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import pycutlass
from pycutlass import *
from pycutlass.test import *
from pycutlass.test.gemm_testbed import GemmUniversalLauncher

if __name__ == '__main__':
    pycutlass.get_memory_pool(2**32, 2**32)
    pycutlass.compiler.nvcc()

    math_inst = MathInstruction(
        instruction_shape=[16, 8, 16],
        element_a=cutlass.float16, element_b=cutlass.float16,
        element_accumulator=cutlass.float32, opcode_class=cutlass.OpClass.TensorOp,
        math_operation=MathOperation.multiply_add
    )

    tile_description = TileDescription(
        threadblock_shape=[256, 128, 32],
        stages=3, warp_count=[4, 2, 1],
        math_instruction=math_inst
    )

    A = TensorDescription(
        element=cutlass.float16, layout=cutlass.RowMajor,
        alignment=4
    )
    B = TensorDescription(
        element=cutlass.float16, layout=cutlass.RowMajor,
        alignment=4
    )
    C = TensorDescription(
        element=cutlass.float32, layout=cutlass.ColumnMajor,
        alignment=4
    )

    element_epilogue = cutlass.float32

    epilogue_functor = LinearCombination(cutlass.float32, 4, cutlass.float32, cutlass.float32)
    
    swizzling_functor = cutlass.IdentitySwizzle1

    operation = GemmOperationUniversal(
        arch=80, tile_description=tile_description,
        A=A, B=B, C=C, element_epilogue=element_epilogue,
        epilogue_functor=epilogue_functor, swizzling_functor=swizzling_functor
    )

    profiler = GemmUniversalLauncher(operation, verification=False, profiling=True)
    python_runtime = profiler.run(
        mode=cutlass.gemm.Mode.Gemm, 
        problem_size=cutlass.gemm.GemmCoord(4096, 4096, 4096)
    )

    cpp_runtime = profiler.run_cutlass_profiler(
        mode=cutlass.gemm.Mode.Gemm,
        problem_size=cutlass.gemm.GemmCoord(4096, 4096, 4096),
    )

    print(cpp_runtime / python_runtime)
