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

Debugging Metal Kernels with torch.mps.compile_shader

Use torch.mps.compile_shader to JIT-compile and test individual Metal kernels in isolation. This is invaluable for debugging multi-kernel pipelines where you need to verify each stage independently.

Basic Usage

import torch

source = '''
#include <metal_stdlib>
using namespace metal;

kernel void my_kernel(
    const device float* input [[buffer(0)]],
    device float* output [[buffer(1)]],
    uint tid [[thread_position_in_grid]]) {
  output[tid] = input[tid] * 2.0;
}
'''

lib = torch.mps.compile_shader(source)

inp = torch.tensor([1.0, 2.0, 3.0], device='mps')
out = torch.zeros(3, device='mps')
lib.my_kernel(inp, out, threads=[3, 1, 1], group_size=[3, 1, 1])
torch.mps.synchronize()
print(out)  # tensor([2., 4., 6.], device='mps:0')

Dispatch Semantics

compile_shader uses dispatchThreads semantics (same as mtl_dispatch1DJob in PyTorch):

• threads=[N, 1, 1] — total number of threads (NOT threadgroups)
• group_size=[G, 1, 1] — threads per threadgroup

This differs from the dispatchThreadgroups API used by some host-side code. To match dispatchThreadgroups:MTLSizeMake(num_tgs, num_slices, 1) threadsPerThreadgroup:MTLSizeMake(TG_SIZE, 1, 1):

# Equivalent compile_shader call:
lib.kernel(args...,
    threads=[num_tgs * TG_SIZE, num_slices, 1],
    group_size=[TG_SIZE, 1, 1])

Constant Buffer Parameters

Pass scalar constants as single-element tensors:

slice_size = torch.tensor([1024], dtype=torch.int32, device='mps')
lib.my_kernel(data, output, slice_size, threads=[1024, 1, 1], group_size=[256, 1, 1])

Debugging Strategy for Multi-Kernel Pipelines

When a pipeline of kernels (e.g., histogram → prefix_sum → scatter) produces wrong results, test each kernel individually and verify its output against a Python/NumPy reference:

# 1. Run GPU kernel
lib.histogram(keys, hist, ..., threads=[N, 1, 1], group_size=[256, 1, 1])
torch.mps.synchronize()

# 2. Compute reference in Python
ref_hist = compute_histogram_cpu(keys.cpu().numpy(), ...)

# 3. Compare
assert np.array_equal(hist.cpu().numpy(), ref_hist), "Histogram mismatch!"

This isolates which kernel in the pipeline is broken, rather than debugging the entire pipeline at once.

Common Pitfalls

• Wrong threads count — threads is total threads, not threadgroups. For 5 threadgroups of 256, use threads=[1280, 1, 1].
• Threadgroup memory — compile_shader doesn't support [[threadgroup(N)]] parameters directly. If your kernel needs threadgroup memory, restructure to use threadgroup arrays declared inside the kernel body instead.

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)