vllm/csrc/quantization/w8a8/fp8/per_token_group_quant.cu

#include <ATen/cuda/CUDAContext.h>

#include "quantization/w8a8/per_token_group_quant_8bit.h"

#include <cmath>

#include <cuda_fp8.h>

#include <torch/all.h>

#include "quantization/vectorization.cuh"
#include "quantization/vectorization_utils.cuh"
#include "dispatch_utils.h"

__device__ __forceinline__ float GroupReduceMax(float val) {
  unsigned mask = threadIdx.x % 32 >= 16 ? 0xffff0000 : 0x0000ffff;

  val = fmaxf(val, __shfl_xor_sync(mask, val, 8));
  val = fmaxf(val, __shfl_xor_sync(mask, val, 4));
  val = fmaxf(val, __shfl_xor_sync(mask, val, 2));
  val = fmaxf(val, __shfl_xor_sync(mask, val, 1));
  return val;
}

template <typename T, typename DST_DTYPE, bool IS_COLUMN_MAJOR = false,
          bool SCALE_UE8M0 = false, typename scale_packed_t = float>
__global__ void per_token_group_quant_8bit_kernel(
    const T* __restrict__ input, void* __restrict__ output_q,
    scale_packed_t* __restrict__ output_s, const int group_size,
    const int num_groups, const int groups_per_block, const float eps,
    const float min_8bit, const float max_8bit, const int scale_num_rows = 0,
    const int scale_stride = 0) {
  const int threads_per_group = 16;
  const int64_t local_group_id = threadIdx.x / threads_per_group;
  const int lane_id = threadIdx.x % threads_per_group;

  const int64_t block_group_id = blockIdx.x * groups_per_block;
  const int64_t global_group_id = block_group_id + local_group_id;
  const int64_t block_group_offset = global_group_id * group_size;

  float local_absmax = eps;

  using scale_element_t = float;
  static_assert(sizeof(scale_packed_t) % sizeof(scale_element_t) == 0);

  const T* group_input = input + block_group_offset;
  DST_DTYPE* group_output =
      static_cast<DST_DTYPE*>(output_q) + block_group_offset;
  scale_element_t* scale_output;

  if constexpr (IS_COLUMN_MAJOR) {
    const int num_elems_per_pack =
        static_cast<int>(sizeof(scale_packed_t) / sizeof(scale_element_t));
    const int scale_num_rows_element = scale_num_rows * num_elems_per_pack;
    const int row_idx = global_group_id / scale_num_rows_element;
    const int col_idx_raw = global_group_id % scale_num_rows_element;
    const int col_idx = col_idx_raw / num_elems_per_pack;
    const int pack_idx = col_idx_raw % num_elems_per_pack;
    scale_output = reinterpret_cast<scale_element_t*>(output_s) +
                   (col_idx * scale_stride * num_elems_per_pack +
                    row_idx * num_elems_per_pack + pack_idx);
  } else {
    scale_output = output_s + global_group_id;
  }

  // shared memory to cache each group's data to avoid double DRAM reads.
  extern __shared__ __align__(16) char smem_raw[];
  T* smem = reinterpret_cast<T*>(smem_raw);
  T* smem_group = smem + local_group_id * group_size;

  constexpr int vec_size = 16 / sizeof(T);
  using vec_t = vllm::vec_n_t<T, vec_size>;

  // copy global -> shared & compute absmax
  auto scalar_op_cache = [&] __device__(T & dst, const T& src) {
    float abs_v = fabsf(static_cast<float>(src));
    local_absmax = fmaxf(local_absmax, abs_v);
    dst = src;
  };

  vllm::vectorize_with_alignment<vec_size>(
      group_input,        // in
      smem_group,         // out (shared)
      group_size,         // elements per group
      lane_id,            // thread id
      threads_per_group,  // stride in group
      scalar_op_cache);   // scalar handler

  local_absmax = GroupReduceMax(local_absmax);

  float y_s = local_absmax / max_8bit;
  if constexpr (SCALE_UE8M0) {
    y_s = exp2f(ceilf(log2f(fmaxf(fabsf(y_s), 1e-10f))));
  }

  scale_element_t y_s_quant = y_s;

  if (lane_id == 0) {
    *scale_output = y_s_quant;
  }

  __syncthreads();

  // quantize shared -> global 8-bit
  auto scalar_op_quant = [&] __device__(DST_DTYPE & dst, const T& src) {
    float q = fminf(fmaxf(static_cast<float>(src) / y_s, min_8bit), max_8bit);
    dst = DST_DTYPE(q);
  };

  vllm::vectorize_with_alignment<vec_size>(
      smem_group,         // in (shared)
      group_output,       // out (global quant tensor)
      group_size,         // elements
      lane_id,            // tid
      threads_per_group,  // stride
      scalar_op_quant);   // scalar handler
}

void per_token_group_quant_8bit(const torch::Tensor& input,
                                torch::Tensor& output_q,
                                torch::Tensor& output_s, int64_t group_size,
                                double eps, double min_8bit, double max_8bit,
                                bool scale_ue8m0) {
  TORCH_CHECK(input.is_contiguous());
  TORCH_CHECK(output_q.is_contiguous());

  const int num_groups = input.numel() / group_size;

  TORCH_CHECK(input.numel() % group_size == 0);
  TORCH_CHECK(output_s.dim() == 2);

  cudaStream_t stream = at::cuda::getCurrentCUDAStream();

  constexpr int THREADS_PER_GROUP = 16;

  int groups_per_block = 1;

  if (num_groups % 16 == 0) {
    groups_per_block = 16;
  } else if (num_groups % 8 == 0) {
    groups_per_block = 8;
  } else if (num_groups % 4 == 0) {
    groups_per_block = 4;
  } else if (num_groups % 2 == 0) {
    groups_per_block = 2;
  }

  auto dst_type = output_q.scalar_type();
  const int num_blocks = num_groups / groups_per_block;
  const int num_threads = groups_per_block * THREADS_PER_GROUP;

  const bool is_column_major = output_s.stride(0) < output_s.stride(1);
  const int scale_num_rows = output_s.size(1);
  const int scale_stride = output_s.stride(1);

#define LAUNCH_KERNEL(T, DST_DTYPE)                                        \
  do {                                                                     \
    dim3 grid(num_blocks);                                                 \
    dim3 block(num_threads);                                               \
    size_t smem_bytes =                                                    \
        static_cast<size_t>(groups_per_block) * group_size * sizeof(T);    \
    if (is_column_major) {                                                 \
      if (scale_ue8m0) {                                                   \
        per_token_group_quant_8bit_kernel<T, DST_DTYPE, true, true>        \
            <<<grid, block, smem_bytes, stream>>>(                         \
                static_cast<T*>(input.data_ptr()), output_q.data_ptr(),    \
                static_cast<float*>(output_s.data_ptr()), group_size,      \
                num_groups, groups_per_block, (float)eps, (float)min_8bit, \
                (float)max_8bit, scale_num_rows, scale_stride);            \
      } else {                                                             \
        per_token_group_quant_8bit_kernel<T, DST_DTYPE, true, false>       \
            <<<grid, block, smem_bytes, stream>>>(                         \
                static_cast<T*>(input.data_ptr()), output_q.data_ptr(),    \
                static_cast<float*>(output_s.data_ptr()), group_size,      \
                num_groups, groups_per_block, (float)eps, (float)min_8bit, \
                (float)max_8bit, scale_num_rows, scale_stride);            \
      }                                                                    \
    } else {                                                               \
      if (scale_ue8m0) {                                                   \
        per_token_group_quant_8bit_kernel<T, DST_DTYPE, false, true>       \
            <<<grid, block, smem_bytes, stream>>>(                         \
                static_cast<T*>(input.data_ptr()), output_q.data_ptr(),    \
                static_cast<float*>(output_s.data_ptr()), group_size,      \
                num_groups, groups_per_block, (float)eps, (float)min_8bit, \
                (float)max_8bit);                                          \
      } else {                                                             \
        per_token_group_quant_8bit_kernel<T, DST_DTYPE, false, false>      \
            <<<grid, block, smem_bytes, stream>>>(                         \
                static_cast<T*>(input.data_ptr()), output_q.data_ptr(),    \
                static_cast<float*>(output_s.data_ptr()), group_size,      \
                num_groups, groups_per_block, (float)eps, (float)min_8bit, \
                (float)max_8bit);                                          \
      }                                                                    \
    }                                                                      \
  } while (0)

  VLLM_DISPATCH_FLOATING_TYPES(
      input.scalar_type(), "per_token_group_quant_8bit", ([&] {
        if (dst_type == at::ScalarType::Float8_e4m3fn) {
          LAUNCH_KERNEL(scalar_t, __nv_fp8_e4m3);
        } else if (dst_type == at::ScalarType::Char) {
          LAUNCH_KERNEL(scalar_t, int8_t);
        }
      }));

#undef LAUNCH_KERNEL
}

void per_token_group_quant_fp8(const torch::Tensor& input,
                               torch::Tensor& output_q, torch::Tensor& output_s,
                               int64_t group_size, double eps, double fp8_min,
                               double fp8_max, bool scale_ue8m0) {
  per_token_group_quant_8bit(input, output_q, output_s, group_size, eps,
                             fp8_min, fp8_max, scale_ue8m0);
}