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 #include "tensorflow/core/common_runtime/function_testlib.h" 16 17 #include "tensorflow/core/common_runtime/device.h" 18 #include "tensorflow/core/framework/common_shape_fns.h" 19 #include "tensorflow/core/framework/node_def.pb.h" 20 #include "tensorflow/core/framework/node_def_builder.h" 21 #include "tensorflow/core/framework/op_kernel.h" 22 23 namespace tensorflow { 24 namespace test { 25 namespace function { 26 27 typedef FunctionDefHelper FDH; 28 29 class FindDeviceOpKernel : public OpKernel { 30 public: 31 explicit FindDeviceOpKernel(OpKernelConstruction* ctx) : OpKernel(ctx) {} 32 void Compute(OpKernelContext* ctx) override { 33 Tensor* device_tensor = nullptr; 34 OP_REQUIRES_OK(ctx, ctx->allocate_output("device_name", TensorShape{}, 35 &device_tensor)); 36 device_tensor->scalar<string>()() = 37 ctx->function_library()->device()->name(); 38 } 39 }; 40 41 REGISTER_KERNEL_BUILDER(Name("FindDeviceOp").Device(tensorflow::DEVICE_CPU), 42 FindDeviceOpKernel); 43 REGISTER_OP("FindDeviceOp") 44 .Output("device_name: string") 45 .SetShapeFn(shape_inference::UnknownShape); 46 47 FunctionDef FindDevice() { 48 return FDH::Define( 49 // Name 50 "FindDevice", 51 // Args 52 {}, 53 // Return values 54 {"device_name: string"}, 55 // Attr def 56 {}, 57 // Nodes 58 {{{"device_name"}, "FindDeviceOp", {}, {}}}); 59 } 60 61 void BlockingOpState::AwaitState(int awaiting_state) { 62 mutex_lock ml(mu_); 63 while (state_ != awaiting_state) { 64 cv_.wait(ml); 65 } 66 } 67 68 void BlockingOpState::MoveToState(int expected_current, int next) { 69 mutex_lock ml(mu_); 70 CHECK_EQ(expected_current, state_); 71 state_ = next; 72 cv_.notify_all(); 73 } 74 75 BlockingOpState* blocking_op_state = nullptr; 76 77 // BlockingOp blocks on the global <blocking_op_state's> state, 78 // and also updates it when it is unblocked and finishing computation. 79 class BlockingOp : public OpKernel { 80 public: 81 explicit BlockingOp(OpKernelConstruction* ctx) : OpKernel(ctx) {} 82 void Compute(OpKernelContext* ctx) override { 83 blocking_op_state->MoveToState(0, 1); 84 blocking_op_state->AwaitState(2); 85 blocking_op_state->MoveToState(2, 3); 86 87 Tensor* out = nullptr; 88 const Tensor& in = ctx->input(0); 89 OP_REQUIRES_OK(ctx, ctx->allocate_output(0, in.shape(), &out)); 90 out->flat<float>() = in.flat<float>(); 91 } 92 }; 93 REGISTER_KERNEL_BUILDER(Name("BlockingOp").Device(DEVICE_CPU), BlockingOp); 94 REGISTER_OP("BlockingOp") 95 .Input("x: float") 96 .Output("y: float") 97 .Doc("") 98 .SetShapeFn(shape_inference::UnknownShape); 99 100 FunctionDef BlockingOpFn() { 101 return FDH::Define( 102 // Name 103 "BlockingOpFn", 104 // Args 105 {"x: float"}, 106 // Return values 107 {"y: float"}, 108 // Attr def 109 {}, 110 // Nodes 111 {{{"y"}, "BlockingOp", {"x"}, {}}}); 112 } 113 114 // TODO(phawkins): replace with C++ API for calling functions, when that exists. 115 Output Call(Scope* scope, const string& op_name, const string& fn_name, 116 gtl::ArraySlice<Input> inputs) { 117 NodeDef def; 118 NodeDefBuilder builder(op_name, fn_name, scope->graph()->op_registry()); 119 for (const Input& input : inputs) { 120 builder.Input(input.node()->name(), input.index(), 121 input.node()->output_type(input.index())); 122 } 123 TF_CHECK_OK(builder.Finalize(&def)); 124 Status status; 125 Node* n = scope->graph()->AddNode(def, &status); 126 TF_CHECK_OK(status); 127 TF_CHECK_OK(scope->DoShapeInference(n)); 128 for (int i = 0; i < inputs.size(); ++i) { 129 scope->graph()->AddEdge(inputs[i].node(), inputs[i].index(), n, i); 130 } 131 return Output(n); 132 } 133 134 } // namespace function 135 } // namespace test 136 } // namespace tensorflow 137