!508 [Dataset] Adding sync_wait operator for dataset

Merge pull request !508 from EricZ/master

mindspore/ccsrc/dataset/api/de_pipeline.cc

@@ -48,6 +48,7 @@ static std::unordered_map<uint32_t, pFunction> g_parse_op_func_ = {{kStorage, &D
                                                                    {kMap, &DEPipeline::ParseMapOp},
                                                                    {kFilter, &DEPipeline::ParseFilterOp},
                                                                    {kBatch, &DEPipeline::ParseBatchOp},
+                                                                   {kBarrier, &DEPipeline::ParseBarrierOp},
                                                                    {kRepeat, &DEPipeline::ParseRepeatOp},
                                                                    {kSkip, &DEPipeline::ParseSkipOp},
                                                                    {kZip, &DEPipeline::ParseZipOp},
@@ -627,6 +628,30 @@ Status DEPipeline::ParseBatchOp(const py::dict &args, std::shared_ptr<DatasetOp>
   return Status::OK();
 }
 
+Status DEPipeline::ParseBarrierOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr) {
+  std::shared_ptr<BarrierOp::Builder> builder = std::make_shared<BarrierOp::Builder>();
+  // Right now barrier should only take num_rows_per_buffer = 1
+  // The reason for this is because having it otherwise can lead to blocking issues
+  // See barrier_op.h for more details
+  (void)builder->SetRowsPerBuffer(1);
+  for (auto arg : args) {
+    std::string key = py::str(arg.first);
+    py::handle value = arg.second;
+    if (!value.is_none()) {
+      if (key == "condition_name") {
+        (void)builder->SetConditionName(ToString(value));
+      } else if (key == "condition_func") {
+        (void)builder->SetConditionFunc(value.cast<py::function>());
+      }
+    }
+  }
+
+  std::shared_ptr<BarrierOp> op;
+  RETURN_IF_NOT_OK(builder->Build(&op));
+  *ptr = op;
+  return Status::OK();
+}
+
 Status DEPipeline::ParseDeviceQueueOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr) {
   int32_t prefetch_size = 0;
   if (args.contains("prefetch_size")) {

mindspore/ccsrc/dataset/api/de_pipeline.h

@@ -40,6 +40,7 @@ enum OpName {
   kShuffle,
   kMindrecord,
   kBatch,
+  kBarrier,
   kCache,
   kRepeat,
   kSkip,
@@ -115,6 +116,8 @@ class DEPipeline {
 
   Status ParseBatchOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr);
 
+  Status ParseBarrierOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr);
+
   Status ParseGeneratorOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr);
 
   Status ParseRenameOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr);

mindspore/ccsrc/dataset/api/python_bindings.cc

@@ -481,6 +481,7 @@ PYBIND11_MODULE(_c_dataengine, m) {
     .value("STORAGE", OpName::kStorage)
     .value("SHUFFLE", OpName::kShuffle)
     .value("BATCH", OpName::kBatch)
+    .value("BARRIER", OpName::kBarrier)
     .value("MINDRECORD", OpName::kMindrecord)
     .value("CACHE", OpName::kCache)
     .value("REPEAT", OpName::kRepeat)

mindspore/ccsrc/dataset/core/client.h

@@ -25,6 +25,7 @@
 #include "dataset/core/tensor_shape.h"
 #include "dataset/engine/data_schema.h"
 #include "dataset/engine/dataset_iterator.h"
+#include "dataset/engine/datasetops/barrier_op.h"
 #include "dataset/engine/datasetops/batch_op.h"
 #include "dataset/engine/datasetops/dataset_op.h"
 #include "dataset/engine/datasetops/device_queue_op.h"

mindspore/ccsrc/dataset/engine/datasetops/CMakeLists.txt

@@ -4,6 +4,7 @@ add_library(engine-datasetops OBJECT
     dataset_op.cc
     parallel_op.cc
     pipeline_op.cc
+    barrier_op.cc
     batch_op.cc
     device_queue_op.cc
     map_op.cc

mindspore/ccsrc/dataset/engine/datasetops/barrier_op.cc

+/**
+ * Copyright 2020 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/barrier_op.h"
+#include <utility>
+#include "dataset/core/constants.h"
+#include "dataset/engine/data_buffer.h"
+#include "dataset/engine/db_connector.h"
+#include "dataset/core/config_manager.h"
+#include "dataset/core/global_context.h"
+#include "utils/log_adapter.h"
+
+namespace mindspore {
+namespace dataset {
+BarrierOp::Builder::Builder() {
+  // Some arguments to the BarrierOp 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 BarrierOp 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 BarrierOp::Builder::SanityCheck() const { return Status::OK(); }
+
+Status BarrierOp::Builder::Build(std::shared_ptr<BarrierOp> *ptr) {
+  RETURN_IF_NOT_OK(SanityCheck());
+  *ptr = std::make_shared<BarrierOp>(builder_rows_per_buffer_, builder_op_connector_size_, builder_condition_name_,
+                                     builder_condition_func_);
+  return Status::OK();
+}
+
+// Construct BarrierOp here, local variables initialized in operator due to tree construction restrictions
+BarrierOp::BarrierOp(int32_t rows_per_buffer, int32_t op_connector_size, const std::string &condition_name,
+                     py::function condition_func)
+    : PipelineOp(op_connector_size),
+      rows_per_buffer_(rows_per_buffer),
+      buffer_id_(0),
+      clean_up_(false),
+      eof_(false),
+      condition_name_(condition_name),
+      condition_function_(condition_func) {}
+
+// destructor
+BarrierOp::~BarrierOp() {}
+
+// Entry point for Barrier, called by launch()
+Status BarrierOp::operator()() {
+  // The children_num_ parameter needs to be put here
+  // Synchronize with TaskManager once the thread is created.
+  TaskManager::FindMe()->Post();
+
+  // create child iterator, right now this barrier is a pipeline operator
+  int32_t worker_id = 0;
+  int32_t child_idx = 0;
+  child_iterator_ = std::make_unique<ChildIterator>(this, worker_id, child_idx);
+
+  // Loop until eof is true
+  while (!eof_) {
+    // Create new table to put the new tensor rows
+    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;
+    }
+
+    // we have to output new buffer with possibly different buffer size, possibly one row
+    while (!clean_up_) {
+      // 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: clean_up mode might get turned on if epoch is finished
+      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));
+        curr_buffer->set_column_name_map(col_name_id_map_);
+        MS_LOG(DEBUG) << "Barrier operator finished one buffer, pushing, rows " << curr_buffer->NumRows() << ", cols "
+                      << curr_buffer->NumCols() << ", map " << col_name_id_map_.size() << ".";
+        RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(curr_buffer)));
+        buffer_id_++;
+      }
+    }
+
+    // 4 handle drain state.
+    if (clean_up_) {
+      MS_LOG(DEBUG) << "Barrier operator sending epoch ending signal.";
+      // 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(INFO) << "Barrier 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 BarrierOp::prepare(TensorQTable *const table) {
+  MS_LOG(DEBUG) << "Barrier operator prepares for new epoch.";
+  clean_up_ = false;
+  buffer_id_ = 0;
+  if (table == nullptr) {
+    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "BarrierOp prepare phase requires a tensor table.");
+  }
+  // fill initial row
+  TensorRow new_row = {};
+  // use iterator to get next row and invoke pyfunc wait
+  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()) {
+    // This epoch is empty
+    return Status::OK();
+  }
+  // Pack this first row into our tensor table
+  // first row we also have to check if we should block
+  RETURN_IF_NOT_OK(blockCond());
+
+  table->push_back(std::move(new_row));
+  // At this point we have 1 row produced, we take the old column map id and use it in the new table
+  // Initializing col_name_id_map_ from the first data buffer.
+  col_name_id_map_ = child_iterator_->col_name_id_map();
+  // the update code below shouldn't do anything bad if the column name already exists.
+  return Status::OK();
+}
+
+// fillBuffer always expects a new table to fill
+Status BarrierOp::fillBuffer(TensorQTable *const table) {
+  if (table == nullptr) {
+    return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "BarrierOp 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.
+    RETURN_IF_NOT_OK(blockCond());
+
+    table->push_back(std::move(new_row));
+  }
+  return Status::OK();
+}
+
+// function executes a py_func and blocks until condition becomes true.
+Status BarrierOp::blockCond() {
+  {
+    py::gil_scoped_acquire gil_acquire;
+    if (Py_IsInitialized() == 0) {
+      return Status(StatusCode::kPythonInterpreterFailure, "Python Interpreter is finalized");
+    }
+    // we have condition name, however the flexibility is in python today
+    try {
+      // Invoke python function
+      py::object ret_py_obj = condition_function_();
+      // Process the return value
+      if (!py::isinstance<py::bool_>(ret_py_obj)) {
+        return Status(StatusCode::kPyFuncException, "Condition wait function should return true/false");
+      }
+    } catch (const py::error_already_set &e) {
+      return Status(StatusCode::kPyFuncException, e.what());
+    }
+  }
+  return Status::OK();
+}
+
+// fetches next Barrier buffer row
+Status BarrierOp::getNextTensorRow(TensorRow *new_row) {
+  // iterate over all iterators and generate a row
+  RETURN_IF_NOT_OK((child_iterator_)->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(INFO) << "Barrier operator child iterator produced empty row.";
+    clean_up_ = true;
+    // If we picked up an eof here, then we are completely done.
+    if ((child_iterator_)->eof_handled()) {
+      MS_LOG(INFO) << "Barrier operator iterator got EOF.";
+      eof_ = true;
+    }
+    return Status::OK();
+  }
+  return Status::OK();
+}
+
+// A function that prints info about the Operator
+void BarrierOp::Print(std::ostream &out, bool show_all) const {
+  // Call base class printer first
+  PipelineOp::Print(out, show_all);
+  out << "\nBarrierOp:\n"
+      << "\nCondition " << condition_name_ << "\n\n";
+}
+
+// overwrite function and handle eof
+Status BarrierOp::EofReceived(int32_t) {
+  MS_LOG(DEBUG) << "Barrier operator EOF received, do nothing now.";
+  return Status::OK();
+}
+
+// overwrite function and handle eoe
+Status BarrierOp::EoeReceived(int32_t) {
+  state_ = OpState::kDeOpIdle;
+  return Status::OK();
+}
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/dataset/engine/datasetops/barrier_op.h

+/**
+ * Copyright 2020 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.
+ */
+#ifndef DATASET_ENGINE_DATASETOPS_BARRIER_OP_H_
+#define DATASET_ENGINE_DATASETOPS_BARRIER_OP_H_
+
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+#include "dataset/core/tensor.h"
+#include "dataset/engine/dataset_iterator.h"
+#include "dataset/engine/datasetops/pipeline_op.h"
+#include "dataset/kernels/tensor_op.h"
+
+namespace mindspore {
+namespace dataset {
+// Forward declare
+class DataBuffer;
+class ExecutionTree;
+
+// BarrierOp class implements the Barrier operator. It will block sending of rows until a signal has
+// been received. This signal is given from python layer. The current barrier design respects the
+// rows per buffer design and will only output a buffer with rows once it has received rows per buffer
+// signals from python.
+
+class BarrierOp : public PipelineOp {
+ public:
+  //  The nested builder class inside of the BarrierOp is used to help manage all of
+  //  the arguments for constructing it.  Use the builder by setting each argument
+  //  with the provided set methods, and then finally call the build method to execute
+  //  the actual construction.
+
+  class Builder {
+   public:
+    // Builder constructor.  Creates the builder object.
+    // @note No default args
+    // @return This is a constructor.
+    Builder();
+
+    // Default destructor
+    ~Builder() = default;
+
+    // Setter method.
+    // @return Builder setter method returns reference to the builder.
+    Builder &SetRowsPerBuffer(int32_t rows_per_buffer) {
+      builder_rows_per_buffer_ = rows_per_buffer;
+      return *this;
+    }
+
+    // Setter method.
+    // @param int32_t op_connector_size
+    // @return Builder setter method returns reference to the builder.
+    Builder &SetOpConnectorSize(int32_t op_connector_size) {
+      builder_op_connector_size_ = op_connector_size;
+      return *this;
+    }
+
+    // Setter method.
+    // @param const std::string & condition_name
+    // @return Builder setter method returns reference to the builder.
+    Builder &SetConditionName(const std::string &condition_name) {
+      builder_condition_name_ = condition_name;
+      return *this;
+    }
+
+    // Setter method.
+    // @param py::function condition_func - blocking condition function
+    // @return Builder setter method returns reference to the builder.
+    Builder &SetConditionFunc(py::function condition_func) {
+      builder_condition_func_ = condition_func;
+      return *this;
+    }
+
+    // The builder "build" method creates the BarrierOp dataset Operator.
+    // @return shared_ptr to the new BarrierOp object
+    Status Build(std::shared_ptr<BarrierOp> *);
+
+   private:
+    int32_t builder_rows_per_buffer_;
+    int32_t builder_op_connector_size_;
+    std::string builder_condition_name_;
+    py::function builder_condition_func_;
+
+    Status SanityCheck() const;
+  };
+
+  // Constructor for BarrierOp
+  // @param rows_per_buffer - number of rows in output buffer
+  // @param op_connector_size - connector size
+  // @param condition_name - the condition name associated with this operator
+  // @param condition_func - the blocking condition check per row
+  // @note - currently rows_per_buffer should = 1 for barrier.
+  // The reason for this is having other values would complicate how the pipeline behaves with other operators
+  // One example of such case is having batch after barrier. Batch would be waiting for data and having
+  // rows per buffer in this case can result in hanging
+  BarrierOp(int32_t rows_per_buffer, int32_t op_connector_size, const std::string &condition_name,
+            py::function condition_func);
+
+  // Destructor
+  ~BarrierOp();
+
+  Status EofReceived(int32_t) override;
+
+  Status EoeReceived(int32_t) override;
+
+  // Print function for Barrier
+  // @param out - output stream to print to
+  // @param show_all - if it should print everything
+  void Print(std::ostream &out, bool show_all) const override;
+
+  // Provide stream operator for displaying it
+  friend std::ostream &operator<<(std::ostream &out, const BarrierOp &bo) {
+    bo.Print(out, false);
+    return out;
+  }
+
+  // Class functor operator () override.
+  // All dataset ops operate by launching a thread (see ExecutionTree). This class functor will
+  // provide the master loop that drives the logic for performing the work
+  // @return Status - The error code return
+  Status operator()() override;
+
+  // Handles preprocessing of the main loop, used when starting new epoch
+  // @param table - a table of tensors to be moved into a buffer
+  Status prepare(TensorQTable *const table);
+
+  // This function calls takes a table repeatedly adds rows to it.
+  // @param table - a table of tensors to be moved into a buffer
+  Status fillBuffer(TensorQTable *const table);
+
+  // Gets next tensor row and sets control signals
+  Status getNextTensorRow(TensorRow *new_row);
+
+  // This function runs the wait function on condition
+  Status blockCond();
+
+ private:
+  // clean up variable to return imcomplete buffer
+  bool clean_up_;
+  // end of file state, we stop reading data and shut down
+  bool eof_;
+  // rows per buffer
+  int32_t rows_per_buffer_;
+  // buffer_id
+  int32_t buffer_id_;
+  // local variable to keep track of the buffer information
+  std::unordered_map<std::string, int32_t> col_name_id_map_;
+  // iterator to pull new rows, we only have one child
+  std::unique_ptr<ChildIterator> child_iterator_;
+  // condition name, to support multiple barriers
+  std::string condition_name_;
+  // Function pointer of blocking function
+  py::function condition_function_;
+};
+}  // namespace dataset
+}  // namespace mindspore
+
+#endif  // DATASET_ENGINE_DATASETOPS_BARRIER_OP_H_

mindspore/ccsrc/dataset/engine/datasetops/zip_op.h

@@ -34,7 +34,7 @@ class DataBuffer;
 
 class ZipOp : public PipelineOp {
  public:
-  //  The nested builder class inside of the BatchOp is used to help manage all of
+  //  The nested builder class inside of the ZipOp is used to help manage all of
   //  the arguments for constructing it.  Use the builder by setting each argument
   //  with the provided set methods, and then finally call the build method to execute
   //  the actual construction.
@@ -76,8 +76,8 @@ class ZipOp : public PipelineOp {
   };
 
   // Constructor for ZipOp
-  // @param rows_per_buffer number of rows in output buffer
-  // @param op_connector_size connector
+  // @param rows_per_buffer - number of rows in output buffer
+  // @param op_connector_size - connector size
   ZipOp(int32_t rows_per_buffer, int32_t op_connector_size);
 
   // Destructor
@@ -88,8 +88,8 @@ class ZipOp : public PipelineOp {
   Status EoeReceived(int32_t) override;
 
   // Print function for Zip
-  // @param out output stream to print to
-  // @param show_all if it should print everything
+  // @param out - output stream to print to
+  // @param show_all - if it should print everything
   void Print(std::ostream &out, bool show_all) const override;
 
   // Provide stream operator for displaying it
@@ -113,14 +113,14 @@ class ZipOp : public PipelineOp {
   Status fillBuffer(TensorQTable *const table);
 
   // Special handle case where an empty row has been received from child iterator
-  // @note we need to drain eoe signals from all children connectors.
-  // @details when this function is called, then we encountered eoe at child iterator
+  // @note - we need to drain eoe signals from all children connectors.
+  // @details - when this function is called, then we encountered eoe at child iterator
   // we have to drain rows from other child iterators until we hit eoe from all other child iterators
   Status drainPipeline();
 
   // Merges 1 row from each childIterator together
-  // @param new_zip_row input and output, will return a non-empty row if all rows from childConnectors are non-empty
-  // @param updateColumnMapping generates a new column name to index mapping (mColNameIdMap) if set to true
+  // @param new_zip_row - input and output, will be a non-empty row if all rows from childConnectors are non-empty
+  // @param updateColumnMapping - generates a new column name to index mapping (mColNameIdMap) if set to true
   // @details merge rows from iterator together. This is the main functionality for ZipOp
   //          this function takes one row and fills it with tensors from rows fetched
   //          from childIterators.

mindspore/dataset/engine/datasets.py

@@ -28,6 +28,7 @@ import multiprocessing
 import queue
 from enum import Enum
 from importlib import import_module
+import threading
 
 import numpy as np
 from mindspore._c_dataengine import DataType, TFReaderOp, ImageFolderOp, CifarOp, MnistOp, ManifestOp, \
@@ -40,7 +41,7 @@ from .iterators import DictIterator, TupleIterator
 from .validators import check, check_batch, check_shuffle, check_map, check_filter, check_repeat, check_skip, check_zip, check_rename, \
     check_take, check_project, check_imagefolderdatasetv2, check_mnist_cifar_dataset, check_manifestdataset, \
     check_tfrecorddataset, check_vocdataset, check_celebadataset, check_minddataset, check_generatordataset, \
-    check_zip_dataset, check_add_column, check_textfiledataset
+    check_sync_wait, check_zip_dataset, check_add_column, check_textfiledataset
 from ..core.datatypes import mstype_to_detype, mstypelist_to_detypelist
 
 try:
@@ -141,6 +142,7 @@ class Dataset:
         self._batch_size = None
         self._num_classes = None
         self._repeat_count = None
+        self._sync = False
 
     def get_args(self):
         """
@@ -198,6 +200,30 @@ class Dataset:
         """
         return BatchDataset(self, batch_size, drop_remainder, num_parallel_workers, per_batch_map, input_columns)
 
+    @check_sync_wait
+    def sync_wait(self, condition_name, num_batch=1, callback=None):
+        '''
+        Add a blocking condition to the input Dataset
+
+        Args:
+            input_dataset (Dataset): Input dataset to apply flow control
+            num_batch (int): the number of batches without blocking at the start of each epoch
+            condition_name (str): The condition name that is used to toggle sending next row
+            callback (function): The callback funciton that will be invoked when sync_update is called
+
+        Raises:
+            RuntimeError: If condition name already exists.
+
+        Examples:
+            >>> import mindspore.dataset as ds
+            >>> # data is an instance of Dataset object.
+            >>> data = data.sync_wait("callback1")
+            >>> data = data.batch(batch_size)
+            >>> for batch_data in data.create_dict_iterator():
+            >>>     data = data.sync_update("callback1")
+        '''
+        return SyncWaitDataset(self, condition_name, num_batch, callback)
+
     @check_shuffle
     def shuffle(self, buffer_size):
         """
@@ -220,6 +246,9 @@ class Dataset:
         Returns:
             ShuffleDataset, dataset shuffled.
 
+        Raises:
+            RuntimeError: If exist sync operators before shuffle.
+
         Examples:
             >>> import mindspore.dataset as ds
             >>> # data is an instance of Dataset object
@@ -821,6 +850,9 @@ class Dataset:
         self._input_indexs = value
 
     def _get_pipeline_info(self):
+        """
+        Gets pipeline information.
+        """
         device_iter = TupleIterator(self)
         self._output_shapes = device_iter.get_output_shapes()
         self._output_types = device_iter.get_output_types()
@@ -875,6 +907,30 @@ class Dataset:
             return self.input[0].num_classes()
         return None
 
+    def get_sync_notifiers(self):
+        if self.input:
+            return self.input[0].get_sync_notifiers()
+        return {}
+
+    def is_sync(self):
+        if self.input:
+            return self.input[0].is_sync()
+        return False
+
+    def sync_update(self, condition_name, num_batch=None, data=None):
+        """
+        condition_name (str): The condition name that is used to toggle sending next row
+        step_size (int or None): The number of steps(rows) that are released
+                         when pass_rows is None, will update the same number as sync_wait specified
+        data (dict or None): The data passed to the callback
+        """
+        notifiers_dict = self.get_sync_notifiers()
+        if condition_name not in notifiers_dict:
+            raise RuntimeError("Condition name not found")
+        if num_batch is not None:
+            num_batch *= self.get_batch_size()
+        notifiers_dict[condition_name](num_batch, data)
+
     def get_batch_size(self):
         """
         Get the size of a batch.
@@ -978,6 +1034,8 @@ class BatchDataset(DatasetOp):
         if BatchDataset._is_ancestor_of_repeat(input_dataset):
             logger.warning("Repeat is located before batch, data from two epochs can be batched together.")
 
+        BatchDataset._update_batch_size_for_syncwait(input_dataset, batch_size)
+
         self.batch_size = batch_size
         self.drop_remainder = drop_remainder
         self.per_batch_map = per_batch_map
@@ -1034,6 +1092,20 @@ class BatchDataset(DatasetOp):
             flag = flag | BatchDataset._is_ancestor_of_repeat(input_dataset)
         return flag
 
+    @staticmethod
+    def _update_batch_size_for_syncwait(dataset, batch_size):
+        """
+        Utility function to notify batch size to sync_wait.
+
+        Args:
+             dataset (Dataset): dataset to be checked
+             batchsize (int): batch size to notify
+        """
+        if isinstance(dataset, SyncWaitDataset):
+            dataset.update_sync_batch_size(batch_size)
+        for input_dataset in dataset.input:
+            BatchDataset._update_batch_size_for_syncwait(input_dataset, batch_size)
+
 
 class BatchInfo(CBatchInfo):
     """
@@ -1058,6 +1130,108 @@ class BatchInfo(CBatchInfo):
         """
         return
 
+class BlockReleasePair:
+    """
+    The blocking condition class used by SyncWaitDataset
+
+    Args:
+        init_release_rows (int): Number of lines to allow through the pipeline
+        callback (function): The callback funciton that will be called when release is called
+    """
+    def __init__(self, init_release_rows, callback=None):
+        self.row_count = -init_release_rows
+        self.cv = threading.Condition()
+        self.callback = callback
+        self.default_rows = init_release_rows
+
+    def __deepcopy__(self, memodict):
+        if id(self) in memodict:
+            return memodict[id(self)]
+        memodict[id(self)] = self
+        # condition variable and callback are the same, but reset the counter
+        self.reset()
+        return self
+
+    def reset(self):
+        with self.cv:
+            self.row_count = -self.default_rows
+            self.cv.notify_all()
+
+    def update_batched_size(self, batch_size):
+        # should only use before the pipeline creates
+        self.row_count *= batch_size
+        self.default_rows *= batch_size
+
+    def block_func(self):
+        with self.cv:
+            self.cv.wait_for(lambda: self.row_count < 0)
+            self.row_count += 1
+        return True
+
+    def release_func(self, pass_rows=None, data=None):
+        with self.cv:
+            if pass_rows is None:
+                pass_rows = self.default_rows
+            self.row_count -= pass_rows
+            if self.callback is not None:
+                self.callback(data)
+            self.cv.notify_all()
+
+class SyncWaitDataset(DatasetOp):
+    """
+    The result of adding a blocking condition to the input Dataset
+
+    Args:
+        input_dataset (Dataset): Input dataset to apply flow control
+        num_batch (int): the number of batches without blocking at the start of each epoch
+        condition_name (str): The condition name that is used to toggle sending next row
+        callback (function): The callback funciton that will be invoked when sync_update is called
+
+    Raises:
+        RuntimeError: If condition name already exists.
+    """
+
+    def __init__(self, input_dataset, condition_name, num_batch, callback=None):
+        super().__init__()
+        self.input.append(input_dataset)
+        input_dataset.output.append(self)
+        # set to the default value, waiting for the batch to update it
+        self._condition_name = condition_name
+        self._pair = BlockReleasePair(num_batch, callback)
+        if self._condition_name in self.input[0].get_sync_notifiers():
+            raise RuntimeError("Condition name is already in use")
+
+    def get_sync_notifiers(self):
+        return {**self.input[0].get_sync_notifiers(), **{self._condition_name: self._pair.release_func}}
+
+    def is_sync(self):
+        return True
+
+    def get_args(self):
+        args = super().get_args()
+        args["condition_name"] = self._condition_name
+        args["condition_func"] = self._pair.block_func
+        return args
+
+    def update_sync_batch_size(self, batch_size):
+        self._pair.update_batched_size(batch_size)
+
+    @staticmethod
+    def _is_ancestor_of_batch(dataset):
+        """
+        Utility function to find the case where sync_wait is used before batch.
+
+        Args:
+             dataset (Dataset): dataset to be checked
+        Return:
+            True or False
+        """
+        if isinstance(dataset, BatchDataset):
+            return True
+        flag = False
+        for input_dataset in dataset.input:
+            flag = flag | SyncWaitDataset._is_ancestor_of_batch(input_dataset)
+        return flag
 
 class ShuffleDataset(DatasetOp):
     """
@@ -1066,6 +1240,9 @@ class ShuffleDataset(DatasetOp):
     Args:
         input_dataset (Dataset): Input Dataset to be shuffled.
         buffer_size (int): The size of the buffer.
+
+    Raises:
+        RuntimeError: If exist sync operators before shuffle.
     """
 
     def __init__(self, input_dataset, buffer_size):
@@ -1074,6 +1251,8 @@ class ShuffleDataset(DatasetOp):
         self.input.append(input_dataset)
         input_dataset.output.append(self)
         self._input_indexs = input_dataset.input_indexs
+        if self.is_sync():
+            raise RuntimeError("No shuffle after sync operators")
 
     def get_args(self):
         args = super().get_args()
@@ -1427,6 +1606,9 @@ class ZipDataset(DatasetOp):
         """
         return None
 
+    def is_sync(self):
+        return any([c.is_sync() for c in self.input])
+
     def get_args(self):
         args = super().get_args()
         return args

mindspore/dataset/engine/iterators.py

@@ -129,6 +129,8 @@ class Iterator:
             op_type = OpName.MINDRECORD
         elif isinstance(dataset, de.BatchDataset):
             op_type = OpName.BATCH
+        elif isinstance(dataset, de.SyncWaitDataset):
+            op_type = OpName.BARRIER
         elif isinstance(dataset, de.ZipDataset):
             op_type = OpName.ZIP
         elif isinstance(dataset, de.MapDataset):

mindspore/dataset/engine/validators.py

@@ -652,6 +652,22 @@ def check_batch(method):
 
     return new_method
 
+def check_sync_wait(method):
+    """check the input arguments of sync_wait."""
+    @wraps(method)
+    def new_method(*args, **kwargs):
+        param_dict = make_param_dict(method, args, kwargs)
+
+        nreq_param_str = ['condition_name']
+        nreq_param_int = ['step_size']
+
+        check_param_type(nreq_param_int, param_dict, int)
+
+        check_param_type(nreq_param_str, param_dict, str)
+
+        return method(*args, **kwargs)
+
+    return new_method
 
 def check_shuffle(method):
     """check the input arguments of shuffle."""

tests/ut/python/dataset/test_config.py

@@ -12,8 +12,18 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
+"""
+Testing configuration manager 
+"""
+import filecmp
+import glob
+import os
+
 import mindspore.dataset as ds
+import mindspore.dataset.transforms.vision.c_transforms as vision
 
+DATA_DIR = ["../data/dataset/test_tf_file_3_images/train-0000-of-0001.data"]
+SCHEMA_DIR = "../data/dataset/test_tf_file_3_images/datasetSchema.json"
 
 def test_basic():
     ds.config.load('../data/dataset/declient.cfg')
@@ -36,6 +46,34 @@ def test_basic():
     assert ds.config.get_prefetch_size() == 4
     assert ds.config.get_seed() == 5
 
+def test_pipeline():
+    """ 
+    Test that our configuration pipeline works when we set parameters at dataset interval 
+    """
+    data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
+    ds.config.set_num_parallel_workers(2)
+    data1 = data1.map(input_columns=["image"], operations=[vision.Decode(True)])
+    ds.serialize(data1, "testpipeline.json")
+
+    data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, shuffle=False)
+    ds.config.set_num_parallel_workers(4)
+    data2 = data2.map(input_columns=["image"], operations=[vision.Decode(True)])
+    ds.serialize(data2, "testpipeline2.json")
+
+    # check that the generated output is different 
+    assert (filecmp.cmp('testpipeline.json', 'testpipeline2.json'))
+
+    # this test passes currently because our num_parallel_workers don't get updated. 
+
+    # remove generated jason files 
+    file_list = glob.glob('*.json')
+    for f in file_list:
+        try:
+            os.remove(f)
+        except IOError:
+            logger.info("Error while deleting: {}".format(f))
+
 
 if __name__ == '__main__':
     test_basic()
+    test_pipeline()

tests/ut/python/dataset/test_sync_wait.py

+# 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.
+# ==============================================================================
+
+import mindspore.dataset as ds
+from mindspore import log as logger
+import time
+import numpy as np
+
+
+def gen():
+    for i in range(100):
+        yield np.array(i),
+
+
+class Augment:
+    def __init__(self, loss):
+        self.loss = loss
+    
+    def preprocess(self, input):
+        return input
+        
+    def update(self, data):
+        self.loss = data["loss"]
+
+
+def test_simple_sync_wait():
+    """
+    Test simple sync wait: test sync in dataset pipeline  
+    """
+    logger.info("test_simple_sync_wait")
+    batch_size = 4
+    dataset = ds.GeneratorDataset(gen, column_names=["input"])
+
+    aug = Augment(0)
+    dataset = dataset.sync_wait(condition_name="policy", callback=aug.update)
+    dataset = dataset.map(input_columns=["input"], operations=[aug.preprocess])
+    dataset = dataset.batch(batch_size)
+
+    count = 0
+    for data in dataset.create_dict_iterator():
+        assert (data["input"][0] == count)
+        count += batch_size
+        data = {"loss": count}
+        dataset.sync_update(condition_name="policy", data=data)
+
+
+def test_simple_shuffle_sync():
+    """
+    Test simple shuffle sync: test shuffle before sync  
+    """
+    logger.info("test_simple_shuffle_sync")
+    shuffle_size = 4
+    batch_size = 10
+
+    dataset = ds.GeneratorDataset(gen, column_names=["input"])
+
+    aug = Augment(0)
+    dataset = dataset.shuffle(shuffle_size)
+    dataset = dataset.sync_wait(condition_name="policy", callback=aug.update)
+    dataset = dataset.map(input_columns=["input"], operations=[aug.preprocess])
+    dataset = dataset.batch(batch_size)
+
+    count = 0
+    for data in dataset.create_dict_iterator():
+        count += 1
+        #time.sleep(0.5)
+        data = {"loss": count}
+        dataset.sync_update(condition_name="policy", data=data)
+
+
+def test_two_sync():
+    """
+    Test two sync: dataset pipeline with with two sync_operators  
+    """
+    logger.info("test_two_sync")
+    batch_size = 6
+
+    dataset = ds.GeneratorDataset(gen, column_names=["input"])
+
+    aug = Augment(0)
+    # notice that with our design, we need to have step_size = shuffle size
+    dataset = dataset.sync_wait(condition_name="every batch", callback=aug.update)
+
+    dataset = dataset.map(input_columns=["input"], operations=[aug.preprocess])
+    
+    dataset = dataset.sync_wait(num_batch=2, condition_name="every 2 batches")
+    
+    dataset = dataset.batch(batch_size)
+
+    count = 0
+    for data in dataset.create_dict_iterator():
+        count += 1
+        data = {"loss": count}
+        dataset.sync_update(condition_name="every batch", data=data)
+        if count % 2 == 0:
+            dataset.sync_update(condition_name="every 2 batches")
+
+def test_sync_epoch():
+    """
+    Test sync wait with epochs: test sync with epochs in dataset pipeline  
+    """
+    logger.info("test_sync_epoch")
+    batch_size = 30
+    dataset = ds.GeneratorDataset(gen, column_names=["input"])
+
+    aug = Augment(0)
+    dataset = dataset.sync_wait(condition_name="policy", callback=aug.update)
+    dataset = dataset.map(input_columns=["input"], operations=[aug.preprocess])
+    dataset = dataset.batch(batch_size, drop_remainder=True)
+
+    for epochs in range(3):
+        aug.update({"loss": 0})
+        count = 0
+        for data in dataset.create_dict_iterator():
+            assert (data["input"][0] == count)
+            count += batch_size
+            data = {"loss": count}
+            dataset.sync_update(condition_name="policy", data=data)
+
+
+def test_sync_exception_01():
+    """
+    Test sync: with shuffle in sync mode 
+    """
+    logger.info("test_sync_exception_01")
+    shuffle_size = 4
+    batch_size = 10
+
+    dataset = ds.GeneratorDataset(gen, column_names=["input"])
+
+    aug = Augment(0)
+    dataset = dataset.sync_wait(condition_name="policy", callback=aug.update)
+    dataset = dataset.map(input_columns=["input"], operations=[aug.preprocess])
+
+    try:
+        dataset = dataset.shuffle(shuffle_size)
+    except BaseException as e:
+        assert "shuffle" in str(e)
+    dataset = dataset.batch(batch_size)
+
+
+def test_sync_exception_02():
+    """
+    Test sync: with duplicated condition name  
+    """
+    logger.info("test_sync_exception_02")
+    batch_size = 6
+
+    dataset = ds.GeneratorDataset(gen, column_names=["input"])
+
+    aug = Augment(0)
+    # notice that with our design, we need to have step_size = shuffle size
+    dataset = dataset.sync_wait(condition_name="every batch", callback=aug.update)
+    
+    dataset = dataset.map(input_columns=["input"], operations=[aug.preprocess])
+    
+    try:
+        dataset = dataset.sync_wait(num_batch=2, condition_name="every batch")
+    except BaseException as e:
+        assert "name" in str(e)
+    dataset = dataset.batch(batch_size)
+    
+    
+if __name__ == "__main__":
+    test_simple_sync_wait()
+    test_simple_shuffle_sync()
+    test_two_sync()
+    test_sync_exception_01()
+    test_sync_exception_02()
+    test_sync_epoch()
\ No newline at end of file