Metal Kernel Writing Guide

This skill guides you through implementing Metal kernels for PyTorch operators on Apple Silicon.

Important: The goal of this skill is to use native Metal capabilities via the

c10/metal/

infrastructure, NOT MPSGraph. Native Metal kernels provide better control, performance, and maintainability.

Overview

There are two workflows covered by this skill:

1. Adding new MPS support - Implementing a new operator from scratch
2. Migrating from MPSGraph - Converting existing MPSGraph-based operators to native Metal

Both workflows involve:

1. Update dispatch in

   aten/src/ATen/native/native_functions.yaml

2. Write Metal kernel in

   aten/src/ATen/native/mps/kernels/

3. Implement host-side stub in

   aten/src/ATen/native/mps/operations/

Step 1: Update native_functions.yaml

Location:

aten/src/ATen/native/native_functions.yaml

For New Operators

Find the operator entry and add MPS dispatch:

# Simple MPS-specific implementation
- func: my_op(Tensor self) -> Tensor
  dispatch:
    CPU: my_op_cpu
    CUDA: my_op_cuda
    MPS: my_op_mps

# Shared implementation across devices (preferred for structured kernels)
- func: my_op.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
  dispatch:
    CPU, CUDA, MPS: my_op_out

# Structured kernel (preferred for new ops)
- func: my_op.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
  structured: True
  structured_inherits: TensorIteratorBase
  dispatch:
    CPU, CUDA, MPS: my_op_out

For Migrating from MPSGraph

When migrating an existing operator from MPSGraph to native Metal, consolidate the dispatch entry:

# BEFORE (MPSGraph-based, separate dispatch)
- func: atan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
  structured: True
  structured_inherits: TensorIteratorBase
  dispatch:
    CPU, CUDA: atan2_out
    MPS: atan2_out_mps  # Separate MPS implementation

# AFTER (native Metal, shared dispatch via stub)
- func: atan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)
  structured: True
  structured_inherits: TensorIteratorBase
  dispatch:
    CPU, CUDA, MPS: atan2_out  # MPS now uses the same stub mechanism

Key change: Replace

MPS: my_op_out_mps

with adding

MPS

to the shared dispatch line (e.g.,

CPU, CUDA, MPS: my_op_out

).

Dispatch naming conventions:

• MPS: function_name_mps

  - MPS-specific implementation (old MPSGraph pattern)

• CPU, CUDA, MPS: function_name

  - Shared stub implementation (native Metal pattern)

Step 2: Implement Metal Kernel

Location:

aten/src/ATen/native/mps/kernels/

Unary Kernel Pattern

// MyKernel.metal
#include <c10/metal/indexing.h>
#include <c10/metal/utils.h>
#include <metal_stdlib>

using namespace metal;
using namespace c10::metal;

// Define operation functor
struct my_op_functor {
  template <typename T>
  inline T operator()(const T x) {
    return /* your operation */;
  }
};

// Register for supported types
REGISTER_UNARY_OP(my_op, float, float);
REGISTER_UNARY_OP(my_op, half, half);
REGISTER_UNARY_OP(my_op, bfloat, bfloat);

Binary Kernel Pattern

struct my_binary_functor {
  template <typename T>
  inline T operator()(const T a, const T b) {
    return /* your operation */;
  }
};

REGISTER_BINARY_OP(my_binary, float, float);
REGISTER_BINARY_OP(my_binary, half, half);

Binary Kernel Type Registration Macros

For binary operations, use the convenience macros defined in

BinaryKernel.metal

:

// Floating-point types only (float, half, bfloat)
REGISTER_FLOAT_BINARY_OP(my_op);

// Integral types with float output (for math ops like atan2, copysign)
// Registers: long->float, int->float, short->float, uchar->float, char->float, bool->float
REGISTER_INT2FLOAT_BINARY_OP(my_op);

// Integral types with same-type output (for bitwise/logical ops)
// Registers: long, int, short, uchar, char, bool
REGISTER_INTEGER_BINARY_OP(my_op);

// Floating-point with opmath precision (for ops needing higher precision)
REGISTER_OPMATH_FLOAT_BINARY_OP(my_op);

Common patterns:

• Math functions (atan2, copysign, logaddexp): Use both

  REGISTER_FLOAT_BINARY_OP

  and

  REGISTER_INT2FLOAT_BINARY_OP

• Comparison/logical ops (maximum, minimum): Use both

  REGISTER_FLOAT_BINARY_OP

  and

  REGISTER_INTEGER_BINARY_OP

• Arithmetic ops (add, sub, mul): Use both

  REGISTER_FLOAT_BINARY_OP

  and

  REGISTER_INTEGER_BINARY_OP

Example for atan2 (supports both float and int inputs):

struct atan2_functor {
  template <typename T, enable_if_t<is_floating_point_v<T>, bool> = true>
  inline T operator()(const T a, const T b) {
    return static_cast<T>(precise::atan2(float(a), float(b)));
  }
  template <typename T, enable_if_t<is_integral_v<T>, bool> = true>
  inline float operator()(const T a, const T b) {
    return precise::atan2(float(a), float(b));
  }
};

REGISTER_FLOAT_BINARY_OP(atan2);
REGISTER_INT2FLOAT_BINARY_OP(atan2);

With Scalar Parameter

struct my_alpha_functor {
  template <typename T>
  inline T operator()(const T a, const T b, const T alpha) {
    return a + c10::metal::mul(alpha, b);
  }
};

REGISTER_UNARY_ALPHA_OP(my_alpha, float, float, float);
REGISTER_UNARY_ALPHA_OP(my_alpha, half, half, half);

Type-Specialized Functor

struct special_functor {
  // Floating point types
  template <typename T, enable_if_t<is_scalar_floating_point_v<T>, bool> = true>
  inline T operator()(const T x) {
    return precise::exp(x);  // Use precise math
  }

  // Integral types
  template <typename T, enable_if_t<is_scalar_integral_v<T>, bool> = true>
  inline float operator()(const T x) {
    return precise::exp(float(x));
  }

  // Complex types (float2 for cfloat, half2 for chalf)
  template <typename T, enable_if_t<is_complex_v<T>, bool> = true>
  inline T operator()(const T x) {
    // x.x = real, x.y = imaginary
    return T(/* real */, /* imag */);
  }
};

Note on complex types: Complex numbers in Metal are represented as vector types:

• c10::complex<float>

  maps to

  float2

  (x = real, y = imaginary)

• c10::complex<half>

  maps to

  half2

Use

is_complex_v<T>

to specialize for complex types in functors.

Available c10/metal Utilities

utils.h:

• opmath_t<T>

  - Operation math type (half->float)

• accum_t<T>

  - Accumulation type for reductions

• max()

  ,

  min()

  with NaN propagation

special_math.h:

• precise::exp()

  ,

  precise::log()

  ,

  precise::sqrt()

• precise::sin()

  ,

  precise::cos()

  ,

  precise::tan()

• erf()

  ,

  erfc()

  ,

  erfinv()

indexing.h:

• REGISTER_UNARY_OP(name, in_type, out_type)

• REGISTER_BINARY_OP(name, in_type, out_type)

• REGISTER_UNARY_ALPHA_OP(name, in_type, alpha_type, out_type)

Step 3: Implement Host-Side Stub

Location:

aten/src/ATen/native/mps/operations/

Choose or create an appropriate file based on operation type:

• UnaryKernel.mm

  - Single input operations via stub dispatch

• BinaryKernel.mm

  - Two input operations via stub dispatch

• UnaryOps.mm

  /

  BinaryOps.mm

  - Legacy MPSGraph implementations (for reference)

• ReduceOps.mm

  - Reductions (sum, mean, max, etc.)
• Create new file for distinct operation categories

Stub Registration Pattern (Preferred for Native Metal)

For structured kernels that use the TensorIterator pattern:

// In BinaryKernel.mm (or appropriate file)

static void my_op_mps_kernel(TensorIteratorBase& iter) {
  lib.exec_binary_kernel(iter, "my_op");  // "my_op" matches the functor name in .metal
}

// Register the MPS stub - this connects to the dispatch system
REGISTER_DISPATCH(my_op_stub, &my_op_mps_kernel)

For unary operations:

static void my_unary_mps_kernel(TensorIteratorBase& iter) {
  lib.exec_unary_kernel(iter, "my_unary");
}

REGISTER_DISPATCH(my_unary_stub, &my_unary_mps_kernel)

Migration: Removing Old MPSGraph Implementation

When migrating from MPSGraph, also remove the old implementation:

1. Remove from BinaryOps.mm (or UnaryOps.mm):

   • Delete the

     TORCH_IMPL_FUNC(my_op_out_mps)

     implementation
   • Remove the corresponding

     #include <ATen/ops/my_op_native.h>

     header

2. Add to BinaryKernel.mm (or UnaryKernel.mm):

   • Add the static kernel function
   • Add the

     REGISTER_DISPATCH

     call

Step 4: Compile

After making changes, compile to verify everything builds correctly:

cd build && ninja torch_cpu

Testing

Basic operator support is already tested by

test_output_match

in

test/test_mps.py

. After implementing an operator, enable testing by removing expected failures:

1. Remove from common_mps.py

Location:

torch/testing/_internal/common_mps.py

Find and remove the operator from skip/xfail lists:

# Remove entries like:
MPS_XFAILLIST = {
    "my_op": ...,  # Remove this line
}

MPS_SKIPLIST = {
    "my_op": ...,  # Remove this line
}

2. Remove from OpInfo decorators

Location:

torch/testing/_internal/common_methods_invocations.py

(or related files)

Remove MPS-specific decorators from the OpInfo:

OpInfo(
    "my_op",
    # Remove decorators like:
    # decorators=[skipMPS, expectedFailureMPS("reason")],
    ...
)

3. Run tests to verify

# Run the specific operator test
python test/test_mps.py -k test_output_match_my_op

# Or run full MPS test suite
python test/test_mps.py

Checklist

• Added MPS dispatch to

  native_functions.yaml

• Implemented Metal kernel in

  kernels/

• Implemented host-side operator in

  operations/

• Handles empty tensors
• Handles non-contiguous tensors
• Supports required dtypes (float32, float16, bfloat16, and often complex types via float2/half2)
• Removed expected failures from

  torch/testing/_internal/common_mps.py

• Removed skip/xfail decorators from OpInfo (if applicable)