// Copyright © 2025 Apple Inc.

#include "mlx/backend/cuda/quantized/quantized.h"
#include "mlx/backend/cuda/device.h"
#include "mlx/backend/cuda/quantized/qmm/qmm.h"
#include "mlx/backend/cuda/quantized/quantized_utils.h"
#include "mlx/dtype_utils.h"
#include "mlx/fast_primitives.h"
#include "mlx/primitives.h"

#include <nvtx3/nvtx3.hpp>

namespace mlx::core {

void QuantizedMatmul::eval_gpu(const std::vector<array>& inputs, array& out) {
  nvtx3::scoped_range r("QuantizedMatmul::eval_gpu");
  auto& s = stream();
  auto& encoder = cu::get_command_encoder(s);

  const array& x = inputs[0];
  const array& w = inputs[1];
  const array& scales = inputs[2];
  std::optional<array> biases;
  if (inputs.size() > 3) {
    biases = inputs[3];
  }

  auto supports = [&](auto&& f) {
    return f(
        x,
        w,
        scales,
        biases,
        out,
        transpose_,
        bits_,
        group_size_,
        mode_,
        encoder.device());
  };
  bool can_use_qmm_sm90 = supports(supports_qmm_sm90);
  bool can_use_qmm_sm80 = supports(supports_qmm_sm80);
  bool can_use_qmm_naive = supports(supports_qmm_naive);
  bool can_use_fp_qmv = supports(supports_fp_qmv);
  bool can_use_qmv = supports(supports_qmv) || can_use_fp_qmv;

  auto call_qmm_sm90 = [&]() {
    out.set_data(cu::malloc_async(out.nbytes(), encoder));
    qmm_sm90(x, w, scales, *biases, out, bits_, group_size_, encoder, s);
  };
  auto call_qmm_sm80 = [&]() {
    out.set_data(cu::malloc_async(out.nbytes(), encoder));
    qmm_sm80(
        x,
        w,
        scales,
        biases,
        std::nullopt,
        std::nullopt,
        out,
        bits_,
        group_size_,
        mode_,
        encoder);
  };
  auto call_qmm_naive = [&]() {
    out.set_data(cu::malloc_async(out.nbytes(), encoder));
    qmm_naive(
        x,
        w,
        scales,
        biases,
        std::nullopt,
        std::nullopt,
        out,
        transpose_,
        bits_,
        group_size_,
        mode_,
        encoder);
  };
  auto call_qmv = [&]() {
    out.set_data(cu::malloc_async(out.nbytes(), encoder));
    if (can_use_fp_qmv) {
      fp_qmv(x, w, scales, out, bits_, group_size_, encoder, s);
    } else {
      qmv(x, w, scales, biases, out, bits_, group_size_, mode_, encoder);
    }
  };

  int M = out.ndim() > 1 ? out.shape(-2) : 1;
  int N = out.shape(-1);
  int K = x.shape(-1);
  int B = out.size() / (M * N);

  if (can_use_qmm_sm90) {
    if (can_use_qmv && (M == 1 && B == 1 && N <= 16384 && K <= 16384)) {
      call_qmv();
    } else {
      call_qmm_sm90();
    }
    return;
  }

  if (can_use_qmm_sm80) {
    if (can_use_qmv && (M * B < 8)) {
      call_qmv();
    } else {
      call_qmm_sm80();
    }
    return;
  }

  if (can_use_qmm_naive) {
    if (can_use_qmv && (M * B < 8)) {
      call_qmv();
    } else {
      call_qmm_naive();
    }
    return;
  }

  if (can_use_qmv) {
    call_qmv();
    return;
  }

  throw std::runtime_error(
      fmt::format(
          "[quantized_matmul] No implementation for "
          "problem shape: {}x{}x{}x{}, transpose: {}, "
          "activation: {}, bits: {}, group size: {}, mode: \"{}\".",
          M,
          N,
          K,
          B,
          transpose_,
          dtype_to_string(x.dtype()),
          bits_,
          group_size_,
          quantization_mode_to_string(mode_)));
}

void GatherQMM::eval_gpu(const std::vector<array>& inputs, array& out) {
  nvtx3::scoped_range r("GatherQMM::eval_gpu");
  auto& s = stream();
  auto& encoder = cu::get_command_encoder(s);

  const array& x = inputs[0];
  const array& w = inputs[1];
  const array& scales = inputs[2];
  std::optional<array> biases;
  if (inputs.size() == 6) {
    biases = inputs[3];
  }
  array lhs_indices = ensure_contiguous(inputs[inputs.size() - 2], encoder, s);
  array rhs_indices = ensure_contiguous(inputs[inputs.size() - 1], encoder, s);

  int M = out.ndim() > 1 ? out.shape(-2) : 1;
  int N = out.shape(-1);
  int K = x.shape(-1);
  int B = out.size() / (M * N);

  auto supports = [&](auto&& f) {
    return f(
        x,
        w,
        scales,
        biases,
        out,
        transpose_,
        bits_,
        group_size_,
        mode_,
        encoder.device());
  };
  bool can_use_qmm_sm80 = supports(supports_qmm_sm80);
  bool can_use_qmm_naive = supports(supports_qmm_naive);
  bool can_use_qmv = supports(supports_qmv);

  auto call_qmm_sm80 = [&]() {
    out.set_data(cu::malloc_async(out.nbytes(), encoder));
    qmm_sm80(
        x,
        w,
        scales,
        biases,
        lhs_indices,
        rhs_indices,
        out,
        bits_,
        group_size_,
        mode_,
        encoder);
  };
  auto call_qmm_naive = [&]() {
    out.set_data(cu::malloc_async(out.nbytes(), encoder));
    qmm_naive(
        x,
        w,
        scales,
        biases,
        lhs_indices,
        rhs_indices,
        out,
        transpose_,
        bits_,
        group_size_,
        mode_,
        encoder);
  };
  auto call_qmv = [&]() {
    out.set_data(cu::malloc_async(out.nbytes(), encoder));
    gather_qmv(
        x,
        w,
        scales,
        biases,
        lhs_indices,
        rhs_indices,
        out,
        bits_,
        group_size_,
        mode_,
        encoder);
  };

  if (can_use_qmm_sm80) {
    if (can_use_qmv && (M * B < 8)) {
      call_qmv();
    } else {
      call_qmm_sm80();
    }
    return;
  }

  if (can_use_qmm_naive) {
    if (can_use_qmv && (M * B < 8)) {
      call_qmv();
    } else {
      call_qmm_naive();
    }
    return;
  }

  if (can_use_qmv) {
    call_qmv();
    return;
  }

  throw std::runtime_error(
      fmt::format(
          "[gather_qmm] No implementation for "
          "problem shape: {}x{}x{}x{}, transpose: {}, "
          "activation: {}, bits: {}, group size: {}, mode: \"{}\".",
          M,
          N,
          K,
          B,
          transpose_,
          dtype_to_string(x.dtype()),
          bits_,
          group_size_,
          quantization_mode_to_string(mode_)));
}

void fast::Quantize::eval_gpu(
    const std::vector<array>& inputs,
    std::vector<array>& outputs) {
  nvtx3::scoped_range r("Quantize::eval_gpu");
  auto& s = stream();
  auto& enc = cu::get_command_encoder(s);
  if (dequantize_) {
    auto wq = ensure_row_contiguous(inputs[0], enc, s);
    auto scales = ensure_row_contiguous(inputs[1], enc, s);
    auto& w = outputs[0];

    w.set_data(cu::malloc_async(w.nbytes(), enc));

    if (mode_ == QuantizationMode::Affine) {
      auto biases = ensure_row_contiguous(inputs[2], enc, s);
      affine_dequantize(wq, scales, biases, w, group_size_, bits_, enc, s);
    } else {
      // 0 -- xq, 1 -- scales, 2 -- could be global scale for nvfp4
      bool use_global_scale =
          mode_ == QuantizationMode::Nvfp4 && inputs.size() > 2;
      std::optional<array> global_scale =
          use_global_scale ? std::make_optional(inputs[2]) : std::nullopt;
      fp_dequantize(wq, scales, w, group_size_, bits_, global_scale, enc, s);
    }
  } else {
    auto w = ensure_contiguous(inputs[0], enc, s);
    auto& wq = outputs[0];
    auto& scales = outputs[1];

    wq.set_data(cu::malloc_async(wq.nbytes(), enc));
    scales.set_data(cu::malloc_async(scales.nbytes(), enc));

    if (mode_ == QuantizationMode::Affine) {
      auto& biases = outputs[2];
      biases.set_data(cu::malloc_async(biases.nbytes(), enc));
      affine_quantize(w, wq, scales, biases, group_size_, bits_, enc, s);
    } else {
      bool use_global_scale =
          mode_ == QuantizationMode::Nvfp4 && inputs.size() > 1;
      std::optional<array> global_scale =
          use_global_scale ? std::make_optional(inputs[1]) : std::nullopt;
      fp_quantize(w, wq, scales, group_size_, bits_, global_scale, enc, s);
    }
  }
}

} // namespace mlx::core
