pytorch

Fix bugs reported in PyTorch GitHub issues by reproducing, root-causing, and implementing a fix in the local working tree. Use when the user asks to fix a PyTorch GitHub issue.

Write Metal/MPS kernels for PyTorch operators. Use when adding MPS device support to operators, implementing Metal shaders, or porting CUDA kernels to Apple Silicon. Covers native_functions.yaml dispatch, host-side operators, and Metal kernel implementation.

Triages GitHub issues by routing to oncall teams, applying labels, and closing questions. Use when processing new PyTorch issues or when asked to triage an issue.

Sub-triages issues in the oncall:distributed queue by assigning distributed module labels, routing to sub-oncalls, and marking triaged. Use when an issue has been routed to oncall:distributed and needs second-level triage.

Migrate a file to use stricter Pyrefly type checking with annotations required for all functions, classes, and attributes.

Review PyTorch pull requests for code quality, test coverage, security, and backward compatibility. Use when reviewing PRs, when asked to review code changes, or when the user mentions "review PR", "code review", or "check this PR".

Debug PyTorch 2 compiler stack failures including Dynamo graph breaks, Inductor codegen errors, AOTAutograd crashes, and accuracy mismatches. Use when encountering torch.compile errors, BackendCompilerFailed exceptions, recompilation issues, Triton kernel failures, FX graph problems, or when the user mentions debugging PT2, Dynamo, Inductor, or compiled model issues.

Fetch, analyze, reproduce, and minimize GitHub issue reproductions. Use when asked to check if an issue reproduces, minimize a repro, analyze a bug report, or scrub/triage an issue for reproducibility.

Build, test, or develop the QNN (Qualcomm AI Engine Direct) backend. Use when working on backends/qualcomm/, building QNN (use backends/qualcomm/scripts/build.sh), adding new ops or passes, running QNN delegate tests, or exporting models for Qualcomm HTP/GPU targets. Also exposes a Buck-vs-CMake parity workflow — invoke as `/qualcomm buck-fix`, `/qualcomm buck-cmake fix`, `/qualcomm buck-parity`, or any user request to fix `test-qnn-buck-build-linux` CI failures or check buck/cmake drift in backends/qualcomm/.

Build and configure ExecuTorch as a Zephyr RTOS module for embedded boards. Use when setting up a Zephyr workspace with ET, adding board support (overlays, confs, memory layout), building with west, or debugging linker memory overflow.

Search the ExecuTorch tribal knowledge base covering QNN, XNNPACK, Vulkan, CoreML, Arm, and Cadence backends, quantization recipes, export pitfalls, runtime errors, and SoC compatibility. Use when debugging ExecuTorch errors, choosing quantization configs, checking backend op support, or answering questions about Qualcomm HTP / Snapdragon / Apple Neural Engine behavior.

Build ExecuTorch from source — Python package, C++ runtime, runners, cross-compilation, and backend-specific builds. Use when compiling anything in the ExecuTorch repo, diagnosing build failures, or setting up platform-specific builds.

Analyze and reduce ExecuTorch binary size. Use when investigating binary size, running size tests, or optimizing the runtime for size-constrained deployments.

Export a PyTorch model to .pte format for ExecuTorch. Use when converting models, lowering to edge, or generating .pte files.

Build, test, or develop the Cortex-M (CMSIS-NN) backend. Use when working on backends/cortex_m/, running Cortex-M tests, or exporting models for Cortex-M targets.

Profile ExecuTorch model execution. Use when measuring performance, analyzing operator timing, or debugging slow models.

Step-by-step playbook for migrating a pytorch/pytorch .github/workflows/*.yml from EC2 to OSDC (ARC) runners — covers both dial-up and 100% opt-in patterns, with the inputs that must be plumbed through _linux-build.yml / _linux-test.yml. Use when migrating a workflow off EC2 onto on-site data center / EKS-hosted self-hosted runners (OSDC / ARC), enabling the ARC experiment, or wiring up `use-arc` / `runner_prefix` / `ci-docker-hash` / `python-version` / `compiler` / `cuda-version` inputs.

Update tools/scripts/generate_binary_build_matrix.py when a PyTorch release goes live. Advances CURRENT_STABLE_VERSION to the new stable, promotes the release-channel CUDA/ROCm arches to match the candidate/test channel, and regenerates the JSON test fixtures. Triggered by mentions of "release go live", "advance stable version", "promote release matrix", or "update binary build matrix" for a new PyTorch X.Y release.

Triage a failing vLLM Buildkite CI build for a PyTorch version-bump PR, isolate new regressions vs. pre-existing failures on main by comparing against recent "Full CI run - nightly/daily" builds, classify by root cause, and file one grouped issue per root cause in pytorch/pytorch (linked under an umbrella issue). Use when the user points at a Buildkite build for a torch/triton upgrade and asks "what's broken that isn't broken on main" or "file issues for each failure".

Disable or re-enable Cloudflare R2 (download-r2.pytorch.org) usage in manage_v2.py during R2 outages. Can toggle R2 off/on for nightly builds, prod/stable builds, or both. The affected packages are PT_FOUNDATION_PACKAGES (torch, torchvision, torchaudio, fbgemm_gpu, fbgemm_gpu_genai, triton, pytorch_triton, pytorch_triton_rocm, pytorch_triton_xpu).

Generate a PyTorch release validation checklist issue by pulling open/closed issues from a GitHub milestone and cherry-picks from a release tracker issue. Triggered by mentions of "validation issue", "validation checklist", "release validation", or "release checklist".

Analyze PyTorch nightly wheel sizes over a date range using GitHub Actions artifacts API. Use when tracking binary size changes, identifying wheel size regressions, or producing wheel size reports for manywheel builds. Supports specifying CUDA version (cuda12_6, cuda12_8, cuda12_9, cuda13_0), CPU, XPU, or ROCm variants. Triggered by mentions of "wheel size", "binary size", "package size", "manywheel size", or "nightly build size".

Address CI failures and unresolved review comments on a Helion pull request. Auto-activate when the user mentions a URL like https://github.com/pytorch/helion/pull/<number>.

Auto-activate when the user mentions runpod or scripts/runpod.py

Context for working on Helion's TPU/Pallas backend. Auto-activate when the user mentions TPU or Pallas in the context of Helion development.

Capture and compare per-op activations between two TorchTitan runs to spot numerics divergence (eager vs aot_fx_trace, FSDP vs no-FSDP, before vs after a refactor). Use when the user wants to debug bitwise / numeric drift in training, or invokes /numerics_debugging.

Bisect PyTorch commits to find the regression that breaks TorchTitan. Use when the user wants to bisect PyTorch or invokes /torch_bisect.

Review PyTorch tutorials pull requests for content quality, code correctness, build compatibility, and style. Use when reviewing PRs, when asked to review code changes, or when the user mentions "review PR", "code review", or "check this PR".

Update the third_party/kineto submodule in PyTorch to the latest commit from this kineto repo and commit the change. Use when updating the kineto submodule hash for a release.