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+/* Copyright 2017 The TensorFlow 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.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_INSTRUCTION_FUSION_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_INSTRUCTION_FUSION_H_
+
+#include "tensorflow/compiler/xla/service/hlo_computation.h"
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/service/hlo_pass.h"
+#include "tensorflow/core/platform/macros.h"
+
+namespace xla {
+
+// Returns true if the computation of the given instruction is significantly
+// more expensive than just writing all the values of the instructions' result
+// array. Expensive operations should not be duplicated.
+bool IsExpensive(const HloInstruction& instruction);
+
+// Returns true if fusing producer into consumer would cause producer to be
+// duplicated. This is the case if producer has uses other than consumer.
+bool FusionWouldDuplicate(HloInstruction* producer, HloInstruction* consumer);
+
+// HLO pass which performs instruction fusion. Instructions are fused
+// "vertically", meaning producing instructions are fused into their consumers
+// with the intent that the loops which compute their values will be fused in
+// code generation. Derived classes define ShouldFuse method to select which
+// instructions to fuse.
+class InstructionFusion : public HloPass {
+ public:
+  explicit InstructionFusion(bool may_duplicate = true)
+      : HloPass("fusion"), may_duplicate_(may_duplicate) {}
+  ~InstructionFusion() override {}
+
+  // Run instruction fusion on the given computation. Returns whether the
+  // computation was changed (instructions were fused).
+  StatusOr<bool> Run(HloModule* module) override;
+
+ protected:
+  // Returns whether the given producer instruction should be fused into the
+  // given consumer instruction. producer is necessarily an operand of consumer.
+  // Derived classes should define this method to specify which instructions
+  // should be fused. `operand_index` is which operand of the consumer the
+  // producer is.
+  //
+  // Instructions are traversed in reverse post order (computation root to
+  // leaves). This method is called for each operand of the instruction (where
+  // the operand is 'producer' and the instruction is 'consumer')
+  //
+  // Subtypes can override this with target-specific heuristics.
+  virtual bool ShouldFuse(HloInstruction* consumer, int64 operand_index);
+
+  // Chooses a fusion kind for `producer` and `consumer`.
+  // Default method chooses `kLoop`.
+  virtual HloInstruction::FusionKind ChooseKind(const HloInstruction* producer,
+                                                const HloInstruction* consumer);
+
+  // Current HloComputation instance the loop fuser is traversing.
+  HloComputation* computation_;
+
+ private:
+  HloInstruction* Fuse(HloInstruction* producer, HloInstruction* consumer);
+
+  // Returns whether we may duplicate an instruction if we want to fuse it.
+  bool may_duplicate_;
+
+  TF_DISALLOW_COPY_AND_ASSIGN(InstructionFusion);
+};
+
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_INSTRUCTION_FUSION_H_