/*
 * Copyright © Advanced Micro Devices, Inc. All rights reserved.
 * Copyright (c) 2024, The vLLM team.
 *
 * 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.
 */
#include <torch/all.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>

#include "dispatch_utils.h"
// #include "attention/attention_dtypes.h"
#ifndef USE_ROCM
#include <cuda_bf16.h>
#include <cuda_fp16.h>
#include <cub/util_type.cuh>
#include <cub/cub.cuh>
#else
#include <hip/hip_bf16.h>
#include <hip/hip_fp16.h>
#include <hipcub/util_type.hpp>
#include <hipcub/hipcub.hpp>
// #include "quantization/fp8/amd/hip_float8.h"
// #include "quantization/fp8/amd/quant_utils.cuh"

using __nv_bfloat16 = __hip_bfloat16;
using __nv_bfloat162 = __hip_bfloat162;
#endif

namespace vllm
{

  template <typename scalar_t>
  struct __align__(16) vec8_t
  {
    scalar_t x, y, z, w, u, v, s, t;

    __device__ vec8_t() : x(0), y(0), z(0), w(0), u(0), v(0), s(0), t(0) {}
    __device__ vec8_t(scalar_t x, scalar_t y, scalar_t z, scalar_t w, scalar_t u,
                      scalar_t v, scalar_t s, scalar_t t)
        : x(x), y(y), z(z), w(w), u(u), v(v), s(s), t(t) {}

    __device__ vec8_t operator*(const vec8_t &other) const
    {
      return vec8_t(x * other.x, y * other.y, z * other.z, w * other.w,
                    u * other.u, v * other.v, s * other.s, t * other.t);
    }

    __device__ vec8_t operator*(const float &scale) const
    {
      return vec8_t(x * scale, y * scale, z * scale, w * scale, u * scale,
                    v * scale, s * scale, t * scale);
    }

    __device__ vec8_t operator+(const vec8_t &other) const
    {
      return vec8_t(x + other.x, y + other.y, z + other.z, w + other.w,
                    u + other.u, v + other.v, s + other.s, t + other.t);
    }

    __device__ void operator+=(const vec8_t &other)
    {
      x += other.x;
      y += other.y;
      z += other.z;
      w += other.w;
      u += other.u;
      v += other.v;
      s += other.s;
      t += other.t;
    }

    __device__ scalar_t sum() const { return x + y + z + w + u + v + s + t; }
  };

  // TODO(woosuk): Further optimize this kernel.
  template <typename scalar_t>
  __global__ void rms_norm_kernel(
      scalar_t *__restrict__ out,          // [..., hidden_size]
      const scalar_t *__restrict__ input,  // [..., hidden_size]
      const scalar_t *__restrict__ weight, // [hidden_size]
      const float epsilon, const int num_tokens, const int hidden_size)
  {
    __shared__ float s_variance;

    vec8_t<scalar_t> v8_variance = {0, 0, 0, 0, 0, 0, 0, 0};

    vec8_t<scalar_t> *vectorized_out = reinterpret_cast<vec8_t<scalar_t> *>(out);
    vec8_t<scalar_t> const *vectorized_in =
        reinterpret_cast<vec8_t<scalar_t> const *>(input);
    vec8_t<scalar_t> const *vectorized_weight =
        reinterpret_cast<vec8_t<scalar_t> const *>(weight);
    const int vec_hidden_size = hidden_size >> 3;

    // Compute variance. Be careful, hidden_size should multiple of 4.
    for (int idx = threadIdx.x; idx < vec_hidden_size; idx += blockDim.x)
    {
      vec8_t<scalar_t> x = vectorized_in[blockIdx.x * vec_hidden_size + idx];
      v8_variance += x * x;
    }
    float v8_variance_sum = v8_variance.sum();

    using BlockReduce = cub::BlockReduce<float, 1024>;
    __shared__ typename BlockReduce::TempStorage reduceStore;
    float variance =
        BlockReduce(reduceStore).Reduce(v8_variance_sum, cub::Sum{}, blockDim.x);

    if (threadIdx.x == 0)
    {
      s_variance = rsqrtf(variance / hidden_size + epsilon);
    }
    __syncthreads();

    for (int idx = threadIdx.x; idx < vec_hidden_size; idx += blockDim.x)
    {
      vec8_t<scalar_t> v8_in = vectorized_in[blockIdx.x * vec_hidden_size + idx];
      vec8_t<scalar_t> v8_w = vectorized_weight[idx];
      vectorized_out[blockIdx.x * vec_hidden_size + idx] =
          v8_in * s_variance * v8_w;
    }
  }

  // template <typename scalar_t>
  // __global__ void scaled_rms_norm_kernel(
  //     c10::Float8_e4m3fnuz* __restrict__ out,  // [..., hidden_size]
  //     const scalar_t* __restrict__ input,      // [..., hidden_size]
  //     const scalar_t* __restrict__ weight,     // [hidden_size]
  //     const float scale, const float epsilon, const int num_tokens,
  //     const int hidden_size) {
  //   __shared__ float s_variance;
  //   float variance = 0.0f;

  //   for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
  //     const float x = (float)input[blockIdx.x * hidden_size + idx];
  //     variance += x * x;
  //   }

  //   using BlockReduce = cub::BlockReduce<float, 1024>;
  //   __shared__ typename BlockReduce::TempStorage reduceStore;
  //   variance = BlockReduce(reduceStore).Reduce(variance, cub::Sum{}, blockDim.x);

  //   if (threadIdx.x == 0) {
  //     s_variance = rsqrtf(variance / hidden_size + epsilon);
  //   }
  //   __syncthreads();

  //   for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
  //     float x = (float)input[blockIdx.x * hidden_size + idx];
  //     float r = (x * s_variance) * weight[idx] * scale;
  //     out[blockIdx.x * hidden_size + idx] = c10::Float8_e4m3fnuz(
  //         hip_fp8(r).data, c10::Float8_e4m3fnuz::from_bits());
  //   }
  // }

  /* Converter structs for the conversion from torch types to HIP/CUDA types,
     and the associated type conversions within HIP/CUDA. These helpers need
     to be implemented for now because the relevant type conversion
     operators/constructors are not consistently implemented by HIP/CUDA, so
     a generic conversion via type casts cannot be implemented.

     Each struct should have the member static constexpr bool `exists`:
     If false, the optimized kernel is not used for the corresponding torch type.
     If true, the struct should be fully defined as shown in the examples below.
   */
  template <typename torch_type>
  struct _typeConvert
  {
    static constexpr bool exists = false;
  };

#if defined(USE_ROCM) || (defined(CUDA_VERSION) && (CUDA_VERSION >= 12000))
  // CUDA < 12.0 runs into issues with packed type conversion
  template <>
  struct _typeConvert<c10::Half>
  {
    static constexpr bool exists = true;
    using hip_type = __half;
    using packed_hip_type = __half2;

    __device__ static inline float convert(hip_type x) { return __half2float(x); }
    __device__ static inline float2 convert(packed_hip_type x)
    {
      return __half22float2(x);
    }
    __device__ static inline hip_type convert(float x)
    {
      return __float2half_rn(x);
    }
    __device__ static inline packed_hip_type convert(float2 x)
    {
      return __float22half2_rn(x);
    }
  };

#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800
  // CUDA_ARCH < 800 does not have BF16 support
  // TODO: Add in ROCm support once public headers handle bf16 maturely
  template <>
  struct _typeConvert<c10::BFloat16>
  {
    static constexpr bool exists = true;
    using hip_type = __nv_bfloat16;
    using packed_hip_type = __nv_bfloat162;

    __device__ static inline float convert(hip_type x)
    {
      return __bfloat162float(x);
    }
    __device__ static inline float2 convert(packed_hip_type x)
    {
      return __bfloat1622float2(x);
    }
    __device__ static inline hip_type convert(float x)
    {
      return __float2bfloat16(x);
    }
    __device__ static inline packed_hip_type convert(float2 x)
    {
      return __float22bfloat162_rn(x);
    }
  };
#endif // defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800
#endif // defined(USE_ROCM) || (defined(CUDA_VERSION) && (CUDA_VERSION >=
       // 12000))

  /* Vector POD struct to generate vectorized and packed FP16/BF16 ops
     for appropriate specializations of fused_add_rms_norm_kernel.
     Only functions that are necessary in that kernel are implemented.
     Alignment to 16 bytes is required to use 128-bit global memory ops.
   */
  template <typename scalar_t, int width>
  struct alignas(16) _f16Vec
  {
    /* Not theoretically necessary that width is a power of 2 but should
       almost always be the case for optimization purposes */
    static_assert(width > 0 && (width & (width - 1)) == 0,
                  "Width is not a positive power of 2!");
    using Converter = _typeConvert<scalar_t>;
    using T1 = typename Converter::hip_type;
    using T2 = typename Converter::packed_hip_type;
    T1 data[width];

    __device__ _f16Vec &operator+=(const _f16Vec<scalar_t, width> &other)
    {
      if constexpr (width % 2 == 0)
      {
#pragma unroll
        for (int i = 0; i < width; i += 2)
        {
          T2 temp{data[i], data[i + 1]};
          temp += T2{other.data[i], other.data[i + 1]};
          data[i] = temp.x;
          data[i + 1] = temp.y;
        }
      }
      else
      {
#pragma unroll
        for (int i = 0; i < width; ++i)
          data[i] += other.data[i];
      }
      return *this;
    }

    __device__ _f16Vec &operator*=(const _f16Vec<scalar_t, width> &other)
    {
      if constexpr (width % 2 == 0)
      {
#pragma unroll
        for (int i = 0; i < width; i += 2)
        {
          T2 temp{data[i], data[i + 1]};
          temp *= T2{other.data[i], other.data[i + 1]};
          data[i] = temp.x;
          data[i + 1] = temp.y;
        }
      }
      else
      {
#pragma unroll
        for (int i = 0; i < width; ++i)
          data[i] *= other.data[i];
      }
      return *this;
    }

    __device__ _f16Vec &operator*=(const float scale)
    {
      if constexpr (width % 2 == 0)
      {
#pragma unroll
        for (int i = 0; i < width; i += 2)
        {
          float2 temp_f = Converter::convert(T2{data[i], data[i + 1]});
          temp_f.x *= scale;
          temp_f.y *= scale;
          T2 temp = Converter::convert(temp_f);
          data[i] = temp.x;
          data[i + 1] = temp.y;
        }
      }
      else
      {
#pragma unroll
        for (int i = 0; i < width; ++i)
        {
          float temp = Converter::convert(data[i]) * scale;
          data[i] = Converter::convert(temp);
        }
      }
      return *this;
    }

    __device__ float sum_squares() const
    {
      float result = 0.0f;
      if constexpr (width % 2 == 0)
      {
#pragma unroll
        for (int i = 0; i < width; i += 2)
        {
          float2 z = Converter::convert(T2{data[i], data[i + 1]});
          result += z.x * z.x + z.y * z.y;
        }
      }
      else
      {
#pragma unroll
        for (int i = 0; i < width; ++i)
        {
          float x = Converter::convert(data[i]);
          result += x * x;
        }
      }
      return result;
    }
  };

  /* Function specialization in the case of FP16/BF16 tensors.
     Additional optimizations we can make in this case are
     packed and vectorized operations, which help with the
     memory latency bottleneck. */
  template <typename scalar_t, int width>
  __global__ std::enable_if_t<(width > 0) && _typeConvert<scalar_t>::exists>
  fused_add_rms_norm_kernel(
      scalar_t *__restrict__ input,        // [..., hidden_size]
      scalar_t *__restrict__ residual,     // [..., hidden_size]
      const scalar_t *__restrict__ weight, // [hidden_size]
      const float epsilon, const int num_tokens, const int hidden_size)
  {
    // Sanity checks on our vector struct and type-punned pointer arithmetic
    static_assert(std::is_pod_v<_f16Vec<scalar_t, width>>);
    static_assert(sizeof(_f16Vec<scalar_t, width>) == sizeof(scalar_t) * width);

    const int vec_hidden_size = hidden_size / width;
    __shared__ float s_variance;
    float variance = 0.0f;
    /* These and the argument pointers are all declared `restrict` as they are
       not aliased in practice. Argument pointers should not be dereferenced
       in this kernel as that would be undefined behavior */
    auto *__restrict__ input_v =
        reinterpret_cast<_f16Vec<scalar_t, width> *>(input);
    auto *__restrict__ residual_v =
        reinterpret_cast<_f16Vec<scalar_t, width> *>(residual);
    auto *__restrict__ weight_v =
        reinterpret_cast<const _f16Vec<scalar_t, width> *>(weight);

    for (int idx = threadIdx.x; idx < vec_hidden_size; idx += blockDim.x)
    {
      int id = blockIdx.x * vec_hidden_size + idx;
      _f16Vec<scalar_t, width> temp = input_v[id];
      temp += residual_v[id];
      variance += temp.sum_squares();
      residual_v[id] = temp;
    }

    using BlockReduce = cub::BlockReduce<float, 1024>;
    __shared__ typename BlockReduce::TempStorage reduceStore;
    variance = BlockReduce(reduceStore).Reduce(variance, cub::Sum{}, blockDim.x);

    if (threadIdx.x == 0)
    {
      s_variance = rsqrtf(variance / hidden_size + epsilon);
    }
    __syncthreads();

    for (int idx = threadIdx.x; idx < vec_hidden_size; idx += blockDim.x)
    {
      int id = blockIdx.x * vec_hidden_size + idx;
      _f16Vec<scalar_t, width> temp = residual_v[id];
      temp *= s_variance;
      temp *= weight_v[idx];
      input_v[id] = temp;
    }
  }

  /* Generic fused_add_rms_norm_kernel
     The width field is not used here but necessary for other specializations.
   */
  template <typename scalar_t, int width>
  __global__ std::enable_if_t<(width == 0) || !_typeConvert<scalar_t>::exists>
  fused_add_rms_norm_kernel(
      scalar_t *__restrict__ input,        // [..., hidden_size]
      scalar_t *__restrict__ residual,     // [..., hidden_size]
      const scalar_t *__restrict__ weight, // [hidden_size]
      const float epsilon, const int num_tokens, const int hidden_size)
  {
    __shared__ float s_variance;
    float variance = 0.0f;

    for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x)
    {
      scalar_t z = input[blockIdx.x * hidden_size + idx];
      z += residual[blockIdx.x * hidden_size + idx];
      float x = (float)z;
      variance += x * x;
      residual[blockIdx.x * hidden_size + idx] = z;
    }

    using BlockReduce = cub::BlockReduce<float, 1024>;
    __shared__ typename BlockReduce::TempStorage reduceStore;
    variance = BlockReduce(reduceStore).Reduce(variance, cub::Sum{}, blockDim.x);

    if (threadIdx.x == 0)
    {
      s_variance = rsqrtf(variance / hidden_size + epsilon);
    }
    __syncthreads();

    for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x)
    {
      float x = (float)residual[blockIdx.x * hidden_size + idx];
      input[blockIdx.x * hidden_size + idx] =
          ((scalar_t)(x * s_variance)) * weight[idx];
    }
  }

  /* Function specialization in the case of FP16/BF16 tensors.
     Additional optimizations we can make in this case are
     packed and vectorized operations, which help with the
     memory latency bottleneck. */

  // template <>
  // struct Vec<c10::Float8_e4m3fnuz, 8> {
  //   using Type = uint2;
  // };

  // template <>
  // struct Vec<c10::Half, 8> {
  //   using Type = uint4;
  // };

  // template <>
  // struct Vec<c10::BFloat16, 8> {
  //   using Type = bf16_8_t;
  // };

  // template <typename scalar_t, int width>
  // __global__ std::enable_if_t<(width > 0) && _typeConvert<scalar_t>::exists>
  // scaled_fused_add_rms_norm_kernel(
  //     c10::Float8_e4m3fnuz* __restrict__ out,  // [..., hidden_size]
  //     scalar_t* __restrict__ input,            // [..., hidden_size]
  //     scalar_t* __restrict__ residual,         // [..., hidden_size]
  //     const scalar_t* __restrict__ weight,     // [hidden_size]
  //     const float epsilon, const float scale, const int num_tokens,
  //     const int hidden_size) {
  //   using in_v_t = typename Vec<scalar_t, width>::Type;
  //   using out_v_t = typename Vec<c10::Float8_e4m3fnuz, width>::Type;
  //   // Sanity checks on our vector struct and type-punned pointer arithmetic
  //   static_assert(std::is_pod_v<_f16Vec<scalar_t, width>>);
  //   static_assert(sizeof(_f16Vec<scalar_t, width>) == sizeof(scalar_t) * width);

  //   const int vec_hidden_size = hidden_size / width;
  //   __shared__ float s_variance;
  //   float variance = 0.0f;
  //   /* These and the argument pointers are all declared `restrict` as they are
  //      not aliased in practice. Argument pointers should not be dereferenced
  //      in this kernel as that would be undefined behavior */
  //   auto* __restrict__ out_v = reinterpret_cast<out_v_t*>(out);
  //   auto* __restrict__ input_v =
  //       reinterpret_cast<_f16Vec<scalar_t, width>*>(input);
  //   auto* __restrict__ residual_v =
  //       reinterpret_cast<_f16Vec<scalar_t, width>*>(residual);
  //   auto* __restrict__ weight_v =
  //       reinterpret_cast<const _f16Vec<scalar_t, width>*>(weight);

  //   for (int idx = threadIdx.x; idx < vec_hidden_size; idx += blockDim.x) {
  //     int id = blockIdx.x * vec_hidden_size + idx;
  //     _f16Vec<scalar_t, width> temp = input_v[id];
  //     temp += residual_v[id];
  //     variance += temp.sum_squares();
  //     residual_v[id] = temp;
  //   }

  //   using BlockReduce = cub::BlockReduce<float, 1024>;
  //   __shared__ typename BlockReduce::TempStorage reduceStore;
  //   variance = BlockReduce(reduceStore).Reduce(variance, cub::Sum{}, blockDim.x);

  //   if (threadIdx.x == 0) {
  //     s_variance = rsqrtf(variance / hidden_size + epsilon);
  //   }
  //   __syncthreads();

  //   for (int idx = threadIdx.x; idx < vec_hidden_size; idx += blockDim.x) {
  //     int id = blockIdx.x * vec_hidden_size + idx;
  //     _f16Vec<scalar_t, width> temp = residual_v[id];
  //     temp *= s_variance;
  //     temp *= weight_v[idx];
  //     out_v_t temp_quant = fp8::scaled_vec_conversion<out_v_t, in_v_t>(
  //         *reinterpret_cast<in_v_t*>(&temp), scale);
  //     out_v[id] = temp_quant;
  //   }
  // }

  /* Generic scaled_fused_add_rms_norm_kernel
     The width field is not used here but necessary for other specializations.
   */
  // template <typename scalar_t, int width>
  // __global__ std::enable_if_t<(width == 0) || !_typeConvert<scalar_t>::exists>
  // scaled_fused_add_rms_norm_kernel(
  //     c10::Float8_e4m3fnuz* __restrict__ out,  // [..., hidden_size]
  //     scalar_t* __restrict__ input,            // [..., hidden_size]
  //     scalar_t* __restrict__ residual,         // [..., hidden_size]
  //     const scalar_t* __restrict__ weight,     // [hidden_size]
  //     const float epsilon, const float scale, const int num_tokens,
  //     const int hidden_size) {
  //   __shared__ float s_variance;
  //   float variance = 0.0f;

  //   for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
  //     scalar_t z = input[blockIdx.x * hidden_size + idx];
  //     z += residual[blockIdx.x * hidden_size + idx];
  //     float x = (float)z;
  //     variance += x * x;
  //     residual[blockIdx.x * hidden_size + idx] = z;
  //   }

  //   using BlockReduce = cub::BlockReduce<float, 1024>;
  //   __shared__ typename BlockReduce::TempStorage reduceStore;
  //   variance = BlockReduce(reduceStore).Reduce(variance, cub::Sum{}, blockDim.x);

  //   if (threadIdx.x == 0) {
  //     s_variance = rsqrtf(variance / hidden_size + epsilon);
  //   }
  //   __syncthreads();

  //   for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
  //     float x = (float)residual[blockIdx.x * hidden_size + idx];
  //     float r = (x * s_variance) * (float)weight[idx] / scale;
  //     out[blockIdx.x * hidden_size + idx] = c10::Float8_e4m3fnuz(
  //         hip_fp8(r).data, c10::Float8_e4m3fnuz::from_bits());
  //   }
  // }

} // namespace vllm

void rms_norm(torch::Tensor &out,    // [..., hidden_size]
              torch::Tensor &input,  // [..., hidden_size]
              torch::Tensor &weight, // [hidden_size]
              double epsilon)
{
  int hidden_size = input.size(-1);
  int num_tokens = input.numel() / hidden_size;

  dim3 grid(num_tokens);
  dim3 block(std::min(hidden_size, 1024));
  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
  VLLM_DISPATCH_FLOATING_TYPES(input.scalar_type(), "rms_norm_kernel", [&]
                               { vllm::rms_norm_kernel<scalar_t><<<grid, block, 0, stream>>>(
                                     out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(),
                                     weight.data_ptr<scalar_t>(), epsilon, num_tokens, hidden_size); });
}

// void scaled_rms_norm(torch::Tensor& out,     // [..., hidden_size]
//                      torch::Tensor& input,   // [..., hidden_size]
//                      torch::Tensor& weight,  // [hidden_size]
//                      torch::Tensor& scale, double epsilon) {
//   int hidden_size = input.size(-1);
//   int num_tokens = input.numel() / hidden_size;

//   dim3 grid(num_tokens);
//   dim3 block(std::min(hidden_size, 1024));
//   const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
//   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
//   VLLM_DISPATCH_FLOATING_TYPES(
//       input.scalar_type(), "scaled_rms_norm_kernel", [&] {
//         vllm::scaled_rms_norm_kernel<scalar_t><<<grid, block, 0, stream>>>(
//             out.data_ptr<c10::Float8_e4m3fnuz>(), input.data_ptr<scalar_t>(),
//             weight.data_ptr<scalar_t>(), 1.0 / (*scale.data_ptr<float>()),
//             epsilon, num_tokens, hidden_size);
//       });
// }

#define LAUNCH_FUSED_ADD_RMS_NORM(width)                                                                                             \
  VLLM_DISPATCH_FLOATING_TYPES(                                                                                                      \
      input.scalar_type(), "fused_add_rms_norm_kernel", [&] { vllm::fused_add_rms_norm_kernel<scalar_t, width>                       \
                                                                  <<<grid, block, 0, stream>>>(input.data_ptr<scalar_t>(),           \
                                                                                               residual.data_ptr<scalar_t>(),        \
                                                                                               weight.data_ptr<scalar_t>(), epsilon, \
                                                                                               num_tokens, hidden_size); });

void fused_add_rms_norm(torch::Tensor &input,    // [..., hidden_size]
                        torch::Tensor &residual, // [..., hidden_size]
                        torch::Tensor &weight,   // [hidden_size]
                        double epsilon)
{
  int hidden_size = input.size(-1);
  int num_tokens = input.numel() / hidden_size;

  dim3 grid(num_tokens);
  /* This kernel is memory-latency bound in many scenarios.
     When num_tokens is large, a smaller block size allows
     for increased block occupancy on CUs and better latency
     hiding on global mem ops. */
  const int max_block_size = (num_tokens < 256) ? 1024 : 256;
  dim3 block(std::min(hidden_size, max_block_size));
  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
  /*If the tensor types are FP16/BF16, try to use the optimized kernel
    with packed + vectorized ops.
    Max optimization is achieved with a width-8 vector of FP16/BF16s
    since we can load at most 128 bits at once in a global memory op.
    However, this requires each tensor's data to be aligned to 16
    bytes.
   */
  auto inp_ptr = reinterpret_cast<std::uintptr_t>(input.data_ptr());
  auto res_ptr = reinterpret_cast<std::uintptr_t>(residual.data_ptr());
  auto wt_ptr = reinterpret_cast<std::uintptr_t>(weight.data_ptr());
  bool ptrs_are_aligned =
      inp_ptr % 16 == 0 && res_ptr % 16 == 0 && wt_ptr % 16 == 0;
  if (ptrs_are_aligned && hidden_size % 8 == 0)
  {
    LAUNCH_FUSED_ADD_RMS_NORM(8);
  }
  else
  {
    LAUNCH_FUSED_ADD_RMS_NORM(0);
  }
}

// #define LAUNCH_SCALED_FUSED_ADD_RMS_NORM(width)                            \
//   VLLM_DISPATCH_FLOATING_TYPES(                                            \
//       input.scalar_type(), "scaled_fused_add_rms_norm_kernel", [&] {       \
//         vllm::scaled_fused_add_rms_norm_kernel<scalar_t, width>            \
//             <<<grid, block, 0, stream>>>(                                  \
//                 out.data_ptr<c10::Float8_e4m3fnuz>(),                      \
//                 input.data_ptr<scalar_t>(), residual.data_ptr<scalar_t>(), \
//                 weight.data_ptr<scalar_t>(), epsilon,                      \
//                 *scale.data_ptr<float>(), num_tokens, hidden_size);        \
//       });

// void scaled_fused_add_rms_norm(torch::Tensor& out,       // [..., hidden_size]
//                                torch::Tensor& input,     // [..., hidden_size]
//                                torch::Tensor& residual,  // [..., hidden_size]
//                                torch::Tensor& weight,    // [hidden_size]
//                                torch::Tensor& scale, double epsilon) {
//   int hidden_size = input.size(-1);
//   int num_tokens = input.numel() / hidden_size;

//   dim3 grid(num_tokens);
//   /* This kernel is memory-latency bound in many scenarios.
//      When num_tokens is large, a smaller block size allows
//      for increased block occupancy on CUs and better latency
//      hiding on global mem ops. */
//   const int max_block_size = (num_tokens < 256) ? 1024 : 256;
//   dim3 block(std::min(hidden_size, max_block_size));
//   const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
//   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
//   /*If the tensor types are FP16/BF16, try to use the optimized kernel
//     with packed + vectorized ops.
//     Max optimization is achieved with a width-8 vector of FP16/BF16s
//     since we can load at most 128 bits at once in a global memory op.
//     However, this requires each tensor's data to be aligned to 16
//     bytes.
//    */
//   auto inp_ptr = reinterpret_cast<std::uintptr_t>(input.data_ptr());
//   auto res_ptr = reinterpret_cast<std::uintptr_t>(residual.data_ptr());
//   auto wt_ptr = reinterpret_cast<std::uintptr_t>(weight.data_ptr());
//   bool ptrs_are_aligned =
//       inp_ptr % 16 == 0 && res_ptr % 16 == 0 && wt_ptr % 16 == 0;
//   if (ptrs_are_aligned && hidden_size % 8 == 0) {
//     LAUNCH_SCALED_FUSED_ADD_RMS_NORM(8);
//   } else {
//     LAUNCH_SCALED_FUSED_ADD_RMS_NORM(0);
//   }
// }