transpose_op.cc

// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#include <bmcompiler_defs.h>
#include <bmcompiler_if.h>
#include "lite/kernels/bm/bridges/graph.h"
#include "lite/kernels/bm/bridges/utility.h"
#include "lite/kernels/npu/bridges/registry.h"

namespace paddle {
namespace lite {
namespace subgraph {
namespace bm {

int TransposeConverter(void* ctx, OpLite* op, KernelBase* kernel) {
  CHECK(ctx != nullptr);
  CHECK(op != nullptr);
  auto graph = static_cast<Graph*>(ctx);
  auto scope = op->scope();
  auto op_info = op->op_info();
  auto op_type = op_info->Type();
  auto x_var_name = op_info->Input("X").front();
  auto x = scope->FindVar(x_var_name)->GetMutable<lite::Tensor>();
  auto x_dims = x->dims();
  auto output_var_name = op_info->Output("Out").front();
  auto output = scope->FindVar(output_var_name)->GetMutable<lite::Tensor>();
  auto output_dims = output->dims();
  const int64_t* x_shape_data = const_cast<const int64_t*>(&x_dims.data()[0]);
  const int64_t* output_shape_data =
      const_cast<const int64_t*>(&output_dims.data()[0]);
  std::vector<int32_t> i_x_shape_data(x_dims.size());
  std::vector<int32_t> i_output_shape_data(x_dims.size());
  for (size_t i = 0; i < x_dims.size(); i++) {
    i_x_shape_data[i] = static_cast<int>(x_shape_data[i]);
  }
  auto out_name = output_var_name;
  if (x_dims.size() > output_dims.size()) {
    for (size_t i = 0; i < (x_dims.size() - output_dims.size()); i++) {
      i_output_shape_data[i] = 1;
    }
    out_name = lite::subgraph::bm::UniqueName(op_type);
  }

  for (size_t i = (x_dims.size() - output_dims.size()); i < output_dims.size();
       i++) {
    i_output_shape_data[i] = static_cast<int>(output_shape_data[i]);
  }
  auto axis = op_info->GetAttr<std::vector<int>>("axis");
  CHECK_EQ(axis.size(), x_dims.size());
  add_transpose_layer_v2(graph->GetCompilerHandle(),
                         static_cast<const char*>(x_var_name.c_str()),
                         const_cast<const int*>(&i_x_shape_data[0]),
                         x_dims.size(),
                         DTYPE_FP32,
                         static_cast<const char*>(out_name.c_str()),
                         NULL,
                         const_cast<const int*>(&axis[0]));
  if (x_dims.size() > output_dims.size()) {
    std::vector<int32_t> i_real_output_shape_data(output_dims.size());
    for (size_t i = 0; i < output_dims.size(); i++) {
      i_real_output_shape_data[i] = static_cast<int>(output_shape_data[i]);
    }
    add_reshape_layer_v2(graph->GetCompilerHandle(),
                         static_cast<const char*>(out_name.c_str()),
                         const_cast<const int*>(&i_output_shape_data[0]),
                         i_output_shape_data.size(),
                         static_cast<const char*>(output_var_name.c_str()),
                         const_cast<const int*>(&i_real_output_shape_data[0]),
                         output_dims.size());
  }
  graph->AddNode(output_var_name);
  return SUCCESS;
}

}  // namespace bm
}  // namespace subgraph
}  // namespace lite
}  // namespace paddle

REGISTER_SUBGRAPH_BRIDGE(transpose,
                         kBM,
                         paddle::lite::subgraph::bm::TransposeConverter);
REGISTER_SUBGRAPH_BRIDGE(transpose2,
                         kBM,
                         paddle::lite::subgraph::bm::TransposeConverter);