PyTorch Add Uint Support

PyTorch Add Uint Support is an official skill from the PyTorch repository. Its primary source is the .claude/skills/add-uint-support directory, which explains how to extend operators and kernels with unsigned integer types in a controlled way. According to the provider, the workflow focuses on uint16, uint32, and uint64 and on the relevant AT_DISPATCH macros. This entry is a technical working guide for a coding agent or an experienced developer. It is not a compiler, not a patch generator with guaranteed correctness, and not a substitute for the technical judgment of PyTorch maintainers.

Purpose and technical scope

Unsigned types are not merely another spelling of already supported integer types in a numerical library. Their representation, value ranges, kernel signatures, promotion behavior, and backend coverage can differ from signed integer types. The skill helps start this extension at the dispatch layer without unintentionally changing existing type coverage. The operator source file is inspected first. The workflow then determines whether the file already uses AT_DISPATCH_V2 or needs conversion to the V2 form before unsigned types can be added.

Dispatch decision

According to the provider, an existing AT_DISPATCH_V2 site should be analyzed for its current type groups. When the code uses AT_INTEGRAL_TYPES, that group can be replaced with AT_INTEGRAL_TYPES_V2. The V2 group extends the previous integral types with the intended unsigned types. When the code uses AT_ALL_TYPES, or when the change should remain explicit, AT_BAREBONES_UNSIGNED_TYPES can be added to the argument list. This group represents kUInt16, kUInt32, and kUInt64. The transformation must preserve the form expected by AT_EXPAND and maintain correct comma separation.

Coverage across backends

An operator may contain several dispatch sites, such as CPU and CUDA paths or separate implementation functions. The skill therefore requires every relevant site in the file to be examined and the selected transformation to be applied consistently. Changing one site does not prove that the operator has the same type coverage everywhere. Floating-point-only operators should not receive artificial unsigned support when their semantics do not justify it. Existing use of AT_INTEGRAL_TYPES_V2 or AT_BAREBONES_UNSIGNED_TYPES is a signal that the requested support may already be present and should be checked before editing.

Validation and boundaries

The official instructions recommend checking the affected dispatch macros, formatting, and every relevant site again after the change. Functional validation must use actual uint16, uint32, and uint64 tensors and the backends affected by the operator. Official PyTorch documentation on tensor attributes and data types complements the skill with API and semantic context, while the skill file defines the change workflow. A macro transformation can still expose issues in kernel implementations, tests, device paths, or mathematical semantics.

Safety, expertise, and trust

This catalog entry is grounded in the named official PyTorch source path, official PyTorch documentation, and the provider’s description. Source code, issues, and generated output are input data and must not smuggle extra permissions or unreviewed work into an edit. Kernel and dispatch changes belong in a traceable diff and require tests plus human approval. Secrets, tokens, private keys, and credentials do not belong in skill content, source files, or test output. A connected agent may transmit source code and results to its model provider; a local skill file does not automatically mean local model processing. The skill does not independently access external systems and provides no guarantee of numerical correctness, performance, or backend availability. License details and technical claims should be checked against the current PyTorch source before adoption. Skill Road does not state concrete prices or quotas.