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 #include "tensorflow/compiler/tf2xla/type_util.h" 17 #include "tensorflow/compiler/tf2xla/xla_compiler.h" 18 #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" 19 #include "tensorflow/compiler/tf2xla/xla_op_registry.h" 20 #include "tensorflow/core/framework/kernel_def_builder.h" 21 #include "tensorflow/core/framework/tensor.pb.h" 22 23 namespace tensorflow { 24 namespace { 25 26 class ConstOp : public XlaOpKernel { 27 public: 28 explicit ConstOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) { 29 const TensorProto* proto = nullptr; 30 OP_REQUIRES_OK(ctx, ctx->GetAttr("value", &proto)); 31 proto_ = *proto; 32 OP_REQUIRES( 33 ctx, ctx->output_type(0) == proto_.dtype(), 34 errors::InvalidArgument("Type mismatch between value (", 35 DataTypeString(proto_.dtype()), ") and dtype (", 36 DataTypeString(ctx->output_type(0)), ")")); 37 OP_REQUIRES_OK(ctx, TensorShape::IsValidShape(proto_.tensor_shape())); 38 } 39 40 void Compile(XlaOpKernelContext* ctx) override { 41 TensorShape shape(proto_.tensor_shape()); 42 43 if (proto_.dtype() == DT_STRING) { 44 LOG(WARNING) << "Not computing Const of type DT_STRING"; 45 ctx->SetInvalidOutput(0); 46 return; 47 } 48 xla::ComputationBuilder* b = ctx->builder(); 49 50 // To avoid blowups for large constants filled with the same value, 51 // recognize that case and emit a scalar broadcast instead. 52 if (shape.num_elements() > 1) { 53 switch (proto_.dtype()) { 54 case DT_BOOL: 55 if (proto_.bool_val_size() == 1) { 56 ctx->SetOutput(0, 57 b->Broadcast(b->ConstantR0<bool>(proto_.bool_val(0)), 58 shape.dim_sizes())); 59 return; 60 } 61 break; 62 case DT_FLOAT: 63 if (proto_.float_val_size() == 1) { 64 ctx->SetOutput( 65 0, b->Broadcast(b->ConstantR0<float>(proto_.float_val(0)), 66 shape.dim_sizes())); 67 return; 68 } 69 break; 70 case DT_DOUBLE: 71 if (proto_.double_val_size() == 1) { 72 ctx->SetOutput( 73 0, b->Broadcast(b->ConstantR0<double>(proto_.double_val(0)), 74 shape.dim_sizes())); 75 return; 76 } 77 break; 78 case DT_INT32: 79 if (proto_.int_val_size() == 1) { 80 ctx->SetOutput(0, 81 b->Broadcast(b->ConstantR0<int32>(proto_.int_val(0)), 82 shape.dim_sizes())); 83 return; 84 } 85 break; 86 case DT_INT64: 87 if (proto_.int64_val_size() == 1) { 88 ctx->SetOutput( 89 0, b->Broadcast(b->ConstantR0<int64>(proto_.int64_val(0)), 90 shape.dim_sizes())); 91 return; 92 } 93 break; 94 default: 95 break; 96 } 97 } 98 99 // General case 100 Tensor tensor(proto_.dtype()); 101 OP_REQUIRES(ctx, tensor.FromProto(cpu_allocator(), proto_), 102 errors::InvalidArgument("Cannot parse tensor from proto: ", 103 proto_.DebugString())); 104 ctx->SetConstantOutput(0, tensor); 105 } 106 107 private: 108 TensorProto proto_; 109 TF_DISALLOW_COPY_AND_ASSIGN(ConstOp); 110 }; 111 112 // XLA_* devices also register a "real" Const operator so we suppress the 113 // dummy operator using CompilationOnly(). 114 REGISTER_XLA_OP(Name("Const").CompilationOnly(), ConstOp); 115 116 } // namespace 117 } // namespace tensorflow 118
