ColossalAI/colossalai/kernel/cuda_native/csrc/layer_norm_cuda.cpp

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

#include <torch/extension.h>
#include <vector>
#include <cassert>
#include "compat.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);
}
}

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)");
}