/*
 * 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 <cassert>
#include <iostream>
#include <sstream>
#include <string>

namespace tensorrt_llm
{
namespace cutlass_extensions
{
// Note: The shapes are in the format MxNxK. The K shape of the runtime config MUST match the K shape
//       in the kernel layout details when doing weight only quantization.
enum class CutlassTileConfig
{
    // Signals that we should run heuristics do choose a config
    Undefined,

    // Signals that we should run heuristics do choose a config
    ChooseWithHeuristic,

    // SiMT config
    CtaShape128x128x8_WarpShape64x64x8,

    // TensorCore configs CTA_N = 128, CTA_K = 64
    // Warp configs for M=16
    CtaShape16x128x64_WarpShape16x32x64,
    // Warp configs for M=32
    CtaShape32x128x64_WarpShape32x32x64,

    // Warp configs for M=64
    CtaShape64x128x64_WarpShape32x64x64,
    CtaShape64x64x128_WarpShape32x64x64,
    CtaShape64x128x64_WarpShape64x32x64,

    // Warp configs for M=128
    CtaShape128x64x64_WarpShape64x32x64,
    CtaShape128x128x64_WarpShape64x32x64,
    CtaShape128x128x64_WarpShape64x64x64,
    CtaShape128x128x64_WarpShape128x32x64,
    CtaShape128x256x64_WarpShape64x64x64,

    // Warp configs for M=256
    CtaShape256x128x64_WarpShape64x64x64,

    // TensorCore config CTA_N = 64, CTA_K = 128
    CtaShape128x64x128_WarpShape64x32x128,

    // TensorCore config CTA_N = 256, CTA_K = 64
    CtaShape16x256x64_WarpShape16x64x64,

    // TensorCore config CTA_N = 256, CTA_K = 128
    CtaShape16x256x128_WarpShape16x64x128

};

enum class SplitKStyle
{
    NO_SPLIT_K,
    SPLIT_K_SERIAL,
    STREAM_K, // Sm80+
    // SPLIT_K_PARALLEL // Not supported yet
};

enum class CutlassTileConfigSM90
{
    // Signals that we should run heuristics do choose a config
    Undefined,

    // Signals that we should run heuristics do choose a config
    ChooseWithHeuristic,

    // CTA configs for M=64
    CtaShape64x16x128B,
    CtaShape64x32x128B,
    CtaShape64x64x128B,
    CtaShape64x128x128B,
    CtaShape64x256x128B,

    // CTA configs for M=128
    CtaShape128x16x128B,
    CtaShape128x32x128B,
    CtaShape128x64x128B,
    CtaShape128x128x128B,
    CtaShape128x256x128B,

    // CTA configs for M=128
    CtaShape256x128x128B,
};

enum class MainloopScheduleType
{
    AUTO // Automatically selects between pingpong and cooperative schedules on Hopper. On older architectures, this
         // defaults to the "legacy" main loop schedule.
};

enum class EpilogueScheduleType
{
    AUTO // Automatically chooses an epilogue schedule compatible with the selected main loop schedule for Hopper. For
         // architectures older than hopper, the epilogue is always performed by the same thread block as the main loop.
};

enum class ClusterShape
{
    ClusterShape_1x1x1,
    ClusterShape_2x1x1,
    ClusterShape_1x2x1,
    ClusterShape_2x2x1,
    ClusterShape_1x8x1,
    ClusterShape_8x1x1
};

struct CutlassGemmConfig
{
    enum CandidateConfigTypeParam : int
    {
        NONE = 0,
        WEIGHT_ONLY = 1u << 0,
        SIMT_ONLY = 1u << 1,
        INT8_ONLY = 1u << 2,
        HOPPER = 1u << 3,
        GROUPED_GEMM = 1u << 4,
        FP8_ONLY = 1u << 5,
    };

    CutlassTileConfig tile_config = CutlassTileConfig::ChooseWithHeuristic;
    SplitKStyle split_k_style = SplitKStyle::NO_SPLIT_K;
    int split_k_factor = -1;
    int stages = -1;

    // config options for sm90
    CutlassTileConfigSM90 tile_config_sm90 = CutlassTileConfigSM90::ChooseWithHeuristic;
    MainloopScheduleType mainloop_schedule = MainloopScheduleType::AUTO;
    EpilogueScheduleType epilogue_schedule = EpilogueScheduleType::AUTO;
    ClusterShape cluster_shape = ClusterShape::ClusterShape_1x1x1;
    bool is_sm90 = false;

    CutlassGemmConfig() {}

    CutlassGemmConfig(CutlassTileConfig tile_config, SplitKStyle split_k_style, int split_k_factor, int stages)
        : tile_config(tile_config)
        , split_k_style(split_k_style)
        , split_k_factor(split_k_factor)
        , stages(stages)
        , is_sm90(false)
    {
    }

    CutlassGemmConfig(CutlassTileConfigSM90 tile_config_sm90, MainloopScheduleType mainloop_schedule,
        EpilogueScheduleType epilogue_schedule, ClusterShape cluster_shape)
        : tile_config_sm90(tile_config_sm90)
        , mainloop_schedule(mainloop_schedule)
        , epilogue_schedule(epilogue_schedule)
        , cluster_shape(cluster_shape)
        , is_sm90(true)
    {
    }

    std::string toString() const
    {
        std::stringstream tactic;
        tactic << "Cutlass GEMM Tactic";
        if (tile_config_sm90 != tensorrt_llm::cutlass_extensions::CutlassTileConfigSM90::ChooseWithHeuristic)
        {
            assert(is_sm90 && "Invalid cutlass GEMM config");
            tactic << "\n\tstyle=TMA"
                   << "\n\ttile shape ID: " << (int) tile_config_sm90 << "\n\tcluster shape ID: " << (int) cluster_shape
                   << "\n\tmainloop sched: " << (int) mainloop_schedule << "\n\tepi sched: " << (int) epilogue_schedule;
        }
        else if (tile_config != tensorrt_llm::cutlass_extensions::CutlassTileConfig::ChooseWithHeuristic)
        {
            assert(!is_sm90 && "Invalid cutlass GEMM config");
            tactic << "\n\tstyle=compatible"
                   << "\n\ttile shape ID: " << (int) tile_config << "\n\tstages: " << (int) stages
                   << "\n\tsplit k: " << (int) split_k_factor;
        }
        else
        {
            tactic << "\n\tundefined";
        }
        tactic << "\n";
        return tactic.str();
    }
};

inline std::ostream& operator<<(std::ostream& out, CutlassGemmConfig const& config)
{
    // clang-format off
    if (config.is_sm90)
    {
        out << "tile_config_sm90_enum: " << int(config.tile_config_sm90)
            << ", mainloop_schedule_enum: " << int(config.mainloop_schedule)
            << ", epilogue_schedule_enum: " << int(config.epilogue_schedule)
            << ", cluster_shape_enum: " << int(config.cluster_shape);
    }
    else
    {
        out << "tile_config_enum: " << int(config.tile_config)
            << ", split_k_style_enum: " << int(config.split_k_style)
            << ", split_k_factor: " << config.split_k_factor
            << ", stages: " << config.stages;
    }
    // clang-format on
    return out;
}

} // namespace cutlass_extensions
} // namespace tensorrt_llm
