csrc/fused_dense.cpp

#include <torch/extension.h>
#include <torch/torch.h>
#include <vector>

#include <stdio.h>


template <typename T>
int linear_bias_forward_cuda(at::Tensor input, T *weight, at::Tensor bias, int in_features, int batch_size, int out_features, at::Tensor output, void *lt_workspace);

template <typename T>
int linear_bias_backward_cuda(T *input, T *weight, T *d_output, int in_features, int batch_size, int out_features, T *d_weight, T *d_bias, T *d_input,  void *lt_workspace);

template <typename T>
int linear_gelu_linear_forward_cuda(T *input, T *weight1, T *bias1, T *weight2, T *bias2, int in_features, int hidden_features, int batch_size, int out_features, T *output1, T *output2, T *gelu_in, void *lt_workspace) ;

template <typename T>
int linear_gelu_linear_backward_cuda(T *input, T *gelu_in, T *output1, T *weight1, T *weight2, T *d_output1, T *d_output2, int in_features, int batch_size, int hidden_features, int out_features, T *d_weight1, T *d_weight2, T *d_bias1, T *d_bias2, T *d_input, void *lt_workspace);

at::Tensor linear_bias_forward(at::Tensor input, at::Tensor weight, at::Tensor bias) {

  auto batch_size = input.size(0);
  auto in_features = input.size(1);

  int out_features = weight.size(0);

  //auto reserved_size = get_mlp_reserved_space(batch_size, num_layers, output_features.data());

  // create output/workspace tensor
  auto out = at::empty({batch_size, out_features}, input.type());
  //auto reserved_space = at::empty({reserved_size}, inputs[0].type());
  // allocate fixed 4MB workspace for cublaslt for now, and this gets at least 4 MB
  auto lt_workspace = at::empty({1 << 22}, input.type());

  AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.type(), "linear_bias_forward", [&] {
    scalar_t* w_ptr = weight.data_ptr<scalar_t>();
    scalar_t* b_ptr = bias.data_ptr<scalar_t>();
    auto result = linear_bias_forward_cuda<scalar_t>(
        input,
        w_ptr,
        bias,
        in_features,
        batch_size,
        out_features,
        out,
        //out.data_ptr<scalar_t>(),
       // reserved_space.data_ptr<scalar_t>(),
        (void*) (lt_workspace.data_ptr<scalar_t>()));
  });

  return {out};
}

std::vector<at::Tensor> linear_bias_backward(at::Tensor input, at::Tensor weight, at::Tensor d_output) {

  auto batch_size = input.size(0);
  auto in_features = input.size(1);

  int out_features = weight.size(0);

  //auto reserved_size = get_mlp_reserved_space(batch_size, num_layers, output_features.data());

  // create output/workspace tensor
  auto d_weight = at::empty({out_features, in_features}, input.type());
#if defined(CUBLAS_VERSION) && CUBLAS_VERSION < 11600
  auto d_bias = d_output.view({-1, out_features}).sum(0, false);
#else                                                                              
  auto d_bias = at::empty({out_features}, input.type());
#endif                                                                              
  auto d_input = at::empty({batch_size, in_features}, input.type());
  //auto reserved_space = at::empty({reserved_size}, inputs[0].type());
  // allocate fixed 4MB workspace for cublaslt for now, and this gets at least 4 MB
  auto lt_workspace = at::empty({1 << 22}, input.type());

  AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.type(), "linear_bias_backward", [&] {
    scalar_t* w_ptr = weight.data_ptr<scalar_t>();
    scalar_t* d_b_ptr = d_bias.data_ptr<scalar_t>();
    auto result = linear_bias_backward_cuda<scalar_t>(
        input.data_ptr<scalar_t>(),
        w_ptr,
        d_output.data_ptr<scalar_t>(),
        in_features,
        batch_size,
        out_features,
        d_weight.data_ptr<scalar_t>(),
        d_bias.data_ptr<scalar_t>(),
        d_input.data_ptr<scalar_t>(),
       // reserved_space.data_ptr<scalar_t>(),
        (void*) (lt_workspace.data_ptr<scalar_t>()));
  });

  return {d_input, d_weight, d_bias};
}

std::vector<at::Tensor> linear_gelu_linear_forward(at::Tensor input, at::Tensor weight1, at::Tensor bias1, at::Tensor weight2, at::Tensor bias2) {

  auto batch_size = input.size(0);
  auto in_features = input.size(1);

  int hidden_features = weight1.size(0);
  int out_features = weight2.size(0);

  //auto reserved_size = get_mlp_reserved_space(batch_size, num_layers, output_features.data());

  // create output/workspace tensor
  auto output1 = at::empty({batch_size, hidden_features}, input.type());
  auto gelu_in = at::empty({batch_size, hidden_features}, input.type());
  auto output2 = at::empty({batch_size, out_features}, input.type());
  //auto reserved_space = at::empty({reserved_size}, inputs[0].type());
  // allocate fixed 4MB workspace for cublaslt for now, and this gets at least 4 MB
  auto lt_workspace = at::empty({1 << 22}, input.type());

  AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.type(), "linear_gelu_linear_forward", [&] {
    scalar_t* w1_ptr = weight1.data_ptr<scalar_t>();
    scalar_t* b1_ptr = bias1.data_ptr<scalar_t>();
    scalar_t* w2_ptr = weight2.data_ptr<scalar_t>();
    scalar_t* b2_ptr = bias2.data_ptr<scalar_t>();
    auto result = linear_gelu_linear_forward_cuda<scalar_t>(
        input.data_ptr<scalar_t>(),
        w1_ptr,
        b1_ptr,
	w2_ptr,
	b2_ptr,
        in_features,
	hidden_features,
        batch_size,
        out_features,
        output1.data_ptr<scalar_t>(),
        output2.data_ptr<scalar_t>(),
        gelu_in.data_ptr<scalar_t>(),
       // reserved_space.data_ptr<scalar_t>(),
        (void*) (lt_workspace.data_ptr<scalar_t>()));
  });

  return {output1, output2, gelu_in};
}

std::vector<at::Tensor> linear_gelu_linear_backward(at::Tensor input, at::Tensor gelu_in, at::Tensor output1, at::Tensor weight1, at::Tensor weight2, at::Tensor d_output2) {

  auto batch_size = input.size(0);
  auto in_features = input.size(1);

  int hidden_features = weight1.size(0);
  int out_features = weight2.size(0);

  //auto reserved_size = get_mlp_reserved_space(batch_size, num_layers, output_features.data());

  // create output/workspace tensor
  auto d_weight1 = at::empty({hidden_features, in_features}, input.type());
  auto d_weight2 = at::empty({out_features, hidden_features}, input.type());
  auto d_bias1 = at::empty({hidden_features}, input.type());
  auto d_bias2 = at::empty({out_features}, input.type());
  auto d_input = at::empty({batch_size, in_features}, input.type());
  auto d_output1 = at::empty({batch_size, hidden_features}, input.type());
  //auto reserved_space = at::empty({reserved_size}, inputs[0].type());
  // allocate fixed 4MB workspace for cublaslt for now, and this gets at least 4 MB
  auto lt_workspace = at::empty({1 << 22}, input.type());

  AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.type(), "linear_bias_backward", [&] {
    //scalar_t* w_ptr = weight.data_ptr<scalar_t>();
    //scalar_t* d_b_ptr = d_bias.data_ptr<scalar_t>();
    auto result = linear_gelu_linear_backward_cuda<scalar_t>(
        input.data_ptr<scalar_t>(),
        gelu_in.data_ptr<scalar_t>(),
        output1.data_ptr<scalar_t>(),
        weight1.data_ptr<scalar_t>(),
        weight2.data_ptr<scalar_t>(),
        d_output1.data_ptr<scalar_t>(),
        d_output2.data_ptr<scalar_t>(),
        in_features,
        batch_size,
        hidden_features,
        out_features,
        d_weight1.data_ptr<scalar_t>(),
        d_weight2.data_ptr<scalar_t>(),
        d_bias1.data_ptr<scalar_t>(),
        d_bias2.data_ptr<scalar_t>(),
        d_input.data_ptr<scalar_t>(),
       // reserved_space.data_ptr<scalar_t>(),
        (void*) (lt_workspace.data_ptr<scalar_t>()));
  });

  return {d_input, d_weight1, d_bias1, d_weight2, d_bias2};
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
  m.def("linear_bias_forward", &linear_bias_forward, "linear bias forward");
  m.def("linear_bias_backward", &linear_bias_backward, "linear bias backward");
  m.def("linear_gelu_linear_forward", &linear_gelu_linear_forward, "linear gelu linear forward");
  m.def("linear_gelu_linear_backward", &linear_gelu_linear_backward, "linear gelu linear backward");
}