cutlass/tools/library/scripts/pycutlass/test/gemm/gemm_bf16_sm90.py

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from functools import partial
import pycutlass
from pycutlass import *
from pycutlass import library
from pycutlass.test import *
import unittest

from pycutlass.test.utils import LayoutCombination, get_name
from pycutlass.test.gemm_testbed import test_all_gemm
from pycutlass.utils.device import device_cc


name_fn = partial(get_name, element_a=cutlass.bfloat16, element_b=cutlass.bfloat16, arch=90)

def add_test(cls, layouts, alignments, element_output, element_accumulator, element_epilogue,
             cluster_shape, threadblock_shape, stages, opclass, persistent=False):
    """
    Create a test-running function with the given specification and set it as a method of `cls`.

    :param cls: class to which the generated method will be added
    :type cls: type
    :param layouts: indexable container of layouts of A, B, and C operands
    :param alignments: indexable container of alignments of A, B, and C operands
    :param element_output: data type of the output element
    :param element_accumulator: data type used in accumulation
    :param element_epilogue: data type used in computing the epilogue
    :param cluster_shape: indexable container of dimensions of threadblock cluster to be launched
    :param threadblock_shape: indexable container of dimensions of threadblock tiles
    :param stages: number of pipeline stages to use in the kernel
    :type stages: int
    :param opclass: class of operation being performed (e.g., SIMT, Tensor Core)
    :type opclass: cutlass.OpClass
    :param persistent: whether this is a persistent warp-specialized kernel
    :type persistent: bool
    """

    def run(self):
        """
        Dynamically-generated function that constructs a GEMM operation and verifies it against
        multiple test cases.
        """
        element_A = cutlass.bfloat16
        element_B = cutlass.bfloat16
        inst_shape = [1, 1, 1] if opclass == cutlass.OpClass.Simt else None
        warp_count = [2, 2, 1] if opclass == cutlass.OpClass.Simt else None
        math_inst = MathInstruction(
            instruction_shape=inst_shape,
            element_a=element_A, element_b=element_B, element_accumulator=element_accumulator,
            opcode_class=opclass, math_operation=MathOperation.multiply_add
        )

        tile_description = TileDescription(
            threadblock_shape=threadblock_shape,
            cluster_shape=cluster_shape,
            stages=stages, warp_count=warp_count,
            math_instruction=math_inst,
            persistent=persistent
        )

        A = TensorDescription(element=element_A, layout=layouts[0], alignment=alignments[0])
        B = TensorDescription(element=element_B, layout=layouts[1], alignment=alignments[1])
        C = TensorDescription(element=element_output, layout=layouts[2], alignment=alignments[2])

        epilogue_functor = LinearCombination(C.element, C.alignment, math_inst.element_accumulator, element_epilogue)

        swizzling_functor = cutlass.IdentitySwizzle1

        operation = GemmOperationUniversal(
            arch=90, tile_description=tile_description, A=A, B=B, C=C,
            epilogue_functor=epilogue_functor, swizzling_functor=swizzling_functor)

        self.assertTrue(test_all_gemm(operation, "universal"))

    if persistent:
        suffix = "_persistent"
    else:
        suffix = ""

    name = name_fn(layouts, alignments, element_output, element_accumulator,
                  element_epilogue, cluster_shape, threadblock_shape, stages, opclass=opclass, suffix=suffix)
    setattr(cls, name, run)

    return run


@unittest.skipIf(device_cc() < 90, "Device compute capability is insufficient for SM90 tests.")
class GemmBF16Sm90(unittest.TestCase):
    """
    Wrapper class to which tests will be added dynamically in __main__
    """
    pass


add_test_tensorop = partial(add_test, opclass=cutlass.OpClass.TensorOp)
add_test_simt = partial(add_test, opclass=cutlass.OpClass.Simt)

add_test_tensorop(GemmBF16Sm90, LayoutCombination.NNN, [8, 8, 8], cutlass.bfloat16, cutlass.float32, cutlass.float32, [1, 1, 1], [128, 128, 32], 3)
add_test_tensorop(GemmBF16Sm90, LayoutCombination.NNN, [4, 4, 8], cutlass.bfloat16, cutlass.float32, cutlass.float32, [1, 1, 1], [128, 128, 32], 5)
add_test_tensorop(GemmBF16Sm90, LayoutCombination.TNN, [8, 8, 8], cutlass.bfloat16, cutlass.float32, cutlass.float32, [2, 1, 1], [128, 128, 32], None)
add_test_tensorop(GemmBF16Sm90, LayoutCombination.TNN, [8, 8, 8], cutlass.bfloat16, cutlass.float32, cutlass.float32, [2, 1, 1], [128, 128, 32], None, persistent=True)
add_test_simt(GemmBF16Sm90, LayoutCombination.NNN, [1, 1, 1], cutlass.bfloat16, cutlass.float32, cutlass.float32, [1, 1, 1], [128, 128, 8], 2)


if __name__ == '__main__':
    pycutlass.get_memory_pool(2**30, 2**30)
    unittest.main()