pytorch

Choose 32-bit vs 64-bit index math in PyTorch CUDA kernels. Use when fixing large-tensor indexing overflows, deciding whether to use int64_t, canUse32BitIndexMath, CUDA_KERNEL_LOOP_TYPE, or AT_DISPATCH_INDEX_TYPES, and when considering binary-size or performance impact of index-type templating.

Query PyTorch CI, GitHub Actions, HUD, Grafana, and infrastructure metrics. Use when users ask about CI duration, job failures, queue times, workflow trends, runner health, dashboard data, or PyTorch infrastructure metrics.

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

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.

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".

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.

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.

Read-only CLI commands for debugging OSDC clusters: kubectl, aws, helm, tofu. Includes command references, safety boundaries, and the deploy-audit ConfigMaps in osdc-system (what version is running where, who deployed it, when, did it succeed, is a deploy stuck). Applies to ~/meta/ci-infra/osdc. Load when investigating cluster state, debugging pods, inspecting infrastructure, comparing deployed versions across clusters, or diagnosing a failed or stuck deploy.

OSDC deployment workflow, just recipes, base deploy order, module deploy order, clusters.yaml configuration, Terraform architecture, smoke tests, justfile conventions. Applies to ~/meta/ci-infra/osdc. Load when deploying, adding modules, or modifying deploy scripts.

OSDC runners, NodePools, BuildKit, GitHub Actions constraints, EKS node taints, image mirroring, and the runner/nodepool change checklist. Applies to ~/meta/ci-infra/osdc. Load when modifying runners, nodepools, BuildKit, or node configurations.

OSDC observability: monitoring (metrics) and logging pipelines, three-Alloy architecture, Grafana Cloud Loki + Mimir queries, label strategy, credential setup, and troubleshooting. Applies to ~/meta/ci-infra/osdc. Load when working on monitoring, logging, Alloy, or querying logs.

OSDC platform architecture, project organization, directory tree, key design decisions, external knowledge base, key files, and docs index. Applies to ~/meta/ci-infra/osdc. Load when working on OSDC to understand project layout and architecture.

Harbor pull-through cache configuration, Helm chart gotchas (inconsistent value paths, per-component image overrides, taint tolerations), and image mirroring strategy. Applies to ~/meta/ci-infra/osdc. Load when working on Harbor, image mirroring, or container registry configuration.

OSDC NodeLocal DNSCache (NLD) base component — per-node CoreDNS DaemonSet under base/kubernetes/nodelocaldns/. Covers iptables-mode rationale (vs IPVS-mode), dynamic kube-dns ClusterIP substitution via deploy.sh (validates the kube-dns ClusterIP is IPv6 and fails fast on IPv4 — OSDC EKS is IPv6-only by design and NLD is single-family by deploy-script enforcement; the IPv6-only cluster recreate has already shipped in commit a6b4c8c), Service-before-DaemonSet apply ordering (KUBE_DNS_UPSTREAM env-var injection), kube-dns-upstream auxiliary Service, two metrics ports (9253 CoreDNS plugin / 9353 binary setup_errors_total), the coredns_nodecache_* vs nodelocaldns_* metric-name confusion, no preStop / no memory limit / no startup taint design decisions, Harbor proxy-cache image pull (no pre-mirror), IPv6 ULA bind address (fd00::10), and the ≥3-day soak gate (applies to any FUTURE cluster-wide NLD cutover). Applies to ~/meta/ci-infra/osdc. Load when developing, debugging, or maintaining the nodelocaldns base compone

OSDC PyPI wheel cache module — per-CUDA-slug nginx+pypiserver fanout backed by shared EFS wheelhouse, fed by an external wheel-build pipeline via S3. Covers architecture (4 components), slug naming, EFS PVC, NVMe nginx cache, S3 bucket layout, needbuild override, prebuilt-cache.txt matrix invalidation, njs merge handler, /whl/ rewrites, NetworkPolicy, IRSA roles, log rotation, pod resource computation, cache-enforcer SNI matching. Applies to ~/meta/ci-infra/osdc. Load when developing, debugging, or maintaining the pypi-cache module, investigating pip install failures on runners, adding CUDA versions, or working with the wants/wheel-syncer pipeline.

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.

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/. Also covers QNN intermediate-output / per-layer accuracy debugging — trigger on phrases like "QNN accuracy issue", "QNN output doesn't match CPU", "debug per-layer for QNN", "find which QNN layer is wrong".

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.

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.

Find reverts that landed on pytorch/pytorch main but are missing from a release branch (release/X.Y) because the reverted commit was already shipped in a release candidate, and open one cherry-pick PR per missing revert against the release branch (on a fork branch, never pushing to release/X.Y directly), optionally posting a cherry-pick nomination comment for each on the release tracker issue. Driven by tools/analytics/github_analyze.py --analyze-missing-reverts-from-branch (the "GitHub Analytics Daily" workflow). Triggered by "missing reverts", "analyze reverts for release", "cherry-pick missing reverts", "reverts not in release/X.Y", or pointing at a GitHub Analytics Daily run and asking to cherry-pick the flagged reverts.

Pin (or re-pin) the Linux manywheel builder docker images used by the nightly/release binary build workflows in pytorch/pytorch to a fixed .ci/docker build. Takes the release version (e.g. 2.13) as input. Updates DOCKER_IMAGE_PIN in .github/scripts/generate_binary_build_matrix.py and regenerates the workflows. Triggered by mentions of "pin docker image", "update docker pin", "docker image pin", "pin builder images", or "bump the manywheel image pin" for a release branch.

Generate (and optionally open) a PyTorch release tracker / cherry-pick tracking issue from a release announcement, like https://github.com/pytorch/pytorch/issues/180506. Triggered by mentions of "release tracker", "release tracking issue", "cherry-pick tracker", or "cut a release branch issue".

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".

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).

Investigate a Helion benchmark dashboard regression (helionlang.com/dashboard) — find the cause and classify it. Auto-activate when the user reports a perf drop/spike on a dashboard platform (e.g. b200 cute) around a given date.

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.

Profiler-driven hill-climbing to close the inference throughput gap between TorchTitan's unified model (running inside vLLM) and vLLM's native model. Benchmark with generate.py --benchmark, climb optimization rungs (compile / cudagraph / fused kernels), profile torchtitan vs the native target, then patch the single biggest gap at a time and re-measure. Use when the user wants to benchmark or optimize RL inference generation speed, reproduce previous hill climbing study, or invokes /inference_perf_hillclimb.

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.

Analyze torch-tensorrt local test results and drive failures to a fix. Use when the user pastes a test report / summary, asks why tests failed, asks to triage or fix failing tests, or mentions the JUnit/test-summary output from `just tests-report` / `just test-summary`. Covers where the JUnit XMLs live, how to read the consolidated report, how to reproduce a single failure, and how to categorize (real bug vs torch-API change vs OOM/skip vs flake).

Build torch-tensorrt locally — install/pin the matching PyTorch nightly, drive Bazel through setup.py, do a clean rebuild after libtorch ABI changes, and recover from common build failures (undefined symbol, stale _C.so, libtorchtrt.so missing). Invoke whenever the user asks to build, rebuild, install editable, or upgrade the torch nightly; or when an import fails with an undefined-symbol error tying torch_tensorrt to libtorch.

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.