/*
 * Copyright (c) 2020-2023, NVIDIA CORPORATION.  All rights reserved.
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

#pragma once

#include <cuda_runtime_api.h>

#include "../../include/moe_gemm_kernels.h"

namespace tensorrt_llm::kernels::cutlass_kernels_oss {
using tensorrt_llm::kernels::cutlass_kernels::TmaWarpSpecializedGroupedGemmInput;
// Keep in sync with the signature generated by generate_kernels.py
template <typename Arch, typename T, typename WeightType, typename OutputType,
          typename EpilogueSchedule, typename EpilogueTag,
          TmaWarpSpecializedGroupedGemmInput::EpilogueFusion FUSION, typename TileShape,
          typename ClusterShape, bool IsMXFPX, bool DYNAMIC_CGA, bool BIAS, bool SwapAB>
void tma_warp_specialized_generic_moe_gemm_kernelLauncher(
    TmaWarpSpecializedGroupedGemmInput hopper_input, int num_experts, int multi_processor_count,
    cudaStream_t stream, int* kernel_occupancy, size_t* workspace_size,
    cute::Shape<int32_t, int32_t, cute::_1> dynamic_cluster_shape,
    cute::Shape<int32_t, int32_t, cute::_1> fallback_cluster_shape);

}  // namespace tensorrt_llm::kernels::cutlass_kernels_oss
