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/compiler/xla/client/xla_builder.h" 21 #include "tensorflow/core/framework/kernel_def_builder.h" 22 #include "tensorflow/core/framework/tensor.pb.h" 23 #include "tensorflow/core/framework/types.pb.h" 24 25 namespace tensorflow { 26 namespace { 27 28 class ConstOp : public XlaOpKernel { 29 public: ConstOp(OpKernelConstruction * ctx)30 explicit ConstOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) { 31 const TensorProto* proto = nullptr; 32 OP_REQUIRES_OK(ctx, ctx->GetAttr("value", &proto)); 33 proto_ = *proto; 34 OP_REQUIRES( 35 ctx, ctx->output_type(0) == proto_.dtype(), 36 errors::InvalidArgument("Type mismatch between value (", 37 DataTypeString(proto_.dtype()), ") and dtype (", 38 DataTypeString(ctx->output_type(0)), ")")); 39 OP_REQUIRES_OK(ctx, TensorShape::IsValidShape(proto_.tensor_shape())); 40 } 41 Compile(XlaOpKernelContext * ctx)42 void Compile(XlaOpKernelContext* ctx) override { 43 TensorShape shape(proto_.tensor_shape()); 44 45 xla::XlaBuilder* b = ctx->builder(); 46 47 // To avoid blowups for large constants filled with the same value, 48 // recognize that case and emit a scalar broadcast instead. 49 if (shape.num_elements() > 1) { 50 switch (proto_.dtype()) { 51 case DT_BOOL: 52 if (proto_.bool_val_size() == 1) { 53 ctx->SetOutput( 54 0, xla::Broadcast(xla::ConstantR0<bool>(b, proto_.bool_val(0)), 55 shape.dim_sizes())); 56 return; 57 } 58 break; 59 case DT_FLOAT: 60 if (proto_.float_val_size() == 1) { 61 ctx->SetOutput(0, xla::Broadcast(xla::ConstantR0<float>( 62 b, proto_.float_val(0)), 63 shape.dim_sizes())); 64 return; 65 } 66 break; 67 case DT_DOUBLE: 68 if (proto_.double_val_size() == 1) { 69 ctx->SetOutput(0, xla::Broadcast(xla::ConstantR0<double>( 70 b, proto_.double_val(0)), 71 shape.dim_sizes())); 72 return; 73 } 74 break; 75 case DT_COMPLEX64: 76 if (proto_.scomplex_val_size() == 2) { 77 ctx->SetOutput( 78 0, 79 xla::Broadcast(xla::ConstantR0<xla::complex64>( 80 b, xla::complex64(proto_.scomplex_val(0), 81 proto_.scomplex_val(1))), 82 shape.dim_sizes())); 83 return; 84 } 85 break; 86 case DT_COMPLEX128: 87 if (proto_.scomplex_val_size() == 2) { 88 ctx->SetOutput( 89 0, 90 xla::Broadcast(xla::ConstantR0<xla::complex128>( 91 b, xla::complex128(proto_.dcomplex_val(0), 92 proto_.dcomplex_val(1))), 93 shape.dim_sizes())); 94 return; 95 } 96 break; 97 case DT_INT32: 98 if (proto_.int_val_size() == 1) { 99 ctx->SetOutput( 100 0, xla::Broadcast(xla::ConstantR0<int32>(b, proto_.int_val(0)), 101 shape.dim_sizes())); 102 return; 103 } 104 break; 105 case DT_INT64: 106 if (proto_.int64_val_size() == 1) { 107 ctx->SetOutput(0, xla::Broadcast(xla::ConstantR0<int64>( 108 b, proto_.int64_val(0)), 109 shape.dim_sizes())); 110 return; 111 } 112 break; 113 default: 114 break; 115 } 116 } 117 118 // General case 119 Tensor tensor(proto_.dtype()); 120 OP_REQUIRES(ctx, tensor.FromProto(cpu_allocator(), proto_), 121 errors::InvalidArgument("Cannot parse tensor from proto: ", 122 proto_.DebugString())); 123 ctx->SetConstantOutput(0, tensor); 124 } 125 126 private: 127 TensorProto proto_; 128 TF_DISALLOW_COPY_AND_ASSIGN(ConstOp); 129 }; 130 131 // XLA_* devices also register a "real" Const operator so we suppress the 132 // dummy operator using CompilationOnly(). 133 REGISTER_XLA_OP(Name("Const").CompilationOnly(), ConstOp); 134 135 } // namespace 136 } // namespace tensorflow 137