reshape_compute.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 <vector>

#include "lite/backends/fpga/KD/debugger.hpp"
#include "lite/kernels/fpga/reshape_compute.h"
#include "lite/operators/reshape_op.h"

namespace paddle {
namespace lite {
namespace kernels {
namespace fpga {

using float16 = zynqmp::float16;

void FlattenCompute::Run() {
  auto& param = Param<operators::ReshapeParam>();
  auto x = param.x;
  auto output = param.output;
  output->mutable_data<float16>();
  auto output_dims = output->dims();
  if (param.inplace) {
    output->ShareDataWith(*x);
  } else {
    // output->CopyDataFrom(*x);
  }
  x->ZynqTensor()->unalignImage();
  // x->ZynqTensor()->saveToFile("fi", true);

  output->ZynqTensor()->copyFrom(x->ZynqTensor());
  // output->ZynqTensor()->saveToFile("fo", true);
  output->ZynqTensor()->flush();
  output->ZynqTensor()->setAligned(x->ZynqTensor()->aligned());
  output->Resize(output_dims);

#ifdef FPGA_PRINT_TENSOR
  Debugger::get_instance().registerOutput("flatten", output->ZynqTensor());
#endif
}

void ReshapeCompute::PrepareForRun() {
  auto& param = Param<operators::ReshapeParam>();
  auto x = param.x;
  auto output = param.output;
  auto output_dims = output->dims();

  output->Resize(output_dims);
  output->mutable_data<float16>();
}

void ReshapeCompute::Run() {
  auto& param = Param<operators::ReshapeParam>();
  auto x = param.x;
  auto output = param.output;
  // auto output_dims = output->dims();

  // x->ZynqTensor()->invalidate();// TODO
  x->ZynqTensor()->unalignImage();
  x->ZynqTensor()->flush();

  // output->Resize(output_dims);
  // output->mutable_data<float16>();

  if (param.inplace) {
    // output->ShareDataWith(*x);
  } else {
    // output->CopyDataFrom(*x);
  }

  output->ZynqTensor()->copyFrom(x->ZynqTensor());
  // output->ZynqTensor()->saveToFile("ro", true);
  output->ZynqTensor()->flush();
// output->ZynqTensor()->setAligned(x->ZynqTensor()->aligned());

#ifdef FPGA_PRINT_TENSOR
  Debugger::get_instance().registerOutput("reshape", output->ZynqTensor());
#endif
}

}  // namespace fpga
}  // namespace kernels
}  // namespace lite
}  // namespace paddle

REGISTER_LITE_KERNEL(reshape,
                     kFPGA,
                     kFP16,
                     kNHWC,
                     paddle::lite::kernels::fpga::ReshapeCompute,
                     def)
    .BindInput("X",
               {LiteType::GetTensorTy(TARGET(kFPGA),
                                      PRECISION(kFP16),
                                      DATALAYOUT(kNHWC))})
    .BindInput("Shape",
               {LiteType::GetTensorTy(TARGET(kHost),
                                      PRECISION(kAny),
                                      DATALAYOUT(kAny))})
    .BindOutput("Out",
                {LiteType::GetTensorTy(TARGET(kFPGA),
                                       PRECISION(kFP16),
                                       DATALAYOUT(kNHWC))})
    .Finalize();

REGISTER_LITE_KERNEL(reshape2,
                     kFPGA,
                     kFP16,
                     kNHWC,
                     paddle::lite::kernels::fpga::ReshapeCompute,
                     def)
    .BindInput("X",
               {LiteType::GetTensorTy(TARGET(kFPGA),
                                      PRECISION(kFP16),
                                      DATALAYOUT(kNHWC))})
    .BindInput("Shape",
               {LiteType::GetTensorTy(TARGET(kHost),
                                      PRECISION(kAny),
                                      DATALAYOUT(kAny))})
    .BindOutput("Out",
                {LiteType::GetTensorTy(TARGET(kFPGA),
                                       PRECISION(kFP16),
                                       DATALAYOUT(kNHWC))})
    .BindOutput("XShape",
                {LiteType::GetTensorTy(TARGET(kFPGA),
                                       PRECISION(kFP16),
                                       DATALAYOUT(kNHWC))})
    .Finalize();

REGISTER_LITE_KERNEL(flatten,
                     kFPGA,
                     kFP16,
                     kNHWC,
                     paddle::lite::kernels::fpga::FlattenCompute,
                     def)
    .BindInput("X",
               {LiteType::GetTensorTy(TARGET(kFPGA),
                                      PRECISION(kFP16),
                                      DATALAYOUT(kNHWC))})
    .BindInput("Shape",
               {LiteType::GetTensorTy(TARGET(kHost),
                                      PRECISION(kAny),
                                      DATALAYOUT(kAny))})
    .BindOutput("Out",
                {LiteType::GetTensorTy(TARGET(kFPGA),
                                       PRECISION(kFP16),
                                       DATALAYOUT(kNHWC))})
    .Finalize();

REGISTER_LITE_KERNEL(flatten2,
                     kFPGA,
                     kFP16,
                     kNHWC,
                     paddle::lite::kernels::fpga::FlattenCompute,
                     def)
    .BindInput("X",
               {LiteType::GetTensorTy(TARGET(kFPGA),
                                      PRECISION(kFP16),
                                      DATALAYOUT(kNHWC))})
    .BindInput("Shape",
               {LiteType::GetTensorTy(TARGET(kHost),
                                      PRECISION(kAny),
                                      DATALAYOUT(kAny))})
    .BindOutput("Out",
                {LiteType::GetTensorTy(TARGET(kFPGA),
                                       PRECISION(kFP16),
                                       DATALAYOUT(kNHWC))})
    .BindOutput("XShape",
                {LiteType::GetTensorTy(TARGET(kFPGA),
                                       PRECISION(kFP16),
                                       DATALAYOUT(kNHWC))})
    .Finalize();