#include "common.cuh"
#include "dispatch_utils.h"
#include "cub_helpers.h"
#include "libtorch_stable/quantization/vectorization_utils.cuh"
#include <c10/cuda/CUDAGuard.h>
#include <ATen/cuda/Exceptions.h>
#include <tuple>

namespace vllm {

// STRIDE_I_ZERO: true if scale_stride_i == 0 (per-tensor or per-channel)
// STRIDE_J_ZERO: true if scale_stride_j == 0 (per-tensor or per-token)
template <typename scalar_t, typename fp8_type, bool STRIDE_I_ZERO,
          bool STRIDE_J_ZERO>
__global__ void scaled_fp8_quant_kernel_strided_group_shape(
    fp8_type* __restrict__ out, const scalar_t* __restrict__ input,
    const float* __restrict__ scale, int hidden_size, int64_t in_row_stride,
    int64_t out_row_stride, int group_m, int group_n, int64_t scale_stride_i,
    int64_t scale_stride_j) {
  const int64_t token_idx = blockIdx.x;
  const int tid = threadIdx.x;

  const scalar_t* token_in = input + token_idx * in_row_stride;
  fp8_type* token_out = out + token_idx * out_row_stride;

  // Precompute row-level base offset for scale access (compile-time eliminated
  // when STRIDE_I_ZERO)
  const int64_t scale_row_base =
      STRIDE_I_ZERO ? 0
                    : static_cast<int>(token_idx) / group_m * scale_stride_i;

  auto get_inv_scale = [&](int gj) {
    return 1.0f / scale[scale_row_base + gj * scale_stride_j];
  };

  int cached_gj = -1;
  float cached_inv_scale = 0.0f;
  auto get_inv_scale_cached = [&](int gj) {
    if (gj != cached_gj) {
      cached_inv_scale = 1.0f / scale[scale_row_base + gj * scale_stride_j];
      cached_gj = gj;
    }
    return cached_inv_scale;
  };

  constexpr int VEC_SIZE = 16;  // FP8 so vectorize to 128 bits
  auto scaled_fp8_conversion_vectorized = [&](const scalar_t* in, fp8_type* out,
                                              int size, float inv_scale) {
    vectorize_with_alignment<VEC_SIZE>(
        in, out, size, tid, blockDim.x,
        [=] __device__(fp8_type & dst, const scalar_t& src) {
          dst = scaled_fp8_conversion<true, fp8_type>(static_cast<float>(src),
                                                      inv_scale);
        });
  };

  if (STRIDE_J_ZERO && hidden_size % VEC_SIZE == 0) {
    // Per-tensor or per-token: single scale per row, vectorize full row
    scaled_fp8_conversion_vectorized(token_in, token_out, hidden_size,
                                     get_inv_scale(0));
  } else if (group_n % VEC_SIZE == 0) {
    // Multiple column groups with vectorization
    const int num_groups_n = hidden_size / group_n;

    for (int gj = 0; gj < num_groups_n; gj++) {
      scaled_fp8_conversion_vectorized(token_in + gj * group_n,
                                       token_out + gj * group_n, group_n,
                                       get_inv_scale(gj));
    }
  } else {
    // Scalar path for small column groups (group_n < VEC_SIZE)
    for (int n = tid; n < hidden_size; n += blockDim.x) {
      const int gj = n / group_n;
      token_out[n] = scaled_fp8_conversion<true, fp8_type>(
          static_cast<float>(token_in[n]), get_inv_scale_cached(gj));
    }
  }
}

template <typename scalar_t, typename fp8_type>
__global__ void segmented_max_reduction_strided(
    float* __restrict__ scale, const scalar_t* __restrict__ input,
    int hidden_size, int64_t in_row_stride, int64_t num_tokens) {
  __shared__ float cache[256];
  const int tid = threadIdx.x;
  int64_t token_idx = blockIdx.x;

  // one block per token. Guard in case gridDim.x > num_tokens.
  if (token_idx >= num_tokens) {
    return;
  }

  const scalar_t* row_ptr = input + token_idx * in_row_stride;

  // each thread scans elements of the row in a strided fashion.
  float thread_max = 0.0f;
  for (int e = tid; e < hidden_size; e += blockDim.x) {
    float v = fabsf(static_cast<float>(row_ptr[e]));
    thread_max = fmaxf(thread_max, v);
  }

  cache[tid] = thread_max;
  __syncthreads();

  // parallel reduction to find row max.
  for (int offset = blockDim.x / 2; offset > 0; offset >>= 1) {
    if (tid < offset) {
      cache[tid] = fmaxf(cache[tid], cache[tid + offset]);
    }
    __syncthreads();
  }

  // thread 0 updates global scale (per-tensor) atomically.
  if (tid == 0) {
    atomicMaxFloat(scale, cache[0] / quant_type_max_v<fp8_type>);
  }
}

template <typename scalar_t, typename fp8_type>
__global__ void scaled_fp8_quant_kernel_strided_dynamic(
    fp8_type* __restrict__ out, const scalar_t* __restrict__ input,
    const float* __restrict__ scale, int hidden_size, int64_t in_row_stride,
    int64_t out_row_stride) {
  const int64_t token_idx = blockIdx.x;
  const int tid = threadIdx.x;

  const scalar_t* token_in = input + token_idx * in_row_stride;
  fp8_type* token_out = out + token_idx * out_row_stride;

  const float reciprocal_scale = 1.0f / (*scale);
  vectorize_with_alignment<16>(
      token_in, token_out, hidden_size, tid, blockDim.x,
      [=] __device__(fp8_type & dst, const scalar_t& src) {
        dst = scaled_fp8_conversion<true, fp8_type>(static_cast<float>(src),
                                                    reciprocal_scale);
      });
}

template <typename scalar_t, typename fp8_type>
__global__ void dynamic_per_token_scaled_fp8_quant_kernel_strided(
    fp8_type* __restrict__ out, float* __restrict__ scale,
    const scalar_t* __restrict__ input, const float* __restrict__ scale_ub,
    int hidden_size, int64_t in_row_stride, int64_t out_row_stride) {
  const int64_t token_idx = blockIdx.x;
  const int tid = threadIdx.x;

  // Use int64 to avoid overflowing an int32 when calculating this offset
  int64_t in_offset = static_cast<int64_t>(token_idx) * in_row_stride;
  int64_t out_offset = static_cast<int64_t>(token_idx) * out_row_stride;
  const scalar_t* token_in = input + in_offset;
  fp8_type* token_out = out + out_offset;

  // 1) per-token absmax
  float absmax_val = 0.f;
  vectorize_read_with_alignment<16>(
      token_in, hidden_size, tid, blockDim.x, [&] __device__(scalar_t v) {
        absmax_val = fmaxf(absmax_val, fabsf(static_cast<float>(v)));
      });

  using BlockReduce = cub::BlockReduce<float, 256>;
  __shared__ typename BlockReduce::TempStorage tmp;
  const float block_max =
      BlockReduce(tmp).Reduce(absmax_val, CubMaxOp{}, blockDim.x);

  __shared__ float token_scale;
  if (tid == 0) {
    token_scale = scale_ub ? fminf(block_max, *scale_ub) : block_max;
    token_scale = fmaxf(token_scale / quant_type_max_v<fp8_type>,
                        min_scaling_factor<fp8_type>::val());
    scale[token_idx] = token_scale;
  }
  __syncthreads();

  // 2) quantize
  vectorize_with_alignment<16>(
      token_in, token_out, hidden_size, tid, blockDim.x,
      [=] __device__(fp8_type & dst, const scalar_t& src) {
        dst = scaled_fp8_conversion<false, fp8_type>(static_cast<float>(src),
                                                     token_scale);
      });
}

}  // namespace vllm

void static_scaled_fp8_quant(
    torch::Tensor& out,          // [..., d]
    torch::Tensor const& input,  // [..., d]
    torch::Tensor const& scale,  // various shapes
    std::optional<std::tuple<int64_t, int64_t>>
        opt_group_shape)  // optional explicit (group_m, group_n)
{
  TORCH_CHECK(input.stride(-1) == 1,
              "last dimension of input must be contiguous");
  TORCH_CHECK(out.stride(-1) == 1,
              "last dimension of output must be contiguous");

  const int hidden_size = input.size(-1);              // N (columns)
  const int num_tokens = input.numel() / hidden_size;  // M (rows)

  // Determine group_m, group_n, and scale strides from scale shape
  // Scale indexing: scale[gi * scale_stride_j + gj * scale_stride_i]
  // where gi = m / group_m, gj = n / group_n
  int group_m, group_n;
  int64_t scale_stride_i, scale_stride_j;

  if (scale.dim() == 0 || scale.numel() == 1) {
    // Per-tensor: one scale for the entire tensor
    group_m = num_tokens;
    group_n = hidden_size;
    scale_stride_i = 0;
    scale_stride_j = 0;
  } else if (scale.dim() == 1) {
    // 1D scale: require explicit group_shape to disambiguate per-channel vs
    // per-token (avoids edge case where num_tokens == hidden_size)
    TORCH_CHECK(opt_group_shape.has_value(),
                "1D scale requires explicit group_shape to disambiguate "
                "per-channel vs per-token quantization. "
                "Use group_shape=(-1, 1) for per-channel or group_shape=(1, "
                "-1) for per-token.");

    const auto& [opt_group_m, opt_group_n] = opt_group_shape.value();
    group_m = opt_group_m == -1 ? num_tokens : static_cast<int>(opt_group_m);
    group_n = opt_group_n == -1 ? hidden_size : static_cast<int>(opt_group_n);

    // Validate the explicit group shape matches the 1D scale
    const int64_t scale_len = scale.numel();
    const int64_t expected_scale_m = num_tokens / group_m;
    const int64_t expected_scale_n = hidden_size / group_n;
    const int64_t expected_scale_numel = expected_scale_m * expected_scale_n;

    TORCH_CHECK(scale_len == expected_scale_numel, "1D scale length (",
                scale_len, ") does not match expected size (",
                expected_scale_numel, ") for group_shape (", opt_group_m, ", ",
                opt_group_n, ") with input shape (", num_tokens, ", ",
                hidden_size, ")");

    // For 1D scale, determine strides based on which dim is trivial
    // Scale indexing: scale[gi * scale_stride_i + gj * scale_stride_j]
    // where gi = m / group_m (row group), gj = n / group_n (col group)
    if (expected_scale_m == 1) {
      // Per-channel style: one scale in M dim, scale varies along N
      // gi = 0 always, gj varies, so stride_1 traverses the scale
      scale_stride_i = 0;
      scale_stride_j = scale.stride(0);
    } else if (expected_scale_n == 1) {
      // Per-token style: one scale in N dim, scale varies along M
      // gj = 0 always, gi varies, so stride_0 traverses the scale
      scale_stride_i = scale.stride(0);
      scale_stride_j = 0;
    } else {
      TORCH_CHECK(
          false,
          "1D scale can only be used when one of the scale dimensions is 1. "
          "For 2D group scaling, use a 2D scale tensor.");
    }
  } else if (scale.dim() == 2) {
    // 2D scale: infer group sizes from scale dimensions (or use explicit if
    // provided)
    const int64_t scale_size_0 = scale.size(0);
    const int64_t scale_size_1 = scale.size(1);

    TORCH_CHECK(num_tokens % scale_size_0 == 0, "num_tokens (", num_tokens,
                ") must be divisible by scale.size(0) (", scale_size_0, ")");
    TORCH_CHECK(hidden_size % scale_size_1 == 0, "hidden_size (", hidden_size,
                ") must be divisible by scale.size(1) (", scale_size_1, ")");

    // Infer from 2D scale shape
    int inferred_group_m = num_tokens / scale_size_0;
    int inferred_group_n = hidden_size / scale_size_1;

    // Use explicit if provided, otherwise use inferred
    if (opt_group_shape.has_value()) {
      const auto& [opt_group_m, opt_group_n] = opt_group_shape.value();
      group_m = opt_group_m == -1 ? num_tokens : static_cast<int>(opt_group_m);
      group_n = opt_group_n == -1 ? hidden_size : static_cast<int>(opt_group_n);

      // Validate explicit matches inferred
      TORCH_CHECK(group_m == inferred_group_m && group_n == inferred_group_n,
                  "Explicit group_shape (", opt_group_m, ", ", opt_group_n,
                  ") does not match inferred group shape (", inferred_group_m,
                  ", ", inferred_group_n, ") from 2D scale tensor shape (",
                  scale_size_0, ", ", scale_size_1, ")");
    } else {
      group_m = inferred_group_m;
      group_n = inferred_group_n;
    }

    scale_stride_i = scale.stride(0);
    scale_stride_j = scale.stride(1);
  } else {
    TORCH_CHECK(false, "scale must be 0D, 1D, or 2D tensor, but got ",
                scale.dim(), "D");
  }

  const int block_size = 256;
  dim3 grid(num_tokens);
  dim3 block(block_size);

  const int64_t in_row_stride = input.stride(-2);
  const int64_t out_row_stride = out.stride(-2);

  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();

  // Dispatch to template-specialized kernel based on stride pattern
  VLLM_DISPATCH_FLOATING_TYPES(
      input.scalar_type(), "scaled_fp8_quant_kernel_scalar_type", [&] {
        VLLM_DISPATCH_FP8_TYPES(
            out.scalar_type(), "scaled_fp8_quant_kernel_fp8_type", [&] {
              VLLM_DISPATCH_BOOL(scale_stride_i == 0, S0_ZERO, [&] {
                VLLM_DISPATCH_BOOL(scale_stride_j == 0, S1_ZERO, [&] {
                  vllm::scaled_fp8_quant_kernel_strided_group_shape<
                      scalar_t, fp8_t, S0_ZERO, S1_ZERO>
                      <<<grid, block, 0, stream>>>(
                          out.data_ptr<fp8_t>(), input.data_ptr<scalar_t>(),
                          scale.data_ptr<float>(), hidden_size, in_row_stride,
                          out_row_stride, group_m, group_n, scale_stride_i,
                          scale_stride_j);
                });
              });
            });
      });
}

void dynamic_scaled_fp8_quant(torch::Tensor& out,          // [..., d]
                              torch::Tensor const& input,  // [..., d]
                              torch::Tensor& scale)        // [1]
{
  TORCH_CHECK(input.stride(-1) == 1,
              "last dimension of input must be contiguous");
  TORCH_CHECK(out.stride(-1) == 1,
              "last dimension of output must be contiguous");

  const int hidden_size = input.size(-1);
  const int num_tokens = input.numel() / hidden_size;
  const int block_size = 256;
  dim3 grid(num_tokens);
  dim3 block(block_size);

  const int64_t in_row_stride = input.stride(-2);
  const int64_t out_row_stride = out.stride(-2);

  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();

  // scale tensor should be initialised to <=0 before reduction
  AT_CUDA_CHECK(
      cudaMemsetAsync(scale.data_ptr<float>(), 0, sizeof(float), stream));

  VLLM_DISPATCH_FLOATING_TYPES(
      input.scalar_type(), "scaled_fp8_quant_kernel_scalar_type", [&] {
        VLLM_DISPATCH_FP8_TYPES(
            out.scalar_type(), "scaled_fp8_quant_kernel_fp8_type", [&] {
              vllm::segmented_max_reduction_strided<scalar_t, fp8_t>
                  <<<grid, block, 0, stream>>>(
                      scale.data_ptr<float>(), input.data_ptr<scalar_t>(),
                      hidden_size, in_row_stride,
                      static_cast<int64_t>(num_tokens));

              vllm::scaled_fp8_quant_kernel_strided_dynamic<scalar_t, fp8_t>
                  <<<grid, block, 0, stream>>>(
                      out.data_ptr<fp8_t>(), input.data_ptr<scalar_t>(),
                      scale.data_ptr<float>(), hidden_size, in_row_stride,
                      out_row_stride);
            });
      });
}

void dynamic_per_token_scaled_fp8_quant(
    torch::Tensor& out,          // [..., d]
    torch::Tensor const& input,  // [..., d]
    torch::Tensor& scales, std::optional<at::Tensor> const& scale_ub) {
  TORCH_CHECK(input.stride(-1) == 1,
              "last dimension of input must be contiguous");
  TORCH_CHECK(out.stride(-1) == 1,
              "last dimension of output must be contiguous");

  const int hidden_size = input.size(-1);
  const int num_tokens = input.numel() / hidden_size;
  const int block_size = 256;
  dim3 grid(num_tokens);
  dim3 block(std::min(hidden_size, block_size));

  const int64_t in_row_stride = input.stride(-2);
  const int64_t out_row_stride = out.stride(-2);

  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
  VLLM_DISPATCH_FLOATING_TYPES(
      input.scalar_type(),
      "dynamic_per_token_scaled_fp8_quant_kernel_scalar_type", [&] {
        VLLM_DISPATCH_FP8_TYPES(
            out.scalar_type(),
            "dynamic_per_token_scaled_fp8_quant_kernel_fp8_type", [&] {
              vllm::dynamic_per_token_scaled_fp8_quant_kernel_strided<
                  scalar_t, fp8_t><<<grid, block, 0, stream>>>(
                  out.data_ptr<fp8_t>(), scales.data_ptr<float>(),
                  input.data_ptr<scalar_t>(),
                  scale_ub.has_value() ? scale_ub->data_ptr<float>() : nullptr,
                  hidden_size, in_row_stride, out_row_stride);
            });
      });
}
