dense_tensor_kernels.cc

// Copyright (c) 2022 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 "paddle/infrt/kernel/phi/dense_tensor_kernels.h"
#include "llvm/Support/ErrorHandling.h"
#include "paddle/infrt/common/string.h"
#include "paddle/infrt/dialect/phi/data_type.h"
#include "paddle/infrt/kernel/phi/context_kernels.h"
#include "paddle/infrt/paddle/model_parser.h"
#include "paddle/infrt/paddle/scope.h"
#include "paddle/phi/backends/all_context.h"
#include "paddle/phi/common/place.h"

#ifdef INFRT_WITH_GPU
#include <cuda_runtime.h>
#endif

namespace paddle {
namespace platform {
using DeviceContext = ::phi::DeviceContext;
}  // namespace platform
namespace framework {
using LoDTensor = ::phi::DenseTensor;
void DeserializeFromStream(std::istream& is,
                           LoDTensor* tensor,
                           const platform::DeviceContext& dev_ctx);
}
}  // namespace paddle

namespace infrt {
namespace kernel {
namespace phi {

::phi::DenseTensor CreateDenseTensor(
    const ::phi::CPUContext& context,
    host_context::Attribute<std::vector<int64_t>> dims,
    host_context::Attribute<std::vector<int64_t>> lod,
    host_context::Attribute<::infrt::LayoutType> layout,
    host_context::Attribute<::infrt::PrecisionType> precision) {
  return ::phi::DenseTensor(
      const_cast<::phi::Allocator*>(&context.GetAllocator()),
      ::phi::DenseTensorMeta(ConvertPrecisionToPhi(precision.get()),
                             ::phi::make_ddim(dims.get()),
                             ConvertLayoutToPhi(layout.get()),
                             {}));
}

::phi::DenseTensor CreateInitedDenseTensorF32(
    const ::phi::CPUContext& context,
    host_context::Attribute<std::vector<int64_t>> dims,
    host_context::Attribute<std::vector<int64_t>> lod,
    host_context::Attribute<::infrt::LayoutType> layout,
    host_context::Attribute<float> value) {
  ::phi::DenseTensor dense_tensor(
      const_cast<::phi::Allocator*>(&context.GetAllocator()),
      ::phi::DenseTensorMeta(
          ConvertPrecisionToPhi(::infrt::PrecisionType::FLOAT32),
          ::phi::make_ddim(dims.get()),
          ConvertLayoutToPhi(layout.get()),
          {}));
  float* a_data = dense_tensor.mutable_data<float>(::phi::CPUPlace());
  for (int64_t i = 0; i < dense_tensor.numel(); ++i) {
    a_data[i] = value.get();
  }
  return dense_tensor;
}

::phi::DenseTensor CreateGPUDenseTensor(
    const ::phi::GPUContext& context,
    host_context::Attribute<std::vector<int64_t>> dims,
    host_context::Attribute<std::vector<int64_t>> lod,
    host_context::Attribute<::infrt::LayoutType> layout,
    host_context::Attribute<::infrt::PrecisionType> precision) {
  return ::phi::DenseTensor(
      const_cast<::phi::Allocator*>(&context.GetAllocator()),
      ::phi::DenseTensorMeta(ConvertPrecisionToPhi(precision.get()),
                             ::phi::make_ddim(dims.get()),
                             ConvertLayoutToPhi(layout.get()),
                             {}));
}

void FillDenseTensorF32(::phi::DenseTensor* dense_tensor,
                        host_context::Attribute<std::vector<float>> value) {
  auto place = dense_tensor->place();
  float* a_data = dense_tensor->mutable_data<float>(place);
  if (place.GetType() == ::phi::AllocationType::CPU) {
    for (int64_t i = 0; i < dense_tensor->numel(); ++i) {
      a_data[i] = (value.get())[i];
    }
  } else if (place.GetType() == ::phi::AllocationType::GPU) {
#ifdef INFRT_WITH_GPU
    // TODO(wilber): how to set the stream parameter to copy with stream.
    cudaMemcpy(a_data,
               value.get().data(),
               sizeof(float) * value.get().size(),
               cudaMemcpyHostToDevice);
#endif
  } else {
    llvm_unreachable("temporarily not support other target.");
  }
}

void PrintDenseTensor(::phi::DenseTensor* dense_tensor) {
#ifndef INFRT_WITH_GPU
#define PRINT_META_DATA(PHI_DATATYPE, DTYPE)                \
  case ::phi::DataType::PHI_DATATYPE: {                     \
    auto place = dense_tensor->place();                     \
    if (place.GetType() == ::phi::AllocationType::CPU) {    \
      DTYPE* data = dense_tensor->data<DTYPE>();            \
      if (dense_tensor->numel() == 0) break;                \
      std::cout << data[0];                                 \
      for (int64_t i = 1; i < dense_tensor->numel(); i++) { \
        std::cout << "," << data[i];                        \
      }                                                     \
    }                                                       \
    break;                                                  \
  }
#else
#define PRINT_META_DATA(PHI_DATATYPE, DTYPE)                     \
  case ::phi::DataType::PHI_DATATYPE: {                          \
    auto place = dense_tensor->place();                          \
    DTYPE* data = dense_tensor->data<DTYPE>();                   \
    if (dense_tensor->numel() == 0) break;                       \
    if (place.GetType() == ::phi::AllocationType::CPU) {         \
      std::cout << data[0];                                      \
      for (int64_t i = 1; i < dense_tensor->numel(); i++) {      \
        std::cout << "," << data[i];                             \
      }                                                          \
    } else if (place.GetType() == ::phi::AllocationType::GPU) {  \
      std::vector<DTYPE> host_data(dense_tensor->numel(), 0);    \
      cudaMemcpy(host_data.data(),                               \
                 data,                                           \
                 sizeof(DTYPE) * dense_tensor->numel(),          \
                 cudaMemcpyDeviceToHost);                        \
      std::cout << host_data[0];                                 \
      for (int64_t i = 1; i < dense_tensor->numel(); i++) {      \
        std::cout << "," << host_data[i];                        \
      }                                                          \
    } else {                                                     \
      llvm_unreachable("temporarily not support other target."); \
    }                                                            \
    break;                                                       \
  }
#endif

  ::phi::DDim dims = dense_tensor->dims();
  std::cout << "dense_tensor: shape=shape" << dims.to_str() << ","
            << " value=[";
  switch (dense_tensor->dtype()) {
    PRINT_META_DATA(FLOAT32, float);
    PRINT_META_DATA(INT32, int32_t);
    default:
      std::cout << "Error! Unsupported data type!\n";
  }
  std::cout << "]\n";
#undef PRINT_META_DATA
}

::infrt::phi::DenseTensorMap LoadParams(
    host_context::Attribute<std::string> path) {
  const auto& file_path = path.get();
  std::cout << "loading params from: " << file_path << std::endl;
  ::infrt::phi::DenseTensorMap map;

  const std::string model_path = file_path + "/__model__";
  auto pb_proto_prog = paddle::LoadProgram(model_path);
  auto main_block = pb_proto_prog->blocks(0);

  for (auto& var : main_block.vars()) {
    if (var.name() == "feed" || var.name() == "fetch" || !var.persistable())
      continue;
    std::string param_path = file_path + "/" + var.name();
    std::ifstream param_file(param_path, std::ios::binary);
    switch (var.type().type()) {
      case ::paddle::framework::proto::VarType_Type_LOD_TENSOR: {
        std::unique_ptr<::phi::DenseTensor> tensor{
            std::make_unique<::phi::DenseTensor>()};
        ::phi::CPUContext ctx;
        ::paddle::framework::DeserializeFromStream(
            param_file, tensor.get(), ctx);
        map.SetDenseTensor(var.name(), std::move(tensor));
      } break;
      default: {
        LOG(WARNING) << "Var `" << var.name() << "` type `"
                     << static_cast<int>(var.type().type())
                     << "` has not been supported now.";
      }
    }
  }
  return map;
}

::infrt::phi::DenseTensorMap LoadCombinedParams(
    host_context::Attribute<std::string> model_path,
    host_context::Attribute<std::string> params_path) {
  const auto& model = model_path.get();
  std::cout << "loading params from: " << model << std::endl;
  ::infrt::phi::DenseTensorMap map;

  auto pb_proto_prog = paddle::LoadProgram(model);
  auto main_block = pb_proto_prog->blocks(0);

  std::ifstream param_file(params_path.get(), std::ios::binary);

  std::set<std::string> tmp;
  for (auto& var : main_block.vars()) {
    if (var.name() == "feed" || var.name() == "fetch" || !var.persistable()) {
      continue;
    }
    if (var.type().type() ==
        ::paddle::framework::proto::VarType_Type_LOD_TENSOR) {
      tmp.emplace(var.name());
    } else {
      llvm_unreachable("the tensor type is illegal.");
    }
  }

  for (auto& var : tmp) {
    std::unique_ptr<::phi::DenseTensor> tensor{
        std::make_unique<::phi::DenseTensor>()};
    ::phi::CPUContext ctx;
    ::paddle::framework::DeserializeFromStream(param_file, tensor.get(), ctx);
    map.SetDenseTensor(var, std::move(tensor));
  }

  return map;
}

::phi::DenseTensor TensorMapGetTensor(
    const ::infrt::phi::DenseTensorMap& map,
    host_context::Attribute<std::string> name) {
  auto* tensor = map.GetDenseTensor(name.get());
  CHECK(tensor);
  return *tensor;
}

int32_t TensorMapGetSize(const ::infrt::phi::DenseTensorMap& map) {
  return map.size();
}

#ifdef INFRT_WITH_GPU
inline size_t SizeOfDataType(::phi::DataType data_type) {
  switch (data_type) {
    case ::phi::DataType::BOOL:
    case ::phi::DataType::UINT8:
    case ::phi::DataType::INT8:
      return 1;
    case ::phi::DataType::BFLOAT16:
    case ::phi::DataType::FLOAT16:
    case ::phi::DataType::INT16:
    case ::phi::DataType::UINT16:
      return 2;
    case ::phi::DataType::FLOAT32:
    case ::phi::DataType::INT32:
    case ::phi::DataType::UINT32:
      return 4;
    case ::phi::DataType::FLOAT64:
    case ::phi::DataType::INT64:
    case ::phi::DataType::UINT64:
    case ::phi::DataType::COMPLEX64:
      return 8;
    case ::phi::DataType::COMPLEX128:
      return 16;
    case ::phi::DataType::UNDEFINED:
      return 0;
    default:
      llvm_unreachable("should not reach here");
      return 0;
  }
  return 0;
}
::phi::DenseTensor GpuMemCpy(const ::phi::DenseTensor& input,
                             const ::phi::GPUContext& context,
                             bool d2h) {
  if (d2h) {
    ::phi::DenseTensor ret(
        const_cast<::phi::Allocator*>(&context.GetHostAllocator()),
        input.meta());
    CHECK(input.place().GetType() == ::phi::AllocationType::GPU);
    // TODO(wilber): Add sync op and stream.
    cudaMemcpyAsync(ret.data(),
                    input.data(),
                    SizeOfDataType(input.dtype()) * input.numel(),
                    cudaMemcpyDeviceToHost,
                    nullptr);
    return ret;
  } else {
    // h2d
    ::phi::DenseTensor ret(
        const_cast<::phi::Allocator*>(&context.GetAllocator()), input.meta());
    CHECK(input.place().GetType() == ::phi::AllocationType::CPU ||
          input.place().GetType() == ::phi::AllocationType::GPUPINNED);
    // TODO(wilber): Add sync op and stream.
    cudaMemcpyAsync(ret.data(),
                    input.data(),
                    SizeOfDataType(input.dtype()) * input.numel(),
                    cudaMemcpyHostToDevice,
                    nullptr);
    return ret;
  }
}
#endif

}  // namespace phi
}  // namespace kernel
}  // namespace infrt