add Histogram summary operator

clean clang format errors and cpplint errors

add some test cases for histogram summary operator

mindspore/ccsrc/ir/anf.cc

@@ -103,7 +103,8 @@ std::string CNode::fullname_with_scope() {
     return fullname_with_scope_;
   }
 
-  if (IsApply(prim::kPrimScalarSummary) || IsApply(prim::kPrimTensorSummary) || IsApply(prim::kPrimImageSummary)) {
+  if (IsApply(prim::kPrimScalarSummary) || IsApply(prim::kPrimTensorSummary) || IsApply(prim::kPrimImageSummary) ||
+      IsApply(prim::kPrimHistogramSummary)) {
     std::string tag = GetValue<std::string>(GetValueNode(input(1)));
     if (tag == "") {
       MS_LOG(EXCEPTION) << "The tag name is null, should be valid string";
@@ -111,10 +112,12 @@ std::string CNode::fullname_with_scope() {
     std::string name;
     if (IsApply(prim::kPrimScalarSummary)) {
       name = tag + "[:Scalar]";
-    } else if (IsApply(prim::kPrimTensorSummary)) {
-      name = tag + "[:Tensor]";
-    } else {
+    } else if (IsApply(prim::kPrimImageSummary)) {
       name = tag + "[:Image]";
+    } else if (IsApply(prim::kPrimHistogramSummary)) {
+      name = tag + "[:Histogram]";
+    } else {
+      name = tag + "[:Tensor]";
     }
     fullname_with_scope_ = name;
   } else {

mindspore/ccsrc/operator/ops.cc

@@ -235,6 +235,7 @@ const PrimitivePtr kPrimVirtualDataset = std::make_shared<Primitive>("_VirtualDa
 const PrimitivePtr kPrimScalarSummary = std::make_shared<Primitive>("ScalarSummary");
 const PrimitivePtr kPrimImageSummary = std::make_shared<Primitive>("ImageSummary");
 const PrimitivePtr kPrimTensorSummary = std::make_shared<Primitive>("TensorSummary");
+const PrimitivePtr kPrimHistogramSummary = std::make_shared<Primitive>("HistogramSummary");
 
 ValuePtr GetPythonOps(const std::string& op_name, const std::string& module_name) {
   py::object obj = parse::python_adapter::GetPyFn(module_name, op_name);

mindspore/ccsrc/operator/ops.h

@@ -225,6 +225,7 @@ extern const PrimitivePtr kPrimStateSetItem;
 extern const PrimitivePtr kPrimScalarSummary;
 extern const PrimitivePtr kPrimImageSummary;
 extern const PrimitivePtr kPrimTensorSummary;
+extern const PrimitivePtr kPrimHistogramSummary;
 extern const PrimitivePtr kPrimBroadcastGradientArgs;
 extern const PrimitivePtr kPrimControlDepend;
 extern const PrimitivePtr kPrimIs_;

mindspore/ccsrc/operator/prim_debug.cc

@@ -69,7 +69,7 @@ AbstractBasePtr InferImplTensorSummary(const AnalysisEnginePtr &, const Primitiv
 
   int tensor_rank = SizeToInt(tensor_value->shape()->shape().size());
   if (tensor_rank == 0) {
-    MS_LOG(EXCEPTION) << "Tensor/Image Summary evaluator second arg should be an tensor, but got a scalar";
+    MS_LOG(EXCEPTION) << op_name << " summary evaluator second arg should be an tensor, but got a scalar, rank is 0";
   }
 
   // Reomve the force check to support batch set summary use 'for' loop

mindspore/ccsrc/optimizer/irpass/branch_culling.cc

@@ -51,25 +51,14 @@ bool InConvertWhiteList(const AnfNodePtr &node, size_t index) {
   // node because it is attribute or ge specific reason.
   // Example : when convert CNode(kPrimReduceSum, x, axis), node of index 2 in CNode->inputs is axis which should not be
   // converted to switch guarded.
-  std::vector<std::pair<PrimitivePtr, std::vector<size_t>>> white_list({{prim::kPrimApplyMomentum, {1, 2}},
-                                                                        {prim::kPrimMomentum, {2, 3}},
-                                                                        {prim::kPrimStateSetItem, {1}},
-                                                                        {prim::kPrimEnvGetItem, {1}},
-                                                                        {prim::kPrimEnvSetItem, {1}},
-                                                                        {prim::kPrimReduceSum, {2}},
-                                                                        {prim::kPrimReduceMean, {2}},
-                                                                        {prim::kPrimReduceAll, {2}},
-                                                                        {prim::kPrimCast, {2}},
-                                                                        {prim::kPrimTranspose, {2}},
-                                                                        {prim::kPrimOneHot, {2}},
-                                                                        {prim::kPrimGatherV2, {3}},
-                                                                        {prim::kPrimReshape, {2}},
-                                                                        {prim::kPrimAssign, {1}},
-                                                                        {prim::kPrimAssignAdd, {1}},
-                                                                        {prim::kPrimAssignSub, {1}},
-                                                                        {prim::kPrimTensorSummary, {1}},
-                                                                        {prim::kPrimImageSummary, {1}},
-                                                                        {prim::kPrimScalarSummary, {1}}});
+  std::vector<std::pair<PrimitivePtr, std::vector<size_t>>> white_list(
+    {{prim::kPrimApplyMomentum, {1, 2}}, {prim::kPrimMomentum, {2, 3}},     {prim::kPrimStateSetItem, {1}},
+     {prim::kPrimEnvGetItem, {1}},       {prim::kPrimEnvSetItem, {1}},      {prim::kPrimReduceSum, {2}},
+     {prim::kPrimReduceMean, {2}},       {prim::kPrimReduceAll, {2}},       {prim::kPrimCast, {2}},
+     {prim::kPrimTranspose, {2}},        {prim::kPrimOneHot, {2}},          {prim::kPrimGatherV2, {3}},
+     {prim::kPrimReshape, {2}},          {prim::kPrimAssign, {1}},          {prim::kPrimAssignAdd, {1}},
+     {prim::kPrimAssignSub, {1}},        {prim::kPrimTensorSummary, {1}},   {prim::kPrimImageSummary, {1}},
+     {prim::kPrimScalarSummary, {1}},    {prim::kPrimHistogramSummary, {1}}});
   for (auto &item : white_list) {
     auto matched = std::any_of(item.second.begin(), item.second.end(), [&item, &node, &index](size_t idx) {
       return IsPrimitiveCNode(node, item.first) && idx == index;

mindspore/ccsrc/parallel/node_check.cc

@@ -66,6 +66,7 @@ const std::set<std::string> BLACK_LIST = {TUPLE_GETITEM,
                                           SCALARSUMMARY,
                                           IMAGESUMMARY,
                                           TENSORSUMMARY,
+                                          HISTOGRAMSUMMARY,
                                           COL2IMV1,
                                           RESOLVE,
                                           BROADCASTGRADIENTARGS,

mindspore/ccsrc/parallel/ops_info/ops_utils.h

@@ -246,6 +246,7 @@ constexpr char STATESETITEM[] = "state_setitem";
 constexpr char SCALARSUMMARY[] = "ScalarSummary";
 constexpr char IMAGESUMMARY[] = "ImageSummary";
 constexpr char TENSORSUMMARY[] = "TensorSummary";
+constexpr char HISTOGRAMSUMMARY[] = "HistogramSummary";
 constexpr char BROADCASTGRADIENTARGS[] = "BroadcastGradientArgs";
 constexpr char INVERTPERMUTATION[] = "InvertPermutation";
 constexpr char CONTROLDEPEND[] = "ControlDepend";

mindspore/ccsrc/pipeline/static_analysis/prim.cc

@@ -131,6 +131,7 @@ PrimitiveEvalImplMap &GetPrimitiveToEvalImplMap() {
     {prim::kPrimScalarSummary, {InferImplScalarSummary, true}},
     {prim::kPrimImageSummary, {InferImplTensorSummary, true}},
     {prim::kPrimTensorSummary, {InferImplTensorSummary, true}},
+    {prim::kPrimHistogramSummary, {InferImplTensorSummary, true}},
   };
   return prim_eval_implement_map;
 }

mindspore/ccsrc/session/anf_runtime_algorithm.cc

@@ -714,7 +714,8 @@ bool AnfRuntimeAlgorithm::IsRealKernel(const AnfNodePtr &node) {
   }
   auto input = cnode->inputs()[0];
   bool is_virtual_node = IsPrimitive(input, prim::kPrimImageSummary) || IsPrimitive(input, prim::kPrimScalarSummary) ||
-                         IsPrimitive(input, prim::kPrimTensorSummary) || IsPrimitive(input, prim::kPrimMakeTuple) ||
+                         IsPrimitive(input, prim::kPrimTensorSummary) ||
+                         IsPrimitive(input, prim::kPrimHistogramSummary) || IsPrimitive(input, prim::kPrimMakeTuple) ||
                          IsPrimitive(input, prim::kPrimStateSetItem) || IsPrimitive(input, prim::kPrimDepend) ||
                          IsPrimitive(input, prim::kPrimTupleGetItem) || IsPrimitive(input, prim::kPrimControlDepend) ||
                          IsPrimitive(input, prim::kPrimReturn);

mindspore/ccsrc/session/session_basic.cc

@@ -45,7 +45,7 @@ void GetSummaryNodes(const KernelGraph *graph, std::unordered_map<std::string, s
   for (auto &n : apply_list) {
     MS_EXCEPTION_IF_NULL(n);
     if (IsPrimitiveCNode(n, prim::kPrimScalarSummary) || IsPrimitiveCNode(n, prim::kPrimTensorSummary) ||
-        IsPrimitiveCNode(n, prim::kPrimImageSummary)) {
+        IsPrimitiveCNode(n, prim::kPrimImageSummary) || IsPrimitiveCNode(n, prim::kPrimHistogramSummary)) {
       int index = 0;
       auto cnode = n->cast<CNodePtr>();
       MS_EXCEPTION_IF_NULL(cnode);
@@ -83,7 +83,7 @@ bool ExistSummaryNode(const KernelGraph *graph) {
   auto all_nodes = DeepLinkedGraphSearch(ret);
   for (auto &n : all_nodes) {
     if (IsPrimitiveCNode(n, prim::kPrimScalarSummary) || IsPrimitiveCNode(n, prim::kPrimTensorSummary) ||
-        IsPrimitiveCNode(n, prim::kPrimImageSummary)) {
+        IsPrimitiveCNode(n, prim::kPrimImageSummary) || IsPrimitiveCNode(n, prim::kPrimHistogramSummary)) {
       return true;
     }
   }

mindspore/ccsrc/transform/convert.cc

@@ -353,6 +353,7 @@ std::unordered_map<std::string, OpAdapterDescPtr> &DfGraphConvertor::get_adpt_ma
     {prim::kPrimScalarSummary->name(), ADPT_DESC(Summary)},
     {prim::kPrimImageSummary->name(), ADPT_DESC(Summary)},
     {prim::kPrimTensorSummary->name(), ADPT_DESC(Summary)},
+    {prim::kPrimHistogramSummary->name(), ADPT_DESC(Summary)},
     {prim::kPrimTensorAdd->name(),
      std::make_shared<OpAdapterDesc>(std::make_shared<OpAdapter<Add>>(ExtraAttr({{"mode", MakeValue(1)}})),
                                      std::make_shared<OpAdapter<Add>>(ExtraAttr({{"mode", MakeValue(1)}})))},

mindspore/ccsrc/utils/callbacks_ge.cc

@@ -131,7 +131,7 @@ static TensorPtr GetMeTensorForSummary(const std::string& name, const std::share
     auto shape = std::vector<int>({ONE_SHAPE});
     return TransformUtil::ConvertGeTensor(ge_tensor_ptr, shape);
   }
-  if (tname == "[:Tensor]") {
+  if (tname == "[:Tensor]" || tname == "[:Histogram]") {
     MS_LOG(DEBUG) << "The summary(" << name << ") is Tensor";
     // process the tensor summary
     // Now we can't get the real shape, so we keep same shape with GE

mindspore/ops/_grad/grad_debug_ops.py

@@ -49,6 +49,15 @@ def get_bprop_image_summary(self):
     return bprop
 
 
+@bprop_getters.register(P.HistogramSummary)
+def get_bprop_histogram_summary(self):
+    """Generate bprop for HistogramSummary"""
+
+    def bprop(tag, x, out, dout):
+        return tag, zeros_like(x)
+    return bprop
+
+
 @bprop_getters.register(P.InsertGradientOf)
 def get_bprop_insert_gradient_of(self):
     """Generate bprop for InsertGradientOf"""

mindspore/ops/operations/__init__.py

@@ -34,7 +34,7 @@ from .comm_ops import (AllGather, AllReduce, _AlltoAll, ReduceScatter, Broadcast
                        _MirrorOperator, ReduceOp, _VirtualDataset,
                        _VirtualDiv, _GetTensorSlice)
 from .debug_ops import (ImageSummary, InsertGradientOf, ScalarSummary,
-                        TensorSummary, Print)
+                        TensorSummary, HistogramSummary, Print)
 from .control_ops import ControlDepend, GeSwitch, Merge
 from .inner_ops import ScalarCast
 from .math_ops import (Abs, ACos, AddN, AssignAdd, AssignSub, Atan2, BatchMatMul,
@@ -148,6 +148,7 @@ __all__ = [
     'ScalarSummary',
     'ImageSummary',
     'TensorSummary',
+    'HistogramSummary',
     "Print",
     'InsertGradientOf',
     'InvertPermutation',

mindspore/ops/operations/debug_ops.py

@@ -98,6 +98,33 @@ class TensorSummary(Primitive):
         """init"""
 
 
+class HistogramSummary(Primitive):
+    """
+    Output tensor to protocol buffer through histogram summary operator.
+
+    Inputs:
+        - **name** (str) - The name of the input variable.
+        - **value** (Tensor) - The value of tensor, and the rank of tensor should be greater than 0.
+
+    Examples:
+        >>> class SummaryDemo(nn.Cell):
+        >>>     def __init__(self,):
+        >>>         super(SummaryDemo, self).__init__()
+        >>>         self.summary = P.HistogramSummary()
+        >>>         self.add = P.TensorAdd()
+        >>>
+        >>>     def construct(self, x, y):
+        >>>         x = self.add(x, y)
+        >>>         name = "x"
+        >>>         self.summary(name, x)
+        >>>         return x
+    """
+
+    @prim_attr_register
+    def __init__(self):
+        """init"""
+
+
 class InsertGradientOf(PrimitiveWithInfer):
     """
     Attach callback to graph node that will be invoked on the node's gradient.

tests/st/summary/test_gpu_summary.py

@@ -24,17 +24,6 @@ from mindspore.common.tensor import Tensor
 from mindspore.ops import operations as P
 from mindspore.train.summary.summary_record import SummaryRecord
 
-'''
-    This testcase is used for save summary data only. You need install MindData first and uncomment the commented 
-    packages to analyse summary data.
-    Using "minddata start --datalog='./test_me_summary_event_file/' --host=0.0.0.0" to make data visible. 
-'''
-# from minddata.datavisual.data_transform.data_manager import DataManager
-# from minddata.datavisual.visual.train_visual.train_task_manager import TrainTaskManager
-# from minddata.datavisual.visual.train_visual.scalars_processor import ScalarsProcessor
-# from minddata.datavisual.common.enums import PluginNameEnum
-# from minddata.datavisual.common.enums import DataManagerStatus
-
 
 context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
 
@@ -43,6 +32,7 @@ CUR_DIR = os.getcwd()
 SUMMARY_DIR_ME = CUR_DIR + "/test_me_summary_event_file/"
 SUMMARY_DIR_ME_TEMP = CUR_DIR + "/test_me_temp_summary_event_file/"
 
+
 def clean_environment_file(srcDir):
     if os.path.exists(srcDir):
         ls = os.listdir(srcDir)
@@ -50,6 +40,8 @@ def clean_environment_file(srcDir):
             filePath = os.path.join(srcDir, line)
             os.remove(filePath)
         os.removedirs(srcDir)
+
+
 def save_summary_events_file(srcDir, desDir):
     if not os.path.exists(desDir):
         print("-- create desDir")
@@ -64,12 +56,14 @@ def save_summary_events_file(srcDir, desDir):
         os.remove(filePath)
     os.removedirs(srcDir)
 
+
 class SummaryNet(nn.Cell):
     def __init__(self, tag_tuple=None, scalar=1):
         super(SummaryNet, self).__init__()
         self.summary_s = P.ScalarSummary()
         self.summary_i = P.ImageSummary()
         self.summary_t = P.TensorSummary()
+        self.histogram_summary = P.HistogramSummary()
         self.add = P.TensorAdd()
         self.tag_tuple = tag_tuple
         self.scalar = scalar
@@ -79,8 +73,10 @@ class SummaryNet(nn.Cell):
         self.summary_s("x1", x)
         z = self.add(x, y)
         self.summary_t("z1", z)
+        self.histogram_summary("histogram", z)
         return z
 
+
 def train_summary_record_scalar_for_1(test_writer, steps, fwd_x, fwd_y):
     net = SummaryNet()
     out_me_dict = {}
@@ -93,6 +89,7 @@ def train_summary_record_scalar_for_1(test_writer, steps, fwd_x, fwd_y):
         out_me_dict[i] = out_put.asnumpy()
     return out_me_dict
 
+
 def me_scalar_summary(steps, tag=None, value=None):
     test_writer = SummaryRecord(SUMMARY_DIR_ME_TEMP)
 
@@ -104,44 +101,6 @@ def me_scalar_summary(steps, tag=None, value=None):
     test_writer.close()
     return out_me_dict
 
-def print_scalar_data():
-    print("============start print_scalar_data\n")
-    data_manager = DataManager()
-    data_manager.start_load_data(path=SUMMARY_DIR_ME)
-    while data_manager.get_status() != DataManagerStatus.DONE:
-        time.sleep(0.1)
-    task_manager = TrainTaskManager(data_manager)
-    train_jobs = task_manager.get_all_train_tasks(PluginNameEnum.scalar)
-    print(train_jobs)
-    """
-        train_jobs
-        ['train_jobs': {
-	    'id': '12-123',
-	    'name': 'train_job_name',
-	    'tags': ['x1', 'y1']
-	                    }]
-    """
-    scalar_processor = ScalarsProcessor(data_manager)
-    metadata = scalar_processor.get_metadata_list(train_job_ids=train_jobs['train_jobs'][0]['id'], tag=train_jobs['train_jobs'][0]['tags'][0])
-    print(metadata)
-    '''
-    metadata
-    {
-        'scalars' : [
-            {
-                'train_job_id' : '12-12',
-                'metadatas' : [
-                    {
-                        'wall_time' : 0.1,
-                        'step' : 1,
-                        'value' : 0.1
-                    }
-                ]
-            }
-        ]
-    }
-    '''
-    print("============end print_scalar_data\n")
 
 @pytest.mark.level0
 @pytest.mark.platform_x86_gpu_training

tests/ut/cpp/transform/convert_test.cc

@@ -621,6 +621,12 @@ TEST_F(TestConvert, TestTensorSummaryOps) {
   ASSERT_TRUE(ret);
 }
 
+TEST_F(TestConvert, TestHistogramSummaryOps) {
+  auto prim = prim::kPrimHistogramSummary;
+  bool ret = MakeDfGraph(prim, 2);
+  ASSERT_TRUE(ret);
+}
+
 TEST_F(TestConvert, TestGreaterOps) {
   auto prim = std::make_shared<Primitive>("Greater");
   bool ret = MakeDfGraph(prim, 2);

tests/ut/cpp/transform/transform_base_test.cc

@@ -73,7 +73,8 @@ FuncGraphPtr MakeFuncGraph(const PrimitivePtr prim, unsigned int nparam) {
   std::vector<AnfNodePtr> inputs;
   inputs.push_back(NewValueNode(prim));
   for (unsigned int i = 0; i < nparam; i++) {
-    if ((prim->name() == "ScalarSummary" || prim->name() == "TensorSummary" || prim->name() == "ImageSummary") &&
+    if ((prim->name() == "ScalarSummary" || prim->name() == "TensorSummary" || 
+        prim->name() == "ImageSummary" || prim->name() == "HistogramSummary") &&
         i == 0) {
       auto input = NewValueNode("testSummary");
       inputs.push_back(input);

tests/ut/python/ops/test_nn_ops.py

@@ -198,6 +198,19 @@ class ScalarSummaryNet(nn.Cell):
         return out
 
 
+class HistogramSummaryNet(nn.Cell):
+    """HistogramSummaryNet definition"""
+
+    def __init__(self):
+        super(HistogramSummaryNet, self).__init__()
+        self.summary = P.HistogramSummary()
+
+    def construct(self, tensor):
+        string_in = "wight_value"
+        out = self.summary(string_in, tensor)
+        return out
+
+
 class FusedBatchNormGrad(nn.Cell):
     """ FusedBatchNormGrad definition """
 
@@ -443,6 +456,10 @@ test_cases = [
         'block': ScalarSummaryNet(),
         'desc_inputs': [2.2],
     }),
+    ('HistogramSummary', {
+        'block': HistogramSummaryNet(),
+        'desc_inputs': [[1,2,3]],
+    }),
     ('FusedBatchNormGrad', {
         'block': FusedBatchNormGrad(nn.BatchNorm2d(num_features=512, eps=1e-5, momentum=0.1)),
         'desc_inputs': [[64, 512, 7, 7], [64, 512, 7, 7]],

tests/ut/python/ops/test_ops.py

@@ -160,6 +160,19 @@ class SummaryNet(nn.Cell):
         return self.add(x, y)
 
 
+class HistogramSummaryNet(nn.Cell):
+    def __init__(self,):
+        super(HistogramSummaryNet, self).__init__()
+        self.summary = P.HistogramSummary()
+        self.add = P.TensorAdd()
+
+    def construct(self, x, y):
+        out = self.add(x, y)
+        string_in = "out"
+        self.summary(string_in, out)
+        return out
+
+
 test_case_math_ops = [
     ('Neg', {
         'block': P.Neg(),
@@ -1104,6 +1117,12 @@ test_case_other_ops = [
         'desc_inputs': [Tensor(np.array([1.1]).astype(np.float32)),
                         Tensor(np.array([1.2]).astype(np.float32))],
         'skip': ['backward']}),
+    ('HistogramSummary', {
+        'block': HistogramSummaryNet(),
+        'desc_inputs': [Tensor(np.array([1.1]).astype(np.float32)),
+                        Tensor(np.array([1.2]).astype(np.float32))],
+        'skip': ['backward']}),
+    
 ]
 
 test_case_lists = [test_case_nn_ops, test_case_math_ops, test_case_array_ops, test_case_other_ops]

tests/ut/python/train/summary/test_summary.py

@@ -132,6 +132,7 @@ class SummaryDemo(nn.Cell):
     def __init__(self,):
         super(SummaryDemo, self).__init__()
         self.s = P.ScalarSummary()
+        self.histogram_summary = P.HistogramSummary()
         self.add = P.TensorAdd()
 
     def construct(self, x, y):
@@ -139,6 +140,7 @@ class SummaryDemo(nn.Cell):
         z = self.add(x, y)
         self.s("z1", z)
         self.s("y1", y)
+        self.histogram_summary("histogram", z)
         return z
 
 

tests/ut/python/train/summary/test_summary_ops_params_valid_check.py

@@ -40,6 +40,7 @@ class SummaryDemoTag(nn.Cell):
     def __init__(self, tag1, tag2, tag3):
         super(SummaryDemoTag, self).__init__()
         self.s = P.ScalarSummary()
+        self.histogram_summary = P.HistogramSummary()
         self.add = P.TensorAdd()
         self.tag1 = tag1
         self.tag2 = tag2
@@ -50,6 +51,7 @@ class SummaryDemoTag(nn.Cell):
         z = self.add(x, y)
         self.s(self.tag2, z)
         self.s(self.tag3, y)
+        self.histogram_summary(self.tag1, x)
         return z
 
 
@@ -58,6 +60,7 @@ class SummaryDemoTagForSet(nn.Cell):
     def __init__(self, tag_tuple):
         super(SummaryDemoTagForSet, self).__init__()
         self.s = P.ScalarSummary()
+        self.histogram_summary = P.HistogramSummary()
         self.add = P.TensorAdd()
         self.tag_tuple = tag_tuple
 
@@ -65,6 +68,7 @@ class SummaryDemoTagForSet(nn.Cell):
         z = self.add(x, y)
         for tag in self.tag_tuple:
             self.s(tag, x)
+            self.histogram_summary(tag, x)
         return z
 
 
@@ -98,6 +102,19 @@ class SummaryDemoValueForSet(nn.Cell):
             self.s(tag, self.v)
         return z
 
+
+class HistogramSummaryNet(nn.Cell):
+    "HistogramSummaryNet definition"
+    def __init__(self, value):
+        self.histogram_summary = P.HistogramSummary()
+        self.add = P.TensorAdd()
+        self.value = value
+    
+    def construct(self, tensors1, tensor2):
+        self.histogram_summary("value", self.value)
+        return self.add(tensors1, tensor2)
+
+
 def run_case(net):
     """ run_case """
     # step 0: create the thread
@@ -121,8 +138,8 @@ def run_case(net):
 
 
 # Test 1: use the repeat tag
-def test_scalar_summary_use_repeat_tag():
-    log.debug("begin test_scalar_summary_use_repeat_tag")
+def test_summary_use_repeat_tag():
+    log.debug("begin test_summary_use_repeat_tag")
     net = SummaryDemoTag("x", "x", "x")
     try:
         run_case(net)
@@ -130,12 +147,12 @@ def test_scalar_summary_use_repeat_tag():
         assert False
     else:
         assert True
-    log.debug("finished test_scalar_summary_use_repeat_tag")
+    log.debug("finished test_summary_use_repeat_tag")
 
 
 # Test 2: repeat tag use for set summary
-def test_scalar_summary_use_repeat_tag_for_set():
-    log.debug("begin test_scalar_summary_use_repeat_tag_for_set")
+def test_summary_use_repeat_tag_for_set():
+    log.debug("begin test_summary_use_repeat_tag_for_set")
     net = SummaryDemoTagForSet(("x", "x", "x"))
     try:
         run_case(net)
@@ -143,12 +160,12 @@ def test_scalar_summary_use_repeat_tag_for_set():
         assert False
     else:
         assert True
-    log.debug("finished test_scalar_summary_use_repeat_tag_for_set")
+    log.debug("finished test_summary_use_repeat_tag_for_set")
 
 
 # Test3: test with invalid tag(None, bool, "", int)
-def test_scalar_summary_use_invalid_tag_None():
-    log.debug("begin test_scalar_summary_use_invalid_tag_None")
+def test_summary_use_invalid_tag_None():
+    log.debug("begin test_summary_use_invalid_tag_None")
     net = SummaryDemoTag(None, None, None)
     try:
         run_case(net)
@@ -156,31 +173,31 @@ def test_scalar_summary_use_invalid_tag_None():
         assert True
     else:
         assert False
-    log.debug("finished test_scalar_summary_use_invalid_tag_None")
+    log.debug("finished test_summary_use_invalid_tag_None")
 
 
 # Test4: test with invalid tag(None, bool, "", int)
-def test_scalar_summary_use_invalid_tag_Bool():
-    log.debug("begin test_scalar_summary_use_invalid_tag_Bool")
+def test_summary_use_invalid_tag_Bool():
+    log.debug("begin test_summary_use_invalid_tag_Bool")
     net = SummaryDemoTag(True, True, True)
     run_case(net)
-    log.debug("finished test_scalar_summary_use_invalid_tag_Bool")
+    log.debug("finished test_summary_use_invalid_tag_Bool")
 
 
 # Test5: test with invalid tag(None, bool, "", int)
-def test_scalar_summary_use_invalid_tag_null():
-    log.debug("begin test_scalar_summary_use_invalid_tag_null")
+def test_summary_use_invalid_tag_null():
+    log.debug("begin test_summary_use_invalid_tag_null")
     net = SummaryDemoTag("", "", "")
     run_case(net)
-    log.debug("finished test_scalar_summary_use_invalid_tag_null")
+    log.debug("finished test_summary_use_invalid_tag_null")
 
 
 # Test6: test with invalid tag(None, bool, "", int)
-def test_scalar_summary_use_invalid_tag_Int():
-    log.debug("begin test_scalar_summary_use_invalid_tag_Int")
+def test_summary_use_invalid_tag_Int():
+    log.debug("begin test_summary_use_invalid_tag_Int")
     net = SummaryDemoTag(1, 2, 3)
     run_case(net)
-    log.debug("finished test_scalar_summary_use_invalid_tag_Int")
+    log.debug("finished test_summary_use_invalid_tag_Int")
 
 
 # Test7: test with invalid value(None, "")
@@ -196,7 +213,6 @@ def test_scalar_summary_use_invalid_value_None():
     log.debug("finished test_scalar_summary_use_invalid_tag_Int")
 
 
-
 # Test8: test with invalid value(None, "")
 def test_scalar_summary_use_invalid_value_None_ForSet():
     log.debug("begin test_scalar_summary_use_invalid_value_None_ForSet")
@@ -221,3 +237,30 @@ def test_scalar_summary_use_invalid_value_null():
     else:
         assert False
     log.debug("finished test_scalar_summary_use_invalid_value_null")
+
+
+def test_histogram_summary_use_valid_value():
+    """Test histogram summary with valid value"""
+    log.debug("Begin test_histogram_summary_use_valid_value")
+    try:
+        net = HistogramSummaryNet(Tensor(np.array([1,2,3])))
+        run_case(net)
+    except:
+        assert True
+    else:
+        assert False
+    log.debug("Finished test_histogram_summary_use_valid_value")
+
+
+def test_histogram_summary_use_scalar_value():
+    """Test histogram summary use scalar value"""
+    log.debug("Begin test_histogram_summary_use_scalar_value")
+    try:
+        scalar = Tensor(1)
+        net = HistogramSummaryNet(scalar)
+        run_case(net)
+    except:
+        assert True
+    else:
+        assert False
+    log.debug("Finished test_histogram_summary_use_scalar_value")