support padding samples

mindspore/ccsrc/dataset/api/de_pipeline.cc

@@ -32,6 +32,7 @@
 #include "dataset/engine/datasetops/source/text_file_op.h"
 #include "dataset/engine/datasetops/filter_op.h"
 #include "mindrecord/include/shard_category.h"
+#include "mindrecord/include/shard_distributed_sample.h"
 #include "mindrecord/include/shard_sample.h"
 #include "mindrecord/include/shard_shuffle.h"
 #include "dataset/util/random.h"
@@ -400,7 +401,7 @@ Status DEPipeline::CheckMindRecordPartitionInfo(const py::dict &args, std::vecto
     RETURN_STATUS_UNEXPECTED(err_msg);
   }
 
-  constexpr int kMaxPartitions = 64;
+  constexpr int kMaxPartitions = 1024;
   if (in_partitions->at(0) <= 0 || in_partitions->at(0) > kMaxPartitions) {
     std::string err_msg = "Error: partitions is invalid or not set.";
     RETURN_STATUS_UNEXPECTED(err_msg);
@@ -438,6 +439,10 @@ Status DEPipeline::ParseMindRecordOp(const py::dict &args, std::shared_ptr<Datas
     (void)builder->SetColumnsToLoad(in_col_names);
   }
 
+  if (!args["padded_sample"].is_none()) {
+    (void)builder->SetPaddedSample(args["padded_sample"]);
+    (void)builder->SetNumToPadSamples(ToInt(args["num_padded"]));
+  }
   std::vector<std::shared_ptr<mindrecord::ShardOperator>> operators;
   for (auto arg : args) {
     std::string key = py::str(arg.first);
@@ -447,14 +452,15 @@ Status DEPipeline::ParseMindRecordOp(const py::dict &args, std::shared_ptr<Datas
         (void)builder->SetNumMindRecordWorkers(ToInt(value));
       } else if (key == "block_reader" && ToBool(value) == true) {
         (void)builder->SetBlockReader();
-      } else if (key == "global_shuffle" && ToBool(value) == true) {
-        uint32_t seed = args["partitions"].is_none() ? GetSeed() : 0;
+      } else if (key == "shuffle_option" && ToBool(value) == true) {
+        if (!args["partitions"].is_none()) continue;
+        uint32_t seed = GetSeed();
         operators.push_back(std::make_shared<mindrecord::ShardShuffle>(seed));
       } else if (key == "sampler") {
-        auto create = py::reinterpret_borrow<py::object>(value).attr("_create_for_minddataset");
-        std::shared_ptr<mindrecord::ShardOperator> sample_op =
-          create().cast<std::shared_ptr<mindrecord::ShardOperator>>();
-        operators.push_back(sample_op);
+        auto sampler = py::reinterpret_borrow<py::object>(value);
+        auto create = sampler.attr("_create_for_minddataset");
+        auto op = create().cast<std::shared_ptr<mindrecord::ShardOperator>>();
+        operators.push_back(op);
       }
     }
   }
@@ -465,7 +471,13 @@ Status DEPipeline::ParseMindRecordOp(const py::dict &args, std::shared_ptr<Datas
     if (Status::OK() != ret) {
       return ret;
     }
-    operators.push_back(std::make_shared<mindrecord::ShardSample>(1, in_partitions[0], in_partitions[1]));
+    auto shuffle = ToBool(args["shuffle_option"]);
+    int num_padded = 0;
+    if (!args["num_padded"].is_none()) {
+      num_padded = ToInt(args["num_padded"]);
+    }
+    operators.push_back(
+      std::make_shared<mindrecord::ShardDistributedSample>(in_partitions[0], in_partitions[1], num_padded, shuffle, 0));
   }
 
   if (!operators.empty()) {

mindspore/ccsrc/dataset/api/python_bindings.cc

@@ -66,6 +66,7 @@
 #include "dataset/util/random.h"
 #include "mindrecord/include/shard_operator.h"
 #include "mindrecord/include/shard_pk_sample.h"
+#include "mindrecord/include/shard_distributed_sample.h"
 #include "mindrecord/include/shard_sample.h"
 #include "pybind11/pybind11.h"
 #include "pybind11/stl.h"
@@ -157,17 +158,17 @@ void bindDatasetOps(py::module *m) {
     });
 
   (void)py::class_<MindRecordOp, DatasetOp, std::shared_ptr<MindRecordOp>>(*m, "MindRecordOp")
-    .def_static("get_num_rows",
-                [](const std::vector<std::string> &paths, bool load_dataset, const py::object &sampler) {
-                  int64_t count = 0;
-                  std::shared_ptr<mindrecord::ShardOperator> op;
-                  if (py::hasattr(sampler, "_create_for_minddataset")) {
-                    auto create = sampler.attr("_create_for_minddataset");
-                    op = create().cast<std::shared_ptr<mindrecord::ShardOperator>>();
-                  }
-                  THROW_IF_ERROR(MindRecordOp::CountTotalRows(paths, load_dataset, op, &count));
-                  return count;
-                });
+    .def_static("get_num_rows", [](const std::vector<std::string> &paths, bool load_dataset, const py::object &sampler,
+                                   const int64_t num_padded) {
+      int64_t count = 0;
+      std::shared_ptr<mindrecord::ShardOperator> op;
+      if (py::hasattr(sampler, "_create_for_minddataset")) {
+        auto create = sampler.attr("_create_for_minddataset");
+        op = create().cast<std::shared_ptr<mindrecord::ShardOperator>>();
+      }
+      THROW_IF_ERROR(MindRecordOp::CountTotalRows(paths, load_dataset, op, &count, num_padded));
+      return count;
+    });
 
   (void)py::class_<ManifestOp, DatasetOp, std::shared_ptr<ManifestOp>>(*m, "ManifestOp")
     .def_static("get_num_rows_and_classes",
@@ -472,6 +473,7 @@ void bindSamplerOps(py::module *m) {
   (void)py::class_<mindrecord::ShardSample, mindrecord::ShardOperator, std::shared_ptr<mindrecord::ShardSample>>(
     *m, "MindrecordSubsetRandomSampler")
     .def(py::init<std::vector<int64_t>, uint32_t>(), py::arg("indices"), py::arg("seed") = GetSeed());
+
   (void)py::class_<mindrecord::ShardPkSample, mindrecord::ShardOperator, std::shared_ptr<mindrecord::ShardPkSample>>(
     *m, "MindrecordPkSampler")
     .def(py::init([](int64_t kVal, std::string kColumn, bool shuffle) {

mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.cc

@@ -53,6 +53,8 @@ MindRecordOp::Builder::Builder() : build_dataset_file_({}) {
   build_op_connector_queue_size_ = cfg->op_connector_size();
   build_block_reader_ = false;
   builder_num_workers_ = 0;
+  build_num_padded_ = 0;
+  build_sample_ = nullptr;
 }
 
 // The builder "build" method creates the final object.
@@ -63,24 +65,57 @@ Status MindRecordOp::Builder::Build(std::shared_ptr<MindRecordOp> *ptr) {
     return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__,
                   "Building a MindRecordOp that has not provided a file.");
   }
-
+  mindrecord::json sample_json;
+  if (build_num_padded_ > 0) {
+    sample_json = ToJson(build_sample_);
+  }
   new_mind_record_op = std::make_shared<MindRecordOp>(
     build_num_mind_record_workers_, build_rows_per_buffer_, build_dataset_file_, build_load_dataset_,
-    build_op_connector_queue_size_, build_columns_to_load_, build_operators_, build_block_reader_);
+    build_op_connector_queue_size_, build_columns_to_load_, build_operators_, build_block_reader_, build_num_padded_,
+    sample_json, build_sample_bytes_);
 
   RETURN_IF_NOT_OK(new_mind_record_op->Init());
-
   *ptr = std::move(new_mind_record_op);
   return Status::OK();
 }
 
 Status MindRecordOp::Builder::SanityCheck() const { return Status::OK(); }
 
+mindrecord::json MindRecordOp::Builder::ToJson(const py::handle &obj) {
+  if (obj.is_none()) {
+    return nullptr;
+  }
+  if (py::isinstance<py::int_>(obj)) {
+    return obj.cast<int64_t>();
+  }
+  if (py::isinstance<py::float_>(obj)) {
+    return obj.cast<double>();
+  }
+  if (py::isinstance<py::str>(obj)) {  // also catch py::bytes
+    return obj.cast<std::string>();
+  }
+  if (py::isinstance<py::dict>(obj)) {
+    auto out = mindrecord::json::object();
+    for (const py::handle &key : obj) {
+      if (py::isinstance<py::bytes>(obj[key])) {
+        build_sample_bytes_[py::str(key).cast<std::string>()] = obj[key].cast<std::string>();
+      } else {
+        out[py::str(key).cast<std::string>()] = ToJson(obj[key]);
+      }
+    }
+    return out;
+  }
+  MS_LOG(ERROR) << "Python object convert to json failed, object is: " << py::cast<std::string>(obj);
+  return mindrecord::json();
+}
+
 // Constructor of the MindRecordOp.
 MindRecordOp::MindRecordOp(int32_t num_mind_record_workers, int32_t rows_per_buffer,
                            std::vector<std::string> dataset_file, bool load_dataset, int32_t op_connector_queue_size,
                            const std::vector<std::string> &columns_to_load,
-                           const std::vector<std::shared_ptr<ShardOperator>> &operators, const bool &block_reader)
+                           const std::vector<std::shared_ptr<ShardOperator>> &operators, const bool &block_reader,
+                           int64_t num_padded, const mindrecord::json &sample_json,
+                           const std::map<std::string, std::string> &sample_bytes)
     : ParallelOp(num_mind_record_workers, op_connector_queue_size),
       rows_per_buffer_(rows_per_buffer),
       dataset_file_(dataset_file),
@@ -92,7 +127,10 @@ MindRecordOp::MindRecordOp(int32_t num_mind_record_workers, int32_t rows_per_buf
       buffers_needed_(0),
       buf_cnt_(0),
       ended_worker_(0),
-      buffer_water_mark_(0) {
+      buffer_water_mark_(0),
+      num_padded_(num_padded),
+      sample_json_(sample_json),
+      sample_bytes_(sample_bytes) {
   io_blk_queues_.Init(num_workers_, op_connector_queue_size);
   if (!block_reader_) return;
   for (int32_t i = 0; i < num_workers_; ++i) {
@@ -104,7 +142,7 @@ MindRecordOp::MindRecordOp(int32_t num_mind_record_workers, int32_t rows_per_buf
 Status MindRecordOp::Init() {
   shard_reader_ = std::make_unique<ShardReader>();
   auto rc = shard_reader_->Open(dataset_file_, load_dataset_, num_mind_record_workers_, columns_to_load_, operators_,
-                                block_reader_);
+                                block_reader_, num_padded_);
 
   CHECK_FAIL_RETURN_UNEXPECTED(rc == MSRStatus::SUCCESS,
                                "MindRecordOp init failed. Error message: " + ErrnoToMessage(rc));
@@ -161,10 +199,6 @@ Status MindRecordOp::Init() {
     column_name_id_map_[columns_to_load_[i]] = i;
   }
 
-  num_rows_ = shard_reader_->GetNumRows();
-  // Compute how many buffers we would need to accomplish rowsPerBuffer
-  buffers_needed_ = (num_rows_ + rows_per_buffer_ - 1) / rows_per_buffer_;
-
   return Status::OK();
 }
 
@@ -261,20 +295,30 @@ Status MindRecordOp::GetBufferFromReader(std::unique_ptr<DataBuffer> *fetched_bu
   std::unique_ptr<TensorQTable> tensor_table = std::make_unique<TensorQTable>();
   for (int32_t i = 0; i < rows_per_buffer_; ++i) {
     ShardTuple tupled_buffer;
+    mindrecord::TaskType task_type = mindrecord::TaskType::kCommonTask;
     if (block_reader_) {
       if (i >= block_buffer_[buffer_id % num_workers_]->size()) break;
       tupled_buffer = block_buffer_[buffer_id % num_workers_]->at(i);
     } else {
       int32_t row_id = buffer_id * rows_per_buffer_ + i;
-      tupled_buffer = shard_reader_->GetNextById(row_id, worker_id);
+      auto rc = shard_reader_->GetNextById(row_id, worker_id);
+      task_type = rc.first;
+      tupled_buffer = rc.second;
+      if (task_type == mindrecord::TaskType::kPaddedTask) {
+        TensorRow tensor_row;
+        RETURN_IF_NOT_OK(LoadTensorRow(&tensor_row, {}, mindrecord::json(), task_type));
+        tensor_table->push_back(std::move(tensor_row));
+      }
       if (tupled_buffer.empty()) break;
     }
-    for (const auto &tupled_row : tupled_buffer) {
-      std::vector<uint8_t> columns_blob = std::get<0>(tupled_row);
-      mindrecord::json columns_json = std::get<1>(tupled_row);
-      TensorRow tensor_row;
-      RETURN_IF_NOT_OK(LoadTensorRow(&tensor_row, columns_blob, columns_json));
-      tensor_table->push_back(std::move(tensor_row));
+    if (task_type == mindrecord::TaskType::kCommonTask) {
+      for (const auto &tupled_row : tupled_buffer) {
+        std::vector<uint8_t> columns_blob = std::get<0>(tupled_row);
+        mindrecord::json columns_json = std::get<1>(tupled_row);
+        TensorRow tensor_row;
+        RETURN_IF_NOT_OK(LoadTensorRow(&tensor_row, columns_blob, columns_json, task_type));
+        tensor_table->push_back(std::move(tensor_row));
+      }
     }
   }
 
@@ -284,7 +328,7 @@ Status MindRecordOp::GetBufferFromReader(std::unique_ptr<DataBuffer> *fetched_bu
 }
 
 Status MindRecordOp::LoadTensorRow(TensorRow *tensor_row, const std::vector<uint8_t> &columns_blob,
-                                   const mindrecord::json &columns_json) {
+                                   const mindrecord::json &columns_json, const mindrecord::TaskType task_type) {
   for (uint32_t i_col = 0; i_col < columns_to_load_.size(); i_col++) {
     auto column_name = columns_to_load_[i_col];
 
@@ -297,11 +341,39 @@ Status MindRecordOp::LoadTensorRow(TensorRow *tensor_row, const std::vector<uint
     std::vector<int64_t> column_shape;
 
     // Get column data
-    auto has_column = shard_reader_->GetShardColumn()->GetColumnValueByName(
-      column_name, columns_blob, columns_json, &data, &data_ptr, &n_bytes, &column_data_type, &column_data_type_size,
-      &column_shape);
-    if (has_column == MSRStatus::FAILED) {
-      RETURN_STATUS_UNEXPECTED("Failed to retrieve data from mindrecord reader.");
+    auto shard_column = shard_reader_->GetShardColumn();
+    if (num_padded_ > 0 && task_type == mindrecord::TaskType::kPaddedTask) {
+      auto rc =
+        shard_column->GetColumnTypeByName(column_name, &column_data_type, &column_data_type_size, &column_shape);
+      if (rc.first != MSRStatus::SUCCESS) {
+        RETURN_STATUS_UNEXPECTED("Failed to retrieve data type.");
+      }
+      if (rc.second == mindrecord::ColumnInRaw) {
+        auto has_column = shard_column->GetColumnFromJson(column_name, sample_json_, &data_ptr, &n_bytes);
+        if (has_column == MSRStatus::FAILED) {
+          RETURN_STATUS_UNEXPECTED("Failed to retrieve raw data from padding sample.");
+        }
+      } else if (rc.second == mindrecord::ColumnInBlob) {
+        if (sample_bytes_.find(column_name) == sample_bytes_.end()) {
+          RETURN_STATUS_UNEXPECTED("Failed to retrieve blob data from padding sample.");
+        }
+        std::string ss(sample_bytes_[column_name]);
+        n_bytes = ss.size();
+        data_ptr = std::make_unique<unsigned char[]>(n_bytes);
+        std::copy(ss.begin(), ss.end(), data_ptr.get());
+      } else {
+        RETURN_STATUS_UNEXPECTED("Retrieved data type is unknown.");
+      }
+      if (data == nullptr) {
+        data = reinterpret_cast<const unsigned char *>(data_ptr.get());
+      }
+    } else {
+      auto has_column =
+        shard_column->GetColumnValueByName(column_name, columns_blob, columns_json, &data, &data_ptr, &n_bytes,
+                                           &column_data_type, &column_data_type_size, &column_shape);
+      if (has_column == MSRStatus::FAILED) {
+        RETURN_STATUS_UNEXPECTED("Failed to retrieve data from mindrecord reader.");
+      }
     }
 
     std::shared_ptr<Tensor> tensor;
@@ -334,7 +406,8 @@ Status MindRecordOp::FetchBlockBuffer(const int32_t &buffer_id) {
   }
   for (int32_t i = 0; i < rows_per_buffer_; i++) {
     // Block reader does NOT care about argument
-    ShardTuple tuple_buffer = shard_reader_->GetNextById(i, i);
+    auto rc = shard_reader_->GetNextById(i, i);
+    ShardTuple tuple_buffer = rc.second;
     if (tuple_buffer.empty()) break;
     block_buffer_[buffer_id % num_workers_]->push_back(std::move(tuple_buffer));
   }
@@ -348,11 +421,8 @@ Status MindRecordOp::FetchBlockBuffer(const int32_t &buffer_id) {
 Status MindRecordOp::operator()() {
   RETURN_IF_NOT_OK(LaunchThreadAndInitOp());
   num_rows_ = shard_reader_->GetNumRows();
-
-  buffers_needed_ = num_rows_ / rows_per_buffer_;
-  if (num_rows_ % rows_per_buffer_ != 0) {
-    buffers_needed_++;
-  }
+  // Compute how many buffers we would need to accomplish rowsPerBuffer
+  buffers_needed_ = (num_rows_ + rows_per_buffer_ - 1) / rows_per_buffer_;
 
   while (true) {  // each iterator is 1 epoch
     for (int32_t i = 0; i < buffers_needed_; ++i) {
@@ -417,9 +487,9 @@ Status MindRecordOp::LaunchThreadAndInitOp() {
 }
 
 Status MindRecordOp::CountTotalRows(const std::vector<std::string> dataset_path, bool load_dataset,
-                                    const std::shared_ptr<ShardOperator> &op, int64_t *count) {
+                                    const std::shared_ptr<ShardOperator> &op, int64_t *count, int64_t num_padded) {
   std::unique_ptr<ShardReader> shard_reader = std::make_unique<ShardReader>();
-  MSRStatus rc = shard_reader->CountTotalRows(dataset_path, load_dataset, op, count);
+  MSRStatus rc = shard_reader->CountTotalRows(dataset_path, load_dataset, op, count, num_padded);
   if (rc == MSRStatus::FAILED) {
     RETURN_STATUS_UNEXPECTED("MindRecordOp count total rows failed.");
   }

mindspore/ccsrc/dataset/engine/datasetops/source/mindrecord_op.h

@@ -104,10 +104,22 @@ class MindRecordOp : public ParallelOp {
       return *this;
     }
 
+    Builder &SetNumToPadSamples(int64_t num_padded) {
+      build_num_padded_ = num_padded;
+      return *this;
+    }
+
+    Builder &SetPaddedSample(const py::handle &sample) {
+      build_sample_ = sample;
+      return *this;
+    }
+
     Status SanityCheck() const;
 
     static int32_t num_mind_record_workers() { return kDefaultMindRecordWorkers; }
 
+    mindrecord::json ToJson(const py::handle &obj);
+
    private:
     static constexpr int32_t kDefaultMindRecordWorkers = 4;
     // The builder saves all MindRecordOp construction arguments internally.
@@ -121,6 +133,9 @@ class MindRecordOp : public ParallelOp {
     std::vector<std::string> build_columns_to_load_;
     std::vector<std::shared_ptr<ShardOperator>> build_operators_;
     bool build_block_reader_;
+    int64_t build_num_padded_;
+    py::handle build_sample_;
+    std::map<std::string, std::string> build_sample_bytes_;
   };
 
   // Constructor of the MindRecordOp.
@@ -133,7 +148,9 @@ class MindRecordOp : public ParallelOp {
   // @param operators - ShardOperators for Shuffle, Category, Sample
   MindRecordOp(int32_t num_mind_record_workers, int32_t rows_per_buffer, std::vector<std::string> dataset_file,
                bool load_dataset, int32_t op_connector_queue_size, const std::vector<std::string> &columns_to_load,
-               const std::vector<std::shared_ptr<ShardOperator>> &operators, const bool &block_reader);
+               const std::vector<std::shared_ptr<ShardOperator>> &operators, const bool &block_reader,
+               int64_t num_padded_, const mindrecord::json &sample_json,
+               const std::map<std::string, std::string> &sample_bytes_);
 
   // Destructor
   ~MindRecordOp() override;
@@ -178,7 +195,7 @@ class MindRecordOp : public ParallelOp {
   int32_t num_rows() const { return num_rows_; }
 
   static Status CountTotalRows(const std::vector<std::string> dataset_path, bool load_dataset,
-                               const std::shared_ptr<ShardOperator> &op, int64_t *count);
+                               const std::shared_ptr<ShardOperator> &op, int64_t *count, int64_t num_padded);
 
   // Getter method
   int32_t rows_per_buffer() const { return rows_per_buffer_; }
@@ -209,7 +226,7 @@ class MindRecordOp : public ParallelOp {
   // @param columns_blob - the blob data received from the reader
   // @param columns_json - the data for fields received from the reader
   Status LoadTensorRow(TensorRow *tensor_row, const std::vector<uint8_t> &columns_blob,
-                       const mindrecord::json &columns_json);
+                       const mindrecord::json &columns_json, const mindrecord::TaskType task_type);
 
   Status FetchBlockBuffer(const int32_t &buffer_id);
 
@@ -226,6 +243,10 @@ class MindRecordOp : public ParallelOp {
   std::atomic<int32_t> ended_worker_;
   std::atomic<int32_t> buffer_water_mark_;
 
+  int64_t num_padded_;
+  mindrecord::json sample_json_;
+  std::map<std::string, std::string> sample_bytes_;
+
   std::unique_ptr<DataSchema> data_schema_;  // Data schema for column typing
   std::vector<std::string> columns_blob_;    // Blob Columns to load from dataset
   std::vector<int32_t> columns_blob_index_;  // Blob Columns to load from dataset

mindspore/ccsrc/dataset/engine/gnn/graph_loader.cc

@@ -203,7 +203,8 @@ Status GraphLoader::LoadFeatureIndex(const std::string &key, const std::vector<u
 Status GraphLoader::WorkerEntry(int32_t worker_id) {
   // Handshake
   TaskManager::FindMe()->Post();
-  ShardTuple rows = shard_reader_->GetNextById(row_id_++, worker_id);
+  auto ret = shard_reader_->GetNextById(row_id_++, worker_id);
+  ShardTuple rows = ret.second;
   while (rows.empty() == false) {
     RETURN_IF_INTERRUPTED();
     for (const auto &tupled_row : rows) {
@@ -224,7 +225,8 @@ Status GraphLoader::WorkerEntry(int32_t worker_id) {
         MS_LOG(WARNING) << "attribute:" << attr << " is neither edge nor node.";
       }
     }
-    rows = shard_reader_->GetNextById(row_id_++, worker_id);
+    auto rc = shard_reader_->GetNextById(row_id_++, worker_id);
+    rows = rc.second;
   }
   return Status::OK();
 }

mindspore/ccsrc/mindrecord/include/common/shard_utils.h

@@ -73,6 +73,10 @@ enum ShardType {
   kCV = 1,
 };
 
+enum TaskType {
+  kCommonTask = 0,
+  kPaddedTask = 1,
+};
 enum SamplerType { kCustomTopNSampler, kCustomTopPercentSampler, kSubsetRandomSampler, kPKSampler };
 
 enum ShuffleType { kShuffleCategory, kShuffleSample };

mindspore/ccsrc/mindrecord/include/shard_column.h

@@ -89,12 +89,16 @@ class ShardColumn {
   MSRStatus GetColumnFromBlob(const std::string &column_name, const std::vector<uint8_t> &columns_blob,
                               const unsigned char **data, std::unique_ptr<unsigned char[]> *data_ptr,
                               uint64_t *n_bytes);
+  std::pair<MSRStatus, ColumnCategory> GetColumnTypeByName(const std::string &column_name,
+                                                           ColumnDataType *column_data_type,
+                                                           uint64_t *column_data_type_size,
+                                                           std::vector<int64_t> *column_shape);
 
- private:
   /// \brief get column value from json
   MSRStatus GetColumnFromJson(const std::string &column_name, const json &columns_json,
                               std::unique_ptr<unsigned char[]> *data_ptr, uint64_t *n_bytes);
 
+ private:
   /// \brief get float value from json
   template <typename T>
   MSRStatus GetFloat(std::unique_ptr<unsigned char[]> *data_ptr, const json &json_column_value, bool use_double);

mindspore/ccsrc/mindrecord/include/shard_distributed_sample.h

@@ -0,0 +1,47 @@
+/**
+ * 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 MINDRECORD_INCLUDE_SHARD_DISTRIBUTED_SAMPLE_H_
+#define MINDRECORD_INCLUDE_SHARD_DISTRIBUTED_SAMPLE_H_
+
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+#include "mindrecord/include/shard_operator.h"
+#include "mindrecord/include/shard_shuffle.h"
+#include "mindrecord/include/shard_sample.h"
+
+namespace mindspore {
+namespace mindrecord {
+class ShardDistributedSample : public ShardSample {
+ public:
+  ShardDistributedSample(int num_shards, int shard_id, int no_of_padded_samples, bool shuffle, uint32_t seed);
+
+  ~ShardDistributedSample() override{};
+
+  MSRStatus PreExecute(ShardTask &tasks) override;
+
+  int64_t GetNumSamples(int64_t dataset_size, int64_t num_classes) override;
+
+ private:
+  bool shuffle_;
+  int no_of_padded_samples_;
+};
+}  // namespace mindrecord
+}  // namespace mindspore
+
+#endif  // MINDRECORD_INCLUDE_SHARD_DISTRIBUTED_SAMPLE_H_

mindspore/ccsrc/mindrecord/include/shard_reader.h

@@ -58,7 +58,8 @@ using ROW_GROUPS =
   std::tuple<MSRStatus, std::vector<std::vector<std::vector<uint64_t>>>, std::vector<std::vector<json>>>;
 using ROW_GROUP_BRIEF =
   std::tuple<MSRStatus, std::string, int, uint64_t, std::vector<std::vector<uint64_t>>, std::vector<json>>;
-using TASK_RETURN_CONTENT = std::pair<MSRStatus, std::vector<std::tuple<std::vector<uint8_t>, json>>>;
+using TASK_RETURN_CONTENT =
+  std::pair<MSRStatus, std::pair<TaskType, std::vector<std::tuple<std::vector<uint8_t>, json>>>>;
 const int kNumBatchInMap = 1000;  // iterator buffer size in row-reader mode
 const int kNumPageInBuffer = 16;  // page buffer size in block-reader mode
 
@@ -78,7 +79,8 @@ class ShardReader {
   /// \return MSRStatus the status of MSRStatus
   MSRStatus Open(const std::vector<std::string> &file_paths, bool load_dataset, int n_consumer = 4,
                  const std::vector<std::string> &selected_columns = {},
-                 const std::vector<std::shared_ptr<ShardOperator>> &operators = {}, const bool &block_reader = false);
+                 const std::vector<std::shared_ptr<ShardOperator>> &operators = {}, const bool &block_reader = false,
+                 const int num_padded = 0);
 
   /// \brief open files and initialize reader, python API
   /// \param[in] file_paths the path of ONE file, any file in dataset is fine or file list
@@ -127,7 +129,7 @@ class ShardReader {
   /// \param[out] count # of rows
   /// \return MSRStatus the status of MSRStatus
   MSRStatus CountTotalRows(const std::vector<std::string> &file_paths, bool load_dataset,
-                           const std::shared_ptr<ShardOperator> &op, int64_t *count);
+                           const std::shared_ptr<ShardOperator> &op, int64_t *count, const int num_padded);
 
   /// \brief shuffle task with incremental seed
   /// \return void
@@ -182,7 +184,8 @@ class ShardReader {
 
   /// \brief return a row by id
   /// \return a batch of images and image data
-  std::vector<std::tuple<std::vector<uint8_t>, json>> GetNextById(const int64_t &task_id, const int32_t &consumer_id);
+  std::pair<TaskType, std::vector<std::tuple<std::vector<uint8_t>, json>>> GetNextById(const int64_t &task_id,
+                                                                                       const int32_t &consumer_id);
 
   /// \brief return a batch in block-reader mode, given that one is ready
   /// \return a batch of images and image data
@@ -330,6 +333,8 @@ class ShardReader {
   bool all_in_index_ = true;  // if all columns are stored in index-table
   bool interrupt_ = false;    // reader interrupted
 
+  int num_padded_;  // number of padding samples
+
   // Delivery/Iterator mode begin
   const std::string kThreadName = "THRD_ITER_";  // prefix of thread name
   std::vector<std::thread> thread_set_;          // thread list

mindspore/ccsrc/mindrecord/include/shard_sample.h

@@ -38,22 +38,22 @@ class ShardSample : public ShardOperator {
 
   ~ShardSample() override{};
 
-  const std::pair<int, int> GetPartitions() const;
-
   MSRStatus Execute(ShardTask &tasks) override;
 
   MSRStatus SufExecute(ShardTask &tasks) override;
 
   int64_t GetNumSamples(int64_t dataset_size, int64_t num_classes) override;
 
- private:
+ protected:
   int numerator_;
   int denominator_;
-  int no_of_samples_;
   int partition_id_;
+  std::shared_ptr<ShardShuffle> shuffle_op_;
+
+ private:
+  int no_of_samples_;
   std::vector<int64_t> indices_;
   SamplerType sampler_type_;
-  std::shared_ptr<ShardShuffle> shuffle_op_;
 };
 }  // namespace mindrecord
 }  // namespace mindspore

mindspore/ccsrc/mindrecord/include/shard_task.h

@@ -29,9 +29,10 @@ class ShardTask {
  public:
   void MakePerm();
 
-  void InsertTask(int shard_id, int group_id, const std::vector<uint64_t> &offset, const json &label);
+  void InsertTask(TaskType task_type, int shard_id, int group_id, const std::vector<uint64_t> &offset,
+                  const json &label);
 
-  void InsertTask(std::tuple<std::tuple<int, int>, std::vector<uint64_t>, json> task);
+  void InsertTask(std::tuple<TaskType, std::tuple<int, int>, std::vector<uint64_t>, json> task);
 
   void PopBack();
 
@@ -39,15 +40,15 @@ class ShardTask {
 
   uint32_t SizeOfRows() const;
 
-  std::tuple<std::tuple<int, int>, std::vector<uint64_t>, json> &GetTaskByID(size_t id);
+  std::tuple<TaskType, std::tuple<int, int>, std::vector<uint64_t>, json> &GetTaskByID(size_t id);
 
-  std::tuple<std::tuple<int, int>, std::vector<uint64_t>, json> &GetRandomTask();
+  std::tuple<TaskType, std::tuple<int, int>, std::vector<uint64_t>, json> &GetRandomTask();
 
   static ShardTask Combine(std::vector<ShardTask> &category_tasks, bool replacement, int64_t num_elements);
 
   uint32_t categories = 1;
 
-  std::vector<std::tuple<std::tuple<int, int>, std::vector<uint64_t>, json>> task_list_;
+  std::vector<std::tuple<TaskType, std::tuple<int, int>, std::vector<uint64_t>, json>> task_list_;
   std::vector<int> permutation_;
 };
 }  // namespace mindrecord

mindspore/ccsrc/mindrecord/io/shard_reader.cc

@@ -45,6 +45,7 @@ ShardReader::ShardReader() {
   row_id_ = 0;
   num_blocks_ = 0;
   block_reader_ = false;
+  num_padded_ = 0;
 }
 
 std::pair<MSRStatus, std::vector<std::string>> ShardReader::GetMeta(const std::string &file_path, json &meta_data) {
@@ -790,7 +791,7 @@ int64_t ShardReader::GetNumClasses(const std::string &category_field) {
 }
 
 MSRStatus ShardReader::CountTotalRows(const std::vector<std::string> &file_paths, bool load_dataset,
-                                      const std::shared_ptr<ShardOperator> &op, int64_t *count) {
+                                      const std::shared_ptr<ShardOperator> &op, int64_t *count, const int num_padded) {
   if (SUCCESS != Init(file_paths, load_dataset)) {
     return FAILED;
   }
@@ -802,11 +803,12 @@ MSRStatus ShardReader::CountTotalRows(const std::vector<std::string> &file_paths
     num_samples = category_op->GetNumSamples(num_rows_, num_classes);
   } else if (std::dynamic_pointer_cast<ShardSample>(op)) {
     num_samples = op->GetNumSamples(num_rows_, 0);
+    if (-1 == num_samples) {
+      MS_LOG(ERROR) << "Dataset size plus number of padded samples is not divisible by number of shards.";
+      return FAILED;
+    }
   } else {
-  }
-  if (-1 == num_samples) {
-    MS_LOG(ERROR) << "Failed to get dataset size.";
-    return FAILED;
+    if (num_padded > 0) num_samples += num_padded;
   }
   *count = num_samples;
   return SUCCESS;
@@ -814,7 +816,8 @@ MSRStatus ShardReader::CountTotalRows(const std::vector<std::string> &file_paths
 
 MSRStatus ShardReader::Open(const std::vector<std::string> &file_paths, bool load_dataset, int n_consumer,
                             const std::vector<std::string> &selected_columns,
-                            const std::vector<std::shared_ptr<ShardOperator>> &operators, const bool &block_reader) {
+                            const std::vector<std::shared_ptr<ShardOperator>> &operators, const bool &block_reader,
+                            int num_padded) {
   // Open file and set header by ShardReader
   auto ret = Init(file_paths, load_dataset);
   if (SUCCESS != ret) {
@@ -844,6 +847,7 @@ MSRStatus ShardReader::Open(const std::vector<std::string> &file_paths, bool loa
   // Initialize argument
   shard_count_ = static_cast<int>(file_paths_.size());
   n_consumer_ = n_consumer;
+  num_padded_ = num_padded;
 
   operators_ = operators;
 
@@ -935,7 +939,7 @@ MSRStatus ShardReader::CreateTasksByBlock(const std::vector<std::tuple<int, int,
     auto shard_id = std::get<0>(rg);
     auto group_id = std::get<1>(rg);
     auto n_Rows = std::get<3>(rg);
-    tasks_.InsertTask(shard_id, group_id, std::vector<uint64_t>{n_Rows}, json{});
+    tasks_.InsertTask(TaskType::kCommonTask, shard_id, group_id, std::vector<uint64_t>{n_Rows}, json{});
   }
   return SUCCESS;
 }
@@ -986,7 +990,7 @@ MSRStatus ShardReader::CreateTasksByCategory(const std::vector<std::tuple<int, i
       auto number_of_rows = offsets.size();
       for (uint32_t iStart = 0; iStart < number_of_rows; iStart += 1) {
         if (category_index < num_elements) {
-          categoryTasks[categoryNo].InsertTask(shard_id, group_id, std::get<4>(details)[iStart],
+          categoryTasks[categoryNo].InsertTask(TaskType::kCommonTask, shard_id, group_id, std::get<4>(details)[iStart],
                                                std::get<5>(details)[iStart]);
           category_index++;
         }
@@ -1014,7 +1018,7 @@ MSRStatus ShardReader::CreateTasksByRow(const std::vector<std::tuple<int, int, i
   if (shard_count_ <= kMaxShardCount) {
     for (int shard_id = 0; shard_id < shard_count_; shard_id++) {
       for (uint32_t i = 0; i < offsets[shard_id].size(); i += 1) {
-        tasks_.InsertTask(offsets[shard_id][i][0], offsets[shard_id][i][1],
+        tasks_.InsertTask(TaskType::kCommonTask, offsets[shard_id][i][0], offsets[shard_id][i][1],
                           std::vector<uint64_t>{offsets[shard_id][i][2], offsets[shard_id][i][3]},
                           local_columns[shard_id][i]);
       }
@@ -1044,6 +1048,11 @@ MSRStatus ShardReader::CreateTasks(const std::vector<std::tuple<int, int, int, u
       if (SUCCESS != CreateTasksByRow(row_group_summary, operators)) {
         return FAILED;
       }
+      if (num_padded_ > 0) {
+        for (int i = 0; i < num_padded_; ++i) {
+          tasks_.InsertTask(TaskType::kPaddedTask, 0, 0, {}, json());
+        }
+      }
     } else {
       if (SUCCESS != CreateTasksByCategory(row_group_summary, operators[category_operator])) {
         return FAILED;
@@ -1070,18 +1079,27 @@ MSRStatus ShardReader::CreateTasks(const std::vector<std::tuple<int, int, int, u
 TASK_RETURN_CONTENT ShardReader::ConsumerOneTask(int task_id, uint32_t consumer_id) {
   // All tasks are done
   if (task_id >= static_cast<int>(tasks_.Size())) {
-    return std::make_pair(FAILED, std::vector<std::tuple<std::vector<uint8_t>, json>>());
+    return std::make_pair(FAILED,
+                          std::make_pair(TaskType::kCommonTask, std::vector<std::tuple<std::vector<uint8_t>, json>>()));
   }
 
   // Pick up task from task list
   auto task = tasks_.GetTaskByID(tasks_.permutation_[task_id]);
 
-  auto shard_id = std::get<0>(std::get<0>(task));
-  auto group_id = std::get<1>(std::get<0>(task));
-  auto addr = std::get<1>(task);
+  // check task type
+  auto task_type = std::get<0>(task);
+  if (task_type == TaskType::kPaddedTask) {
+    return std::make_pair(SUCCESS,
+                          std::make_pair(TaskType::kPaddedTask, std::vector<std::tuple<std::vector<uint8_t>, json>>()));
+  }
+
+  auto shard_id = std::get<0>(std::get<1>(task));
+  auto group_id = std::get<1>(std::get<1>(task));
+  auto addr = std::get<2>(task);
   const auto &ret = shard_header_->GetPageByGroupId(group_id, shard_id);
   if (SUCCESS != ret.first) {
-    return std::make_pair(FAILED, std::vector<std::tuple<std::vector<uint8_t>, json>>());
+    return std::make_pair(FAILED,
+                          std::make_pair(TaskType::kCommonTask, std::vector<std::tuple<std::vector<uint8_t>, json>>()));
   }
   const std::shared_ptr<Page> &page = ret.second;
 
@@ -1093,7 +1111,8 @@ TASK_RETURN_CONTENT ShardReader::ConsumerOneTask(int task_id, uint32_t consumer_
   if (!io_seekg.good() || io_seekg.fail() || io_seekg.bad()) {
     MS_LOG(ERROR) << "File seekg failed";
     file_streams_random_[consumer_id][shard_id]->close();
-    return std::make_pair(FAILED, std::vector<std::tuple<std::vector<uint8_t>, json>>());
+    return std::make_pair(FAILED,
+                          std::make_pair(TaskType::kCommonTask, std::vector<std::tuple<std::vector<uint8_t>, json>>()));
   }
 
   auto &io_read =
@@ -1101,14 +1120,15 @@ TASK_RETURN_CONTENT ShardReader::ConsumerOneTask(int task_id, uint32_t consumer_
   if (!io_read.good() || io_read.fail() || io_read.bad()) {
     MS_LOG(ERROR) << "File read failed";
     file_streams_random_[consumer_id][shard_id]->close();
-    return std::make_pair(FAILED, std::vector<std::tuple<std::vector<uint8_t>, json>>());
+    return std::make_pair(FAILED,
+                          std::pair(TaskType::kCommonTask, std::vector<std::tuple<std::vector<uint8_t>, json>>()));
   }
 
   // Deliver batch data to output map
   std::vector<std::tuple<std::vector<uint8_t>, json>> batch;
-  batch.emplace_back(std::move(images), std::move(std::get<2>(task)));
+  batch.emplace_back(std::move(images), std::move(std::get<3>(task)));
 
-  return std::make_pair(SUCCESS, std::move(batch));
+  return std::make_pair(SUCCESS, std::make_pair(TaskType::kCommonTask, std::move(batch)));
 }
 
 MSRStatus ShardReader::ConsumerByRow(int consumer_id) {
@@ -1133,7 +1153,7 @@ MSRStatus ShardReader::ConsumerByRow(int consumer_id) {
     if (SUCCESS != ret.first) {
       return FAILED;
     }
-    const auto &batch = ret.second;
+    const auto &batch = (ret.second).second;
     // Hanging if maximum map size exceeded
     //   otherwise, set batch data in map
     {
@@ -1193,8 +1213,8 @@ MSRStatus ShardReader::ConsumerByBlock(int consumer_id) {
     // Pick up task from task list
     auto task = tasks_.GetTaskByID(tasks_.permutation_[task_id]);
 
-    auto shard_id = std::get<0>(std::get<0>(task));
-    auto group_id = std::get<1>(std::get<0>(task));
+    auto shard_id = std::get<0>(std::get<1>(task));
+    auto group_id = std::get<1>(std::get<1>(task));
     auto row_group_brief = ReadRowGroupBrief(group_id, shard_id, selected_columns_);
     if (SUCCESS != std::get<0>(row_group_brief)) {
       return FAILED;
@@ -1302,17 +1322,17 @@ std::vector<std::tuple<std::vector<uint8_t>, json>> ShardReader::GetNext() {
   return *res;
 }
 
-std::vector<std::tuple<std::vector<uint8_t>, json>> ShardReader::GetNextById(const int64_t &task_id,
-                                                                             const int32_t &consumer_id) {
+std::pair<TaskType, std::vector<std::tuple<std::vector<uint8_t>, json>>> ShardReader::GetNextById(
+  const int64_t &task_id, const int32_t &consumer_id) {
   if (interrupt_) {
-    return std::vector<std::tuple<std::vector<uint8_t>, json>>();
+    return std::make_pair(TaskType::kCommonTask, std::vector<std::tuple<std::vector<uint8_t>, json>>());
   }
   if (block_reader_) {
-    return GetBlockNext();
+    return std::make_pair(TaskType::kCommonTask, GetBlockNext());
   }
   const auto &ret = ConsumerOneTask(task_id, consumer_id);
   if (SUCCESS != ret.first) {
-    return std::vector<std::tuple<std::vector<uint8_t>, json>>();
+    return std::make_pair(TaskType::kCommonTask, std::vector<std::tuple<std::vector<uint8_t>, json>>());
   }
   return std::move(ret.second);
 }

mindspore/ccsrc/mindrecord/meta/shard_category.cc

@@ -41,7 +41,7 @@ int64_t ShardCategory::GetNumSamples(int64_t dataset_size, int64_t num_classes)
   if (dataset_size > 0 && num_classes > 0 && num_categories_ > 0 && num_elements_ > 0) {
     return std::min(num_categories_, num_classes) * num_elements_;
   }
-  return -1;
+  return 0;
 }
 }  // namespace mindrecord
 }  // namespace mindspore

mindspore/ccsrc/mindrecord/meta/shard_column.cc

@@ -66,6 +66,25 @@ ShardColumn::ShardColumn(const std::shared_ptr<ShardHeader> &shard_header, bool
   num_blob_column_ = blob_column_.size();
 }
 
+std::pair<MSRStatus, ColumnCategory> ShardColumn::GetColumnTypeByName(const std::string &column_name,
+                                                                      ColumnDataType *column_data_type,
+                                                                      uint64_t *column_data_type_size,
+                                                                      std::vector<int64_t> *column_shape) {
+  // Skip if column not found
+  auto column_category = CheckColumnName(column_name);
+  if (column_category == ColumnNotFound) {
+    return {FAILED, ColumnNotFound};
+  }
+
+  // Get data type and size
+  auto column_id = column_name_id_[column_name];
+  *column_data_type = column_data_type_[column_id];
+  *column_data_type_size = ColumnDataTypeSize[*column_data_type];
+  *column_shape = column_shape_[column_id];
+
+  return {SUCCESS, column_category};
+}
+
 MSRStatus ShardColumn::GetColumnValueByName(const std::string &column_name, const std::vector<uint8_t> &columns_blob,
                                             const json &columns_json, const unsigned char **data,
                                             std::unique_ptr<unsigned char[]> *data_ptr, uint64_t *n_bytes,

mindspore/ccsrc/mindrecord/meta/shard_distributed_sample.cc

@@ -0,0 +1,64 @@
+/**
+ * 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 "mindrecord/include/shard_distributed_sample.h"
+
+using mindspore::LogStream;
+using mindspore::ExceptionType::NoExceptionType;
+using mindspore::MsLogLevel::ERROR;
+
+namespace mindspore {
+namespace mindrecord {
+ShardDistributedSample::ShardDistributedSample(int num_shards, int shard_id, int no_of_padded_samples, bool shuffle,
+                                               uint32_t seed)
+    : ShardSample(1, num_shards, shard_id), shuffle_(shuffle), no_of_padded_samples_(no_of_padded_samples) {
+  shuffle_op_ = std::make_shared<ShardShuffle>(seed, kShuffleSample);
+}
+
+int64_t ShardDistributedSample::GetNumSamples(int64_t dataset_size, int64_t num_classes) {
+  if (no_of_padded_samples_ <= 0) {
+    if (dataset_size % denominator_ == 0) {
+      return dataset_size / denominator_ * numerator_;
+    } else {
+      return dataset_size / denominator_ * numerator_ + 1;
+    }
+  } else {
+    auto padded_size = dataset_size + no_of_padded_samples_;
+    if (padded_size % denominator_ == 0) {
+      return padded_size / denominator_ * numerator_;
+    } else {
+      return -1;
+    }
+  }
+  return 0;
+}
+MSRStatus ShardDistributedSample::PreExecute(ShardTask &tasks) {
+  auto total_no = tasks.Size();
+  if (no_of_padded_samples_ > 0) {
+    if (total_no % denominator_ != 0) {
+      MS_LOG(ERROR) << "Dataset size plus number of padded samples is not divisible by number of shards.";
+      return FAILED;
+    }
+  }
+  if (shuffle_ == true) {
+    if (SUCCESS != (*shuffle_op_)(tasks)) {
+      return FAILED;
+    }
+  }
+  return SUCCESS;
+}
+}  // namespace mindrecord
+}  // namespace mindspore

mindspore/ccsrc/mindrecord/meta/shard_sample.cc

@@ -25,32 +25,32 @@ namespace mindrecord {
 ShardSample::ShardSample(int n)
     : numerator_(0),
       denominator_(0),
-      no_of_samples_(n),
       partition_id_(0),
+      no_of_samples_(n),
       indices_({}),
       sampler_type_(kCustomTopNSampler) {}
 
 ShardSample::ShardSample(int num, int den)
     : numerator_(num),
       denominator_(den),
-      no_of_samples_(0),
       partition_id_(0),
+      no_of_samples_(0),
       indices_({}),
       sampler_type_(kCustomTopPercentSampler) {}
 
 ShardSample::ShardSample(int num, int den, int par)
     : numerator_(num),
       denominator_(den),
-      no_of_samples_(0),
       partition_id_(par),
+      no_of_samples_(0),
       indices_({}),
       sampler_type_(kCustomTopPercentSampler) {}
 
 ShardSample::ShardSample(const std::vector<int64_t> &indices, uint32_t seed)
     : numerator_(0),
       denominator_(0),
-      no_of_samples_(0),
       partition_id_(0),
+      no_of_samples_(0),
       indices_(indices),
       sampler_type_(kSubsetRandomSampler) {
   shuffle_op_ = std::make_shared<ShardShuffle>(seed);
@@ -71,19 +71,12 @@ int64_t ShardSample::GetNumSamples(int64_t dataset_size, int64_t num_classes) {
   if (sampler_type_ == kSubsetRandomSampler) {
     return indices_.size();
   }
-  return -1;
-}
-
-const std::pair<int, int> ShardSample::GetPartitions() const {
-  if (numerator_ == 1 && denominator_ > 1) {
-    return std::pair<int, int>(denominator_, partition_id_);
-  }
-  return std::pair<int, int>(-1, -1);
+  return 0;
 }
 
 MSRStatus ShardSample::Execute(ShardTask &tasks) {
   int no_of_categories = static_cast<int>(tasks.categories);
-  int total_no = static_cast<int>(tasks.Size());
+  int total_no = static_cast<int>(tasks.Size());  // make sure task_size
 
   int taking = 0;
   if (sampler_type_ == kCustomTopNSampler) {  // non sharding case constructor #1
@@ -97,7 +90,7 @@ MSRStatus ShardSample::Execute(ShardTask &tasks) {
   } else {  // constructor TopPercent
     if (numerator_ > 0 && denominator_ > 0 && numerator_ <= denominator_) {
       if (numerator_ == 1 && denominator_ > 1) {  // sharding
-        taking = (total_no / denominator_) + (total_no % denominator_ == 0 ? 0 : 1);
+        taking = (total_no + denominator_ - 1) / denominator_;
       } else {  // non sharding
         taking = total_no * numerator_ / denominator_;
         taking -= (taking % no_of_categories);

mindspore/ccsrc/mindrecord/meta/shard_task.cc

@@ -31,16 +31,18 @@ void ShardTask::MakePerm() {
   }
 }
 
-void ShardTask::InsertTask(int shard_id, int group_id, const std::vector<uint64_t> &offset, const json &label) {
+void ShardTask::InsertTask(TaskType task_type, int shard_id, int group_id, const std::vector<uint64_t> &offset,
+                           const json &label) {
   MS_LOG(DEBUG) << "Into insert task, shard_id: " << shard_id << ", group_id: " << group_id
                 << ", label: " << label.dump() << ", size of task_list_: " << task_list_.size() << ".";
-  task_list_.emplace_back(std::make_tuple(shard_id, group_id), offset, label);
+  task_list_.emplace_back(task_type, std::make_tuple(shard_id, group_id), offset, label);
 }
 
-void ShardTask::InsertTask(std::tuple<std::tuple<int, int>, std::vector<uint64_t>, json> task) {
-  MS_LOG(DEBUG) << "Into insert task, shard_id: " << std::get<0>(std::get<0>(task))
-                << ", group_id: " << std::get<1>(std::get<0>(task)) << ", label: " << std::get<2>(task).dump()
+void ShardTask::InsertTask(std::tuple<TaskType, std::tuple<int, int>, std::vector<uint64_t>, json> task) {
+  MS_LOG(DEBUG) << "Into insert task, shard_id: " << std::get<0>(std::get<1>(task))
+                << ", group_id: " << std::get<1>(std::get<1>(task)) << ", label: " << std::get<3>(task).dump()
                 << ", size of task_list_: " << task_list_.size() << ".";
+
   task_list_.push_back(std::move(task));
 }
 
@@ -52,19 +54,19 @@ uint32_t ShardTask::SizeOfRows() const {
   if (task_list_.size() == 0) return static_cast<uint32_t>(0);
 
   // 1 task is 1 page
-  auto sum_num_rows = [](int x, std::tuple<std::tuple<int, int>, std::vector<uint64_t>, json> y) {
-    return x + std::get<1>(y)[0];
+  auto sum_num_rows = [](int x, std::tuple<TaskType, std::tuple<int, int>, std::vector<uint64_t>, json> y) {
+    return x + std::get<2>(y)[0];
   };
   uint32_t nRows = std::accumulate(task_list_.begin(), task_list_.end(), 0, sum_num_rows);
   return nRows;
 }
 
-std::tuple<std::tuple<int, int>, std::vector<uint64_t>, json> &ShardTask::GetTaskByID(size_t id) {
+std::tuple<TaskType, std::tuple<int, int>, std::vector<uint64_t>, json> &ShardTask::GetTaskByID(size_t id) {
   MS_ASSERT(id < task_list_.size());
   return task_list_[id];
 }
 
-std::tuple<std::tuple<int, int>, std::vector<uint64_t>, json> &ShardTask::GetRandomTask() {
+std::tuple<TaskType, std::tuple<int, int>, std::vector<uint64_t>, json> &ShardTask::GetRandomTask() {
   std::random_device rd;
   std::mt19937 gen(rd());
   std::uniform_int_distribution<> dis(0, task_list_.size() - 1);

mindspore/dataset/engine/datasets.py

@@ -2548,7 +2548,11 @@ class MindDataset(SourceDataset):
         sampler (Sampler, optional): Object used to choose samples from the
             dataset (default=None, sampler is exclusive
             with shuffle and block_reader). Support list: SubsetRandomSampler,
-            PkSampler
+            PkSampler.
+        padded_sample (dict, optional): Samples will be appended to dataset, which
+            keys are the same as column_list.
+        num_padded (int, optional): Number of padding samples.Dataset size
+            plus num_padded should be divisible by num_shards.
 
     Raises:
         ValueError: If num_shards is specified but shard_id is None.
@@ -2559,7 +2563,8 @@ class MindDataset(SourceDataset):
     @check_minddataset
     def __init__(self, dataset_file, columns_list=None, num_parallel_workers=None,
                  shuffle=None, num_shards=None, shard_id=None,
-                 block_reader=False, sampler=None):
+                 block_reader=False, sampler=None, padded_sample=None,
+                 num_padded=None):
         super().__init__(num_parallel_workers)
         if isinstance(dataset_file, list):
             self.load_dataset = False
@@ -2567,7 +2572,7 @@ class MindDataset(SourceDataset):
             self.load_dataset = True
         self.dataset_file = dataset_file
         self.columns_list = columns_list
-        self.global_shuffle = shuffle
+        self.shuffle_option = shuffle
         self.distribution = ""
         self.sampler = sampler
 
@@ -2598,22 +2603,36 @@ class MindDataset(SourceDataset):
             raise ValueError("shuffle not allowed when use sampler")
 
         if block_reader is False and sampler is None:
-            self.global_shuffle = not bool(shuffle is False)
+            self.shuffle_option = not bool(shuffle is False)
+
+        if num_padded is None:
+            num_padded = 0
 
         self.num_shards = num_shards
         self.shard_id = shard_id
         self.block_reader = block_reader
+        self.padded_sample = padded_sample
+        self.num_padded = num_padded
 
     def get_args(self):
         args = super().get_args()
+        padded_sample = {}
+        if self.padded_sample:
+            for k, v in self.padded_sample.items():
+                if isinstance(v, np.ndarray):
+                    padded_sample[k] = v.tobytes()
+                else:
+                    padded_sample[k] = v
         args["dataset_file"] = self.dataset_file
         args["load_dataset"] = self.load_dataset
         args["columns_list"] = self.columns_list
-        args["global_shuffle"] = self.global_shuffle
+        args["shuffle_option"] = self.shuffle_option
         args["partitions"] = self.partitions
         args["block_reader"] = self.block_reader
         args["num_shards"] = self.num_shards
         args["shard_id"] = self.shard_id
+        args["num_padded"] = self.num_padded
+        args["padded_sample"] = padded_sample
         args["sampler"] = self.sampler
         return args
 
@@ -2628,19 +2647,22 @@ class MindDataset(SourceDataset):
             dataset_file = [self.dataset_file]
         else:
             dataset_file = self.dataset_file
-        num_rows = MindRecordOp.get_num_rows(dataset_file, self.load_dataset, self.sampler)
+        num_rows = MindRecordOp.get_num_rows(dataset_file, self.load_dataset, self.sampler, self.num_padded)
         if self.partitions is not None and self.partitions[0] > 0:
             if num_rows % self.partitions[0] == 0:
                 num_rows = num_rows // self.partitions[0]
             else:
+                if self.num_padded > 0:
+                    raise RuntimeError(
+                        "Dataset size plus number of padded samples is not divisible by number of shards.")
                 num_rows = num_rows // self.partitions[0] + 1
         return num_rows
 
     def is_shuffled(self):
-        if self.global_shuffle is None:
+        if self.shuffle_option is None:
             return True
 
-        return self.global_shuffle or self.sampler.is_shuffled()
+        return self.shuffle_option or self.sampler.is_shuffled()
 
     def is_sharded(self):
         if self.num_shards is not None:

mindspore/dataset/engine/validators.py

@@ -323,6 +323,27 @@ def check_sampler_shuffle_shard_options(param_dict):
         raise RuntimeError("shard_id is specified but num_shards is not.")
 
 
+def check_padding_options(param_dict):
+    """ check for valid padded_sample and num_padded of padded samples"""
+    columns_list = param_dict.get('columns_list')
+    block_reader = param_dict.get('block_reader')
+    padded_sample, num_padded = param_dict.get('padded_sample'), param_dict.get('num_padded')
+    if padded_sample is not None:
+        if num_padded is None:
+            raise RuntimeError("padded_sample is specified and requires num_padded as well.")
+        if num_padded < 0:
+            raise ValueError("num_padded is invalid, num_padded={}.".format(num_padded))
+        if columns_list is None:
+            raise RuntimeError("padded_sample is specified and requires columns_list as well.")
+        for column in columns_list:
+            if column not in padded_sample:
+                raise ValueError("padded_sample cannot match columns_list.")
+        if block_reader:
+            raise RuntimeError("block_reader and padded_sample cannot be specified at the same time.")
+
+    if padded_sample is None and num_padded is not None:
+        raise RuntimeError("num_padded is specified but padded_sample is not.")
+
 def check_imagefolderdatasetv2(method):
     """A wrapper that wrap a parameter checker to the original Dataset(ImageFolderDatasetV2)."""
 
@@ -549,9 +570,10 @@ def check_minddataset(method):
     def new_method(*args, **kwargs):
         param_dict = make_param_dict(method, args, kwargs)
 
-        nreq_param_int = ['num_samples', 'num_parallel_workers', 'seed', 'num_shards', 'shard_id']
+        nreq_param_int = ['num_samples', 'num_parallel_workers', 'seed', 'num_shards', 'shard_id', 'num_padded']
         nreq_param_list = ['columns_list']
         nreq_param_bool = ['block_reader']
+        nreq_param_dict = ['padded_sample']
 
         # check dataset_file; required argument
         dataset_file = param_dict.get('dataset_file')
@@ -569,12 +591,11 @@ def check_minddataset(method):
 
         check_param_type(nreq_param_bool, param_dict, bool)
 
-        num_shards, shard_id = param_dict.get('num_shards'), param_dict.get('shard_id')
-        if (num_shards is not None and shard_id is None) or (num_shards is None and shard_id is not None):
-            raise ValueError("num_shards and shard_id need to be set or not set at the same time")
+        check_param_type(nreq_param_dict, param_dict, dict)
 
         check_sampler_shuffle_shard_options(param_dict)
 
+        check_padding_options(param_dict)
         return method(*args, **kwargs)
 
     return new_method

tests/ut/cpp/mindrecord/ut_shard_operator_test.cc

@@ -139,9 +139,6 @@ TEST_F(TestShardOperator, TestShardSamplePartition) {
   const int kPar = 2;
   std::vector<std::shared_ptr<ShardOperator>> ops;
   ops.push_back(std::make_shared<ShardSample>(kNum, kDen, kPar));
-  auto partitions = std::dynamic_pointer_cast<ShardSample>(ops[0])->GetPartitions();
-  ASSERT_TRUE(partitions.first == 4);
-  ASSERT_TRUE(partitions.second == 2);
 
   ShardReader dataset;
   dataset.Open({file_name}, true, 4, column_list, ops);

tests/ut/python/dataset/test_minddataset.py

@@ -227,10 +227,9 @@ def test_cv_minddataset_partition_tutorial(add_and_remove_cv_file):
                                       num_shards=num_shards, shard_id=partition_id)
             num_iter = 0
             for item in data_set.create_dict_iterator():
-                logger.info(
-                    "-------------- partition : {} ------------------------".format(partition_id))
-                logger.info(
-                    "-------------- item[label]: {} -----------------------".format(item["label"]))
+                logger.info("-------------- partition : {} ------------------------".format(partition_id))
+                logger.info("-------------- item[file_name]: {}-----------------------".format(item["file_name"]))
+                logger.info("-------------- item[label]: {} -----------------------".format(item["label"]))
                 num_iter += 1
         return num_iter
 
@@ -321,12 +320,11 @@ def test_cv_minddataset_issue_888(add_and_remove_cv_file):
     """issue 888 test."""
     columns_list = ["data", "label"]
     num_readers = 2
-    data = ds.MindDataset(CV_FILE_NAME + "0", columns_list,
-                          num_readers, shuffle=False, num_shards=5, shard_id=1)
-    data = data.shuffle(2)
-    data = data.repeat(9)
+    data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers, shuffle=False, num_shards=5, shard_id=1)
+    data_set = data_set.shuffle(2)
+    data_set = data_set.repeat(9)
     num_iter = 0
-    for _ in data.create_dict_iterator():
+    for _ in data_set.create_dict_iterator():
         num_iter += 1
     assert num_iter == 18
 
@@ -335,8 +333,7 @@ def test_cv_minddataset_blockreader_tutorial(add_and_remove_cv_file):
     """tutorial for cv minddataset."""
     columns_list = ["data", "label"]
     num_readers = 4
-    data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
-                              block_reader=True)
+    data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers, block_reader=True)
     assert data_set.get_dataset_size() == 10
     repeat_num = 2
     data_set = data_set.repeat(repeat_num)
@@ -544,7 +541,6 @@ def test_cv_minddataset_reader_basic_tutorial(add_and_remove_cv_file):
         num_iter += 1
     assert num_iter == 10
 
-
 def test_nlp_minddataset_reader_basic_tutorial(add_and_remove_nlp_file):
     """tutorial for nlp minderdataset."""
     num_readers = 4

tests/ut/python/dataset/test_minddataset_padded.py

@@ -0,0 +1,444 @@
+# 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.
+# ==============================================================================
+"""
+This is the test module for mindrecord
+"""
+import collections
+import json
+import numpy as np
+import os
+import pytest
+import re
+import string
+
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.vision.c_transforms as vision
+from mindspore import log as logger
+from mindspore.dataset.transforms.vision import Inter
+from mindspore.mindrecord import FileWriter
+
+FILES_NUM = 4
+CV_FILE_NAME = "../data/mindrecord/imagenet.mindrecord"
+CV1_FILE_NAME = "../data/mindrecord/imagenet1.mindrecord"
+CV2_FILE_NAME = "../data/mindrecord/imagenet2.mindrecord"
+CV_DIR_NAME = "../data/mindrecord/testImageNetData"
+NLP_FILE_NAME = "../data/mindrecord/aclImdb.mindrecord"
+NLP_FILE_POS = "../data/mindrecord/testAclImdbData/pos"
+NLP_FILE_VOCAB = "../data/mindrecord/testAclImdbData/vocab.txt"
+
+
+@pytest.fixture
+def add_and_remove_cv_file():
+    """add/remove cv file"""
+    paths = ["{}{}".format(CV_FILE_NAME, str(x).rjust(1, '0'))
+             for x in range(FILES_NUM)]
+    for x in paths:
+        os.remove("{}".format(x)) if os.path.exists("{}".format(x)) else None
+        os.remove("{}.db".format(x)) if os.path.exists(
+            "{}.db".format(x)) else None
+    writer = FileWriter(CV_FILE_NAME, FILES_NUM)
+    data = get_data(CV_DIR_NAME)
+    cv_schema_json = {"id": {"type": "int32"},
+                      "file_name": {"type": "string"},
+                      "label": {"type": "int32"},
+                      "data": {"type": "bytes"}}
+    writer.add_schema(cv_schema_json, "img_schema")
+    writer.add_index(["file_name", "label"])
+    writer.write_raw_data(data)
+    writer.commit()
+    yield "yield_cv_data"
+    for x in paths:
+        os.remove("{}".format(x))
+        os.remove("{}.db".format(x))
+
+
+@pytest.fixture
+def add_and_remove_nlp_file():
+    """add/remove nlp file"""
+    paths = ["{}{}".format(NLP_FILE_NAME, str(x).rjust(1, '0'))
+             for x in range(FILES_NUM)]
+    for x in paths:
+        if os.path.exists("{}".format(x)):
+            os.remove("{}".format(x))
+        if os.path.exists("{}.db".format(x)):
+            os.remove("{}.db".format(x))
+    writer = FileWriter(NLP_FILE_NAME, FILES_NUM)
+    data = [x for x in get_nlp_data(NLP_FILE_POS, NLP_FILE_VOCAB, 10)]
+    nlp_schema_json = {"id": {"type": "string"}, "label": {"type": "int32"},
+                       "rating": {"type": "float32"},
+                       "input_ids": {"type": "int64",
+                                     "shape": [-1]},
+                       "input_mask": {"type": "int64",
+                                      "shape": [1, -1]},
+                       "segment_ids": {"type": "int64",
+                                       "shape": [2, -1]}
+                       }
+    writer.set_header_size(1 << 14)
+    writer.set_page_size(1 << 15)
+    writer.add_schema(nlp_schema_json, "nlp_schema")
+    writer.add_index(["id", "rating"])
+    writer.write_raw_data(data)
+    writer.commit()
+    yield "yield_nlp_data"
+    for x in paths:
+        os.remove("{}".format(x))
+        os.remove("{}.db".format(x))
+
+def test_cv_minddataset_reader_basic_padded_samples(add_and_remove_cv_file):
+    """tutorial for cv minderdataset."""
+    columns_list = ["label", "file_name", "data"]
+
+    data = get_data(CV_DIR_NAME)
+    padded_sample = data[0]
+    padded_sample['label'] = -1
+    padded_sample['file_name'] = 'dummy.jpg'
+    num_readers = 4
+    data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers, padded_sample=padded_sample, num_padded=5)
+    assert data_set.get_dataset_size() == 15
+    num_iter = 0
+    num_padded_iter = 0
+    for item in data_set.create_dict_iterator():
+        logger.info("-------------- cv reader basic: {} ------------------------".format(num_iter))
+        logger.info("-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
+        logger.info("-------------- item[label]: {} ----------------------------".format(item["label"]))
+        if item['label'] == -1:
+            num_padded_iter += 1
+            assert item['file_name'] == bytes(padded_sample['file_name'],
+                    encoding='utf8')
+            assert item['label'] == padded_sample['label']
+            assert (item['data'] == np.array(list(padded_sample['data']))).all()
+        num_iter += 1 
+    assert num_padded_iter ==5
+    assert num_iter == 15
+
+
+def test_cv_minddataset_partition_padded_samples(add_and_remove_cv_file):
+    """tutorial for cv minddataset."""
+    columns_list = ["data", "file_name", "label"]
+
+    data = get_data(CV_DIR_NAME)
+    padded_sample = data[0]
+    padded_sample['label'] = -2
+    padded_sample['file_name'] = 'dummy.jpg'
+    num_readers = 4
+
+    def partitions(num_shards, num_padded, dataset_size):
+        for partition_id in range(num_shards):
+            data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
+                                      num_shards=num_shards,
+                                      shard_id=partition_id,
+                                      padded_sample=padded_sample,
+                                      num_padded=num_padded)
+            assert data_set.get_dataset_size() == dataset_size
+            num_iter = 0
+            num_padded_iter = 0
+            for item in data_set.create_dict_iterator():
+                logger.info("-------------- partition : {} ------------------------".format(partition_id))
+                logger.info("-------------- len(item[data]): {} ------------------------".format(len(item["data"])))
+                logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
+                logger.info("-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
+                logger.info("-------------- item[label]: {} -----------------------".format(item["label"]))
+                if item['label'] == -2:
+                    num_padded_iter += 1
+                    assert item['file_name'] == bytes(padded_sample['file_name'], encoding='utf8')
+                    assert item['label'] == padded_sample['label']
+                    assert (item['data'] == np.array(list(padded_sample['data']))).all()
+                num_iter += 1
+        return num_iter
+
+    assert partitions(4, 2, 3) == 3
+    assert partitions(5, 5, 3) == 3
+    assert partitions(9, 8, 2) == 2
+
+def test_cv_minddataset_partition_padded_samples_no_dividsible(add_and_remove_cv_file):
+    """tutorial for cv minddataset."""
+    columns_list = ["data", "file_name", "label"]
+
+    data = get_data(CV_DIR_NAME)
+    padded_sample = data[0]
+    padded_sample['label'] = -2
+    padded_sample['file_name'] = 'dummy.jpg'
+    num_readers = 4
+
+    def partitions(num_shards, num_padded):
+        for partition_id in range(num_shards):
+            data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
+                                      num_shards=num_shards,
+                                      shard_id=partition_id,
+                                      padded_sample=padded_sample,
+                                      num_padded=num_padded)
+            num_iter = 0
+            for item in data_set.create_dict_iterator():
+                num_iter += 1
+        return num_iter
+
+    with pytest.raises(RuntimeError):
+        partitions(4, 1)
+
+def test_cv_minddataset_partition_padded_samples_dataset_size_no_divisible(add_and_remove_cv_file):
+    columns_list = ["data", "file_name", "label"]
+
+    data = get_data(CV_DIR_NAME)
+    padded_sample = data[0]
+    padded_sample['label'] = -2
+    padded_sample['file_name'] = 'dummy.jpg'
+    num_readers = 4
+
+    def partitions(num_shards, num_padded):
+        for partition_id in range(num_shards):
+            data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
+                                      num_shards=num_shards,
+                                      shard_id=partition_id,
+                                      padded_sample=padded_sample,
+                                      num_padded=num_padded)
+            with pytest.raises(RuntimeError):
+                data_set.get_dataset_size() == 3
+    partitions(4, 1)
+
+def test_cv_minddataset_partition_padded_samples_no_equal_column_list(add_and_remove_cv_file):
+    columns_list = ["data", "file_name", "label"]
+
+    data = get_data(CV_DIR_NAME)
+    padded_sample = data[0]
+    padded_sample.pop('label', None)
+    padded_sample['file_name'] = 'dummy.jpg'
+    num_readers = 4
+
+    def partitions(num_shards, num_padded):
+        for partition_id in range(num_shards):
+            data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
+                                      num_shards=num_shards,
+                                      shard_id=partition_id,
+                                      padded_sample=padded_sample,
+                                      num_padded=num_padded)
+        for item in data_set.create_dict_iterator():
+            logger.info("-------------- partition : {} ------------------------".format(partition_id))
+            logger.info("-------------- len(item[data]): {} ------------------------".format(len(item["data"])))
+            logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
+            logger.info("-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
+    with pytest.raises(Exception, match="padded_sample cannot match columns_list."):
+        partitions(4, 2)
+
+def test_cv_minddataset_partition_padded_samples_no_column_list(add_and_remove_cv_file):
+    data = get_data(CV_DIR_NAME)
+    padded_sample = data[0]
+    padded_sample['label'] = -2
+    padded_sample['file_name'] = 'dummy.jpg'
+    num_readers = 4
+
+    def partitions(num_shards, num_padded):
+        for partition_id in range(num_shards):
+            data_set = ds.MindDataset(CV_FILE_NAME + "0", None, num_readers,
+                                      num_shards=num_shards,
+                                      shard_id=partition_id,
+                                      padded_sample=padded_sample,
+                                      num_padded=num_padded)
+        for item in data_set.create_dict_iterator():
+            logger.info("-------------- partition : {} ------------------------".format(partition_id))
+            logger.info("-------------- len(item[data]): {} ------------------------".format(len(item["data"])))
+            logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
+            logger.info("-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
+    with pytest.raises(Exception, match="padded_sample is specified and requires columns_list as well."):
+        partitions(4, 2)
+
+def test_cv_minddataset_partition_padded_samples_no_num_padded(add_and_remove_cv_file):
+    columns_list = ["data", "file_name", "label"]
+    data = get_data(CV_DIR_NAME)
+    padded_sample = data[0]
+    padded_sample['file_name'] = 'dummy.jpg'
+    num_readers = 4
+
+    def partitions(num_shards, num_padded):
+        for partition_id in range(num_shards):
+            data_set = ds.MindDataset(CV_FILE_NAME + "0", None, num_readers,
+                                      num_shards=num_shards,
+                                      shard_id=partition_id,
+                                      padded_sample=padded_sample)
+        for item in data_set.create_dict_iterator():
+            logger.info("-------------- partition : {} ------------------------".format(partition_id))
+            logger.info("-------------- len(item[data]): {} ------------------------".format(len(item["data"])))
+            logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
+            logger.info("-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
+    with pytest.raises(Exception, match="padded_sample is specified and requires num_padded as well."):
+        partitions(4, 2)
+
+def test_cv_minddataset_partition_padded_samples_no_padded_samples(add_and_remove_cv_file):
+    columns_list = ["data", "file_name", "label"]
+    data = get_data(CV_DIR_NAME)
+    padded_sample = data[0]
+    padded_sample['file_name'] = 'dummy.jpg'
+    num_readers = 4
+
+    def partitions(num_shards, num_padded):
+        for partition_id in range(num_shards):
+            data_set = ds.MindDataset(CV_FILE_NAME + "0", None, num_readers,
+                                      num_shards=num_shards,
+                                      shard_id=partition_id,
+                                      num_padded=num_padded)
+        for item in data_set.create_dict_iterator():
+            logger.info("-------------- partition : {} ------------------------".format(partition_id))
+            logger.info("-------------- len(item[data]): {} ------------------------".format(len(item["data"])))
+            logger.info("-------------- item[data]: {} -----------------------------".format(item["data"]))
+            logger.info("-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
+    with pytest.raises(Exception, match="num_padded is specified but padded_sample is not."):
+        partitions(4, 2)
+
+
+
+def test_nlp_minddataset_reader_basic_padded_samples(add_and_remove_nlp_file):
+    columns_list = ["input_ids", "id", "rating"]
+
+    data = [x for x in get_nlp_data(NLP_FILE_POS, NLP_FILE_VOCAB, 10)]
+    padded_sample = data[0]
+    padded_sample['id'] = "-1"
+    padded_sample['input_ids'] = np.array([-1,-1,-1,-1], dtype=np.int64)
+    padded_sample['rating'] = 1.0
+    num_readers = 4
+
+    def partitions(num_shards, num_padded, dataset_size):
+        for partition_id in range(num_shards):
+            data_set = ds.MindDataset(NLP_FILE_NAME + "0", columns_list, num_readers,
+                                      num_shards=num_shards,
+                                      shard_id=partition_id,
+                                      padded_sample=padded_sample,
+                                      num_padded=num_padded)
+            assert data_set.get_dataset_size() == dataset_size
+            num_iter = 0
+            for item in data_set.create_dict_iterator():
+                logger.info("-------------- item[id]: {} ------------------------".format(item["id"]))
+                logger.info("-------------- item[rating]: {} --------------------".format(item["rating"]))
+                logger.info("-------------- item[input_ids]: {}, shape: {} -----------------".format(item["input_ids"], item["input_ids"].shape))
+                if item['id'] == '-1':
+                    num_padded_iter += 1
+                    assert item['id'] == padded_sample['id']
+                    assert item['input_ids'] == padded_sample['input_ids']
+                    assert item['rating'] == padded_sample['rating']
+                num_iter += 1
+        return num_iter
+
+    assert partitions(4, 6, 4) == 4
+    assert partitions(5, 5, 3) == 3
+    assert partitions(9, 8, 2) == 2
+
+def get_data(dir_name):
+    """
+    usage: get data from imagenet dataset
+    params:
+    dir_name: directory containing folder images and annotation information
+
+    """
+    if not os.path.isdir(dir_name):
+        raise IOError("Directory {} not exists".format(dir_name))
+    img_dir = os.path.join(dir_name, "images")
+    ann_file = os.path.join(dir_name, "annotation.txt")
+    with open(ann_file, "r") as file_reader:
+        lines = file_reader.readlines()
+
+    data_list = []
+    for i, line in enumerate(lines):
+        try:
+            filename, label = line.split(",")
+            label = label.strip("\n")
+            with open(os.path.join(img_dir, filename), "rb") as file_reader:
+                img = file_reader.read()
+            data_json = {"id": i,
+                         "file_name": filename,
+                         "data": img,
+                         "label": int(label)}
+            data_list.append(data_json)
+        except FileNotFoundError:
+            continue
+    return data_list
+
+
+def get_nlp_data(dir_name, vocab_file, num):
+    """
+    Return raw data of aclImdb dataset.
+
+    Args:
+        dir_name (str): String of aclImdb dataset's path.
+        vocab_file (str): String of dictionary's path.
+        num (int): Number of sample.
+
+    Returns:
+        List
+    """
+    if not os.path.isdir(dir_name):
+        raise IOError("Directory {} not exists".format(dir_name))
+    for root, dirs, files in os.walk(dir_name):
+        for index, file_name_extension in enumerate(files):
+            if index < num:
+                file_path = os.path.join(root, file_name_extension)
+                file_name, _ = file_name_extension.split('.', 1)
+                id_, rating = file_name.split('_', 1)
+                with open(file_path, 'r') as f:
+                    raw_content = f.read()
+
+                dictionary = load_vocab(vocab_file)
+                vectors = [dictionary.get('[CLS]')]
+                vectors += [dictionary.get(i) if i in dictionary
+                            else dictionary.get('[UNK]')
+                            for i in re.findall(r"[\w']+|[{}]"
+                                                .format(string.punctuation),
+                                                raw_content)]
+                vectors += [dictionary.get('[SEP]')]
+                input_, mask, segment = inputs(vectors)
+                input_ids = np.reshape(np.array(input_), [-1])
+                input_mask = np.reshape(np.array(mask), [1, -1])
+                segment_ids = np.reshape(np.array(segment), [2, -1])
+                data = {
+                    "label": 1,
+                    "id": id_,
+                    "rating": float(rating),
+                    "input_ids": input_ids,
+                    "input_mask": input_mask,
+                    "segment_ids": segment_ids
+                }
+                yield data
+
+
+def convert_to_uni(text):
+    if isinstance(text, str):
+        return text
+    if isinstance(text, bytes):
+        return text.decode('utf-8', 'ignore')
+    raise Exception("The type %s does not convert!" % type(text))
+
+
+def load_vocab(vocab_file):
+    """load vocabulary to translate statement."""
+    vocab = collections.OrderedDict()
+    vocab.setdefault('blank', 2)
+    index = 0
+    with open(vocab_file) as reader:
+        while True:
+            tmp = reader.readline()
+            if not tmp:
+                break
+            token = convert_to_uni(tmp)
+            token = token.strip()
+            vocab[token] = index
+            index += 1
+    return vocab
+
+
+def inputs(vectors, maxlen=50):
+    length = len(vectors)
+    if length > maxlen:
+        return vectors[0:maxlen], [1] * maxlen, [0] * maxlen
+    input_ = vectors + [0] * (maxlen - length)
+    mask = [1] * length + [0] * (maxlen - length)
+    segment = [0] * maxlen
+    return input_, mask, segment