Merge pull request #926 from NiklasGustafsson/missing

Missing methods implemented

Author: Niklas Gustafsson

RELEASENOTES.md

@@ -11,6 +11,11 @@ Adding allow_tf32<br/>
 Adding overloads of Module.save() and Module.load() taking a 'Stream' argument.<br/>
 Adding torch.softmax() and Tensor.softmax() as aliases for torch.special.softmax()<br/>
 Adding torch.from_file()<br/>
+Adding a number of missing pointwise Tensor operations.<br/>
+Adding select_scatter, diagonal_scatter, and slice_scatter<br/>
+Adding torch.set_printoptions<br/>
+Adding torch.cartesian_prod, combinations, and cov.<br/>
+Adding torch.cdist, diag_embed, rot90, triu_indices, tril_indices<br/>
 
 __Fixed Bugs__:
 

src/Native/LibTorchSharp/THSLinearAlgebra.cpp

@@ -47,6 +47,11 @@ Tensor THSLinalg_det(const Tensor tensor)
     CATCH_TENSOR(torch::linalg::det(*tensor));
 }
 
+Tensor THSTensor_logdet(const Tensor tensor)
+{
+    CATCH_TENSOR(torch::logdet(*tensor));
+}
+
 Tensor THSLinalg_slogdet(const Tensor tensor, Tensor* logabsdet)
 {
     std::tuple<at::Tensor, at::Tensor> res;
@@ -63,6 +68,13 @@ Tensor THSLinalg_eig(const Tensor tensor, Tensor* eigenvectors)
     return ResultTensor(std::get<0>(res));
 }
 
+Tensor THSTensor_geqrf(const Tensor tensor, Tensor* tau)
+{
+    std::tuple<at::Tensor, at::Tensor> res;
+    CATCH(res = torch::geqrf(*tensor);)
+    *tau = ResultTensor(std::get<1>(res));
+    return ResultTensor(std::get<0>(res));
+}
 
 #if 0
 Tensor THSTensor_eig(const Tensor tensor, bool vectors, Tensor* eigenvectors)
@@ -98,6 +110,11 @@ Tensor THSLinalg_eigvalsh(const Tensor tensor, const char UPLO)
     CATCH_TENSOR(torch::linalg::eigvalsh(*tensor, _uplo));
 }
 
+Tensor THSLinalg_householder_product(const Tensor tensor, const Tensor tau)
+{
+    CATCH_TENSOR(torch::linalg::householder_product(*tensor, *tau));
+}
+
 Tensor THSLinalg_inv(const Tensor tensor)
 {
     CATCH_TENSOR(torch::linalg::inv(*tensor));

src/Native/LibTorchSharp/THSTensor.cpp

@@ -66,6 +66,12 @@ Tensor THSTensor_any_along_dimension(const Tensor tensor, const int64_t dim, boo
 {
     CATCH_TENSOR(tensor->any(dim, keepdim));
 }
+
+Tensor THSTensor_adjoint(const Tensor tensor)
+{
+    CATCH_TENSOR(tensor->adjoint());
+}
+
 Tensor THSTensor_argmax(const Tensor tensor)
 {
     CATCH_TENSOR(tensor->argmax());
@@ -86,6 +92,11 @@ Tensor THSTensor_argmin_along_dimension(const Tensor tensor, const int64_t dim,
     CATCH_TENSOR(tensor->argmin(dim, keepdim));
 }
 
+Tensor THSTensor_argwhere(const Tensor tensor)
+{
+    CATCH_TENSOR(tensor->argwhere());
+}
+
 Tensor THSTensor_atleast_1d(const Tensor tensor)
 {
     CATCH_TENSOR(torch::atleast_1d(*tensor));
@@ -159,6 +170,11 @@ void THSTensor_vector_to_parameters(const Tensor vec, const Tensor* tensors, con
     CATCH(torch::nn::utils::vector_to_parameters(*vec, toTensors<at::Tensor>((torch::Tensor**)tensors, length)););
 }
 
+Tensor THSTensor_cartesian_prod(const Tensor* tensors, const int length)
+{
+    CATCH_TENSOR(torch::cartesian_prod(toTensors<at::Tensor>((torch::Tensor**)tensors, length)));
+}
+
 double THSTensor_clip_grad_norm_(const Tensor* tensors, const int length, const double max_norm, const double norm_type)
 {
     double res = 0.0;
@@ -258,6 +274,11 @@ Tensor THSTensor_clone(const Tensor tensor)
     CATCH_TENSOR(tensor->clone());
 }
 
+Tensor THSTensor_combinations(const Tensor tensor, const int r, const bool with_replacement)
+{
+    CATCH_TENSOR(torch::combinations(*tensor, r, with_replacement));
+}
+
 Tensor THSTensor_copy_(const Tensor input, const Tensor other, const bool non_blocking)
 {
     CATCH_TENSOR(input->copy_(*other, non_blocking));
@@ -285,6 +306,13 @@ int THSTensor_is_contiguous(const Tensor tensor)
     return result;
 }
 
+int64_t THSTensor_is_nonzero(const Tensor tensor)
+{
+    bool result = false;
+    CATCH(result = tensor->is_nonzero();)
+    return result;
+}
+
 Tensor THSTensor_copysign(const Tensor input, const Tensor other)
 {
     CATCH_TENSOR(input->copysign(*other));
@@ -295,13 +323,6 @@ Tensor THSTensor_corrcoef(const Tensor tensor)
     CATCH_TENSOR(tensor->corrcoef());
 }
 
-Tensor THSTensor_cov(const Tensor input, int64_t correction, const Tensor fweights, const Tensor aweights)
-{
-    c10::optional<at::Tensor> fw = (fweights == nullptr) ? c10::optional<at::Tensor>() : *fweights;
-    c10::optional<at::Tensor> aw = (aweights == nullptr) ? c10::optional<at::Tensor>() : *aweights;
-    CATCH_TENSOR(input->cov(correction, fw, aw));
-}
-
 bool THSTensor_is_cpu(const Tensor tensor)
 {
     bool result = true;
@@ -402,6 +423,11 @@ int THSTensor_device_type(const Tensor tensor)
     return (int)device.type();
 }
 
+Tensor THSTensor_diag_embed(const Tensor tensor, const int64_t offset, const int64_t dim1, const int64_t dim2)
+{
+    CATCH_TENSOR(tensor->diag_embed(offset, dim1, dim2));
+}
+
 Tensor THSTensor_diff(const Tensor tensor, const int64_t n, const int64_t dim, const Tensor prepend, const Tensor append)
 {
     c10::optional<at::Tensor> prep = prepend != nullptr ? *prepend : c10::optional<at::Tensor>(c10::nullopt);
@@ -473,6 +499,11 @@ Tensor THSTensor_repeat_interleave_int64(const Tensor tensor, const int64_t repe
     CATCH_TENSOR(tensor->repeat_interleave(repeats, _dim, _output_size));
 }
 
+int THSTensor_result_type(const Tensor left, const Tensor right)
+{
+    CATCH_RETURN_RES(int, -1, res = (int)torch::result_type(*left, *right));
+}
+
 Tensor THSTensor_movedim(const Tensor tensor, const int64_t* src, const int src_len, const int64_t* dst, const int dst_len)
 {
     CATCH_TENSOR(tensor->movedim(at::ArrayRef<int64_t>(src, src_len), at::ArrayRef<int64_t>(dst, dst_len)));
@@ -1070,6 +1101,11 @@ Tensor THSTensor_outer(const Tensor left, const Tensor right)
     CATCH_TENSOR(left->outer(*right));
 }
 
+Tensor THSTensor_ormqr(const Tensor input, const Tensor tau, const Tensor other, bool left, bool transpose)
+{
+    CATCH_TENSOR(torch::ormqr(*input, *tau, *other, left, transpose));
+}
+
 Tensor THSTensor_mH(const Tensor tensor)
 {
     CATCH_TENSOR(tensor->mH());
@@ -1161,6 +1197,11 @@ Tensor THSTensor_reshape(const Tensor tensor, const int64_t* shape, const int le
     CATCH_TENSOR(tensor->reshape(at::ArrayRef<int64_t>(shape, length)));
 }
 
+Tensor THSTensor_rot90(const Tensor tensor, const int64_t k, const int64_t dim1, const int64_t dim2)
+{
+    CATCH_TENSOR(tensor->rot90(k, { dim1, dim2 }));
+}
+
 Tensor THSTensor_roll(const Tensor tensor, const int64_t* shifts, const int shLength, const int64_t* dims, const int dimLength)
 {
     CATCH_TENSOR(
@@ -1194,6 +1235,36 @@ Tensor THSTensor_scatter_(
     CATCH_TENSOR(tensor->scatter_(dim, *index, *source));
 }
 
+Tensor THSTensor_select_scatter(
+    const Tensor tensor,
+    const Tensor source,
+    const int64_t dim,
+    const int64_t index)
+{
+    CATCH_TENSOR(torch::select_scatter(*tensor, *source, dim, index));
+}
+
+Tensor THSTensor_diagonal_scatter(
+    const Tensor tensor,
+    const Tensor source,
+    const int64_t offset,
+    const int64_t dim1,
+    const int64_t dim2)
+{
+    CATCH_TENSOR(torch::diagonal_scatter(*tensor, *source, offset, dim1, dim2));
+}
+
+Tensor THSTensor_slice_scatter(
+    const Tensor tensor,
+    const Tensor source,
+    const int64_t dim,
+    const int64_t *start,
+    const int64_t *end,
+    const int64_t step)
+{
+    CATCH_TENSOR(torch::slice_scatter(*tensor, *source, dim, start == nullptr ? c10::optional<int64_t>() : c10::optional<int64_t>(*start), end == nullptr ? c10::optional<int64_t>() : c10::optional<int64_t>(*end), step));
+}
+
 Tensor THSTensor_scatter_add(
     const Tensor tensor,
     const int64_t dim,
@@ -1762,6 +1833,23 @@ Tensor THSTensor_tril(const Tensor tensor, const int64_t diagonal)
     CATCH_TENSOR(tensor->tril(diagonal));
 }
 
+Tensor THSTensor_tril_indices(const int64_t row, const int64_t col, const int64_t offset, const int8_t scalar_type, const int device_type, const int device_index)
+{
+    auto options = at::TensorOptions()
+        .dtype(at::ScalarType(scalar_type))
+        .device(c10::Device((c10::DeviceType)device_type, (c10::DeviceIndex)device_index));
+    CATCH_TENSOR(torch::tril_indices(row, col, offset, options));
+}
+
+Tensor THSTensor_triu_indices(const int64_t row, const int64_t col, const int64_t offset, const int8_t scalar_type, const int device_type, const int device_index)
+{
+    auto options = at::TensorOptions()
+        .dtype(at::ScalarType(scalar_type))
+        .device(c10::Device((c10::DeviceType)device_type, (c10::DeviceIndex)device_index));
+    CATCH_TENSOR(torch::triu_indices(row, col, offset, options));
+}
+
+
 Tensor THSTensor_transpose(const Tensor tensor, const int64_t dim1, const int64_t dim2)
 {
     CATCH_TENSOR(tensor->transpose(dim1, dim2));