at-dispatch-v2

name	at-dispatch-v2
description	Convert PyTorch AT_DISPATCH macros to AT_DISPATCH_V2 format in ATen C++ code. Use when porting AT_DISPATCH_ALL_TYPES_AND*, AT_DISPATCH_FLOATING_TYPES*, or other dispatch macros to the new v2 API. For ATen kernel files, CUDA kernels, and native operator implementations.

AT_DISPATCH to AT_DISPATCH_V2 Converter

This skill helps convert PyTorch's legacy AT_DISPATCH macros to the new AT_DISPATCH_V2 format, as defined in aten/src/ATen/Dispatch_v2.h.

When to use this skill

Use this skill when:

• Converting AT_DISPATCH_* macros to AT_DISPATCH_V2
• Porting ATen kernels to use the new dispatch API
• Working with files in aten/src/ATen/native/ that use dispatch macros
• User mentions "AT_DISPATCH", "dispatch v2", "Dispatch_v2.h", or macro conversion

Quick reference

Old format:

AT_DISPATCH_ALL_TYPES_AND3(kBFloat16, kHalf, kBool, dtype, "kernel_name", [&]() {
  // lambda body
});

New format:

AT_DISPATCH_V2(dtype, "kernel_name", AT_WRAP([&]() {
  // lambda body
}), AT_EXPAND(AT_ALL_TYPES), kBFloat16, kHalf, kBool);

Key transformations

1. Reorder arguments: scalar_type and name come first, then lambda, then types
2. Wrap the lambda: Use AT_WRAP(lambda) to handle internal commas
3. Expand type groups: Use AT_EXPAND(AT_ALL_TYPES) instead of implicit expansion
4. List individual types: Add extra types (kHalf, kBFloat16, etc.) after expanded groups
5. Add include: #include <ATen/Dispatch_v2.h> near other Dispatch includes

Instructions

Step 1: Add the Dispatch_v2.h include

Add the v2 header near the existing #include <ATen/Dispatch.h>:

#include <ATen/Dispatch.h>
#include <ATen/Dispatch_v2.h>

Keep the old Dispatch.h include for now (other code may still need it).

Step 2: Identify the old dispatch pattern

Common patterns to convert:

• AT_DISPATCH_ALL_TYPES_AND{2,3,4}(type1, type2, ..., scalar_type, name, lambda)
• AT_DISPATCH_FLOATING_TYPES_AND{2,3}(type1, type2, ..., scalar_type, name, lambda)
• AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND{2,3}(type1, ..., scalar_type, name, lambda)
• AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND{2,3}(type1, ..., scalar_type, name, lambda)

Step 3: Map the old macro to type groups

Identify which type group macro corresponds to the base types:

Old macro base	AT_DISPATCH_V2 type group
ALL_TYPES	AT_EXPAND(AT_ALL_TYPES)
FLOATING_TYPES	AT_EXPAND(AT_FLOATING_TYPES)
INTEGRAL_TYPES	AT_EXPAND(AT_INTEGRAL_TYPES)
COMPLEX_TYPES	AT_EXPAND(AT_COMPLEX_TYPES)
ALL_TYPES_AND_COMPLEX	AT_EXPAND(AT_ALL_TYPES_AND_COMPLEX)

For combined patterns, use multiple AT_EXPAND() entries:

// Old: AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2(...)
// New: AT_EXPAND(AT_ALL_TYPES), AT_EXPAND(AT_COMPLEX_TYPES), type1, type2

Step 4: Extract the individual types

From AT_DISPATCH_*_AND2(type1, type2, ...) or AT_DISPATCH_*_AND3(type1, type2, type3, ...), extract the individual types (type1, type2, etc.).

These become the trailing arguments after the type group:

AT_DISPATCH_V2(..., AT_EXPAND(AT_ALL_TYPES), kBFloat16, kHalf, kBool)
                                             ^^^^^^^^^^^^^^^^^^^^^^^^
                                             Individual types from AND3

Step 5: Transform to AT_DISPATCH_V2

Apply the transformation:

Pattern:

AT_DISPATCH_V2(
  scalar_type,           // 1st: The dtype expression
  "name",                // 2nd: The debug string
  AT_WRAP(lambda),       // 3rd: The lambda wrapped in AT_WRAP
  type_groups,           // 4th+: Type groups with AT_EXPAND()
  individual_types       // Last: Individual types
)

Example transformation:

// BEFORE
AT_DISPATCH_ALL_TYPES_AND3(
    kBFloat16, kHalf, kBool,
    iter.dtype(),
    "min_values_cuda",
    [&]() {
      min_values_kernel_cuda_impl<scalar_t>(iter);
    }
);

// AFTER
AT_DISPATCH_V2(
    iter.dtype(),
    "min_values_cuda",
    AT_WRAP([&]() {
      min_values_kernel_cuda_impl<scalar_t>(iter);
    }),
    AT_EXPAND(AT_ALL_TYPES),
    kBFloat16, kHalf, kBool
);

Step 6: Handle multi-line lambdas

For lambdas with internal commas or complex expressions, AT_WRAP is essential:

AT_DISPATCH_V2(
    dtype,
    "complex_kernel",
    AT_WRAP([&]() {
      gpu_reduce_kernel<scalar_t, scalar_t>(
        iter,
        MinOps<scalar_t>{},
        thrust::pair<scalar_t, int64_t>(upper_bound(), 0)  // Commas inside!
      );
    }),
    AT_EXPAND(AT_ALL_TYPES)
);

Step 7: Verify the conversion

Check that:

• AT_WRAP() wraps the entire lambda
• Type groups use AT_EXPAND()
• Individual types don't have AT_EXPAND() (just kBFloat16, not AT_EXPAND(kBFloat16))
• Argument order is: scalar_type, name, lambda, types
• Include added: #include <ATen/Dispatch_v2.h>

Type group reference

Available type group macros (use with AT_EXPAND()):

AT_INTEGRAL_TYPES      // kByte, kChar, kInt, kLong, kShort
AT_FLOATING_TYPES      // kDouble, kFloat
AT_COMPLEX_TYPES       // kComplexDouble, kComplexFloat
AT_QINT_TYPES         // kQInt8, kQUInt8, kQInt32
AT_ALL_TYPES          // INTEGRAL_TYPES + FLOATING_TYPES
AT_ALL_TYPES_AND_COMPLEX  // ALL_TYPES + COMPLEX_TYPES
AT_INTEGRAL_TYPES_V2  // INTEGRAL_TYPES + unsigned types
AT_BAREBONES_UNSIGNED_TYPES  // kUInt16, kUInt32, kUInt64
AT_FLOAT8_TYPES       // Float8 variants

Common patterns

Pattern: AT_DISPATCH_ALL_TYPES_AND2

// Before
AT_DISPATCH_ALL_TYPES_AND2(kHalf, kBFloat16, dtype, "op", [&]() {
  kernel<scalar_t>(data);
});

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

Pattern: AT_DISPATCH_FLOATING_TYPES_AND3

// Before
AT_DISPATCH_FLOATING_TYPES_AND3(kHalf, kBFloat16, kFloat8_e4m3fn,
    tensor.scalar_type(), "float_op", [&] {
  process<scalar_t>(tensor);
});

// After
AT_DISPATCH_V2(tensor.scalar_type(), "float_op", AT_WRAP([&] {
  process<scalar_t>(tensor);
}), AT_EXPAND(AT_FLOATING_TYPES), kHalf, kBFloat16, kFloat8_e4m3fn);

Pattern: AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2

// Before
AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2(
    kComplexHalf, kHalf,
    self.scalar_type(),
    "complex_op",
    [&] {
      result = compute<scalar_t>(self);
    }
);

// After
AT_DISPATCH_V2(
    self.scalar_type(),
    "complex_op",
    AT_WRAP([&] {
      result = compute<scalar_t>(self);
    }),
    AT_EXPAND(AT_ALL_TYPES),
    AT_EXPAND(AT_COMPLEX_TYPES),
    kComplexHalf,
    kHalf
);

Edge cases

Case 1: No extra types (rare)

// Before
AT_DISPATCH_ALL_TYPES(dtype, "op", [&]() { kernel<scalar_t>(); });

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

Case 2: Many individual types (AND4, AND5, etc.)

// Before
AT_DISPATCH_FLOATING_TYPES_AND4(kHalf, kBFloat16, kFloat8_e4m3fn, kFloat8_e5m2,
    dtype, "float8_op", [&]() { kernel<scalar_t>(); });

// After
AT_DISPATCH_V2(dtype, "float8_op", AT_WRAP([&]() {
  kernel<scalar_t>();
}), AT_EXPAND(AT_FLOATING_TYPES), kHalf, kBFloat16, kFloat8_e4m3fn, kFloat8_e5m2);

Case 3: Lambda with no captures

// Before
AT_DISPATCH_ALL_TYPES_AND2(kHalf, kBool, dtype, "op", []() {
  static_kernel<scalar_t>();
});

// After
AT_DISPATCH_V2(dtype, "op", AT_WRAP([]() {
  static_kernel<scalar_t>();
}), AT_EXPAND(AT_ALL_TYPES), kHalf, kBool);

Benefits of AT_DISPATCH_V2

1. No arity in macro name: Don't need different macros for AND2, AND3, AND4
2. Composable type sets: Mix and match type groups with AT_EXPAND()
3. Extensible: Easy to add more types without hitting macro limits
4. Clearer: Type groups are explicit, not implicit in macro name

Important notes

• Keep #include <ATen/Dispatch.h> - other code may need it
• The AT_WRAP() is mandatory - prevents comma parsing issues in the lambda
• Type groups need AT_EXPAND(), individual types don't
• The v2 API is in aten/src/ATen/Dispatch_v2.h - refer to it for full docs
• See the header file for the Python script to regenerate the macro implementation

Workflow

When asked to convert AT_DISPATCH macros:

1. Read the file to identify all AT_DISPATCH uses
2. Add #include <ATen/Dispatch_v2.h> if not present
3. For each dispatch macro:
   • Identify the pattern and extract components
   • Map the base type group
   • Extract individual types
   • Construct the AT_DISPATCH_V2 call
   • Apply with Edit tool
4. Show the user the complete converted file
5. Explain what was changed

Run repository-defined focused tests and builds for the affected code. If a required check is unavailable, report exactly what was not run and why.