chore: import upstream snapshot with attribution

paddle/phi/common/int_array.cc

@@ -0,0 +1,126 @@
+/* Copyright (c) 2022 PaddlePaddle Authors. 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 "paddle/phi/common/int_array.h"
+
+#include "paddle/common/ddim.h"
+#include "paddle/phi/backends/context_pool.h"
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/common/place.h"
+#include "paddle/phi/core/tensor_utils.h"
+
+namespace paddle::experimental {
+
+template <typename T>
+IntArrayBase<T>::IntArrayBase(const DDim& dims) {
+  AssignData(dims.Get(), dims.size());
+}
+
+template <>
+IntArrayBase<DenseTensor>::IntArrayBase(const DenseTensor& tensor) {  // NOLINT
+  is_from_tensor_ = true;
+  if (tensor.place().GetType() == AllocationType::CPU) {
+    AssignDataFromTensor(tensor);
+  } else {
+    DenseTensor tensor_tmp;
+    phi::DeviceContextPool& pool = phi::DeviceContextPool::Instance();
+    auto dev_ctx = pool.Get(tensor.place());
+    phi::Copy(*dev_ctx, tensor, CPUPlace(), true, &tensor_tmp);
+    AssignDataFromTensor(tensor_tmp);
+  }
+}
+
+template <>
+IntArrayBase<DenseTensor>::IntArrayBase(
+    const std::vector<phi::TensorRef>& tensor_ref_list) {
+  is_from_tensor_ = true;
+  for (auto tensor_ref : tensor_ref_list) {
+    DataType data_type = tensor_ref.Get()->dtype();
+    switch (data_type) {
+      case DataType::INT32:
+        if (tensor_ref.Get()->place().GetType() == AllocationType::CPU) {
+          array_.push_back(*tensor_ref.Get()->template data<int32_t>());
+        } else {
+          DenseTensor tensor_tmp;
+          phi::DeviceContextPool& pool = phi::DeviceContextPool::Instance();
+          auto dev_ctx = pool.Get(tensor_ref.Get()->place());
+          phi::Copy(
+              *dev_ctx, *(tensor_ref.Get()), CPUPlace(), true, &tensor_tmp);
+          array_.push_back(*tensor_tmp.template data<int32_t>());
+        }
+        break;
+      case DataType::INT64:
+        if (tensor_ref.Get()->place().GetType() == AllocationType::CPU) {
+          array_.push_back(*tensor_ref.Get()->template data<int64_t>());
+        } else {
+          DenseTensor tensor_tmp;
+          phi::DeviceContextPool& pool = phi::DeviceContextPool::Instance();
+          auto dev_ctx = pool.Get(tensor_ref.Get()->place());
+          phi::Copy(
+              *dev_ctx, *(tensor_ref.Get()), CPUPlace(), true, &tensor_tmp);
+          array_.push_back(*tensor_tmp.template data<int64_t>());
+        }
+        break;
+      default:
+        PD_THROW(
+            "Data type error. Currently, The data type of IntArrayBase "
+            "only supports Tensor with int32 and int64, "
+            "but now received `",
+            data_type,
+            "`.");
+    }
+  }
+}
+
+template <>
+IntArrayBase<DenseTensor>::IntArrayBase(
+    const std::vector<DenseTensor>& tensor_list) {
+  is_from_tensor_ = true;
+  for (const auto& tensor_item : tensor_list) {
+    DataType data_type = tensor_item.dtype();
+    switch (data_type) {
+      case DataType::INT32:
+        if (tensor_item.place().GetType() == AllocationType::CPU) {
+          array_.push_back(*tensor_item.template data<int32_t>());
+        } else {
+          DenseTensor tensor_tmp;
+          phi::DeviceContextPool& pool = phi::DeviceContextPool::Instance();
+          auto dev_ctx = pool.Get(tensor_item.place());
+          phi::Copy(*dev_ctx, tensor_item, CPUPlace(), true, &tensor_tmp);
+          array_.push_back(*tensor_tmp.template data<int32_t>());
+        }
+        break;
+      case DataType::INT64:
+        if (tensor_item.place().GetType() == AllocationType::CPU) {
+          array_.push_back(*tensor_item.template data<int64_t>());
+        } else {
+          DenseTensor tensor_tmp;
+          phi::DeviceContextPool& pool = phi::DeviceContextPool::Instance();
+          auto dev_ctx = pool.Get(tensor_item.place());
+          phi::Copy(*dev_ctx, tensor_item, CPUPlace(), true, &tensor_tmp);
+          array_.push_back(*tensor_tmp.template data<int64_t>());
+        }
+        break;
+      default:
+        PD_THROW(
+            "Data type error. Currently, The data type of IntArrayBase "
+            "only supports Tensor with int32 and int64, "
+            "but now received `",
+            data_type,
+            "`.");
+    }
+  }
+}
+
+}  // namespace paddle::experimental