compiler/locomotiv/src/Node/TensorBroadcast.cpp

   1 /*
   2  * Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
   3  *
   4  * Licensed under the Apache License, Version 2.0 (the "License");
   5  * you may not use this file except in compliance with the License.
   6  * You may obtain a copy of the License at
   7  *
   8  *    http://www.apache.org/licenses/LICENSE-2.0
   9  *
  10  * Unless required by applicable law or agreed to in writing, software
  11  * distributed under the License is distributed on an "AS IS" BASIS,
  12  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  13  * See the License for the specific language governing permissions and
  14  * limitations under the License.
  15  */
  16
  17 #include "NodeExecution.h"
  18
  19 #include "NodeDataImpl.h"
  20 #include "NodeDomain.h"
  21
  22 #include <nncc/core/ADT/tensor/Shape.h>
  23 #include <nncc/core/ADT/tensor/Buffer.h>
  24 #include <nncc/core/ADT/tensor/Index.h>
  25 #include <nncc/core/ADT/tensor/IndexEnumerator.h>
  26 #include <nncc/core/ADT/tensor/LexicalLayout.h>
  27
  28 using nncc::core::ADT::tensor::Index;
  29 using nncc::core::ADT::tensor::IndexEnumerator;
  30 using nncc::core::ADT::tensor::LexicalLayout;
  31 using nncc::core::ADT::tensor::make_buffer;
  32 using nncc::core::ADT::tensor::Shape;
  33
  34 #include <cassert>
  35 #include <stdexcept>
  36
  37 namespace
  38 {
  39
  40 using namespace locomotiv;
  41
  42 void execute_node(loco::TensorBroadcast *tensor_broadcast)
  43 {
  44   auto input_data = annot_data(tensor_broadcast->input());
  45
  46   // Calculate output shape
  47   Shape input_shape = *(input_data->shape());
  48
  49   // TODO Reuse "ShapeInferenceService"
  50   Shape output_shape;
  51
  52   output_shape.resize(input_shape.rank());
  53   for (uint32_t axis = 0; axis < input_shape.rank(); ++axis)
  54   {
  55     if (tensor_broadcast->mapping()->defined(axis))
  56     {
  57       assert(input_shape.dim(axis) == 1); // Required by TensorBroadcast definition
  58       output_shape.dim(axis) = tensor_broadcast->mapping()->dim(axis).value();
  59     }
  60     else
  61     {
  62       output_shape.dim(axis) = input_shape.dim(axis);
  63     }
  64   }
  65
  66   assert(input_shape.rank() == output_shape.rank());
  67
  68   uint32_t const rank = input_shape.rank();
  69
  70   std::unique_ptr<NodeData> output_data = nullptr;
  71
  72   switch (input_data->dtype())
  73   {
  74     // TODO Use type-generic implementation!
  75     case loco::DataType::FLOAT32:
  76     {
  77       auto input_bufptr = input_data->as_f32_bufptr();
  78       auto output_buf = make_buffer<float, LexicalLayout>(output_shape);
  79
  80       for (IndexEnumerator e{output_shape}; e.valid(); e.advance())
  81       {
  82         auto input_index = e.current();
  83         const auto &output_index = e.current();
  84
  85         for (uint32_t axis = 0; axis < rank; ++axis)
  86         {
  87           if (tensor_broadcast->mapping()->defined(axis))
  88           {
  89             input_index.at(axis) = 0;
  90           }
  91         }
  92
  93         output_buf.at(output_index) = input_bufptr->at(input_index);
  94       }
  95
  96       output_data = make_data(output_buf);
  97       break;
  98     }
  99     default:
 100       throw std::runtime_error("Not yet supported");
 101   }
 102
 103   assert(output_data != nullptr);
 104   annot_data(tensor_broadcast, std::move(output_data));
 105   annot_domain(tensor_broadcast, loco::Domain::Tensor);
 106 }
 107
 108 } // namespace
 109
 110 namespace locomotiv
 111 {
 112
 113 void NodeExecution::execute(loco::TensorBroadcast *tensor_broadcast)
 114 {
 115   execute_node(tensor_broadcast);
 116 }
 117
 118 } // namespace locomotiv