!49858 export primitive instance name

Merge pull request !49858 from lianliguang/r2.0.0-beta

config/op_info.config

@@ -283,3 +283,4 @@
 {"op_name": "QuantDTypeCast", "inputs": [{"index": 0, "name": "x", "param_type": "required"},{"index": 1, "name": "scales", "param_type": "required"},{"index": 2, "name": "zps", "param_type": "required"},{"index": 3, "name": "mean_corrs", "param_type": "required"},{"index": 4, "name": "var_corr", "param_type": "required"}], "outputs": [{"index": 0, "name": "y", "param_type": "required"}], "attr": [{"name": "src_t", "type": "int"},{"name": "dst_t", "type": "int"},{"name": "axis", "type": "int"}], "fusion_type": "OPAQUE", "dtype_format": [[["int8", "DefaultFormat"], ["float32", "DefaultFormat"], ["int32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"]],[["int8", "DefaultFormat"], ["float32", "DefaultFormat"], ["int32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float16", "FRACTAL_NZ"]]],  "imply_type": "AiCPU"}
 {"op_name": "FSEDecode", "inputs": [{"index": 0, "name": "x", "param_type": "required"},{"index": 1, "name": "states_table", "param_type": "required"},{"index": 2, "name": "bit_count_table", "param_type": "required"},{"index": 3, "name": "symbol_table", "param_type": "required"},{"index": 4, "name": "centroids", "param_type": "required"},{"index": 5, "name": "input_shape", "param_type": "required"}], "outputs": [{"index": 0, "name": "y", "param_type": "required"}], "attr": [{"name": "dst_t", "type": "int"}, {"name": "curr_chunk", "type": "int"}, {"name": "curr_chunk_index", "type": "int"}, {"name": "curr_bit_count", "type": "int"}, {"name": "table_log", "type": "int"}], "fusion_type": "OPAQUE", "dtype_format": [[["int8", "DefaultFormat"], ["uint16", "DefaultFormat"], ["uint8", "DefaultFormat"], ["uint16", "DefaultFormat"], ["float32", "DefaultFormat"], ["int32", "DefaultFormat"],  ["float32", "DefaultFormat"]],[["int8", "DefaultFormat"], ["uint16", "DefaultFormat"], ["uint8", "DefaultFormat"], ["uint16", "DefaultFormat"], ["float32", "DefaultFormat"], ["int32", "DefaultFormat"], ["float16", "FRACTAL_NZ"]]],  "imply_type": "AiCPU"}
 {"op_name": "AssignAdd", "inputs": [{"index": 0, "name": "ref", "needCompile": false, "paramType": "required", "shape": "all"}, {"index": 1, "name": "value", "needCompile": false, "paramType": "required", "shape": "all"}], "outputs": [{"index": 0, "name": "ref", "need_compile": false, "paramType": "required", "shape": "all"}], "attr": [], "fusion_type": "OPAQUE", "dtype_format": [[["int8", "DefaultFormat"], ["int8", "DefaultFormat"], ["int8", "DefaultFormat"]], [["int8", "NC1HWC0"], ["int8", "NC1HWC0"], ["int8", "NC1HWC0"]], [["int8", "C1HWNCoC0"], ["int8", "C1HWNCoC0"], ["int8", "C1HWNCoC0"]], [["int8", "FRACTAL_Z"], ["int8", "FRACTAL_Z"], ["int8", "FRACTAL_Z"]], [["uint8", "DefaultFormat"], ["uint8", "DefaultFormat"], ["uint8", "DefaultFormat"]], [["uint8", "NC1HWC0"], ["uint8", "NC1HWC0"], ["uint8", "NC1HWC0"]], [["uint8", "C1HWNCoC0"], ["uint8", "C1HWNCoC0"], ["uint8", "C1HWNCoC0"]], [["uint8", "FRACTAL_Z"], ["uint8", "FRACTAL_Z"], ["uint8", "FRACTAL_Z"]], [["int32", "DefaultFormat"], ["int32", "DefaultFormat"], ["int32", "DefaultFormat"]], [["int32", "NC1HWC0"], ["int32", "NC1HWC0"], ["int32", "NC1HWC0"]], [["int32", "C1HWNCoC0"], ["int32", "C1HWNCoC0"], ["int32", "C1HWNCoC0"]], [["int32", "FRACTAL_Z"], ["int32", "FRACTAL_Z"], ["int32", "FRACTAL_Z"]], [["int64", "DefaultFormat"], ["int64", "DefaultFormat"], ["int64", "DefaultFormat"]], [["int64", "NC1HWC0"], ["int64", "NC1HWC0"], ["int64", "NC1HWC0"]], [["int64", "C1HWNCoC0"], ["int64", "C1HWNCoC0"], ["int64", "C1HWNCoC0"]], [["int64", "FRACTAL_Z"], ["int64", "FRACTAL_Z"], ["int64", "FRACTAL_Z"]], [["float16", "DefaultFormat"], ["float16", "DefaultFormat"], ["float16", "DefaultFormat"]], [["float16", "NC1HWC0"], ["float16", "NC1HWC0"], ["float16", "NC1HWC0"]], [["float16", "C1HWNCoC0"], ["float16", "C1HWNCoC0"], ["float16", "C1HWNCoC0"]], [["float16", "FRACTAL_Z"], ["float16", "FRACTAL_Z"], ["float16", "FRACTAL_Z"]], [["float32", "DefaultFormat"], ["float32", "DefaultFormat"], ["float32", "DefaultFormat"]], [["float32", "NC1HWC0"], ["float32", "NC1HWC0"], ["float32", "NC1HWC0"]], [["float32", "C1HWNCoC0"], ["float32", "C1HWNCoC0"], ["float32", "C1HWNCoC0"]], [["float32", "FRACTAL_Z"], ["float32", "FRACTAL_Z"], ["float32", "FRACTAL_Z"]]], "imply_type": "TBE", "async_flag": false, "binfile": "assign_add.so", "compute_cost": 10, "kernel": "assign_add", "partial_flag": true, "reshape_type": "", "dynamicRankSupport": false, "dynamicShapeSupport": true, "dynamicCompileStatic": true, "needCheckSupport": false, "dynamicFormat": false, "op_pattern": "", "real_input_index": [], "input_to_attr_index": [], "unknown_shape_formats": []}
+{'op_name': 'AtomicAddrClean', 'inputs': [], 'outputs': [], 'attr': [{'name': 'automic_add_mem_size', 'paramType': 'required', 'type': 'listInt64', 'value': 'all'}], 'fusion_type': 'ELEMWISE', 'dtype_format': [], 'imply_type': 'TBE', 'async_flag': False, 'binfile': 'atomic_addr_clean.so', 'compute_cost': 10, 'kernel': 'atomic_addr_clean', 'partial_flag': True, 'reshape_type': '', 'dynamicRankSupport': False, 'dynamicShapeSupport': False, 'dynamicCompileStatic': False, 'needCheckSupport': False, 'dynamicFormat': False, 'op_pattern': '', 'real_input_index': [], 'input_to_attr_index': [], 'unknown_shape_formats': []}

config/super_bar_config.json

@@ -415,7 +415,8 @@
     "TransData ": "support boll",
     "ScatterNdD ": "Accuracy issues",
     "Trace": "Hadn't adapted tbe implementation",
-    "AssignAdd": "Frac_nz in pangu not support"
+    "AssignAdd": "Frac_nz in pangu not support",
+    "AtomicAddrClean": "need to clean addr larger than 2G, int32 is not enough"
   },
   "SkipNodes": [
     "BroadcastTo",
@@ -444,7 +445,9 @@
     "ACos",
     "TransData",
     "ScatterNdD",
-    "AssignAdd"
+    "AssignAdd",
+    "Assign",
+    "AtomicAddrClean"
   ],
   "FallbackOps": {
     "DeformableOffsets": [

docs/api/api_python/mindrecord/mindspore.mindrecord.ImageNetToMR.rst

@@ -8,10 +8,12 @@
 
           .. code-block::
 
-              n02119789 0
-              n02100735 1
-              n02110185 2
-              n02096294 3
+              n01440764 0
+              n01443537 1
+              n01484850 2
+              n01491361 3
+              ...
+              n15075141 999
 
         - **image_dir** (str) - ImageNet数据集的目录路径，目录中包含类似n02119789、n02100735、n02110185和n02096294的子目录。
         - **destination** (str) - 转换生成的MindRecord文件路径，需提前创建目录并且目录下不能存在同名文件。

mindspore/ccsrc/frontend/parallel/graph_util/pipeline_split_utils.cc

@@ -104,6 +104,10 @@ void SetStridedSliceStrategy(const AnfNodePtr &node) {
     if (skip_redis && !full_batch && input_strategy.size() > 0) {
       input_strategy[0] = dev_num < shape_list[1][0][0] ? dev_num : shape_list[1][0][0];
       auto prim = GetCNodePrimitive(node);
+      if (prim->HasAttr("out_shard_size")) {
+        auto out_shard_size = GetValue<int64_t>(prim->GetAttr("out_shard_size"));
+        input_strategy[0] = out_shard_size;
+      }
       auto attrs = prim->attrs();
       attrs[parallel::SKIP_REDISTRIBUTION] = MakeValue<bool>(true);
       prim->SetAttrs(attrs);

mindspore/ccsrc/frontend/parallel/ops_info/gather_info.cc

@@ -354,6 +354,7 @@ Status GatherInfo::CheckStrategy(const StrategyPtr &strategy) {
 
   // parameter not split axis
   if (param_strategy.at(LongToSize(axis_)) == 1) {
+    SetAttribute(strategy);
     return SUCCESS;
   }
 

mindspore/ccsrc/frontend/parallel/ops_info/operator_info.cc

@@ -456,21 +456,11 @@ void AddCommOpMeanFlag(const CNodePtr &comm_node) {
   (void)prim->SetAttrs(attrs);
 }
 
-void AddCommOpMirrorFlag(const CNodePtr &comm_node, bool do_mirror) {
+void AddCNodePrimAttr(const CNodePtr &comm_node, const std::string &attr_name, const ValuePtr &attr_val) {
   MS_EXCEPTION_IF_NULL(comm_node);
   auto prim = GetValueNode<PrimitivePtr>(comm_node->input(0));
   auto attrs = prim->attrs();
-  MS_EXCEPTION_IF_NULL(ParallelContext::GetInstance());
-  attrs[DO_MIRROR] = MakeValue<bool>(do_mirror);
-  (void)prim->SetAttrs(attrs);
-}
-
-void AddCommOpAddAccuFlag(const CNodePtr &comm_node, bool add_accu) {
-  MS_EXCEPTION_IF_NULL(comm_node);
-  auto prim = GetValueNode<PrimitivePtr>(comm_node->input(0));
-  auto attrs = prim->attrs();
-  MS_EXCEPTION_IF_NULL(ParallelContext::GetInstance());
-  attrs[ADD_ACCU] = MakeValue<bool>(add_accu);
+  attrs[attr_name] = attr_val;
   (void)prim->SetAttrs(attrs);
 }
 

mindspore/ccsrc/frontend/parallel/ops_info/operator_info.h

@@ -351,9 +351,8 @@ Operator CreateAllGatherOp(const std::string &group);
 Operator CreateCastOp(TypePtr type);
 Operator CreateDivOp(float scale);
 Operator CreateMiniStepAllGatherOp(const std::string &group);
+void AddCNodePrimAttr(const CNodePtr &comm_node, const std::string &attr_name, const ValuePtr &attr_val);
 int32_t AddCommOpFusionType(const CNodePtr &comm_node, const AnfNodePtr &param_node);
-void AddCommOpMirrorFlag(const CNodePtr &comm_node, bool do_mirror);
-void AddCommOpAddAccuFlag(const CNodePtr &comm_node, bool add_accu);
 Operator CreateMicroStepAllGatherOp(const std::string &group);
 void AddCommOpMeanFlag(const CNodePtr &comm_node);
 void AddCommOpParamFlag(const CNodePtr &comm_node);

mindspore/ccsrc/frontend/parallel/parallel_optimizer/opt_param_mgr.cc

@@ -117,9 +117,9 @@ class OptParamMgrImpl : public OptParamMgr {
       return false;
     }
 
-    if (!ParameterRequireGrad(parameter)) {
-      // only trainable parameters need parallel optimizer
-      MS_LOG(INFO) << "Parallel optimizer: " << parameter->ToString() << " is not trainable parameter.";
+    auto param_ptr = parameter->cast<ParameterPtr>();
+    if ((!param_ptr) || (!param_ptr->has_default())) {
+      MS_LOG(INFO) << "Parallel optimizer: " << parameter->ToString() << " is not a parameter.";
       return false;
     }
 

mindspore/ccsrc/frontend/parallel/parameter_manager.cc

@@ -396,10 +396,13 @@ void SliceParameterObj(const ParameterPtr &parameter, const TensorLayoutPtr &ten
   // create python layout obj
   const auto &device_arrangement = tensor_layout->device_arrangement().array();
   const auto &tensor_map = tensor_layout->tensor_map().array();
-  const auto &slice_shape = tensor_layout->slice_shape().array();
+  auto slice_shape = tensor_layout->slice_shape().array();
   int64_t field_size = tensor_layout->get_field_size();
   bool uniform_split = tensor_layout->uniform_split();
   std::string opt_shard_group = tensor_layout->opt_shard_group();
+  if (!opt_shard_group.empty()) {
+    slice_shape = tensor_layout->opt_shard_slice_shape();
+  }
   py::tuple layout =
     py::make_tuple(device_arrangement, tensor_map, slice_shape, field_size, uniform_split, opt_shard_group);
 

mindspore/ccsrc/frontend/parallel/step_parallel.cc

@@ -1265,12 +1265,18 @@ static void InsertAllGatherOp(const FuncGraphPtr &root, const std::string &group
     op = CreateAllGatherOp(group);
   }
   CNodePtr cast_node = InsertAllGatherAfterCast(cnode);
-  std::string opt_shard_mirror_group;
+  bool is_with_mirror = false;
   auto param_ptr = node->cast<ParameterPtr>();
   MS_EXCEPTION_IF_NULL(param_ptr);
   if (param_ptr->user_data<TensorLayout>()) {
-    opt_shard_mirror_group = param_ptr->user_data<TensorLayout>()->opt_shard_mirror_group();
+    auto opt_shard_mirror_group = param_ptr->user_data<TensorLayout>()->opt_shard_mirror_group();
+    is_with_mirror = !opt_shard_mirror_group.empty();
+    if (!param_ptr->param_info()->parallel_optimizer()) {
+      auto mirror_group = mirror_group_list(param_ptr->user_data<TensorLayout>());
+      is_with_mirror = mirror_group.size() > 1;
     }
+  }
+
   if (!is_shared_param && cast_node) {
     allgather = ReplaceNode(op, cast_node, graph, PARALLEL_OPTIMIZER_ALLGATHER_NOT_COMPUTE, param_name, root);
     MS_LOG(INFO) << "Parallel optimizer is applied before Cast for " << param_name;
@@ -1294,16 +1300,16 @@ static void InsertAllGatherOp(const FuncGraphPtr &root, const std::string &group
   AddNodeFusionInfo(cnode, allgather, "reduce_scatter", fusion_id);
   // add gradients mean
   AddCommOpMeanFlag(allgather);
+  AddCNodePrimAttr(allgather, "with_mirror_operator", MakeValue<bool>(is_with_mirror));
   if (op_name == MICRO_STEP_ALL_GATHER) {
     // When grad_accumulation_shard is enabled, the ReduceScatter is inserted at each micro step
     // so no need to do backward for the micro_step_allgather
-    AddCommOpMirrorFlag(allgather, !grad_accumulation_shard);
+    AddCNodePrimAttr(allgather, DO_MIRROR, MakeValue<bool>(!grad_accumulation_shard));
   } else if (op_name == MINI_STEP_ALL_GATHER) {
     // We need to manually set the add_accu to be false if it's father node is MirrorMiniStep
     bool add_accu = root->has_flag(kAccumulation);
-    bool is_with_mirror = opt_shard_mirror_group.size() > 1;
-    AddCommOpAddAccuFlag(allgather, !add_accu && !is_with_mirror);
-    AddCommOpMirrorFlag(allgather, grad_accumulation_shard || !add_accu);
+    AddCNodePrimAttr(allgather, ADD_ACCU, MakeValue<bool>(!add_accu && !is_with_mirror));
+    AddCNodePrimAttr(allgather, DO_MIRROR, MakeValue<bool>(!grad_accumulation_shard || !add_accu));
   }
 }
 
@@ -1311,17 +1317,20 @@ static void ApplyParallelOptOnParam(const FuncGraphPtr &root, const AnfNodePtr &
                                     const std::string &opt_shard_group) {
   int32_t split_stage_num = ParallelContext::GetInstance()->pipeline_stage_split_num();
   auto enable_opt_shard = ParallelContext::GetInstance()->enable_parallel_optimizer();
-  if ((opt_shard_group.empty() && split_stage_num <= 1) || (!enable_opt_shard) || (!ParameterRequireGrad(parameter))) {
+  if ((opt_shard_group.empty() && split_stage_num <= 1) || (!enable_opt_shard)) {
     return;
   }
 
+  if (opt_shard_group.empty() && !ParameterRequireGrad(parameter)) {
+    return;
+  }
   // set all gather type
   MS_EXCEPTION_IF_NULL(parameter);
   int64_t grad_accumulation_step = ParallelContext::GetInstance()->grad_accumulation_step();
   std::string op_name;
   if (grad_accumulation_step > 1) {
     op_name = MINI_STEP_ALL_GATHER;
-  } else if (split_stage_num > 1) {
+  } else if (split_stage_num > 1 && ParameterRequireGrad(parameter)) {
     op_name = MICRO_STEP_ALL_GATHER;
   } else {
     op_name = ALL_GATHER;

mindspore/ccsrc/frontend/parallel/step_parallel_utils.cc

@@ -1515,6 +1515,17 @@ TensorLayout GetInputLayoutFromCNode(const std::pair<AnfNodePtr, int64_t> &node_
   return tensorlayout_in;
 }
 
+Shape mirror_group_list(const TensorLayoutPtr &layout) {
+  int64_t rank = g_device_manager->global_rank();
+  auto stage_dev_list = g_device_manager->GetDeviceListInThisStage();
+  DeviceMatrix dev_matrix(rank, stage_dev_list, layout->device_arrangement().array());
+  RankList group_devices;
+  if (dev_matrix.GetDevicesByTensorMap(layout->tensor_map().array(), &group_devices) != SUCCESS) {
+    MS_LOG(EXCEPTION) << "For layout:" << layout->ToString() << ", infer mirror failed";
+  }
+  return group_devices;
+}
+
 std::string GetSerialNumberString(size_t number) {
   std::string suffix = "th";
   if (number == kSizeOne) {

mindspore/ccsrc/frontend/parallel/step_parallel_utils.h

@@ -111,7 +111,7 @@ StrategyPtr GenerateStandAloneStrategy(const Shapes &inputs_shape);
 StrategyPtr GenerateBatchParallelStrategy(const OperatorInfoPtr operator_, const PrimitivePtr prim);
 bool IsInsertVirtualOutput(const FuncGraphPtr &root);
 TensorLayout GetInputLayoutFromCNode(const std::pair<AnfNodePtr, int64_t> &node_pair);
-
+Shape mirror_group_list(const TensorLayoutPtr &layout);
 // Transfer number to serial number string
 std::string GetSerialNumberString(size_t number);
 }  // namespace parallel

mindspore/ccsrc/minddata/mindrecord/common/shard_pybind.cc

@@ -129,8 +129,9 @@ void BindShardWriter(py::module *m) {
            return SUCCESS;
          })
     .def("write_raw_data",
-         [](ShardWriter &s, std::map<uint64_t, std::vector<py::handle>> &raw_data, vector<vector<uint8_t>> &blob_data,
+         [](ShardWriter &s, std::map<uint64_t, std::vector<py::handle>> &raw_data, vector<py::bytes> &blob_data,
             bool sign, bool parallel_writer) {
+           // convert the raw_data from dict to json
            std::map<uint64_t, std::vector<json>> raw_data_json;
            (void)std::transform(raw_data.begin(), raw_data.end(), std::inserter(raw_data_json, raw_data_json.end()),
                                 [](const std::pair<uint64_t, std::vector<py::handle>> &p) {
@@ -141,7 +142,54 @@ void BindShardWriter(py::module *m) {
                                     [](const py::handle &obj) { return nlohmann::detail::ToJsonImpl(obj); });
                                   return std::make_pair(p.first, std::move(json_raw_data));
                                 });
-           THROW_IF_ERROR(s.WriteRawData(raw_data_json, blob_data, sign, parallel_writer));
+
+           // parallel convert blob_data from vector<py::bytes> to vector<vector<uint8_t>>
+           int32_t parallel_convert = kParallelConvert;
+           if (parallel_convert > blob_data.size()) {
+             parallel_convert = blob_data.size();
+           }
+           parallel_convert = parallel_convert != 0 ? parallel_convert : 1;
+           std::vector<std::thread> thread_set(parallel_convert);
+           vector<vector<uint8_t>> vector_blob_data(blob_data.size());
+           uint32_t step = uint32_t(blob_data.size() / parallel_convert);
+           if (blob_data.size() % parallel_convert != 0) {
+             step = step + 1;
+           }
+           for (int x = 0; x < parallel_convert; ++x) {
+             uint32_t start = x * step;
+             uint32_t end = ((x + 1) * step) < blob_data.size() ? ((x + 1) * step) : blob_data.size();
+             thread_set[x] = std::thread([&vector_blob_data, &blob_data, start, end]() {
+               for (auto i = start; i < end; i++) {
+                 char *buffer = nullptr;
+                 ssize_t length = 0;
+                 if (PYBIND11_BYTES_AS_STRING_AND_SIZE(blob_data[i].ptr(), &buffer, &length)) {
+                   MS_LOG(ERROR) << "Unable to extract bytes contents!";
+                   return FAILED;
+                 }
+                 vector<uint8_t> blob_data_item(length);
+                 if (length < SECUREC_MEM_MAX_LEN) {
+                   int ret_code = memcpy_s(&blob_data_item[0], length, buffer, length);
+                   if (ret_code != EOK) {
+                     MS_LOG(ERROR) << "memcpy_s failed for py::bytes to vector<uint8_t>.";
+                     return FAILED;
+                   }
+                 } else {
+                   auto ret_code = std::memcpy(&blob_data_item[0], buffer, length);
+                   if (ret_code != &blob_data_item[0]) {
+                     MS_LOG(ERROR) << "memcpy failed for py::bytes to vector<uint8_t>.";
+                     return FAILED;
+                   }
+                 }
+                 vector_blob_data[i] = blob_data_item;
+               }
+             });
+           }
+
+           // wait for the threads join
+           for (int x = 0; x < parallel_convert; ++x) {
+             thread_set[x].join();
+           }
+           THROW_IF_ERROR(s.WriteRawData(raw_data_json, vector_blob_data, sign, parallel_writer));
            return SUCCESS;
          })
     .def("commit", [](ShardWriter &s) {

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

@@ -160,6 +160,9 @@ const std::unordered_map<std::string, std::string> kTypesMap = {
 /// \brief the max number of samples to enable lazy load
 const uint32_t LAZY_LOAD_THRESHOLD = 5000000;
 
+/// \brief parallel convert from vector<py::bytes> to vector<vector<uint8_t>>
+const uint32_t kParallelConvert = 4;
+
 /// \brief split a string using a character
 /// \param[in] field target string
 /// \param[in] separator a character for splitting

mindspore/ccsrc/pipeline/jit/action.cc

@@ -1500,7 +1500,8 @@ static std::vector<ActionItem> CommonPipeline() {
   auto parallel_mode = parallel_context->parallel_mode();
   const bool is_parallel_mode =
     parallel_mode == parallel::kSemiAutoParallel || parallel_mode == parallel::kAutoParallel;
-  if (!is_cluster_initialized && !is_parallel_mode && pipeline::GetJitLevel() != "O0") {
+  static const auto combine_like_graphs = (common::GetEnv("COMBINE_LIKE_GRAPHS") == "1");
+  if (!is_cluster_initialized && (!is_parallel_mode || combine_like_graphs) && pipeline::GetJitLevel() != "O0") {
     (void)actions.emplace_back(std::make_pair("combine_like_graphs", CombineLikeGraphs));
   }
 

mindspore/ccsrc/plugin/device/ascend/hal/device/kernel_adjust.cc

@@ -567,7 +567,7 @@ CNodePtr KernelAdjust::CreateStreamSwitchOp(const std::shared_ptr<session::Kerne
   ValuePtr cond = MakeValue(condition);
   common::AnfAlgo::SetNodeAttr(kAttrSwitchCondition, cond, stream_switch_app);
   // set attr:data_type
-  int data_type = static_cast<int>(RT_SWITCH_INT64);
+  int data_type = static_cast<int>(RT_SWITCH_INT32);
   ValuePtr dt = MakeValue(data_type);
   common::AnfAlgo::SetNodeAttr(kAttrDataType, dt, stream_switch_app);
   // set distinction label and graph id

mindspore/ccsrc/plugin/device/ascend/hal/device/kernel_build_ascend.cc

@@ -239,11 +239,11 @@ bool IfAtomicOpNeedFusion(const size_t clean_total_num, const CNodePtr &first_no
   return false;
 }
 
-std::vector<int32_t> GetClearSize(const CNodePtr &pre_node) {
+std::vector<int64_t> GetClearSize(const CNodePtr &pre_node) {
   MS_EXCEPTION_IF_NULL(pre_node);
   auto kernel_mod = AnfAlgo::GetKernelMod(pre_node);
   MS_EXCEPTION_IF_NULL(kernel_mod);
-  std::vector<int32_t> clean_size_list;
+  std::vector<int64_t> clean_size_list;
   constexpr size_t kAlignBytes = 32 - 1;
   // clean output
   if (common::AnfAlgo::HasNodeAttr(kAttrAtomicOutputIndexs, pre_node)) {
@@ -251,7 +251,7 @@ std::vector<int32_t> GetClearSize(const CNodePtr &pre_node) {
     auto output_men_size = kernel_mod->GetOutputSizeList();
     for (auto index : output_indexes) {
       auto clean_item =
-        SizeToInt((output_men_size.at(index) + kMemAlignSize + kAlignBytes) / kMemAlignSize * kMemAlignSize);
+        SizeToLong((output_men_size.at(index) + kMemAlignSize + kAlignBytes) / kMemAlignSize * kMemAlignSize);
       (void)clean_size_list.emplace_back(clean_item);
     }
   }
@@ -261,7 +261,7 @@ std::vector<int32_t> GetClearSize(const CNodePtr &pre_node) {
     auto workspace_men_sizes = kernel_mod->GetWorkspaceSizeList();
     for (const auto &index : workspace_indexes) {
       auto clean_item =
-        SizeToInt((workspace_men_sizes.at(index) + kMemAlignSize + kAlignBytes) / kMemAlignSize * kMemAlignSize);
+        SizeToLong((workspace_men_sizes.at(index) + kMemAlignSize + kAlignBytes) / kMemAlignSize * kMemAlignSize);
       (void)clean_size_list.emplace_back(clean_item);
     }
   }
@@ -303,7 +303,7 @@ CNodePtr NewAtomicOp(const CNodePtr &pre_node, const std::vector<AnfNodePtr> &fu
 }
 
 void InsertFusionAtomicOp(const CNodePtr &first_clear_node, const std::vector<AnfNodePtr> &fusion_clear_inputs,
-                          const std::vector<int32_t> &clean_size_list, CleanOpsMap *clean_ops) {
+                          const std::vector<int64_t> &clean_size_list, CleanOpsMap *clean_ops) {
   MS_EXCEPTION_IF_NULL(first_clear_node);
   MS_EXCEPTION_IF_NULL(clean_ops);
   auto clear_zero = NewAtomicOp(first_clear_node, fusion_clear_inputs);
@@ -355,7 +355,7 @@ void SpecialAkgOps(const std::string &op_name, const CNodePtr &node, CleanOpsMap
 
 void ProcessAtomicFusion(const std::vector<CNodePtr> &kernels, CleanOpsMap *clean_ops) {
   MS_EXCEPTION_IF_NULL(clean_ops);
-  std::vector<int32_t> clean_size_list;
+  std::vector<int64_t> clean_size_list;
   std::vector<AnfNodePtr> fusion_clear_inputs;
   CNodePtr first_node = nullptr;
   for (const auto &anf_node : kernels) {

mindspore/ccsrc/plugin/device/ascend/hal/device/tasksink/task_generator.cc

@@ -153,7 +153,7 @@ void TaskGenerator::LaunchAddrCleanKernel(const CNodePtr &anf_node_ptr, AddressP
       MS_LOG(DEBUG) << "AtomicAddClean clean workspace size:" << clean_workspace_indexs.size();
     }
   }
-  auto clear_mems = common::AnfAlgo::GetNodeAttr<std::vector<int32_t>>(anf_node_ptr, kAttrAtomicAddMemSize);
+  auto clear_mems = common::AnfAlgo::GetNodeAttr<std::vector<int64_t>>(anf_node_ptr, kAttrAtomicAddMemSize);
   if (kernel_inputs->size() != clear_mems.size()) {
     MS_LOG(EXCEPTION) << "AtomicAddClean kernel inputs size not equal clear memory size, kernel inputs size:"
                       << kernel_inputs->size() << ",clean mem size" << clear_mems.size();

mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_json/tbe_json_creator.cc

@@ -48,6 +48,7 @@ static std::unordered_map<std::string, ATTR_DTYPE> type_attr_dtype_map = {
   {kVTypeFloat, ATTR_DTYPE::ATTR_FLOAT32},
   {kVTypeListInt, ATTR_DTYPE::ATTR_LIST_INT32},
   {kVTypeListFloat, ATTR_DTYPE::ATTR_LIST_FLOAT32},
+  {kVTypeListInt64, ATTR_DTYPE::ATTR_LIST_INT64},
   {kVTypeListUInt64, ATTR_DTYPE::ATTR_LIST_UINT64},
   {kVTypeListListInt, ATTR_DTYPE::ATTR_LIST_LIST_INT64}};
 
@@ -181,6 +182,7 @@ bool ParseAttrValue(const std::string &type, const mindspore::ValuePtr &value, n
     case ATTR_DTYPE::ATTR_FLOAT32:
       return ParseAttrFloat(value, attr_obj);
     case ATTR_DTYPE::ATTR_LIST_INT32:
+    case ATTR_DTYPE::ATTR_LIST_INT64:
       return ParseAttrListInt(value, attr_obj);
     case ATTR_DTYPE::ATTR_LIST_FLOAT32:
       return ParseAttrListFloat(value, attr_obj);
@@ -232,7 +234,8 @@ bool ParseAttrDefaultValue(const std::string &type, const std::string &value, nl
     case ATTR_DTYPE::ATTR_FLOAT32:
       (*attr_obj)[kJValue] = std::stof(value);
       break;
-    case ATTR_DTYPE::ATTR_LIST_INT32: {
+    case ATTR_DTYPE::ATTR_LIST_INT32:
+    case ATTR_DTYPE::ATTR_LIST_INT64: {
       std::stringstream string_value(value);
       std::string list_elem;
       std::vector<int64_t> attrs_value;

mindspore/ccsrc/plugin/device/ascend/kernel/tbe/tbe_json/tbe_json_utils.h

@@ -60,6 +60,7 @@ constexpr auto kVTypeFloat32 = "float32";
 constexpr auto kVTypeListInt = "listInt";
 constexpr auto kVTypeInt32 = "Int32";
 constexpr auto kVTypeInt64 = "Int64";
+constexpr auto kVTypeListInt64 = "listInt64";
 constexpr auto kVTypeListUInt64 = "listUInt64";
 constexpr auto kVTypeListFloat = "listFloat";
 constexpr auto kVTypeListListInt = "listListInt";

mindspore/ccsrc/plugin/device/ascend/optimizer/ascend_comm_op_reuse.cc

@@ -40,7 +40,7 @@ std::string VecToString(const std::vector<T> &vec) {
   return res;
 }
 
-std::string GenCommOpKey(const CNodePtr &node) {
+std::string GenCommOpKey(const CNodePtr &node, const KernelGraphPtr &root_graph) {
   std::string op_key;
   MS_EXCEPTION_IF_NULL(node);
   auto comm_prim = GetCNodePrimitive(node);
@@ -68,6 +68,8 @@ std::string GenCommOpKey(const CNodePtr &node) {
   if (comm_prim->HasAttr(kAttrRecvRankIds)) {
     op_key += "_" + VecToString(GetValue<std::vector<int64_t>>(comm_prim->GetAttr(kAttrRecvRankIds)));
   }
+  // model identifier, aka. root_graph_id
+  op_key += "_" + std::to_string(root_graph->root_graph_id());
   MS_LOG(INFO) << node->DebugString() << " key " << op_key;
   return op_key;
 }
@@ -198,7 +200,7 @@ void AscendCommOpReuse::AnalyseCommOpReuse() {
     if (!IsReusable(comm_op)) {
       continue;
     }
-    reuse_map[GenCommOpKey(comm_op)].push_back(comm_op);
+    reuse_map[GenCommOpKey(comm_op, root_graph_)].push_back(comm_op);
   }
 
   for (const auto &[key, comm_op_set] : reuse_map) {
@@ -255,7 +257,7 @@ KernelGraphPtr AscendCommOpReuse::CreateCommSubGraph(const CNodePtr &comm_op) {
   MS_EXCEPTION_IF_NULL(new_comm_op);
   new_comm_op->set_abstract(comm_op->abstract());
 
-  std::string group_name = GenCommOpKey(comm_op);
+  std::string group_name = GenCommOpKey(comm_op, root_graph_);
   auto rank_list = common::AnfAlgo::GetNodeAttr<std::vector<unsigned int>>(comm_op, kAttrRankList);
   if (!CommManager::GetInstance().CreateGroupSync(group_name, rank_list)) {
     MS_LOG(EXCEPTION) << "Create new group " << group_name << " failed, rank list = " << VecToString(rank_list);

mindspore/ccsrc/transform/express_ir/mindir_exporter.cc

@@ -246,6 +246,8 @@ bool IrExportBuilder::BuildPrimitives() {
       prim = real_prim;
     }
 
+    prim_proto->set_instance_name(prim->instance_name());
+
     // Set primitive attributes
     for (const auto &attr : prim->attrs()) {
       MS_LOG(DEBUG) << "attr: " << attr.first << " " << attr.second->DumpText() << " " << attr.second->type_name();

mindspore/core/load_mindir/anf_model_parser.cc

@@ -2009,16 +2009,16 @@ bool MSANFModelParser::BuildPrimitiveNode(const mind_ir::PrimitiveProto &primiti
     if (prim_type.compare(0, strlen(kDoSignaturePrimitivePrefix), kDoSignaturePrimitivePrefix) == 0) {
       auto op_name = prim_type.substr(strlen(kDoSignaturePrimitivePrefix));
       prim = std::make_shared<prim::DoSignaturePrimitive>(op_name, std::make_shared<Primitive>(op_name));
-      MS_EXCEPTION_IF_NULL(prim);
-      prim->set_instance_name(op_name);
     } else {
       MS_LOG(DEBUG) << "Special node_type: " << prim_type;
       prim = std::make_shared<Primitive>(prim_type);
-      MS_EXCEPTION_IF_NULL(prim);
-      prim->set_instance_name(prim_type);
     }
   }
 
+  if (primitive_proto.has_instance_name()) {
+    prim->set_instance_name(primitive_proto.instance_name());
+  }
+
   // Set primitive attributes
   auto prim_to_add_attr = GetValueWithoutDoSignature(prim)->cast<PrimitivePtr>();
   MS_EXCEPTION_IF_NULL(prim_to_add_attr);

mindspore/core/proto/mind_ir.proto

@@ -222,4 +222,5 @@ message PrimitiveProto {
   optional string name = 1;
   optional string op_type = 2;
   repeated AttributeProto attribute = 3;
+  optional string instance_name = 4;
 }

mindspore/python/mindspore/common/parameter.py

@@ -710,7 +710,7 @@ class Parameter(Tensor_):
                 raise TypeError("The argument 'layout' should be tuple, but got {}.".format(type(layout)))
             if len(layout) < 6:
                 raise ValueError("The length of 'layout' must be larger than 5, but got {}.".format(len(layout)))
-            slice_index = int(_get_slice_index(layout[0], layout[1]))
+            slice_index = int(_get_slice_index(layout[0], layout[1], layout[5]))
             init_data_args += (slice_index, layout[2], layout[5])
         return init_data_args
 

mindspore/python/mindspore/common/tensor.py

@@ -20,7 +20,7 @@ import math
 import numbers
 import numpy as np
 
-from mindspore.communication.management import get_rank, get_group_size
+from mindspore.communication.management import get_group_size
 from mindspore.common._utils import is_shape_unknown, is_stub_tensor
 from mindspore.common.seed import get_seed
 from mindspore import context
@@ -2280,9 +2280,9 @@ class Tensor(Tensor_):
                 self._np_seed = np.random.get_state()[1][0]
                 self.need_set_seed = (slice_index is not None)
                 self._global_seed = global_seed
-                self._device_num = 1
+                self._seed_offset = 1
                 if self.need_set_seed:
-                    self._device_num = get_group_size()
+                    self._seed_offset = get_group_size() * 2
 
             def __enter__(self):
                 if self.need_set_seed:
@@ -2293,7 +2293,7 @@ class Tensor(Tensor_):
                     else:
                         np.random.seed(slice_index + Tensor.delta_seed)
                         self.init.seed = slice_index + Tensor.delta_seed
-                        Tensor.delta_seed += self._device_num
+                        Tensor.delta_seed += self._seed_offset
 
             def __exit__(self, ptype, value, trace):
                 if self.need_set_seed:
@@ -2302,10 +2302,6 @@ class Tensor(Tensor_):
 
         with seed_context(self.init):
             self.init(data)
-        if opt_shard_group:
-            rank = get_rank(opt_shard_group)
-            size = get_group_size(opt_shard_group)
-            data = np.split(data, size)[rank]
         self.init = None
 
         # At embedding cache scenes. When size of tensor is out of range, we store data to persistent storage

mindspore/python/mindspore/mindrecord/filewriter.py

@@ -17,8 +17,11 @@ This module is to write data into mindrecord.
 """
 import os
 import platform
+import queue
 import re
 import stat
+import time
+import multiprocessing as mp
 import numpy as np
 from mindspore import log as logger
 from .shardwriter import ShardWriter
@@ -26,7 +29,7 @@ from .shardreader import ShardReader
 from .shardheader import ShardHeader
 from .shardindexgenerator import ShardIndexGenerator
 from .shardutils import MIN_SHARD_COUNT, MAX_SHARD_COUNT, VALID_ATTRIBUTES, VALID_ARRAY_ATTRIBUTES, \
-    check_filename, VALUE_TYPE_MAP
+    check_filename, VALUE_TYPE_MAP, SUCCESS
 from .common.exceptions import ParamValueError, ParamTypeError, MRMInvalidSchemaError, MRMDefineIndexError
 
 __all__ = ['FileWriter']
@@ -103,6 +106,13 @@ class FileWriter:
         self._writer = ShardWriter()
         self._generator = None
 
+        # parallel write mode
+        self._parallel_writer = None
+        self._writers = None
+        self._queue = None
+        self._workers = None
+        self._index_workers = None
+
     @classmethod
     def open_for_append(cls, file_name):
         r"""
@@ -259,6 +269,11 @@ class FileWriter:
             MRMSetHeaderError: If failed to set header.
             MRMWriteDatasetError: If failed to write dataset.
         """
+        if self._parallel_writer is None:
+            self._parallel_writer = parallel_writer
+        if self._parallel_writer != parallel_writer:
+            raise RuntimeError("The parameter `parallel_writer` must be consistent during use.")
+        if not self._parallel_writer:
             if not self._writer.is_open:
                 self._writer.open(self._paths, self._overwrite)
             if not self._writer.get_shard_header():
@@ -267,8 +282,8 @@ class FileWriter:
                 raise ParamTypeError('raw_data', 'list')
             if self._flush and not self._append:
                 raise RuntimeError("Not allowed to call `write_raw_data` on flushed MindRecord files." \
-                               "When creating new Mindrecord files, please remove `commit` before `write_raw_data`." \
-                               "In other cases, when appending to existing MindRecord files, " \
+                                   "When creating new Mindrecord files, please remove `commit` before " \
+                                   "`write_raw_data`. In other cases, when appending to existing MindRecord files, " \
                                    "please call `open_for_append` first and then `write_raw_data`.")
             for each_raw in raw_data:
                 if not isinstance(each_raw, dict):
@@ -276,6 +291,46 @@ class FileWriter:
             self._verify_based_on_schema(raw_data)
             return self._writer.write_raw_data(raw_data, True, parallel_writer)
 
+        ## parallel write mode
+        # init the _writers and launch the workers
+        if self._writers is None:
+            self._writers = [None] * len(self._paths)  # writers used by worker
+            self._queue = mp.Queue(len(self._paths) * 2)  # queue for worker
+            self._workers = [None] * len(self._paths)  # worker process
+            for i, path in enumerate(self._paths):
+                self._writers[i] = ShardWriter()
+                self._writers[i].open([path], self._overwrite)
+                self._writers[i].set_shard_header(self._header)
+
+                # launch the workers for parallel write
+                self._queue._joincancelled = True  # pylint: disable=W0212
+                p = mp.Process(target=self._write_worker, args=(i, self._queue))
+                p.daemon = True
+                p.start()
+                logger.info("Start worker process(pid:{}) to parallel write.".format(p.pid))
+                self._workers[i] = p
+
+        # fill the self._queue
+        check_interval = 0.5  # 0.5s
+        start_time = time.time()
+        while True:
+            try:
+                self._queue.put(raw_data, block=False)
+            except queue.Full:
+                if time.time() - start_time > check_interval:
+                    start_time = time.time()
+                    logger.warning("Because there are too few MindRecord file shards, the efficiency of parallel " \
+                                   "writing is too low. You can stop the current task and add the parameter " \
+                                   "`shard_num` of `FileWriter` to upgrade the task.")
+
+                # check the status of worker process
+                for i in range(len(self._paths)):
+                    if not self._workers[i].is_alive():
+                        raise RuntimeError("Worker process(pid:{}) has stopped. Please check " \
+                                           "the above log".format(self._workers[i].pid))
+                continue
+            return SUCCESS
+
     def set_header_size(self, header_size):
         """
         Set the size of header which contains shard information, schema information, \
@@ -326,7 +381,7 @@ class FileWriter:
         """
         return self._writer.set_page_size(page_size)
 
-    def commit(self):
+    def commit(self):  # pylint: disable=W0212
         """
         Flush data in memory to disk and generate the corresponding database files.
 
@@ -343,13 +398,14 @@ class FileWriter:
             MRMGenerateIndexError: If failed to write to database.
             MRMCommitError: If failed to flush data to disk.
         """
+        if not self._parallel_writer:
             self._flush = True
             if not self._writer.is_open:
                 self._writer.open(self._paths, self._overwrite)
             # permit commit without data
             if not self._writer.get_shard_header():
                 self._writer.set_shard_header(self._header)
-        ret = self._writer.commit()
+            self._writer.commit()
             if self._index_generator:
                 if self._append:
                     self._generator = ShardIndexGenerator(self._file_name, self._append)
@@ -357,10 +413,20 @@ class FileWriter:
                     self._generator = ShardIndexGenerator(os.path.realpath(self._paths[0]), self._append)
                 self._generator.build()
                 self._generator.write_to_db()
+        else:
+            # maybe a empty mindrecord, so need check _writers
+            if self._writers is None:
+                self._writers = [None] * len(self._paths)
+                for i, path in enumerate(self._paths):
+                    self._writers[i] = ShardWriter()
+                    self._writers[i].open(path, self._overwrite)
+                    self._writers[i].set_shard_header(self._header)
 
+            self._parallel_commit()
+
+        # change the file mode to 600
         mindrecord_files = []
         index_files = []
-        # change the file mode to 600
         for item in self._paths:
             if os.path.exists(item):
                 os.chmod(item, stat.S_IRUSR | stat.S_IWUSR)
@@ -373,7 +439,62 @@ class FileWriter:
         logger.info("The list of mindrecord files created are: {}, and the list of index files are: {}".format(
             mindrecord_files, index_files))
 
-        return ret
+        return SUCCESS
+
+    def _index_worker(self, i):
+        """The worker do the index generator"""
+        generator = ShardIndexGenerator(os.path.realpath(self._paths[i]), False)
+        generator.build()
+        generator.write_to_db()
+
+    def _parallel_commit(self):
+        """Parallel commit"""
+        # send EOF to worker process
+        for _ in range(len(self._paths)):
+            while True:
+                try:
+                    self._queue.put("EOF", block=False)
+                except queue.Full:
+                    time.sleep(1)
+                    continue
+                break
+
+        # wait the worker processing
+        while True:
+            if not self._queue.empty():
+                logger.info("Waiting for worker process write done.")
+                time.sleep(1)
+                continue
+            break
+
+        del self._queue
+
+        # wait for worker process stop
+        for index in range(len(self._paths)):
+            while True:
+                logger.info("Waiting for the worker process(pid:{}) to process all the data.".format(
+                    self._workers[index].pid))
+                if self._workers[index].is_alive():
+                    time.sleep(1)
+                    continue
+                elif self._workers[index].exitcode != 0:
+                    raise RuntimeError("Worker process(pid:{}) has stopped abnormal. Please check " \
+                                       "the above log".format(self._workers[index].pid))
+                break
+
+        if self._index_generator:
+            # use parallel index workers to generator index
+            self._index_workers = [None] * len(self._paths)
+            for index in range(len(self._paths)):
+                p = mp.Process(target=self._index_worker, args=(index,))
+                p.daemon = True
+                p.start()
+                logger.info("Start worker process(pid:{}) to generate index.".format(p.pid))
+                self._index_workers[index] = p
+
+            # wait the index workers stop
+            for index in range(len(self._paths)):
+                self._index_workers[index].join()
 
     def _validate_array(self, k, v):
         """
@@ -487,3 +608,29 @@ class FileWriter:
                 error = "Field '{}' should be dict.".format(k)
                 return False, error
         return True, error
+
+    def _write_worker(self, i, in_queue):
+        """The worker do the data check and write to disk for parallel mode"""
+        while True:
+            # try to get new raw_data from master
+            try:
+                raw_data = in_queue.get()
+            except queue.Empty:
+                continue
+
+            # get EOF from master, worker should commit and stop
+            if raw_data == "EOF":
+                ret = self._writers[i].commit()
+                if ret != SUCCESS:
+                    raise RuntimeError("Commit the {}th shard of MindRecord file failed.".format(index))
+                break
+
+            # check the raw_data
+            if not isinstance(raw_data, list):
+                raise ParamTypeError('raw_data', 'list')
+            for each_raw in raw_data:
+                if not isinstance(each_raw, dict):
+                    raise ParamTypeError('raw_data item', 'dict')
+
+            self._verify_based_on_schema(raw_data)
+            self._writers[i].write_raw_data(raw_data, True, False)

mindspore/python/mindspore/mindrecord/shardwriter.py

@@ -173,7 +173,7 @@ class ShardWriter:
         for item in data:
             row_blob = self._merge_blob({field: item[field] for field in self._header.blob_fields})
             if row_blob:
-                blob_data.append(list(row_blob))
+                blob_data.append(row_blob)
             # filter raw data according to schema
             row_raw = {field: self.convert_np_types(item[field])
                        for field in self._header.schema.keys() - self._header.blob_fields if field in item}

mindspore/python/mindspore/mindrecord/tools/imagenet_to_mr.py

@@ -38,10 +38,12 @@ class ImageNetToMR:
 
             .. code-block::
 
-              n02119789 0
-              n02100735 1
-              n02110185 2
-              n02096294 3
+              n01440764 0
+              n01443537 1
+              n01484850 2
+              n01491361 3
+              ...
+              n15075141 999
 
         image_dir (str): Image directory contains n02119789, n02100735, n02110185 and n02096294 directory.
         destination (str): MindRecord file path to transform into, ensure that the directory is created in advance and
@@ -108,11 +110,11 @@ class ImageNetToMR:
                 for _ in range(batch_size):
                     data_list.append(imagenet_iter.__next__())
                     transform_count += 1
-                self.writer.write_raw_data(data_list)
+                self.writer.write_raw_data(data_list, True)
                 logger.info("transformed {} record...".format(transform_count))
             except StopIteration:
                 if data_list:
-                    self.writer.write_raw_data(data_list)
+                    self.writer.write_raw_data(data_list, True)
                     logger.info("transformed {} record...".format(transform_count))
                 break
 

mindspore/python/mindspore/mindrecord/tools/tfrecord_to_mr.py

@@ -307,11 +307,11 @@ class TFRecordToMR:
                     data_list.append(tf_iter.__next__())
                     transform_count += 1
 
-                writer.write_raw_data(data_list)
+                writer.write_raw_data(data_list, True)
                 logger.info("Transformed {} records...".format(transform_count))
             except StopIteration:
                 if data_list:
-                    writer.write_raw_data(data_list)
+                    writer.write_raw_data(data_list, True)
                     logger.info("Transformed {} records...".format(transform_count))
                 break
         return writer.commit()

mindspore/python/mindspore/ops/_grad/grad_comm_ops.py

@@ -198,7 +198,6 @@ def get_bprop_mirror_micro_step_operator(self):
         assign.add_prim_attr("parameter_micro", 0)
     out_tensor = Tensor(1.0, mstype.float16)
     opt_shard = _get_enable_parallel_optimizer()
-
     def bprop(x, z, out, dout):
         real_grad = z
         assign_out = dout
@@ -207,16 +206,16 @@ def get_bprop_mirror_micro_step_operator(self):
                 z = F.depend(z, dout)
                 real_grad = all_reduce(z)
                 real_grad = F.tensor_mul(real_grad, scale)
-                assign(z, real_grad)
-                assign_out = z
+                if opt_shard:
+                    return (real_grad, cast(out_tensor, dtype(z)))
+                return F.depend((cast(out_tensor, dtype(x)), cast(out_tensor, dtype(z))), assign(z, real_grad))
         else:
             if issubclass_(F.typeof(dout), mstype.tensor):
                 z = F.depend(z, dout)
                 real_grad = all_reduce(z)
-                assign(z, real_grad)
-                assign_out = z
                 if opt_shard:
                     return (real_grad, cast(out_tensor, dtype(z)))
+                return F.depend((cast(out_tensor, dtype(x)), cast(out_tensor, dtype(z))), assign(z, real_grad))
         return F.depend((cast(out_tensor, dtype(x)), cast(out_tensor, dtype(z))), assign_out)
     return bprop
 
@@ -314,9 +313,18 @@ def get_bprop_micro_step_all_gather(self):
     cast = P.Cast()
     dtype = P.DType()
     out_tensor = Tensor(1.0, mstype.float16)
+    with_mirror_operator = self.get_attr_dict()["with_mirror_operator"]
 
     # z: accu_grad
     def bprop(x, z, out, dout):
+        if with_mirror_operator:
+            if not do_mirror:
+                return (dout, cast(out_tensor, dtype(z)))
+            real_grad = all_reduce(dout)
+            real_grad = split(real_grad)[rank]
+            if mean_flag:
+                real_grad = F.tensor_mul(real_grad, scale)
+            return (real_grad, cast(out_tensor, dtype(z)))
         z = F.depend(z, dout)
         if not do_mirror:
             return (z, cast(out_tensor, dtype(z)))

mindspore/python/mindspore/ops/_op_impl/tbe/__init__.py

@@ -35,3 +35,5 @@ from .acos import _acos_tbe # Accuracy issues(task error in parallel)
 from .trans_data_ds import _trans_data_ds_tbe # support bool
 from .scatter_nd_d import _scatter_nd_d_tbe # in python no check supported
 from .assign_add_ds import _assign_add_ds_tbe # "Frac_nz in pangu not support"
+from .assign import _assign_tbe # Different formats of assign inputs cause memory to increase
+from .atomic_addr_clean import _atomic_addr_clean_tbe # need to clean addr larger than 2G, int32 is not enough

mindspore/python/mindspore/ops/_op_impl/tbe/atomic_addr_clean.py

@@ -23,7 +23,7 @@ atomic_addr_clean_op_info = TBERegOp("AtomicAddrClean") \
     .compute_cost(10) \
     .kernel_name("atomic_addr_clean") \
     .partial_flag(True) \
-    .attr("automic_add_mem_size", "required", "listInt", "all") \
+    .attr("automic_add_mem_size", "required", "listInt64", "all") \
     .get_op_info()
 
 

mindspore/python/mindspore/ops/operations/comm_ops.py

@@ -1186,6 +1186,7 @@ class _MirrorMicroStepOperator(PrimitiveWithInfer):
         self.dev_num = dev_num
         self.mean_flag = mean_flag
         self.add_prim_attr('order_enforce_skip', True)
+        self.add_prim_attr('side_effect_backprop_mem', True)
 
     def infer_shape(self, x_shape, z_shape):
         return x_shape

mindspore/python/mindspore/parallel/_tensor.py

@@ -175,20 +175,26 @@ def _chunk_tensor_by_strategy(np_tensor, strategy):
     return _chunk_tensor(np_tensor, strategy, len(strategy))
 
 
-def _get_slice_index(dev_mat, tensor_map):
+def _get_slice_index(dev_mat, tensor_map, opt_shard_group):
     """
     Get the slice index for current slice.
 
     Args:
         dev_mat (list): The device matrix of devices.
         tensor_map (list): The split strategy of tensor.
+        opt_shard_group(string): The group of optimizer shard
 
     Returns:
         Integer, the slice index for slice on this device.
     """
     rank = get_rank()
+    dev_num = get_group_size()
     tensor_strategy = _get_tensor_strategy(dev_mat, tensor_map)
     tensor_slice_index = _get_tensor_slice_index(dev_mat, tensor_strategy, tensor_map, rank)
+    if opt_shard_group:
+        tensor_slice_index += dev_num
+        opt_rank = get_rank(opt_shard_group)
+        tensor_slice_index += opt_rank
     return tensor_slice_index
 
 

tests/st/ops/ascend/test_tbe_ops/test_deformable_offsets_grad.py

@@ -1,97 +0,0 @@
-# Copyright 2022 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 numpy as np
-import pytest
-
-import mindspore.context as context
-import mindspore.nn as nn
-from mindspore import Tensor
-from mindspore.ops.composite import GradOperation
-from mindspore.ops import operations as P
-from mindspore.ops.operations import nn_ops as NN
-
-
-class Net(nn.Cell):
-    def __init__(self, out_channel, kernel_size, pad, stride, dilation):
-        super(Net, self).__init__()
-        self.net = NN.DeformableOffsets(ksize=(kernel_size, kernel_size),
-                                        pads=(pad, pad, pad, pad),
-                                        strides=(stride, stride, stride, stride),
-                                        dilations=(dilation, dilation, dilation, dilation),
-                                        deformable_groups=1,
-                                        modulated=True,
-                                        data_format="NCHW")
-        self.conv = P.Conv2D(out_channel,
-                             kernel_size,
-                             mode=1,
-                             pad_mode="pad",
-                             pad=pad,
-                             stride=kernel_size,
-                             dilation=1,
-                             group=1,
-                             data_format="NCHW")
-
-    def construct(self, x, w, offset):
-        x = self.net(x, offset)
-        return self.conv(x, w)
-
-
-class Grad(nn.Cell):
-    def __init__(self, network):
-        super(Grad, self).__init__()
-        self.grad = GradOperation(get_all=True, sens_param=True)
-        self.network = network
-
-    def construct(self, x, w, offset, output_grad):
-        return self.grad(self.network)(x, w, offset, output_grad)
-
-
-@pytest.mark.level0
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.env_onecard
-def test_deformable_conv2d_grad():
-    """"
-    Feature: deformable_conv2d_grad function
-    Description: Test case for simplest deformable_conv2d_grad
-    Expectation: The results are as expected
-    """
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=True)
-    kernel_size = 2
-    stride = 1
-    pad = 0
-    dilation = 1
-    # x shape [1, 64, 2, 2]
-    x = Tensor(np.ones([1, 64, 2, 2]).astype(np.float32) * 0.1)
-    # weight shape [1, 64, 2, 2]
-    weight = Tensor(np.ones([1, 64, 2, 2]).astype(np.float32) * 0.1)
-    # offsets shape [1, 12, 1, 1]
-    offsets = Tensor(np.ones([1, 12, 1, 1]).astype(np.float32) * 0.1)
-    # out_channel, kernel_size, pad, stride, dilation
-    dfm_conv2d_net = Net(1, kernel_size, pad, stride, dilation)
-    out = dfm_conv2d_net(x, weight, offsets)
-    grad_net = Grad(dfm_conv2d_net)
-    grad_output = grad_net(x, weight, offsets, out)
-    expect_out = np.array([[[[0.2310471]]]]).astype(np.float32)
-    expect_grad_x = np.array([[[[0.00187125, 0.00207916], [0.00207916, 0.00231018]]] * 64]).astype(np.float32)
-    expect_grad_weight = np.array([[[[0.00231128, 0.00208033], [0.00208033, 0.0018723]]] * 64]).astype((np.float32))
-    expect_grad_offset = np.array([[[0]], [[-0.01478]], [[0]], [[-0.01331]],
-                                   [[0]], [[0]], [[-0.01478]], [[-0.01331]],
-                                   [[0.14785]], [[0.13307]], [[0.13307]], [[0.11976]]]).astype((np.float32))
-    assert np.allclose(out.asnumpy(), expect_out, 0.0001, 0.0001)
-    assert np.allclose(grad_output[0].asnumpy(), expect_grad_x, 0.0001, 0.0001)
-    assert np.allclose(grad_output[1].asnumpy(), expect_grad_weight, 0.0001, 0.0001)
-    assert np.allclose(grad_output[2].asnumpy(), expect_grad_offset, 0.0001, 0.0001)

tests/st/ops/cpu/test_deformable_offsets_grad.py

@@ -1,217 +0,0 @@
-# Copyright 2022 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 pytest
-import numpy as np
-
-from mindspore.ops.operations import _grad_ops as G
-from mindspore.ops import composite as C
-from mindspore import nn
-from mindspore import Tensor
-from mindspore import dtype
-from mindspore.ops.operations import nn_ops
-from mindspore.ops import functional as F
-
-grad_all = C.GradOperation(get_all=True)
-
-
-class TestNetwork(nn.Cell):
-    def __init__(self):
-        super(TestNetwork, self).__init__()
-        stride = (1, 1, 1, 1)
-        pad = (0, 0, 0, 0)
-        ksize = (2, 2)
-        self.deformable_offsets_grad_op = G.DeformableOffsetsGrad(stride, pad, ksize)
-
-    def construct(self, dout, x, offsets):
-        output = self.deformable_offsets_grad_op(dout, x, offsets)
-        return output
-
-
-def test_grad_infer():
-    """
-    Feature: CPU operation.
-    Description: Test of CPU operation: DeformableOffsetsGrad
-    Expectation: No exception raised.
-    """
-    dout = Tensor(np.ones([1, 1, 2, 2]), dtype.float32)
-    x = Tensor(np.ones([1, 1, 2, 2]), dtype.float32)
-    offsets = Tensor(np.array([0.1] * 12).astype(np.float32).reshape([1, 12, 1, 1]))
-
-    net = TestNetwork()
-    grad = net(dout, x, offsets)
-    print("grad_x:", grad[0])
-    print("grad_offset:", grad[1])
-    return grad
-
-
-class ForwardNet(nn.Cell):
-    def __init__(self):
-        super(ForwardNet, self).__init__()
-        stride = (1, 1, 1, 1)
-        pad = (0, 0, 0, 0)
-        ksize = (2, 2)
-        self.deformable_offsets_grad_op = nn_ops.DeformableOffsets(stride, pad, ksize)
-
-    def construct(self, x, offsets):
-        output = self.deformable_offsets_grad_op(x, offsets)
-        return output
-
-
-class BackwardNet(nn.Cell):
-    def __init__(self, net):
-        super(BackwardNet, self).__init__()
-        self.net = net
-
-    def construct(self, *inputs):
-        out = self.net(*inputs)
-        return out, grad_all(self.net)(*inputs)
-
-
-def test_auto_diff():
-    """
-    Feature: CPU operation.
-    Description: Test of CPU operation: DeformableOffsetsGrad by auto diff.
-    Expectation: No exception raised.
-    """
-    x = Tensor(np.ones([1, 1, 2, 2]), dtype.float32)
-    offsets = Tensor(np.array([0.1] * 12).astype(np.float32).reshape([1, 12, 1, 1]))
-    forward_net = ForwardNet()
-    net = BackwardNet(forward_net)
-    grad = net(x, offsets)
-    print("grad_x:", grad[0])
-    print("grad_offset:", grad[1])
-    return grad
-
-
-class NetDeformableOffsetsGrad(nn.Cell):
-    def __init__(self, data_format):
-        super(NetDeformableOffsetsGrad, self).__init__()
-        strides = (1, 1, 1, 1)
-        pads = (0, 0, 0, 0)
-        ksize = (3, 3)
-        self.grad_op = G.DeformableOffsetsGrad(strides, pads, ksize, data_format=data_format)
-
-    def construct(self, grad, input_x, offsets):
-        return self.grad_op(grad, input_x, offsets)
-
-
-@pytest.mark.level0
-@pytest.mark.platform_x86_cpu
-@pytest.mark.env_onecard
-@pytest.mark.parametrize('data_type', [np.float16, np.float32])
-def test_deformable_offsets_grad_nchw(data_type):
-    """
-    Feature: DeformableOffsetsGrad cpu kernel
-    Description: test the rightness of DeformableOffsetsGrad gpu kernel
-    Expectation: the output is same as expected result
-    """
-    net = NetDeformableOffsetsGrad(data_format="NCHW")
-    dout = Tensor(np.ones([1, 2, 3, 3]).astype(data_type))
-    x = Tensor(np.ones([1, 2, 4, 4]).astype(data_type))
-    offsets = Tensor(np.ones([1, 27, 1, 1]).astype(data_type) * 0.1)
-    output = net(dout, x, offsets)
-
-    expect_grad_x = np.array([[[0.081, 0.09, 0.09, 0.009],
-                               [0.09, 0.1, 0.1, 0.01],
-                               [0.09, 0.1, 0.1, 0.01],
-                               [0.009, 0.01, 0.01, 0.001]],
-                              [[0.081, 0.09, 0.09, 0.009],
-                               [0.09, 0.1, 0.1, 0.01],
-                               [0.09, 0.1, 0.1, 0.01],
-                               [0.009, 0.01, 0.01, 0.001]]]
-                             ).astype(data_type)
-    expect_grad_offset = np.array([0] * 18 + [2.0] * 9).astype(data_type).reshape([1, 27, 1, 1])
-    rtol = 1e-5
-    if data_type == np.float16:
-        rtol = 1e-3
-    assert np.allclose(output[0].asnumpy(), expect_grad_x, rtol)
-    assert np.allclose(output[1].asnumpy(), expect_grad_offset, rtol)
-
-
-@pytest.mark.level0
-@pytest.mark.platform_x86_cpu
-@pytest.mark.env_onecard
-@pytest.mark.parametrize('data_type', [np.float16, np.float32])
-def test_deformable_offsets_grad_nhwc(data_type):
-    """
-    Feature: DeformableOffsetsGrad cpu kernel
-    Description: test the rightness of DeformableOffsetsGrad gpu kernel
-    Expectation: the output is same as expected result
-    """
-    net = NetDeformableOffsetsGrad(data_format="NHWC")
-    dout = Tensor(np.ones([1, 3, 3, 2]).astype(data_type))
-    x = Tensor(np.ones([1, 4, 4, 2]).astype(data_type))
-    offsets = Tensor(np.ones([1, 1, 1, 27]).astype(data_type) * 0.1)
-    output = net(dout, x, offsets)
-    expect_grad_x = np.array([[[0.081, 0.081],
-                               [0.09, 0.09],
-                               [0.09, 0.09],
-                               [0.009, 0.009]],
-                              [[0.09, 0.09],
-                               [0.1, 0.1],
-                               [0.1, 0.1],
-                               [0.01, 0.01]],
-                              [[0.09, 0.09],
-                               [0.1, 0.1],
-                               [0.1, 0.1],
-                               [0.01, 0.01]],
-                              [[0.009, 0.009],
-                               [0.01, 0.01],
-                               [0.01, 0.01],
-                               [0.001, 0.001]]
-                              ]
-                             ).astype(data_type)
-    expect_grad_offset = np.array([0] * 18 + [2.0] * 9).astype(data_type).reshape([1, 1, 1, 27])
-    rtol = 1e-5
-    if data_type == np.float16:
-        rtol = 1e-3
-    assert np.allclose(output[0].asnumpy(), expect_grad_x, rtol)
-    assert np.allclose(output[1].asnumpy(), expect_grad_offset, rtol)
-
-
-@pytest.mark.level0
-@pytest.mark.platform_x86_cpu
-@pytest.mark.env_onecard
-def test_vmap():
-    """"
-    Feature: Feature: DeformableOffsetsGrad cpu kernel
-    Description: Test case with vmap.
-    Expectation: The results are as expected.
-    """
-
-    def cal_deformable_offsets_grad(dout, x, offsets):
-        net = NetDeformableOffsetsGrad(data_format="NCHW")
-        return net(dout, x, offsets)
-
-    dout = Tensor(np.arange(2 * 1 * 2 * 3 * 3).reshape(2, 1, 2, 3, 3), dtype.float32)
-    x = Tensor(np.arange(2 * 1 * 2 * 4 * 4).reshape(2, 1, 2, 4, 4), dtype.float32)
-    offsets = Tensor(np.arange(2 * 1 * 27 * 1 * 1).reshape(2, 1, 27, 1, 1) * 0.1, dtype.float32)
-
-    vmap_deformable_offset_grad = F.vmap(cal_deformable_offsets_grad, in_axes=(0, 0, 0), out_axes=0)
-    out1 = vmap_deformable_offset_grad(dout, x, offsets)
-
-    def manually_batched(dout, x, offsets):
-        output_dx = []
-        output_d_offsets = []
-        for i in range(x.shape[0]):
-            dx, d_offsets = cal_deformable_offsets_grad(dout[i], x[i], offsets[i])
-            output_dx.append(dx)
-            output_d_offsets.append(d_offsets)
-
-        return F.stack(output_dx), F.stack(output_d_offsets)
-
-    out2 = manually_batched(dout, x, offsets)
-    assert np.allclose(out1[0].asnumpy(), out2[0].asnumpy())
-    assert np.allclose(out1[1].asnumpy(), out2[1].asnumpy())

tests/st/ops/graph_kernel/custom/test_ms_kernel.py

@@ -1,295 +0,0 @@
-# Copyright 2022 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 platform
-import pytest
-import numpy as np
-from mindspore import context, Tensor
-from mindspore.nn import Cell
-import mindspore.ops as ops
-from mindspore.ops import kernel
-
-
-@kernel
-def dtype_and_cast_example(a, b):
-    """
-    test function for dtype and cast in Hybrid DSL
-    """
-    d = allocate(a.shape, "float16")
-    c = output_tensor(a.shape, "float16")
-
-    for i0 in range(a.shape[0]):
-        for i1 in range(a.shape[1]):
-            d[i0, i1] = float16(1.0)
-            c[i0, i1] = d[i0, i1] + float16(a[i0, i1])
-            c[i0, i1] = c[i0, i1] * float16(b[i0, i1])
-    return c
-
-
-@kernel
-def allocate_and_math_intrin_example(a, b):
-    """
-    test function for allocate and math function in Hybrid DSL
-    """
-    d = allocate(a.shape, a.dtype)
-    c = output_tensor(a.shape, a.dtype)
-
-    for i0 in range(a.shape[0]):
-        for i1 in range(b.shape[1]):
-            d[i0, i1] = abs(a[i0, i1])
-            c[i0, i1] = d[i0, i1] + b[i0, i1]
-    return c
-
-
-@kernel
-def grid_example(a, b):
-    """
-    test function for grid in Hybrid DSL
-    """
-    c = output_tensor(a.shape, a.dtype)
-
-    for arg in grid(a.shape):
-        c[arg] = a[arg] + b[arg[0], arg[1]]
-    return c
-
-
-class TestMsHybridDSL(Cell):
-    """Net definition"""
-
-    def __init__(self, func, func_type, out_shape=None, out_dtype=None):
-        super(TestMsHybridDSL, self).__init__()
-
-        self.program = ops.Custom(func, out_shape=out_shape, out_dtype=out_dtype, func_type=func_type)
-
-    def construct(self, x, y):
-        return self.program(x, y)
-
-
-def ms_kernel_cast_with_infer():
-    """
-    test case Custom Op with functions written in Hybrid DSL and infer functions
-    """
-    np.random.seed(10)
-    input_x = np.random.normal(0, 1, [4, 4]).astype(np.float16)
-    input_y = np.random.normal(0, 1, [4, 4]).astype(np.float16)
-
-    test = TestMsHybridDSL(dtype_and_cast_example, "hybrid", lambda x, _: x, lambda x, _: x)
-    output = test(Tensor(input_x), Tensor(input_y))
-    expect = dtype_and_cast_example(input_x, input_y)
-    compare_res = np.allclose(expect, output.asnumpy(), 0.001, 0.001)
-    if not compare_res:
-        raise ValueError("Precision error, compare result: {}".format(compare_res))
-
-
-def ms_kernel_cast_without_infer():
-    """
-    test case Custom Op with functions written in Hybrid DSL and without infer functions
-    """
-    np.random.seed(10)
-    input_x = np.random.normal(0, 1, [4, 4]).astype(np.float16)
-    input_y = np.random.normal(0, 1, [4, 4]).astype(np.float16)
-
-    test = TestMsHybridDSL(dtype_and_cast_example, "hybrid")
-    output = test(Tensor(input_x), Tensor(input_y))
-    expect = dtype_and_cast_example(input_x, input_y)
-    compare_res = np.allclose(expect, output.asnumpy(), 0.001, 0.001)
-    if not compare_res:
-        raise ValueError("Precision error, compare result: {}".format(compare_res))
-
-
-def ms_kernel_cast_pyfunc():
-    """
-    test case Custom Op with functions written in Hybrid DSL and func_type pyfunc
-    """
-    np.random.seed(10)
-    input_x = np.random.normal(0, 1, [4, 4]).astype(np.float16)
-    input_y = np.random.normal(0, 1, [4, 4]).astype(np.float16)
-
-    test = TestMsHybridDSL(dtype_and_cast_example, "pyfunc", lambda x, _: x, lambda x, _: x)
-    output = test(Tensor(input_x), Tensor(input_y))
-    expect = dtype_and_cast_example(input_x, input_y)
-    compare_res = np.allclose(expect, output.asnumpy(), 0.001, 0.001)
-    if not compare_res:
-        raise ValueError("Precision error, compare result: {}".format(compare_res))
-
-
-def ms_kernel_allocate():
-    """
-    test case Custom Op with functions written in Hybrid DSL about math functions and allocate
-    """
-    np.random.seed(10)
-    input_x = np.random.normal(0, 1, [4, 4]).astype(np.float16)
-    input_y = np.random.normal(0, 1, [4, 4]).astype(np.float16)
-
-    test = TestMsHybridDSL(allocate_and_math_intrin_example, "hybrid", lambda x, _: x, lambda x, _: x)
-    output = test(Tensor(input_x), Tensor(input_y))
-    expect = allocate_and_math_intrin_example(input_x, input_y)
-    compare_res = np.allclose(expect, output.asnumpy(), 0.001, 0.001)
-    if not compare_res:
-        raise ValueError("Precision error, compare result: {}".format(compare_res))
-
-
-def ms_kernel_allocate_cpu():
-    """
-    test case Custom Op with functions written in Hybrid DSL about math functions and allocate
-    for cpu, we test fp32 to avoid env diff in support of data types.
-    """
-    np.random.seed(10)
-    input_x = np.ones((4, 4)).astype(np.float32)
-    input_y = np.ones((4, 4)).astype(np.float32)
-
-    test = TestMsHybridDSL(allocate_and_math_intrin_example, "hybrid", lambda x, _: x, lambda x, _: x)
-    output = test(Tensor(input_x), Tensor(input_y))
-    expect = allocate_and_math_intrin_example(input_x, input_y)
-    compare_res = np.allclose(expect, output.asnumpy(), 0.001, 0.001)
-    if not compare_res:
-        raise ValueError("Precision error, compare result: {}".format(compare_res))
-
-
-def ms_kernel_grid():
-    """
-    test case Custom Op with functions written in Hybrid DSL about grid
-    """
-    np.random.seed(10)
-    input_x = np.random.normal(0, 1, [4, 4]).astype(np.float16)
-    input_y = np.random.normal(0, 1, [4, 4]).astype(np.float16)
-
-    test = TestMsHybridDSL(grid_example, "hybrid", lambda x, _: x, lambda x, _: x)
-    output = test(Tensor(input_x), Tensor(input_y))
-    expect = grid_example(input_x, input_y)
-    compare_res = np.allclose(expect, output.asnumpy(), 0.001, 0.001)
-    if not compare_res:
-        raise ValueError("Precision error, compare result: {}".format(compare_res))
-
-
-def ms_kernel_grid_cpu():
-    """
-    test case Custom Op with functions written in Hybrid DSL about grid
-    """
-    np.random.seed(10)
-    input_x = np.random.normal(0, 1, [4, 4]).astype(np.float32)
-    input_y = np.random.normal(0, 1, [4, 4]).astype(np.float32)
-
-    test = TestMsHybridDSL(grid_example, "hybrid", lambda x, _: x, lambda x, _: x)
-    output = test(Tensor(input_x), Tensor(input_y))
-    expect = grid_example(input_x, input_y)
-    compare_res = np.allclose(expect, output.asnumpy(), 0.001, 0.001)
-    if not compare_res:
-        raise ValueError("Precision error, compare result: {}".format(compare_res))
-
-
-@pytest.mark.level0
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_onecard
-def test_ms_kernel_ascend_graph_mode():
-    """
-    Feature: test case for Custom op with func_type="kernel"
-    Description: ascend test case, Python DSL with kernel decorator in GRAPH_MODE.
-    Expectation: the result match with numpy result
-    """
-    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-    ms_kernel_cast_pyfunc()
-    ms_kernel_cast_with_infer()
-    ms_kernel_cast_without_infer()
-    ms_kernel_allocate()
-    ms_kernel_grid()
-
-
-@pytest.mark.level0
-@pytest.mark.platform_arm_ascend_training
-@pytest.mark.platform_x86_ascend_training
-@pytest.mark.env_onecard
-def test_ms_kernel_ascend_pynative_mode():
-    """
-    Feature: test case for Custom op with func_type="kernel"
-    Description: ascend test case, Python DSL with kernel decorator in PYNATIVE_MODE.
-    Expectation: the result match with numpy result
-    """
-    context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
-    ms_kernel_cast_pyfunc()
-    ms_kernel_cast_with_infer()
-    ms_kernel_cast_without_infer()
-    ms_kernel_allocate()
-    ms_kernel_grid()
-
-
-@pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
-@pytest.mark.env_onecard
-def test_ms_kernel_gpu_graph_mode():
-    """
-    Feature: test case for Custom op with func_type="kernel"
-    Description: gpu test case, Python DSL with kernel decorator in GRAPH_MODE.
-    Expectation: the result match with numpy result
-    """
-    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
-    ms_kernel_cast_pyfunc()
-    ms_kernel_cast_with_infer()
-    ms_kernel_cast_without_infer()
-    ms_kernel_allocate()
-    ms_kernel_grid()
-
-
-@pytest.mark.level0
-@pytest.mark.platform_x86_gpu_training
-@pytest.mark.env_onecard
-def test_ms_kernel_gpu_pynative_mode():
-    """
-    Feature: test case for Custom op with func_type="kernel"
-    Description: gpu test case, Python DSL with kernel decorator in PYNATIVE_MODE.
-    Expectation: the result match with numpy result
-    """
-    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
-    ms_kernel_cast_pyfunc()
-    ms_kernel_cast_with_infer()
-    ms_kernel_cast_without_infer()
-    ms_kernel_allocate()
-    ms_kernel_grid()
-
-
-@pytest.mark.level0
-@pytest.mark.platform_x86_cpu
-@pytest.mark.env_onecard
-def test_ms_kernel_cpu_graph_mode():
-    """
-    Feature: test case for Custom op with func_type="kernel"
-    Description: cpu test case, Python DSL with kernel decorator in GRAPH_MODE.
-    Expectation: the result match with numpy result
-    """
-    if platform.system().lower() in {"windows", "darwin"}:
-        # skip window and mac, same for pynative below
-        pass
-    else:
-        context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
-        ms_kernel_allocate_cpu()
-        ms_kernel_grid_cpu()
-
-
-@pytest.mark.level0
-@pytest.mark.platform_x86_cpu
-@pytest.mark.env_onecard
-def test_ms_kernel_cpu_pynative_mode():
-    """
-    Feature: test case for Custom op with func_type="kernel"
-    Description: cpu test case, Python DSL with kernel decorator in PYNATIVE_MODE.
-    Expectation: the result match with numpy result
-    """
-    if platform.system().lower() in {"windows", "darwin"}:
-        pass
-    else:
-        context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
-        ms_kernel_allocate_cpu()
-        ms_kernel_grid_cpu()

tests/ut/python/mindrecord/test_imagenet_to_mindrecord.py

@@ -67,7 +67,10 @@ def test_imagenet_to_mindrecord(fixture_file):
     for i in range(PARTITION_NUMBER):
         assert os.path.exists(file_name + str(i))
         assert os.path.exists(file_name + str(i) + ".db")
-    read(file_name + "0")
+    read([file_name + "0",
+          file_name + "1",
+          file_name + "2",
+          file_name + "3"])
 
 def test_imagenet_to_mindrecord_default_partition_number(fixture_file):
     """
@@ -76,7 +79,7 @@ def test_imagenet_to_mindrecord_default_partition_number(fixture_file):
     """
     file_name = os.environ.get('PYTEST_CURRENT_TEST').split(':')[-1].split(' ')[0]
     imagenet_transformer = ImageNetToMR(IMAGENET_MAP_FILE, IMAGENET_IMAGE_DIR,
-                                        file_name)
+                                        file_name, 1)
     imagenet_transformer.transform()
     assert os.path.exists(file_name)
     assert os.path.exists(file_name + ".db")

tests/ut/python/mindrecord/test_mindrecord_base.py

@@ -1270,3 +1270,106 @@ def test_cv_file_overwrite_exception_02():
         writer.write_raw_data(data)
     assert 'Invalid file, mindrecord files already exist. Please check file path:' in str(err.value)
     remove_multi_files(mindrecord_file_name, FILES_NUM)
+
+
+def test_file_writer_parallel(file_name=None, remove_file=True):
+    """
+    Feature: FileWriter
+    Description: parallel for writer
+    Expectation: generated mindrecord file
+    """
+    if not file_name:
+        file_name = os.environ.get('PYTEST_CURRENT_TEST').split(':')[-1].split(' ')[0]
+
+    # single file
+    remove_one_file(file_name)
+    remove_one_file(file_name + ".db")
+    writer = FileWriter(file_name)
+    data = get_data("../data/mindrecord/testImageNetData/")
+    cv_schema_json = {"file_name": {"type": "string"},
+                      "label": {"type": "int64"}, "data": {"type": "bytes"}}
+    writer.add_schema(cv_schema_json, "img_schema")
+    writer.add_index(["file_name", "label"])
+    for _ in range(5):
+        writer.write_raw_data(data, True)
+    writer.commit()
+    if remove_file:
+        remove_one_file(file_name)
+        remove_one_file(file_name + ".db")
+
+    # write_raw_data with empty
+    remove_one_file(file_name)
+    remove_one_file(file_name + ".db")
+    writer = FileWriter(file_name)
+    data = get_data("../data/mindrecord/testImageNetData/")
+    cv_schema_json = {"file_name": {"type": "string"},
+                      "label": {"type": "int64"}, "data": {"type": "bytes"}}
+    writer.add_schema(cv_schema_json, "img_schema")
+    writer.add_index(["file_name", "label"])
+    with pytest.raises(RuntimeError):
+        writer.write_raw_data([])
+
+    # multi files
+    # len(data) > FILES_NUM which is parallel size
+    remove_multi_files(file_name, FILES_NUM)
+    writer = FileWriter(file_name, FILES_NUM)
+    data = get_data("../data/mindrecord/testImageNetData/")
+    cv_schema_json = {"file_name": {"type": "string"},
+                      "label": {"type": "int64"}, "data": {"type": "bytes"}}
+    writer.add_schema(cv_schema_json, "img_schema")
+    writer.add_index(["file_name", "label"])
+    for _ in range(10):
+        writer.write_raw_data(data, True)
+    writer.commit()
+    if remove_file:
+        remove_multi_files(file_name, FILES_NUM)
+
+    # len(data) < FILES_NUM which is parallel size
+    remove_multi_files(file_name, FILES_NUM)
+    writer = FileWriter(file_name, FILES_NUM)
+    data = get_data("../data/mindrecord/testImageNetData/")
+    cv_schema_json = {"file_name": {"type": "string"},
+                      "label": {"type": "int64"}, "data": {"type": "bytes"}}
+    writer.add_schema(cv_schema_json, "img_schema")
+    writer.add_index(["file_name", "label"])
+    for _ in range(2):
+        writer.write_raw_data(data[0:2], True)
+    writer.commit()
+    if remove_file:
+        remove_multi_files(file_name, FILES_NUM)
+
+    # write_raw_data(.., True) and write_raw_data(.., False)
+    remove_multi_files(file_name, FILES_NUM)
+    writer = FileWriter(file_name, FILES_NUM)
+    data = get_data("../data/mindrecord/testImageNetData/")
+    cv_schema_json = {"file_name": {"type": "string"},
+                      "label": {"type": "int64"}, "data": {"type": "bytes"}}
+    writer.add_schema(cv_schema_json, "img_schema")
+    writer.add_index(["file_name", "label"])
+    with pytest.raises(RuntimeError):
+        writer.write_raw_data(data[0:2], True)
+        writer.write_raw_data(data[0:2])
+
+    # without write_raw_data
+    remove_multi_files(file_name, FILES_NUM)
+    writer = FileWriter(file_name, FILES_NUM)
+    data = get_data("../data/mindrecord/testImageNetData/")
+    cv_schema_json = {"file_name": {"type": "string"},
+                      "label": {"type": "int64"}, "data": {"type": "bytes"}}
+    writer.add_schema(cv_schema_json, "img_schema")
+    writer.add_index(["file_name", "label"])
+    writer.commit()
+    if remove_file:
+        remove_multi_files(file_name, FILES_NUM)
+
+    # write_raw_data with empty
+    remove_multi_files(file_name, FILES_NUM)
+    writer = FileWriter(file_name, FILES_NUM)
+    data = get_data("../data/mindrecord/testImageNetData/")
+    cv_schema_json = {"file_name": {"type": "string"},
+                      "label": {"type": "int64"}, "data": {"type": "bytes"}}
+    writer.add_schema(cv_schema_json, "img_schema")
+    writer.add_index(["file_name", "label"])
+    with pytest.raises(RuntimeError):
+        writer.write_raw_data([], True)
+        writer.commit()

tests/ut/python/parallel/test_parallel_optimizer_without_grad.py

@@ -0,0 +1,133 @@
+# Copyright 2023 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 numpy as np
+
+import mindspore as ms
+import mindspore.nn as nn
+from mindspore import Tensor, Parameter
+from mindspore import context, Model
+from mindspore.common.api import _cell_graph_executor
+from mindspore.ops import composite as C
+from mindspore.ops import operations as P
+from mindspore.nn.wrap.cell_wrapper import PipelineCell
+from tests.ut.python.ops.test_math_ops import VirtualLoss
+from parallel.utils.utils import ParallelValidator
+from .test_pipeline_split import DatasetLenet
+
+
+def setup_function():
+    context.set_auto_parallel_context(dataset_strategy="full_batch")
+
+
+grad_all = C.GradOperation(get_all=True)
+
+
+class NetWithLoss(nn.Cell):
+    def __init__(self, network):
+        super(NetWithLoss, self).__init__()
+        self.loss = VirtualLoss()
+        self.network = network
+
+    def construct(self, x, y):
+        predict = self.network(x, y)
+        return self.loss(predict)
+
+
+class GradWrap(nn.Cell):
+    def __init__(self, network):
+        super(GradWrap, self).__init__()
+        self.network = network
+
+    def construct(self, x, y):
+        return grad_all(self.network)(x, y)
+
+
+def test_opt_parallel_without_grad():
+    """
+    Feature: Test optimizer parallel with parameter's requires_grad=False.
+    Description: Need insert AllGather.
+    Expectation: Successful graph compilation.
+    """
+    class Net(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.fc1 = P.MatMul().shard(((4, 1), (1, 2)))
+            self.fc2 = P.MatMul().shard(((2, 2), (2, 1)))
+            self.p1 = Parameter(Tensor(np.ones([1024, 1024]).astype(np.float32)), name="weight1", requires_grad=False)
+            self.p2 = Parameter(Tensor(np.ones([1024, 64]).astype(np.float32)), name="weight2")
+
+        def construct(self, x, y):
+            x = self.fc1(x, self.p1)
+            x = self.fc2(x, self.p2)
+            return x - y
+    context.reset_auto_parallel_context()
+    context.set_auto_parallel_context(device_num=8, global_rank=0, enable_parallel_optimizer=True)
+    net = GradWrap(NetWithLoss(Net()))
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
+
+    x = Tensor(np.ones([128, 1024]), dtype=ms.float32)
+    y = Tensor(np.ones([128, 64]), dtype=ms.float32)
+    net.set_train()
+    phase, _ = _cell_graph_executor.compile(net, x, y)
+    validator = ParallelValidator(net, phase)
+    expect_layout = ([4, 2], [-1, 0], [1024, 512], 0, True, '4-5226697808808137312')
+    assert validator.check_parameter_layout("network.network.p1", expect_layout)
+
+
+def test_opt_parallel_without_grad_pipeline():
+    """
+    Feature: Test optimizer parallel + pipeline with parameter's requires_grad=False.
+    Description: Need insert AllGather.
+    Expectation: Successful graph compilation.
+    """
+    class MatMulNet(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.fc1 = P.MatMul().shard(((4, 1), (1, 2)))
+            self.fc2 = P.MatMul().shard(((2, 2), (2, 1)))
+            self.p1 = Parameter(Tensor(np.ones([1024, 1024]).astype(np.float32)), name="weight1", requires_grad=False)
+            self.p2 = Parameter(Tensor(np.ones([1024, 1024]).astype(np.float32)), name="weight2")
+
+        def construct(self, x):
+            x = self.fc1(x, self.p1)
+            x = self.fc2(x, self.p2)
+            return x
+
+    class Net(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.block = nn.CellList()
+            for i in range(2):
+                cell = MatMulNet()
+                cell.pipeline_stage = i
+                self.block.append(cell)
+
+        def construct(self, x, y):
+            for i in range(2):
+                x = self.block[i](x)
+            return x
+    context.reset_auto_parallel_context()
+    context.set_auto_parallel_context(device_num=16, global_rank=0, enable_parallel_optimizer=True)
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", pipeline_stages=2)
+    net = PipelineCell(Net(), 4)
+    x = Tensor(np.ones([128, 1024]), dtype=ms.float32)
+    y = Tensor(np.ones([128, 128]), dtype=ms.float32)
+    dataset = DatasetLenet(x, y, 3)
+    optimizer = nn.Lamb(net.trainable_params(), learning_rate=0.01)
+    model = Model(net, optimizer=optimizer)
+    model.train(2, dataset, dataset_sink_mode=False)
+    assert net.network.block[0].p1.shape == (256, 512)