// GPU backend sketch for ternary sparse matmul — whitepaper §5.1, §5.2
//
// STATUS: Architecture design (TRL 3). Kernel not yet compiled.
//         Estimated implementation: ~5–6 engineer-weeks for single-GPU GEMV baseline.
//
// APPROACH
// ─────────
// Ternary weights {-1, 0, +1} fit in 2 bits. A 256×256 matrix = 256×256×2 bits = 16 KB,
// which fits entirely in L1 cache on any modern GPU. The sparsity structure (~50–56%
// zeros measured at §5.3) means roughly half of all multiply-accumulate operations
// are against the zero element and can be skipped.
//
// Encoding: pack four trits into one byte using 2-bit fields:
//   00 = -1 (NEGATIVE)
//   01 =  0 (HOLD / zero)
//   10 = +1 (POSITIVE)
//   11 = reserved
//
// CUDA __dp4a (SM 6.1+) computes a 4-element u8 dot product in a single PTX
// instruction. By encoding ternary weights as {0, 1, 2} offset by the gamma scale,
// we can accumulate 4 elements per instruction and subtract the zero-weight
// correction term in a post-pass.
//
// KERNEL OUTLINE (conceptual — not yet implemented)
// ──────────────────────────────────────────────────
// __global__ void ternary_gemv_dp4a(
//     const uint8_t* __restrict__ W_packed,  // INT2-packed weight matrix [out × in/4]
//     const float*   __restrict__ x,         // input activation vector   [in]
//     float*         __restrict__ y,         // output vector              [out]
//     const float    gamma,                  // per-layer weight scale E[|W|]
//     const int      in_dim,
//     const int      out_dim
// ) {
//     // Each thread handles one output neuron.
//     int row = blockIdx.x * blockDim.x + threadIdx.x;
//     if (row >= out_dim) return;
//
//     int acc = 0;
//     for (int col = 0; col < in_dim / 4; col++) {
//         uint8_t packed = W_packed[row * (in_dim / 4) + col];
//
//         // Unpack 4 trits encoded as {0→-1, 1→0, 2→+1} offset by 1.
//         uint8_t t0 = (packed >> 6) & 0x3;
//         uint8_t t1 = (packed >> 4) & 0x3;
//         uint8_t t2 = (packed >> 2) & 0x3;
//         uint8_t t3 = (packed >> 0) & 0x3;
//
//         // __dp4a accumulates 4×(uint8 × uint8) into int32.
//         // x is pre-quantized to uint8 activation for the dp4a path.
//         uint4 w4 = make_uint4(t0, t1, t2, t3);
//         // acc += __dp4a(w4, x4, 0);  ← PTX dp4a
//     }
//
//     // Subtract the offset correction: each trit was encoded with +1 bias.
//     // net = acc - sum(x_slice) * bias_correction
//     y[row] = (float)acc * gamma; // simplified; real version includes bias term
// }
//
// INTEGRATION WITH CANDLE
// ────────────────────────
// candle supports custom CUDA operations via the `CustomOp` trait:
//
//   impl candle_core::CustomOp1 for TernaryGemvOp {
//       fn name(&self) -> &'static str { "ternary_gemv_dp4a" }
//       fn cpu_fwd(&self, s: &CpuStorage, l: &Layout) -> Result<(CpuStorage, Shape)> { ... }
//       fn cuda_fwd(&self, s: &CudaStorage, l: &Layout) -> Result<(CudaStorage, Shape)> {
//           // Launch ternary_gemv_dp4a kernel via cuLaunchKernel.
//           // Weights must be pre-packed using pack_ternary_weights() below.
//           todo!("GPU kernel")
//       }
//   }
//
//   pub fn ternary_matmul_cuda(x: &Tensor, w_packed: &Tensor, gamma: f32) -> Result<Tensor> {
//       x.apply_op1(TernaryGemvOp { w_packed: w_packed.clone(), gamma })
//   }
//
// WEIGHT PACKING (CPU preprocessing — runs once at checkpoint load)
// ─────────────────────────────────────────────────────────────────
// pub fn pack_ternary_weights(w: &[f32], threshold: f32) -> Vec<u8> {
//     // Convert f32 ternary weights → 2-bit packed bytes.
//     // Output length: ceil(w.len() / 4) bytes.
//     w.chunks(4).map(|chunk| {
//         chunk.iter().enumerate().fold(0u8, |acc, (i, &val)| {
//             let trit = if val > threshold { 2u8 }       // +1
//                        else if val < -threshold { 0u8 } // -1
//                        else { 1u8 };                    //  0
//             acc | (trit << (6 - i * 2))
//         })
//     }).collect()
// }
//
// EXPECTED PERFORMANCE (projected from §5.3 benchmarks)
// ──────────────────────────────────────────────────────
// At 56% sparsity (measured), dp4a eliminates ~56% of multiply-accumulate ops.
// On an A100 (312 TFLOPS FP16, 624 TOPS INT8):
//   Dense INT8 GEMV 256×256:    ~0.033 μs
//   Ternary dp4a 256×256 @56%:  ~0.015 μs (projected 2.2× speedup)
//   vs. current F32 CPU GEMV:   ~3.2 ms  (projected 213× speedup vs CPU baseline)
//
// ROADMAP
// ────────
// Phase 1 (3 weeks): Single-GPU GEMV kernel + candle CustomOp wiring + unit tests
// Phase 2 (2 weeks): Batched GEMM, fused STE quantization, benchmark suite
// Phase 3 (1 week):  INT2 tensor-core path (SM 8.0+), optional Triton reimplementation
//
// CUDA file: moe-llm-core/kernels/ternary_matmul.cu (to be added in Phase 1)
// Requires:  CUDA >= 11.0, compute capability >= 6.1 (for __dp4a)

use candle_core::{Result, Tensor};

/// Placeholder: selects CPU or GPU ternary matmul based on device.
/// On CPU, delegates to the existing `TernaryLinear` STE path.
/// GPU path is not yet implemented — see kernel outline above.
pub fn ternary_matmul(x: &Tensor, w: &Tensor, gamma: f32, threshold: f32) -> Result<Tensor> {
    use crate::model::ste::ternarize_ste_with_gamma;

    // CPU path: standard STE-quantized matmul (production-ready).
    let gamma_t = Tensor::new(&[gamma], x.device())?;
    let w_tern = ternarize_ste_with_gamma(w, threshold, &gamma_t)?;

    let dims = x.dims();
    if dims.len() == 3 {
        let (b, s, h) = (dims[0], dims[1], dims[2]);
        let x2 = x.reshape((b * s, h))?;
        let out = x2.matmul(&w_tern.t()?)?;
        out.reshape((b, s, out.dims()[1]))
    } else {
        x.matmul(&w_tern.t()?)
    }

    // GPU path (future — Phase 1):
    // if x.device().is_cuda() {
    //     return ternary_matmul_cuda(x, &pack_ternary_weights_tensor(w, threshold)?, gamma);
    // }
}
