Atlas / Skills / pytorch / Add Uint Support

Add Uint SupportCAUTION

skills/pytorch/add-uint-support

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Verdict
CAUTION
Grade
B
Trust score
89 /100
Version
—
Hosts
—
License
NOASSERTION
Stars
104,945
01

Overview

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Read from source at commit bc18adab2c3eOBSERVED · 2026-10-20
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02

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: add-uint-support
description: Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
---

# Add Unsigned Integer (uint) Support to Operators

This skill helps add support for unsigned integer types (uint16, uint32, uint64) to PyTorch operators by updating their AT_DISPATCH macros.

## When to use this skill

Use this skill when:
- Adding uint16, uint32, or uint64 support to an operator
- User mentions "unsigned types", "uint support", "barebones unsigned types"
- Enabling support for kUInt16, kUInt32, kUInt64 in kernels
- Working with operator implementations that need expanded type coverage

## Quick reference

**Add unsigned types to existing dispatch:**
```cpp
// Before
AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
  kernel<scalar_t>();
}), AT_EXPAND(AT_ALL_TYPES));

// After (method 1: add unsigned types explicitly)
AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
  kernel<scalar_t>();
}), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES));

// After (method 2: use V2 integral types if AT_INTEGRAL_TYPES present)
AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
  kernel<scalar_t>();
}), AT_EXPAND(AT_INTEGRAL_TYPES_V2), AT_EXPAND(AT_FLOATING_TYPES));
```

## Type group reference

**Unsigned type groups:**
- `AT_BAREBONES_UNSIGNED_TYPES`: kUInt16, kUInt32, kUInt64
- `AT_INTEGRAL_TYPES_V2`: AT_INTEGRAL_TYPES + AT_BAREBONES_UNSIGNED_TYPES

**Relationship:**
```cpp
AT_INTEGRAL_TYPES          // kByte, kChar, kInt, kLong, kShort
AT_BAREBONES_UNSIGNED_TYPES  // kUInt16, kUInt32, kUInt64
AT_INTEGRAL_TYPES_V2       // INTEGRAL_TYPES + BAREBONES_UNSIGNED_TYPES
```

## Instructions

### Step 1: Determine if conversion to V2 is needed

Check if the file uses AT_DISPATCH_V2:

**If using old AT_DISPATCH:**
- First convert to AT_DISPATCH_V2 using the at-dispatch-v2 skill
- Then proceed with adding uint support

**If already using AT_DISPATCH_V2:**
- Proceed directly to Step 2

### Step 2: Analyze the current dispatch macro

Identify what type groups are currently in use:

```cpp
AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
  // body
}), AT_EXPAND(AT_ALL_TYPES), kHalf, kBFloat16);
    ^^^^^^^^^^^^^^^^^^^^^^^^^
    Current type coverage
```

Common patterns:
- `AT_EXPAND(AT_ALL_TYPES)` → includes AT_INTEGRAL_TYPES + AT_FLOATING_TYPES
- `AT_EXPAND(AT_INTEGRAL_TYPES)` → signed integers only
- `AT_EXPAND(AT_FLOATING_TYPES)` → floating point types

### Step 3: Choose the uint addition method

Two approaches:

**Method 1: Add AT_BAREBONES_UNSIGNED_TYPES explicitly**
- Use when: You want to be explicit about adding uint support
- Add `AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES)` to the type list

**Method 2: Substitute AT_INTEGRAL_TYPES with AT_INTEGRAL_TYPES_V2**
- Use when: The dispatch already uses `AT_EXPAND(AT_INTEGRAL_TYPES)`
- More concise: replaces one type group with its superset
- Only applicable if AT_INTEGRAL_TYPES is present

### Step 4: Apply the transformation

**Method 1 example:**
```cpp
// Before
AT_DISPATCH_V2(
    dtype,
    "min_values_cuda",
    AT_WRAP([&]() {
      kernel_impl<scalar_t>(iter);
    }),
    AT_EXPAND(AT_ALL_TYPES),
    kBFloat16, kHalf, kBool
);

// After (add unsigned types)
AT_DISPATCH_V2(
    dtype,
    "min_values_cuda",
    AT_WRAP([&]() {
      kernel_impl<scalar_t>(iter);
    }),
    AT_EXPAND(AT_ALL_TYPES),
    AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES),
    kBFloat16, kHalf, kBool
);
```

**Method 2 example:**
```cpp
// Before
AT_DISPATCH_V2(
    dtype,
    "integral_op",
    AT_WRAP([&]() {
      kernel<scalar_t>();
    }),
    AT_EXPAND(AT_INTEGRAL_TYPES)
);

// After (substitute with V2)
AT_DISPATCH_V2(
    dtype,
    "integral_op",
    AT_WRAP([&]() {
      kernel<scalar_t>();
    }),
    AT_EXPAND(AT_INTEGRAL_TYPES_V2)
);
```

### Step 5: Handle AT_ALL_TYPES vs individual type groups

If the dispatch uses `AT_EXPAND(AT_ALL_TYPES)`:
- `AT_ALL_TYPES` = `AT_INTEGRAL_TYPES` + `AT_FLOATING_TYPES`
- To add uint: add `AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES)` to the list

If the dispatch separately lists INTEGRAL and FLOATING:
```cpp
// Before
AT_EXPAND(AT_INTEGRAL_TYPES), AT_EXPAND(AT_FLOATING_TYPES)

// After (Method 2 preferred)
AT_EXPAND(AT_INTEGRAL_TYPES_V2), AT_EXPAND(AT_FLOATING_TYPES)
```

### Step 6: Verify all dispatch sites

Check the file for ALL dispatch macros that need uint support:
- Some operators have multiple dispatch sites (CPU, CUDA, different functions)
- Apply the transformation consistently across all sites
- Ensure each gets the same type coverage updates

### Step 7: Validate the changes

Check that:
- [ ] AT_DISPATCH_V2 format is used (not old AT_DISPATCH)
- [ ] Unsigned types are added via one of the two methods
- [ ] All relevant dispatch sites in the file are updated
- [ ] Type groups use `AT_EXPAND()`
- [ ] Arguments are properly formatted and comma-separated

## Common patterns

### Pattern 1: AT_ALL_TYPES + extras

```cpp
// Before
AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
  kernel<scalar_t>();
}), AT_EXPAND(AT_ALL_TYPES), kHalf, kBFloat16);

// After
AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
  kernel<scalar_t>();
}), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kHalf, kBFloat16);
```

### Pattern 2: Separate INTEGRAL + FLOATING

```cpp
// Before
AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
  kernel<scalar_t>();
}), AT_EXPAND(AT_INTEGRAL_TYPES), AT_EXPAND(AT_FLOATING_TYPES));

// After
AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
  kernel<scalar_t>();
}), AT_EXPAND(AT_INTEGRAL_TYPES_V2), AT_EXPAND(AT_FLOATING_TYPES));
```

### Pattern 3: Old dispatch needs conversion first

```cpp
// Before (needs v2 conversion first)
AT_DISPATCH_ALL_TYPES_AND2(kHalf, kBFloat16, dtype, "op", [&]() {
  kernel<scalar_t>();
});

// After v2 conversion
AT_DISPATCH_V2(dtype, "op", AT_WRAP([&]() {
  kernel<scalar_t>();
}), AT
03

Trust audit

CAUTIONgrade B · trust 89/100 Install with care. The audit found things worth knowing before you trust its output.

LayerWhat it checksResult
L0Provenance & inventoryWARN
L1Static analysis of the codePASS
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
none-observed
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (5)

MEDIUMInventory / provenance · inv.symlink · CWE-1104
.claude/skills
.claude/skills
Why it matters. link not followed
MEDIUMInventory / provenance · inv.symlink · CWE-1104
.dockerignore
.dockerignore
Why it matters. link not followed
LOWInventory / provenance · inv.symlink · CWE-1104
.github/ci_commit_pins/triton.txt
.github/ci_commit_pins/triton.txt
Why it matters. link not followed
LOWInventory / provenance · inv.symlink · CWE-1104
AGENTS.md
AGENTS.md
Why it matters. link not followed
LOWInventory / provenance · inv.symlink · CWE-1104
docs/requirements.txt
docs/requirements.txt
Why it matters. link not followed

Gates applied: no_behavioural_pass.

Audited 2026-10-20 · audit v0.4.1 · source sha bc18adab2c3efull audit observations/trust-audit/skill/pytorch__add-uint-support.json · Report an issue / request a re-scan
04

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-10-20bc18adab2c3eCAUTIONB89first audit
05

Questions

What does the add-uint-support — pytorch/pytorch skill do?

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Is add-uint-support — pytorch/pytorch safe to install?

With care. The audit graded it B (89/100) and found 5 things worth knowing before you trust this skill, listed below with the exact line each was found on.

What can add-uint-support — pytorch/pytorch access on my machine?

The audit observed no filesystem, network or shell use at all in its source.

How current is this page?

The grade is for one exact copy of the source (bc18adab2c3e), read on 2026-10-20. The repository is watched, and a new audit runs when it changes — this is the first audit.

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