1 files changed, 74 insertions, 0 deletions
diff --git a/compiler/mir/src/TensorVariant.cpp b/compiler/mir/src/TensorVariant.cpp
new file mode 100644
index 000000000..9e57dbaf0
--- /dev/null
+++ b/compiler/mir/src/TensorVariant.cpp
@@ -0,0 +1,74 @@
+/*
+ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include "mir/TensorVariant.h"
+#include <cstring>
+
+namespace mir
+{
+
+TensorVariant::TensorVariant(const TensorType &type) : _type(type), _strides(type.getShape().rank())
+{
+  _element_size = getDataTypeSize(getElementType());
+  std::size_t data_size = getShape().numElements() * _element_size;
+  _data.reset(new char[data_size], std::default_delete<char[]>());
+
+  int stride = 1;
+  for (int d = getShape().rank() - 1; d >= 0; --d)
+  {
+    _strides[d] = stride;
+    stride *= getShape().dim(d);
+  }
+}
+
+TensorVariant::TensorVariant(DataType element_type, const Shape &shape)
+    : TensorVariant(TensorType(element_type, shape))
+{
+}
+
+TensorVariant::TensorVariant(const TensorType &type, const void *data) : TensorVariant(type)
+{
+  std::size_t data_size = getShape().numElements() * _element_size;
+  std::memcpy(_data.get(), data, data_size);
+}
+
+TensorVariant::TensorVariant(DataType element_type, const Shape &shape, const void *data)
+    : TensorVariant(TensorType(element_type, shape), data)
+{
+}
+
+/**
+ * @brief Construct a TensorVariant from t_old that has strides with 0 where dim = 1
+ * Used for broadcasting
+ * @param t_old TensorVariant to use as base
+ * @param shape shape to broadcast to
+ */
+TensorVariant::TensorVariant(const TensorVariant &t_old, const Shape &shape)
+    : _type(t_old.getType().getElementType(), shape), _data(t_old._data),
+      _strides(static_cast<size_t>(shape.rank())), _element_size(t_old._element_size)
+{
+  int axis_old = t_old.getShape().rank() - 1;
+  for (int d = shape.rank() - 1; d >= 0; d--)
+  {
+    if (axis_old == -1)
+      break;
+    if (t_old.getShape().dim(axis_old) != 1)
+      _strides[d] = t_old._strides[axis_old];
+    axis_old--;
+  }
+}
+
+} // namespace mir