This skill helps add support for unsigned integer types (uint16, uint32, uint64) to PyTorch operators by updating their AT_DISPATCH macros.
Use this skill when:
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));Unsigned type groups:
AT_BAREBONES_UNSIGNED_TYPES: kUInt16, kUInt32, kUInt64AT_INTEGRAL_TYPES_V2: AT_INTEGRAL_TYPES + AT_BAREBONES_UNSIGNED_TYPESRelationship:
AT_INTEGRAL_TYPES // kByte, kChar, kInt, kLong, kShort
AT_BAREBONES_UNSIGNED_TYPES // kUInt16, kUInt32, kUInt64
AT_INTEGRAL_TYPES_V2 // INTEGRAL_TYPES + BAREBONES_UNSIGNED_TYPESCheck if the file uses AT_DISPATCH_V2:
If using old AT_DISPATCH:
If already using AT_DISPATCH_V2:
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 coverageCommon patterns:
AT_EXPAND(AT_ALL_TYPES) → includes AT_INTEGRAL_TYPES + AT_FLOATING_TYPESAT_EXPAND(AT_INTEGRAL_TYPES) → signed integers onlyAT_EXPAND(AT_FLOATING_TYPES) → floating point typesTwo approaches:
Method 1: Add AT_BAREBONES_UNSIGNED_TYPES explicitly
AT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES) to the type listMethod 2: Substitute AT_INTEGRAL_TYPES with AT_INTEGRAL_TYPES_V2
AT_EXPAND(AT_INTEGRAL_TYPES)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)
);If the dispatch uses AT_EXPAND(AT_ALL_TYPES):
AT_ALL_TYPES = AT_INTEGRAL_TYPES + AT_FLOATING_TYPESAT_EXPAND(AT_BAREBONES_UNSIGNED_TYPES) to the listIf 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)Check the file for ALL dispatch macros that need uint support:
Check that:
AT_EXPAND()// 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);// 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));// 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);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
}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 listIf 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);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);Check if uint types are already present:
AT_INTEGRAL_TYPES_V2 is used → already has uint supportAT_BAREBONES_UNSIGNED_TYPES is already in list → already has uint supportWhen asked to add uint support:
After adding uint support, the operator should accept uint16, uint32, and uint64 tensors. The user is responsible for functional testing.
npx skills add pytorch/pytorch/.claude/skills/add-uint-supportCopy the installation command above and run it in your terminal to install globally.
Add the required environment variables to your MCP client according to the skill description.
Configure and enable this skill in any MCP-compatible app (e.g. Claude or Cursor).