!1932 Add CLUE dataset

Merge pull request !1932 from jiangzhiwen/dataset/clue

mindspore/ccsrc/dataset/api/de_pipeline.cc

@@ -31,6 +31,7 @@
 #include "dataset/engine/datasetops/source/celeba_op.h"
 #include "dataset/engine/datasetops/source/random_data_op.h"
 #include "dataset/engine/datasetops/source/text_file_op.h"
+#include "dataset/engine/datasetops/source/clue_op.h"
 #include "dataset/engine/datasetops/filter_op.h"
 #include "mindrecord/include/shard_category.h"
 #include "mindrecord/include/shard_distributed_sample.h"
@@ -72,7 +73,8 @@ static std::unordered_map<uint32_t, pFunction> g_parse_op_func_ = {{kStorage, &D
                                                                    {kCelebA, &DEPipeline::ParseCelebAOp},
                                                                    {kRandomData, &DEPipeline::ParseRandomDataOp},
                                                                    {kTextFile, &DEPipeline::ParseTextFileOp},
-                                                                   {kBuildVocab, &DEPipeline::ParseBuildVocabOp}};
+                                                                   {kBuildVocab, &DEPipeline::ParseBuildVocabOp},
+                                                                   {kClue, &DEPipeline::ParseClueOp}};
 
 DEPipeline::DEPipeline() : iterator_(nullptr) {
   try {
@@ -1210,6 +1212,7 @@ Status DEPipeline::ParseTextFileOp(const py::dict &args, std::shared_ptr<Dataset
   *ptr = op;
   return Status::OK();
 }
+
 Status DEPipeline::ParsePadInfo(py::handle value, PadInfo *pad_info) {
   for (auto p : py::reinterpret_borrow<py::dict>(value)) {
     if (!p.second.is_none()) {
@@ -1236,6 +1239,7 @@ Status DEPipeline::ParsePadInfo(py::handle value, PadInfo *pad_info) {
   }
   return Status::OK();
 }
+
 Status DEPipeline::ParseBuildVocabOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr) {
   std::shared_ptr<BuildVocabOp::Builder> builder = std::make_shared<BuildVocabOp::Builder>();
   for (auto arg : args) {
@@ -1267,5 +1271,45 @@ Status DEPipeline::ParseBuildVocabOp(const py::dict &args, std::shared_ptr<Datas
   return Status::OK();
 }
 
+Status DEPipeline::ParseClueOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr) {
+  std::shared_ptr<ClueOp::Builder> builder = std::make_shared<ClueOp::Builder>();
+  if (!args["dataset_files"].is_none()) {
+    (void)builder->SetClueFilesList(ToStringVector(args["dataset_files"]));
+  } else {
+    RETURN_STATUS_UNEXPECTED("Error: dataset_files is missing");
+  }
+  // Optional arguments
+  for (auto arg : args) {
+    std::string key = py::str(arg.first);
+    py::handle value = arg.second;
+    if (!value.is_none()) {
+      if (key == "num_parallel_workers") {
+        (void)builder->SetNumWorkers(ToInt(value));
+      } else if (key == "shuffle_files") {
+        (void)builder->SetShuffleFiles(ToBool(value));
+      } else if (key == "num_samples") {
+        (void)builder->SetNumSamples(ToInt(value));
+      } else if (key == "num_shards") {
+        (void)builder->SetNumDevices(ToInt(value));
+      } else if (key == "shard_id") {
+        (void)builder->SetDeviceId(ToInt(value));
+      } else if (key == "cols_to_keyword") {
+        std::map<std::string, std::string> map_dict;
+        for (auto p : py::reinterpret_borrow<py::dict>(value)) {
+          if (!p.second.is_none()) {
+            map_dict.insert({ToString(p.first), ToString(p.second)});
+          } else {
+            map_dict.insert({ToString(p.first), ToString(p.first)});
+          }
+        }
+        (void)builder->SetColsKeyMap(map_dict);
+      }
+    }
+  }
+  std::shared_ptr<ClueOp> op;
+  RETURN_IF_NOT_OK(builder->Build(&op));
+  *ptr = op;
+  return Status::OK();
+}
 }  // namespace dataset
 }  // namespace mindspore

mindspore/ccsrc/dataset/api/de_pipeline.h

@@ -64,7 +64,8 @@ enum OpName {
   kCelebA,
   kRandomData,
   kTextFile,
-  kBuildVocab
+  kBuildVocab,
+  kClue
 };
 
 // The C++ binder class that we expose to the python script.
@@ -166,6 +167,8 @@ class DEPipeline {
 
   Status ParseBuildVocabOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr);
 
+  Status ParseClueOp(const py::dict &args, std::shared_ptr<DatasetOp> *ptr);
+
  private:
   // Execution tree that links the dataset operators.
   std::shared_ptr<ExecutionTree> tree_;

mindspore/ccsrc/dataset/api/python_bindings.cc

@@ -55,6 +55,7 @@
 #include "dataset/engine/datasetops/source/tf_reader_op.h"
 #include "dataset/engine/jagged_connector.h"
 #include "dataset/engine/datasetops/source/text_file_op.h"
+#include "dataset/engine/datasetops/source/clue_op.h"
 #include "dataset/engine/datasetops/source/voc_op.h"
 #include "dataset/engine/datasetops/source/coco_op.h"
 #include "dataset/engine/gnn/graph.h"
@@ -201,6 +202,18 @@ void bindDatasetOps(py::module *m) {
       THROW_IF_ERROR(TextFileOp::CountAllFileRows(filenames, &count));
       return count;
     });
+
+  (void)py::class_<ClueOp, DatasetOp, std::shared_ptr<ClueOp>>(*m, "ClueOp")
+    .def_static("get_num_rows", [](const py::list &files) {
+      int64_t count = 0;
+      std::vector<std::string> filenames;
+      for (auto file : files) {
+        file.is_none() ? (void)filenames.emplace_back("") : filenames.push_back(py::str(file));
+      }
+      THROW_IF_ERROR(ClueOp::CountAllFileRows(filenames, &count));
+      return count;
+    });
+
   (void)py::class_<VOCOp, DatasetOp, std::shared_ptr<VOCOp>>(*m, "VOCOp")
     .def_static("get_num_rows",
                 [](const std::string &dir, const std::string &task_type, const std::string &task_mode,
@@ -629,7 +642,8 @@ PYBIND11_MODULE(_c_dataengine, m) {
     .value("RANDOMDATA", OpName::kRandomData)
     .value("BUILDVOCAB", OpName::kBuildVocab)
     .value("CELEBA", OpName::kCelebA)
-    .value("TEXTFILE", OpName::kTextFile);
+    .value("TEXTFILE", OpName::kTextFile)
+    .value("CLUE", OpName::kClue);
 
   (void)py::enum_<JiebaMode>(m, "JiebaMode", py::arithmetic())
     .value("DE_JIEBA_MIX", JiebaMode::kMix)

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

@@ -19,4 +19,5 @@ add_library(engine-datasetops-source OBJECT
     random_data_op.cc
     celeba_op.cc
     text_file_op.cc
+    clue_op.cc
     )
\ No newline at end of file

mindspore/ccsrc/dataset/engine/datasetops/source/clue_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/source/clue_op.h"
+
+#include <string>
+#include <vector>
+#include <fstream>
+#include <iomanip>
+#include <utility>
+
+#include "dataset/core/config_manager.h"
+#include "dataset/util/task_manager.h"
+#include "dataset/engine/jagged_connector.h"
+#include "dataset/engine/execution_tree.h"
+#include "dataset/engine/datasetops/source/io_block.h"
+#include "dataset/util/random.h"
+
+namespace mindspore {
+namespace dataset {
+
+ClueOp::Builder::Builder()
+    : builder_device_id_(0), builder_num_devices_(1), builder_num_samples_(0), builder_shuffle_files_(false) {
+  std::shared_ptr<ConfigManager> config_manager = GlobalContext::config_manager();
+  builder_num_workers_ = config_manager->num_parallel_workers();
+  builder_op_connector_size_ = config_manager->op_connector_size();
+  builder_rows_per_buffer_ = config_manager->rows_per_buffer();
+  builder_worker_connector_size_ = config_manager->worker_connector_size();
+}
+
+Status ClueOp::Builder::ValidateInputs() const {
+  std::string err;
+  err += builder_num_workers_ <= 0 ? "Number of parallel workers should be greater than 0\n" : "";
+  err += (builder_device_id_ >= builder_num_devices_ || builder_num_devices_ < 1) ? "Wrong sharding configs\n" : "";
+  return err.empty() ? Status::OK() : Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, err);
+}
+
+Status ClueOp::Builder::Build(std::shared_ptr<ClueOp> *op) {
+  RETURN_IF_NOT_OK(ValidateInputs());
+
+  // Throttle the number of workers if we have more workers than files!
+  if (static_cast<size_t>(builder_num_workers_) > builder_clue_files_list_.size()) {
+    builder_num_workers_ = builder_clue_files_list_.size();
+    MS_LOG(WARNING) << "ClueOp operator parallelism reduced to " << builder_num_workers_ << " workers.";
+  }
+
+  ColKeyMap ck_map;
+  for (auto &p : builder_cols_to_keyword_) {
+    ck_map.insert({p.first, split(p.second, '/')});
+  }
+
+  std::shared_ptr<ClueOp> clue_op = std::make_shared<ClueOp>(
+    builder_num_workers_, builder_rows_per_buffer_, builder_num_samples_, builder_worker_connector_size_, ck_map,
+    builder_clue_files_list_, builder_op_connector_size_, builder_shuffle_files_, builder_num_devices_,
+    builder_device_id_);
+  RETURN_IF_NOT_OK(clue_op->Init());
+  *op = std::move(clue_op);
+
+  return Status::OK();
+}
+
+std::vector<std::string> ClueOp::Builder::split(const std::string &s, char delim) {
+  std::vector<std::string> res;
+  std::stringstream ss(s);
+  std::string item;
+
+  while (getline(ss, item, delim)) {
+    res.push_back(item);
+  }
+  return res;
+}
+
+ClueOp::ClueOp(int32_t num_workers, int64_t rows_per_buffer, int64_t num_samples, int32_t worker_connector_size,
+               ColKeyMap cols_to_keyword, std::vector<std::string> clue_files_list, int32_t op_connector_size,
+               bool shuffle_files, int32_t num_device, int32_t device_id)
+    : ParallelOp(num_workers, op_connector_size),
+      rows_per_buffer_(rows_per_buffer),
+      num_rows_per_shard_(0),
+      all_num_rows_(0),
+      num_samples_(num_samples),
+      filename_index_(std::make_unique<StringIndex>()),
+      clue_files_list_(std::move(clue_files_list)),
+      load_jagged_connector_(true),
+      cols_to_keyword_(cols_to_keyword),
+      shuffle_files_(shuffle_files),
+      finished_reading_dataset_(false),
+      num_devices_(num_device),
+      device_id_(device_id),
+      load_io_block_queue_(true) {
+  worker_connector_size_ = worker_connector_size;
+}
+
+Status ClueOp::Init() {
+  RETURN_IF_NOT_OK(filename_index_->insert(clue_files_list_));
+
+  int32_t safe_queue_size = static_cast<int32_t>(std::ceil(clue_files_list_.size() / num_workers_) + 1);
+  io_block_queues_.Init(num_workers_, safe_queue_size);
+
+  // Set the column name mapping (base class field)
+  int count = 0;
+  for (auto &p : cols_to_keyword_) {
+    column_name_id_map_[p.first] = count;
+    count++;
+  }
+
+  RETURN_IF_NOT_OK(ParallelOp::CreateWorkerConnector(worker_connector_size_));
+  jagged_buffer_connector_ = std::make_unique<JaggedConnector>(num_workers_, 1, worker_connector_size_);
+
+  return Status::OK();
+}
+
+Status ClueOp::Reset() {
+  load_jagged_connector_ = true;
+  load_io_block_queue_ = true;
+
+  RETURN_IF_NOT_OK(ParallelOp::Reset());
+  NotifyToFillIOBlockQueue();
+  return Status::OK();
+}
+
+Status ClueOp::LoadTensor(const std::string &line, std::unique_ptr<TensorQTable> *tensor_table, int64_t row) {
+  TensorRow tRow(1, nullptr);
+  (*tensor_table)->push_back(std::move(tRow));
+
+  std::shared_ptr<Tensor> tensor;
+  RETURN_IF_NOT_OK(Tensor::CreateTensor(&tensor, {line}, TensorShape::CreateScalar()));
+  (**tensor_table)[row][0] = std::move(tensor);
+  return Status::OK();
+}
+
+Status ClueOp::GetValue(const nlohmann::json &js, std::vector<std::string> key_chain, std::shared_ptr<Tensor> *t) {
+  nlohmann::json cursor = js;
+  for (int i = 0; i < key_chain.size(); i++) {
+    if (cursor.find(key_chain[i]) != cursor.end()) {
+      cursor = cursor[key_chain[i]];
+    } else {
+      RETURN_STATUS_UNEXPECTED("Failed to find key: " + key_chain[i]);
+    }
+  }
+  std::string final_str = key_chain.back();
+  switch (cursor.type()) {
+    case nlohmann::detail::value_t::string:
+      RETURN_IF_NOT_OK(Tensor::CreateTensor(t, {cursor.get<std::string>()}, TensorShape::CreateScalar()));
+      break;
+
+    case nlohmann::detail::value_t::number_integer:
+      RETURN_IF_NOT_OK(
+        Tensor::CreateTensor(t, TensorImpl::kFlexible, TensorShape::CreateScalar(), DataType(DataType::DE_INT32)));
+      (*t)->SetItemAt<int32_t>({0}, cursor.get<int32_t>());
+      break;
+    case nlohmann::detail::value_t::number_unsigned:
+      RETURN_IF_NOT_OK(
+        Tensor::CreateTensor(t, TensorImpl::kFlexible, TensorShape::CreateScalar(), DataType(DataType::DE_INT32)));
+      (*t)->SetItemAt<int32_t>({0}, cursor.get<uint32_t>());
+      break;
+    case nlohmann::detail::value_t::number_float:
+      RETURN_IF_NOT_OK(
+        Tensor::CreateTensor(t, TensorImpl::kFlexible, TensorShape::CreateScalar(), DataType(DataType::DE_FLOAT32)));
+      (*t)->SetItemAt<int32_t>({0}, cursor.get<float>());
+      break;
+    case nlohmann::detail::value_t::array:
+      RETURN_IF_NOT_OK(Tensor::CreateTensor(t, {cursor.get<std::vector<std::string>>()}, TensorShape::CreateScalar()));
+      break;
+    default:
+      break;
+  }
+  return Status::OK();
+}
+
+Status ClueOp::LoadFile(const std::string &file, const int64_t start_offset, const int64_t end_offset,
+                        const int32_t worker_id) {
+  std::ifstream handle(file);
+  if (!handle.is_open()) {
+    RETURN_STATUS_UNEXPECTED("Failed to open file " + file);
+  }
+
+  int64_t rows_each_buffer = 0;
+  int64_t rows_total = 0;
+  std::string line;
+  std::unique_ptr<DataBuffer> cur_buffer = std::make_unique<DataBuffer>(0, DataBuffer::BufferFlags::kDeBFlagNone);
+  std::unique_ptr<TensorQTable> tensor_table = std::make_unique<TensorQTable>();
+
+  while (getline(handle, line)) {
+    if (line.empty()) {
+      continue;
+    }
+    // If read to the end offset of this file, break.
+    if (rows_total >= end_offset) {
+      break;
+    }
+    // Skip line before start offset.
+    if (rows_total < start_offset) {
+      rows_total++;
+      continue;
+    }
+
+    try {
+      nlohmann::json js = nlohmann::json::parse(line);
+      int cols_count = cols_to_keyword_.size();
+      TensorRow tRow(cols_count, nullptr);
+      tensor_table->push_back(std::move(tRow));
+
+      int cout = 0;
+      for (auto &p : cols_to_keyword_) {
+        std::shared_ptr<Tensor> tensor;
+        RETURN_IF_NOT_OK(GetValue(js, p.second, &tensor));
+        (*tensor_table)[rows_each_buffer][cout] = std::move(tensor);
+        cout++;
+      }
+    } catch (const std::exception &err) {
+      // Catch any exception and convert to Status return code
+      RETURN_STATUS_UNEXPECTED("Failed to load json file");
+    }
+
+    // RETURN_IF_NOT_OK(LoadTensor(line, &tensor_table, rows_each_buffer));
+    rows_each_buffer++;
+    rows_total++;
+    if (rows_each_buffer == rows_per_buffer_) {
+      cur_buffer->set_tensor_table(std::move(tensor_table));
+      RETURN_IF_NOT_OK(jagged_buffer_connector_->Add(worker_id, std::move(cur_buffer)));
+
+      cur_buffer = std::make_unique<DataBuffer>(0, DataBuffer::BufferFlags::kDeBFlagNone);
+      tensor_table = std::make_unique<TensorQTable>();
+      rows_each_buffer = 0;
+    }
+  }
+
+  if (rows_each_buffer > 0) {
+    cur_buffer->set_tensor_table(std::move(tensor_table));
+    RETURN_IF_NOT_OK(jagged_buffer_connector_->Add(worker_id, std::move(cur_buffer)));
+  }
+  return Status::OK();
+}
+
+Status ClueOp::operator()() {
+  RETURN_IF_NOT_OK(CalculateNumRowsPerShard());
+
+  // launch one thread, responsible for filling IoBlockQueue
+  RETURN_IF_NOT_OK(tree_->LaunchWorkers(1, std::bind(&ClueOp::WaitToFillIOBlockQueue, this)));
+
+  RETURN_IF_NOT_OK(tree_->LaunchWorkers(num_workers_, std::bind(&ClueOp::WorkerEntry, this, std::placeholders::_1)));
+
+  // must be called after launching workers.
+  TaskManager::FindMe()->Post();
+  RETURN_IF_NOT_OK(io_block_queue_wait_post_.Register(tree_->AllTasks()));
+  NotifyToFillIOBlockQueue();
+
+  while (!finished_reading_dataset_) {
+    int64_t buffer_id = 0;
+    int32_t workers_done = 0;
+    int64_t rows_read = 0;
+    load_io_block_queue_ = true;
+
+    while (workers_done < num_workers_) {
+      std::unique_ptr<DataBuffer> buffer;
+      RETURN_IF_NOT_OK(jagged_buffer_connector_->Pop(0, &buffer));
+      if (buffer->eoe()) {
+        workers_done++;
+      } else if (num_samples_ == 0 || rows_read < num_samples_) {
+        if ((num_samples_ > 0) && (rows_read + buffer->NumRows() > num_samples_)) {
+          int64_t rowsToRemove = buffer->NumRows() - (num_samples_ - rows_read);
+          RETURN_IF_NOT_OK(buffer->SliceOff(rowsToRemove));
+        }
+        rows_read += buffer->NumRows();
+        buffer->set_id(buffer_id++);
+        RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(buffer)));
+      } else {
+        // end of epoch
+        load_jagged_connector_ = false;
+        load_io_block_queue_ = false;
+      }
+    }
+
+    std::unique_ptr<DataBuffer> eoe_buffer = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
+    RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(eoe_buffer)));
+
+    if (!BitTest(op_ctrl_flags_, kDeOpRepeated) || BitTest(op_ctrl_flags_, kDeOpLastRepeat)) {
+      finished_reading_dataset_ = true;
+      NotifyToFillIOBlockQueue();
+    } else {
+      jagged_buffer_connector_->DoReset();
+      buffer_id = 0;
+    }
+  }
+  std::unique_ptr<DataBuffer> eof_buffer = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOF);
+  RETURN_IF_NOT_OK(out_connector_->Add(0, std::move(eof_buffer)));
+
+  RETURN_IF_NOT_OK(PostEndOfData());
+  return Status::OK();
+}
+
+Status ClueOp::WorkerEntry(int32_t worker_id) {
+  TaskManager::FindMe()->Post();
+  std::unique_ptr<FilenameBlock> io_block;
+  RETURN_IF_NOT_OK(PopIoBlockQueue(worker_id, &io_block));
+  while (!io_block->eof()) {
+    if (!io_block->eoe()) {
+      if (load_jagged_connector_) {
+        std::string filename;
+        RETURN_IF_NOT_OK(io_block->GetFilename(&filename, *filename_index_));
+        int64_t start_offset = io_block->GetStartOffset();
+        int64_t end_offset = io_block->GetEndOffset();
+        RETURN_IF_NOT_OK(LoadFile(filename, start_offset, end_offset, worker_id));
+      }
+    } else {
+      std::unique_ptr<DataBuffer> eoe_buffer = std::make_unique<DataBuffer>(0, DataBuffer::kDeBFlagEOE);
+      RETURN_IF_NOT_OK(jagged_buffer_connector_->Add(worker_id, std::move(eoe_buffer)));
+    }
+
+    RETURN_IF_NOT_OK(PopIoBlockQueue(worker_id, &io_block));
+  }
+  return Status::OK();
+}
+
+// A print method typically used for debugging
+void ClueOp::Print(std::ostream &out, bool show_all) const {
+  // Always show the id and name as first line regardless if this summary or detailed print
+  out << "(" << std::setw(2) << operator_id_ << ") <ClueOp>:";
+  if (!show_all) {
+    // Call the super class for displaying any common 1-liner info
+    ParallelOp::Print(out, show_all);
+    // Then show any custom derived-internal 1-liner info for this op
+    out << "\n";
+  } else {
+    // Call the super class for displaying any common detailed info
+    ParallelOp::Print(out, show_all);
+    // Then show any custom derived-internal stuff
+    out << "\nRows per buffer: " << rows_per_buffer_ << "\nSample count: " << num_samples_
+        << "\nDevice id: " << device_id_ << "\nNumber of devices: " << num_devices_
+        << "\nShuffle files: " << ((shuffle_files_) ? "yes" : "no") << "\nClue files list:\n";
+    for (int i = 0; i < clue_files_list_.size(); ++i) {
+      out << " " << clue_files_list_[i];
+    }
+    out << "\n\n";
+  }
+}
+
+// Pops an element from a queue in io_block_queues
+Status ClueOp::PopIoBlockQueue(int32_t index, std::unique_ptr<FilenameBlock> *out_block) {
+  RETURN_IF_NOT_OK(io_block_queues_[index]->PopFront(out_block));
+
+  return Status::OK();
+}
+
+// Pushes an element to a queue in io_block_queues
+Status ClueOp::PushIoBlockQueue(int32_t index, std::unique_ptr<FilenameBlock> &&io_block) {
+  RETURN_IF_NOT_OK(io_block_queues_[index]->Add(std::move(io_block)));
+
+  return Status::OK();
+}
+
+static void ShuffleKeys(std::vector<int64_t> *i_keys, uint32_t seed) {
+  std::mt19937 rng(seed);
+  std::shuffle(i_keys->begin(), i_keys->end(), rng);
+}
+
+Status ClueOp::WaitToFillIOBlockQueue() {
+  // must be called first if called by worker spanwed by taskgroup
+  TaskManager::FindMe()->Post();
+
+  std::vector<int64_t> i_keys;
+  if (shuffle_files_) {
+    for (auto it = filename_index_->begin(); it != filename_index_->end(); ++it) {
+      i_keys.push_back(it.key());
+    }
+  }
+  uint32_t seed = 0;
+  while (true) {
+    RETURN_IF_NOT_OK(io_block_queue_wait_post_.Wait());
+    io_block_queue_wait_post_.Clear();
+
+    if (finished_reading_dataset_) {
+      break;
+    }
+
+    if (shuffle_files_) {
+      ShuffleKeys(&i_keys, num_devices_ == 1 ? GetSeed() : ++seed);
+    }
+    RETURN_IF_NOT_OK(FillIOBlockQueue(i_keys));
+  }
+  return Status::OK();
+}
+
+Status ClueOp::FillIOBlockQueue(const std::vector<int64_t> &i_keys) {
+  int32_t queue_index = 0;
+  int64_t pre_count = 0;
+  int64_t start_offset = 0;
+  int64_t end_offset = 0;
+  bool finish = false;
+  while (!finish) {
+    std::vector<std::pair<std::string, int64_t>> file_index;
+    if (!i_keys.empty()) {
+      for (auto it = i_keys.begin(); it != i_keys.end(); ++it) {
+        {
+          if (!load_io_block_queue_) {
+            break;
+          }
+        }
+        auto file_it = filename_index_->Search(*it);
+        file_index.emplace_back(std::pair<std::string, int64_t>(file_it.value(), *it));
+      }
+    } else {
+      for (auto it = filename_index_->begin(); it != filename_index_->end(); ++it) {
+        {
+          if (!load_io_block_queue_) {
+            break;
+          }
+        }
+        file_index.emplace_back(std::pair<std::string, int64_t>(it.value(), it.key()));
+      }
+    }
+    for (auto file_info : file_index) {
+      if (NeedPushFileToBlockQueue(file_info.first, &start_offset, &end_offset, pre_count)) {
+        auto ioBlock =
+          std::make_unique<FilenameBlock>(file_info.second, start_offset, end_offset, IOBlock::kDeIoBlockNone);
+        RETURN_IF_NOT_OK(PushIoBlockQueue(queue_index, std::move(ioBlock)));
+        queue_index = (queue_index + 1) % num_workers_;
+      }
+
+      pre_count += filename_numrows_[file_info.first];
+    }
+
+    if (pre_count < (static_cast<int64_t>(device_id_) + 1) * num_rows_per_shard_) {
+      finish = false;
+    } else {
+      finish = true;
+    }
+  }
+
+  RETURN_IF_NOT_OK(PostEndOfEpoch(queue_index));
+  return Status::OK();
+}
+
+void ClueOp::NotifyToFillIOBlockQueue() { io_block_queue_wait_post_.Set(); }
+
+bool ClueOp::NeedPushFileToBlockQueue(const std::string &file_name, int64_t *start_offset, int64_t *end_offset,
+                                      const int64_t &pre_count) {
+  *start_offset = 0;
+  *end_offset = 0;
+  bool push = false;
+  int64_t start_index = device_id_ * num_rows_per_shard_;
+  if (device_id_ + 1 < 0) {
+    MS_LOG(ERROR) << "Device id is invalid";
+    return false;
+  }
+
+  int64_t end_index = (static_cast<int64_t>(device_id_) + 1) * num_rows_per_shard_;
+  if (pre_count <= start_index && pre_count + filename_numrows_[file_name] > start_index) {
+    *start_offset = start_index - pre_count;
+    push = true;
+    if (pre_count < end_index && pre_count + filename_numrows_[file_name] >= end_index) {
+      *end_offset = end_index - pre_count;
+    } else {
+      *end_offset = filename_numrows_[file_name];
+    }
+  }
+
+  if (pre_count >= start_index && pre_count < end_index) {
+    *start_offset = 0;
+    push = true;
+    if (pre_count + filename_numrows_[file_name] >= end_index) {
+      *end_offset = end_index - pre_count;
+    } else {
+      *end_offset = filename_numrows_[file_name];
+    }
+  }
+
+  return push;
+}
+
+// Pushes a control indicator onto the IOBlockQueue for each worker to consume. When the worker
+// pops this control indicator, it will wait until the next epoch starts and then resume execution.
+Status ClueOp::PostEndOfEpoch(int32_t queue_index) {
+  for (int i = 0; i < num_workers_; ++i) {
+    std::unique_ptr<FilenameBlock> eoe = std::make_unique<FilenameBlock>(IOBlock::kDeIoBlockFlagEoe);
+    RETURN_IF_NOT_OK(PushIoBlockQueue((queue_index + i) % num_workers_, std::move(eoe)));
+  }
+
+  return Status::OK();
+}
+
+Status ClueOp::CalculateNumRowsPerShard() {
+  for (auto it = filename_index_->begin(); it != filename_index_->end(); ++it) {
+    int64_t count = CountTotalRows(it.value());
+    filename_numrows_[it.value()] = count;
+    all_num_rows_ += count;
+  }
+  if (all_num_rows_ == 0) {
+    RETURN_STATUS_UNEXPECTED(
+      "There is no valid data matching the dataset API CLUEDataset. Please check file path or dataset API "
+      "validation first.");
+  }
+
+  num_rows_per_shard_ = static_cast<int64_t>(std::ceil(all_num_rows_ * 1.0 / num_devices_));
+  MS_LOG(DEBUG) << "Number rows per shard is " << num_rows_per_shard_;
+  return Status::OK();
+}
+
+int64_t ClueOp::CountTotalRows(const std::string &file) {
+  std::ifstream handle(file);
+  if (!handle.is_open()) {
+    MS_LOG(ERROR) << "Failed to open file: " << file;
+    return 0;
+  }
+
+  std::string line;
+  int64_t count = 0;
+  while (getline(handle, line)) {
+    if (!line.empty()) {
+      count++;
+    }
+  }
+
+  return count;
+}
+
+// Pushes a control indicator onto the IOBlockQueue for each worker to consume.
+// When the worker pops this control indicator, it will shut itself down gracefully.
+Status ClueOp::PostEndOfData() {
+  for (int i = 0; i < num_workers_; ++i) {
+    std::unique_ptr<FilenameBlock> eof = std::make_unique<FilenameBlock>(IOBlock::kDeIoBlockFlagEof);
+    RETURN_IF_NOT_OK(PushIoBlockQueue(i, std::move(eof)));
+  }
+
+  return Status::OK();
+}
+
+Status ClueOp::CountAllFileRows(const std::vector<std::string> &files, int64_t *count) {
+  std::shared_ptr<ClueOp> op;
+  *count = 0;
+  RETURN_IF_NOT_OK(Builder().SetClueFilesList(files).Build(&op));
+  for (auto file : files) {
+    *count += op->CountTotalRows(file);
+  }
+  return Status::OK();
+}
+
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/dataset/engine/datasetops/source/clue_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_SOURCE_CLUE_OP_H_
+#define DATASET_ENGINE_DATASETOPS_SOURCE_CLUE_OP_H_
+
+#include <memory>
+#include <map>
+#include <mutex>
+#include <string>
+#include <vector>
+#include <nlohmann/json.hpp>
+
+#include "dataset/util/auto_index.h"
+#include "dataset/engine/datasetops/parallel_op.h"
+#include "dataset/engine/datasetops/source/io_block.h"
+
+namespace mindspore {
+namespace dataset {
+using StringIndex = AutoIndexObj<std::string>;
+using ColKeyMap = std::map<std::string, std::vector<std::string>>;
+
+class JaggedConnector;
+
+class ClueOp : public ParallelOp {
+ public:
+  class Builder {
+   public:
+    // Builder constructor. Creates the builder object.
+    // @note No default args
+    // @return This is a constructor.
+    Builder();
+
+    // Default destructor
+    ~Builder() = default;
+
+    // Checks if the inputs of the builder is valid.
+    // @return Status - the error code returned.
+    Status ValidateInputs() const;
+
+    // Create the final object.
+    // @param op - dataset op.
+    // @return - the error code return.
+    Status Build(std::shared_ptr<ClueOp> *op);
+
+    // Setter method.
+    // @return Builder - setter method returns reference to the builder.
+    Builder &SetNumWorkers(int32_t num_workers) {
+      builder_num_workers_ = num_workers;
+      return *this;
+    }
+
+    // Setter method.
+    // @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.
+    // @return Builder - setter method returns reference to the builder.
+    Builder &SetRowsPerBuffer(int64_t rows_per_buffer) {
+      builder_rows_per_buffer_ = rows_per_buffer;
+      return *this;
+    }
+
+    // Setter method.
+    // @return Builder - setter method returns reference to the builder.
+    Builder &SetNumDevices(int64_t num_dev) {
+      builder_num_devices_ = num_dev;
+      return *this;
+    }
+
+    // Setter method.
+    // @return Builder - setter method returns reference to the builder.
+    Builder &SetDeviceId(int64_t dev_id) {
+      builder_device_id_ = dev_id;
+      return *this;
+    }
+
+    // Setter method.
+    // @return Builder - setter method returns reference to the builder.
+    Builder &SetClueFilesList(const std::vector<std::string> &files_list) {
+      builder_clue_files_list_ = files_list;
+      return *this;
+    }
+
+    // Setter method.
+    // @return Builder - setter method returns reference to the builder.
+    Builder &SetShuffleFiles(bool shuffle_files) {
+      builder_shuffle_files_ = shuffle_files;
+      return *this;
+    }
+
+    // Setter method.
+    // @return Builder - setter method returns reference to the builder.
+    Builder &SetNumSamples(int64_t num_samples) {
+      builder_num_samples_ = num_samples;
+      return *this;
+    }
+
+    // Setter method.
+    // @return Builder - setter method returns reference to the builder.
+    Builder &SetColsKeyMap(const std::map<std::string, std::string> &cols_to_key) {
+      builder_cols_to_keyword_ = cols_to_key;
+      return *this;
+    }
+
+    // Split string based on a character delimiter
+    // @return - the a string vector
+    std::vector<std::string> split(const std::string &s, char delim);
+
+   private:
+    int32_t builder_device_id_;
+    int32_t builder_num_devices_;
+    int32_t builder_num_workers_;
+    int32_t builder_op_connector_size_;
+    int64_t builder_rows_per_buffer_;
+    int64_t builder_num_samples_;
+    int32_t builder_worker_connector_size_;
+    std::vector<std::string> builder_clue_files_list_;
+    bool builder_shuffle_files_;
+    std::map<std::string, std::string> builder_cols_to_keyword_;
+  };
+
+  // Constructor of ClueOp
+  ClueOp(int32_t num_workers, int64_t rows_per_buffer, int64_t num_samples, int32_t worker_connector_size,
+         ColKeyMap cols_to_keyword, std::vector<std::string> clue_files_list, int32_t op_connector_size,
+         bool shuffle_files, int32_t num_devices, int32_t device_id);
+
+  // Default destructor
+  ~ClueOp() = default;
+
+  // A print method typically used for debugging
+  // @param out - The output stream to write output to
+  // @param show_all - A bool to control if you want to show all info or just a summary
+  void Print(std::ostream &out, bool show_all) const override;
+
+  // Instantiates the internal queues and connectors
+  // @return Status - the error code returned
+  Status Init();
+
+  // Class functor operator () override.
+  // All dataset operators 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 returned.
+  Status operator()() override;
+
+  // Overrides base class reset method. Cleans up any state info from it's previous execution
+  // reinitializes itself so that it can be executed again, as if it was just created.
+  // @return Status - the error code returned.
+  Status Reset() override;
+
+  // Get total rows in files.
+  // @param files - all clue files.
+  // @param count - number of rows.
+  // @return Status - the error coed returned.
+  static Status CountAllFileRows(const std::vector<std::string> &files, int64_t *count);
+
+ private:
+  // The entry point for when workers are launched.
+  // @param worker_id - the id of the worker that is executing this function.
+  // @return Status - the error code returned.
+  Status WorkerEntry(int32_t worker_id) override;
+
+  // Parses a single row and puts the data into a tensor table.
+  // @param line - the content of the row.
+  // @param tensor_table - the tensor table to put the parsed data in.
+  // @param row - the id of the row filled in the tensor table.
+  // @return Status - the error code returned.
+  Status LoadTensor(const std::string &line, std::unique_ptr<TensorQTable> *tensor_table, int64_t row);
+
+  // Reads a clue file and loads the data into multiple buffers.
+  // @param file - the file to read.
+  // @param start_offset - the start offset of file.
+  // @param end_offset - the end offset of file.
+  // @param worker_id - the id of the worker that is executing this function.
+  // @return Status - the error code returned.
+  Status LoadFile(const std::string &file, const int64_t start_offset, const int64_t end_offset,
+                  const int32_t worker_id);
+
+  // Pops an element from a queue in IOBlockQueue.
+  // @param index - the index of the queue to pop from.
+  // @param out_block - the popped element.
+  // @return Status - the error code returned.
+  Status PopIoBlockQueue(int32_t index, std::unique_ptr<FilenameBlock> *out_block);
+
+  // Pushes an element to a queue in IOBlockQueue.
+  // @param index - the index of the queue to push to.
+  // @param io_block - the element to push onto the queue.
+  // @return Status - the error code returned.
+  Status PushIoBlockQueue(int32_t index, std::unique_ptr<FilenameBlock> &&io_block);
+
+  // Called asynchronously by another thread. Will wait until notified to fill the IOBlockQueue.
+  // @return Status - the error code returned.
+  Status WaitToFillIOBlockQueue();
+
+  // Fill the IOBlockQueue.
+  // @para i_keys - keys of file to fill to the IOBlockQueue
+  // @return Status - the error code returned.
+  Status FillIOBlockQueue(const std::vector<int64_t> &i_keys);
+
+  // Notifies the thread which called FillIoBlockQueue to resume execution
+  void NotifyToFillIOBlockQueue();
+
+  // Select file and push it to the block queue.
+  // @param file_name - File name.
+  // @param start_file - If file contains the first sample of data.
+  // @param end_file - If file contains the end sample of data.
+  // @param pre_count - Total rows of previous files.
+  // @return Status - the error code returned.
+  bool NeedPushFileToBlockQueue(const std::string &file_name, int64_t *start_offset, int64_t *end_offset,
+                                const int64_t &pre_count);
+
+  // Pushes a control indicator onto the IOBlockQueue for each worker to consume. When the worker
+  // pops this control indicator, it will wait until the next epoch starts and then resume execution.
+  // @return Status - the error code returned.
+  Status PostEndOfEpoch(int32_t queue_index);
+
+  // Calculate number of rows in each shard.
+  // @return Status - the error code returned.
+  Status CalculateNumRowsPerShard();
+
+  // Count number of rows in each file.
+  // @param filename - clue file name.
+  // @return int64_t - the total number of rows in file.
+  int64_t CountTotalRows(const std::string &file);
+
+  // Pushes a control indicator onto the IOBlockQueue for each worker to consume.
+  // When the worker pops this control indicator, it will shut itself down gracefully.
+  // @return Status - the error code returned.
+  Status PostEndOfData();
+
+  // @return Status - the error code returned.
+  Status GetValue(const nlohmann::json &js, std::vector<std::string> key_chain, std::shared_ptr<Tensor> *t);
+
+  int32_t device_id_;
+  bool shuffle_files_;
+  bool finished_reading_dataset_;
+  int32_t num_devices_;
+  int64_t rows_per_buffer_;
+  bool load_io_block_queue_;
+  int64_t num_rows_per_shard_;
+  int64_t all_num_rows_;
+  int64_t num_samples_;
+  std::map<std::string, int64_t> filename_numrows_;
+  std::unique_ptr<StringIndex> filename_index_;
+  std::vector<std::string> clue_files_list_;
+  WaitPost io_block_queue_wait_post_;
+  std::unique_ptr<JaggedConnector> jagged_buffer_connector_;
+  QueueList<std::unique_ptr<FilenameBlock>> io_block_queues_;
+  bool load_jagged_connector_;
+  ColKeyMap cols_to_keyword_;
+};
+
+}  // namespace dataset
+}  // namespace mindspore
+#endif  // DATASET_ENGINE_DATASETOPS_SOURCE_CLUE_OP_H_

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

@@ -43,7 +43,7 @@ TextFileOp::Builder::Builder()
 
 Status TextFileOp::Builder::ValidateInputs() const {
   std::string err_msg;
-  err_msg += builder_num_workers_ <= 0 ? "Number of parallel workers should be greate than 0\n" : "";
+  err_msg += builder_num_workers_ <= 0 ? "Number of parallel workers should be greater than 0\n" : "";
   err_msg += builder_device_id_ >= builder_num_devices_ || builder_num_devices_ < 1 ? "Wrong sharding configs\n" : "";
   return err_msg.empty() ? Status::OK() : Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, err_msg);
 }

mindspore/dataset/__init__.py

@@ -21,7 +21,7 @@ can also create samplers with this module to sample data.
 from .core.configuration import config
 from .engine.datasets import TFRecordDataset, ImageFolderDatasetV2, MnistDataset, MindDataset, NumpySlicesDataset, \
     GeneratorDataset, ManifestDataset, Cifar10Dataset, Cifar100Dataset, VOCDataset, CocoDataset, CelebADataset,\
-    TextFileDataset, Schema, Shuffle, zip, RandomDataset
+    TextFileDataset, CLUEDataset, Schema, Shuffle, zip, RandomDataset
 from .engine.samplers import DistributedSampler, PKSampler, RandomSampler, SequentialSampler, SubsetRandomSampler, \
     WeightedRandomSampler, Sampler
 from .engine.serializer_deserializer import serialize, deserialize, show
@@ -29,6 +29,6 @@ from .engine.graphdata import GraphData
 
 __all__ = ["config", "ImageFolderDatasetV2", "MnistDataset",
            "MindDataset", "GeneratorDataset", "TFRecordDataset",
-           "ManifestDataset", "Cifar10Dataset", "Cifar100Dataset", "CelebADataset", "NumpySlicesDataset",
-           "VOCDataset", "CocoDataset", "TextFileDataset", "Schema", "DistributedSampler", "PKSampler", "RandomSampler",
-           "SequentialSampler", "SubsetRandomSampler", "WeightedRandomSampler", "zip", "GraphData"]
+           "ManifestDataset", "Cifar10Dataset", "Cifar100Dataset", "CelebADataset", "NumpySlicesDataset", "VOCDataset",
+           "CocoDataset", "TextFileDataset", "CLUEDataset", "Schema", "DistributedSampler", "PKSampler",
+           "RandomSampler", "SequentialSampler", "SubsetRandomSampler", "WeightedRandomSampler", "zip", "GraphData"]

mindspore/dataset/engine/__init__.py

@@ -30,7 +30,7 @@ from ..core.configuration import config, ConfigurationManager
 
 __all__ = ["config", "ConfigurationManager", "zip",
            "ImageFolderDatasetV2", "MnistDataset",
-           "MindDataset", "GeneratorDataset", "TFRecordDataset",
+           "MindDataset", "GeneratorDataset", "TFRecordDataset", "CLUEDataset",
            "ManifestDataset", "Cifar10Dataset", "Cifar100Dataset", "CelebADataset",
            "VOCDataset", "CocoDataset", "TextFileDataset", "BuildVocabDataset", "Schema", "Schema",
            "DistributedSampler", "PKSampler",

mindspore/dataset/engine/datasets.py

@@ -33,7 +33,7 @@ import copy
 import numpy as np
 
 from mindspore._c_dataengine import DataType, TFReaderOp, ImageFolderOp, CifarOp, MnistOp, ManifestOp, \
-    MindRecordOp, TextFileOp, VOCOp, CocoOp, CBatchInfo
+    MindRecordOp, TextFileOp, ClueOp, VOCOp, CocoOp, CBatchInfo
 from mindspore._c_expression import typing
 
 from mindspore import log as logger
@@ -44,7 +44,7 @@ from .validators import check_batch, check_shuffle, check_map, check_filter, che
     check_take, check_project, check_imagefolderdatasetv2, check_mnist_cifar_dataset, check_manifestdataset, \
     check_tfrecorddataset, check_vocdataset, check_cocodataset, check_celebadataset, check_minddataset, \
     check_generatordataset, check_sync_wait, check_zip_dataset, check_add_column, check_textfiledataset, check_concat, \
-    check_split
+    check_split, check_cluedataset
 from ..core.datatypes import mstype_to_detype, mstypelist_to_detypelist
 
 try:
@@ -4317,6 +4317,222 @@ class CelebADataset(MappableDataset):
         return self.sampler.is_sharded()
 
 
+class CLUEDataset(SourceDataset):
+    """
+    A source dataset that reads and parses CLUE datasets.
+    CLUE, the Chinese Language Understanding Evaluation Benchmark, a collection of datasets, baselines, pre-trained
+    models, corpus and leaderboard. Here we bring in classification task of CLUE, which are AFQMC, TNEWS, IFLYTEK,
+    CMNLI, WSC and CSL.
+
+    Args:
+        dataset_files (str or list[str]): String or list of files to be read or glob strings to search for a pattern of
+            files. The list will be sorted in a lexicographical order.
+        task (str, optional): The kind of task, one of 'AFQMC', 'TNEWS', 'IFLYTEK', 'CMNLI', 'WSC' and 'CSL'.
+            (default=AFQMC).
+        usage (str, optional): Need train, test or eval data (default="train").
+        num_samples (int, optional): number of samples(rows) to read (default=None, reads the full dataset).
+        num_parallel_workers (int, optional): number of workers to read the data
+            (default=None, number set in the config).
+        shuffle (bool, Shuffle level, optional): perform reshuffling of the data every epoch (default=Shuffle.GLOBAL).
+            If shuffle is False, no shuffling will be performed;
+            If shuffle is True, the behavior is the same as setting shuffle to be Shuffle.GLOBAL
+            Otherwise, there are two levels of shuffling:
+
+            - Shuffle.GLOBAL: Shuffle both the files and samples.
+
+            - Shuffle.FILES: Shuffle files only.
+
+        num_shards (int, optional): Number of shards that the dataset should be divided into (default=None).
+        shard_id (int, optional): The shard ID within num_shards (default=None). This
+            argument should be specified only when num_shards is also specified.
+
+    Examples:
+        >>> import mindspore.dataset as ds
+        >>> dataset_files = ["/path/to/1", "/path/to/2"] # contains 1 or multiple text files
+        >>> dataset = ds.CLUEDataset(dataset_files=dataset_files, task='AFQMC', usage='train')
+
+    """
+
+    @check_cluedataset
+    def __init__(self, dataset_files, task='AFQMC', usage='train', num_samples=None,
+                 num_parallel_workers=None, shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None):
+        super().__init__(num_parallel_workers)
+        self.dataset_files = self._find_files(dataset_files)
+        self.dataset_files.sort()
+        self.num_samples = num_samples
+        self.task_dict = {
+            'AFQMC': {
+                'train': {
+                    'sentence1': 'sentence1',
+                    'sentence2': 'sentence2',
+                    'label': 'label'
+                },
+                'test': {
+                    'id': 'id',
+                    'sentence1': 'sentence1',
+                    'sentence2': 'sentence2'
+                },
+                'eval': {
+                    'sentence1': 'sentence1',
+                    'sentence2': 'sentence2',
+                    'label': 'label'
+                }
+            },
+            'CMNLI': {
+                'train': {
+                    'sentence1': 'sentence1',
+                    'sentence2': 'sentence2',
+                    'label': 'label'
+                },
+                'test': {
+                    'id': 'id',
+                    'sentence1': 'sentence1',
+                    'sentence2': 'sentence2'
+                },
+                'eval': {
+                    'sentence1': 'sentence1',
+                    'sentence2': 'sentence2',
+                    'label': 'label'
+                }
+            },
+            'CSL': {
+                'train': {
+                    'id': 'id',
+                    'abst': 'abst',
+                    'keyword': 'keyword',
+                    'label': 'label'
+                },
+                'test': {
+                    'id': 'id',
+                    'abst': 'abst',
+                    'keyword': 'keyword'
+                },
+                'eval': {
+                    'id': 'id',
+                    'abst': 'abst',
+                    'keyword': 'keyword',
+                    'label': 'label'
+                }
+            },
+            'IFLYTEK': {
+                'train': {
+                    'label': 'label',
+                    'label_des': 'label_des',
+                    'sentence': 'sentence'
+                },
+                'test': {
+                    'id': 'id',
+                    'sentence': 'sentence',
+                },
+                'eval': {
+                    'label': 'label',
+                    'label_des': 'label_des',
+                    'sentence': 'sentence'
+                }
+            },
+            'TNEWS': {
+                'train': {
+                    'label': 'label',
+                    'label_desc': 'label_desc',
+                    'sentence': 'sentence',
+                    'keywords': 'keywords'
+                },
+                'test': {
+                    'id': 'id',
+                    'sentence': 'sentence',
+                    'keywords': 'keywords'
+                },
+                'eval': {
+                    'label': 'label',
+                    'label_desc': 'label_desc',
+                    'sentence': 'sentence',
+                    'keywords': 'keywords'
+                }
+            },
+            'WSC': {
+                'train': {
+                    'span1_index': 'target/span1_index',
+                    'span2_index': 'target/span2_index',
+                    'span1_text': 'target/span1_text',
+                    'span2_text': 'target/span2_text',
+                    'idx': 'idx',
+                    'label': 'label',
+                    'text': 'text'
+                },
+                'test': {
+                    'span1_index': 'target/span1_index',
+                    'span2_index': 'target/span2_index',
+                    'span1_text': 'target/span1_text',
+                    'span2_text': 'target/span2_text',
+                    'idx': 'idx',
+                    'text': 'text'
+                },
+                'eval': {
+                    'span1_index': 'target/span1_index',
+                    'span2_index': 'target/span2_index',
+                    'span1_text': 'target/span1_text',
+                    'span2_text': 'target/span2_text',
+                    'idx': 'idx',
+                    'label': 'label',
+                    'text': 'text'
+                }
+            }
+        }
+        self.cols_to_keyword = self.task_dict[task][usage]
+
+        if not isinstance(shuffle, (bool, Shuffle)):
+            raise TypeError("shuffle should be of boolean or enum 'Shuffle'.")
+        if not isinstance(shuffle, Shuffle):
+            if shuffle:
+                self.shuffle_level = Shuffle.GLOBAL
+                self.shuffle_files = True
+            else:
+                self.shuffle_level = None
+                self.shuffle_files = False
+        else:
+            self.shuffle_level = shuffle
+            self.shuffle_files = True
+
+        self.num_shards = num_shards
+        self.shard_id = shard_id
+
+    def get_args(self):
+        args = super().get_args()
+        args["dataset_files"] = self.dataset_files
+        args["num_samples"] = self.num_samples
+        if self.shuffle_files is not None:
+            args["shuffle_files"] = self.shuffle_files
+        args["shuffle"] = self.shuffle_level
+        args["num_shards"] = self.num_shards
+        args["shard_id"] = self.shard_id
+        args["cols_to_keyword"] = self.cols_to_keyword
+        return args
+
+    def get_dataset_size(self):
+        """
+        Get the number of batches in an epoch.
+
+        Return:
+            Number, number of batches.
+        """
+        if self._dataset_size is None:
+            num_rows = ClueOp.get_num_rows(self.dataset_files)
+            num_rows = get_num_rows(num_rows, self.num_shards)
+            if self.num_samples is None:
+                return num_rows
+            return min(self.num_samples, num_rows)
+        return self._dataset_size
+
+    def is_shuffled(self):
+        return self.shuffle_files
+
+    def is_sharded(self):
+        if self.num_shards is not None:
+            return self.num_shards > 1
+
+        return False
+
+
 class TextFileDataset(SourceDataset):
     """
     A source dataset that reads and parses datasets stored on disk in text format.

mindspore/dataset/engine/iterators.py

@@ -50,7 +50,8 @@ def alter_tree(node):
 
 def _alter_node(node):
     """Performing some alteration to a dataset node. A common alteration is to insert a node."""
-    if isinstance(node, (de.TFRecordDataset, de.TextFileDataset)) and node.shuffle_level == de.Shuffle.GLOBAL:
+    if isinstance(node, (de.TFRecordDataset, de.TextFileDataset, de.CLUEDataset)) \
+        and node.shuffle_level == de.Shuffle.GLOBAL:
         # Remove the connection between the parent's node to the current node because we are inserting a node.
         if node.output:
             node.output.pop()
@@ -179,6 +180,8 @@ class Iterator:
             op_type = OpName.TEXTFILE
         elif isinstance(dataset, de.BuildVocabDataset):
             op_type = OpName.BUILDVOCAB
+        elif isinstance(dataset, de.CLUEDataset):
+            op_type = OpName.CLUE
         else:
             raise ValueError("Unsupported DatasetOp")
 

mindspore/dataset/engine/validators.py

@@ -1075,6 +1075,41 @@ def check_add_column(method):
     return new_method
 
 
+def check_cluedataset(method):
+    """A wrapper that wrap a parameter checker to the original Dataset(CLUEDataset)."""
+
+    @wraps(method)
+    def new_method(*args, **kwargs):
+        param_dict = make_param_dict(method, args, kwargs)
+
+        nreq_param_int = ['num_samples', 'num_parallel_workers', 'num_shards', 'shard_id']
+
+        # check dataset_files; required argument
+        dataset_files = param_dict.get('dataset_files')
+        if dataset_files is None:
+            raise ValueError("dataset_files is not provided.")
+        if not isinstance(dataset_files, (str, list)):
+            raise TypeError("dataset_files should be of type str or a list of strings.")
+
+        # check task
+        task_param = param_dict.get('task')
+        if task_param not in ['AFQMC', 'TNEWS', 'IFLYTEK', 'CMNLI', 'WSC', 'CSL']:
+            raise ValueError("task should be AFQMC, TNEWS, IFLYTEK, CMNLI, WSC or CSL")
+
+        # check usage
+        usage_param = param_dict.get('usage')
+        if usage_param not in ['train', 'test', 'eval']:
+            raise ValueError("usage should be train, test or eval")
+
+        check_param_type(nreq_param_int, param_dict, int)
+
+        check_sampler_shuffle_shard_options(param_dict)
+
+        return method(*args, **kwargs)
+
+    return new_method
+
+
 def check_textfiledataset(method):
     """A wrapper that wrap a parameter checker to the original Dataset(TextFileDataset)."""
 

tests/ut/cpp/dataset/CMakeLists.txt

@@ -65,6 +65,7 @@ SET(DE_UT_SRCS
     cifar_op_test.cc
     celeba_op_test.cc
     take_op_test.cc
+    clue_op_test.cc
     text_file_op_test.cc
     filter_op_test.cc
     concat_op_test.cc

tests/ut/cpp/dataset/clue_op_test.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 <iostream>
+#include <memory>
+#include <vector>
+
+#include "dataset/core/client.h"
+#include "common/common.h"
+#include "common/utils.h"
+#include "gtest/gtest.h"
+#include "utils/log_adapter.h"
+#include "dataset/engine/datasetops/source/clue_op.h"
+#include "dataset/util/status.h"
+
+namespace common = mindspore::common;
+
+using namespace mindspore::dataset;
+using mindspore::MsLogLevel::INFO;
+using mindspore::ExceptionType::NoExceptionType;
+using mindspore::LogStream;
+
+class MindDataTestCLUEOp : public UT::DatasetOpTesting {
+
+};
+
+TEST_F(MindDataTestCLUEOp, TestCLUEBasic) {
+  // Start with an empty execution tree
+  auto tree = std::make_shared<ExecutionTree>();
+
+  std::string dataset_path;
+  dataset_path = datasets_root_path_ + "/testCLUE/afqmc/train.json";
+  std::map<std::string, std::string> key_map;
+  key_map["sentence1"] = "sentence1";
+  key_map["sentence2"] = "sentence2";
+  key_map["label"] = "label";
+
+  std::shared_ptr<ClueOp> op;
+  ClueOp::Builder builder;
+  builder.SetClueFilesList({dataset_path})
+      .SetRowsPerBuffer(16)
+      .SetNumWorkers(16)
+      .SetOpConnectorSize(2)
+      .SetColsKeyMap(key_map);
+
+  Status rc = builder.Build(&op);
+  ASSERT_TRUE(rc.IsOk());
+
+  rc = tree->AssociateNode(op);
+  ASSERT_TRUE(rc.IsOk());
+
+  rc = tree->AssignRoot(op);
+  ASSERT_TRUE(rc.IsOk());
+
+  MS_LOG(INFO) << "Launching tree and begin iteration.";
+  rc = tree->Prepare();
+  ASSERT_TRUE(rc.IsOk());
+
+  rc = tree->Launch();
+  ASSERT_TRUE(rc.IsOk());
+
+  // Start the loop of reading tensors from our pipeline
+  DatasetIterator di(tree);
+  TensorRow tensor_list;
+  rc = di.FetchNextTensorRow(&tensor_list);
+  ASSERT_TRUE(rc.IsOk());
+
+  int row_count = 0;
+  while (!tensor_list.empty()) {
+    // Display the tensor by calling the printer on it
+    for (int i = 0; i < tensor_list.size(); i++) {
+      std::ostringstream ss;
+      ss << "(" << tensor_list[i] << "): " << *tensor_list[i] << std::endl;
+      MS_LOG(INFO) << "Tensor print: " << ss.str() << ".";
+    }
+
+    rc = di.FetchNextTensorRow(&tensor_list);
+    ASSERT_TRUE(rc.IsOk());
+    row_count++;
+  }
+
+  ASSERT_EQ(row_count, 3);
+}
+
+TEST_F(MindDataTestCLUEOp, TestTotalRows) {
+  std::string tf_file1 = datasets_root_path_ + "/testCLUE/afqmc/train.json";
+  std::string tf_file2 = datasets_root_path_ + "/testCLUE/afqmc/dev.json";
+  std::vector<std::string> files;
+  files.push_back(tf_file1);
+  int64_t total_rows = 0;
+  ClueOp::CountAllFileRows(files, &total_rows);
+  ASSERT_EQ(total_rows, 3);
+  files.clear();
+
+  files.push_back(tf_file2);
+  ClueOp::CountAllFileRows(files, &total_rows);
+  ASSERT_EQ(total_rows, 3);
+  files.clear();
+
+  files.push_back(tf_file1);
+  files.push_back(tf_file2);
+  ClueOp::CountAllFileRows(files, &total_rows);
+  ASSERT_EQ(total_rows, 6);
+  files.clear();
+}

tests/ut/data/dataset/testCLUE/afqmc/dev.json

+{"sentence1": "你有花呗吗", "sentence2": "我的花呗没额度了", "label": "0"}
+{"sentence1": "吃饭能用花呗吗", "sentence2": "花呗太方便了", "label": "0"}
+{"sentence1": "蚂蚁花呗支付金额有什么限制", "sentence2": "我到实体店消费用花呗支付受金额限制", "label": "1"}

tests/ut/data/dataset/testCLUE/afqmc/test.json

+{"id": 0, "sentence1": "借呗取消的时间", "sentence2": "蚂蚁借呗恢复的月数"}
+{"id": 1, "sentence1": "网商贷用什么方法转变成借呗", "sentence2": "什么手段能将网商贷切换为借呗"}
+{"id": 2, "sentence1": "我的借呗为什么开通不了", "sentence2": "我为啥没法开通借呗"}

tests/ut/data/dataset/testCLUE/afqmc/train.json

+{"sentence1": "蚂蚁借呗等额还款能否换成先息后本", "sentence2": "借呗可以先息到期还本吗", "label": "0"}
+{"sentence1": "蚂蚁花呗说我违约了", "sentence2": "蚂蚁花呗违约行为是啥", "label": "0"}
+{"sentence1": "帮我看看本月花呗账单结清了没", "sentence2": "上月的花呗账单", "label": "0"}

tests/ut/data/dataset/testCLUE/cmnli/dev.json

+{"sentence1": "每个人都有权利", "sentence2": "每个人都有福利", "label": "neutral"}
+{"sentence1": "有时候我喜欢他，但我也喜欢看到有人打他", "sentence2": "说实话，我有点喜欢他，但还是喜欢看到有人打他。", "label": "entailment"}
+{"sentence1": "我最喜欢的餐馆是离你最近的一家", "sentence2": "我最喜欢的餐馆离你家至少一百英里远。", "label": "contradiction"}

tests/ut/data/dataset/testCLUE/cmnli/test.json

+{"id": 0, "sentence1": "今天，全球都在看着最新航天飞机的处女航。", "sentence2": "全世界都在看最新的航天飞机发射。"}
+{"id": 1, "sentence1": "而我们把竹篮放在一个地方，把玻璃瓶放在另一处，把书放在另一处，满了要把它放到车里", "sentence2": "我们没有分开任何东西，都把它全扔进一个箱子里。"}
+{"id": 2, "sentence1": "她占用了我的很多时间，她给我读了很多关于灵异的故事，我觉得很无聊。", "sentence2": "我喜欢和她一起读鬼故事。"}

tests/ut/data/dataset/testCLUE/cmnli/train.json

+{"sentence1": "你应该给这件衣服定一个价格。", "sentence2": "不同的衣服有不同的价格。", "label": "neutral"}
+{"sentence1": "我怎么知道他要说什么", "sentence2": "他说什么我并不知道。", "label": "entailment"}
+{"sentence1": "向左。", "sentence2": "向右。", "label": "contradiction"}

tests/ut/data/dataset/testCLUE/csl/dev.json

+{"id": 1, "abst": "这是第一段很长的文本", "keyword": ["关键词1", "关键词2", "关键词3", "关键词4"], "label": "1"}
+{"id": 2, "abst": "这是第二段很长的文本", "keyword": ["关键词1", "关键词2", "关键词3", "关键词4"], "label": "1"}
+{"id": 3, "abst": "这是第三段很长的文本", "keyword": ["1", "2", "3"], "label": "0"}

tests/ut/data/dataset/testCLUE/csl/test.json

+{"id": 2415, "abst": "长文本1", "keyword": ["关键词1", "关键词2"]}
+{"id": 2565, "abst": "长文本2", "keyword": ["关键词1", "关键词2", "关键词3"]}
+{"id": 2625, "abst": "长文本3", "keyword": ["关键词1", "关键词2", "关键词3", "关键词4"]}

tests/ut/data/dataset/testCLUE/csl/train.json

+{"id": 1, "abst": "这是一段长文本", "keyword": ["关键词1", "关键词2", "关键词3", "关键词4"], "label": "0"}
+{"id": 2, "abst": "这是一段长文本", "keyword": ["关键词5", "关键词6", "关键词7", "关键词8"], "label": "0"}
+{"id": 3, "abst": "这是一段长文本", "keyword": ["关键词9", "关键词10", "关键词11", "关键词12"], "label": "0"}

tests/ut/data/dataset/testCLUE/iflytek/dev.json

+{"label": "110", "label_des": "社区超市", "sentence": "这是第一段文本"}
+{"label": "70", "label_des": "工具", "sentence": "这是第二段文本"}
+{"label": "10", "label_des": "社区服务", "sentence": "这是第三段文本"}

tests/ut/data/dataset/testCLUE/iflytek/test.json

+{"id": 0, "sentence": "文本1"}
+{"id": 1, "sentence": "文本2"}
+{"id": 2, "sentence": "文本3"}

tests/ut/data/dataset/testCLUE/iflytek/train.json

+{"label": "11", "label_des": "薅羊毛", "sentence": "第一个文本"}
+{"label": "95", "label_des": "借贷", "sentence": "第二个文本"}
+{"label": "74", "label_des": "违章", "sentence": "第三个文本"}

tests/ut/data/dataset/testCLUE/tnews/dev.json

+{"label": "102", "label_desc": "news_entertainment", "sentence": "新闻1", "keywords": "关键词一,关键词二,关键词三,关键词四"}
+{"label": "110", "label_desc": "news_military", "sentence": "新闻2", "keywords": "关键词一,关键词二,关键词三,关键词四，关键词五"}
+{"label": "104", "label_desc": "news_finance", "sentence": "新闻3", "keywords": "关键词一,关键词二,关键词三,关键词四,关键词五"}

tests/ut/data/dataset/testCLUE/tnews/test.json

+{"id": 0, "sentence": "新闻1", "keywords": "关键词1,关键词2,关键词3,关键词4,关键词5"}
+{"id": 1, "sentence": "新闻2", "keywords": "关键词1,关键词2,关键词3,关键词4"}
+{"id": 2, "sentence": "新闻3", "keywords": ""}

tests/ut/data/dataset/testCLUE/tnews/train.json

+{"label": "108", "label_desc": "news_edu", "sentence": "新闻1", "keywords": ""}
+{"label": "104", "label_desc": "news_finance", "sentence": "新闻2", "keywords": "关键词1,关键词2,关键词3,关键词4,关键词5,关键词6"}
+{"label": "106", "label_desc": "news_house", "sentence": "新闻3", "keywords": ""}

tests/ut/data/dataset/testCLUE/wsc/dev.json

+{"target": {"span1_index": 0, "span1_text": "小明", "span2_index": 4, "span2_text": "他"}, "idx": 0, "text": "小明呢，他在哪？", "label": "true"}
+{"target": {"span1_index": 0, "span1_text": "小红", "span2_index": 9, "span2_text": "他"}, "idx": 1, "text": "小红刚刚看到小明，他在操场", "label": "false"}
+{"target": {"span1_index": 6, "span1_text": "小张", "span2_index": 8, "span2_text": "你"}, "idx": 2, "text": "等小明回来，小张你叫他交作业", "label": "true"}

tests/ut/data/dataset/testCLUE/wsc/test.json

+{"target": {"span1_index": 0, "span1_text": "小明", "span2_index": 4, "span2_text": "他"}, "idx": 0, "text": "小明呢，他在哪？"}
+{"target": {"span1_index": 0, "span1_text": "小红", "span2_index": 9, "span2_text": "他"}, "idx": 1, "text": "小红刚刚看到小明，他在操场"}
+{"target": {"span1_index": 6, "span1_text": "小张", "span2_index": 8, "span2_text": "你"}, "idx": 2, "text": "等小明回来，小张你叫他交作业"}

tests/ut/data/dataset/testCLUE/wsc/train.json

+{"target": {"span1_index": 0, "span1_text": "小明", "span2_index": 4, "span2_text": "他"}, "idx": 0, "text": "小明呢，他在哪？", "label": "true"}
+{"target": {"span1_index": 0, "span1_text": "小红", "span2_index": 9, "span2_text": "他"}, "idx": 1, "text": "小红刚刚看到小明，他在操场", "label": "false"}
+{"target": {"span1_index": 6, "span1_text": "小张", "span2_index": 8, "span2_text": "你"}, "idx": 2, "text": "等小明回来，小张你叫他交作业", "label": "true"}

tests/ut/python/dataset/test_datasets_clue.py

+# 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.
+# ==============================================================================
+
+import mindspore.dataset as ds
+
+
+def test_clue():
+    """
+    Test CLUE with repeat, skip and so on
+    """
+    TRAIN_FILE = '../data/dataset/testCLUE/afqmc/train.json'
+
+    buffer = []
+    data = ds.CLUEDataset(TRAIN_FILE, task='AFQMC', usage='train', shuffle=False)
+    data = data.repeat(2)
+    data = data.skip(3)
+    for d in data.create_dict_iterator():
+        buffer.append({
+            'label': d['label'].item().decode("utf8"),
+            'sentence1': d['sentence1'].item().decode("utf8"),
+            'sentence2': d['sentence2'].item().decode("utf8")
+        })
+    assert len(buffer) == 3
+
+
+def test_clue_num_shards():
+    """
+    Test num_shards param of CLUE dataset
+    """
+    TRAIN_FILE = '../data/dataset/testCLUE/afqmc/train.json'
+
+    buffer = []
+    data = ds.CLUEDataset(TRAIN_FILE, task='AFQMC', usage='train', num_shards=3, shard_id=1)
+    for d in data.create_dict_iterator():
+        buffer.append({
+            'label': d['label'].item().decode("utf8"),
+            'sentence1': d['sentence1'].item().decode("utf8"),
+            'sentence2': d['sentence2'].item().decode("utf8")
+        })
+    assert len(buffer) == 1
+
+
+def test_clue_num_samples():
+    """
+    Test num_samples param of CLUE dataset
+    """
+    TRAIN_FILE = '../data/dataset/testCLUE/afqmc/train.json'
+
+    data = ds.CLUEDataset(TRAIN_FILE, task='AFQMC', usage='train', num_samples=2)
+    count = 0
+    for _ in data.create_dict_iterator():
+        count += 1
+    assert count == 2
+
+
+def test_textline_dataset_get_datasetsize():
+    """
+    Test get_dataset_size of CLUE dataset
+    """
+    TRAIN_FILE = '../data/dataset/testCLUE/afqmc/train.json'
+
+    data = ds.TextFileDataset(TRAIN_FILE)
+    size = data.get_dataset_size()
+    assert size == 3
+
+
+def test_clue_afqmc():
+    """
+    Test AFQMC for train, test and evaluation
+    """
+    TRAIN_FILE = '../data/dataset/testCLUE/afqmc/train.json'
+    TEST_FILE = '../data/dataset/testCLUE/afqmc/test.json'
+    EVAL_FILE = '../data/dataset/testCLUE/afqmc/dev.json'
+
+    # train
+    buffer = []
+    data = ds.CLUEDataset(TRAIN_FILE, task='AFQMC', usage='train', shuffle=False)
+    for d in data.create_dict_iterator():
+        buffer.append({
+            'label': d['label'].item().decode("utf8"),
+            'sentence1': d['sentence1'].item().decode("utf8"),
+            'sentence2': d['sentence2'].item().decode("utf8")
+        })
+    assert len(buffer) == 3
+
+    # test
+    buffer = []
+    data = ds.CLUEDataset(TEST_FILE, task='AFQMC', usage='test', shuffle=False)
+    for d in data.create_dict_iterator():
+        buffer.append({
+            'id': d['id'],
+            'sentence1': d['sentence1'].item().decode("utf8"),
+            'sentence2': d['sentence2'].item().decode("utf8")
+        })
+    assert len(buffer) == 3
+
+    # evaluation
+    buffer = []
+    data = ds.CLUEDataset(EVAL_FILE, task='AFQMC', usage='eval', shuffle=False)
+    for d in data.create_dict_iterator():
+        buffer.append({
+            'label': d['label'].item().decode("utf8"),
+            'sentence1': d['sentence1'].item().decode("utf8"),
+            'sentence2': d['sentence2'].item().decode("utf8")
+        })
+    assert len(buffer) == 3
+
+
+def test_clue_cmnli():
+    """
+    Test CMNLI for train, test and evaluation
+    """
+    TRAIN_FILE = '../data/dataset/testCLUE/cmnli/train.json'
+    TEST_FILE = '../data/dataset/testCLUE/cmnli/test.json'
+    EVAL_FILE = '../data/dataset/testCLUE/cmnli/dev.json'
+
+    # train
+    buffer = []
+    data = ds.CLUEDataset(TRAIN_FILE, task='CMNLI', usage='train', shuffle=False)
+    for d in data.create_dict_iterator():
+        buffer.append({
+            'label': d['label'].item().decode("utf8"),
+            'sentence1': d['sentence1'].item().decode("utf8"),
+            'sentence2': d['sentence2'].item().decode("utf8")
+        })
+    assert len(buffer) == 3
+
+    # test
+    buffer = []
+    data = ds.CLUEDataset(TEST_FILE, task='CMNLI', usage='test', shuffle=False)
+    for d in data.create_dict_iterator():
+        buffer.append({
+            'id': d['id'],
+            'sentence1': d['sentence1'],
+            'sentence2': d['sentence2']
+        })
+    assert len(buffer) == 3
+
+    # eval
+    buffer = []
+    data = ds.CLUEDataset(EVAL_FILE, task='CMNLI', usage='eval', shuffle=False)
+    for d in data.create_dict_iterator():
+        buffer.append({
+            'label': d['label'],
+            'sentence1': d['sentence1'],
+            'sentence2': d['sentence2']
+        })
+    assert len(buffer) == 3
+
+
+def test_clue_csl():
+    """
+    Test CSL for train, test and evaluation
+    """
+    TRAIN_FILE = '../data/dataset/testCLUE/csl/train.json'
+    TEST_FILE = '../data/dataset/testCLUE/csl/test.json'
+    EVAL_FILE = '../data/dataset/testCLUE/csl/dev.json'
+
+    # train
+    buffer = []
+    data = ds.CLUEDataset(TRAIN_FILE, task='CSL', usage='train', shuffle=False)
+    for d in data.create_dict_iterator():
+        buffer.append({
+            'id': d['id'],
+            'abst': d['abst'].item().decode("utf8"),
+            'keyword': [i.item().decode("utf8") for i in d['keyword']],
+            'label': d['label'].item().decode("utf8")
+        })
+    assert len(buffer) == 3
+
+    # test
+    buffer = []
+    data = ds.CLUEDataset(TEST_FILE, task='CSL', usage='test', shuffle=False)
+    for d in data.create_dict_iterator():
+        buffer.append({
+            'id': d['id'],
+            'abst': d['abst'].item().decode("utf8"),
+            'keyword': [i.item().decode("utf8") for i in d['keyword']],
+        })
+    assert len(buffer) == 3
+
+    # eval
+    buffer = []
+    data = ds.CLUEDataset(EVAL_FILE, task='CSL', usage='eval', shuffle=False)
+    for d in data.create_dict_iterator():
+        buffer.append({
+            'id': d['id'],
+            'abst': d['abst'].item().decode("utf8"),
+            'keyword': [i.item().decode("utf8") for i in d['keyword']],
+            'label': d['label'].item().decode("utf8")
+        })
+    assert len(buffer) == 3
+
+
+def test_clue_iflytek():
+    """
+    Test IFLYTEK for train, test and evaluation
+    """
+    TRAIN_FILE = '../data/dataset/testCLUE/iflytek/train.json'
+    TEST_FILE = '../data/dataset/testCLUE/iflytek/test.json'
+    EVAL_FILE = '../data/dataset/testCLUE/iflytek/dev.json'
+
+    # train
+    buffer = []
+    data = ds.CLUEDataset(TRAIN_FILE, task='IFLYTEK', usage='train', shuffle=False)
+    for d in data.create_dict_iterator():
+        buffer.append({
+            'label': d['label'].item().decode("utf8"),
+            'label_des': d['label_des'].item().decode("utf8"),
+            'sentence': d['sentence'].item().decode("utf8"),
+        })
+    assert len(buffer) == 3
+
+    # test
+    buffer = []
+    data = ds.CLUEDataset(TEST_FILE, task='IFLYTEK', usage='test', shuffle=False)
+    for d in data.create_dict_iterator():
+        buffer.append({
+            'id': d['id'],
+            'sentence': d['sentence'].item().decode("utf8")
+        })
+    assert len(buffer) == 3
+
+    # eval
+    buffer = []
+    data = ds.CLUEDataset(EVAL_FILE, task='IFLYTEK', usage='eval', shuffle=False)
+    for d in data.create_dict_iterator():
+        buffer.append({
+            'label': d['label'].item().decode("utf8"),
+            'label_des': d['label_des'].item().decode("utf8"),
+            'sentence': d['sentence'].item().decode("utf8")
+        })
+    assert len(buffer) == 3
+
+
+def test_clue_tnews():
+    """
+    Test TNEWS for train, test and evaluation
+    """
+    TRAIN_FILE = '../data/dataset/testCLUE/tnews/train.json'
+    TEST_FILE = '../data/dataset/testCLUE/tnews/test.json'
+    EVAL_FILE = '../data/dataset/testCLUE/tnews/dev.json'
+
+    # train
+    buffer = []
+    data = ds.CLUEDataset(TRAIN_FILE, task='TNEWS', usage='train', shuffle=False)
+    for d in data.create_dict_iterator():
+        buffer.append({
+            'label': d['label'].item().decode("utf8"),
+            'label_desc': d['label_desc'].item().decode("utf8"),
+            'sentence': d['sentence'].item().decode("utf8"),
+            'keywords':
+                d['keywords'].item().decode("utf8") if d['keywords'].size > 0 else d['keywords']
+        })
+    assert len(buffer) == 3
+
+    # test
+    buffer = []
+    data = ds.CLUEDataset(TEST_FILE, task='TNEWS', usage='test', shuffle=False)
+    for d in data.create_dict_iterator():
+        buffer.append({
+            'id': d['id'],
+            'sentence': d['sentence'].item().decode("utf8"),
+            'keywords':
+                d['keywords'].item().decode("utf8") if d['keywords'].size > 0 else d['keywords']
+        })
+    assert len(buffer) == 3
+
+    # eval
+    buffer = []
+    data = ds.CLUEDataset(EVAL_FILE, task='TNEWS', usage='eval', shuffle=False)
+    for d in data.create_dict_iterator():
+        buffer.append({
+            'label': d['label'].item().decode("utf8"),
+            'label_desc': d['label_desc'].item().decode("utf8"),
+            'sentence': d['sentence'].item().decode("utf8"),
+            'keywords':
+                d['keywords'].item().decode("utf8") if d['keywords'].size > 0 else d['keywords']
+        })
+    assert len(buffer) == 3
+
+
+def test_clue_wsc():
+    """
+    Test WSC for train, test and evaluation
+    """
+    TRAIN_FILE = '../data/dataset/testCLUE/wsc/train.json'
+    TEST_FILE = '../data/dataset/testCLUE/wsc/test.json'
+    EVAL_FILE = '../data/dataset/testCLUE/wsc/dev.json'
+
+    # train
+    buffer = []
+    data = ds.CLUEDataset(TRAIN_FILE, task='WSC', usage='train')
+    for d in data.create_dict_iterator():
+        buffer.append({
+            'span1_index': d['span1_index'],
+            'span2_index': d['span2_index'],
+            'span1_text': d['span1_text'].item().decode("utf8"),
+            'span2_text': d['span2_text'].item().decode("utf8"),
+            'idx': d['idx'],
+            'label': d['label'].item().decode("utf8"),
+            'text': d['text'].item().decode("utf8")
+        })
+    assert len(buffer) == 3
+
+    # test
+    buffer = []
+    data = ds.CLUEDataset(TEST_FILE, task='WSC', usage='test')
+    for d in data.create_dict_iterator():
+        buffer.append({
+            'span1_index': d['span1_index'],
+            'span2_index': d['span2_index'],
+            'span1_text': d['span1_text'].item().decode("utf8"),
+            'span2_text': d['span2_text'].item().decode("utf8"),
+            'idx': d['idx'],
+            'text': d['text'].item().decode("utf8")
+        })
+    assert len(buffer) == 3
+
+    # eval
+    buffer = []
+    data = ds.CLUEDataset(EVAL_FILE, task='WSC', usage='eval')
+    for d in data.create_dict_iterator():
+        buffer.append({
+            'span1_index': d['span1_index'],
+            'span2_index': d['span2_index'],
+            'span1_text': d['span1_text'].item().decode("utf8"),
+            'span2_text': d['span2_text'].item().decode("utf8"),
+            'idx': d['idx'],
+            'label': d['label'].item().decode("utf8"),
+            'text': d['text'].item().decode("utf8")
+        })
+    assert len(buffer) == 3
+
+
+if __name__ == "__main__":
+    test_clue()
+    test_clue_afqmc()
+    test_clue_cmnli()
+    test_clue_csl()
+    test_clue_iflytek()
+    test_clue_tnews()
+    test_clue_wsc()