aphrodite-engine/kernels/activation_kernels.cu

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

#include <cmath>

#include "cuda_compat.h"
#include "dispatch_utils.h"

namespace aphrodite {

// Activation and gating kernel template.
template <typename scalar_t, scalar_t (*ACT_FN)(const scalar_t&)>
__global__ void act_and_mul_kernel(
    scalar_t* __restrict__ out,          // [..., d]
    const scalar_t* __restrict__ input,  // [..., 2, d]
    const int d) {
  const int64_t token_idx = blockIdx.x;
  for (int64_t idx = threadIdx.x; idx < d; idx += blockDim.x) {
    const scalar_t x = APHRODITE_LDG(&input[token_idx * 2 * d + idx]);
    const scalar_t y = APHRODITE_LDG(&input[token_idx * 2 * d + d + idx]);
    out[token_idx * d + idx] = ACT_FN(x) * y;
  }
}

template <typename T>
__device__ __forceinline__ T silu_kernel(const T& x) {
  // x * sigmoid(x)
  return (T)(((float)x) / (1.0f + expf((float)-x)));
}

template <typename T>
__device__ __forceinline__ T gelu_kernel(const T& x) {
  // Equivalent to PyTorch GELU with 'none' approximation.
  // Refer to:
  // https://github.com/pytorch/pytorch/blob/8ac9b20d4b090c213799e81acf48a55ea8d437d6/aten/src/ATen/native/cuda/ActivationGeluKernel.cu#L38
  const float f = (float)x;
  constexpr float ALPHA = M_SQRT1_2;
  return (T)(f * 0.5f * (1.0f + ::erf(f * ALPHA)));
}

template <typename T>
__device__ __forceinline__ T gelu_tanh_kernel(const T& x) {
  // Equivalent to PyTorch GELU with `tanh` approximation
  const float f = (float)x;
  constexpr float BETA = M_SQRT2 * M_2_SQRTPI * 0.5f;
  constexpr float KAPPA = 0.044715;
  float x_cube = f * f * f;
  float inner = BETA * (f + KAPPA * x_cube);
  return (T)(0.5f * f * (1.0f + ::tanhf(inner)));
}

}  // namespace aphrodite

// Launch activation and gating kernel.
#define LAUNCH_ACTIVATION_GATE_KERNEL(KERNEL)                            \
  int d = input.size(-1) / 2;                                            \
  int64_t num_tokens = input.numel() / input.size(-1);                   \
  dim3 grid(num_tokens);                                                 \
  dim3 block(std::min(d, 1024));                                         \
  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));      \
  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();          \
  APHRODITE_DISPATCH_FLOATING_TYPES(                                     \
      input.scalar_type(), "act_and_mul_kernel", [&] {                   \
        aphrodite::act_and_mul_kernel<scalar_t, KERNEL<scalar_t>>        \
            <<<grid, block, 0, stream>>>(out.data_ptr<scalar_t>(),       \
                                         input.data_ptr<scalar_t>(), d); \
      });

void silu_and_mul(torch::Tensor& out,    // [..., d]
                  torch::Tensor& input)  // [..., 2 * d]
{
  LAUNCH_ACTIVATION_GATE_KERNEL(aphrodite::silu_kernel);
}

void gelu_and_mul(torch::Tensor& out,    // [..., d]
                  torch::Tensor& input)  // [..., 2 * d]
{
  LAUNCH_ACTIVATION_GATE_KERNEL(aphrodite::gelu_kernel);
}

void gelu_tanh_and_mul(torch::Tensor& out,    // [..., d]
                       torch::Tensor& input)  // [..., 2 * d]
{
  LAUNCH_ACTIVATION_GATE_KERNEL(aphrodite::gelu_tanh_kernel);
}

namespace aphrodite {

// Element-wise activation kernel template.
template <typename scalar_t, scalar_t (*ACT_FN)(const scalar_t&)>
__global__ void activation_kernel(
    scalar_t* __restrict__ out,          // [..., d]
    const scalar_t* __restrict__ input,  // [..., d]
    const int d) {
  const int64_t token_idx = blockIdx.x;
  for (int64_t idx = threadIdx.x; idx < d; idx += blockDim.x) {
    const scalar_t x = APHRODITE_LDG(&input[token_idx * d + idx]);
    out[token_idx * d + idx] = ACT_FN(x);
  }
}

}  // namespace aphrodite

// Launch element-wise activation kernel.
#define LAUNCH_ACTIVATION_KERNEL(KERNEL)                                 \
  int d = input.size(-1);                                                \
  int64_t num_tokens = input.numel() / d;                                \
  dim3 grid(num_tokens);                                                 \
  dim3 block(std::min(d, 1024));                                         \
  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));      \
  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();          \
  APHRODITE_DISPATCH_FLOATING_TYPES(                                     \
      input.scalar_type(), "activation_kernel", [&] {                    \
        aphrodite::activation_kernel<scalar_t, KERNEL<scalar_t>>         \
            <<<grid, block, 0, stream>>>(out.data_ptr<scalar_t>(),       \
                                         input.data_ptr<scalar_t>(), d); \
      });

namespace aphrodite {

template <typename T>
__device__ __forceinline__ T gelu_new_kernel(const T& x) {
  const float x3 = (float)(x * x * x);
  const T t = (T)tanhf((T)(0.79788456f * (float)(x + (T)(0.044715f * x3))));
  return ((T)0.5) * x * (((T)1.0) + t);
}

template <typename T>
__device__ __forceinline__ T gelu_fast_kernel(const T& x) {
  const float f = (float)x;
  const T t =
      (T)tanhf(((T)(f * 0.79788456f)) * (((T)1.0) + (T)(0.044715f * f) * x));
  return ((T)0.5) * x * (((T)1.0) + t);
}

template <typename T>
__device__ __forceinline__ T gelu_quick_kernel(const T& x) {
  // x * sigmoid(1.702 * x)
  return (T)(((float)x) / (1.0f + expf(-1.702f * (float)x)));
}

}  // namespace aphrodite

void gelu_new(torch::Tensor& out,    // [..., d]
              torch::Tensor& input)  // [..., d]
{
  LAUNCH_ACTIVATION_KERNEL(aphrodite::gelu_new_kernel);
}

void gelu_fast(torch::Tensor& out,    // [..., d]
               torch::Tensor& input)  // [..., d]
{
  LAUNCH_ACTIVATION_KERNEL(aphrodite::gelu_fast_kernel);
}

void gelu_quick(torch::Tensor& out,    // [..., d]
                torch::Tensor& input)  // [..., d]
{
  LAUNCH_ACTIVATION_KERNEL(aphrodite::gelu_quick_kernel);
}