forked from kscience/kmath
108 lines
3.2 KiB
C++
108 lines
3.2 KiB
C++
#include <torch/torch.h>
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#include "ctorch.h"
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namespace ctorch
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{
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template <typename Dtype>
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inline c10::ScalarType dtype()
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{
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return torch::kFloat64;
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}
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template <>
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inline c10::ScalarType dtype<float>()
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{
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return torch::kFloat32;
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}
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template <>
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inline c10::ScalarType dtype<long>()
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{
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return torch::kInt64;
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}
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template <>
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inline c10::ScalarType dtype<int>()
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{
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return torch::kInt32;
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}
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inline torch::Tensor &cast(const TorchTensorHandle &tensor_handle)
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{
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return *static_cast<torch::Tensor *>(tensor_handle);
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}
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inline int device_to_int(const torch::Tensor &tensor)
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{
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return (tensor.device().type() == torch::kCPU) ? 0 : 1 + tensor.device().index();
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}
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inline torch::Device int_to_device(int device_int)
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{
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return (device_int == 0) ? torch::kCPU : torch::Device(torch::kCUDA, device_int - 1);
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}
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inline std::vector<int64_t> to_vec_int(int *arr, int arr_size)
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{
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auto vec = std::vector<int64_t>(arr_size);
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vec.assign(arr, arr + arr_size);
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return vec;
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}
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inline std::vector<at::indexing::TensorIndex> to_index(int *arr, int arr_size)
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{
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std::vector<at::indexing::TensorIndex> index;
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for (int i = 0; i < arr_size; i++)
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{
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index.emplace_back(arr[i]);
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}
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return index;
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}
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template <typename Dtype>
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inline torch::Tensor from_blob(Dtype *data, std::vector<int64_t> shape, torch::Device device, bool copy)
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{
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return torch::from_blob(data, shape, dtype<Dtype>()).to(torch::TensorOptions().layout(torch::kStrided).device(device), false, copy);
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}
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template <typename NumType>
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inline NumType get(const TorchTensorHandle &tensor_handle, int *index)
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{
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auto ten = ctorch::cast(tensor_handle);
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return ten.index(to_index(index, ten.dim())).item<NumType>();
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}
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template <typename NumType>
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inline void set(TorchTensorHandle &tensor_handle, int *index, NumType value)
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{
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auto ten = ctorch::cast(tensor_handle);
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ten.index(to_index(index, ten.dim())) = value;
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}
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template <typename Dtype>
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inline torch::Tensor randn(std::vector<int64_t> shape, torch::Device device)
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{
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return torch::randn(shape, torch::TensorOptions().dtype(dtype<Dtype>()).layout(torch::kStrided).device(device));
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}
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template <typename Dtype>
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inline torch::Tensor rand(std::vector<int64_t> shape, torch::Device device)
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{
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return torch::rand(shape, torch::TensorOptions().dtype(dtype<Dtype>()).layout(torch::kStrided).device(device));
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}
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template <typename Dtype>
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inline torch::Tensor randint(Dtype low, Dtype high, std::vector<int64_t> shape, torch::Device device)
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{
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return torch::randint(low, high, shape, torch::TensorOptions().dtype(dtype<Dtype>()).layout(torch::kStrided).device(device));
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}
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template <typename Dtype>
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inline torch::Tensor full(Dtype value, std::vector<int64_t> shape, torch::Device device)
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{
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return torch::full(shape, value, torch::TensorOptions().dtype(dtype<Dtype>()).layout(torch::kStrided).device(device));
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}
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} // namespace ctorch
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