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

namespace multihead_attn {
namespace self_norm_add {
namespace rocblas_gemmex {

std::vector<torch::Tensor> fwd_cuda(bool use_time_mask, bool is_training,
                                    int heads, torch::Tensor const &inputs,
                                    torch::Tensor const &lyr_nrm_gamma_weights,
                                    torch::Tensor const &lyr_nrm_beta_weights,
                                    torch::Tensor const &input_weights,
                                    torch::Tensor const &output_weights,
                                    const uint8_t *pad_mask,
                                    float dropout_prob);

std::vector<torch::Tensor> bwd_cuda(
    int heads, torch::Tensor const &output_grads,
    torch::Tensor const &matmul2_results, torch::Tensor const &dropout_results,
    torch::Tensor const &softmax_results,
    torch::Tensor const &input_lin_results,
    torch::Tensor const &lyr_nrm_results, torch::Tensor const &lyr_nrm_mean,
    torch::Tensor const &lyr_nrm_invvar, torch::Tensor const &inputs,
    torch::Tensor const &lyr_nrm_gamma_weights,
    torch::Tensor const &lyr_nrm_beta_weights,
    torch::Tensor const &input_weights, torch::Tensor const &output_weights,
    torch::Tensor const &dropout_mask, torch::Tensor const &dropout_add_mask,
    float dropout_prob);

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

std::vector<torch::Tensor>
fwd(bool use_mask, bool use_time_mask, bool is_training, int heads,
    torch::Tensor const &inputs, torch::Tensor const &lyr_nrm_gamma_weights,
    torch::Tensor const &lyr_nrm_beta_weights,
    torch::Tensor const &input_weights, torch::Tensor const &output_weights,
    torch::Tensor const &pad_mask, float dropout_prob) {
  AT_ASSERTM(inputs.dim() == 3, "expected 3D tensor");
  AT_ASSERTM(lyr_nrm_gamma_weights.dim() == 1, "expected 1D tensor");
  AT_ASSERTM(lyr_nrm_beta_weights.dim() == 1, "expected 1D tensor");
  AT_ASSERTM(input_weights.dim() == 2, "expected 2D tensor");
  AT_ASSERTM(output_weights.dim() == 2, "expected 2D tensor");

  AT_ASSERTM(inputs.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(lyr_nrm_gamma_weights.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(lyr_nrm_beta_weights.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(input_weights.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(output_weights.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");

  if (use_mask) {
    AT_ASSERTM(pad_mask.dim() == 2, "expected 2D tensor");
    AT_ASSERTM(pad_mask.type().scalarType() == at::ScalarType::Byte,
               "Only BYTE is supported");
  }

  return fwd_cuda(
      use_time_mask, is_training, heads, inputs, lyr_nrm_gamma_weights,
      lyr_nrm_beta_weights, input_weights, output_weights,
      use_mask ? static_cast<const uint8_t *>(pad_mask.data_ptr()) : nullptr,
      dropout_prob);
}

std::vector<torch::Tensor>
bwd(int heads, torch::Tensor const &output_grads,
    torch::Tensor const &matmul2_results, torch::Tensor const &dropout_results,
    torch::Tensor const &softmax_results,
    torch::Tensor const &input_lin_results,
    torch::Tensor const &lyr_nrm_results, torch::Tensor const &lyr_nrm_mean,
    torch::Tensor const &lyr_nrm_invvar, torch::Tensor const &inputs,
    torch::Tensor const &lyr_nrm_gamma_weights,
    torch::Tensor const &lyr_nrm_beta_weights,
    torch::Tensor const &input_weights, torch::Tensor const &output_weights,
    torch::Tensor const &dropout_mask, torch::Tensor const &dropout_add_mask,
    float dropout_prob) {
  AT_ASSERTM(output_grads.dim() == 3, "expected 3D tensor");
  AT_ASSERTM(matmul2_results.dim() == 3, "expected 3D tensor");
  AT_ASSERTM(dropout_results.dim() == 3, "expected 3D tensor");
  AT_ASSERTM(softmax_results.dim() == 3, "expected 3D tensor");
  AT_ASSERTM(input_lin_results.dim() == 3, "expected 3D tensor");
  AT_ASSERTM(lyr_nrm_results.dim() == 3, "expected 3D tensor");
  AT_ASSERTM(lyr_nrm_mean.dim() == 1, "expected 1D tensor");
  AT_ASSERTM(lyr_nrm_invvar.dim() == 1, "expected 1D tensor");
  AT_ASSERTM(inputs.dim() == 3, "expected 3D tensor");
  AT_ASSERTM(lyr_nrm_gamma_weights.dim() == 1, "expected 1D tensor");
  AT_ASSERTM(lyr_nrm_beta_weights.dim() == 1, "expected 1D tensor");
  AT_ASSERTM(input_weights.dim() == 2, "expected 2D tensor");
  AT_ASSERTM(output_weights.dim() == 2, "expected 2D tensor");
  AT_ASSERTM(dropout_mask.dim() == 3, "expected 3D tensor");
  AT_ASSERTM(dropout_add_mask.dim() == 3, "expected 3D tensor");

  AT_ASSERTM(output_grads.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(matmul2_results.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(dropout_results.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(softmax_results.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(input_lin_results.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(lyr_nrm_results.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(lyr_nrm_mean.type().scalarType() == at::ScalarType::Float,
             "Only FLOAT is supported");
  AT_ASSERTM(lyr_nrm_invvar.type().scalarType() == at::ScalarType::Float,
             "Only FLOAT is supported");
  AT_ASSERTM(inputs.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(lyr_nrm_gamma_weights.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(lyr_nrm_beta_weights.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(input_weights.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(output_weights.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(dropout_mask.type().scalarType() == at::ScalarType::Byte,
             "Only BYTE is supported");
  AT_ASSERTM(dropout_add_mask.type().scalarType() == at::ScalarType::Byte,
             "Only BYTE is supported");

  return bwd_cuda(heads, output_grads, matmul2_results, dropout_results,
                  softmax_results, input_lin_results, lyr_nrm_results,
                  lyr_nrm_mean, lyr_nrm_invvar, inputs, lyr_nrm_gamma_weights,
                  lyr_nrm_beta_weights, input_weights, output_weights,
                  dropout_mask, dropout_add_mask, dropout_prob);
}

} 
} 
} 

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
  m.def("forward", &multihead_attn::self_norm_add::rocblas_gemmex::fwd, "Self Multihead Attention Plus Layer Norm and Residual Add Forward.");
  m.def("backward", &multihead_attn::self_norm_add::rocblas_gemmex::bwd, "Self Multihead Attention Plus Layer Norm and Residual Add Backward.");
}
#include <torch/extension.h>
#include <vector>

namespace multihead_attn {
namespace self_norm_add {
namespace rocblas_gemmex {

std::vector<torch::Tensor> fwd_cuda(bool use_time_mask, bool is_training,
                                    int heads, torch::Tensor const &inputs,
                                    torch::Tensor const &lyr_nrm_gamma_weights,
                                    torch::Tensor const &lyr_nrm_beta_weights,
                                    torch::Tensor const &input_weights,
                                    torch::Tensor const &output_weights,
                                    const uint8_t *pad_mask,
                                    float dropout_prob);

std::vector<torch::Tensor> bwd_cuda(
    int heads, torch::Tensor const &output_grads,
    torch::Tensor const &matmul2_results, torch::Tensor const &dropout_results,
    torch::Tensor const &softmax_results,
    torch::Tensor const &input_lin_results,
    torch::Tensor const &lyr_nrm_results, torch::Tensor const &lyr_nrm_mean,
    torch::Tensor const &lyr_nrm_invvar, torch::Tensor const &inputs,
    torch::Tensor const &lyr_nrm_gamma_weights,
    torch::Tensor const &lyr_nrm_beta_weights,
    torch::Tensor const &input_weights, torch::Tensor const &output_weights,
    torch::Tensor const &dropout_mask, torch::Tensor const &dropout_add_mask,
    float dropout_prob);

// C++ interface

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

std::vector<torch::Tensor>
fwd(bool use_mask, bool use_time_mask, bool is_training, int heads,
    torch::Tensor const &inputs, torch::Tensor const &lyr_nrm_gamma_weights,
    torch::Tensor const &lyr_nrm_beta_weights,
    torch::Tensor const &input_weights, torch::Tensor const &output_weights,
    torch::Tensor const &pad_mask, float dropout_prob) {
  AT_ASSERTM(inputs.dim() == 3, "expected 3D tensor");
  AT_ASSERTM(lyr_nrm_gamma_weights.dim() == 1, "expected 1D tensor");
  AT_ASSERTM(lyr_nrm_beta_weights.dim() == 1, "expected 1D tensor");
  AT_ASSERTM(input_weights.dim() == 2, "expected 2D tensor");
  AT_ASSERTM(output_weights.dim() == 2, "expected 2D tensor");

  AT_ASSERTM(inputs.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(lyr_nrm_gamma_weights.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(lyr_nrm_beta_weights.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(input_weights.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(output_weights.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");

  if (use_mask) {
    AT_ASSERTM(pad_mask.dim() == 2, "expected 2D tensor");
    AT_ASSERTM(pad_mask.type().scalarType() == at::ScalarType::Byte,
               "Only BYTE is supported");
  }

  return fwd_cuda(
      use_time_mask, is_training, heads, inputs, lyr_nrm_gamma_weights,
      lyr_nrm_beta_weights, input_weights, output_weights,
      use_mask ? static_cast<const uint8_t *>(pad_mask.data_ptr()) : nullptr,
      dropout_prob);
}

std::vector<torch::Tensor>
bwd(int heads, torch::Tensor const &output_grads,
    torch::Tensor const &matmul2_results, torch::Tensor const &dropout_results,
    torch::Tensor const &softmax_results,
    torch::Tensor const &input_lin_results,
    torch::Tensor const &lyr_nrm_results, torch::Tensor const &lyr_nrm_mean,
    torch::Tensor const &lyr_nrm_invvar, torch::Tensor const &inputs,
    torch::Tensor const &lyr_nrm_gamma_weights,
    torch::Tensor const &lyr_nrm_beta_weights,
    torch::Tensor const &input_weights, torch::Tensor const &output_weights,
    torch::Tensor const &dropout_mask, torch::Tensor const &dropout_add_mask,
    float dropout_prob) {
  AT_ASSERTM(output_grads.dim() == 3, "expected 3D tensor");
  AT_ASSERTM(matmul2_results.dim() == 3, "expected 3D tensor");
  AT_ASSERTM(dropout_results.dim() == 3, "expected 3D tensor");
  AT_ASSERTM(softmax_results.dim() == 3, "expected 3D tensor");
  AT_ASSERTM(input_lin_results.dim() == 3, "expected 3D tensor");
  AT_ASSERTM(lyr_nrm_results.dim() == 3, "expected 3D tensor");
  AT_ASSERTM(lyr_nrm_mean.dim() == 1, "expected 1D tensor");
  AT_ASSERTM(lyr_nrm_invvar.dim() == 1, "expected 1D tensor");
  AT_ASSERTM(inputs.dim() == 3, "expected 3D tensor");
  AT_ASSERTM(lyr_nrm_gamma_weights.dim() == 1, "expected 1D tensor");
  AT_ASSERTM(lyr_nrm_beta_weights.dim() == 1, "expected 1D tensor");
  AT_ASSERTM(input_weights.dim() == 2, "expected 2D tensor");
  AT_ASSERTM(output_weights.dim() == 2, "expected 2D tensor");
  AT_ASSERTM(dropout_mask.dim() == 3, "expected 3D tensor");
  AT_ASSERTM(dropout_add_mask.dim() == 3, "expected 3D tensor");

  AT_ASSERTM(output_grads.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(matmul2_results.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(dropout_results.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(softmax_results.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(input_lin_results.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(lyr_nrm_results.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(lyr_nrm_mean.type().scalarType() == at::ScalarType::Float,
             "Only FLOAT is supported");
  AT_ASSERTM(lyr_nrm_invvar.type().scalarType() == at::ScalarType::Float,
             "Only FLOAT is supported");
  AT_ASSERTM(inputs.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(lyr_nrm_gamma_weights.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(lyr_nrm_beta_weights.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(input_weights.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(output_weights.type().scalarType() == at::ScalarType::Half,
             "Only HALF is supported");
  AT_ASSERTM(dropout_mask.type().scalarType() == at::ScalarType::Byte,
             "Only BYTE is supported");
  AT_ASSERTM(dropout_add_mask.type().scalarType() == at::ScalarType::Byte,
             "Only BYTE is supported");

  return bwd_cuda(heads, output_grads, matmul2_results, dropout_results,
                  softmax_results, input_lin_results, lyr_nrm_results,
                  lyr_nrm_mean, lyr_nrm_invvar, inputs, lyr_nrm_gamma_weights,
                  lyr_nrm_beta_weights, input_weights, output_weights,
                  dropout_mask, dropout_add_mask, dropout_prob);
}

} // end namespace cublas_gemmex
} // end namespace self_norm_add
} // end namespace multihead_attn

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
  m.def("forward", &multihead_attn::self_norm_add::rocblas_gemmex::fwd, "Self Multihead Attention Plus Layer Norm and Residual Add Forward.");
  m.def("backward", &multihead_attn::self_norm_add::rocblas_gemmex::bwd, "Self Multihead Attention Plus Layer Norm and Residual Add Backward.");
}
