Home | History | Annotate | Download | only in service 
      1 /* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
      2 
      3 Licensed under the Apache License, Version 2.0 (the "License");
      4 you may not use this file except in compliance with the License.
      5 You may obtain a copy of the License at
      6 
      7     http://www.apache.org/licenses/LICENSE-2.0
      8 
      9 Unless required by applicable law or agreed to in writing, software
     10 distributed under the License is distributed on an "AS IS" BASIS,
     11 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
     12 See the License for the specific language governing permissions and
     13 limitations under the License.
     14 ==============================================================================*/
     15 
     16 #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_COMPUTATION_PLACER_H_
     17 #define TENSORFLOW_COMPILER_XLA_SERVICE_COMPUTATION_PLACER_H_
     18 
     19 #include <map>
     20 #include <memory>
     21 #include <vector>
     22 
     23 #include "tensorflow/compiler/xla/array2d.h"
     24 #include "tensorflow/compiler/xla/status.h"
     25 #include "tensorflow/compiler/xla/statusor.h"
     26 #include "tensorflow/compiler/xla/xla_data.pb.h"
     27 #include "tensorflow/core/lib/core/status.h"
     28 #include "tensorflow/core/platform/macros.h"
     29 #include "tensorflow/core/platform/stream_executor_no_cuda.h"
     30 #include "tensorflow/core/platform/types.h"
     31 
     32 namespace xla {
     33 
     34 // Class that represents the device assignment for a set of XLA replicated
     35 // computations. For R replicas and C computations, R * C devices are required
     36 // execute the computation in parallel. The assigned device ids can be accessed
     37 // by assignment(replica, computation).
     38 class DeviceAssignment : public Array2D<int> {
     39  public:
     40   DeviceAssignment() {}
     41   DeviceAssignment(int replica_count, int computation_count)
     42       : Array2D<int>(replica_count, computation_count, -1) {
     43     CHECK_GT(replica_count, 0);
     44     CHECK_GT(computation_count, 0);
     45   }
     46 
     47   int replica_count() const { return height(); }
     48   int computation_count() const { return width(); }
     49 
     50   // Protocol buffer serialization and deserialization.
     51   Status Serialize(DeviceAssignmentProto* proto) const;
     52 
     53   // Return a std::unique_ptr<DeviceAssignment> instead of a DeviceAssignment
     54   // directly because one of the supported TF platforms (mac) does not compile
     55   // due to a StatusOr of an incomplete type (DeviceAssignment).
     56   static StatusOr<std::unique_ptr<DeviceAssignment>> Deserialize(
     57       const DeviceAssignmentProto& proto);
     58 
     59   string ToString() const;
     60 };
     61 
     62 // A generic implementation of the XLA computation placer, which assigns device
     63 // ids to a set of replicated computations.
     64 class ComputationPlacer {
     65  public:
     66   ComputationPlacer() {}
     67   virtual ~ComputationPlacer() {}
     68 
     69   // Returns the device id assigned to the given replica and computation
     70   // instance for [replica_count x computation_count] setup. The returned device
     71   // id must match the assignement from PlaceReplicatedComputation().
     72   virtual StatusOr<int> DeviceId(int replica, int computation,
     73                                  int replica_count, int computation_count);
     74 
     75   // Returns the device ids assigned to a set of replicated computations, given
     76   // the number of replicas and the number of computations.
     77   virtual StatusOr<DeviceAssignment> AssignDevices(int replica_count,
     78                                                    int computation_count);
     79 
     80   using ComputationPlacerCreationFunction =
     81       std::unique_ptr<ComputationPlacer> (*)();
     82 
     83   // Registers a computation placer creation function for a particular platform.
     84   static void RegisterComputationPlacer(
     85       se::Platform::Id platform_id,
     86       ComputationPlacerCreationFunction creation_function);
     87 
     88   // Returns the computation placer singleton pointer if it is available for the
     89   // given platform, or an error status if it is not.
     90   static StatusOr<ComputationPlacer*> GetForPlatform(
     91       const se::Platform* platform);
     92 
     93  private:
     94   // The mutex that guards the platform-to-computation placer map.
     95   static tensorflow::mutex platform_computation_placer_mutex_;
     96 
     97   // State kept for each kind of ComputationPlacer. Registration functions set
     98   // up creation_function, and then we use that to lazily create "placer" the
     99   // first time GetForPlatform is invoked for a particular id.
    100   struct State {
    101     std::unique_ptr<ComputationPlacer> placer;
    102     ComputationPlacerCreationFunction creation_function = nullptr;
    103   };
    104 
    105   // Map from platform kind to computation placer singleton.
    106   static std::map<se::Platform::Id, State>* GetPlatformComputationPlacers();
    107 
    108   TF_DISALLOW_COPY_AND_ASSIGN(ComputationPlacer);
    109 };
    110 
    111 }  // namespace xla
    112 
    113 #endif  // TENSORFLOW_COMPILER_XLA_SERVICE_COMPUTATION_PLACER_H_
    114