ColossalAI/extensions/pybind/layernorm/layer_norm.cpp

/*This code from NVIDIA apex:
 *     https://github.com/NVIDIA/apex
 *     with minor changes. */

#include <torch/extension.h>

#include <cassert>
#include <vector>

#include "common/micros.h"

namespace {

void compute_n1_n2(at::Tensor input, at::IntArrayRef normalized_shape, int &n1,
                   int &n2) {
  int idiff = input.ndimension() - normalized_shape.size();
  n2 = 1;
  for (int i = 0; i < (int)normalized_shape.size(); ++i) {
    assert(input.sizes()[i + idiff] == normalized_shape[i]);
    n2 *= normalized_shape[i];
  }
  n1 = 1;
  for (int i = 0; i < idiff; ++i) {
    n1 *= input.sizes()[i];
  }
}

void check_args(at::IntArrayRef normalized_shape, at::Tensor gamma,
                at::Tensor beta) {
  TORCH_CHECK(!gamma.defined() || gamma.sizes().equals(normalized_shape));
  TORCH_CHECK(!beta.defined() || beta.sizes().equals(normalized_shape));
}

void check_args(at::Tensor input, at::IntArrayRef normalized_shape, int &n1,
                int &n2) {
  int64_t normalized_ndim = normalized_shape.size();

  if (normalized_ndim < 1) {
    std::stringstream ss;
    ss << "Expected normalized_shape to be at least 1-dimensional, i.e., "
       << "containing at least one element, but got normalized_shape="
       << normalized_shape;
    throw std::runtime_error(ss.str());
  }

  auto input_shape = input.sizes();
  auto input_ndim = input.dim();

  if (input_ndim < normalized_ndim ||
      !input_shape.slice(input_ndim - normalized_ndim)
           .equals(normalized_shape)) {
    std::stringstream ss;
    ss << "Given normalized_shape=" << normalized_shape
       << ", expected input with shape [*";
    for (auto size : normalized_shape) {
      ss << ", " << size;
    }
    ss << "], but got input of size" << input_shape;
    throw std::runtime_error(ss.str());
  }

  compute_n1_n2(input, normalized_shape, n1, n2);
}

void check_args(at::Tensor input, at::IntArrayRef normalized_shape,
                at::Tensor gamma, at::Tensor beta, int &n1, int &n2) {
  check_args(input, normalized_shape, n1, n2);
  check_args(normalized_shape, gamma, beta);
}
}  // namespace

void cuda_layer_norm(at::Tensor *output, at::Tensor *mean, at::Tensor *invvar,
                     at::Tensor *input, int n1, int n2,
                     at::IntArrayRef normalized_shape, at::Tensor *gamma,
                     at::Tensor *beta, double epsilon);

#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x " must be a CUDA tensor")
#define CHECK_CONTIGUOUS(x) \
  TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
#define CHECK_INPUT(x) \
  CHECK_CUDA(x);       \
  CHECK_CONTIGUOUS(x)

std::vector<at::Tensor> layer_norm_affine(at::Tensor input,
                                          at::IntArrayRef normalized_shape,
                                          at::Tensor gamma, at::Tensor beta,
                                          double epsilon) {
  CHECK_INPUT(input);
  CHECK_INPUT(gamma);
  CHECK_INPUT(beta);
  int n1, n2;
  check_args(input, normalized_shape, gamma, beta, n1, n2);

  at::Tensor output =
      at::empty_like(input, gamma.options().dtype(gamma.scalar_type()));
  at::Tensor mean =
      at::empty({n1}, input.options().dtype(at::ScalarType::Float));
  at::Tensor invvar = at::empty_like(mean);

  cuda_layer_norm(&output, &mean, &invvar, &input, n1, n2, normalized_shape,
                  &gamma, &beta, epsilon);

  return {output, mean, invvar};
}

void cuda_layer_norm_gradient(at::Tensor *dout, at::Tensor *mean,
                              at::Tensor *invvar, at::Tensor *input, int n1,
                              int n2, at::IntArrayRef normalized_shape,
                              at::Tensor *gamma, at::Tensor *beta,
                              double epsilon, at::Tensor *grad_input,
                              at::Tensor *grad_gamma, at::Tensor *grad_beta);

std::vector<at::Tensor> layer_norm_gradient_affine(
    at::Tensor dout, at::Tensor mean, at::Tensor invvar, at::Tensor input,
    at::IntArrayRef normalized_shape, at::Tensor gamma, at::Tensor beta,
    double epsilon) {
  CHECK_INPUT(dout);
  CHECK_INPUT(mean);
  CHECK_INPUT(invvar);
  CHECK_INPUT(input);
  CHECK_INPUT(gamma);
  CHECK_INPUT(beta);
  int n1, n2;
  check_args(input, normalized_shape, gamma, beta, n1, n2);

  at::Tensor grad_input = at::empty_like(input);
  at::Tensor grad_gamma = at::empty_like(gamma);
  at::Tensor grad_beta = at::empty_like(beta);

  cuda_layer_norm_gradient(&dout, &mean, &invvar, &input, n1, n2,
                           normalized_shape, &gamma, &beta, epsilon,
                           &grad_input, &grad_gamma, &grad_beta);

  return {grad_input, grad_gamma, grad_beta};
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
  m.def("forward_affine", &layer_norm_affine, "LayerNorm forward (CUDA)");
  m.def("backward_affine", &layer_norm_gradient_affine,
        "LayerNorm backward (CUDA)");
}