type_convert.cuh

#pragma once

#include <torch/all.h>

#ifndef USE_ROCM
  #include <cuda_bf16.h>
  #include <cuda_fp16.h>
#else
  #include <hip/hip_bf16.h>
  #include <hip/hip_fp16.h>

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

namespace vllm {
/* 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;
  }
};
}  // namespace vllm