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# from typeguard import typechecked

import ctypes
from typing import Union

from cuda import cuda
import cutlass_bindings
import numpy as np

from cutlass.backend.arguments import ArgumentBase
from cutlass.backend.c_types import Conv2DProblemSize, TensorRef_, get_conv2d_arguments
from cutlass.backend.library import (
    EmissionType,
    ConvKindNames,
    ConvKindTag,
    DataTypeNames,
    DataTypeSize,
    DataTypeTag,
    IteratorAlgorithmNames,
    IteratorAlgorithmTag,
    LayoutTag,
    MathOperation,
    MathOperationTag,
    OpcodeClassNames,
    OpcodeClassTag,
    OperationKind,
    ShortDataTypeNames,
    ShortLayoutTypeNames,
    StrideSupport,
    StrideSupportTag,
    TensorDescription,
    TileDescription,
    get_complex_from_real,
)
from cutlass.backend.memory_manager import device_mem_alloc
from cutlass.backend.operation import ExecutableOperation, LaunchConfiguration
from cutlass.backend.tensor_ref import TensorRef
from cutlass.backend.utils.software import CheckPackages, SubstituteTemplate

if CheckPackages().check_torch():
    import torch


# @typechecked
class Conv2dArguments(ArgumentBase):
    """
    Argument wrapper for Conv2d. It encodes problem information and
    user-provide tensors into the kernel's argument.

    :param operation: the Conv2d operation to take the argument
    :type operation: :class:`cutlass.backend.Conv2dOperation`
    :param problem_size: the Conv2d problem size
    :type problem_size: :class:`cutlass_bindings.conv.Conv2dProblemSize`
    :param A: tensor A
    :type A: cuda.CUdeviceptr | numpy.ndarray | torch.Tensor | cupy.ndarray
    :param B: tensor B
    :type B: cuda.CUdeviceptr | numpy.ndarray | torch.Tensor | cupy.ndarray
    :param C: tensor C
    :type C: cuda.CUdeviceptr | numpy.ndarray | torch.Tensor | cupy.ndarray
    :param D: tensor D
    :type D: cuda.CUdeviceptr | numpy.ndarray | torch.Tensor | cupy.ndarray
    :param split_k_mode: conv2d split K mode, defaults to cutlass_bindings.conv.SplitKMode.Serial
    :type split_k_mode: cutlass_bindings.conv.SplitKMode, optional
    :param output_op: output operator, optional
    :type output_op: :class:`cutlass.backend.LinearCombinationFunctorArguments`
    """

    def __init__(
        self,
        operation: "Conv2dOperation",
        problem_size: "cutlass_bindings.conv.Conv2dProblemSize",
        A: "Union[cuda.CUdeviceptr, np.ndarray, torch.Tensor]",
        B: "Union[cuda.CUdeviceptr, np.ndarray, torch.Tensor]",
        C: "Union[cuda.CUdeviceptr, np.ndarray, torch.Tensor]",
        D: "Union[cuda.CUdeviceptr, np.ndarray, torch.Tensor]",
        split_k_mode: "cutlass_bindings.conv.SplitKMode" = cutlass_bindings.conv.SplitKMode.Serial,
        **kwargs,
    ) -> None:
        self.operation = operation
        #: convolution kind
        self.conv_kind: cutlass_bindings.conv.Operator = operation.conv_kind
        self.layout_A: cutlass_bindings.layout = operation.A.layout
        self.layout_B: cutlass_bindings.layout = operation.B.layout
        self.layout_C: cutlass_bindings.layout = operation.C.layout

        self.element_A = operation.A.element
        self.element_B = operation.B.element
        self.element_C = operation.C.element

        if self.layout_C == cutlass_bindings.TensorNC32HW32:
            B = self.reorder_tensor_B(B, problem_size)

        super().__init__(A, B, C, D, **kwargs)
        # preprocessing output ops

        if "split_k_slices" in kwargs.keys() and kwargs["split_k_slices"] > 1:
            self.split_k_mode = split_k_mode
            self.split_k_slices = kwargs["split_k_slices"]
        else:
            self.split_k_mode = cutlass_bindings.conv.SplitKMode.Serial
            self.split_k_slices = 1
            
        if "output_op" in kwargs.keys() and self.split_k_mode != cutlass_bindings.conv.SplitKMode.Parallel:
            self.output_op = kwargs["output_op"]
        else:
            self.output_op = self.operation.epilogue_type(1.0, 0.0)

        #: problem_size
        self.problem_size: cutlass_bindings.conv.Conv2dProblemSize = problem_size
        self.problem_size.split_k_slices = self.split_k_slices

        if hasattr(self, "tensor_c_numel"):
            c_coord = cutlass_bindings.conv.implicit_gemm_tensor_c_extent(
                self.conv_kind, problem_size)
            if self.tensor_c_numel == c_coord.at(3) and self.tensor_c_numel < c_coord.size():
                self.bias = True

        #
        # initialize the argument
        #
        self.initialize()

    # @typechecked
    def reorder_tensor_B(self, tensor_B: "np.ndarray",
            problem_size: "cutlass_bindings.conv.Conv2dProblemSize"):
        """
        Reorder tensor_B for interleaved layout

        :param tensor_B: input tensor B
        :type tensor_B: numpy.ndarray
        :param problem_size: Conv2d problem size
        :type problem_size: :class:`cutlass_bindings.conv.Conv2dProblemSize`

        :return: reordered tensor B
        :rtype: numpy.ndarray
        """
        reordered_tensor_B = np.empty_like(tensor_B)
        tensor_ref_B = self.get_tensor_ref(
            tensor_B, self.element_B, self.layout_B, problem_size, "b")
        reordered_tensor_ref_B = self.get_tensor_ref(
            reordered_tensor_B, self.element_B, self.layout_B, problem_size, "b")
        cutlass_bindings.conv.host.reorder_convK(
            reordered_tensor_ref_B, tensor_ref_B, self.conv_kind, problem_size)

        return reordered_tensor_B

    def get_tensor_ref(
        self, tensor, dtype, tensor_layout, problem_size, operand):
        if operand == "a":
            tensor_coord = cutlass_bindings.conv.implicit_gemm_tensor_a_extent(
                self.conv_kind, problem_size)
        elif operand == "b":
            tensor_coord = cutlass_bindings.conv.implicit_gemm_tensor_b_extent(
                self.conv_kind, problem_size)
        elif operand in ["c", "d"]:
            tensor_coord = cutlass_bindings.conv.implicit_gemm_tensor_c_extent(
                self.conv_kind, problem_size)
        else:
            raise ValueError("unknown operand: " + operand)
        # Zero stride trick
        if operand == "c" and self.bias:
            tensor_coord = cutlass_bindings.Tensor4DCoord(0, 0, 0, 0)

        layout = tensor_layout.packed(tensor_coord)

        return TensorRef(tensor, dtype, layout).tensor_ref

    def get_arguments(self, semaphore):
        ref_A = TensorRef_(self.get_tensor_ref(
            self.ptr_A, self.element_A, self.layout_A, self.problem_size, "a"))
        ref_B = TensorRef_(self.get_tensor_ref(
            self.ptr_B, self.element_B, self.layout_B, self.problem_size, "b"))
        ref_C = TensorRef_(self.get_tensor_ref(
            self.ptr_C, self.element_C, self.layout_C, self.problem_size, "c"))
        ref_D = TensorRef_(self.get_tensor_ref(
            self.ptr_D, self.element_C, self.layout_C, self.problem_size, "d"))

        self.c_arguments = self.operation.argument_type(
            Conv2DProblemSize(self.problem_size),
            ref_A, ref_B, ref_C, ref_D, self.output_op, self.split_k_mode)

        self.semaphore = semaphore

    def initialize(self):
        # Get launch configuration
        self.launch_config = self.operation.rt_module.plan(self)

        # Allocate and initialize device workspace
        device_workspace_size = self.operation.rt_module.get_device_workspace_size(self)

        if device_workspace_size > 0:
            self.workspace_buffer = device_mem_alloc(device_workspace_size)
            workspace_ptr = self.workspace_buffer.ptr
            err, = cuda.cuMemsetD32(
                workspace_ptr, 0, device_workspace_size // 4)
        else:
            workspace_ptr = None

        # Get kernel params as a bytearray
        semaphore = 0
        if (workspace_ptr is not None
            and self.split_k_mode == cutlass_bindings.conv.SplitKMode.Parallel):
            self.ptr_D = workspace_ptr
        elif (workspace_ptr is not None
              and self.split_k_mode == cutlass_bindings.conv.SplitKMode.Serial):
            semaphore = workspace_ptr

        self.get_arguments(semaphore)

        params_ = self.operation.rt_module.get_args(
            ctypes.byref(self.c_arguments), ctypes.c_void_p(int(self.semaphore)))
        self.host_workspace = bytearray(params_.contents)
        self.device_workspace = None

    def sync(self):
        """
        Synchronize the arguments. If the input tensor is in host,
        copy it from device to host.
        """
        return super().sync()


# @typechecked
class Conv2dRT(ExecutableOperation):
    """
    Conv2dRT manages the CUTLASS runtime components
    """

    KernelTemplate = r"""
extern "C"
__global__ void
${operation_name}(${operation_name}${operation_suffix}::Params params) {

  // Dynamic shared memory base pointer
  extern __shared__ int SharedStorageBase[];

  // Declare pointer to dynamic shared memory.
  ${operation_name}${operation_suffix}::SharedStorage *shared_storage =
      reinterpret_cast<${operation_name}${operation_suffix}::SharedStorage *>(SharedStorageBase);

  ${operation_name}${operation_suffix} op;

  op(params, *shared_storage);
}
    """

    HostTemplate = r"""
extern "C" {
  // Get the size of params in bytes
  int ${operation_name}_get_param_size(){
    return sizeof(${operation_name}${operation_suffix}::Params);
  }

  // Get the size of dynamic shared memory in bytes
  int ${operation_name}_shared_memory_size() {
    return int(sizeof(${operation_name}${operation_suffix}::SharedStorage));
  }

  // Get the params as byte array
  char* ${operation_name}_get_params(${operation_name}${operation_suffix}::Arguments* arguments, int *semaphore=nullptr){
    typename ${operation_name}${operation_suffix}::Params* params;
    params = new ${operation_name}${operation_suffix}::Params(*arguments, semaphore);

    char *bytes = ((char*)(params));
    char *output = new char[sizeof(${operation_name}${operation_suffix}::Params)];
    for (unsigned int i = 0; i < sizeof(${operation_name}${operation_suffix}::Params); i ++)
        output[i] = bytes[i];

    return output;
  }
}

    """

    def __init__(self, operation: "Conv2dOperation"):
        super().__init__(operation)
        self.argument_type, self.epilogue_type = get_conv2d_arguments(operation.epilogue_functor)
        self.argtype = [ctypes.POINTER(self.argument_type), ctypes.c_void_p]
        self.conv_kind = operation.conv_kind

        self.operation: Conv2dOperation = operation

        self.emitter = EmitConv2dInstance("_type")

        self.threads: int = operation.tile_description.num_threads

        self.swizzle_functor = operation.swizzling_functor

    def emit(self):
        return self.emitter.emit(self.operation)

    def get_device_workspace_size(self, arguments: Conv2dArguments):
        workspace_bytes = 0

        launch_config = arguments.launch_config

        self.conv_kind = self.operation.conv_kind

        if arguments.split_k_mode == cutlass_bindings.conv.SplitKMode.Parallel:
            problem_size = arguments.problem_size
            workspace_bytes = DataTypeSize[self.operation.C.element] \
            * launch_config.grid[2] * cutlass_bindings.conv.implicit_gemm_tensor_c_size(
                self.conv_kind, problem_size
            ) // 8

        elif arguments.split_k_mode == cutlass_bindings.conv.SplitKMode.Serial and \
            arguments.split_k_slices > 1:
            workspace_bytes = launch_config.grid[0] * launch_config.grid[1] * 4

        return workspace_bytes

    # @typechecked
    def plan(self, arguments: Conv2dArguments):
        tile_size = cutlass_bindings.gemm.GemmCoord(
            self.operation.tile_description.threadblock_shape[0],
            self.operation.tile_description.threadblock_shape[1],
            self.operation.tile_description.threadblock_shape[2],
        )

        grid = self.swizzle_functor.get_grid_shape(
            self.swizzle_functor.get_tiled_shape(
                self.conv_kind, arguments.problem_size,
                tile_size, arguments.split_k_slices
            )
        )
        return LaunchConfiguration(
            [grid.x, grid.y, grid.z], [self.threads, 1, 1],
            self.shared_memory_capacity)

    def initialize(self):
        err, = cuda.cuFuncSetAttribute(
            self.kernel,
            attrib=cuda.CUfunction_attribute.CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES,
            value=self.shared_memory_capacity)
        if err != cuda.CUresult.CUDA_SUCCESS:
            raise RuntimeError("Cuda Error: {}".format(err))


class Conv2dOperation:
    """
    CUTLASS Conv2d operation description.

    :param conv_kind: convolution operator
    :type conv_kind: :class:`cutlass_bindings.conv.Operator`

    :param iterator_algorithm: Selects among several implementation 
    variants trading off performance with simplicity
    :type iterator_algorithm: :class:`cutlass_bindings.conv.IteratorAlgorithm`

    :param arch: GPU compute capability (sm_xx)
    :type arch: int

    :param tile_description: tile description
    :type tile_description: :class:`cutlass.backend.TileDescription`

    :param A: tensor A description
    :type A: :class:`cutlass.backend.TensorDescription`

    :param B: tensor B description
    :type B: :class:`cutlass.backend.TensorDescription`

    :param C: tensor C description
    :type C: :class:`cutlass.backend.TensorDescription`

    :param D: tensor D description
    :type D: :class:`cutlass.backend.TensorDescription`

    :param element_epilogue: element type for computation in epilogue \
    :type element_epilogue: cutlass_bindings.int8 | cutlass_bindings.int32 | cutlass_bindings.float16 | \
    cutlass_bindings.bfloat16 | cutlass_bindings.float32 | cutlass_bindings.float64

    :param stride_support: distinguish among partial specializations that \
    accelerate certain problems where convolution stride is unit \
    :type stride_support: :class:`cutlass_bindings.conv.StrideSupport`

    :param epilogue_functor: convolution epilogue functor
    :type epilogue_functor: :class:`EpilogueFunctor`

    :param swizzling_functor: threadblock swizzling functor
    """
    def __init__(
        self,
        conv_kind: cutlass_bindings.conv.Operator,
        iterator_algorithm: cutlass_bindings.conv.IteratorAlgorithm,
        arch: int,
        tile_description: TileDescription,
        A: TensorDescription,
        B: TensorDescription,
        C: TensorDescription,
        stride_support,
        epilogue_functor,
        swizzling_functor=cutlass_bindings.IdentitySwizzle1,
        emission_type=EmissionType.Kernel,
        **kwargs
    ):
        self.operation_kind: OperationKind = OperationKind.Conv2d
        self.arch: int = arch
        self.tile_description: TileDescription = tile_description
        self.conv_kind = conv_kind
        self.A: TensorDescription = A
        self.B: TensorDescription = B
        self.C: TensorDescription = C
        self.epilogue_functor = epilogue_functor
        self.iterator_algorithm = iterator_algorithm
        self.stride_support = stride_support
        self.swizzling_functor = swizzling_functor()
        
        self.emission_type = emission_type

        self.rt_module: Conv2dRT = Conv2dRT(self)
        self.argument_type = self.rt_module.argument_type
        self.epilogue_type = self.rt_module.epilogue_type

    def run(self, arguments: Conv2dArguments) -> cuda.CUresult:
        """
        Launch the cuda kernel with input arguments

        :param arguments: conv2d arguments
        :type arguments: :class:`cutlass.backend.Conv2dArguments`
        """

        # launch the kernel
        err = self.rt_module.run(
            arguments.host_workspace,
            arguments.device_workspace,
            arguments.launch_config,
        )

        if err != cuda.CUresult.CUDA_SUCCESS:
            raise RuntimeError("CUDA Error %s" % str(err))

        return err

    #
    # Get function name
    #

    def procedural_name(self):
        """The full procedural name indicates architecture, extended name, tile size, and layout."""
        return self.configuration_name()

    #

    def configuration_name(self):
        """The full procedural name indicates architecture, extended name, tile size, and layout."""

        opcode_class_name = OpcodeClassNames[
            self.tile_description.math_instruction.opcode_class
        ]

        threadblock = "%dx%d_%dx%d" % (
            self.tile_description.threadblock_shape[0],
            self.tile_description.threadblock_shape[1],
            self.tile_description.threadblock_shape[2],
            self.tile_description.stages,
        )

        if self.stride_support == StrideSupport.Unity:
            configuration_name = "cutlass_sm${arch}_${opcode_class}_${extended_name}_${threadblock}_${layout}_unity_stride_align${alignment}"
        else:
            configuration_name = "cutlass_sm${arch}_${opcode_class}_${extended_name}_${threadblock}_${layout}_align${alignment}"

        return SubstituteTemplate(
            configuration_name,
            {
                "arch": str(self.arch),
                "opcode_class": opcode_class_name,
                "extended_name": self.extended_name(),
                "threadblock": threadblock,
                "layout": self.layout_name(),
                "alignment": "%d" % self.A.alignment
            },
        )

    #
    def extended_name(self):
        """Append data types if they differ from compute type."""
        if self.C.element != self.tile_description.math_instruction.element_accumulator and \
                self.A.element != self.tile_description.math_instruction.element_accumulator:
            extended_name = "${element_c}_${core_name}_${element_a}"
        elif self.C.element == self.tile_description.math_instruction.element_accumulator and  \
                self.A.element != self.tile_description.math_instruction.element_accumulator:
            extended_name = "${core_name}_${element_a}"
        else:
            extended_name = "${core_name}"

        extended_name = SubstituteTemplate(extended_name, {
            "element_a": DataTypeNames[self.A.element],
            "element_c": DataTypeNames[self.C.element],
            "core_name": self.core_name(),
        })

        return extended_name

    #
    def layout_name(self):
        return "%s" % (ShortLayoutTypeNames[self.A.layout])

    #
    def core_name(self):
        """The basic operation kind is prefixed with a letter indicating the accumulation type."""

        intermediate_type = ""

        if self.tile_description.math_instruction.opcode_class == cutlass_bindings.OpClass.TensorOp:
            inst_shape = "%dx%dx%d" % tuple(
                self.tile_description.math_instruction.instruction_shape)
            if self.tile_description.math_instruction.element_a != self.A.element and \
                    self.tile_description.math_instruction.element_a != self.accumulator_type():
                intermediate_type = DataTypeNames[self.tile_description.math_instruction.element_a]
        else:
            inst_shape = ""

        return "%s%s%s%s_%s" % (
            ShortDataTypeNames[self.accumulator_type()],
            inst_shape,
            intermediate_type,
            ConvKindNames[self.conv_kind],
            IteratorAlgorithmNames[self.iterator_algorithm]
        )

    #
    def is_complex(self):
        complex_operators = [
            MathOperation.multiply_add_complex,
            MathOperation.multiply_add_complex_gaussian,
        ]
        return self.tile_description.math_instruction.math_operation in complex_operators

    #
    def accumulator_type(self):
        accum = self.tile_description.math_instruction.element_accumulator

        if self.is_complex():
            return get_complex_from_real(accum)

        return accum

    def device_op(self):
        """
        Returns a new Conv2dOperation object that is constructed with emission type
        ``EmissionType.Device``. 
        
        :return: operation ready for device-level code emission
        :rtype: Conv2dOperation
        """
        return Conv2dOperation(
            self.conv_kind, self.iterator_algorithm, self.arch, self.tile_description,
            self.A, self.B, self.C, self.stride_support, self.epilogue_functor, type(self.swizzling_functor),
            emission_type=EmissionType.Device)

###################################################################################################
#
# Emits single instances of a CUTLASS device-wide operator
#
###################################################################################################


class EmitConv2dInstance:
    def __init__(self, operation_suffix=""):
        self.operation_suffix = operation_suffix
        self.includes = [
            "cutlass/cutlass.h",
            "cutlass/conv/kernel/default_conv2d_fprop.h",
            "cutlass/conv/kernel/default_conv2d_dgrad.h",
            "cutlass/conv/kernel/default_conv2d_wgrad.h"
        ]
        self.template = """
// Conv2d${conv_kind_name} ${iterator_algorithm_name} kernel instance "${operation_name}"
using ${operation_name}_base = 
typename cutlass::conv::kernel::DefaultConv2d${conv_kind_name}<
  ${element_a}, 
  ${layout_a},
  ${element_b}, 
  ${layout_b},
  ${element_c}, 
  ${layout_c},
  ${element_accumulator},
  ${opcode_class},
  ${arch},
  cutlass::gemm::GemmShape<${threadblock_shape_m}, ${threadblock_shape_n}, ${threadblock_shape_k}>,
  cutlass::gemm::GemmShape<${warp_shape_m}, ${warp_shape_n}, ${warp_shape_k} >,
  cutlass::gemm::GemmShape<${instruction_shape_m}, ${instruction_shape_n}, ${instruction_shape_k}>,
  ${epilogue_functor},
  ${swizzling_functor},
  ${stages},
  ${math_operator},
  ${iterator_algorithm},
  ${stride_support},
  ${align_a},
  ${align_b}
>::Kernel;

struct ${operation_name}${operation_suffix}:
  public ${operation_name}_base { };

"""

        self.template_device = """
// Conv2d operation ${operation_name}

using Conv2d${conv_kind_name}Kernel = typename cutlass::conv::kernel::DefaultConv2d${conv_kind_name}<
  ${element_a}, 
  ${layout_a},
  ${element_b}, 
  ${layout_b},
  ${element_c}, 
  ${layout_c},
  ${element_accumulator},
  ${opcode_class},
  ${arch},
  cutlass::gemm::GemmShape<${threadblock_shape_m}, ${threadblock_shape_n}, ${threadblock_shape_k}>,
  cutlass::gemm::GemmShape<${warp_shape_m}, ${warp_shape_n}, ${warp_shape_k} >,
  cutlass::gemm::GemmShape<${instruction_shape_m}, ${instruction_shape_n}, ${instruction_shape_k}>,
  ${epilogue_functor},
  ${swizzling_functor},
  ${stages},
  ${math_operator},
  ${iterator_algorithm},
  ${stride_support},
  ${align_a},
  ${align_b}
>::Kernel;

using DeviceKernel =
    typename cutlass::conv::device::ImplicitGemmConvolution<Conv2d${conv_kind_name}Kernel>;
"""

    def emit(self, operation):
        warp_shape = [int(operation.tile_description.threadblock_shape[idx] /
                          operation.tile_description.warp_count[idx]) for idx in range(3)]

        epilogue_vector_length = int(min(
            operation.C.alignment * DataTypeSize[operation.C.element], 128) / DataTypeSize[operation.C.element])

        values = {
            "operation_name": operation.procedural_name(),
            "operation_suffix": self.operation_suffix,
            "conv_kind": ConvKindTag[operation.conv_kind],
            "conv_kind_name": ConvKindNames[operation.conv_kind].capitalize(),
            "element_a": DataTypeTag[operation.A.element],
            "layout_a": LayoutTag[operation.A.layout],
            "element_b": DataTypeTag[operation.B.element],
            "layout_b": LayoutTag[operation.B.layout],
            "element_c": DataTypeTag[operation.C.element],
            "layout_c": LayoutTag[operation.C.layout],
            "element_accumulator": DataTypeTag[operation.accumulator_type()],
            "opcode_class": OpcodeClassTag[operation.tile_description.math_instruction.opcode_class],
            "arch": "cutlass::arch::Sm%d" % operation.arch,
            "threadblock_shape_m": str(operation.tile_description.threadblock_shape[0]),
            "threadblock_shape_n": str(operation.tile_description.threadblock_shape[1]),
            "threadblock_shape_k": str(operation.tile_description.threadblock_shape[2]),
            "warp_shape_m": str(warp_shape[0]),
            "warp_shape_n": str(warp_shape[1]),
            "warp_shape_k": str(warp_shape[2]),
            "instruction_shape_m": str(operation.tile_description.math_instruction.instruction_shape[0]),
            "instruction_shape_n": str(operation.tile_description.math_instruction.instruction_shape[1]),
            "instruction_shape_k": str(operation.tile_description.math_instruction.instruction_shape[2]),
            "epilogue_vector_length": str(epilogue_vector_length),
            "epilogue_functor": operation.epilogue_functor.emit(),
            "swizzling_functor": operation.swizzling_functor.tag(),
            "stages": str(operation.tile_description.stages),
            "iterator_algorithm": IteratorAlgorithmTag[operation.iterator_algorithm],
            "iterator_algorithm_name": IteratorAlgorithmNames[operation.iterator_algorithm].capitalize(),
            "stride_support": StrideSupportTag[operation.stride_support],
            "math_operator": "cutlass::arch::OpMultiplyAddComplex" if operation.is_complex() else MathOperationTag[operation.tile_description.math_instruction.math_operation],
            "align_a": str(operation.A.alignment),
            "align_b": str(operation.B.alignment),
        }
        
        if operation.emission_type == EmissionType.Kernel:
            conv2d_template = self.template
        else:
            conv2d_template = self.template_device

        return SubstituteTemplate(conv2d_template, values)
