init add acltdt handle create and destory

add hostpush part modify

optimize previous code

provide aclhandle access method

modify CMakeList format

add device_id parameter into TransferNode

mindspore/ccsrc/minddata/dataset/CMakeLists.txt

@@ -264,7 +264,7 @@ if(ENABLE_GPUQUE)
 endif()
 
 if(ENABLE_TDTQUE)
-    target_link_libraries(_c_dataengine PRIVATE ${TSDCLIENT})
+    target_link_libraries(_c_dataengine PRIVATE ${ACL})
 endif()
 
 add_dependencies(_c_dataengine _c_mindrecord)

mindspore/ccsrc/minddata/dataset/api/datasets.cc

@@ -131,8 +131,8 @@ std::shared_ptr<Iterator> Dataset::CreateIterator(std::vector<std::string> colum
 
 #ifndef ENABLE_ANDROID
 // Function to return a transferred Node that transfers data through a device.
-bool Dataset::DeviceQueue(std::string queue_name, std::string device_type, int32_t num_epochs, bool send_epoch_end,
-                          int32_t total_batches, bool create_data_info_queue) {
+bool Dataset::DeviceQueue(std::string queue_name, std::string device_type, int32_t device_id, int32_t num_epochs,
+                          bool send_epoch_end, int32_t total_batches, bool create_data_info_queue) {
   Status rc;
 
   // Build and launch tree
@@ -144,8 +144,8 @@ bool Dataset::DeviceQueue(std::string queue_name, std::string device_type, int32
   }
 
   // Add TransferNode IR on top of dataset
-  auto ds = std::make_shared<TransferNode>(shared_from_this()->IRNode(), queue_name, device_type, send_epoch_end,
-                                           total_batches, create_data_info_queue);
+  auto ds = std::make_shared<TransferNode>(shared_from_this()->IRNode(), queue_name, device_type, device_id,
+                                           send_epoch_end, total_batches, create_data_info_queue);
 
   // Get ToDevice consumer
   auto consumer = std::make_unique<ToDevice>(num_epochs);

mindspore/ccsrc/minddata/dataset/api/python/bindings/dataset/include/datasets_bindings.cc

@@ -527,9 +527,10 @@ PYBIND_REGISTER(TransferNode, 2, ([](const py::module *m) {
                   (void)py::class_<TransferNode, DatasetNode, std::shared_ptr<TransferNode>>(*m, "TransferNode",
                                                                                              "to create a TransferNode")
                     .def(py::init([](std::shared_ptr<DatasetNode> self, std::string queue_name, std::string device_type,
-                                     bool send_epoch_end, int32_t total_batch, bool create_data_info_queue) {
-                      auto transfer = std::make_shared<TransferNode>(self, queue_name, device_type, send_epoch_end,
-                                                                     total_batch, create_data_info_queue);
+                                     int32_t device_id, bool send_epoch_end, int32_t total_batch,
+                                     bool create_data_info_queue) {
+                      auto transfer = std::make_shared<TransferNode>(
+                        self, queue_name, device_type, device_id, send_epoch_end, total_batch, create_data_info_queue);
                       THROW_IF_ERROR(transfer->ValidateParams());
                       return transfer;
                     }));

mindspore/ccsrc/minddata/dataset/engine/datasetops/device_queue_op.cc

@@ -62,6 +62,7 @@ DeviceQueueOp::DeviceQueueOp(std::string channel_name, DeviceType device_type, i
 #endif
 #ifdef ENABLE_TDTQUE
   ascend_keep_waiting_ = true;
+  tdtInstancePtr = std::make_shared<TdtPlugin>(channel_name_, device_id_);
 #endif
 }
 
@@ -159,7 +160,7 @@ Status DeviceQueueOp::SendDataToAscend() {
         RETURN_IF_NOT_OK(current_buffer->GetRow(row_id, &currRow));
         WaitContinueSignal();
         auto status = tdtInstancePtr->hostPush(currRow, true, channel_name_, isProfilingEnable, tdt_cost);
-        if (status == TdtStatus::FAILED) {
+        if (status != Status::OK()) {
           if (stop_send_) {
             MS_LOG(INFO) << "stop_send received";
             return Status::OK();
@@ -190,9 +191,9 @@ Status DeviceQueueOp::SendDataToAscend() {
     }
     if (current_buffer->eoe() && send_epoch_end_) {
       TensorRow currRow;
-      auto status =
-        tdtInstancePtr->hostPush(currRow, true, channel_name_, isProfilingEnable, tdt_cost, tdt::TDT_END_OF_SEQUENCE);
-      if (status == TdtStatus::FAILED) {
+      auto status = tdtInstancePtr->hostPush(currRow, true, channel_name_, isProfilingEnable, tdt_cost,
+                                             ACL_TENSOR_DATA_END_OF_SEQUENCE);
+      if (status != Status::OK()) {
         if (stop_send_) {
           MS_LOG(INFO) << "stop_send received";
           return Status::OK();
@@ -209,7 +210,6 @@ Status DeviceQueueOp::SendDataToAscend() {
     }
     RETURN_IF_NOT_OK(GetNextInput(&current_buffer));
   }
-
   tree_->SetFinished();
 
   return Status::OK();

mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/transfer_node.cc

@@ -32,20 +32,20 @@ namespace dataset {
 
 // Constructor for TransferNode
 TransferNode::TransferNode(std::shared_ptr<DatasetNode> child, std::string queue_name, std::string device_type,
-                           bool send_epoch_end, int32_t total_batch, bool create_data_info_queue)
+                           int32_t device_id, bool send_epoch_end, int32_t total_batch, bool create_data_info_queue)
     : prefetch_size_(16),
       queue_name_(std::move(queue_name)),
       device_type_(std::move(device_type)),
       send_epoch_end_(send_epoch_end),
       total_batch_(total_batch),
       create_data_info_queue_(create_data_info_queue),
-      device_id_(0) {
+      device_id_(device_id) {
   this->AddChild(child);
 }
 
 std::shared_ptr<DatasetNode> TransferNode::Copy() {
-  auto node = std::make_shared<TransferNode>(nullptr, queue_name_, device_type_, send_epoch_end_, total_batch_,
-                                             create_data_info_queue_);
+  auto node = std::make_shared<TransferNode>(nullptr, queue_name_, device_type_, device_id_, send_epoch_end_,
+                                             total_batch_, create_data_info_queue_);
   return node;
 }
 
@@ -96,9 +96,9 @@ Status TransferNode::Build(std::vector<std::shared_ptr<DatasetOp>> *const node_o
     RETURN_STATUS_UNEXPECTED(err_msg);
   }
 
-  // Get device ID (shard ID) from children
-  device_id_ = 0;
-  RETURN_IF_NOT_OK(this->GetShardId(&device_id_));
+//  // Get device ID (shard ID) from children
+//  device_id_ = 0;
+//  RETURN_IF_NOT_OK(this->GetShardId(&device_id_));
 
   auto op = std::make_shared<DeviceQueueOp>(queue_name_, type, device_id_, prefetch_size_, send_epoch_end_,
                                             total_batch_, create_data_info_queue_);

mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/transfer_node.h

@@ -29,8 +29,8 @@ namespace dataset {
 class TransferNode : public DatasetNode {
  public:
   /// \brief Constructor
-  TransferNode(std::shared_ptr<DatasetNode> child, std::string queue_name, std::string device_type, bool send_epoch_end,
-               int32_t total_batch, bool create_data_info_queue);
+  TransferNode(std::shared_ptr<DatasetNode> child, std::string queue_name, std::string device_type, int32_t device_id,
+               bool send_epoch_end, int32_t total_batch, bool create_data_info_queue);
 
   /// \brief Destructor
   ~TransferNode() = default;

mindspore/ccsrc/minddata/dataset/engine/tdt/CMakeLists.txt

@@ -1,5 +1,6 @@
-file(GLOB_RECURSE _CURRENT_SRC_FILES RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "*.cc")
+file(
+        GLOB_RECURSE _CURRENT_SRC_FILES
+        RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
+        "*.cc")
 set_property(SOURCE ${_CURRENT_SRC_FILES} PROPERTY COMPILE_DEFINITIONS SUBMODULE_ID=mindspore::SubModuleId::SM_MD)
-add_library(engine-tdt OBJECT
-    tdt_plugin.cc
-    )
+add_library(engine-tdt OBJECT tdt_plugin.cc tdt_handle.cc)

mindspore/ccsrc/minddata/dataset/engine/tdt/tdt_handle.cc

@@ -0,0 +1,39 @@
+/**
+ * Copyright 2021 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 "minddata/dataset/engine/tdt/tdt_handle.h"
+namespace mindspore {
+namespace dataset {
+
+std::vector<acltdtChannelHandle *> TdtHandle::acl_handle = std::vector<acltdtChannelHandle *>();
+
+void TdtHandle::AddHandle(acltdtChannelHandle *handle) {
+  if (handle != nullptr) {
+    acl_handle.emplace_back(handle);
+  }
+}
+
+bool TdtHandle::DestroyHandle() {
+  for (auto handle : acl_handle) {
+    if (handle != nullptr) {
+      if (acltdtDestroyChannel(handle) != ACL_SUCCESS) {
+        return false;
+      }
+    }
+  }
+  return true;
+}
+}  // namespace dataset
+}  // namespace mindspore

mindspore/ccsrc/minddata/dataset/engine/tdt/tdt_handle.h

@@ -0,0 +1,38 @@
+/**
+ * Copyright 2021 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 MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_TDT_TDT_HANDLE_H_
+#define MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_TDT_TDT_HANDLE_H_
+
+#include <iostream>
+#include <vector>
+#include "acl/acl_tdt.h"
+
+namespace mindspore {
+namespace dataset {
+class TdtHandle {
+ public:
+  static void AddHandle(acltdtChannelHandle *handle);
+
+  static bool DestroyHandle();
+
+ private:
+  TdtHandle() {}
+
+  static std::vector<acltdtChannelHandle *> acl_handle;
+};
+}  // namespace dataset
+}  // namespace mindspore
+#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_ENGINE_TDT_TDT_HANDLE_H_

mindspore/ccsrc/minddata/dataset/engine/tdt/tdt_plugin.cc

@@ -23,108 +23,138 @@
 
 namespace mindspore {
 namespace dataset {
-static std::shared_ptr<TdtPlugin> instance_ptr_ = nullptr;
+TdtPlugin::TdtPlugin(const std::string &channel_name, int32_t device_id) {
+  // create acl tdt handle
+  acl_handle_ = acltdtCreateChannel(device_id, channel_name.c_str());
+  if (acl_handle_ == nullptr) {
+    MS_LOG(ERROR) << "Failed to create channel for tdt queue.";
+  }
+  TdtHandle::AddHandle(acl_handle_);
+}
 
-std::shared_ptr<TdtPlugin> TdtPlugin::GetInstance() {
-  if (instance_ptr_ == nullptr) {
-    instance_ptr_ = std::shared_ptr<TdtPlugin>(new TdtPlugin);
+TdtPlugin::~TdtPlugin() {
+  if (acl_handle_ != nullptr && acltdtDestroyChannel(acl_handle_) != ACL_SUCCESS) {
+    MS_LOG(ERROR) << "Failed to destroy channel for tdt queue.";
   }
-  return instance_ptr_;
 }
 
-TdtStatus TdtPlugin::hostPush(TensorRow ts_row, bool is_wait, std::string channel_name, bool profiling, int32_t &time,
-                              tdt::TdtDataType tdt_type) {
+Status TdtPlugin::hostPush(TensorRow ts_row, bool is_wait, std::string channel_name, bool profiling, int32_t &time,
+                           acltdtTensorType tdt_type) {
   MS_LOG(DEBUG) << "TDT channel name is " << channel_name << ".";
-  std::vector<DataItem> items;
+
+  acltdtDataset *acl_dataset = nullptr;
   double start_time;
-  if (tdt_type == tdt::TDT_TENSOR) {
-    auto ret = translate(ts_row, items);
-    if (ret != SUCCESS) {
-      MS_LOG(ERROR) << "TDT converting tensor failed!";
-      return FAILED;
-    }
-  } else if (tdt_type == tdt::TDT_END_OF_SEQUENCE) {
-    DataItem data_item;
-    data_item.dataType_ = tdt::TDT_END_OF_SEQUENCE;
-    items.emplace_back(data_item);
-    MS_LOG(INFO) << "TDT data type is TDT_END_OF_SEQUENCE";
+  auto ret = translate(tdt_type, ts_row, &acl_dataset);
+  if (ret != Status::OK()) {
+    DestroyAclDataset(acl_dataset);
+    RETURN_STATUS_UNEXPECTED("TDT converting tensor failed!");
   }
+
   if (profiling) {
     start_time = ProfilingTime::GetCurMilliSecond();
   }
 #if ENABLE_D
   // Data prefetch only when PS mode enables cache.
-  if (items.size() > 0) {
-    if (!ps::PsDataPrefetch::GetInstance().PrefetchData(channel_name, items[0].dataPtr_.get(), items[0].dataLen_)) {
-      return FAILED;
+  if (acltdtGetDatasetSize(acl_dataset) > 0) {
+    acltdtDataItem *item0 = acltdtGetDataItem(acl_dataset, 0);
+    if (!ps::PsDataPrefetch::GetInstance().PrefetchData(channel_name, acltdtGetDataAddrFromItem(item0),
+                                                        acltdtGetDataSizeFromItem(item0))) {
+      RETURN_STATUS_UNEXPECTED("PrefetchData failed in when pre-processing sending data.");
     }
   }
 #endif
-  if (tdt::TdtHostPushData(channel_name, items) != 0) {
-    return FAILED;
+  auto status = acltdtSendTensor(acl_handle_, acl_dataset, -1);
+  DestroyAclDataset(acl_dataset);
+  if (status != ACL_SUCCESS) {
+    RETURN_STATUS_UNEXPECTED("Tdt Send data failed.");
   }
   if (profiling) {
     double end_time = ProfilingTime::GetCurMilliSecond();
     time = (int32_t)(end_time - start_time);
   }
-  return SUCCESS;
+  return Status::OK();
 }
 
-TdtStatus TdtPlugin::getTdtType(DataType d_type, std::string &datatype) {
+Status TdtPlugin::getTdtType(DataType d_type, aclDataType &datatype) {
   switch (d_type.value()) {
     case DataType::DE_BOOL:
-      datatype = "bool";
+      datatype = ACL_BOOL;
       break;
     case DataType::DE_INT8:
-      datatype = "int8";
+      datatype = ACL_INT8;
       break;
     case DataType::DE_UINT8:
-      datatype = "uint8";
+      datatype = ACL_UINT8;
       break;
     case DataType::DE_INT16:
-      datatype = "int16";
+      datatype = ACL_INT16;
       break;
     case DataType::DE_UINT16:
-      datatype = "uint16";
+      datatype = ACL_UINT16;
       break;
     case DataType::DE_INT32:
-      datatype = "int32";
+      datatype = ACL_INT32;
       break;
     case DataType::DE_UINT32:
-      datatype = "uint32";
+      datatype = ACL_UINT32;
       break;
     case DataType::DE_FLOAT16:
-      datatype = "float16";
+      datatype = ACL_FLOAT16;
       break;
     case DataType::DE_FLOAT32:
-      datatype = "float32";
+      datatype = ACL_FLOAT;
       break;
     case DataType::DE_FLOAT64:
-      datatype = "float64";
+      datatype = ACL_DOUBLE;
       break;
     case DataType::DE_INT64:
-      datatype = "int64";
+      datatype = ACL_INT64;
       break;
     case DataType::DE_UINT64:
-      datatype = "uint64";
+      datatype = ACL_UINT64;
       break;
     default:
-      return FAILED;
+      RETURN_STATUS_UNEXPECTED("Invalid data, got unexpected data type.");
   }
-  return SUCCESS;
+  return Status::OK();
 }
 
-TdtStatus TdtPlugin::translate(const TensorRow &ts_row, std::vector<DataItem> &items) {
-  if (ts_row.size() == 0) {
-    MS_LOG(ERROR) << "TDT the size of row is zero.";
-    return SUCCESS;
+Status TdtPlugin::translate(acltdtTensorType tdt_type, const TensorRow &ts_row, acltdtDataset **output_acl_dataset) {
+  auto acl_dataset = acltdtCreateDataset();
+  if (acl_dataset == nullptr) {
+    RETURN_STATUS_UNEXPECTED("Create tdt dataset failed.");
   }
-  for (auto ts : ts_row) {
-    std::string datatype;
-    TdtStatus status = getTdtType(ts->type(), datatype);
-    if (status != SUCCESS) {
-      return status;
+  auto status = AssembleTensor2AclDataset(tdt_type, ts_row, acl_dataset);
+  if (status != Status::OK()) {
+    DestroyAclDataset(acl_dataset);
+    RETURN_STATUS_UNEXPECTED("Assemble tensor row to tdt dataset failed.");
+  }
+
+  *output_acl_dataset = acl_dataset;
+  return Status::OK();
+}
+
+Status TdtPlugin::AssembleTensor2AclDataset(acltdtTensorType tdt_type, const TensorRow &ts_row,
+                                            acltdtDataset *acl_dataset) {
+  if (tdt_type != ACL_TENSOR_DATA_TENSOR || ts_row.size() == 0) {
+    acltdtDataItem *acl_data = acltdtCreateDataItem(tdt_type, nullptr, 0, ACL_BOOL, nullptr, 0);
+    if (acl_data == nullptr) {
+      RETURN_STATUS_UNEXPECTED("Create data item failed when send data with type:" + std::to_string(tdt_type));
     }
+    if (acltdtAddDataItem(acl_dataset, acl_data) != ACL_SUCCESS) {
+      if (acltdtDestroyDataItem(acl_data) != ACL_SUCCESS) {
+        MS_LOG(ERROR) << "Destroy data item failed when send data with type: " << tdt_type;
+      }
+      RETURN_STATUS_UNEXPECTED("Add data item to tdt dataset failed when send data.");
+    }
+    return Status::OK();
+  }
+
+  for (auto ts : ts_row) {
+    aclDataType datatype;
+    acltdtDataItem *acl_data = nullptr;
+    RETURN_IF_NOT_OK(getTdtType(ts->type(), datatype));
+
     TensorShape tsShape = ts->shape();
     std::string dataShapes = "[";
     for (auto dim : tsShape.AsVector()) {
@@ -132,18 +162,46 @@ TdtStatus TdtPlugin::translate(const TensorRow &ts_row, std::vector<DataItem> &i
     }
     dataShapes.pop_back();
     (void)dataShapes.append("]");
-    DataItem data_item;
-    data_item.dataType_ = tdt::TDT_TENSOR;
-    data_item.tensorShape_ = dataShapes;
-    data_item.tensorType_ = datatype;
-    data_item.dataLen_ = ts->SizeInBytes();
-    data_item.dataPtr_ =
+
+    std::shared_ptr<void> dataPtr =
       std::shared_ptr<void>(reinterpret_cast<uchar *>(&(*ts->begin<uint8_t>())), [](const void *elem) {});
-    items.emplace_back(data_item);
+    size_t dataLen = ts->SizeInBytes();
+    const dsize_t dims = tsShape.Rank();
+    std::vector<int64_t> dataShape;
+    for (auto i = 0; i < dims; i++) {
+      dataShape.emplace_back(tsShape[i]);
+    }
+    acl_data = acltdtCreateDataItem(ACL_TENSOR_DATA_TENSOR, (tsShape.empty() ? nullptr : &dataShape[0]), dims, datatype,
+                                    dataPtr.get(), dataLen);
+    if (acl_data == nullptr) {
+      RETURN_STATUS_UNEXPECTED("Create data item failed when send data.");
+    }
+    if (acltdtAddDataItem(acl_dataset, acl_data) != ACL_SUCCESS) {
+      if (acltdtDestroyDataItem(acl_data) != ACL_SUCCESS) {
+        MS_LOG(ERROR) << "Destroy data item failed when send data with type ACL_TENSOR_DATA_TENSOR.";
+      }
+      RETURN_STATUS_UNEXPECTED("Add data item to tdt dataset failed when send data.");
+    }
+
     MS_LOG(DEBUG) << "TDT data type is TDT_TENSOR, tensor type is " << datatype << ", tensor shape is " << dataShapes
                   << ", data length is " << ts->Size() << ".";
   }
-  return SUCCESS;
+
+  return Status::OK();
+}
+
+Status TdtPlugin::DestroyAclDataset(acltdtDataset *acl_dataset, bool include_data_item) {
+  if (include_data_item) {
+    for (size_t i = 0; i < acltdtGetDatasetSize(acl_dataset); i++) {
+      if (acltdtDestroyDataItem(acltdtGetDataItem(acl_dataset, i)) != ACL_SUCCESS) {
+        RETURN_STATUS_UNEXPECTED("Destroy data item failed when send data.");
+      }
+    }
+  }
+  if (acltdtDestroyDataset(acl_dataset) != ACL_SUCCESS) {
+    RETURN_STATUS_UNEXPECTED("Destroy tdt dataset failed when send data.");
+  }
+  return Status::OK();
 }
 }  // namespace dataset
 }  // namespace mindspore

mindspore/ccsrc/minddata/dataset/engine/tdt/tdt_plugin.h

@@ -22,33 +22,40 @@
 #include <memory>
 #include <string>
 #include <vector>
-#include "tdt/tdt_host_interface.h"
+#include "acl/acl_tdt.h"
+#include "minddata/dataset/engine/tdt/tdt_handle.h"
 
 #include "minddata/dataset/core/data_type.h"
 #include "minddata/dataset/core/tensor.h"
 #include "minddata/dataset/core/tensor_row.h"
+#include "minddata/dataset/util/status.h"
 
 namespace mindspore {
 namespace dataset {
-enum TdtStatus { SUCCESS, FAILED };
-
-using tdt::DataItem;
 
 class TdtPlugin {
  public:
   static std::shared_ptr<TdtPlugin> GetInstance();
 
-  TdtStatus hostPush(TensorRow ts_row, bool is_wait, std::string channel_name, bool profilig, int32_t &time,
-                     tdt::TdtDataType tdt_type = tdt::TDT_TENSOR);
+  Status hostPush(TensorRow ts_row, bool is_wait, std::string channel_name, bool profilig, int32_t &time,
+                  acltdtTensorType tdt_type = ACL_TENSOR_DATA_TENSOR);
+
+  TdtPlugin(const std::string &channel_name, int32_t device_id);
+
+  ~TdtPlugin();
 
  private:
-  TdtPlugin() {}
+  Status DestroyAclDataset(acltdtDataset *acl_dataset, bool include_data_item = true);
 
-  TdtStatus getTdtType(DataType d_type, std::string &datatype);
+  Status AssembleTensor2AclDataset(acltdtTensorType tdt_type, const TensorRow &ts_row, acltdtDataset *acl_dataset);
 
-  TdtStatus translate(const TensorRow &ts_row, std::vector<DataItem> &items);
+  Status getTdtType(DataType d_type, aclDataType &datatype);
+
+  Status translate(acltdtTensorType tdt_type, const TensorRow &ts_row, acltdtDataset **output_acl_dataset);
 
   void *tdt_handle_ = nullptr;
+
+  acltdtChannelHandle *acl_handle_;
 };
 }  // namespace dataset
 }  // namespace mindspore

mindspore/ccsrc/minddata/dataset/include/datasets.h

@@ -152,14 +152,16 @@ class Dataset : public std::enable_shared_from_this<Dataset> {
   ///     of data transmission per time is 256M.
   /// \param[in] queue_name Channel name (default="", create new unique name).
   /// \param[in] device_type Type of device (default="", get from MSContext).
+  /// \param[in] device_id id of device (default=0, get from MSContext).
   /// \param[in] num_epochs Number of epochs (default=-1, infinite epochs).
   /// \param[in] send_epoch_end Whether to send end of sequence to device or not (default=true).
   /// \param[in] total_batches Number of batches to be sent to the device (default=0, all data).
   /// \param[in] create_data_info_queue Whether to create queue which stores types and shapes
   ///     of data or not(default=false).
   /// \return Returns true if no error encountered else false.
-  bool DeviceQueue(std::string queue_name = "", std::string device_type = "", int32_t num_epochs = -1,
-                   bool send_epoch_end = true, int32_t total_batches = 0, bool create_data_info_queue = false);
+  bool DeviceQueue(std::string queue_name = "", std::string device_type = "", int32_t device_id = 0,
+                   int32_t num_epochs = -1, bool send_epoch_end = true, int32_t total_batches = 0,
+                   bool create_data_info_queue = false);
 
   /// \brief Function to create a Saver to save the dynamic data processed by the dataset pipeline
   /// \note Usage restrictions:

mindspore/ccsrc/minddata/dataset/util/task.cc

@@ -21,8 +21,9 @@
 #include "minddata/dataset/util/services.h"
 #endif
 #ifdef ENABLE_TDTQUE
-#include "tdt/tdt_host_interface.h"
+#include "acl/acl_tdt.h"
 #include "tdt/status.h"
+#include "minddata/dataset/engine/tdt/tdt_handle.h"
 #endif
 
 namespace mindspore {
@@ -161,11 +162,10 @@ Status Task::Join(WaitFlag blocking) {
           if (wait_times > 5 && my_name_.find("DeviceQueueOp") != std::string::npos) {
             MS_LOG(WARNING) << "Wait " << wait_times << " seconds, "
                             << "the task: " << my_name_ << " will be destroyed by TdtHostDestory.";
-            int32_t destory_status = tdt::TdtHostDestroy();
-            if (destory_status != TDT_OK_CODE) {
-              MS_LOG(WARNING) << "Destroy tsd failed, status = " << destory_status << ".";
+            if (!TdtHandle::DestroyHandle()) {
+              MS_LOG(WARNING) << "Destroy tdt channel failed.";
             } else {
-              MS_LOG(INFO) << "Destroy tsd success.";
+              MS_LOG(INFO) << "Destroy tdt channel success.";
             }
 
             // just wait 30 seconds

mindspore/ccsrc/runtime/device/CMakeLists.txt

@@ -1,5 +1,6 @@
 file(GLOB_RECURSE DEVICE_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "common/*.cc"
-    "kernel_info.cc" "executor/dynamic_kernel.cc" "executor/executor_callback.cc" "kernel_runtime.cc" "memory_manager.cc" "kernel_runtime_manager.cc" "convert_tensor_utils.cc"
+    "kernel_info.cc" "executor/dynamic_kernel.cc" "executor/executor_callback.cc" "kernel_runtime.cc"
+    "memory_manager.cc" "kernel_runtime_manager.cc" "convert_tensor_utils.cc"
 )
 
 if(ENABLE_GPU)
@@ -9,7 +10,8 @@ else()
 endif()
 
 if(ENABLE_D)
-    file(GLOB_RECURSE D_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "ascend/*.cc" "kernel_adjust.cc")
+    file(GLOB_RECURSE D_SRC_LIST RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "ascend/*.cc" "kernel_adjust.cc"
+        "../../minddata/dataset/engine/tdt/tdt_handle.cc")
 endif()
 
 if(ENABLE_CPU)

mindspore/ccsrc/runtime/device/ascend/ascend_kernel_runtime.cc

@@ -60,8 +60,8 @@
 #include "runtime/device/ascend/profiling/profiling_callback_register.h"
 #include "backend/kernel_compiler/hccl/hccl_context.h"
 #ifdef ENABLE_TDTQUE
-#include "tdt/tdt_host_interface.h"
-#include "tdt/status.h"
+#include "minddata/dataset/engine/tdt/tdt_handle.h"
+using mindspore::dataset::TdtHandle;
 #endif
 
 using ge::model_runner::ModelRunner;
@@ -695,11 +695,10 @@ bool AscendKernelRuntime::RunTask(const session::KernelGraph *graph) {
 #ifdef ENABLE_TDTQUE
     // Run task error, we should call TdtHostDestroy to release tdt to avoid DeviceQueueOp hostPush hung
     // case1: cpu usage 100% cause thread/process exit, but some tdt thread remain in backend
-    int32_t destory_status = tdt::TdtHostDestroy();
-    if (destory_status != TDT_OK_CODE) {
-      MS_LOG(WARNING) << "Destroy tsd failed, status = " << destory_status << ".";
+    if (!TdtHandle::DestroyHandle()) {
+      MS_LOG(WARNING) << "Destroy tdt channel failed.";
     } else {
-      MS_LOG(INFO) << "Destroy tsd success.";
+      MS_LOG(INFO) << "Destroy tdt channel success.";
     }
 #endif
     return false;

mindspore/ccsrc/utils/context/context_extends.cc

@@ -230,7 +230,7 @@ void GetGeOptions(const std::shared_ptr<MsContext> &ms_context_ptr, std::map<std
   } else {
     (*ge_options)["ge.exec.precision_mode"] = "allow_fp32_to_fp16";
   }
-  // Disable the global variable acc, only enable it whlie adding training graph in pipeline
+  // Disable the global variable acc, only enable it while adding training graph in pipeline
   (*ge_options)["ge.exec.variable_acc"] = "0";
 #endif
 }

mindspore/dataset/engine/datasets.py

@@ -2876,10 +2876,11 @@ class TransferDataset(Dataset):
 
     def parse(self, children=None):
         total_batch = 0
+        device_id = context.get_context("device_id")
         if hasattr(self.children[0], "__total_batch__"):
             total_batch = self.children[0].__total_batch__
-        return cde.TransferNode(children[0], self.queue_name, self.device_type, self._send_epoch_end, total_batch,
-                                self._create_data_info_queue)
+        return cde.TransferNode(children[0], self.queue_name, self.device_type, device_id, self._send_epoch_end,
+                                total_batch, self._create_data_info_queue)
 
     def get_args(self):
         args = super().get_args()

tests/st/ops/ascend/test_tensor_print/tensor_print_utils.py

@@ -54,15 +54,20 @@ def get_tensor(is_scalar, input_type):
 
 if __name__ == "__main__":
     net = TensorPrint()
-    net(get_tensor('scalar', mindspore.bool_), get_tensor('scalar', mindspore.uint8),
-        get_tensor('scalar', mindspore.int8), get_tensor('scalar', mindspore.uint16),
-        get_tensor('scalar', mindspore.int16), get_tensor('scalar', mindspore.uint32),
-        get_tensor('scalar', mindspore.int32), get_tensor('scalar', mindspore.uint64),
-        get_tensor('scalar', mindspore.int64), get_tensor('scalar', mindspore.float16),
+    # net(get_tensor('scalar', mindspore.bool_), get_tensor('scalar', mindspore.uint8),
+    #     get_tensor('scalar', mindspore.int8), get_tensor('scalar', mindspore.uint16),
+    #     get_tensor('scalar', mindspore.int16), get_tensor('scalar', mindspore.uint32),
+    #     get_tensor('scalar', mindspore.int32), get_tensor('scalar', mindspore.uint64),
+    #     get_tensor('scalar', mindspore.int64), get_tensor('scalar', mindspore.float16),
+    #     get_tensor('scalar', mindspore.float32), get_tensor('scalar', mindspore.float64),
+    #     get_tensor('array', mindspore.bool_), get_tensor('array', mindspore.uint8),
+    #     get_tensor('array', mindspore.int8), get_tensor('array', mindspore.uint16),
+    #     get_tensor('array', mindspore.int16), get_tensor('array', mindspore.uint32),
+    #     get_tensor('array', mindspore.int32), get_tensor('array', mindspore.uint64),
+    #     get_tensor('array', mindspore.int64), get_tensor('array', mindspore.float16),
+    #     get_tensor('array', mindspore.float32), get_tensor('array', mindspore.float64))
+
+    net(get_tensor('scalar', mindspore.bool_),
         get_tensor('scalar', mindspore.float32), get_tensor('scalar', mindspore.float64),
-        get_tensor('array', mindspore.bool_), get_tensor('array', mindspore.uint8),
-        get_tensor('array', mindspore.int8), get_tensor('array', mindspore.uint16),
-        get_tensor('array', mindspore.int16), get_tensor('array', mindspore.uint32),
-        get_tensor('array', mindspore.int32), get_tensor('array', mindspore.uint64),
-        get_tensor('array', mindspore.int64), get_tensor('array', mindspore.float16),
+        get_tensor('array', mindspore.bool_),
         get_tensor('array', mindspore.float32), get_tensor('array', mindspore.float64))