedding_bag function with include_last_offset=True produces inconsistent results across sum, mean, and max modes when the last bag ends before the end of indices, causing unclear semantics. Additionally, the backward pass of embedding bag kernels causes illegal memory reads due to re-reading mutated indices without proper bounds checking, leading to out-of-bounds access.
  • issues/191965, issues/192445

• Profiler and Tracing Metadata Loss: PyTorch's torch.export(strict=False) drops helper method frames in stack traces, causing all operations to appear from the same call site and losing precise source locations. The profiler also silently drops GPU kernel events at the start of sessions due to timestamp callback re-registration, resulting in incomplete GPU timeline annotations.
  • issues/191967, issues/192021

• Backend and Device Assignment Bugs: The torch._inductor.ir.FallbackKernel.find_device incorrectly assigns devices by selecting the first argument's device instead of following eager mode backend priority, causing inconsistent device assignment and incorrect C-shim naming. Also, after ROCm 6.0 API changes, getDeviceFromPtr misclassifies CPU pointers as invalid CUDA devices, potentially causing runtime errors.
  • issues/192026, issues/192056

• Compilation and CUDA Graph Failures: Using torch.compile with CUDA graphs during distributed all_reduce operations causes crashes due to improper CUDA event handling and invalid argument errors. Additionally, a segmentation fault occurs in CUDA graph compilation when buffers allocated inside compiled regions are passed back as mutated inputs, crashing recurrent models without tracebacks.
  • issues/192000, issues/192748

• Test Failures and Disabled Tests on XPU and ROCm: Several tests in the TestPartitionedScatterOpt suite have been disabled due to failures on the xpu platform. Similarly, the test_alloc_rendezvous_on_current_stream test is disabled on ROCm platforms after CI upgrades, indicating platform-specific instability.
  • issues/192011, issues/192012, issues/192616

• ONNX Export and Graph Translation Errors: Exporting PyTorch models containing while_loops to ONNX fails with a TypeError caused by a 'Node' object not being iterable during graph translation, preventing successful ONNX conversion.
  • issues/192040

• Memory Allocation and Partitioning Bugs: The MapAllocator hardcodes PROT_WRITE flag causing excessive memory usage and OOM errors on integrated CPU/GPU systems due to copy-on-write behavior, with no option for read-only mapping. The ManualOverlapScheduler's custom pass UUID lacks rank information, causing FxGraphCache collisions and CUDA driver errors in multi-rank scenarios.
  • issues/192209, issues/192042

• Regression and Nightly Build Failures: The torch 2.14 nightly build causes multiple regressions including device-side illegal address errors in cuBLAS JIT GEMM kernels crashing vLLM workers, assertion errors due to unknown NCCL backend types in subprocesses, and Triton kernel compilation failures with fp8 zero-padding constants, all absent in stable builds.
  • issues/192061, issues/192062, issues/192063

• Torch.compile and Caching Bugs: Using @torch.compile on classes with caching_precompile=True enabled causes AttributeErrors due to attempts to access __code__ on class objects before proper type checks, leading to failures during model build.
  • issues/192071

• Inductor and Triton Kernel Bugs: Inductor's persistent symmetric-memory allocation test fails due to alloc_id collisions caused by reseeding the global RNG, leading to inconsistent allocation sizes. User-defined Triton kernels compiled with Inductor do not preserve backing storage of tensor views, causing incorrect buffer sizes and potential segmentation faults.
  • issues/192087, issues/192510

• IPC and Multiprocessing Enhancements: A proposal to add pluggable IPC support for third-party accelerator devices by extending PrivateUse1HooksInterface aims to enable device-agnostic storage sharing and multiprocessing reduction, with a reference implementation in OpenReg to support multi-process tensor sharing beyond CUDA-only IPC.
  • issues/192099

• Dynamo and Bytearray Support: Dynamo currently lacks support for the bytearray type, causing graph breaks when used. A proposal suggests adding a ByteArrayVariable model to handle operations and methods to close this gap.
  • issues/192126

• AOTInductor and Embedding Index Bugs: AOTInductor triggers CUDA device-side asserts due to out-of-bounds embedding indices when compiling models using empty torch.nonzero results for embedding lookups, causing failures during compile-time autotuning with synthetic inputs.
  • issues/192143

• Profiler Visibility and Thread Profiling Issues: Several non-dispatched symmetric memory Python functions are invisible in torch.profiler traces; a proposal suggests adding record_function wrappers to improve visibility. Enabling profile_all_threads=True and profile_memory=True causes memory allocation events from worker threads to be missed due to thread-local profiler state queries, resulting in incomplete memory profiling.
  • issues/192181, issues/192290

• Distributed Checkpointing and Optimizer State Bugs: The torch.distributed.checkpoint.state_dict drops optimizer states for frozen parameters during loading, causing incomplete partial state initialization. Using set_optimizer_state_dict with flatten_optimizer_state_dict=True raises unclear errors when trainable parameter states are missing, and round-trip save/load of flattened optimizer states with stateless optimizers creates spurious empty entries.
  • issues/192202, issues/192224, issues/192225

• Triton Kernel Memory Corruption: Using offset or strided tensor views as mutated pointer arguments in compiled Triton kernels with torch.compile causes out-of-bounds reads and silent data corruption due to incorrect handling of storage offsets and buffer sizes during Inductor lowering.
  • issues/192226

• ROCm GPU Hangs and Memory Mapping Issues: On ROCm 7.14, setting PYTORCH_HIP_ALLOC_CONF=expandable_segments:True causes indefinite hangs during GPU tensor transfers due to uncompleted syncobj timeline waits related to DRM kernel versions, requiring process termination.
  • issues/192259

• Inductor Triton Compilation NameError: Inductor-generated Triton modules fail to resolve Python NamedTuple constexpr arguments during compilation with torch.compile(fullgraph=True), causing NameErrors due to undefined NamedTuple classes in generated code, despite direct kernel launches working.
  • issues/192288

• Special Function Numerical Accuracy Issues: torch.special.i0 returns infinity for large finite float64 inputs due to premature overflow, and torch.special.ndtr returns zero for very small but representable float64 inputs, causing loss of precision in both cases.
  • issues/192293, issues/192295

• CUDA Memory Safety Violations: Multiple CUDA memory-safety violations confirmed by NVIDIA Compute Sanitizer are tracked, including illegal memory reads and writes in various CUDA kernels such as segment_reduce_backward_kernel, CUTLASS sparse GEMM, and CTC loss backward kernels, caused by out-of-bounds accesses and mutated tensors after forward validation.
  • issues/192304, issues/192452, issues/192453, issues/192454

• CUDA Expandable Segments Allocator Failures on WSL2: Enabling GPUDirect RDMA with CUDA VMM support on WSL2 with NVIDIA RTX A4000 causes cuMemSetAccess to return unknown driver errors, preventing memory allocation despite advertised capability, contrasting with successful native Windows behavior and suggesting driver inconsistencies.
  • issues/192330

• LocalTensor Process Group Permutation Handling: The LocalTensor _prepare_collective_groups function lacks handling for permutations, preventing process groups with unsorted ranks from running under LocalTensorMode; completing this TODO would maintain original rank order across sibling sets and fibers.
  • issues/192332

• Build Configuration and Python Artifacts: Building PyTorch 2.13.0 with -DBUILD_PYTHON=OFF still installs Python-related files and directories due to CMake statements not properly conditioned on the flag.
  • issues/192334

• Implicit Device-to-Host Copies in torch.bincount: The torch.bincount operation causes implicit device-to-host memory copies that break CUDA Graph capture and interfere with torch.compile optimizations due to dynamic output shapes depending on input data, which tracing tools fail to detect.
  • issues/192343

• Valgrind Failures in CUDA 13.4 Periodic Job: CPU test configurations are disabled in CUDA 13.4 periodic jobs due to valgrind failures caused by uninitialized value reads in libcufft, resulting in loss of test coverage despite passing tests, with potential solutions including suppressions or disabling valgrind.
  • issues/192377

• Documentation Rendering Issues: Many inline equations on the PyTorch autograd documentation page are not rendered properly, affecting content clarity.
  • issues/192405

• Autograd Gradient and Derivative Bugs: The backward pass of torch.expm1 returns zero gradient at -40.0 for float64 instead of a small finite value. The second derivative of torch.exp2 at 1024.0 overflows to infinity incorrectly.
  • issues/192406, issues/192408

• AMD Instinct MI350X GEMM Output Divergence: Batched GEMM operations produce non-identical outputs for bit-identical input rows at small sequence lengths due to kernel tiling differences in BLAS backends, breaking lane-symmetry and causing output divergence in deep autoregressive models.
  • issues/192420

• Illegal Memory Accesses in CUDA Kernels: Multiple CUDA kernels including _embedding_bag_per_sample_weights_backward_kernel and segment_reduce_backward_kernel exhibit illegal memory reads and writes due to out-of-bounds accesses during backward passes.
  • issues/192445, issues/192452

• GPU-Architecture-Gated Test Coverage Gaps: Many sm90+/sm100 gated GPU tests are not included in curated smoke test lists run by CI on H100/B200 hardware, causing silent skips and lack of coverage; a linter-driven mechanism is proposed to keep smoke test lists in sync with gated tests.
  • issues/192465

• DLPack Backend-Agnostic RFC: A proposal to make PyTorch's DLPack implementation backend-agnostic by adding from_blob backend dispatch and pre-export hooks aims to enable third-party accelerator backends to register custom tensor creation and validation, avoiding code duplication and silent data corruption.
  • issues/192473

• Unified Logging Configuration for Accelerators: Adding callback registration and multi-environment-variable support to torch._logging would allow accelerator backends to synchronize C++ logging state with Python logging configuration without monkey-patching, providing unified extensible logging across backends.
  • issues/192481

• torch.linalg.eigh CUDA Bug: For batched complex64 Hermitian inputs on CUDA, torch.linalg.eigh silently returns non-orthonormal eigenvectors after the first call on the same tensor without error, causing persistent incorrect results dependent on batch size and call history.
  • issues/192483

• Checkpoint Consolidation Crash: Consolidating DCP-saved sharded checkpoints into HF safetensors crashes with a TypeError when integer or boolean tensors are present, due to incorrect use of torch.finfo which only supports floating-point types.
  • issues/192485

• Device-Agnostic Symmetric Memory Pool Proposal: A proposal to make the implicit MemPool path in symm_mem.empty() device-agnostic by replacing the CUDA-only gate with checks for registered mempool allocators per device type, dispatching through device-specific modules to support non-CUDA backends.
  • issues/192488

• Inductor Scheduler Hang on AArch64/SVE: The test_vec_compare_op_cpu_only test in Inductor on AArch64/SVE hangs indefinitely due to exponential growth in compile-time expression complexity during scheduler dependency analysis, causing excessive CPU and memory usage.
  • issues/192498

• ROCm hipErrorInvalidConfiguration on Windows: torch.linalg.inv fails with hipErrorInvalidConfiguration on ROCm-enabled Windows with AMD Radeon RX 9070 XT GPUs when inverting large batches of 3x3 matrices exceeding a batch size threshold.
  • issues/192508

• Second-Order Autodiff Failures: Second-order autodiff using torch.func for torch.linalg.det produces NaN or incorrect values with clustered singular values, while forward-mode JVP rules for linalg.det, slogdet, and linalg.solve silently produce incorrect second derivatives due to improper tangent propagation.
  • issues/192521, issues/192540

• Apple MPS Compiled Training Performance Regression: Compiled training on Apple MPS devices is significantly slower than eager execution due to Inductor's layout optimization forcing channels_last memory format, causing large convolution backward penalties; disabling this optimization improves speed.
  • issues/192551

• torch.multinomial Sampling Bugs on MPS and CPU: On Apple Silicon MPS, torch.multinomial(p, 1) intermittently returns zero-probability indices due to exact -0.0 values causing NaNs in Gumbel sampling. On CPU, torch.multinomial with float16 inputs incorrectly selects zero-probability indices due to underflow in exponential_ casting.
  • issues/192577, issues/192812

• rocprofiler-sdk Intermittent Segfaults: Intercepting hipMemcpyAsync calls with rocprofiler-sdk in PyTorch 2.13.0+rocm7.2 causes intermittent segmentation faults due to dangling or uninitialized internal pointers during GPU profiling state changes.
  • issues/192580

• CUDA Symmetric-Memory Signal-Pad Slot Collisions: Signal-pad slots collide across different process groups in CUDA symmetric-memory allocation, causing signals in one group to complete waits in another due to lack of unique group identifiers, leading to silent synchronization errors.
  • issues/192581

• torch.multinomial CUDA Memory Usage Inefficiency: The CUDA implementation of torch.multinomial with replacement and multiple samples unnecessarily allocates a large temporary tensor origDist, increasing peak memory usage and suggesting room for optimization.
  • issues/192594

• ROCm HIP Backend Build Errors: The hip ck backend functions for fmha_fwd_args in PyTorch 2.12.0 and 2.12.1 cause build errors due to type mismatches and improper initialization of members in AMD ROCm HIP code.
  • issues/192610

• nn.MultiHeadAttention XPU Fast Path Bug: The nn.MultiHeadAttention module does not use the fast path on XPU devices, causing an AssertionError with NestedTensor inputs and performance degradation for other inputs.
  • issues/192670

• CPU FP8 scaled_mm NaN Bug: The CPU FP8 scaled_mm operation produces NaNs when using torch.compile with zero-dimension tensors, causing test failures due to mismatched elements compared to eager execution.
  • issues/192709

• torch.sum Backward Runtime Error: The backward pass of torch.sum fails with a runtime error when the dtype argument requests a complex output from a real-valued input tensor requiring gradients, despite successful forward pass.
  • issues/192719

• Autodiff Failures with Boolean Tensors: Reverse- and forward-mode autodiff for logaddexp and logaddexp2 fail with boolean tensor operands due to unsupported subtraction, and forward-mode autodiff fails for mse_loss and smooth_l1_loss with integer or boolean targets, despite correct primal computations.
  • issues/192721, issues/192723

• Distributed Sharding Unevenness and Hangs: Uneven sharding of tensors smaller than the mesh size in Shard._select_split_tensor causes diverging StableHLO graphs and non-SPMD behavior, leading to different TPU ranks running different programs and indefinite hangs during distributed training.
  • issues/192732

• FakeTensor and Functionalize Forward AD Bugs: torch.func.functionalize and Python FunctionalTensor's dispatch_functionalize cause torch.autograd.forward_ad.unpack_dual to incorrectly report missing tangents for dual tensors, leading to silent failures in forward-mode AD and incorrect Jacobian-vector products.
  • issues/192629, issues/192639

• RNG State Mismatch in torch.compile with Randomness: Enabling both fallback_random=True and reorder_for_peak_memory=True in torch.compile causes scheduler reordering of independent dropout calls, mismatching global RNG state consumption and producing different outputs from eager execution; a fix involves threading an ordered effect token.
  • issues/192635

• Static Initialization Order Bug in Quantized Custom-Class Registration: Static initialization order across translation units causes import torch to abort under LTO due to some units not applying a fix, resulting in runtime failures from unspecified dynamic initialization order dependencies.
  • issues/192669

• Inductor FakeTensor Output Stride Bug: FakeTensor returns incorrect output strides for native_batch_norm with noncontiguous CPU inputs, causing failures in torch.compile and torch.export due to stride mismatches with native CPU outputs.
  • issues/192668

• Scheduler Overlap Bucketing Crash: Setting both memory cap parameters to None causes schedule_overlap_bucketing to crash with an AttributeError due to dereferencing a None memory_tracker in multiple scheduling code locations.
  • issues/192618

• In-Place Rank Mutation Detection Proposal: An architectural improvement is proposed to detect in-place rank mutations on placeholders during tracing by intercepting mutations within FakeTensor dispatch, aiming to fail fast and provide accurate error localization without tight eager-compiler coupling.
  • issues/192658

2.4 Closed Issues

This section lists, groups, and then summarizes issues that were closed within the last week in the repository. This section also links the associated pull requests if applicable.

Issues Closed This Week: 1642

Summarized Issues:

• TorchScript and JIT Compilation Issues: Numerous issues report errors, crashes, or unexpected behavior when using TorchScript's torch.jit.script or torch.jit.trace on various models and functions. These include failures due to unsupported operations, type mismatches, incorrect gradient computations, serialization/deserialization problems, and incompatibilities with certain Python features or PyTorch modules, leading to runtime errors, assertion failures, or segmentation faults during scripting, tracing, or model loading.
  [ issues/11967 , issues/13164 , issues/13170 , issues/13208 , issues/13493 , issues/13598 , issues/13655 , issues/13836 , issues/13913 , issues/14030 , issues/14131 , issues/14219 , issues/14332 , issues/14337 , issues/14459 , issues/14685 , issues/14726 , issues/14731 , issues/14940 , issues/15043 , issues/15116 , issues/15120 , issues/15249 , issues/15313 , issues/15342 , issues/15421 , issues/15431 , issues/15432 , issues/15608 , issues/15771 , issues/15869 , issues/16090 , issues/16138 , issues/16263 , issues/16293 , issues/16299 , issues/16385 , issues/16402 , issues/16494 , issues/16556 , issues/16590 , issues/16663 , issues/16744 , issues/168077 , issues/169538 , issues/170680 , issues/171377 , issues/172254 , issues/173492 , issues/17437 , issues/175708 , issues/176770 , issues/177116 , issues/178044 , issues/17907 , issues/17932 , issues/17936 , issues/17970 , issues/18000 , issues/18094 , issues/18101 , issues/18106 , issues/18157 , issues/18212 , issues/18312 , issues/184572 , issues/185405 , issues/186241 , issues/187266 , issues/188198 , issues/188244 , issues/188245 , issues/188336 , issues/188912 , issues/189298 , issues/189309 , issues/189326 , issues/189327 , issues/189488 , issues/189560 , issues/189648 , issues/189711 , issues/189873 , issues/190064 , issues/190663 , issues/190745 , issues/190828 , issues/190874 , issues/190886 , issues/191013 , issues/191378 , issues/191458 , issues/191560 , issues/191564 , issues/191603 , issues/191618 , issues/191620 , issues/191763 , issues/191796 , issues/191809 , issues/191821 , issues/191837 , issues/191849 , issues/191926 , issues/191937 , issues/191942 , issues/191943 , issues/191976 , issues/192002 , issues/192013 , issues/192158 , issues/192212 , issues/192213 , issues/192219 , issues/192246 , issues/192291 , issues/192294 , issues/192387 , issues/192407 , issues/192507 , issues/192528 , issues/192707 ]

• Caffe2 and ONNX Conversion Problems: Several issues highlight difficulties converting models between Caffe, Caffe2, and ONNX formats, including missing operator support, runtime errors due to missing blobs or input mismatches, segmentation faults during training or inference, and incompatibilities in operator schemas. These problems often require manual workarounds, rebuilding with specific flags, or updating conversion tools to handle custom layers or attributes properly.
  [ issues/6505 , issues/7020 , issues/7887 , issues/7945 , issues/9375 , issues/10940 , issues/12515 , issues/13034 , issues/13655 , issues/14222 , issues/16556 , issues/16602 , issues/19244 , issues/22609 , issues/23364 , issues/23766 , issues/25880 , issues/27343 , issues/27839 , issues/27840 , issues/27970 , issues/29108 , issues/29125 , issues/29128 , issues/29177 , issues/29190 , issues/29377 , issues/29419 , issues/29642 , issues/29662 , issues/29750 , issues/29822 , issues/29843 , issues/29987 , issues/30049 , issues/30138 , issues/30155 , issues/30161 , issues/30182 , issues/30365 , issues/30421 , issues/30489 , issues/30532 , issues/30635 , issues/30754 , issues/30786 , issues/30788 , issues/30965 , issues/31007 , issues/31129 , issues/31141 , issues/31191 , issues/31202 , issues/31259 , issues/31386 , issues/31391 , issues/31430 , issues/31545 , issues/31551 , issues/31565 , issues/31574 , issues/31772 , issues/31815 , issues/31842 , issues/31866 , issues/31882 , 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issues/41965 , issues/41966 , issues/41967 , issues/41968 , issues/41969 , issues/41970 , issues/41971 , issues/41972 , issues/41973 , issues/41974 , issues/41975 , issues/41976 , issues/41977 , issues/41978 , issues/41979 , issues/41980 , issues/41981 , issues/41982 , issues/41983 , issues/41984 , issues/41985 , issues/41986 , issues/41987 , issues/41988 , issues/41989 , issues/41990 , issues/41991 , issues/41992 , issues/41993 , issues/41994 , issues/41995 , issues/41996 , issues/41997 , issues/41998 , issues/41999 , issues/42000 , issues/42001 , issues/42002 , issues/42003 , issues/42004 , issues/42005 , issues/42006 , issues/42007 , issues/42008 , issues/42009 , issues/42010 , issues/42011 , issues/42012 , issues/42013 , issues/42014 , issues/42015 , issues/42016 , issues/42017 , issues/42018 , issues/42019 , issues/42020 , issues/42021 , issues/42022 , issues/42023 , issues/42024 , issues/42025 , issues/42026 , issues/42027 , issues/42028 , issues/42029 , issues/42030 , issues/42031 , issues/42032 , issues/42033 , issues/42034 , issues/42035 , issues/42036 , issues/42037 , issues/42038 , issues/42039 , issues/42040 , issues/42041 , issues/42042 , issues/42043 , issues/42044 , issues/42045 , issues/42046 , issues/42047 , issues/42048 , issues/42049 , issues/42050 , issues/42051 , issues/42052 , issues/42053 , issues/42054 , issues/42055 , issues/42056 , issues/42057 , issues/42058 , issues/42059 , issues/42060 , issues/42061 , issues/42062 , issues/42063 , issues/42064 , issues/42065 , issues/42066 , issues/42067 , issues/42068 , issues/42069 , issues/42070 , issues/42071 , issues/42072 , issues/42073 , issues/42074 , issues/42075 , issues/42076 , issues/42077 , issues/42078 , issues/42079 , issues/42080 , issues/42081 , issues/42082 , issues/42083 , issues/42084 , issues/42085 , issues/42086 , issues/42087 , issues/42088 , issues/42089 , issues/42090 , issues/42091 , issues/42092 , issues/42093 , issues/42094 , issues/42095 , issues/42096 , issues/42097 , issues/42098 , issues/42099 , issues/42100 , issues/42101 , issues/42102 , issues/42103 , issues/42104 , issues/42105 , issues/42106 , issues/42107 , issues/42108 , issues/42109 , issues/42110 , issues/42111 , issues/42112 , issues/42113 , issues/42114 , issues/42115 , issues/42116 , issues/42117 , issues/42118 , issues/42119 , issues/42120 , issues/42121 , issues/42122 , issues/42123 , issues/42124 , issues/42125 , issues/42126 , issues/42127 , issues/42128 , issues/42129 , issues/42130 , issues/42131 , issues/42132 , issues/42133 , issues/42134 , issues/42135 , issues/42136 , issues/42137 , issues/42138 , issues/42139 , issues/42140 , issues/42141 , issues/42142 , issues/42143 , issues/42144 , issues/42145 , issues/42146 , issues/42147 , issues/42148 , issues/42149 , issues/42150 , issues/42151 , issues/42152 , issues/42153 , issues/42154 , issues/42155 , issues/42156 , issues/42157 , issues/42158 , issues/42159 , issues/42160 , issues/42161 , issues/42162 , issues/42163 , issues/42164 , issues/42165 , issues/42166 , issues/42167 , issues/42168 , issues/42169 , issues/42170 , issues/42171 , issues/42172 , issues/42173 , issues/42174 , issues/42175 , issues/42176 , issues/42177 , issues/42178 , issues/42179 , issues/42180 , issues/42181 , issues/42182 , issues/42183 , issues/42184 , issues/42185 , issues/42186 , issues/42187 , issues/42188 , issues/42189 , issues/42190 , issues/42191 , issues/42192 , issues/42193 , issues/42194 , issues/42195 , issues/42196 , issues/42197 , issues/42198 , issues/42199 , issues/42200 , issues/42201 , issues/42202 , issues/42203 , issues/42204 , issues/42205 , issues/42206 , issues/42207 , issues/42208 , issues/42209 , issues/42210 , issues/42211 , issues/42212 , issues/42213 , issues/42214 , issues/42215 , issues/42216 , issues/42217 , issues/42218 , issues/42219 , issues/42220 , issues/42221 , issues/42222 , issues/42223 , issues/42224 , issues/42225 , issues/42226 , issues/42227 , issues/42228 , issues/42229 , issues/42230 , issues/42231 , issues/42232 , issues/42233 , issues/42234 , issues/42235 , issues/42236 , issues/42237 , issues/42238 , issues/42239 , issues/42240 , issues/42241 , issues/42242 , issues/42243 , issues/42244 , issues/42245 , issues/42246 , issues/42247 , issues/42248 , issues/42249 , issues/42250 , issues/42251 , issues/42252 , issues/42253 , issues/42254 , issues/42255 , issues/42256 , issues/42257 , issues/42258 , issues/42259 , issues/42260 , issues/42261 , issues/42262 , issues/42263 , issues/42264 , issues/42265 , issues/42266 , issues/42267 , issues/42268 , issues/42269 , issues/42270 , issues/42271 , issues/42272 , issues/42273 , issues/42274 , issues/42275 , issues/42276 , issues/42277 , issues/42278 , issues/42279 , issues/42280 , issues/42281 , issues/42282 , issues/42283 , issues/42284 , issues/42285 , issues/42286 , issues/42287 , issues/42288 , issues/42289 , issues/42290 , issues/42291 , issues/42292 , issues/42293 , issues/42294 , issues/42295 , issues/42296 , issues/42297 , issues/42298 , issues/42299 , issues/42300 , issues/42301 , issues/42302 , issues/42303 , issues/42304 , issues/42305 , issues/42306 , issues/42307 , issues/42308 , issues/42309 , issues/42310 , issues/42311 , issues/42312 , issues/42313 , issues/42314 , issues/42315 , issues/42316 , issues/42317 , issues/42318 , issues/42319 , issues/42320 , issues/42321 , issues/42322 , issues/42323 , issues/42324 , issues/42325 , issues/42326 , issues/42327 , issues/42328 , issues/42329 , issues/42330 , issues/42331 , issues/42332 , issues/42333 , issues/42334 , issues/42335 , issues/42336 , issues/42337 , issues/42338 , issues/42339 , issues/42340 , issues/42341 , issues/42342 , issues/42343 , issues/42344 , issues/42345 , issues/42346 , issues/42347 , issues/42348 , issues/42349 , issues/42350 , issues/42351 , issues/423

2.5 Issue Discussion Insights

This section will analyze the tone and sentiment of discussions within this project's open and closed issues that occurred within the past week. It aims to identify potentially heated exchanges and to maintain a constructive project environment.

Based on our analysis, there are no instances of toxic discussions in the project's open or closed issues from the past week.

---

III. Pull Requests

3.1 Open Pull Requests

This section provides a summary of pull requests that were opened in the repository over the past week. The top three pull requests with the highest number of commits are highlighted as 'key' pull requests. Other pull requests are grouped based on similar characteristics for easier analysis. Up to 25 pull requests are displayed in this section, while any remaining pull requests beyond this limit are omitted for brevity.

Pull Requests Opened This Week: 562

Key Open Pull Requests

1. [ROCm][CI] Fix rocSHMEM testing with TheRock SDKs: This pull request fixes two separate rocSHMEM testing failures in the ROCm 7.14 and 7.15 TheRock CI images by updating the runtime library search paths to include TheRock SDK locations and ensuring the installation of the libnuma-dev package so that CMake can properly detect the NUMA dependency, thereby enabling rocSHMEM support and successful test execution.

• URL: pull/192362

• Associated Commits: 37412, fb970, 5dfb0, 14503, bc8d1, e56af, 50d74, b3c78, c77d3, ded95, 5aac0, f4d33, 6462b, e9ec1, 24470, f356b, 4d0f3, 3716b, 3eec4, 5c2fa, c1b7f, c0492, bff3f, 86a9d, be403, 90642, 245fd, 27b18, 64820, 27680, 33674, fdc91, 65941, 726d8, 72a0d, 18e3a, 39fd7, 771bc, 2ea87, a76de, 8aa8f, eda44, 2f147, 2faae, f7b41, e8789, 6dc97, 5b6d4, 3a588, f696f, cc817, f0de6

2. [inductor] Don't assume alignment of saved user inputs in backward: This pull request fixes a bug in the PyTorch Inductor backend where saved user inputs that are unused in the forward pass but needed in backward were incorrectly assumed to be aligned, causing runtime CUDA errors on misaligned tensors by ensuring these inputs receive the same runtime alignment check and copy_if_misaligned treatment as tangents, with a robust metadata-based staticness classification and accompanying tests verifying the fix.

• URL: pull/192263

• Associated Commits: fa0fc, b4d24, fcdeb, a5dae, 78cac, c948e, 5823c, c40e5, fc4ce, 3cea8, 19b96, b6cfa, a9b35, c6602

3. [xpu] Remove allocator property from MemPool: This pull request removes the allocator property from the MemPool class in the [xpu] backend to align its implementation with CUDA, which no longer provides an allocator.

• URL: pull/192041

• Associated Commits: 89d48, 7e4aa, 43782, fe08b, f8412, b9e15, 1c9d4, 75175, 5851a, 01c6f, 0a88e, 5e580, cffd8

Other Open Pull Requests

• XPU Caching Allocator Enhancements: Multiple pull requests improve the XPU caching allocator by adding support for the use_oom feature and introducing a non-split memory pool to align its behavior with CUDA, enhancing distributed symmetric memory usage and overall allocator functionality. These changes ensure better distributed usage and memory management consistency between XPU and CUDA devices.
  • pull/192038, pull/192033

• PyTorch Dynamo Improvements: Several pull requests enhance PyTorch Dynamo by fixing under-recording issues in invoke_subgraph, enabling multi-graph precompilation across graph breaks, adding sparse compressed tensor creation tracing, and registering a heapq polyfill for better graph tracing. These updates improve compilation correctness, scalability, sparse tensor support, and tracing reliability across different backends.
  • pull/192009, pull/192379, pull/192093, pull/192100

• Associative Scan and vmap Functionality: Two pull requests implement and fix the vmap functionality for the associative_scan operation, consolidating implementations to improve user experience and backward pass efficiency by avoiding performance bottlenecks. These changes unify related vmap implementations and enhance support for compile and pointwise modes.
  • pull/192389, pull/192388

• Sparse Tensor Support in Inductor Backend: A pull request introduces sparse fallback kernels in the Inductor backend by adding a NonStridedLayout and updating sparse tensor recognition to include multiple sparse formats, while marking non-strided arguments as invalid in pattern matching. This implementation is currently inactive pending sparse tensor creation support in Dynamo.
  • pull/192092

• Distributed Backend and Memory Pool Updates: A pull request changes the default NCCL backend to nccl2 with backward compatibility and environment variable control, while another adds IPC support for torch.xpu.Event, enhancing distributed communication and device event handling. These updates improve backend defaults and inter-process communication for XPU devices.
  • pull/192281, pull/192495

• Testing and CI Enhancements: Multiple pull requests refactor tests for device-generic compatibility, enable multi-GPU tests on Intel GPUs, update CI workflows to support MI300 GPUs, and improve flop counter tests by decoupling CUDA-specific checks. These changes broaden hardware support and improve test coverage and reliability across platforms.
  • pull/192422, pull/192262, pull/192494, pull/192130

• Performance and Compilation Optimizations: Several pull requests optimize compilation performance by improving caching for symbolic integers in fake tensors, introducing a LazyJointCallable to reduce redundant graph walks, and enhancing the FakeTensor dispatch cache with a more efficient reconstruction method. These improvements reduce overhead and speed up dynamic shape handling and backward graph construction.
  • pull/191958, pull/191945, pull/192047

• CUDA Graph and Kernel Annotation Improvements: A pull request introduces a new CUPTI-based backend for attributing CUDA graph nodes to scopes via node-creation callbacks, improving kernel annotation accuracy and performance during CUDA graph capture by addressing limitations of the previous method. This enhances profiling and debugging capabilities for CUDA graphs.
  • pull/191960

• Multithreaded Autograd Default Change: One pull request disables multithreaded autograd by default, making backward computations run on the calling thread unless explicitly enabled, which reduces overhead and unexpected behavior in common single-device workloads. Related engine components, tests, and documentation are updated accordingly.
  • pull/192082

• MemPool Functionality Refinement: A pull request refines and improves the MemPool functionality in PyTorch through iterative updates, enhancing memory pool management and efficiency.
  • pull/192048

• Kineto Integration Testing: A pull request focuses on upstream Kineto integration by vendoring source code, enabling Kineto unit tests in PyTorch CI, fixing CUDA 13 compatibility, and ensuring cross-platform test discovery and execution.
  • pull/192519

• New torch.divmod Functionality: A pull request adds the torch.divmod function and Tensor.divmod() method, implementing combined division and modulo operations with fixes, documentation, and tests to address issue #90820.
  • pull/192313

• Group Normalization CUDA NHWC Implementation: A pull request adds a CUDA implementation of Group Normalization for NHWC data format, reusing existing infrastructure to improve performance on H100 GPUs by avoiding channels-first contiguous output constraints.
  • pull/191945

3.2 Closed Pull Requests

This section provides a summary of pull requests that were closed in the repository over the past week. The top three pull requests with the highest number of commits are highlighted as 'key' pull requests. Other pull requests are grouped based on similar characteristics for easier analysis. Up to 25 pull requests are displayed in this section, while any remaining pull requests beyond this limit are omitted for brevity.

Pull Requests Closed This Week: 502

Key Closed Pull Requests

1. [CUDA][cuBLASLt] addmm - Lt path improvements: This pull request proposes improvements to the CUDA cuBLASLt addmm implementation by allowing arbitrary beta values, enabling no-copy bias handling in suitable cases, and retiring the isInputCompliesAddmmCudaLt flag to optimize the Lt path.

• URL: pull/170571

• Associated Commits: 2aaaa, 25068, e6b30, 0d641, 74325, 465b1, dea71, df40c, 89d90, 0023c, 3f994, 1ef7c, 2f2cc, 1f040, 05086, 25e93, 29bf1, 97d9a, 11740, c82ab, 66f20, 54bf0, 6b43c, fdfe6, 26900, b0932, b4430, 8812a, 60c14, 1f0d0, 97303, b3aa2, 922a6, 9d497, ccae5, 916e7, deba3, 5db21, 04da0, 9c64b, 2eef9, 234ee, b5f16, 10482, 8f0c7, 6071e, 7b806, cb1de, 4e478, 287c1, 91658, 2b596, 6c851

2. [profiler][cupti] Perfetto-native (.pftrace) export, full chrome-args parity: This pull request implements native Perfetto (.pftrace) export support in the PyTorch profiler's CUPTI backend, achieving full feature parity with the existing Chrome trace JSON export by encoding detailed GPU counters, per-event arguments, and metadata directly in a compact protobuf format using the vendored protozero library, thereby improving export performance and reducing output size while maintaining compatibility with Perfetto's official trace decoder.

• URL: pull/187898

• Associated Commits: c9dd4, 7f293, dd154, 76a49, b4d41, 606ba, 8d91a, f7059, a4813, 61174, 0c6e1, 282db, f69e6, 5e5d2, 02fef, c98cb, 7204e, e5e0b, cc575, b5a6a, cf714, 7daa0, 62774, 2cbcb, b7061, 306ad, fafee, 9cfd7, 4616c, 984b8, 776df, 80be7, 7651f, 7f077, b85f4, 76b86, 83287, b30be, 0727e

3. [MPS] RDMA for distributed MPS: This pull request introduces an RDMA-only c10d backend for distributed MPS on Apple Silicon Macs connected via Thunderbolt 5, implementing collective operations like allreduce, broadcast, send, recv, and barrier with high-performance RDMA transport while requiring successful ibv_alloc_pd allocation on all ranks and providing fallback guidance to the gloo backend if RDMA is unavailable.

• URL: pull/181087

• Associated Commits: 8228f, fb9a3, ff33c, 1458d, b73d3, b257d, c1bce, ebadb, c3d3f, cc69a, dbe76, ba166, 2a17a, d4a46, 5a107, 9c803, ffa98, 003e5, 520a5, 21aa9, 9611f, f00f3, dc2c6, 29916, c6900, 1ed05, 30182, abf45, 912a2, 41053, 6148d, 03b8e, 04184, 28154, 35956, 9e61a, 32a33

Other Closed Pull Requests

• XPU and Inductor Testing Enhancements: Multiple pull requests improve testing and caching mechanisms for XPU and Inductor backends. These include enabling eager execution tests on Intel GPUs with XPU support and adding a thread-local LRU cache for oneDNN matmul primitives on XPU to optimize repeated operations and avoid redundant rebuilds.
  • pull/176689, pull/180209

• MPS Backend Improvements: Several pull requests enhance the MPS backend by adding support for sparse CSR tensor formats, introducing an opt-in GEMV kernel with tile autotuning, and drafting a port of MPSGraph matrix multiplication operations to Metal for performance gains on Apple hardware.
  • pull/177757, pull/189628, pull/186541

• Profiler and Performance Metrics Enhancements: Pull requests add new GPU counters such as power and temperature to the profiler's JSON export and introduce a new pathway to build typed metadata events directly from Kineto, improving performance tracing and maintainability.
  • pull/188939, pull/191756

• FlexGEMM and Float8 Quantization Support: Updates to the flex_gemm module include support for recognizing and lowering packed NVFP4 main output expressions and adding epilogue lowering for MX E8M0 and NVFP4 E4M3 scale custom operations, enabling native lowering with detailed handling of float8 quantization scenarios.
  • pull/191270, pull/188739

• Distributed and Backend Stability Fixes: Fixes address critical issues in distributed backends and NCCL, including preventing deadlocks caused by caching-allocator trace hooks and correcting environment variable handling in TCPStore to avoid startup hangs under torchrun.
  • pull/192101, pull/191957

• Test Refactoring and Stability Improvements: Several pull requests refactor test files and stabilize flaky tests by normalizing placeholder names, removing unnecessary opt-outs, and improving order invariance in test partitions to ensure consistent test behavior.
  • pull/186352, pull/188175, pull/188176

• DTensor Custom Algorithm Dispatch: A pull request introduces a custom algorithm dispatch mechanism in DTensor, allowing selection of alternate operator implementations during sharding propagation, including a prototype distributed GEMM algorithm that overlaps communication and computation for optimized matrix multiplication.
  • pull/182379

• Build and CI Pipeline Updates: Updates include switching ROCm preview Docker images to wheel-based SDKs, fixing s390x Docker image build issues, and adding a new GitHub Actions workflow for Windows NVIDIA RTX GPU testing to improve CI coverage.
  • pull/188429, pull/181359, pull/176678

• Dynamo and Triton Launcher Fixes: Fixes in PyTorch Dynamo preserve live semantics of dict view objects to prevent stale snapshots, while improvements in the Triton launcher add validation checks and handle edge cases to avoid silent failures.
  • pull/185908, pull/176072

• SM100 GEMM Kernel and Inductor Backend: A pull request adds the SM100 persistent dense GEMM kernel source as a foundational implementation for NVGEMM epilogue fusion in the Inductor backend, with plans for operator registration and scheduler integration in future commits.
  • pull/190819

3.3 Pull Request Discussion Insights

This section will analyze the tone and sentiment of discussions within this project's open and closed pull requests that occurred within the past week. It aims to identify potentially heated exchanges and to maintain a constructive project environment.

Based on our analysis, there are no instances of toxic discussions in the project's open or closed pull requests from the past week.

---

IV. Contributors

4.1 Contributors

Active Contributors:

We consider an active contributor in this project to be any contributor who has made at least 1 commit, opened at least 1 issue, created at least 1 pull request, or made more than 2 comments in the last month.

If there are more than 10 active contributors, the list is truncated to the top 10 based on contribution metrics for better clarity.

Contributor	Commits	Pull Requests	Issues	Comments
janeyx99	35	7	0	1206
mlazos	305	9	0	1
d4l3k	215	22	2	19
drisspg	212	18	2	11
williamwen42	198	14	0	7
bobrenjc93	198	10	0	1
ezyang	162	21	0	19
guangyey	154	13	1	15
frgossen	173	2	1	0
guilhermeleobas	148	16	2	3