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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_CPU_CPU_PARALLELIZATION_PREPARATION_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_CPU_CPU_PARALLELIZATION_PREPARATION_H_
+
+#include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/service/hlo_pass.h"
+
+namespace xla {
+namespace cpu {
+
+// This pass prepares an HLO module for parallel execution by transforming
+// subgraphs of the top-level computation into embedded computations which can
+// be executed in parallel.
+// TODO(b/29630486): Currently, it is limited to turning all instructions (which
+// are not constants or parameters) in the entry computation into embedded
+// computations.  However, it could make sense to coarsen the parallelization to
+// improve cache locality.  Also, we will need to do something to intelligently
+// handle While constructs.
+class ParallelizationPreparation : public HloPass {
+ public:
+  explicit ParallelizationPreparation() : HloPass("cpu-parallel-prepare") {}
+  ~ParallelizationPreparation() override {}
+
+  // Run instruction fusion on the given computation. Returns whether the
+  // computation was changed.
+  StatusOr<bool> Run(HloModule* module) override;
+};
+
+}  // namespace cpu
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_CPU_CPU_PARALLELIZATION_PREPARATION_H_