layer.h 6.4 KB
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// Copyright (c) 2018 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.

#pragma once

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// clang-format off
#include "paddle/fluid/framework/python_headers.h"
// clang-format on

#include <map>     // NOLINT
#include <string>  // NOLINT
#include <vector>  // NOLINT
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#include "paddle/fluid/framework/op_desc.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/var_desc.h"
#include "paddle/fluid/platform/enforce.h"

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#include "paddle/fluid/imperative/type_defs.h"

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namespace paddle {
namespace imperative {

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namespace py = ::pybind11;

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class PreparedOp {
 public:
  PreparedOp(const framework::OperatorBase& op,
             const framework::RuntimeContext& ctx,
             framework::OperatorWithKernel::OpKernelFunc func,
             platform::DeviceContext* dev_ctx)
      : op(op), ctx(ctx), func(func), dev_ctx(dev_ctx) {}

  static PreparedOp Prepare(const framework::RuntimeContext& ctx,
                            const framework::OperatorWithKernel& op,
                            const platform::Place& place) {
    platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
    auto* dev_ctx = pool.Get(place);

    // check if op[type] has kernel registered.
    auto& all_op_kernels = op.AllOpKernels();
    auto kernels_iter = all_op_kernels.find(op.Type());
    if (kernels_iter == all_op_kernels.end()) {
      PADDLE_THROW(
          "There are no kernels which are registered in the %s operator.",
          op.Type());
    }

    framework::OperatorWithKernel::OpKernelMap& kernels = kernels_iter->second;

    auto expected_kernel_key = op.GetExpectedKernelType(
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        framework::ExecutionContext(op, framework::Scope(), *dev_ctx, ctx));
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    VLOG(3) << "expected_kernel_key:" << expected_kernel_key;

    auto kernel_iter = kernels.find(expected_kernel_key);
#ifdef PADDLE_WITH_MKLDNN
    // workaround for missing MKLDNN kernel when FLAGS_use_mkldnn env var is set
    if (kernel_iter == kernels.end() &&
        expected_kernel_key.library_type_ == framework::LibraryType::kMKLDNN) {
      VLOG(3) << "missing MKLDNN kernel: fallbacking to PLAIN one";
      expected_kernel_key.library_type_ = framework::LibraryType::kPlain;
      expected_kernel_key.data_layout_ = framework::DataLayout::kAnyLayout;
      kernel_iter = kernels.find(expected_kernel_key);
    }
#endif
    if (kernel_iter == kernels.end()) {
      PADDLE_THROW("op %s does not have kernel for %s", op.Type(),
                   KernelTypeToString(expected_kernel_key));
    }
    return PreparedOp(op, ctx, kernel_iter->second, dev_ctx);
  }

  const framework::OperatorBase& op;
  const framework::RuntimeContext& ctx;
  framework::OperatorWithKernel::OpKernelFunc func;
  platform::DeviceContext* dev_ctx;
};
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class OpBase;

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/* The wrapper for Variable which holds a Variable and a VarBase of its
 * gradient. This object should be managed totally by Python intepreter.
 *
 * Nearly all interface should be implemented in C++.
 */
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class VarBase {
 public:
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  VarBase() : VarBase(new framework::Variable(), new VarBase(true)) {}
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  // Owns `var` and `grad`
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  VarBase(framework::Variable* var, VarBase* grad)
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      : pre_op_(nullptr),
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        pre_op_out_name_(),
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        pre_op_out_idx_(-1),
        var_desc_(nullptr),
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        var_(var),
        grads_(grad),
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        stop_gradient_(false) {}

  explicit VarBase(bool stop_gradient)
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      : pre_op_(nullptr),
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        pre_op_out_name_(),
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        pre_op_out_idx_(-1),
        var_desc_(nullptr),
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        var_(new framework::Variable()),
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        grads_(stop_gradient ? nullptr : new VarBase(true)),
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        stop_gradient_(stop_gradient) {}
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  virtual ~VarBase() {
    if (var_) {
      delete var_;
    }

    if (grads_) {
      delete grads_;
    }
  }
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  void RunBackward();
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  framework::LoDTensor& GradValue();
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  inline std::string GradName() const {
    PADDLE_ENFORCE(
        var_desc_,
        "Couldn't get gradient variable's name, please call backward() first");
    return string::Sprintf("%s@IGrad", var_desc_->Name());
  }

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  OpBase* pre_op_;
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  std::string pre_op_out_name_;
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  int pre_op_out_idx_;

  framework::VarDesc* var_desc_;
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  framework::Variable* var_;
  VarBase* grads_;
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  bool stop_gradient_;
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};

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/* The wrapper for OpDesc which holds a OpDesc and a OpDesc of its
 * gradient. This object should be managed totally by Python intepreter.
 */
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class OpBase {
 public:
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  OpBase()
      : op_desc_(nullptr),
        forward_id_(-1),
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        grad_op_desc_(nullptr),
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        backward_id_(-1) {}
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  virtual ~OpBase() {
    if (grad_op_desc_) delete grad_op_desc_;
  }

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  std::map<std::string, std::vector<VarBase*>> ApplyGrad();
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  // One of `op_desc_` or `forward_id_` is set, not both.
  // For pure python PyLayer, use `forward_id_`, otherwise, use op_desc_.
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  framework::OpDesc* op_desc_;
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  int forward_id_;
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  // When has backward, one of `grad_op_desc_` or `backward_id_` is set,
  // not both.
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  framework::OpDesc* grad_op_desc_;
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  int backward_id_;
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  VarBasePtrMap input_vars_;
  VarBasePtrMap output_vars_;
  OpBasePtrMap pre_ops_;
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  std::map<std::string, std::vector<int>> pre_ops_out_idx_;
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  framework::VariableValueMap grad_input_vars_;
  framework::VariableValueMap grad_output_vars_;
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  framework::BlockDesc* block_;
};

class Layer {
 public:
  virtual ~Layer() {}

  virtual std::vector<VarBase> Forward(const std::vector<VarBase>& inputs) {
    std::vector<VarBase> vars;
    return vars;
  }
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};
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class PyLayer {
 public:
  virtual ~PyLayer() {}

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  static void RegisterFunc(int func_id, const py::object& py_func);
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  static int NumFuncs();

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  static std::vector<VarBase*> Apply(int func_id,
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                                     const std::vector<VarBase*>& inputs);

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  static std::vector<framework::Variable*> ApplyGrad(
      int func_id, const std::vector<framework::Variable*>& inputs);
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 private:
  static std::vector<framework::Variable*> CallPythonFunc(
      const py::object& callable, const std::vector<framework::Variable*>& ins);
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};

}  // namespace imperative
}  // namespace paddle