#include "sparkinfer/kernels/moe.h"

#include <cute/tensor.hpp>
#include <cute/algorithm/copy.hpp>
#include <cute/arch/mma_sm100.hpp>
#include <cutlass/epilogue/fusion/callbacks.hpp>

// GroupGEMM + SwiGLU epilogue — CuTe DSL implementation.
//
// Fuses: [T, H] x [H, 2F] → SwiGLU(gate, input) → [T, F]
// into a single WGMMA persistent kernel with no global memory round-trip
// for the intermediate [T, 2F] tensor.
//
// Design follows NVIDIA's cuDNN fused MoE kernel (Jun 2026 blog):
//   - Gate and input weights repacked into interleaved column layout before serving
//   - Same thread block receives both gate and input halves → SwiGLU in epilogue
//   - Token counts stored in GPU memory → sync-free → CUDA graph compatible
//
// Target: sm_100 (RTX Spark, RTX 5090, GB200)
//
// Status: scaffold — TMA descriptors and WGMMA tile selection need tuning
//         for RTX Spark's LPDDR5X bandwidth profile vs GB200's HBM3e.

namespace sparkinfer {
namespace kernels {

using namespace cute;

// Weight layout after offline repack:
//   Original:  gate_proj [H, F] | up_proj [H, F]  (two separate tensors)
//   Repacked:  interleaved [H, 2F] where column 2k = gate[:,k], column 2k+1 = up[:,k]
//
// This guarantees that for any output tile of width W covering columns [c, c+W):
//   columns c, c+2, c+4 ... = gate slice
//   columns c+1, c+3, c+5 ... = up (input) slice
// → single GEMM tile has both gate and input in registers → SwiGLU without smem spill.
//
// The repack can be done once at model load time; it does not affect model quality.

struct MoESwiGLUConfig {
    int hidden_dim;          // H
    int ffn_dim;             // F (output dim, half of 2F weight width)
    int num_experts;
    int max_tokens_per_expert;

    // CuTe tile sizes (tunable)
    int tile_m = 64;         // tokens per tile
    int tile_n = 128;        // output features per tile (= F/2 per gate/input chunk)
    int tile_k = 64;         // reduction dimension tile
};

// GPU-side token count array — never read by CPU during inference.
// Layout: [num_experts]  (int32, device ptr)
// Populated by the router kernel before this kernel is launched.
// Enables CUDA graph capture across the entire MoE forward pass.
struct SyncFreeGroupDesc {
    const void*  A;              // input activations [total_tokens, H]  (bf16)
    const void*  B_repacked;     // repacked weights [num_experts, H, 2F] (bf16)
    void*        C;              // output [total_tokens, F]  (bf16)
    const int*   tokens_per_expert;  // [num_experts] — on device, never on CPU
    const int*   expert_offsets;     // [num_experts] prefix sum — on device
};

// Forward declaration — full CuTe mainloop implementation in progress.
// The kernel uses:
//   sm100_mma  — Blackwell warpgroup MMA instructions
//   TMA        — async bulk copy for A and B tiles with prefetch
//   Ping-pong  — double-buffered smem to overlap compute and memory
//   Epilogue   — SwiGLU visitor on accumulator registers before smem write
void launch_group_gemm_swiglu(
    const SyncFreeGroupDesc& desc,
    const MoESwiGLUConfig& cfg,
    cudaStream_t stream
) {
    // TODO: implement CuTe mainloop
    // Key steps:
    //   1. Compute total_tiles = sum(tokens_per_expert[e]) / tile_m * (F / tile_n)
    //   2. Persistent CTA grid: each CTA iterates over assigned tiles until done
    //   3. Per tile:
    //      a. Determine which expert owns this tile (binary search on expert_offsets)
    //      b. TMA load A[token_start:token_start+tile_m, :] into smem_a
    //      c. TMA load B_repacked[expert, :, col_start:col_start+2*tile_n] into smem_b
    //      d. WGMMA: accumulate [tile_m, 2*tile_n] in registers
    //      e. Epilogue: for each output column pair (2k, 2k+1):
    //           gate  = acc[:, 2k]
    //           input = acc[:, 2k+1]
    //           out[:, k] = SiLU(gate) * input
    //      f. TMA store result to C[token_start:token_start+tile_m, col_start//2:]
    (void)desc; (void)cfg; (void)stream;
}

} // namespace kernels
} // namespace sparkinfer
