- [ ] Step 1: Register dispatch interface in ops.py
- [ ] Step 2: Implement cuTile backend
- [ ] Step 3: Register in __init__.py (cutile)
- [ ] Step 4: Add tests
- [ ] Step 5: Add benchmark to tests/benchmark
- [ ] Step 6: Verify (run pytest + lint)

@dispatch(
    "my_op",
)
def my_op(
    input: torch.Tensor,
    out: Optional[torch.Tensor] = None,
    **kwargs: Any,
):
    """
    Description of my_op.

    Args:
        input: Input tensor
        out: Optional preallocated output tensor
        **kwargs: Additional arguments for backend-specific configurations

    Returns:
        torch.Tensor
    """
    raise NotImplementedError(f"my_op is not implemented for {get_current_backend()}")

import torch
import cuda.tile as ct

from tilegym.backend import register_impl


@ct.kernel
def my_op_kernel_ct(x, output, n_elements: ct.Constant[int], BLOCK_SIZE: ct.Constant[int]):
    bid = ct.bid(0)
    indices = bid * BLOCK_SIZE + ct.arange(0, BLOCK_SIZE)
    x_val = ct.gather(x, indices)
    # ... compute ...
    ct.scatter(output, indices, result)


@register_impl("my_op", backend="cutile")
def my_op(input: torch.Tensor, out: torch.Tensor = None, **kwargs) -> torch.Tensor:
    n = input.numel()
    if out is None:
        out = torch.empty_like(input)
    grid = ((n + 1023) // 1024,)
    ct.launch(stream, grid, kernel, (some args, ...))
    return out

from . import my_op

from .my_op import my_op

import pytest
import torch

from tilegym.backend import is_backend_available, set_backend
from .. import common

_backends = ["cutile"]


class Test_MY_OP(common.PyTestCase):
    @staticmethod
    def reference(input):
        """Reference implementation using PyTorch."""
        return torch.some_reference(input)

    @pytest.mark.parametrize("shape, dtype", [
        ((1024,), torch.float16),
        ((1024, 512), torch.float32),
        ((64, 64, 64), torch.bfloat16),
    ])
    @pytest.mark.parametrize("backend", _backends)
    def test_op(self, shape, dtype, backend, arch):
        if backend == "cutile" and not is_backend_available("cutile"):
            pytest.skip("Cutile backend not available")
        try:
            set_backend(backend)
        except Exception as e:
            pytest.skip(f"Backend is not supported: {e}")

        self.setUp()

        from tilegym.ops import my_op

        A = torch.randn(*shape, dtype=dtype, device="cuda")
        self.assertCorrectness(
            my_op, self.reference, {"input": A},
            atol=1e-3, rtol=1e-3,
        )

1. Missing _backends list (inside class)
2. test_op / test_op_xxx — missing @pytest.mark.parametrize("backend", _backends), backend parameter, and tilegym.is_backend_available / tilegym.set_backend pattern

import torch
import triton
import triton.testing

import tilegym
from tilegym.backend import is_backend_available, register_impl

ALL_BACKENDS = [
    ("cutile", "cuTile", ("orange", "-")) if is_backend_available("cutile") else None,
    ("torch", "PyTorch", ("green", "-")),
]

def get_supported_backends():
    return [p for p in ALL_BACKENDS if p is not None]

def reference_my_op(input: torch.Tensor, out: torch.Tensor = None, **kwargs):
    """Reference implementation using PyTorch."""
    ...

register_impl("my_op", "torch")(reference_my_op)

def create_benchmark_config(datatype, ...):
    available_backends = get_supported_backends()
    if not available_backends:
        return None
    backends, names, styles = zip(*available_backends)
    return triton.testing.Benchmark(
        x_names=["M"],  # or other dimension names
        x_vals=[...],
        line_arg="backend",
        line_vals=list(backends),
        line_names=list(names),
        styles=list(styles),
        ylabel="GB/s",  # or TFLOPS
        plot_name="my-op-...",
        args={"datatype": datatype, ...},
    )

@triton.testing.perf_report([
    create_benchmark_config(datatype, ...)
    for datatype in [torch.float16, torch.float32]
    for ... in [...]
])
def bench_my_op(M, backend, datatype, ..., device="cuda"):
    x = torch.randn(..., dtype=datatype, device=device)

    fn = lambda: tilegym.ops.my_op(x, backend=backend)
    ref = lambda: reference_my_op(x)
    torch.testing.assert_close(fn(), ref(), rtol=1e-2, atol=1e-2)

    ms = triton.testing.do_bench(fn)  # or do_bench_cudagraph(fn)
    # Compute metric (e.g. GB/s or TFLOPS) from ms and problem size
    return metric

if __name__ == "__main__":
    bench_my_op.run(print_data=True)

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