1 /* Copyright 2015 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 // Implements a quantized eight-bit version of the bias addition operation. 17 18 #define EIGEN_USE_THREADS 19 20 #include "tensorflow/core/framework/numeric_op.h" 21 #include "tensorflow/core/framework/op_kernel.h" 22 #include "tensorflow/core/framework/tensor.h" 23 #include "tensorflow/core/framework/tensor_shape.h" 24 #include "tensorflow/core/kernels/meta_support.h" 25 #include "tensorflow/core/kernels/ops_util.h" 26 #include "tensorflow/core/kernels/quantization_utils.h" 27 #include "tensorflow/core/lib/core/errors.h" 28 29 namespace tensorflow { 30 31 typedef Eigen::ThreadPoolDevice CPUDevice; 32 33 template <class T1, class T2, class T3> 34 class QuantizedBiasAddOp : public OpKernel { 35 public: QuantizedBiasAddOp(OpKernelConstruction * context)36 explicit QuantizedBiasAddOp(OpKernelConstruction* context) 37 : OpKernel(context) {} 38 Compute(OpKernelContext * context)39 void Compute(OpKernelContext* context) override { 40 const Tensor& input = context->input(0); 41 const Tensor& bias = context->input(1); 42 43 const Tensor& min_input = context->input(2); 44 const Tensor& max_input = context->input(3); 45 const Tensor& min_bias = context->input(4); 46 const Tensor& max_bias = context->input(5); 47 OP_REQUIRES( 48 context, TensorShapeUtils::IsScalar(min_input.shape()), 49 errors::InvalidArgument("`min_input` must be rank 0 but is rank ", 50 min_input.dims())); 51 OP_REQUIRES( 52 context, TensorShapeUtils::IsScalar(max_input.shape()), 53 errors::InvalidArgument("`max_input` must be rank 0 but is rank ", 54 max_input.dims())); 55 OP_REQUIRES(context, TensorShapeUtils::IsScalar(min_bias.shape()), 56 errors::InvalidArgument( 57 "`min_bias` must be rank 0 but is rank ", min_bias.dims())); 58 OP_REQUIRES(context, TensorShapeUtils::IsScalar(max_bias.shape()), 59 errors::InvalidArgument( 60 "`max_bias` must be rank 0 but is rank ", max_bias.dims())); 61 62 const float input_min = min_input.flat<float>()(0); 63 const float input_max = max_input.flat<float>()(0); 64 const float bias_min = min_bias.flat<float>()(0); 65 const float bias_max = max_bias.flat<float>()(0); 66 67 OP_REQUIRES(context, TensorShapeUtils::IsMatrixOrHigher(input.shape()), 68 errors::InvalidArgument("Input tensor must be at least 2D: ", 69 input.shape().DebugString())); 70 OP_REQUIRES(context, TensorShapeUtils::IsVector(bias.shape()), 71 errors::InvalidArgument("Biases must be 1D: ", 72 bias.shape().DebugString())); 73 const auto last_dim = input.shape().dims() - 1; 74 OP_REQUIRES( 75 context, bias.shape().dim_size(0) == input.shape().dim_size(last_dim), 76 errors::InvalidArgument( 77 "Must provide as many biases as the last dimension " 78 "of the input tensor: ", 79 bias.shape().DebugString(), " vs. ", input.shape().DebugString())); 80 OP_REQUIRES(context, bias.NumElements() > 0, 81 errors::InvalidArgument("Must provide at least 1 bias")); 82 83 Tensor* output = nullptr; 84 OP_REQUIRES_OK(context, 85 context->allocate_output(0, input.shape(), &output)); 86 87 float total_min; 88 float total_max; 89 90 if (meta::IsSupportedAndEnabled() && std::is_same<T1, quint8>() && 91 std::is_same<T2, quint8>() && std::is_same<T3, qint32>()) { 92 auto input_ui8_array = input.flat<quint8>(); 93 auto bias_ui8_array = bias.flat<quint8>(); 94 GetOutputMinAndMaxForQuantizedAdd(input_min, input_max, bias_min, 95 bias_max, &total_min, &total_max); 96 meta::QuantizedBiasAdd(context, input_ui8_array.data(), 97 input_ui8_array.size(), bias_ui8_array.data(), 98 bias_ui8_array.size(), input_min, input_max, 99 bias_min, bias_max, total_min, total_max, 100 output->flat<qint32>().data()); 101 } else { 102 QuantizedAddUsingEigen<T1, T2, T3>( 103 context->template eigen_device<CPUDevice>(), input, input_min, 104 input_max, bias, bias_min, bias_max, output, &total_min, &total_max); 105 } 106 107 Tensor* output_min = nullptr; 108 OP_REQUIRES_OK(context, context->allocate_output(1, {}, &output_min)); 109 output_min->flat<float>()(0) = total_min; 110 111 Tensor* output_max = nullptr; 112 OP_REQUIRES_OK(context, context->allocate_output(2, {}, &output_max)); 113 output_max->flat<float>()(0) = total_max; 114 } 115 }; 116 117 REGISTER_KERNEL_BUILDER(Name("QuantizedBiasAdd") 118 .Device(DEVICE_CPU) 119 .TypeConstraint<quint8>("T1") 120 .TypeConstraint<quint8>("T2") 121 .TypeConstraint<qint32>("out_type"), 122 QuantizedBiasAddOp<quint8, quint8, qint32>); 123 REGISTER_KERNEL_BUILDER(Name("QuantizedBiasAdd") 124 .Device(DEVICE_CPU) 125 .TypeConstraint<qint8>("T1") 126 .TypeConstraint<qint8>("T2") 127 .TypeConstraint<qint32>("out_type"), 128 QuantizedBiasAddOp<qint8, qint8, qint32>); 129 } // namespace tensorflow 130