mindspore2022/mindspore/ccsrc/dataset/engine/datasetops/zip_op.cc

/**
 * Copyright 2019 Huawei Technologies Co., Ltd
 *
 * 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 "dataset/engine/datasetops/zip_op.h"
#include <utility>
#include <iomanip>
#include "dataset/core/constants.h"
#include "dataset/engine/data_buffer.h"
#include "dataset/engine/db_connector.h"
#include "dataset/engine/opt/pass.h"
#include "dataset/core/config_manager.h"
#include "dataset/core/global_context.h"
#include "utils/log_adapter.h"

namespace mindspore {
namespace dataset {
ZipOp::Builder::Builder() {
  // Some arguments to the ZipOp constructor have a default argument that is taken
  // from the client config.
  // The user may choose to change these values for the construction of the ZipOp by
  // using the various builder set methods.

  std::shared_ptr<ConfigManager> cfg = GlobalContext::config_manager();
  builder_rows_per_buffer_ = cfg->rows_per_buffer();
  builder_op_connector_size_ = cfg->op_connector_size();
}

Status ZipOp::Builder::SanityCheck() const { return Status::OK(); }

Status ZipOp::Builder::Build(std::shared_ptr<ZipOp> *ptr) {
  RETURN_IF_NOT_OK(SanityCheck());
  *ptr = std::make_shared<ZipOp>(builder_rows_per_buffer_, builder_op_connector_size_);
  return Status::OK();
}

// Construct ZipOp here, local variables initialized in operator due to tree construction restrictions
ZipOp::ZipOp(int32_t rows_per_buffer, int32_t op_connector_size)
    : PipelineOp(op_connector_size),
      children_num_(0),
      rows_per_buffer_(rows_per_buffer),
      buffer_id_(0),
      draining_(false),
      eof_(false) {}

// destructor
ZipOp::~ZipOp() {}

// Entry point for Zip, called by launch()
Status ZipOp::operator()() {
  // The children_num_ parameter needs to be put here
  children_num_ = child_.size();
  // Synchronize with TaskManager once the thread is created.
  TaskManager::FindMe()->Post();

  // initialize the iterators
  for (int32_t i = 0; i < children_num_; ++i) {
    // magic number 0 since Zip is not a parallel Op
    child_iterators_.push_back(std::make_unique<ChildIterator>(this, 0, i));
  }

  // Loop until eof is true
  while (!eof_) {
    // Create tensor table and prepare it by fetching and packing the first zipped row into it.
    std::unique_ptr<TensorQTable> curr_table = std::make_unique<TensorQTable>();
    RETURN_IF_NOT_OK(prepare(curr_table.get()));

    // If an eof got picked up during the above prepare, then we're done
    if (eof_) {
      break;
    }
    while (!draining_) {
      // 1. If a previous loop iteration sent the current table out, then create a new one.
      if (curr_table == nullptr) {
        curr_table = std::make_unique<TensorQTable>();
      }

      // 2 fill the table.  Note: draining mode might get turned on if any of the child inputs were done
      RETURN_IF_NOT_OK(fillBuffer(curr_table.get()));

      // 3 create and update buffer and send it to the out connector
      if (!curr_table->empty()) {
        std::unique_ptr<DataBuffer> curr_buffer = std::make_unique<DataBuffer>(buffer_id_, DataBuffer::kDeBFlagNone);
        curr_buffer->set_tensor_table(std::move(curr_table));
        MS_LOG(DEBUG) << "Zip operator finished one buffer, pushing, rows " << curr_buffer->NumRows() << ", cols "
                      << curr_buffer->NumCols() << ", map " << column_name_id_map_.size() << ".";
        RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(curr_buffer)));
        buffer_id_++;
      }
    }

    // 4 handle drain state.
    if (draining_) {
      MS_LOG(DEBUG) << "Zip operator is now draining child inputs.";
      RETURN_IF_NOT_OK(drainPipeline());
      // Now that we have drained child inputs, send the eoe up.
      RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE))));
    }
  }

  // 5 handle eof
  // propagate eof here.
  MS_LOG(DEBUG) << "Zip operator got EOF, propagating.";
  RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF))));
  return Status::OK();
}

// Handles preprocessing of the main loop, used when starting new epoch
Status ZipOp::prepare(TensorQTable *const table) {
  MS_LOG(DEBUG) << "Zip operator prepares for new epoch.";
  draining_ = false;
  buffer_id_ = 0;
  if (table == nullptr) {
    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "ZipOp prepare phase requires a tensor table.");
  }
  // fill initial row
  TensorRow new_row;
  RETURN_IF_NOT_OK(getNextTensorRow(&new_row));

  // If the first row fetching resulted in eof, then we are done.
  if (eof_) {
    return Status::OK();
  }
  if (new_row.empty()) {
    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "ZipOp prepare phase got empty row!");
  }

  // Pack this first row into our tensor table
  table->push_back(std::move(new_row));

  // At this point we have at least 1 row produced, so all child iterators have their column names such that we
  // can produce our column name map now.
  column_name_id_map_ = {};
  for (int32_t i = 0; i < children_num_; ++i) {
    // Initializing col_name_id_map_ from the first data buffer.
    const std::unordered_map<std::string, int32_t> col_name_id_map = child_iterators_[i]->GetColumnNameMap();
    int32_t colsCurrent = column_name_id_map_.size();
    // the update code below shouldn't do anything bad if the column name already exists.
    for (const auto &pair : col_name_id_map) {
      std::string name = pair.first;
      int32_t old_id = pair.second;
      // check if name already exists in column name descriptor
      if (column_name_id_map_.count(name) == 1) {
        RETURN_STATUS_UNEXPECTED("key already exists when zipping datasets");
      }
      column_name_id_map_[name] = old_id + colsCurrent;
    }
  }
  return Status::OK();
}

// fillBuffer always expects a new table to fill
Status ZipOp::fillBuffer(TensorQTable *const table) {
  if (table == nullptr) {
    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "ZipOp fillBuffer null table pointer.");
  }
  TensorRow new_row;
  while (table->size() < static_cast<size_t>(rows_per_buffer_)) {
    RETURN_IF_NOT_OK(getNextTensorRow(&new_row));
    // Early exit the loop if we got empty row from any of our child iterations
    if (new_row.empty()) {
      return Status::OK();
    }
    // else we got a row so pack it into the tensor table.
    table->push_back(std::move(new_row));
  }
  return Status::OK();
}

// fetches next zip buffer row (merged row)
Status ZipOp::getNextTensorRow(TensorRow *const new_zip_row) {
  // iterate over all iterators and generate a row
  for (int32_t i = 0; i < children_num_; ++i) {
    TensorRow new_row = {};
    RETURN_IF_NOT_OK((child_iterators_[i])->FetchNextTensorRow(&new_row));
    // add each new row to iterator, check if row is empty, if row from iterator is empty return empty row
    if (new_row.empty()) {
      // If we did not get a row from any of the children, then it's the end of an epoch and we can move
      // to drain state.
      MS_LOG(DEBUG) << "Zip operator child iterator produced empty row.";
      draining_ = true;
      new_zip_row->clear();
      // If we picked up an eof here, then we are completely done.
      if ((child_iterators_[i])->eof_handled()) {
        MS_LOG(DEBUG) << "Zip operator iterator got EOF.";
        eof_ = true;
      }
      return Status::OK();
    } else {
      MS_LOG(DEBUG) << "Zip operator got row from child " << i << ". Num cols: " << new_row.size() << ".";
      // if row isn't empty then we can append the fetched row with new_zip_row
      new_zip_row->insert(new_zip_row->end(), new_row.begin(), new_row.end());
    }
  }
  MS_LOG(DEBUG) << "Zip operator builds a zipped row. Number of columns in row: " << new_zip_row->size() << ".";
  return Status::OK();
}

// drain end of epoch messages from iterator for this epoch
Status ZipOp::drainPipeline() {
  // we don't need to drain if we reached eof
  if (eof_) {
    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__,
                  "ZipOp draining should not be done if already at eof!");
  }
  for (int32_t con = 0; con < children_num_; ++con) {
    MS_LOG(DEBUG) << "Zip operator draining child at " << con << ".";
    RETURN_IF_NOT_OK(child_iterators_[con]->Drain());
  }
  // at this point all connectors don't contain end of epoch messages. next iteration should be clean
  return Status::OK();
}

// A function that prints info about the Operator
void ZipOp::Print(std::ostream &out,      // In: The output stream to print to
                  bool show_all) const {  // In: T/F if it should print everything
  // Always show the id and name as first line regardless if this is summary or detailed print
  out << "(" << std::setw(2) << operator_id_ << ") <ZipOp>:";
  if (!show_all) {
    // Call the super class for displaying any common 1-liner info
    PipelineOp::Print(out, show_all);
    // Then show any custom derived-internal 1-liner info for this op
    out << "\n";
  } else {
    // Call the super class for displaying any common detailed info
    PipelineOp::Print(out, show_all);
    // Then show any custom derived-internal stuff
    out << "\nDatasets: " << children_num_ << "\n\n";
  }
}

// overwrite function and handle eof
Status ZipOp::EofReceived(int32_t) {
  MS_LOG(DEBUG) << "Zip operator EOF received, do nothing now.";
  return Status::OK();
}

// overwrite function and handle eoe
Status ZipOp::EoeReceived(int32_t) {
  state_ = OpState::kDeOpIdle;
  return Status::OK();
}

// Visitor accept method for NodePass
Status ZipOp::Accept(NodePass *p, bool *modified) {
  // Downcast shared pointer then call visitor
  return p->RunOnNode(std::static_pointer_cast<ZipOp>(shared_from_this()), modified);
}
}  // namespace dataset
}  // namespace mindspore