aphrodite-engine/kernels/layernorm_kernels.cu

#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>

#include "dispatch_utils.h"
#include "reduction.cuh"

namespace aphrodite {

// TODO: 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;
  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;
  }
  variance = blockReduceSum<float>(variance);
  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];
    out[blockIdx.x * hidden_size + idx] = ((scalar_t) (x * s_variance)) * weight[idx];
  }
}

} // namespace aphrodite

void rms_norm(
  torch::Tensor& out,      // [..., hidden_size]
  torch::Tensor& input,    // [..., hidden_size]
  torch::Tensor& weight,   // [hidden_size]
  float 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 cudaStream_t stream = at::cuda::getCurrentCUDAStream();
  APHRODITE_DISPATCH_FLOATING_TYPES(
    input.scalar_type(),
    "rms_norm_kernel",
    [&] {
      aphrodite::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);
    });
}