Create a ragged tensor from a regular tensor.

Author: csukuangfj

.github/workflows/build-doc.yml

@@ -22,6 +22,7 @@ on:
   push:
     branches:
     - master
+    - doc
 
 env:
   # debug is faster in terms of compilation time

k2/python/csrc/torch/torch_util.cu

@@ -105,7 +105,7 @@ torch::Tensor ToTorch(Array1<Arc> &array) {
 }
 
 template <>
-Array1<Arc> FromTorch<Arc>(torch::Tensor &tensor) {
+Array1<Arc> FromTorch<Arc>(torch::Tensor tensor) {
   K2_CHECK_EQ(tensor.dim(), 2) << "Expected dim: 2. Given: " << tensor.dim();
   K2_CHECK_EQ(tensor.scalar_type(), ToScalarType<int32_t>::value)
       << "Expected scalar type: " << ToScalarType<int32_t>::value
@@ -124,7 +124,7 @@ Array1<Arc> FromTorch<Arc>(torch::Tensor &tensor) {
   return ans;
 }
 
-Tensor FromTorch(torch::Tensor &tensor, TensorTag) {
+Tensor FromTorch(torch::Tensor tensor, TensorTag) {
   Dtype dtype = ScalarTypeToDtype(tensor.scalar_type());
   torch::IntArrayRef sizes = tensor.sizes();
   torch::IntArrayRef strides = tensor.strides();

k2/python/csrc/torch/torch_util.h

@@ -113,7 +113,7 @@ torch::Tensor ToTorch(Array1<T> &array) {
            input tensor.
  */
 template <typename T>
-Array1<T> FromTorch(torch::Tensor &tensor) {
+Array1<T> FromTorch(torch::Tensor tensor) {
   K2_CHECK_EQ(tensor.dim(), 1) << "Expected dim: 1. Given: " << tensor.dim();
   K2_CHECK_EQ(tensor.scalar_type(), ToScalarType<T>::value)
       << "Expected scalar type: " << ToScalarType<T>::value
@@ -158,12 +158,12 @@ torch::Tensor ToTorch(Array1<Arc> &array);
           the input tensor.
  */
 template <>
-Array1<Arc> FromTorch<Arc>(torch::Tensor &tensor);
+Array1<Arc> FromTorch<Arc>(torch::Tensor tensor);
 
 struct Array2Tag {};
 
 template <typename T>
-Array2<T> FromTorch(torch::Tensor &tensor, Array2Tag) {
+Array2<T> FromTorch(torch::Tensor tensor, Array2Tag) {
   K2_CHECK_EQ(tensor.dim(), 2) << "Expected dim: 2. Given: " << tensor.dim();
   K2_CHECK_EQ(tensor.scalar_type(), ToScalarType<T>::value)
       << "Expected scalar type: " << ToScalarType<T>::value
@@ -199,7 +199,7 @@ torch::Tensor ToTorch(Array2<T> &array) {
 
 struct TensorTag {};
 
-Tensor FromTorch(torch::Tensor &tensor, TensorTag);
+Tensor FromTorch(torch::Tensor tensor, TensorTag);
 torch::Tensor ToTorch(Tensor &tensor);
 
 /* Transfer an object to a specific device.

k2/python/csrc/torch/v2/any.cu

@@ -54,10 +54,12 @@ void PybindRaggedAny(py::module &m) {
       }),
       py::arg("s"), py::arg("dtype") = py::none(), kRaggedAnyInitStrDoc);
 
-  // TODO(fangjun): add documentation for it
   any.def(py::init<const RaggedShape &, torch::Tensor>(), py::arg("shape"),
           py::arg("value"), kRaggedInitFromShapeAndTensorDoc);
 
+  any.def(py::init<torch::Tensor>(), py::arg("tensor"),
+          kRaggedAnyInitTensorDoc);
+
   any.def(
       "__str__",
       [](const RaggedAny &self) -> std::string { return self.ToString(); },
@@ -78,8 +80,7 @@ void PybindRaggedAny(py::module &m) {
           K2_CHECK_EQ(self.any.NumAxes(), 2);
           Array1<int32_t> row_split = self.any.RowSplits(1).To(GetCpuContext());
           const int32_t *row_split_data = row_split.Data();
-          int32_t begin = row_split_data[i],
-                  end = row_split_data[i + 1];
+          int32_t begin = row_split_data[i], end = row_split_data[i + 1];
           Dtype t = self.any.GetDtype();
           FOR_REAL_AND_INT32_TYPES(t, T, {
             Array1<T> array =
@@ -100,19 +101,18 @@ void PybindRaggedAny(py::module &m) {
         if (!slice.compute(self.any.Dim0(), &start, &stop, &step, &slicelength))
           throw py::error_already_set();
         int32_t istart = static_cast<int32_t>(start);
-        int32_t istop  = static_cast<int32_t>(stop);
-        int32_t istep  = static_cast<int32_t>(step);
-        K2_CHECK_EQ(istep, 1) << "Only support slicing with step 1, given : "
-                              << istep;
+        int32_t istop = static_cast<int32_t>(stop);
+        int32_t istep = static_cast<int32_t>(step);
+        K2_CHECK_EQ(istep, 1)
+            << "Only support slicing with step 1, given : " << istep;
 
         return self.Arange(/*axis*/ 0, istart, istop);
-      }, py::arg("key"), kRaggedAnyGetItemSliceDoc);
+      },
+      py::arg("key"), kRaggedAnyGetItemSliceDoc);
 
   any.def("index",
-          static_cast<RaggedAny (RaggedAny::*)(RaggedAny &)>(
-              &RaggedAny::Index),
-          py::arg("indexes"),
-          kRaggedAnyRaggedIndexDoc);
+          static_cast<RaggedAny (RaggedAny::*)(RaggedAny &)>(&RaggedAny::Index),
+          py::arg("indexes"), kRaggedAnyRaggedIndexDoc);
 
   any.def("index",
           static_cast<std::pair<RaggedAny, torch::optional<torch::Tensor>> (
@@ -423,6 +423,11 @@ void PybindRaggedAny(py::module &m) {
         return RaggedAny(s, dtype);
       },
       py::arg("s"), py::arg("dtype") = py::none(), kCreateRaggedTensorStrDoc);
+
+  m.def(
+      "create_ragged_tensor",
+      [](torch::Tensor tensor) -> RaggedAny { return RaggedAny(tensor); },
+      py::arg("tensor"), kCreateRaggedTensorTensorDoc);
 }
 
 }  // namespace k2