alloc_continuous_space_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 <sstream>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/operators/math/math_function.h"

namespace paddle {
namespace operators {

static framework::proto::VarType::Type kDefaultDtype =
    framework::proto::VarType::Type::VarType_Type_BOOL;

template <typename DeviceContext, typename T>
class AllocContinuousSpaceKernel : public framework::OpKernel<T> {
 public:
  void Compute(const framework::ExecutionContext &context) const override {
    auto &in_var_names = context.Inputs("Input");
    auto &out_var_names = context.Outputs("Output");
    auto &in_vars = context.MultiInputVar("Input");
    auto out_vars = context.MultiOutputVar("Output");

    PADDLE_ENFORCE_GT(in_var_names.size(), static_cast<size_t>(0));
    PADDLE_ENFORCE_EQ(in_var_names.size(), out_var_names.size());

    for (size_t i = 0; i < in_var_names.size(); ++i) {
      // Only support LoDTensor
      PADDLE_ENFORCE_NOT_NULL(in_vars[i], "%s should not be nullptr,",
                              in_var_names[i]);
      PADDLE_ENFORCE_NOT_NULL(out_vars[i], "%s should not be nullptr,",
                              out_var_names[i]);
      PADDLE_ENFORCE(in_vars[i]->IsType<framework::LoDTensor>());
      PADDLE_ENFORCE(out_vars[i]->IsType<framework::LoDTensor>());
    }

    auto in_tensors = context.MultiInput<framework::LoDTensor>("Input");

    if (context.Attr<bool>("check_name")) {
      for (size_t i = 0; i < in_var_names.size(); ++i) {
        PADDLE_ENFORCE_EQ(in_var_names[i], out_var_names[i]);
      }
    } else {
      // Init the output as input
      for (size_t i = 0; i < in_tensors.size(); ++i) {
        out_vars[i]->GetMutable<framework::LoDTensor>()->Resize(
            in_tensors[i]->dims());
      }
    }

    auto &dev_ctx = context.template device_context<DeviceContext>();

    // Get numel and dtype
    size_t numel = 0;
    auto dtype = kDefaultDtype;
    GetMemSizeAndDtype(in_tensors, in_var_names, &numel, &dtype,
                       context.GetPlace());

    // Alloc the continuous space
    auto fused_tensor = context.Output<framework::LoDTensor>("FusedOutput");
    fused_tensor->Resize(framework::make_ddim({static_cast<int64_t>(numel)}))
        .mutable_data(context.GetPlace(), dtype);

    // Init the continuous space
    auto out_tensors = context.MultiOutput<framework::LoDTensor>("Output");
    size_t offset = 0;
    size_t size_of_dtype = framework::SizeOfType(dtype);
    if (context.Attr<bool>("copy_data")) {
      for (size_t i = 0; i < in_var_names.size(); ++i) {
        size_t len = static_cast<size_t>(in_tensors[i]->numel());
        auto sub_tensor = fused_tensor->Slice(
            static_cast<int64_t>(offset), static_cast<int64_t>(offset + len));
        framework::TensorCopy(*in_tensors[i], context.GetPlace(), dev_ctx,
                              &sub_tensor);

        offset +=
            Alignment(len * size_of_dtype, context.GetPlace()) / size_of_dtype;
      }
    } else if (context.Attr<bool>("set_constant")) {
      math::SetConstant<DeviceContext, T> set_constant;
      set_constant(dev_ctx, fused_tensor,
                   static_cast<T>(context.Attr<float>("constant")));
    }

    // Make the outputs point to the continuous space.
    offset = 0;
    std::stringstream ss;
    ss << "alloc_space_for_vars: ";
    for (size_t i = 0; i < out_tensors.size(); ++i) {
      size_t len = static_cast<size_t>(out_tensors[i]->numel());
      auto dim = out_tensors[i]->dims();
      out_tensors[i]
          ->ShareDataWith(fused_tensor->Slice(
              static_cast<int64_t>(offset), static_cast<int64_t>(offset + len)))
          .Resize(dim);
      len = Alignment(len * size_of_dtype, context.GetPlace()) / size_of_dtype;
      offset += len;
      ss << "output(" << out_var_names[i] << ")  dim:(" << dim << ")"
         << " address: " << out_tensors[i]->data<void>() << ", ";
    }
    VLOG(10) << ss.str();
  }

 private:
  // Note(zcd): Addresses should be aligned, otherwise, the results may have
  // diff.
  size_t Alignment(size_t size, const platform::Place &place) const {
    // Allow to allocate the minimum chunk size is 4 KB.
    size_t alignment = 1 << 12;
    if (platform::is_gpu_place(place)) {
      // Allow to allocate the minimum chunk size is 256 B.
      alignment = 1 << 8;
    }
    size_t remaining = size % alignment;
    return remaining == 0 ? size : size + (alignment - remaining);
  }

  void GetMemSizeAndDtype(
      const std::vector<const framework::LoDTensor *> &lod_tensors,
      const std::vector<std::string> var_names, size_t *numel,
      framework::proto::VarType::Type *dtype,
      const platform::Place &place) const {
    PADDLE_ENFORCE_EQ(lod_tensors.size(), var_names.size());
    *numel = 0;
    size_t size_of_dtype = 0;

    std::stringstream ss;
    ss << "alloc_space_for_vars: ";
    for (size_t i = 0; i < var_names.size(); ++i) {
      PADDLE_ENFORCE(lod_tensors[i]->IsInitialized(), "%s is not initialized.",
                     var_names[i]);

      auto p_dtype = lod_tensors[i]->type();
      if (*dtype == kDefaultDtype) {
        PADDLE_ENFORCE_NE(p_dtype, kDefaultDtype, "%s's type should not be %s.",
                          var_names[i], kDefaultDtype);
        *dtype = p_dtype;
        size_of_dtype = framework::SizeOfType(p_dtype);
      }
      PADDLE_ENFORCE_EQ(p_dtype, *dtype, "Input vars is not equal.");

      auto size = lod_tensors[i]->numel();
      PADDLE_ENFORCE_GT(size, 0);
      ss << "input(" << var_names[i] << ") dim:(" << lod_tensors[i]->dims()
         << "), ";
      *numel += Alignment(static_cast<size_t>(size) * size_of_dtype, place) /
                size_of_dtype;
    }

    VLOG(10) << ss.str();
  }
};

class AllocContinuousSpaceOp : public framework::OperatorWithKernel {
 public:
  using framework::OperatorWithKernel::OperatorWithKernel;

  void InferShape(framework::InferShapeContext *ctx) const override {}
};

class AllocContinuousSpaceOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput("Input",
             "(vector<LoDTensor>) The input tensors of"
             " alloc_continuous_space operator.")
        .AsDuplicable();
    AddOutput("Output",
              "(vector<LoDTensor>) The output "
              "tensors of alloc_continuous_space operator. And the address "
              "of output tensors are continuous, they are sliced from the "
              "tensor of FusedOutput.")
        .AsDuplicable();
    AddOutput("FusedOutput",
              "(LoDTensor) The output tensor "
              "of alloc_continuous_space operator. And the tensors of"
              " Output is sliced from the tensor of FusedOutput.");
    AddAttr<bool>("copy_data", "Whether to copy the Input value to Output.")
        .SetDefault(false);
    AddAttr<bool>("set_constant",
                  "Whether to set the Output with a constant value.")
        .SetDefault(false);
    AddAttr<float>("constant",
                   "If set_constant is true, the constant value will be used "
                   "to set the Output.")
        .SetDefault(0.0);
    AddAttr<bool>("check_name",
                  "Whether to check the name of Input and Output to ensure "
                  "they are the same separately.")
        .SetDefault(false);
    AddComment(R"DOC(
AllocContinuousSpace Operator.

alloc_continuous_space is used to make the address of Output
continuous according to the Input. This Op will alloc a big tensor
according to the tensors of Input, the dtype is the same with those input tensors,
the size is the sum of those input tensors' numel, and the dim of the big
tensor is {sum(numel)}. And the big tensor is stored in FusedOutput.
The tensors of Output are sliced from the tensor of FusedOutput.
Note that, the dtype of Input should be the same, and the dim of Input
and Output should equal.
The tensors of Input and Output could be the same or different. And
alloc_continuous_space allows copying the value of Input to Output, or
setting the Output with a constant value.

)DOC");
  }
};

}  // namespace operators
}  // namespace paddle

REGISTER_OPERATOR(alloc_continuous_space,
                  paddle::operators::AllocContinuousSpaceOp,
                  paddle::operators::AllocContinuousSpaceOpMaker);
namespace ops = paddle::operators;
REGISTER_OP_CPU_KERNEL(
    alloc_continuous_space,
    ops::AllocContinuousSpaceKernel<paddle::platform::CPUDeviceContext, int>,
    ops::AllocContinuousSpaceKernel<paddle::platform::CPUDeviceContext, float>,
    ops::AllocContinuousSpaceKernel<paddle::platform::CPUDeviceContext,
                                    double>);

#ifdef PADDLE_WITH_CUDA
REGISTER_OP_CUDA_KERNEL(
    alloc_continuous_space,
    ops::AllocContinuousSpaceKernel<paddle::platform::CUDADeviceContext, int>,
    ops::AllocContinuousSpaceKernel<paddle::platform::CUDADeviceContext, float>,
    ops::AllocContinuousSpaceKernel<paddle::platform::CUDADeviceContext,
                                    double>);
#endif