/* 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.
==============================================================================*/
#include "tensorflow/core/distributed_runtime/cluster_function_library_runtime.h"

#include "tensorflow/core/common_runtime/function_testlib.h"
#include "tensorflow/core/distributed_runtime/rpc/grpc_channel.h"
#include "tensorflow/core/distributed_runtime/rpc/grpc_testlib.h"
#include "tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.h"
#include "tensorflow/core/distributed_runtime/worker_session.h"
#include "tensorflow/core/framework/function_testlib.h"
#include "tensorflow/core/framework/tensor_testutil.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/util/equal_graph_def.h"

namespace tensorflow {

class ClusterFunctionLibraryRuntimeTest : public ::testing::Test {
 public:
  ClusterFunctionLibraryRuntimeTest() {
    SessionOptions options;
    TF_CHECK_OK(test::TestCluster::MakeTestCluster(options, 2, &cluster_));
    GrpcChannelSpec spec;
    TF_CHECK_OK(spec.AddHostPortsJob("localhost", cluster_->targets()));
    ChannelCreationFunction channel_func =
        ConvertToChannelCreationFunction(NewHostPortGrpcChannel);
    std::unique_ptr<WorkerCacheInterface> worker_cache(
        NewGrpcWorkerCache(std::shared_ptr<GrpcChannelCache>(
            NewGrpcChannelCache(spec, channel_func))));

    worker_session_.reset(new WorkerSession(
        "cluster_test_session", "/job:localhost/replica:0/task:0",
        std::move(worker_cache), std::unique_ptr<DeviceMgr>(),
        std::unique_ptr<GraphMgr>(), nullptr));

    cluster_flr_.reset(new ClusterFunctionLibraryRuntime(worker_session_.get(),
                                                         true, nullptr));
  }

  Status ConstructFunctionGraphHelper(
      const OpDef& sig, test::function::Attrs attrs,
      const FunctionLibraryRuntime::InstantiateOptions& options,
      const FunctionLibraryDefinition& lib_def, GraphDef* g,
      std::vector<string>* send_keys, std::vector<string>* recv_keys) {
    return ClusterFunctionLibraryRuntime::ConstructFunctionGraph(
        sig, attrs, options, lib_def, g, send_keys, recv_keys);
  }

  Status Instantiate(const string& function_name,
                     const FunctionLibraryDefinition& lib_def,
                     test::function::Attrs attrs,
                     const FunctionLibraryRuntime::InstantiateOptions& options,
                     FunctionLibraryRuntime::LocalHandle* local_handle) {
    return cluster_flr_->Instantiate(function_name, lib_def, attrs, options,
                                     local_handle);
  }

  Status InstantiateAndRun(
      const string& function_name, const FunctionLibraryDefinition& lib_def,
      test::function::Attrs attrs,
      const FunctionLibraryRuntime::InstantiateOptions& options,
      const std::vector<Tensor>& args, std::vector<Tensor*> rets) {
    FunctionLibraryRuntime::LocalHandle handle;
    TF_RETURN_IF_ERROR(cluster_flr_->Instantiate(function_name, lib_def, attrs,
                                                 options, &handle));

    Notification done;
    FunctionLibraryRuntime::Options opts;
    std::vector<Tensor> out;
    Status status;
    cluster_flr_->Run(opts, handle, args, &out,
                      [&status, &done](const Status& s) {
                        status = s;
                        done.Notify();
                      });
    done.WaitForNotification();
    if (!status.ok()) {
      return status;
    }
    CHECK_EQ(rets.size(), out.size());
    for (size_t i = 0; i < rets.size(); ++i) {
      *rets[i] = out[i];
    }

    return Status::OK();
  }

 protected:
  std::unique_ptr<test::TestCluster> cluster_;
  std::unique_ptr<WorkerSession> worker_session_;
  std::unique_ptr<ClusterFunctionLibraryRuntime> cluster_flr_;
};

TEST_F(ClusterFunctionLibraryRuntimeTest, ConstructFunctionGraph) {
  GraphDef actual;
  std::vector<string> send_keys, recv_keys;
  FunctionDefLibrary proto;
  *(proto.add_function()) = test::function::Swap();
  FunctionLibraryDefinition lib_def(OpRegistry::Global(), proto);

  FunctionLibraryRuntime::InstantiateOptions instantiate_opts;
  instantiate_opts.target = "/job:a/replica:0/task:0/device:CPU:0";
  TF_CHECK_OK(ConstructFunctionGraphHelper(
      test::function::Swap().signature(), {{"T", DT_FLOAT}}, instantiate_opts,
      lib_def, &actual, &send_keys, &recv_keys));
  GraphDef expected;
  protobuf::TextFormat::ParseFromString(R"(
node {
  name: "_recv_i0_0"
  op: "_Recv"
  device: "/job:a/replica:0/task:0/device:CPU:0"
  attr {
    key: "client_terminated"
    value {
      b: true
    }
  }
  attr {
    key: "recv_device"
    value {
      s: "/job:a/replica:0/task:0/device:CPU:0"
    }
  }
  attr {
    key: "send_device"
    value {
      s: "/job:a/replica:0/task:0/device:CPU:0"
    }
  }
  attr {
    key: "send_device_incarnation"
    value {
      i: 1
    }
  }
  attr {
    key: "tensor_name"
    value {
      s: "i0"
    }
  }
  attr {
    key: "tensor_type"
    value {
      type: DT_FLOAT
    }
  }
}
node {
  name: "_recv_i1_1"
  op: "_Recv"
  device: "/job:a/replica:0/task:0/device:CPU:0"
  attr {
    key: "client_terminated"
    value {
      b: true
    }
  }
  attr {
    key: "recv_device"
    value {
      s: "/job:a/replica:0/task:0/device:CPU:0"
    }
  }
  attr {
    key: "send_device"
    value {
      s: "/job:a/replica:0/task:0/device:CPU:0"
    }
  }
  attr {
    key: "send_device_incarnation"
    value {
      i: 1
    }
  }
  attr {
    key: "tensor_name"
    value {
      s: "i1"
    }
  }
  attr {
    key: "tensor_type"
    value {
      type: DT_FLOAT
    }
  }
}
node {
  name: "Func/Swap/input/_0"
  op: "Identity"
  input: "_recv_i0_0"
  device: "/job:a/replica:0/task:0/device:CPU:0"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
}
node {
  name: "Func/Swap/input/_1"
  op: "Identity"
  input: "_recv_i1_1"
  device: "/job:a/replica:0/task:0/device:CPU:0"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
}
node {
  name: "Swap/o0"
  op: "Identity"
  input: "Func/Swap/input/_1"
  device: "/job:a/replica:0/task:0/device:CPU:0"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
}
node {
  name: "Swap/o1"
  op: "Identity"
  input: "Func/Swap/input/_0"
  device: "/job:a/replica:0/task:0/device:CPU:0"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
}
node {
  name: "Func/Swap/output/_2"
  op: "Identity"
  input: "Swap/o0"
  device: "/job:a/replica:0/task:0/device:CPU:0"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
}
node {
  name: "Func/Swap/output/_3"
  op: "Identity"
  input: "Swap/o1"
  device: "/job:a/replica:0/task:0/device:CPU:0"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
}
node {
  name: "_send_o0_0"
  op: "_Send"
  input: "Func/Swap/output/_2"
  device: "/job:a/replica:0/task:0/device:CPU:0"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "client_terminated"
    value {
      b: true
    }
  }
  attr {
    key: "recv_device"
    value {
      s: "/job:a/replica:0/task:0/device:CPU:0"
    }
  }
  attr {
    key: "send_device"
    value {
      s: "/job:a/replica:0/task:0/device:CPU:0"
    }
  }
  attr {
    key: "send_device_incarnation"
    value {
      i: 1
    }
  }
  attr {
    key: "tensor_name"
    value {
      s: "o0"
    }
  }
}
node {
  name: "_send_o1_1"
  op: "_Send"
  input: "Func/Swap/output/_3"
  device: "/job:a/replica:0/task:0/device:CPU:0"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "client_terminated"
    value {
      b: true
    }
  }
  attr {
    key: "recv_device"
    value {
      s: "/job:a/replica:0/task:0/device:CPU:0"
    }
  }
  attr {
    key: "send_device"
    value {
      s: "/job:a/replica:0/task:0/device:CPU:0"
    }
  }
  attr {
    key: "send_device_incarnation"
    value {
      i: 1
    }
  }
  attr {
    key: "tensor_name"
    value {
      s: "o1"
    }
  }
}
)",
                                        &expected);
  TF_EXPECT_GRAPH_EQ(expected, actual);
}

// Disabling the following two tests since there seem to be some issues with
// GRPC bringing up multiple processes as sub-processes.
// More info at: https://github.com/grpc/grpc/issues/10142.
// TODO(rohanj): Enable tests when the grpc bug is fixed.
TEST_F(ClusterFunctionLibraryRuntimeTest, DISABLED_InstantiateAndRun) {
  FunctionDefLibrary proto;
  *(proto.add_function()) = test::function::XTimesTwoInt32();
  FunctionLibraryDefinition lib_def(OpRegistry::Global(), proto);
  FunctionLibraryRuntime::InstantiateOptions instantiate_opts;
  instantiate_opts.target = "/job:localhost/replica:0/task:1/cpu:0";

  Tensor y;
  auto x = test::AsTensor<int32>({1, 2, 3, 4});
  TF_EXPECT_OK(InstantiateAndRun("XTimesTwoInt32", lib_def, {},
                                 instantiate_opts, {x}, {&y}));
  test::ExpectTensorEqual<int32>(y, test::AsTensor<int32>({2, 4, 6, 8}));
}

TEST_F(ClusterFunctionLibraryRuntimeTest,
       DISABLED_InstantiateAndRunAttrSubstitution) {
  FunctionDefLibrary proto;
  *(proto.add_function()) = test::function::Swap();
  FunctionLibraryDefinition lib_def(OpRegistry::Global(), proto);
  FunctionLibraryRuntime::InstantiateOptions instantiate_opts;
  instantiate_opts.target = "/job:localhost/replica:0/task:1/cpu:0";
  Tensor y1, y2;
  auto x1 = test::AsTensor<float>({1, 2, 3, 4});
  auto x2 = test::AsTensor<float>({4, 3, 2, 1});
  TF_EXPECT_OK(InstantiateAndRun("Swap", lib_def, {{"T", DT_FLOAT}},
                                 instantiate_opts, {x1, x2}, {&y1, &y2}));
  test::ExpectTensorEqual<float>(y1, test::AsTensor<float>({4, 3, 2, 1}));
  test::ExpectTensorEqual<float>(y2, test::AsTensor<float>({1, 2, 3, 4}));
}

}  // namespace tensorflow
