!11074 replace tdt with acltdt interface

From: @ms_yan
Reviewed-by: @lilongfei15
Signed-off-by:

mindspore/ccsrc/CMakeLists.txt

@@ -267,6 +267,8 @@ if(ENABLE_D)
     find_library(REGISTER register ${ASCEND_RUNTIME_PATH} ${ASCEND_TOOLKIT_RUNTIME_PATH})
     find_library(PLATFORM platform ${ASCEND_RUNTIME_PATH} ${ASCEND_TOOLKIT_RUNTIME_PATH})
     find_library(OPTILING optiling ${ASCEND_OPP_PATH} ${ASCEND_TOOLKIT_OPP_PATH})
+    find_library(ACL ascendcl ${ASCEND_RUNTIME_PATH} ${ASCEND_TOOLKIT_RUNTIME_PATH})
+
     # hccl_adpter
     find_library(HCCL_ADPTER hcom_graph_adaptor ${ASCEND_RUNTIME_PATH} ${ASCEND_TOOLKIT_RUNTIME_PATH})
     find_library(HCCL_RA ra ${ASCEND_RUNTIME_PATH} ${ASCEND_TOOLKIT_RUNTIME_PATH})
@@ -281,7 +283,7 @@ if(ENABLE_D)
                           mindspore::protobuf -Wl,--end-group)
     target_link_libraries(mindspore ge_runtime ${CCE_LIB} ${RUNTIME_LIB} ${TSDCLIENT} ${HCCL} ${DATATRANSFER}
                           ${HCCL_ADPTER} ${REGISTER} -Wl,--no-as-needed ${OPTILING} ${HCCL_BUILDER}
-                          ${HCCL_RA} ${PLATFORM})
+                          ${HCCL_RA} ${PLATFORM} ${ACL})
     target_link_libraries(mindspore -Wl,--start-group proto_input mindspore::protobuf -Wl,--end-group)
 elseif(CMAKE_SYSTEM_NAME MATCHES "Windows")
     target_link_libraries(mindspore -Wl,--start-group proto_input mindspore::protobuf mindspore::sentencepiece

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

@@ -521,9 +521,10 @@ 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

@@ -55,6 +55,7 @@ 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
 }
 
@@ -152,7 +153,7 @@ 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();
@@ -183,9 +184,9 @@ 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();
@@ -202,7 +203,6 @@ 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

@@ -23,8 +23,9 @@
 #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 {
@@ -163,11 +164,10 @@ 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

@@ -54,8 +54,8 @@
 #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;
@@ -698,11 +698,10 @@ 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

@@ -22,7 +22,6 @@
 #include <atomic>
 
 #include "pybind11/pybind11.h"
-
 #include "utils/ms_utils.h"
 #include "utils/convert_utils_base.h"
 
@@ -46,7 +45,7 @@ bool OpenTsd(const std::shared_ptr<MsContext> &ms_context_ptr) {
   }
 
   if (ms_context_ptr->get_param<uint32_t>(MS_CTX_TSD_REF)) {
-    MS_LOG(DEBUG) << "TDT Dataset client is already opened.";
+    MS_LOG(DEBUG) << "ACLTDT Dataset client is already opened.";
     ms_context_ptr->increase_param<uint32_t>(MS_CTX_TSD_REF);
     return true;
   }
@@ -56,10 +55,8 @@ bool OpenTsd(const std::shared_ptr<MsContext> &ms_context_ptr) {
     return true;
   }
 
-  unsigned int device_id;
-  unsigned int rank_size = 1;
-
-  device_id = ms_context_ptr->get_param<uint32_t>(MS_CTX_DEVICE_ID);
+  uint32_t rank_size = 1;
+  uint32_t device_id = ms_context_ptr->get_param<uint32_t>(MS_CTX_DEVICE_ID);
 
   auto rank_size_env = common::GetEnv("RANK_SIZE");
   if (rank_size_env.empty()) {
@@ -81,14 +78,14 @@ bool OpenTsd(const std::shared_ptr<MsContext> &ms_context_ptr) {
   }
   ms_context_ptr->increase_param<uint32_t>(MS_CTX_TSD_REF);
 #ifdef ENABLE_TDTQUE
-  int32_t initStatus = tdt::TdtHostInit(device_id);
-  if (initStatus != TDT_OK_CODE) {
-    MS_LOG(EXCEPTION) << "Init tsd failed, status = " << initStatus << ".";
+  acltdtChannelHandle *acl_handle = ms_context_ptr->get_acl_tdt_channel_handle();
+  if (acl_handle == nullptr) {
+    MS_LOG(EXCEPTION) << "Get acltdt handle failed";
     return false;
   }
-  ms_context_ptr->tdt_print_ = std::thread(TensorPrint());
+  ms_context_ptr->acl_tdt_print = std::thread(TensorPrint(acl_handle));
 #endif
-  MS_LOG(INFO) << "Open and init tsd successful, tsd reference = "
+  MS_LOG(INFO) << "Get the acltdt handle successful, tsd reference = "
                << ms_context_ptr->get_param<uint32_t>(MS_CTX_TSD_REF) << ".";
   return true;
 }
@@ -103,28 +100,34 @@ bool CloseTsd(const std::shared_ptr<MsContext> &ms_context_ptr, bool force) {
   ms_context_ptr->decrease_param<uint32_t>(MS_CTX_TSD_REF);
   if (force || ms_context_ptr->get_param<uint32_t>(MS_CTX_TSD_REF) == 0) {
     ms_context_ptr->set_param<uint32_t>(MS_CTX_TSD_REF, 0);
+
 #ifdef ENABLE_TDTQUE
-    int32_t stopStatus = tdt::TdtHostStop(KNpuLog);
-    if (stopStatus != TDT_OK_CODE) {
-      MS_LOG(EXCEPTION) << "Stop tsd failed, status = " << stopStatus << ".";
-      return false;
+    acltdtChannelHandle *acl_handle = ms_context_ptr->get_acl_tdt_channel_handle();
+    aclError stopStatus = acltdtStopChannel(acl_handle);
+    if (stopStatus != ACL_SUCCESS) {
+      MS_LOG(ERROR) << "Failed stop acl data channel for host queue ";
+    } else {
+      MS_LOG(INFO) << "Succeed stop acl data channel for host queue ";
     }
+    MS_LOG(INFO) << "Succeed run cancellation callback of out-feed dequeue op ";
+
     py::gil_scoped_release gil_release;
-    int32_t destroyStatus = tdt::TdtHostDestroy();
-    if (destroyStatus != TDT_OK_CODE) {
-      MS_LOG(EXCEPTION) << "Destroy tsd failed, status = " << destroyStatus << ".";
-      return false;
+    aclError destrodStatus = acltdtDestroyChannel(acl_handle);
+    if (destrodStatus != ACL_SUCCESS) {
+      MS_LOG(ERROR) << "Failed destroy acl channel for out-feed dequeue op ";
+    } else {
+      MS_LOG(INFO) << "Succeed destroy acl channel for out-feed dequeue op ";
     }
     try {
-      if (ms_context_ptr->tdt_print_.joinable()) {
-        MS_LOG(INFO) << "join tdt host receive process";
-        ms_context_ptr->tdt_print_.join();
+      if (ms_context_ptr->acl_tdt_print.joinable()) {
+        MS_LOG(INFO) << "join acl tdt host receive process";
+        ms_context_ptr->acl_tdt_print.join();
       }
     } catch (const std::exception &e) {
       MS_LOG(ERROR) << "tdt thread join failed: " << e.what();
     }
 #endif
-    auto device_id = ms_context_ptr->get_param<uint32_t>(MS_CTX_DEVICE_ID);
+    uint32_t device_id = ms_context_ptr->get_param<uint32_t>(MS_CTX_DEVICE_ID);
     auto ret = rtDeviceReset(device_id);
     if (ret != RT_ERROR_NONE) {
       MS_LOG(EXCEPTION) << "Device " << device_id << " call rtDeviceReset failed, ret[" << static_cast<int>(ret) << "]";
@@ -133,10 +136,9 @@ bool CloseTsd(const std::shared_ptr<MsContext> &ms_context_ptr, bool force) {
     ms_context_ptr->set_param<bool>(MS_CTX_IS_PYNATIVE_GE_INIT, false);
     MS_LOG(INFO) << "Call rtDeviceReset, destroy and close tsd successful, ret[" << static_cast<int>(ret) << "]";
   } else {
-    MS_LOG(DEBUG) << "TDT Dataset client is used, no need to close, tsd reference = "
+    MS_LOG(DEBUG) << "Acltdt Dataset client is used, no need to close, tsd reference = "
                   << ms_context_ptr->get_param<uint32_t>(MS_CTX_TSD_REF) << ".";
   }
-
   return true;
 }
 #else
@@ -230,7 +232,7 @@ 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
 }
@@ -308,6 +310,7 @@ bool PynativeInitGe(const std::shared_ptr<MsContext> &ms_context_ptr) {
       ms_context_ptr->get_param<uint32_t>(MS_CTX_GE_REF) || ms_context_ptr->get_param<uint32_t>(MS_CTX_TSD_REF)) {
     return true;
   }
+
   (void)OpenTsd(ms_context_ptr);
   (void)InitGe(ms_context_ptr);
   ms_context_ptr->set_param(MS_CTX_IS_PYNATIVE_GE_INIT, true);

mindspore/ccsrc/utils/context/context_extends.h

@@ -24,8 +24,8 @@
 #include "utils/tensorprint_utils.h"
 
 #ifndef NO_DLIB
+#include "acl/acl_tdt.h"
 #include "tdt/tsd_client.h"
-#include "tdt/tdt_host_interface.h"
 #include "tdt/data_common.h"
 #include "runtime/dev.h"
 #endif
@@ -35,8 +35,8 @@
 
 namespace mindspore {
 namespace context {
-bool OpenTsd(const std::shared_ptr<MsContext> &inst_context);
-bool CloseTsd(const std::shared_ptr<MsContext> &inst_context, bool force = false);
+bool OpenTsd(const std::shared_ptr<MsContext> &ms_context_ptr);
+bool CloseTsd(const std::shared_ptr<MsContext> &ms_context_ptr, bool force = false);
 void SetHcclOptions(const std::shared_ptr<MsContext> &inst_context, std::map<std::string, std::string> *ge_options);
 void GetGeOptions(const std::shared_ptr<MsContext> &inst_context, std::map<std::string, std::string> *ge_options);
 void SetDisableReuseMemoryFlag(std::map<std::string, std::string> *ge_options);

mindspore/ccsrc/utils/tensorprint_utils.cc

@@ -1,5 +1,5 @@
 /**
- * Copyright 2020 Huawei Technologies Co., Ltd
+ * Copyright 2020-2021 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.
@@ -23,65 +23,35 @@
 #include "pybind11/pybind11.h"
 #include "utils/ms_utils.h"
 #include "utils/shape_utils.h"
-#ifndef NO_DLIB
-#include "tdt/tsd_client.h"
-#include "tdt/tdt_host_interface.h"
-#include "tdt/data_common.h"
-#endif
 
 namespace py = pybind11;
 namespace mindspore {
-const char kShapeSeperator[] = ",";
-const char kShapeScalar[] = "[0]";
-const char kShapeNone[] = "[]";
-static std::map<std::string, TypeId> print_type_map = {
-  {"int8_t", TypeId::kNumberTypeInt8},     {"uint8_t", TypeId::kNumberTypeUInt8},
-  {"int16_t", TypeId::kNumberTypeInt16},   {"uint16_t", TypeId::kNumberTypeUInt16},
-  {"int32_t", TypeId::kNumberTypeInt32},   {"uint32_t", TypeId::kNumberTypeUInt32},
-  {"int64_t", TypeId::kNumberTypeInt64},   {"uint64_t", TypeId::kNumberTypeUInt64},
-  {"float16", TypeId::kNumberTypeFloat16}, {"float", TypeId::kNumberTypeFloat32},
-  {"double", TypeId::kNumberTypeFloat64},  {"bool", TypeId::kNumberTypeBool}};
 
-static std::map<std::string, size_t> type_size_map = {
-  {"int8_t", sizeof(int8_t)},     {"uint8_t", sizeof(uint8_t)},   {"int16_t", sizeof(int16_t)},
-  {"uint16_t", sizeof(uint16_t)}, {"int32_t", sizeof(int32_t)},   {"uint32_t", sizeof(uint32_t)},
-  {"int64_t", sizeof(int64_t)},   {"uint64_t", sizeof(uint64_t)}, {"float16", sizeof(float) / 2},
-  {"float", sizeof(float)},       {"double", sizeof(double)},     {"bool", sizeof(bool)}};
+#ifndef NO_DLIB
+static std::map<aclDataType, TypeId> print_acl_data_type_map = {
+  {ACL_INT8, TypeId::kNumberTypeInt8},       {ACL_UINT8, TypeId::kNumberTypeUInt8},
+  {ACL_INT16, TypeId::kNumberTypeInt16},     {ACL_UINT16, TypeId::kNumberTypeUInt16},
+  {ACL_INT32, TypeId::kNumberTypeInt32},     {ACL_UINT32, TypeId::kNumberTypeUInt32},
+  {ACL_INT64, TypeId::kNumberTypeInt64},     {ACL_UINT64, TypeId::kNumberTypeUInt64},
+  {ACL_FLOAT16, TypeId::kNumberTypeFloat16}, {ACL_FLOAT, TypeId::kNumberTypeFloat32},
+  {ACL_DOUBLE, TypeId::kNumberTypeFloat64},  {ACL_BOOL, TypeId::kNumberTypeBool}};
 
-std::string GetParseType(const std::string &tensorType_) {
-  static const std::map<std::string, std::string> print_parse_map = {
-    {"int8_t", "Int8"},     {"uint8_t", "Uint8"},   {"int16_t", "Int16"},  {"uint16_t", "Uint16"},
-    {"int32_t", "Int32"},   {"uint32_t", "Uint32"}, {"int64_t", "Int64"},  {"uint64_t", "Uint64"},
-    {"float16", "Float16"}, {"float", "Float32"},   {"double", "Float64"}, {"bool", "Bool"}};
-  auto type_iter = print_parse_map.find(tensorType_);
-  if (type_iter == print_parse_map.end()) {
-    MS_LOG(EXCEPTION) << "type of tensor need to print is not support " << tensorType_;
-  }
-  return type_iter->second;
-}
+static std::map<aclDataType, size_t> acl_data_type_size_map = {
+  {ACL_INT8, sizeof(int8_t)},     {ACL_UINT8, sizeof(uint8_t)},   {ACL_INT16, sizeof(int16_t)},
+  {ACL_UINT16, sizeof(uint16_t)}, {ACL_INT32, sizeof(int32_t)},   {ACL_UINT32, sizeof(uint32_t)},
+  {ACL_INT64, sizeof(int64_t)},   {ACL_UINT64, sizeof(uint64_t)}, {ACL_FLOAT16, sizeof(float) / 2},
+  {ACL_FLOAT, sizeof(float)},     {ACL_DOUBLE, sizeof(double)},   {ACL_BOOL, sizeof(bool)}};
 
-bool ParseTensorShape(const std::string &input_shape_str, ShapeVector *const tensor_shape, size_t *dims) {
-  if (tensor_shape == nullptr) {
-    return false;
-  }
-  MS_EXCEPTION_IF_NULL(dims);
-  std::string shape_str = input_shape_str;
-  if (shape_str.size() <= 2) {
-    return false;
-  }
-  (void)shape_str.erase(shape_str.begin());
-  shape_str.pop_back();
-  shape_str += kShapeSeperator;
-  string::size_type pos_begin = 0;
-  string::size_type pos_end = shape_str.find(kShapeSeperator);
-  while (pos_end != std::string::npos) {
-    string dim_str = shape_str.substr(pos_begin, pos_end - pos_begin);
-    tensor_shape->emplace_back(std::stoi(dim_str));
-    (*dims) = (*dims) * std::stoul(dim_str);
-    pos_begin = pos_end + sizeof(kShapeSeperator) - 1;
-    pos_end = shape_str.find(kShapeSeperator, pos_begin);
+std::string GetParseType(const aclDataType &acl_data_type) {
+  static const std::map<aclDataType, std::string> print_tensor_parse_map = {
+    {ACL_INT8, "Int8"},       {ACL_UINT8, "Uint8"},   {ACL_INT16, "Int16"},    {ACL_UINT16, "Uint16"},
+    {ACL_INT32, "Int32"},     {ACL_UINT32, "Uint32"}, {ACL_INT64, "Int64"},    {ACL_UINT64, "Uint64"},
+    {ACL_FLOAT16, "Float16"}, {ACL_FLOAT, "Float32"}, {ACL_DOUBLE, "Float64"}, {ACL_BOOL, "Bool"}};
+  auto type_iter = print_tensor_parse_map.find(acl_data_type);
+  if (type_iter == print_tensor_parse_map.end()) {
+    MS_LOG(EXCEPTION) << "type of tensor need to print is not support " << acl_data_type;
   }
-  return true;
+  return type_iter->second;
 }
 
 bool PrintTensorToString(const char *str_data_ptr, mindspore::tensor::Tensor *const print_tensor,
@@ -90,8 +60,11 @@ bool PrintTensorToString(const char *str_data_ptr, mindspore::tensor::Tensor *co
   MS_EXCEPTION_IF_NULL(print_tensor);
   auto *tensor_data_ptr = static_cast<uint8_t *>(print_tensor->data_c());
   MS_EXCEPTION_IF_NULL(tensor_data_ptr);
-  auto cp_ret =
-    memcpy_s(tensor_data_ptr, static_cast<size_t>(print_tensor->data().nbytes()), str_data_ptr, memory_size);
+
+  size_t dest_size = static_cast<size_t>(print_tensor->data().nbytes());
+  size_t target_size = memory_size;
+
+  auto cp_ret = memcpy_s(tensor_data_ptr, dest_size, str_data_ptr, target_size);
   if (cp_ret != EOK) {
     MS_LOG(ERROR) << "Print op Failed to copy the memory to py::tensor " << cp_ret;
     return false;
@@ -100,10 +73,10 @@ bool PrintTensorToString(const char *str_data_ptr, mindspore::tensor::Tensor *co
 }
 
 template <typename T>
-void PrintScalarToString(const char *str_data_ptr, const string &tensor_type, std::ostringstream *const buf) {
+void PrintScalarToString(const char *str_data_ptr, const aclDataType &acl_data_type, std::ostringstream *const buf) {
   MS_EXCEPTION_IF_NULL(str_data_ptr);
   MS_EXCEPTION_IF_NULL(buf);
-  *buf << "Tensor(shape=[], dtype=" << GetParseType(tensor_type) << ", value=";
+  *buf << "Tensor(shape=[], dtype=" << GetParseType(acl_data_type) << ", value=";
   const T *data_ptr = reinterpret_cast<const T *>(str_data_ptr);
   if constexpr (std::is_same<T, int8_t>::value || std::is_same<T, uint8_t>::value) {
     const int int_data = static_cast<int>(*data_ptr);
@@ -113,11 +86,12 @@ void PrintScalarToString(const char *str_data_ptr, const string &tensor_type, st
   }
 }
 
-void PrintScalarToBoolString(const char *str_data_ptr, const string &tensor_type, std::ostringstream *const buf) {
+void PrintScalarToBoolString(const char *str_data_ptr, const aclDataType &acl_data_type,
+                             std::ostringstream *const buf) {
   MS_EXCEPTION_IF_NULL(str_data_ptr);
   MS_EXCEPTION_IF_NULL(buf);
   const bool *data_ptr = reinterpret_cast<const bool *>(str_data_ptr);
-  *buf << "Tensor(shape=[], dtype=" << GetParseType(tensor_type) << ", value=";
+  *buf << "Tensor(shape=[], dtype=" << GetParseType(acl_data_type) << ", value=";
   if (*data_ptr) {
     *buf << "True)\n";
   } else {
@@ -125,89 +99,99 @@ void PrintScalarToBoolString(const char *str_data_ptr, const string &tensor_type
   }
 }
 
-void convertDataItem2Scalar(const char *str_data_ptr, const string &tensor_type, std::ostringstream *const buf) {
+void convertDataItem2Scalar(const char *str_data_ptr, const aclDataType &acl_data_type, std::ostringstream *const buf) {
   MS_EXCEPTION_IF_NULL(str_data_ptr);
   MS_EXCEPTION_IF_NULL(buf);
-  auto type_iter = print_type_map.find(tensor_type);
+  auto type_iter = print_acl_data_type_map.find(acl_data_type);
   auto type_id = type_iter->second;
   if (type_id == TypeId::kNumberTypeBool) {
-    PrintScalarToBoolString(str_data_ptr, tensor_type, buf);
+    PrintScalarToBoolString(str_data_ptr, acl_data_type, buf);
   } else if (type_id == TypeId::kNumberTypeInt8) {
-    PrintScalarToString<int8_t>(str_data_ptr, tensor_type, buf);
+    PrintScalarToString<int8_t>(str_data_ptr, acl_data_type, buf);
   } else if (type_id == TypeId::kNumberTypeUInt8) {
-    PrintScalarToString<uint8_t>(str_data_ptr, tensor_type, buf);
+    PrintScalarToString<uint8_t>(str_data_ptr, acl_data_type, buf);
   } else if (type_id == TypeId::kNumberTypeInt16) {
-    PrintScalarToString<int16_t>(str_data_ptr, tensor_type, buf);
+    PrintScalarToString<int16_t>(str_data_ptr, acl_data_type, buf);
   } else if (type_id == TypeId::kNumberTypeUInt16) {
-    PrintScalarToString<uint16_t>(str_data_ptr, tensor_type, buf);
+    PrintScalarToString<uint16_t>(str_data_ptr, acl_data_type, buf);
   } else if (type_id == TypeId::kNumberTypeInt32) {
-    PrintScalarToString<int32_t>(str_data_ptr, tensor_type, buf);
+    PrintScalarToString<int32_t>(str_data_ptr, acl_data_type, buf);
   } else if (type_id == TypeId::kNumberTypeUInt32) {
-    PrintScalarToString<uint32_t>(str_data_ptr, tensor_type, buf);
+    PrintScalarToString<uint32_t>(str_data_ptr, acl_data_type, buf);
   } else if (type_id == TypeId::kNumberTypeInt64) {
-    PrintScalarToString<int64_t>(str_data_ptr, tensor_type, buf);
+    PrintScalarToString<int64_t>(str_data_ptr, acl_data_type, buf);
   } else if (type_id == TypeId::kNumberTypeUInt64) {
-    PrintScalarToString<uint64_t>(str_data_ptr, tensor_type, buf);
+    PrintScalarToString<uint64_t>(str_data_ptr, acl_data_type, buf);
   } else if (type_id == TypeId::kNumberTypeFloat16) {
-    PrintScalarToString<float16>(str_data_ptr, tensor_type, buf);
+    PrintScalarToString<float16>(str_data_ptr, acl_data_type, buf);
   } else if (type_id == TypeId::kNumberTypeFloat32) {
-    PrintScalarToString<float>(str_data_ptr, tensor_type, buf);
+    PrintScalarToString<float>(str_data_ptr, acl_data_type, buf);
   } else if (type_id == TypeId::kNumberTypeFloat64) {
-    PrintScalarToString<double>(str_data_ptr, tensor_type, buf);
+    PrintScalarToString<double>(str_data_ptr, acl_data_type, buf);
   } else {
-    MS_LOG(EXCEPTION) << "Cannot print scalar because of unsupported data type: " << tensor_type << ".";
+    MS_LOG(EXCEPTION) << "Cannot print scalar because of unsupported data type: " << GetParseType(acl_data_type) << ".";
   }
 }
 
-bool judgeLengthValid(const size_t str_len, const string &tensor_type) {
-  auto type_iter = type_size_map.find(tensor_type);
-  if (type_iter == type_size_map.end()) {
+bool judgeLengthValid(const size_t str_len, const aclDataType &acl_data_type) {
+  auto type_iter = acl_data_type_size_map.find(acl_data_type);
+  if (type_iter == acl_data_type_size_map.end()) {
     MS_LOG(EXCEPTION) << "type of scalar to print is not support.";
   }
   return str_len == type_iter->second;
 }
 
-#ifndef NO_DLIB
-bool ConvertDataItem2Tensor(const std::vector<tdt::DataItem> &items) {
+bool ConvertDataset2Tensor(acltdtDataset *acl_dataset) {
   //  Acquire Python GIL
   py::gil_scoped_acquire gil_acquire;
   std::ostringstream buf;
   bool ret_end_sequence = false;
-  for (auto &item : items) {
-    if (item.dataType_ == tdt::TDT_END_OF_SEQUENCE) {
+
+  size_t acl_dataset_size = acltdtGetDatasetSize(acl_dataset);
+
+  for (size_t i = 0; i < acl_dataset_size; i++) {
+    acltdtDataItem *item = acltdtGetDataItem(acl_dataset, i);
+    if (acltdtGetTensorTypeFromItem(item) == ACL_TENSOR_DATA_END_OF_SEQUENCE) {
       ret_end_sequence = true;
+      MS_LOG(INFO) << "end of sequence" << std::endl;
       break;
     }
-    std::shared_ptr<std::string> str_data_ptr = std::static_pointer_cast<std::string>(item.dataPtr_);
-    MS_EXCEPTION_IF_NULL(str_data_ptr);
-    if (item.tensorShape_ == kShapeScalar || item.tensorShape_ == kShapeNone) {
-      if (!judgeLengthValid(str_data_ptr->size(), item.tensorType_)) {
-        MS_LOG(EXCEPTION) << "Print op receive data length is invalid.";
-      }
-      convertDataItem2Scalar(str_data_ptr->data(), item.tensorType_, &buf);
-      continue;
+
+    size_t dim_num = acltdtGetDimNumFromItem(item);
+    void *acl_addr = acltdtGetDataAddrFromItem(item);
+    size_t acl_data_size = acltdtGetDataSizeFromItem(item);
+    aclDataType acl_data_type = acltdtGetDataTypeFromItem(item);
+    char *acl_data = reinterpret_cast<char *>(acl_addr);
+    acl_data = const_cast<char *>(reinterpret_cast<std::string *>(acl_data)->c_str());
+    MS_EXCEPTION_IF_NULL(acl_data);
+
+    ShapeVector tensorShape;
+    tensorShape.resize(dim_num);
+
+    if (acltdtGetDimsFromItem(item, tensorShape.data(), dim_num) != ACL_SUCCESS) {
+      MS_LOG(ERROR) << "ACL failed get dim-size from acl channel data";
     }
 
-    ShapeVector tensor_shape;
-    size_t totaldims = 1;
-    if (!ParseTensorShape(item.tensorShape_, &tensor_shape, &totaldims)) {
-      MS_LOG(ERROR) << "Tensor print can not parse tensor shape, receive info" << item.tensorShape_;
+    if ((tensorShape.size() == 1 && tensorShape[0] == 0) || tensorShape.size() == 0) {
+      if (!judgeLengthValid(acl_data_size, acl_data_type)) {
+        MS_LOG(EXCEPTION) << "Print op receive data length is invalid.";
+      }
+      convertDataItem2Scalar(acl_data, acl_data_type, &buf);
       continue;
     }
 
-    if (item.tensorType_ == "string") {
-      std::string data(reinterpret_cast<const char *>(str_data_ptr->c_str()), item.dataLen_);
+    if (acl_data_type == ACL_STRING) {
+      std::string data(reinterpret_cast<const char *>(acl_data), acl_data_size);
       buf << data << std::endl;
     } else {
-      auto type_iter = print_type_map.find(item.tensorType_);
-      if (type_iter == print_type_map.end()) {
-        MS_LOG(ERROR) << "type of tensor need to print is not support " << item.tensorType_;
+      auto type_iter = print_acl_data_type_map.find(acl_data_type);
+      if (type_iter == print_acl_data_type_map.end()) {
+        MS_LOG(ERROR) << "type of tensor need to print is not support " << GetParseType(acl_data_type);
         continue;
       }
       auto type_id = type_iter->second;
-      mindspore::tensor::Tensor print_tensor(type_id, tensor_shape);
-      auto memory_size = totaldims * type_size_map[item.tensorType_];
-      if (PrintTensorToString(str_data_ptr->data(), &print_tensor, memory_size)) {
+      mindspore::tensor::Tensor print_tensor(type_id, tensorShape);
+      if (PrintTensorToString(acl_data, &print_tensor, acl_data_size)) {
         buf << print_tensor.ToStringNoLimit() << std::endl;
       }
     }
@@ -216,44 +200,63 @@ bool ConvertDataItem2Tensor(const std::vector<tdt::DataItem> &items) {
   return ret_end_sequence;
 }
 
-bool SaveDataItem2File(const std::vector<tdt::DataItem> &items, const std::string &print_file_path, prntpb::Print print,
-                       std::fstream *output) {
+bool SaveDataset2File(acltdtDataset *acl_dataset, const std::string &print_file_path, prntpb::Print print,
+                      std::fstream *output) {
   bool ret_end_thread = false;
-  for (auto &item : items) {
-    if (item.dataType_ == tdt::TDT_END_OF_SEQUENCE) {
+
+  for (size_t i = 0; i < acltdtGetDatasetSize(acl_dataset); i++) {
+    acltdtDataItem *item = acltdtGetDataItem(acl_dataset, i);
+    MS_EXCEPTION_IF_NULL(item);
+    acltdtTensorType acl_tensor_type = acltdtGetTensorTypeFromItem(item);
+
+    if (acl_tensor_type == ACL_TENSOR_DATA_END_OF_SEQUENCE) {
+      MS_LOG(INFO) << "Acl channel received end-of-sequence for print op.";
       ret_end_thread = true;
       break;
+    } else if (acl_tensor_type == ACL_TENSOR_DATA_ABNORMAL) {
+      MS_LOG(INFO) << "Acl channel received abnormal for print op.";
+      return true;
+    } else if (acl_tensor_type == ACL_TENSOR_DATA_UNDEFINED) {
+      MS_LOG(INFO) << "Acl channel received undefined message type for print op.";
+      return false;
     }
+
     prntpb::Print_Value *value = print.add_value();
-    std::shared_ptr<std::string> str_data_ptr = std::static_pointer_cast<std::string>(item.dataPtr_);
-    MS_EXCEPTION_IF_NULL(str_data_ptr);
-    if (item.tensorShape_ == kShapeScalar || item.tensorShape_ == kShapeNone) {
-      if (!judgeLengthValid(str_data_ptr->size(), item.tensorType_)) {
+    size_t dim_num = acltdtGetDimNumFromItem(item);
+    void *acl_addr = acltdtGetDataAddrFromItem(item);
+    size_t acl_data_size = acltdtGetDataSizeFromItem(item);
+    aclDataType acl_data_type = acltdtGetDataTypeFromItem(item);
+    char *acl_data = reinterpret_cast<char *>(acl_addr);
+    MS_EXCEPTION_IF_NULL(acl_data);
+
+    ShapeVector tensorShape;
+    tensorShape.resize(dim_num);
+
+    if (acltdtGetDimsFromItem(item, tensorShape.data(), dim_num) != ACL_SUCCESS) {
+      MS_LOG(ERROR) << "ACL failed get dim-size from acl channel data";
+    }
+
+    if ((tensorShape.size() == 1 && tensorShape[0] == 0) || tensorShape.size() == 0) {
+      if (!judgeLengthValid(acl_data_size, acl_data_type)) {
         MS_LOG(ERROR) << "Print op receive data length is invalid.";
         ret_end_thread = true;
       }
     }
 
-    ShapeVector tensor_shape;
-    size_t totaldims = 1;
-    if (!ParseTensorShape(item.tensorShape_, &tensor_shape, &totaldims)) {
-      MS_LOG(ERROR) << "Tensor print can not parse tensor shape, receive info" << item.tensorShape_;
-      ret_end_thread = true;
-    }
-
-    if (item.tensorType_ == "string") {
-      std::string data(reinterpret_cast<const char *>(str_data_ptr->c_str()), item.dataLen_);
+    if (acl_data_type == ACL_STRING) {
+      std::string data(reinterpret_cast<const char *>(acl_data), acl_data_size);
       value->set_desc(data);
     } else {
-      auto parse_type = GetParseType(item.tensorType_);
+      auto parse_type = GetParseType(acl_data_type);
       prntpb::TensorProto *tensor = value->mutable_tensor();
-      if (!(item.tensorShape_ == kShapeScalar) && !(item.tensorShape_ == kShapeNone)) {
-        for (const auto &dim : tensor_shape) {
+      if (tensorShape.size() > 1 || (tensorShape.size() == 1 && tensorShape[0] != 1)) {
+        for (const auto &dim : tensorShape) {
           tensor->add_dims(static_cast<::google::protobuf::int64>(dim));
         }
       }
+
       tensor->set_tensor_type(parse_type);
-      std::string data(reinterpret_cast<const char *>(str_data_ptr->c_str()), item.dataLen_);
+      std::string data(reinterpret_cast<const char *>(acl_data), acl_data_size);
       tensor->set_tensor_content(data);
     }
 
@@ -274,29 +277,37 @@ void TensorPrint::operator()() {
   std::string print_file_path = ms_context->get_param<std::string>(MS_CTX_PRINT_FILE_PATH);
   if (print_file_path == "") {
     while (true) {
-      std::vector<tdt::DataItem> bundle;
-      if (tdt::TdtHostPopData("_npu_log", bundle) != 0) {
+      acltdtDataset *acl_dataset = acltdtCreateDataset();
+      if (acl_dataset == nullptr) {
+        MS_LOG(ERROR) << "Failed create acl dateaset.";
+      }
+      if (acltdtReceiveTensor(acl_handle_, acl_dataset, -1 /* no timeout */) != ACL_SUCCESS) {
+        MS_LOG(ERROR) << "Acltdt receive tensor failed";
         break;
       }
-      if (ConvertDataItem2Tensor(bundle)) {
+      if (ConvertDataset2Tensor(acl_dataset)) {
         break;
       }
     }
   } else {
     std::fstream output(print_file_path, std::ios::out | std::ios::trunc | std::ios::binary);
     while (true) {
-      std::vector<tdt::DataItem> bundle;
-      if (tdt::TdtHostPopData("_npu_log", bundle) != 0) {
+      acltdtDataset *acl_dataset = acltdtCreateDataset();
+      if (acl_dataset == nullptr) {
+        MS_LOG(ERROR) << "Failed create acl dateaset.";
+      }
+      if (acltdtReceiveTensor(acl_handle_, acl_dataset, -1 /* no timeout */) != ACL_SUCCESS) {
+        MS_LOG(ERROR) << "Acltdt receive tensor failed";
         break;
       }
-      if (SaveDataItem2File(bundle, print_file_path, print, &output)) {
+      if (SaveDataset2File(acl_dataset, print_file_path, print, &output)) {
         break;
       }
     }
     output.close();
     std::string path_string = print_file_path;
     if (chmod(common::SafeCStr(path_string), S_IRUSR) == -1) {
-      MS_LOG(ERROR) << "Modify file:" << print_file_path << " to r fail.";
+      MS_LOG(ERROR) << "Modify file:" << print_file_path << " to fail.";
       return;
     }
   }

mindspore/ccsrc/utils/tensorprint_utils.h

@@ -20,9 +20,10 @@
 #include <map>
 #include "ir/dtype/type.h"
 #ifndef NO_DLIB
+#include "acl/acl_tdt.h"
 #include "tdt/tsd_client.h"
-#include "tdt/tdt_host_interface.h"
 #include "tdt/data_common.h"
+#include "tdt/tdt_host_interface.h"
 #include "proto/print.pb.h"
 #include "utils/ms_context.h"
 #endif
@@ -32,7 +33,11 @@ class TensorPrint {
   TensorPrint() {}
   ~TensorPrint() = default;
 #ifndef NO_DLIB
+  explicit TensorPrint(acltdtChannelHandle *acl_handle) { acl_handle_ = acl_handle; }
   void operator()();
+
+ private:
+  acltdtChannelHandle *acl_handle_ = nullptr;
 #endif
 };
 }  // namespace mindspore

mindspore/core/utils/ms_context.cc

@@ -50,6 +50,7 @@ MsContext::MsContext(const std::string &policy, const std::string &target) {
   } else {
     set_param<uint32_t>(MS_CTX_DEVICE_ID, 0);
   }
+
   set_param<uint32_t>(MS_CTX_MAX_CALL_DEPTH, MAX_CALL_DEPTH_DEFAULT);
   set_param<std::string>(MS_CTX_DEVICE_TARGET, target);
   set_param<int>(MS_CTX_EXECUTION_MODE, kPynativeMode);
@@ -107,4 +108,22 @@ std::string MsContext::backend_policy() const {
   }
   return "unknown";
 }
+
+#ifdef ENABLE_TDTQUE
+acltdtChannelHandle *MsContext::get_acl_tdt_channel_handle() {
+  if (acl_handle == nullptr) {
+    std::string kReceivePrefix = "TF_RECEIVE_";
+    std::string channel_name = "_npu_log";
+    uint32_t device_id = get_param<uint32_t>(MS_CTX_DEVICE_ID);
+    acl_handle = acltdtCreateChannel(device_id, (kReceivePrefix + channel_name).c_str());
+    if (acl_handle == nullptr) {
+      MS_LOG(ERROR) << "Failed to create acltdt handle : " << channel_name;
+      return nullptr;
+    }
+    MS_LOG(INFO) << "Success to create acltdt handle: " << channel_name;
+    return acl_handle;
+  }
+  return acl_handle;
+}
+#endif
 }  // namespace mindspore

mindspore/core/utils/ms_context.h

@@ -24,7 +24,10 @@
 #include <string>
 #include <utility>
 #include "utils/log_adapter.h"
-
+#include "utils/ms_utils.h"
+#ifndef NO_DLIB
+#include "acl/acl_tdt.h"
+#endif
 namespace mindspore {
 enum MsBackendPolicy {
   kMsBackendGeOnly = 0,
@@ -129,11 +132,13 @@ class MsContext {
 
   std::string backend_policy() const;
   bool set_backend_policy(const std::string &policy);
-
+#ifdef ENABLE_TDTQUE
+  acltdtChannelHandle *get_acl_tdt_channel_handle();
+#endif
   static void device_seter(DeviceSeter device) { seter_ = device; }
   static void device_type_seter(DeviceTypeSeter device_type) { device_type_seter_ = device_type; }
 
-  std::thread tdt_print_;
+  std::thread acl_tdt_print;
 
   template <typename T>
   void set_param(MsCtxParam param, const T &value) {
@@ -168,6 +173,9 @@ class MsContext {
   std::string string_params_[MsCtxParam::NUM_STRING_PARAMS];
 
   MsBackendPolicy backend_policy_;
+#ifdef ENABLE_TDTQUE
+  acltdtChannelHandle *acl_handle = nullptr;
+#endif
 };
 
 // set method implementation for type bool/int/uint32_t/float/std::string

mindspore/dataset/engine/datasets.py

@@ -2698,10 +2698,11 @@ 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 create_dict_iterator(self, num_epochs=-1, output_numpy=False):
         raise RuntimeError("TransferDataset is not iterable.")

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))