cutlass/operators/test/unit/test_metadata.py

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from dataclasses import dataclass

import pytest
import torch

import cutlass
from cutlass import cute

import cutlass.operators as ops
from cutlass.operators.arguments.base import Operand
from cutlass.operators.arguments.operand import _operand_or_dense
from cutlass.operators.metadata import (
    DenseTensorConstraints,
    OperandConstraints,
    OperandsMetadata,
)
from cutlass.operators.providers.cutedsl.operator import CuteDslOperator
from cutlass.operators.status import Status
from cutlass.operators.typing import TensorLike


@dataclass
class DummyArguments(ops.RuntimeArguments):
    A: Operand
    B: Operand
    out: Operand

    def __init__(
        self,
        A: TensorLike | Operand,
        B: TensorLike | Operand,
        out: TensorLike | Operand,
    ):
        self.A = _operand_or_dense(A).copy()
        self.B = _operand_or_dense(B).copy()
        self.out = _operand_or_dense(out).copy()
        super().__init__()


@dataclass
class DummyOperandsMetadata(OperandsMetadata):
    A: OperandConstraints
    B: OperandConstraints
    out: OperandConstraints

    def supports(self, other: DummyArguments) -> Status:
        return all(
            [
                self.A.supports(other.A),
                self.B.supports(other.B),
                self.out.supports(other.out),
            ]
        )


class NoopOperatorForTesting(CuteDslOperator):
    supported_args_type = DummyArguments
    designed_for_min_cc = 80

    @cute.jit
    def impl(self, A, B, out, stream):
        cute.printf("Called kernel from host successfully!")
        return

    def _compile(self, args: DummyArguments, target_sm: ops.TargetSm | None = None):
        stream = cute.runtime.make_fake_stream()
        return self.cute_compile(
            self.impl,
            args.A.tensor,
            args.B.tensor,
            args.out.tensor,
            stream,
            target_sm=target_sm,
        )

    def _run(
        self,
        args: DummyArguments,
        compiled_artifact,
        stream,
        workspace=None,
    ):
        self.cute_run(
            compiled_artifact.compiled_obj,
            args.A.tensor,
            args.B.tensor,
            args.out.tensor,
            stream,
        )

    @classmethod
    def _generate_operators(cls, _filter, _epilogue_args, _target_sm, _args):
        attrs = DenseTensorConstraints(
            stride=(0, 1),
            dtype=cutlass.Float16,
            divisibility=8,
        )
        metadata = ops.OperatorMetadata(
            operator_name="NoopOperatorForTesting",
            operator_class=cls,
            supported_targets=[ops.TargetSm("80")],
            operands=DummyOperandsMetadata(
                A=attrs,
                B=attrs,
                out=attrs,
            ),
        )
        return [NoopOperatorForTesting(metadata)]


operator = NoopOperatorForTesting.generate_operators(metadata_filter=None)[0]


def test_perfectly_aligned():
    divisibility = operator.metadata.operands.A.divisibility
    A, B, out = [
        torch.randn(divisibility, divisibility * 2, dtype=torch.float16, device="cuda")
        for _ in range(3)
    ]
    args = DummyArguments(A=A, B=B, out=out)
    operator.run(args)


def test_overaligned():
    A, B, out = [
        torch.randn(1024, 1024, dtype=torch.float16, device="cuda") for _ in range(3)
    ]
    args = DummyArguments(A=A, B=B, out=out)
    operator.run(args)


def _check_misaligned_args(error_match_string: str, **tensors):
    """Helper to test various misalignment errors are properly caught.

    With TVM-FFI:
        args creation may succeed, but operator.supports must fail.
        TVM-FFI should still catch errors if user bypasses supports.
    Without TVM-FFI:
        error must be caught early during argument creation itself.
    """
    if ops.GlobalOptions().use_tvm_ffi:
        args = DummyArguments(**tensors)
        assert not operator.supports(args), "Unsupported args should be rejected"

        with pytest.raises(Exception, match=error_match_string):
            operator.run(args, assume_supported_args=True)
    else:
        with pytest.raises(Exception, match=error_match_string):
            DummyArguments(**tensors)


def test_underaligned(fixture_toggle_tvm_ffi):
    divisibility = operator.metadata.operands.A.divisibility
    A, B, out = [
        torch.randn(
            divisibility + divisibility // 2,
            divisibility + divisibility // 4,
            dtype=torch.float16,
            device="cuda",
        )
        for _ in range(3)
    ]
    _check_misaligned_args("divisible", A=A, B=B, out=out)


def test_ptr_misaligned(fixture_toggle_tvm_ffi):
    rows = operator.metadata.operands.A.divisibility * 4
    cols = rows
    offset = 117

    A = torch.randn(rows * cols + offset, dtype=torch.float16, device="cuda")
    B = torch.randn(rows, cols, dtype=torch.float16, device="cuda")
    out = torch.randn(rows, cols, dtype=torch.float16, device="cuda")
    A_offset = torch.as_strided(A[offset:], (rows, cols), (cols, 1))

    _check_misaligned_args("align", A=A_offset, B=B, out=out)