kmath/kmath-torch/ctorch/include/utils.hh

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#include <torch/torch.h>
#include "ctorch.h"
namespace ctorch
{
template <typename Dtype>
inline c10::ScalarType dtype()
{
return torch::kFloat64;
}
template <>
inline c10::ScalarType dtype<float>()
{
return torch::kFloat32;
}
template <>
inline c10::ScalarType dtype<long>()
{
return torch::kInt64;
}
template <>
inline c10::ScalarType dtype<int>()
{
return torch::kInt32;
}
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inline torch::Tensor &cast(const TorchTensorHandle &tensor_handle)
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{
return *static_cast<torch::Tensor *>(tensor_handle);
}
template <typename Dtype>
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inline torch::Tensor copy_from_blob(Dtype *data, int *shape, int dim, torch::Device device)
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{
auto shape_vec = std::vector<int64_t>(dim);
shape_vec.assign(shape, shape + dim);
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return torch::from_blob(data, shape_vec, dtype<Dtype>()).to(
torch::TensorOptions().layout(torch::kStrided).device(device), false, true);
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}
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inline int *to_dynamic_ints(const c10::IntArrayRef &arr)
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{
size_t n = arr.size();
int *res = (int *)malloc(sizeof(int) * n);
for (size_t i = 0; i < n; i++)
{
res[i] = arr[i];
}
return res;
}
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inline std::vector<at::indexing::TensorIndex> offset_to_index(int offset, const c10::IntArrayRef &strides)
{
std::vector<at::indexing::TensorIndex> index;
for (const auto &stride : strides)
{
index.emplace_back(offset / stride);
offset %= stride;
}
return index;
}
template <typename NumType>
inline NumType get_at_offset(const TorchTensorHandle &tensor_handle, int offset)
{
auto ten = ctorch::cast(tensor_handle);
return ten.index(ctorch::offset_to_index(offset, ten.strides())).item<NumType>();
}
template <typename NumType>
inline void set_at_offset(TorchTensorHandle &tensor_handle, int offset, NumType value)
{
auto ten = ctorch::cast(tensor_handle);
ten.index(offset_to_index(offset, ten.strides())) = value;
}
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} // namespace ctorch