nvfp4_quant_kernels_sm120.cu

#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAGuard.h>
#include <cuda.h>
#include <cuda_fp8.h>
#include <cuda_runtime.h>
#include <cuda_runtime_api.h>
#include <torch/all.h>

#include "utils.h"

// Get type2 from type or vice versa (applied to half and bfloat16)
template <typename T>
struct TypeConverter {
  using Type = half2;
};  // keep for generality

template <>
struct TypeConverter<half2> {
  using Type = half;
};

template <>
struct TypeConverter<half> {
  using Type = half2;
};

template <>
struct TypeConverter<__nv_bfloat162> {
  using Type = __nv_bfloat16;
};

template <>
struct TypeConverter<__nv_bfloat16> {
  using Type = __nv_bfloat162;
};

#define ELTS_PER_THREAD 8

constexpr int CVT_FP4_ELTS_PER_THREAD = 8;
constexpr int CVT_FP4_SF_VEC_SIZE = 16;

// Convert 8 float32 values into 8 e2m1 values (represented as one uint32_t).
inline __device__ uint32_t fp32_vec_to_e2m1(float (&array)[8]) {
  // PTX instructions used here requires sm100a.
// #if CUDA_VERSION >= 12080
// #if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 1000) && __CUDA_ARCH_HAS_FEATURE__(SM100_ALL)
  uint32_t val;
  asm volatile(
      "{\n"
      ".reg .b8 byte0;\n"
      ".reg .b8 byte1;\n"
      ".reg .b8 byte2;\n"
      ".reg .b8 byte3;\n"
      "cvt.rn.satfinite.e2m1x2.f32   byte0, %2, %1;\n"
      "cvt.rn.satfinite.e2m1x2.f32   byte1, %4, %3;\n"
      "cvt.rn.satfinite.e2m1x2.f32   byte2, %6, %5;\n"
      "cvt.rn.satfinite.e2m1x2.f32   byte3, %8, %7;\n"
      "mov.b32 %0, {byte0, byte1, byte2, byte3};\n"
      "}"
      : "=r"(val)
      : "f"(array[0]),
        "f"(array[1]),
        "f"(array[2]),
        "f"(array[3]),
        "f"(array[4]),
        "f"(array[5]),
        "f"(array[6]),
        "f"(array[7]));
  return val;
// #else
//   return 0;
// #endif
// #endif
}

// Convert 4 float2 values into 8 e2m1 values (represented as one uint32_t).
inline __device__ uint32_t fp32_vec_to_e2m1(float2 (&array)[4]) {
  // PTX instructions used here requires sm100a.
// #if CUDA_VERSION >= 12080
// #if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 1000) && __CUDA_ARCH_HAS_FEATURE__(SM100_ALL)
  uint32_t val;
  asm volatile(
      "{\n"
      ".reg .b8 byte0;\n"
      ".reg .b8 byte1;\n"
      ".reg .b8 byte2;\n"
      ".reg .b8 byte3;\n"
      "cvt.rn.satfinite.e2m1x2.f32   byte0, %2, %1;\n"
      "cvt.rn.satfinite.e2m1x2.f32   byte1, %4, %3;\n"
      "cvt.rn.satfinite.e2m1x2.f32   byte2, %6, %5;\n"
      "cvt.rn.satfinite.e2m1x2.f32   byte3, %8, %7;\n"
      "mov.b32 %0, {byte0, byte1, byte2, byte3};\n"
      "}"
      : "=r"(val)
      : "f"(array[0].x),
        "f"(array[0].y),
        "f"(array[1].x),
        "f"(array[1].y),
        "f"(array[2].x),
        "f"(array[2].y),
        "f"(array[3].x),
        "f"(array[3].y));
  return val;
// #else
//   return 0;
// #endif
// #endif
}

// Fast reciprocal.
inline __device__ float reciprocal_approximate_ftz(float a) {
  float b;
  asm volatile("rcp.approx.ftz.f32 %0, %1;\n" : "=f"(b) : "f"(a));
  return b;
}

template <class SFType, int CVT_FP4_NUM_THREADS_PER_SF>
__device__ uint8_t* cvt_quant_to_fp4_get_sf_out_offset(int rowIdx, int colIdx, int numCols, SFType* SFout) {
// #if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 1000)
  static_assert(CVT_FP4_NUM_THREADS_PER_SF == 1 || CVT_FP4_NUM_THREADS_PER_SF == 2);

  // One pair of threads write one SF to global memory.
  // TODO: stage through smem for packed STG.32
  // is it better than STG.8 from 4 threads ?
  if (threadIdx.x % CVT_FP4_NUM_THREADS_PER_SF == 0) {
    // SF vector index (16 elements share one SF in the K dimension).
    int32_t kIdx = colIdx / CVT_FP4_NUM_THREADS_PER_SF;
    int32_t mIdx = rowIdx;

    // SF layout [numMTiles, numKTiles, 32 (mTile), 4 (mTile), 4(kTile)]
    // --> index [mTileIdx, kTileIdx, outerMIdx, innerMIdx, innerKIdx]

    int32_t mTileIdx = mIdx / (32 * 4);
    // SF vector size 16.
    int factor = CVT_FP4_SF_VEC_SIZE * 4;
    int32_t numKTiles = (numCols + factor - 1) / factor;
    int64_t mTileStride = numKTiles * 32 * 4 * 4;

    int32_t kTileIdx = (kIdx / 4);
    int64_t kTileStride = 32 * 4 * 4;

    // M tile layout [32, 4] is column-major.
    int32_t outerMIdx = (mIdx % 32);
    int64_t outerMStride = 4 * 4;

    int32_t innerMIdx = (mIdx % (32 * 4)) / 32;
    int64_t innerMStride = 4;

    int32_t innerKIdx = (kIdx % 4);
    int64_t innerKStride = 1;

    // Compute the global offset.
    int64_t SFOffset = mTileIdx * mTileStride + kTileIdx * kTileStride + outerMIdx * outerMStride +
                       innerMIdx * innerMStride + innerKIdx * innerKStride;

    return reinterpret_cast<uint8_t*>(SFout) + SFOffset;
  }
// #endif
  return nullptr;
}

// Define a 16 bytes packed data type.
template <class Type>
struct PackedVec {
  typename TypeConverter<Type>::Type elts[4];
};

template <>
struct PackedVec<__nv_fp8_e4m3> {
  __nv_fp8x2_e4m3 elts[8];
};

// Quantizes the provided PackedVec into the uint32_t output
template <class Type, bool UE8M0_SF = false>
__device__ uint32_t cvt_warp_fp16_to_fp4(PackedVec<Type>& vec, float SFScaleVal, uint8_t* SFout) {
// #if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 1000)
  // Get absolute maximum values among the local 8 values.
  auto localMax = __habs2(vec.elts[0]);

// Local maximum value.
#pragma unroll
  for (int i = 1; i < CVT_FP4_ELTS_PER_THREAD / 2; i++) {
    localMax = __hmax2(localMax, __habs2(vec.elts[i]));
  }

  // Get the absolute maximum among all 16 values (two threads).
  localMax = __hmax2(__shfl_xor_sync(uint32_t(-1), localMax, 1), localMax);
  // Get the final absolute maximum values.
  float vecMax = float(__hmax(localMax.x, localMax.y));

  // Get the SF (max value of the vector / max value of e2m1).
  // maximum value of e2m1 = 6.0.
  // TODO: use half as compute data type.
  float SFValue = SFScaleVal * (vecMax * 0.16666666666666666f);
  // 8 bits representation of the SF.
  uint8_t fp8SFVal;
  // Write the SF to global memory (STG.8).
  if constexpr (UE8M0_SF) {
    __nv_fp8_e8m0 tmp;
    tmp.__x = __nv_cvt_float_to_e8m0(SFValue, __NV_SATFINITE, cudaRoundPosInf);
    SFValue = static_cast<float>(tmp);
    fp8SFVal = tmp.__x;
  } else {
    // Here SFValue is always positive, so E4M3 is the same as UE4M3.
    __nv_fp8_e4m3 tmp = __nv_fp8_e4m3(SFValue);
    fp8SFVal = tmp.__x;
    SFValue = static_cast<float>(tmp);
  }
  // Get the output scale.
  // Recipe: final_scale = reciprocal(fp32(fp8(SFValue * SFScaleVal))) *
  //                       reciprocal(SFScaleVal))
//   float outputScale =
//       SFValue != 0 ? reciprocal_approximate_ftz(SFValue * reciprocal_approximate_ftz(SFScaleVal)) : 0.0f;

  float outputScale =
      SFValue != 0 ? SFScaleVal * reciprocal_approximate_ftz(SFValue) : 0.0f;

  if (SFout) {
    // Write the SF to global memory (STG.8).
    *SFout = fp8SFVal;
  }

  // Convert the input to float.
  float2 fp2Vals[CVT_FP4_ELTS_PER_THREAD / 2];

#pragma unroll
  for (int i = 0; i < CVT_FP4_ELTS_PER_THREAD / 2; i++) {
    if constexpr (std::is_same_v<Type, half>) {
      fp2Vals[i] = __half22float2(vec.elts[i]);
    } else {
      fp2Vals[i] = __bfloat1622float2(vec.elts[i]);
    }
    fp2Vals[i].x *= outputScale;
    fp2Vals[i].y *= outputScale;
  }

  // Convert to e2m1 values.
  uint32_t e2m1Vec = fp32_vec_to_e2m1(fp2Vals);

  // Write the e2m1 values to global memory.
  return e2m1Vec;
// #else
//   return 0;
// #endif
}

// Use UE4M3 by default.
template <class Type, bool UE8M0_SF = false>
__global__ void
// #if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 1000)
__launch_bounds__(256, 6) cvt_fp16_to_fp4(
// #else
// cvt_fp16_to_fp4(
// #endif
    int32_t numRows, int32_t numCols, Type const* in, float const* SFScale, uint32_t* out, uint32_t* SFout) {
// #if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 1000)
  using PackedVec = PackedVec<Type>;
  static constexpr int CVT_FP4_NUM_THREADS_PER_SF = (CVT_FP4_SF_VEC_SIZE / CVT_FP4_ELTS_PER_THREAD);
  static_assert(sizeof(PackedVec) == sizeof(Type) * CVT_FP4_ELTS_PER_THREAD, "Vec size is not matched.");

  // Get the global scaling factor, which will be applied to the SF.
  // Note SFScale is the same as next GEMM's alpha, which is
  // (448.f / (Alpha_A / 6.f)).
  float const SFScaleVal = SFScale == nullptr ? 1.0f : SFScale[0];

  // Input tensor row/col loops.
  for (int rowIdx = blockIdx.x; rowIdx < numRows; rowIdx += gridDim.x) {
    for (int colIdx = threadIdx.x; colIdx < numCols / CVT_FP4_ELTS_PER_THREAD; colIdx += blockDim.x) {
      int64_t inOffset = rowIdx * (numCols / CVT_FP4_ELTS_PER_THREAD) + colIdx;
      PackedVec in_vec = reinterpret_cast<PackedVec const*>(in)[inOffset];
      // Get the output tensor offset.
      // Same as inOffset because 8 elements are packed into one uint32_t.
      int64_t outOffset = inOffset;
      auto& out_pos = out[outOffset];

      auto sf_out =
          cvt_quant_to_fp4_get_sf_out_offset<uint32_t, CVT_FP4_NUM_THREADS_PER_SF>(rowIdx, colIdx, numCols, SFout);

      out_pos = cvt_warp_fp16_to_fp4<Type, UE8M0_SF>(in_vec, SFScaleVal, sf_out);
    }
  }
// #endif
}

template <typename T>
void invokeFP4Quantization(
    int m,
    int n,
    T const* input,
    float const* SFScale,
    int64_t* output,
    int32_t* SFOuput,
    bool useUE8M0,
    int multiProcessorCount,
    cudaStream_t stream) {
  // Grid, Block size.
  // Each thread converts 8 values.
  dim3 block(std::min(int(n / ELTS_PER_THREAD), 256));
  // Get number of blocks per SM (assume we can fully utilize the SM).
  int const numBlocksPerSM = 1536 / block.x;
  dim3 grid(std::min(int(m), multiProcessorCount * numBlocksPerSM));

  // Launch the cvt kernel.
  if (useUE8M0) {
    cvt_fp16_to_fp4<T, true><<<grid, block, 0, stream>>>(
        m, n, input, SFScale, reinterpret_cast<uint32_t*>(output), reinterpret_cast<uint32_t*>(SFOuput));
  } else {
    cvt_fp16_to_fp4<T, false><<<grid, block, 0, stream>>>(
        m, n, input, SFScale, reinterpret_cast<uint32_t*>(output), reinterpret_cast<uint32_t*>(SFOuput));
  }
}

// Instantiate the function.
template void invokeFP4Quantization(
    int m,
    int n,
    half const* input,
    float const* SFScale,
    int64_t* output,
    int32_t* SFOuput,
    bool useUE8M0,
    int multiProcessorCount,
    cudaStream_t stream);

template void invokeFP4Quantization(
    int m,
    int n,
    __nv_bfloat16 const* input,
    float const* SFScale,
    int64_t* output,
    int32_t* SFOuput,
    bool useUE8M0,
    int multiProcessorCount,
    cudaStream_t stream);

inline int getMultiProcessorCount() {
  static int multi_processor_count = []() {
    int device_id = 0;
    int count = 0;

    // Get the current CUDA device ID
    CHECK_CUDA_SUCCESS(cudaGetDevice(&device_id));

    // Get the number of multiprocessors for the current device
    CHECK_CUDA_SUCCESS(cudaDeviceGetAttribute(&count, cudaDevAttrMultiProcessorCount, device_id));

    return count;  // Initialize the static variable
  }();

  return multi_processor_count;  // Return the cached value on subsequent calls
}

void scaled_nvfp4_quant_sm120(
    torch::Tensor& output, torch::Tensor const& input, torch::Tensor& output_sf, torch::Tensor const& input_sf) {
  int32_t m = input.size(0);
  int32_t n = input.size(1);

  TORCH_CHECK(n % 16 == 0, "The N dimension must be multiple of 16.");

  int multiProcessorCount = getMultiProcessorCount();

  auto input_sf_ptr = static_cast<float const*>(input_sf.data_ptr());
  auto sf_out = static_cast<int32_t*>(output_sf.data_ptr());
  auto output_ptr = static_cast<int64_t*>(output.data_ptr());
  at::cuda::CUDAGuard device_guard{(char)input.get_device()};
  const cudaStream_t stream = at::cuda::getCurrentCUDAStream(input.get_device());

  // We don't support e8m0 scales at this moment.
  bool useUE8M0 = false;

  switch (input.scalar_type()) {
    case torch::kHalf: {
      auto input_ptr = reinterpret_cast<half const*>(input.data_ptr());
      invokeFP4Quantization(m, n, input_ptr, input_sf_ptr, output_ptr, sf_out, useUE8M0, multiProcessorCount, stream);
      break;
    }
    case torch::kBFloat16: {
      auto input_ptr = reinterpret_cast<__nv_bfloat16 const*>(input.data_ptr());
      invokeFP4Quantization(m, n, input_ptr, input_sf_ptr, output_ptr, sf_out, useUE8M0, multiProcessorCount, stream);
      break;
    }
    default: {
      std::cerr << "Observing: " << input.scalar_type() << " for the input datatype which is invalid";
      throw std::runtime_error("Unsupported input data type for quantize_to_fp4.");
    }
  }
}