add-uint-support

name	add-uint-support
name_zh	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.
description_zh	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 用户 mentions enabling unsigned types, barebones unsigned types, or uint support.
category	applications
tags	["ai","frontend","python","testing","typescript"]
needs_review	false
source	null
language	en
slug	add-uint-support
version	1.0.0
created	2026-06-12
updated	2026-06-12
inputs	[{"name":"request","type":"string","required":true,"description":"User request or task description"}]
output	{"format":"markdown","description":"Generated content based on the user request"}
author	AI-SKILL
license	MIT

When to use

Use this skill when adding uint16, uint32, or uint64 support to PyTorch operators, when the user mentions "unsigned types", "uint support", or "barebones unsigned types", or when enabling support for kUInt16, kUInt32, kUInt64 in kernels and dispatch macros.

Inputs

User request or task description.

Output

Generated content based on the user request.

Prompt

Follow the guidelines in this skill when working on related tasks.

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:

// 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:

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:

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:

// 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:

// 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:

// 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

// 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

// 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

// 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_EXPAND(AT_ALL_TYPES), kHalf, kBFloat16);

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

Multiple dispatch sites example

For a file with multiple functions:

void min_values_kernel_cuda(TensorIterator& iter) {
  AT_DISPATCH_V2(iter.dtype(), "min_values_cuda", AT_WRAP([&]() {
    impl<scalar_t>(iter);
  }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf);
  //                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  //                           Added uint support
}

void min_launch_kernel(TensorIterator &iter) {
  AT_DISPATCH_V2(iter.input_dtype(), "min_cuda", AT_WRAP([&]() {
    gpu_reduce_kernel<scalar_t>(iter);
  }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kBFloat16, kHalf);
  //                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  //                           Added uint support here too
}

Decision tree

Use this decision tree to determine the approach:

Is the file using AT_DISPATCH_V2?
├─ No → Use at-dispatch-v2 skill first, then continue
└─ Yes
   └─ Does it use AT_EXPAND(AT_INTEGRAL_TYPES)?
      ├─ Yes → Replace with AT_EXPAND(AT_INTEGRAL_TYPES_V2)
      └─ No → Add AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES) to type list

Edge cases

Case 1: Dispatch with only floating types

If the operator only supports floating point types, don't add uint support:

// Leave as-is - floating point only operator
AT_DISPATCH_V2(dtype, "float_op", AT_WRAP([&]() {
  kernel<scalar_t>();
}), AT_EXPAND(AT_FLOATING_TYPES), kHalf);

Case 2: Complex types present

Unsigned types work alongside complex types:

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

Case 3: Already has uint support

Check if uint types are already present:

• If AT_INTEGRAL_TYPES_V2 is used → already has uint support
• If AT_BAREBONES_UNSIGNED_TYPES is already in list → already has uint support
• Skip the file if uint support is already present

Workflow

When asked to add uint support:

1. Read the target file
2. Check if using AT_DISPATCH_V2:
   • If not → use at-dispatch-v2 skill first
3. Identify all dispatch macro sites
4. For each dispatch:
   • Analyze current type groups
   • Choose method (add BAREBONES_UNSIGNED or upgrade to V2)
   • Apply transformation with Edit tool
5. Show the user the changes
6. Explain what was modified

Important notes

• Always check if v2 conversion is needed first
• Apply changes consistently across all dispatch sites in the file
• Method 2 (AT_INTEGRAL_TYPES_V2) is cleaner when applicable
• Method 1 (explicit AT_BAREBONES_UNSIGNED_TYPES) is more explicit
• Unsigned types are: kUInt16, kUInt32, kUInt64 (not kByte which is uint8)
• Some operators may not semantically support unsigned types - use judgment

Testing

After adding uint support, the operator should accept uint16, uint32, and uint64 tensors. The user is responsible for functional testing.

When NOT to use

Do not use this skill for tasks outside its scope.

Example

Adding uint support to a PyTorch CUDA operator:

Before (operator only supports signed types):

// aten/src/ATen/native/cuda/ReduceOpsKernel.cu
void min_values_kernel_cuda(TensorIterator& iter) {
  AT_DISPATCH_V2(iter.dtype(), "min_values_cuda", AT_WRAP([&]() {
    gpu_reduce_kernel<scalar_t, scalar_t>(
      iter, [](scalar_t* out, scalar_t* inp, int n, scalar_t init) {
        return min_impl<scalar_t>().reduce(inp, n, init);
      }, at::numeric_limits<scalar_t>::max());
  }), AT_EXPAND(AT_ALL_TYPES), kHalf, kBFloat16);
}

After (uint16, uint32, uint64 support added):

// aten/src/ATen/native/cuda/ReduceOpsKernel.cu
void min_values_kernel_cuda(TensorIterator& iter) {
  AT_DISPATCH_V2(iter.dtype(), "min_values_cuda", AT_WRAP([&]() {
    gpu_reduce_kernel<scalar_t, scalar_t>(
      iter, [](scalar_t* out, scalar_t* inp, int n, scalar_t init) {
        return min_impl<scalar_t>().reduce(inp, n, init);
      }, at::numeric_limits<scalar_t>::max());
  }), AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES), kHalf, kBFloat16);
  //                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  //                           Added: kUInt16, kUInt32, kUInt64 support
}

Testing the change:

import torch

# Create tensors with unsigned integer types
x_uint16 = torch.tensor([5, 3, 8, 1], dtype=torch.uint16, device='cuda')
x_uint32 = torch.tensor([100, 200, 50], dtype=torch.uint32, device='cuda')
x_uint64 = torch.tensor([1000, 2000, 500], dtype=torch.uint64, device='cuda')

# These now work (previously would raise RuntimeError)
print(torch.min(x_uint16))  # tensor(1, dtype=torch.uint16)
print(torch.min(x_uint32))  # tensor(50, dtype=torch.uint32)
print(torch.min(x_uint64))  # tensor(500, dtype=torch.uint64)