while_op.cc

/* Copyright (c) 2016 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 "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/lod_tensor_array.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/detail/safe_ref.h"

namespace paddle {
namespace operators {

using StepScopeVar = std::vector<framework::Scope *>;
using LoDTensor = framework::LoDTensor;

static constexpr char kStepBlock[] = "sub_block";
static constexpr char kCondition[] = "Condition";
static constexpr char kStepScopes[] = "StepScopes";
static constexpr char kX[] = "X";
static constexpr char kXGRAD[] = "X@GRAD";
static constexpr char kOutputs[] = "Out";

class WhileOp : public framework::OperatorBase {
 public:
  WhileOp(const std::string &type, const framework::VariableNameMap &inputs,
          const framework::VariableNameMap &outputs,
          const framework::AttributeMap &attrs)
      : framework::OperatorBase(type, inputs, outputs, attrs) {}

 private:
  void RunImpl(const framework::Scope &scope,
               const platform::Place &dev_place) const override {
    PADDLE_ENFORCE_NOT_NULL(scope.FindVar(Input(kCondition)));
    auto &cond = scope.FindVar(Input(kCondition))->Get<LoDTensor>();
    PADDLE_ENFORCE_EQ(cond.dims(), paddle::framework::make_ddim({1}));

    framework::Executor executor(dev_place);
    auto *block = Attr<framework::BlockDesc *>(kStepBlock);

    auto *program = block->Program();

    auto step_scopes =
        scope.FindVar(Output(kStepScopes))->GetMutable<StepScopeVar>();

    PADDLE_ENFORCE(platform::is_cpu_place(cond.place()),
                   "Condition of while op must in CPU memory.");
    while (cond.data<bool>()[0]) {
      auto &current_scope = scope.NewScope();
      step_scopes->push_back(&current_scope);

      executor.Run(*program, &current_scope, block->ID(),
                   false /*create_local_scope*/);
    }
  }
};

class WhileOpMaker : public framework::OpProtoAndCheckerMaker {
 public:
  void Make() override {
    AddInput(kX,
             "A set of variables, which are required by operators inside the "
             "block of While Op.")
        .AsDuplicable();
    AddInput(
        kCondition,
        "(Bool) An scalar. When it's False, the While Op will be terminated.")
        .AsDuplicable();
    AddOutput(kOutputs,
              "A set of variables, which will be assigned with values "
              "generated by the operators inside the block of While Op.")
        .AsDuplicable();
    AddOutput(kStepScopes,
              "(StepScopeVar) A vector of local scope, which size equals the "
              "step number of While Op. The i'th scope storages temporary "
              "variables generated in the i'th step.");
    AddAttr<framework::BlockDesc *>(kStepBlock,
                                    "The step block inside WhileOp");
    AddComment(R"DOC(
)DOC");
  }
};

class WhileGradOp : public framework::OperatorBase {
 public:
  WhileGradOp(const std::string &type, const framework::VariableNameMap &inputs,
              const framework::VariableNameMap &outputs,
              const framework::AttributeMap &attrs)
      : framework::OperatorBase(type, inputs, outputs, attrs) {}

 private:
  void RunImpl(const framework::Scope &scope,
               const platform::Place &dev_place) const override {
    // get device context from pool
    platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
    auto &dev_ctx = *pool.Get(dev_place);
    framework::Executor executor(dev_place);
    auto *block = Attr<framework::BlockDesc *>(kStepBlock);
    auto *program = block->Program();

    auto *step_scopes =
        scope.FindVar(Input(kStepScopes))->GetMutable<StepScopeVar>();

    auto outside_og_names = Inputs(framework::GradVarName(kOutputs));
    auto inside_og_names =
        Attr<std::vector<std::string>>("original_output_grad");

    PADDLE_ENFORCE_EQ(outside_og_names.size(), inside_og_names.size());

    for (auto cur_scope_iter = step_scopes->rbegin();
         cur_scope_iter != step_scopes->rend(); ++cur_scope_iter) {
      VLOG(3) << "Start backward at time_step "
              << cur_scope_iter - step_scopes->rbegin();
      framework::Scope &cur_scope = **cur_scope_iter;
      // Link OG from outside to inside
      for (size_t i = 0; i < outside_og_names.size(); ++i) {
        auto outside_og_name = outside_og_names[i];
        auto inside_og_name = inside_og_names[i];
        VLOG(8) << "Linking outside " << outside_og_name << " --> inside "
                << inside_og_name;
        auto &og_outside =
            detail::Ref(scope.FindVar(outside_og_name),
                        "Cannot find Outside Gradient %s", outside_og_name);
        auto &og_inside =
            detail::Ref(cur_scope.Var(inside_og_name),
                        "Cannot find inside gradient %s", inside_og_name);
        if (og_outside.Type().hash_code() ==
            typeid(framework::LoDTensor).hash_code()) {
          auto &outside_tensor = og_outside.Get<framework::LoDTensor>();
          auto &inside_tensor =
              detail::Ref(og_inside.GetMutable<framework::LoDTensor>());
          inside_tensor.set_lod(outside_tensor.lod());
          inside_tensor.ShareDataWith(outside_tensor);
        } else if (og_outside.Type().hash_code() ==
                   typeid(framework::LoDTensorArray).hash_code()) {
          auto &outside_array = og_outside.Get<framework::LoDTensorArray>();
          auto &inside_array =
              detail::Ref(og_inside.GetMutable<framework::LoDTensorArray>());
          VLOG(8) << outside_og_name << " size = " << outside_array.size();
          inside_array.resize(outside_array.size());

          for (size_t j = 0; j < inside_array.size(); ++j) {
            VLOG(8) << j << " " << outside_array[j].numel();
            if (outside_array[j].numel() != 0) {
              inside_array[j].set_lod(outside_array[j].lod());
              inside_array[j].ShareDataWith(outside_array[j]);
            } else {
              PADDLE_ENFORCE_EQ(inside_array[j].numel(), 0);
            }
          }
        }
      }

      executor.Run(*program, *cur_scope_iter, block->ID(), false);

      auto &pg_names = Outputs(kXGRAD);
      auto &p_names = Inputs(kX);
      PADDLE_ENFORCE_EQ(pg_names.size(), p_names.size());
      for (size_t param_id = 0; param_id < pg_names.size(); ++param_id) {
        if (pg_names[param_id] == framework::kEmptyVarName) {
          continue;  // parameter doesn't have gradient
        }
        auto inside_grad_name = framework::GradVarName(p_names[param_id]);

        //  // TODO(tonyyang-svail): Not sure we need the following
        //  // If does not compute gradient of that variable inside rnn,
        //  just
        //  // continue
        //  if (local_var_names.find(inside_grad_name) ==
        //  local_var_names.end()) {
        //    continue;
        //  }

        // zero gradient variable in step 0
        if (cur_scope_iter == step_scopes->rbegin()) {
          auto *var = (*cur_scope_iter)->FindVar(inside_grad_name);
          PADDLE_ENFORCE_NOT_NULL(var, "Can not find var %s", inside_grad_name);
          if (var->IsType<LoDTensor>()) {
            auto &inside_tensor = var->Get<framework::LoDTensor>();
            framework::AttributeMap attrs;
            attrs["dtype"] = framework::ToDataType(inside_tensor.type());
            attrs["shape"] = framework::vectorize2int(inside_tensor.dims());
            attrs["value"] = 0.0f;

            auto var_name = pg_names[param_id];
            auto zero_op = framework::OpRegistry::CreateOp(
                "fill_constant", framework::VariableNameMap{},
                {{"Out", {var_name}}}, attrs);
            zero_op->Run(scope, dev_place);
            scope.FindVar(var_name)
                ->GetMutable<framework::LoDTensor>()
                ->set_lod(inside_tensor.lod());
          }
        }

        auto new_inside_name = cur_scope.Rename(inside_grad_name);
        auto sum_op = framework::OpRegistry::CreateOp(
            "sum", {{"X", {pg_names[param_id], new_inside_name}}},
            {{"Out", {pg_names[param_id]}}}, framework::AttributeMap{});
        sum_op->Run(cur_scope, dev_place);
        cur_scope.Rename(new_inside_name, inside_grad_name);
      }
      dev_ctx.Wait();
      const_cast<framework::Scope &>(scope).DeleteScope(&cur_scope);
    }
  }
};

class WhileGradOpDescMaker : public framework::SingleGradOpDescMaker {
 public:
  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;

 protected:
  std::unique_ptr<framework::OpDesc> Apply() const override {
    auto *while_grad = new framework::OpDesc();
    while_grad->SetType("while_grad");
    while_grad->SetInput(kX, Input(kX));
    while_grad->SetInput(kOutputs, Output(kOutputs));
    while_grad->SetInput(kStepScopes, Output(kStepScopes));

    auto *grad_block = this->grad_block_[0];
    auto *fwd_block = grad_block->ForwardBlock();
    auto *parent_block = grad_block->ParentBlock();

    // Not all of IGs will be generated by inner gradient operators of while op.
    // Ignore IGs that is not generated by the inside block.
    std::unordered_set<std::string> inner_op_outputs;
    for (const auto *op : grad_block->AllOps()) {
      for (auto &oname : op->OutputArgumentNames()) {
        inner_op_outputs.insert(oname);
      }
    }
    auto igs = InputGrad(kX, /*do not drop empty gradient*/ false);
    for (auto &each_ig : igs) {
      if (inner_op_outputs.find(each_ig) == inner_op_outputs.end()) {
        VLOG(8) << "Ignore " << each_ig;
        each_ig = framework::kEmptyVarName;
      }
    }
    while_grad->SetOutput(framework::GradVarName(kX), igs);

    // OG should be re-calculated by step blocks, since many outputs of while op
    // do not need to calculate gradients.
    std::unordered_set<std::string> block_ins;
    block_ins.reserve(Input(kX).size() + Output(kOutputs).size());
    for (auto &p : Input(kX)) {
      block_ins.insert(p);
    }
    for (auto &o : Output(kOutputs)) {
      block_ins.insert(o);
    }
    std::unordered_set<std::string> output_grads;
    for (const auto *op : grad_block->AllOps()) {
      for (auto &input_name : op->InputArgumentNames()) {
        // If the input of Op has been recorded or is generated by the forward
        // block, do not make it as input again.

        // The input is located in I/O or other op's outputs or the variable is
        // located in grad_block's parents
        if (block_ins.find(input_name) != block_ins.end() ||
            (fwd_block->FindVarRecursive(input_name) != nullptr ||
             parent_block->FindVarRecursive(input_name) != nullptr)) {
          continue;
        }
        output_grads.insert(input_name);
      }
      for (auto &output_name : op->OutputArgumentNames()) {
        block_ins.insert(output_name);
      }
    }

    std::vector<std::string> output_grads_list;
    output_grads_list.resize(output_grads.size());
    std::copy(output_grads.begin(), output_grads.end(),
              output_grads_list.begin());
    while_grad->SetInput(framework::GradVarName(kOutputs), output_grads_list);

    while_grad->SetAttrMap(this->Attrs());
    while_grad->SetBlockAttr(kStepBlock, grad_block);
    // record the original output gradient names, since the gradient name of
    // while operator could be renamed.
    while_grad->SetAttr("original_output_grad", output_grads_list);

    return std::unique_ptr<framework::OpDesc>(while_grad);
  }
};

class WhileGradOpVarTypeInference : public framework::VarTypeInference {
 public:
  void operator()(const framework::OpDesc &op_desc,
                  framework::BlockDesc *block) const override {
    auto p_names = op_desc.Input(kX);
    auto pg_names = op_desc.Output(framework::GradVarName(kX));

    for (size_t i = 0; i < p_names.size(); ++i) {
      auto &p_var = detail::Ref(block->FindVarRecursive(p_names[i]));
      auto *g_var = block->FindVarRecursive(pg_names[i]);
      if (g_var != nullptr) {  // Gradient could be @EMPTY@
        VLOG(5) << "Setting " << pg_names[i] << " following " << p_names[i]
                << " type: " << p_var.GetType();
        g_var->SetType(p_var.GetType());
        g_var->SetDataType(p_var.GetDataType());
      }
    }
  }
};

class WhileGradOpShapeInference : public framework::InferShapeBase {
 public:
  void operator()(framework::InferShapeContext *ctx) const override {
    ctx->HasInputs(kX);
    ctx->HasOutputs(framework::GradVarName(kX));
    ctx->HasInputs(kOutputs);
    ctx->HasInputs(framework::GradVarName(kOutputs));

    auto p_names = ctx->Inputs(kX);
    auto pg_names = ctx->Outputs(kXGRAD);
    auto var_types = ctx->GetInputsVarType(kX);
    std::vector<std::string> names_to_set;
    std::vector<framework::DDim> dims_to_set;
    for (size_t i = 0; i < p_names.size(); ++i) {
      if (pg_names[i] == framework::kEmptyVarName) {
        continue;
      }
      auto dims = ctx->GetInputsElementDim(kX, i);
      if (var_types[i] == framework::proto::VarType::LOD_TENSOR) {
        names_to_set.push_back(pg_names[i]);
        dims_to_set.push_back(dims);
      } else if (var_types[i] == framework::proto::VarType::LOD_TENSOR_ARRAY) {
        // not sure how to set the dim of LOD_TENSOR_ARRAY
        names_to_set.push_back(pg_names[i]);
        dims_to_set.push_back(dims);
      }
    }
    ctx->SetDims(names_to_set, dims_to_set);
  }
};

}  // namespace operators
}  // namespace paddle

REGISTER_OPERATOR(while, paddle::operators::WhileOp,
                  paddle::operators::WhileOpMaker,
                  paddle::operators::WhileGradOpDescMaker);
REGISTER_OPERATOR(while_grad, paddle::operators::WhileGradOp,
                  paddle::operators::WhileGradOpShapeInference,
                  paddle::operators::WhileGradOpVarTypeInference);