chore: import upstream snapshot with attribution

paddle/fluid/imperative/basic_engine.h

@@ -0,0 +1,84 @@
+// Copyright (c) 2018 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.
+
+#pragma once
+
+#include <memory>
+#include <unordered_map>
+#include <unordered_set>
+#include <utility>
+#include <vector>
+
+#include "paddle/fluid/imperative/engine.h"
+#include "paddle/fluid/imperative/gradient_accumulator.h"
+#include "paddle/utils/test_macros.h"
+
+namespace paddle {
+namespace imperative {
+
+class VarBase;
+class OpBase;
+
+class TEST_API BasicEngine : public Engine {
+ public:
+  void Init(const std::vector<std::shared_ptr<VarBase>>& tensors,
+            const std::vector<std::shared_ptr<VarBase>>& grad_tensors,
+            bool retain_graph = false);
+
+  void Execute() override;
+
+ private:
+  void PrepareDeps();
+
+  void CheckBackwardInputs(const OpBase& op);
+
+  void PrepareGradAccumulators(
+      const OpBase& op,
+      const std::vector<std::shared_ptr<GradOpNode>>& grad_pending_nodes);
+
+  void Clear();
+
+ private:
+  std::vector<std::shared_ptr<GradOpNode>> init_nodes_;
+  std::unordered_map<GradOpNode*, size_t> node_deps_;
+  // The input and output of Inplace op are the same. If only `var` is used
+  // as the key, then the input and output of inplace op must be gradient
+  // accumulated. Therefore, add the `grad_node` as the key to prevent the
+  // problem of gradient accumulation in inplace op.
+  std::unordered_map<std::shared_ptr<GradOpNode>,
+                     std::unordered_map<VariableWrapper*,
+                                        std::unique_ptr<GradientAccumulator>>>
+      accumulators_with_grad_node_;
+  // Leaf var doesn't have grad_node, and leaf var with `stop_gradient=False`
+  // can't use Inplace strategy. If a var doesn't have grad_node, only use
+  // `var` as the key.
+  std::unordered_map<VariableWrapper*, std::unique_ptr<GradientAccumulator>>
+      accumulators_;
+  // The output grad var of Inplace grad op. Because Inplace grad op does not
+  // use the Inplace strategy, a new output grad var needs to be created.
+  std::vector<std::pair<std::shared_ptr<VariableWrapper>,
+                        std::shared_ptr<VariableWrapper>>>
+      inplace_output_grad_var_list_;
+  std::vector<std::pair<GradientAccumulator*, std::shared_ptr<VariableWrapper>>>
+      need_accu_var_list_;
+  // leaf_accumulators_ is only for leaf tensor(hooks/accumulate grad)
+  // It should be orderly and not repeated, because multiple cards must ensure
+  // that the order of vars is the same.
+  std::vector<GradientAccumulator*> leaf_accumulators_;
+
+  bool retain_graph_;
+};
+
+}  // namespace imperative
+}  // namespace paddle