!1935 Summary callback as collector for summary and lineage

Merge pull request !1935 from 李鸿章/policy_writer

mindspore/ccsrc/CMakeLists.txt

@@ -79,6 +79,7 @@ if (ENABLE_DUMP_PROTO)
     file(GLOB_RECURSE PROTO_PY RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}
         "utils/anf_ir.proto"
         "utils/summary.proto"
+        "utils/lineage.proto"
         "utils/checkpoint.proto"
     )
     ms_protobuf_generate_py(PY_SRCS PY_HDRS PY_PYS ${PROTO_PY})

mindspore/ccsrc/utils/lineage.proto

+// Copyright 2020 Huawei Technologies Co., Ltd.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+syntax = "proto2";
+
+package mindspore.irpb;
+option cc_enable_arenas = true;
+
+
+// Event Protocol buffer, Top define
+message LineageEvent {
+  // Timestamp
+  required double wall_time = 1;
+
+  // The step of train.
+  optional int64 step = 2;
+
+  oneof what {
+    // An event file was started, with the specified version.
+    // Now version is "Mindspore.Event:1"
+    string version = 3;
+
+    // Train lineage
+    TrainLineage train_lineage = 6;
+
+    // Evaluation lineage
+    EvaluationLineage evaluation_lineage = 7;
+
+    // Dataset graph
+    DatasetGraph dataset_graph = 9;
+
+    // User defined info
+    UserDefinedInfo user_defined_info = 10;
+  }
+}
+
+// User defined info
+message UserDefinedInfo{
+    // repeated user defined info
+    repeated UserDefinedInfo user_info = 1;
+
+    // key/value which contains both scalar and dict
+    map<string, UserDefinedInfo> map_dict = 2;
+    map<string, int32> map_int32 = 3;
+    map<string, string>  map_str = 4;
+    map<string, double>  map_double = 5;
+}
+
+// TrainLineage records infos of a train.
+message TrainLineage{
+    message HyperParameters{
+        optional string optimizer = 1;
+        optional float learning_rate = 2;
+        optional string loss_function = 3;
+        optional int32 epoch = 4;
+        optional string parallel_mode = 5;
+        optional int32 device_num = 6;
+        optional int32 batch_size = 8;
+    }
+
+    message TrainDataset{
+        optional string train_dataset_path = 1;
+        optional int32 train_dataset_size = 2;
+    }
+
+    message Algorithm{
+        optional string network = 1;
+        optional float loss = 2;
+    }
+
+    message Model{
+        optional string path = 3;
+        optional int64 size = 4;
+    }
+
+    optional HyperParameters hyper_parameters = 1;
+    optional TrainDataset train_dataset = 2;
+    optional Algorithm algorithm = 3;
+    optional Model model = 4;
+}
+
+//EvalLineage records infos of evaluation.
+message EvaluationLineage{
+    message ValidDataset{
+        optional string valid_dataset_path = 1;
+        optional int32 valid_dataset_size = 2;
+    }
+
+    optional string metric = 2;
+    optional ValidDataset valid_dataset = 3;
+}
+
+
+// DatasetGraph
+message DatasetGraph {
+    repeated DatasetGraph children = 1;
+    optional OperationParameter parameter = 2;
+    repeated Operation operations = 3;
+    optional Operation sampler = 4;
+}
+
+message Operation {
+    optional OperationParameter operationParam = 1;
+    repeated int32 size = 2;
+    repeated float weights = 3;
+}
+
+message OperationParameter{
+    map<string, string> mapStr = 1;
+    map<string, StrList> mapStrList = 2;
+    map<string, bool> mapBool = 3;
+    map<string, int32> mapInt = 4;
+    map<string, double> mapDouble = 5;
+}
+
+message StrList {
+    repeated string strValue = 1;
+}

mindspore/train/_utils.py

@@ -21,6 +21,7 @@ from mindspore.common import dtype as mstype
 from mindspore import log as logger
 from mindspore.common.api import _executor
 
+from .lineage_pb2 import DatasetGraph, TrainLineage, EvaluationLineage, UserDefinedInfo
 
 def _convert_type(types):
     """
@@ -193,3 +194,38 @@ def _to_full_shapes(shapes, device_num):
                 new_shape += (item,)
         new_shapes.append(new_shape)
     return new_shapes
+
+
+def _check_to_numpy(plugin, tensor):
+    """Check the tensor and return a numpy.ndarray."""
+    np_value = tensor.asnumpy()
+    if plugin == 'scalar':
+        if np_value.size == 1:
+            return np_value
+        raise ValueError('The tensor holds more than one value, but the scalar plugin expects on value.')
+    if plugin == 'image':
+        if np_value.ndim == 4:
+            return np_value
+        raise ValueError('The tensor seems not to hold a valid image.')
+    if plugin in ('tensor', 'histogram'):
+        if np_value.ndim > 0:
+            return np_value
+        raise ValueError('The tensor should not be empty.')
+    return np_value
+
+def _check_lineage_value(plugin, value):
+    """Check the lineage value."""
+    def raises(plugin, prototype):
+        raise TypeError(f'Plugin {repr(plugin)} expects a {prototype.__name__} value.')
+
+    if plugin == 'dataset_graph' and not isinstance(value, DatasetGraph):
+        raises(plugin, DatasetGraph)
+
+    if plugin == 'eval_lineage' and not isinstance(value, EvaluationLineage):
+        raises(plugin, EvaluationLineage)
+
+    if plugin == 'train_lineage' and not isinstance(value, TrainLineage):
+        raises(plugin, TrainLineage)
+
+    if plugin == 'custom_lineage_data' and not isinstance(value, UserDefinedInfo):
+        raises(plugin, UserDefinedInfo)

mindspore/train/summary/_lineage_adapter.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Generate the lineage event which conform to proto format."""
+import time
+
+from ..lineage_pb2 import LineageEvent
+
+
+def serialize_to_lineage_event(name, value):
+    """Serialize value to lineage event."""
+    event = LineageEvent()
+    event.wall_time = time.time()
+    content = _get_lineage_content(name, event)
+    content.ParseFromString(value)
+    return event.SerializeToString()
+
+
+def _get_lineage_content(name, event):
+    if name == 'dataset_graph':
+        return event.dataset_graph
+    if name == 'eval_lineage':
+        return event.evaluation_lineage
+    if name == 'train_lineage':
+        return event.train_lineage
+    if name == 'custom_lineage_data':
+        return event.user_defined_info
+    raise KeyError(f'No such field in LineageEvent')

mindspore/train/summary/_summary_adapter.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """Generate the summary event which conform to proto format."""
-import socket
+import platform
 import time
 
 import numpy as np
@@ -51,7 +51,7 @@ def get_event_file_name(prefix, suffix):
     _check_str_by_regular(suffix)
     file_name = ""
     time_second = str(int(time.time()))
-    hostname = socket.gethostname()
+    hostname = platform.node()
 
     if prefix is not None:
         file_name = file_name + prefix

mindspore/train/summary/_summary_writer.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Writes events to disk in a logdir."""
+import os
+import stat
+
+from ..._c_expression import EventWriter_
+from ._summary_adapter import package_init_event
+
+
+class BaseWriter:
+    """BaseWriter to be subclass."""
+
+    def __init__(self, filepath) -> None:
+        self._filepath = filepath
+        self._writer: EventWriter_ = None
+
+    def init_writer(self):
+        """Write some metadata etc."""
+
+    @property
+    def writer(self) -> EventWriter_:
+        """Get the writer."""
+        if self._writer is not None:
+            return self._writer
+
+        with open(self._filepath, 'w'):
+            os.chmod(self._filepath, stat.S_IWUSR | stat.S_IRUSR)
+        self._writer = EventWriter_(self._filepath)
+        self.init_writer()
+        return self._writer
+
+    def write(self, plugin, mode, data):
+        """Write data to file."""
+        raise NotImplementedError()
+
+    def flush(self):
+        """Flush the writer."""
+        if self._writer is not None:
+            self._writer.Flush()
+
+    def close(self):
+        """Close the writer."""
+        if self._writer is not None:
+            self._writer.Shut()
+
+
+class SummaryWriter(BaseWriter):
+    """SummaryWriter for write summaries."""
+
+    def init_writer(self):
+        """Write some metadata etc."""
+        self.writer.Write(package_init_event().SerializeToString())
+
+    def write(self, plugin, mode, data):
+        """Write data to file."""
+        if plugin in ('summary', 'graph'):
+            self.writer.Write(data)
+
+
+class LineageWriter(BaseWriter):
+    """LineageWriter for write lineage."""
+
+    def write(self, plugin, mode, data):
+        """Write data to file."""
+        if plugin in ('dataset_graph', 'train_lineage', 'eval_lineage', 'custom_lineage_data'):
+            self.writer.Write(data)

mindspore/train/summary/_event_writer.py → mindspore/train/summary/_writer_pool.py

@@ -12,74 +12,100 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-"""Writes events to disk in a logdir."""
+"""Write events to disk in a base directory."""
 import os
-import stat
 from collections import deque
 from multiprocessing import Pool, Process, Queue, cpu_count
 
-from ..._c_expression import EventWriter_
-from ._summary_adapter import package_summary_event
+from ._lineage_adapter import serialize_to_lineage_event
+from ._summary_adapter import package_graph_event, package_summary_event
+from ._summary_writer import SummaryWriter, LineageWriter
 
 
-def _pack(result, step):
-    summary_event = package_summary_event(result, step)
-    return summary_event.SerializeToString()
+def _pack_data(datadict):
+    """Pack data according to which plugin."""
+    result = []
+    summaries, step, mode = [], None, None
+    for plugin, datalist in datadict.items():
+        for data in datalist:
+            if plugin == 'graph':
+                result.append([plugin, data.get('mode'), package_graph_event(data.get('value')).SerializeToString()])
+            elif plugin in ('train_lineage', 'eval_lineage', 'custom_lineage_data', 'dataset_graph'):
+                result.append([plugin, data.get('mode'), serialize_to_lineage_event(plugin, data.get('value'))])
+            elif plugin in ('scalar', 'tensor', 'histogram', 'image'):
+                summaries.append({'_type': plugin.title(), 'name': data.get('tag'), 'data': data.get('value')})
+                step = data.get('step')
+                mode = data.get('mode')
+    if summaries:
+        result.append(['summary', mode, package_summary_event(summaries, step).SerializeToString()])
+    return result
 
 
-class EventWriter(Process):
+class WriterPool(Process):
     """
-    Creates a `EventWriter` and write event to file.
+    Use a set of pooled resident processes for writing a list of file.
 
     Args:
-        filepath (str): Summary event file path and file name.
-        flush_interval (int): The flush seconds to flush the pending events to disk. Default: 120.
+        base_dir (str): The base directory to hold all the files.
+        filelist (str): The mapping from short name to long filename.
     """
 
-    def __init__(self, filepath: str, flush_interval: int) -> None:
+    def __init__(self, base_dir, **filedict) -> None:
         super().__init__()
-        _ = flush_interval
-        with open(filepath, 'w'):
-            os.chmod(filepath, stat.S_IWUSR | stat.S_IRUSR)
-        self._writer = EventWriter_(filepath)
+        self._base_dir, self._filedict = base_dir, filedict
         self._queue = Queue(cpu_count() * 2)
         self.start()
 
     def run(self):
+        writers = self._get_writers()
 
-        with Pool(min(cpu_count(), 32)) as pool:
+        with Pool() as pool:
             deq = deque()
             while True:
                 while deq and deq[0].ready():
-                    self._writer.Write(deq.popleft().get())
+                    for plugin, mode, data in deq.popleft().get():
+                        for writer in writers:
+                            writer.write(plugin, mode, data)
 
                 if not self._queue.empty():
                     action, data = self._queue.get()
                     if action == 'WRITE':
-                        if not isinstance(data, (str, bytes)):
-                            deq.append(pool.apply_async(_pack, data))
-                        else:
-                            self._writer.Write(data)
+                        deq.append(pool.apply_async(_pack_data, (data,)))
                     elif action == 'FLUSH':
-                        self._writer.Flush()
+                        for writer in writers:
+                            writer.flush()
                     elif action == 'END':
                         break
-            for res in deq:
-                self._writer.Write(res.get())
+            for result in deq:
+                for plugin, mode, data in result.get():
+                    for writer in writers:
+                        writer.write(plugin, mode, data)
 
-            self._writer.Shut()
+            for writer in writers:
+                writer.close()
+
+    def _get_writers(self):
+        writers = []
+        for plugin, filename in self._filedict.items():
+            filepath = os.path.join(self._base_dir, filename)
+            if plugin == 'summary':
+                writers.append(SummaryWriter(filepath))
+            elif plugin == 'lineage':
+                writers.append(LineageWriter(filepath))
+        return writers
 
     def write(self, data) -> None:
         """
         Write the event to file.
 
         Args:
+            name (str): The key of a specified file.
             data (Optional[str, Tuple[list, int]]): The data to write.
         """
         self._queue.put(('WRITE', data))
 
     def flush(self):
-        """Flush the writer."""
+        """Flush the writer and sync data to disk."""
         self._queue.put(('FLUSH', None))
 
     def close(self) -> None:

mindspore/train/summary/summary_record.py

@@ -21,9 +21,9 @@ from mindspore import log as logger
 
 from ..._c_expression import Tensor
 from ..._checkparam import _check_str_by_regular
-from .._utils import _make_directory
-from ._event_writer import EventWriter
-from ._summary_adapter import get_event_file_name, package_graph_event, package_init_event
+from .._utils import _make_directory, _check_to_numpy, _check_lineage_value
+from ._summary_adapter import get_event_file_name, package_graph_event
+from ._writer_pool import WriterPool
 
 # for the moment, this lock is for caution's sake,
 # there are actually no any concurrencies happening.
@@ -53,16 +53,20 @@ def _get_summary_tensor_data():
         return data
 
 
+def _dictlist():
+    from collections import defaultdict
+    return defaultdict(list)
+
+
 class SummaryRecord:
     """
-    SummaryRecord is used to record the summary value.
+    SummaryRecord is used to record the summary data and lineage data.
 
     Note:
-        The API will create an event file in a given directory and add summaries and events to it.
-        It writes the event log to a file by executing the record method. In addition,
-        if the SummaryRecord object is created and the summary operator is used in the network,
-        even if the record method is not called, the event in the cache will be written to the
-        file at the end of execution. Make sure to close the SummaryRecord object at the end.
+        The API will create a summary file and a lineage file lazily in a given directory and writes data to them.
+        It writes the data to files by executing the record method. In addition to record the data bubbled up from
+        the network by defining the summary operators, SummaryRecord also supports to record extra data which
+        can be added by calling add_value. Finally, make sure to close the SummaryRecord object at the end.
 
     Args:
         log_dir (str): The log_dir is a directory location to save the summary.
@@ -89,10 +93,12 @@ class SummaryRecord:
                  file_suffix="_MS",
                  network=None):
 
-        self._event_writer, self._closed = None, False
+        self._closed, self._mode = False, 'train'
+        self._data_pool = _dictlist()
 
         _check_str_by_regular(file_prefix)
         _check_str_by_regular(file_suffix)
+
         self.log_path = _make_directory(log_dir)
 
         if not isinstance(queue_max_size, int) or not isinstance(flush_time, int):
@@ -123,16 +129,12 @@ class SummaryRecord:
         except Exception as ex:
             raise RuntimeError(ex)
 
-    def _init_event_writer(self):
-        """Init event writer and write metadata."""
-        event_writer = EventWriter(self.full_file_name, self.flush_time)
-        event_writer.write(package_init_event().SerializeToString())
-        return event_writer
+        self._event_writer = WriterPool(log_dir,
+                                        summary=self.full_file_name,
+                                        lineage=get_event_file_name('events', '_lineage'))
 
     def __enter__(self):
         """Enter the context manager."""
-        if not self._event_writer:
-            self._event_writer = self._init_event_writer()
         if self._closed:
             raise ValueError('SummaryRecord has been closed.')
         return self
@@ -141,6 +143,76 @@ class SummaryRecord:
         """Exit the context manager."""
         self.close()
 
+    def set_mode(self, mode):
+        """
+        Set the mode for the recorder to be aware. The mode is set 'train' by default.
+
+        Args:
+            mode (str): The mode to set, which should be 'train' or 'eval'.
+
+        Raises:
+            ValueError: When the mode is not recognized.
+
+        Examples:
+            >>> with SummaryRecord(log_dir="/opt/log", file_prefix="xxx_", file_suffix="_yyy") as summary_record:
+            >>>     summary_record.set_mode('eval')
+        """
+        mode_spec = 'train', 'eval'
+        if mode not in mode_spec:
+            raise ValueError(f'{repr(mode)} is not a recognized mode.')
+        self._mode = mode
+
+    def add_value(self, plugin, name, value):
+        """
+        Add value to be record later on.
+
+        When the plugin is 'tensor', 'scalar', 'image' or 'histogram',
+        the name should be the tag name, and the value should be a Tensor.
+
+        When the plugin plugin is 'graph', the value should be a GraphProto.
+
+        When the plugin 'dataset_graph', 'train_lineage', 'eval_lineage',
+        or 'custom_lineage_data', the value should be a proto message.
+
+
+        Args:
+            plugin (str): The plugin for the value.
+            name (str): The name for the value.
+            value (Union[Tensor, GraphProto, TrainLineage, EvaluationLineage, DatasetGraph, UserDefinedInfo]): \
+                The value to store.
+
+                - GraphProto: The 'value' should be a serialized string this type when the plugin is 'graph'.
+                - Tensor: The 'value' should be this type when the plugin is 'scalar', 'image', 'tensor' or 'histogram'.
+                - TrainLineage: The 'value' should be this type when the plugin is 'train_lineage'.
+                - EvaluationLineage: The 'value' should be this type when the plugin is 'eval_lineage'.
+                - DatasetGraph: The 'value' should be this type when the plugin is 'dataset_graph'.
+                - UserDefinedInfo: The 'value' should be this type when the plugin is 'custom_lineage_data'.
+
+        Raises:
+            ValueError: When the name is not valid.
+            TypeError: When the value is not a Tensor.
+
+        Examples:
+            >>> with SummaryRecord(log_dir="/opt/log", file_prefix="xxx_", file_suffix="_yyy") as summary_record:
+            >>>     summary_record.add_value('scalar', 'loss', Tensor(0.1))
+        """
+        if plugin in ('tensor', 'scalar', 'image', 'histogram'):
+            if not name or not isinstance(name, str):
+                raise ValueError(f'{repr(name)} is not a valid tag name.')
+            if not isinstance(value, Tensor):
+                raise TypeError(f'Expect the value to be Tensor, but got {type(value).__name__}')
+            np_value = _check_to_numpy(plugin, value)
+            self._data_pool[plugin].append(dict(tag=name, mode=self._mode, value=np_value))
+
+        elif plugin in ('train_lineage', 'eval_lineage', 'dataset_graph', 'custom_lineage_data'):
+            _check_lineage_value(plugin, value)
+            self._data_pool[plugin].append(dict(mode=self._mode, value=value.SerializeToString()))
+        elif plugin == 'graph':
+            package_graph_event(value)
+            self._data_pool[plugin].append(dict(mode=self._mode, value=value))
+        else:
+            raise ValueError(f'No such plugin of {repr(plugin)}')
+
     def record(self, step, train_network=None):
         """
         Record the summary.
@@ -149,12 +221,12 @@ class SummaryRecord:
             step (int): Represents training step number.
             train_network (Cell): The network that called the callback.
 
+        Returns:
+            bool, whether the record process is successful or not.
+
         Examples:
             >>> with SummaryRecord(log_dir="/opt/log", file_prefix="xxx_", file_suffix="_yyy") as summary_record:
             >>>     summary_record.record(step=2)
-
-        Returns:
-            bool, whether the record process is successful or not.
         """
         logger.info("SummaryRecord step is %r.", step)
         if self._closed:
@@ -163,10 +235,6 @@ class SummaryRecord:
         if not isinstance(step, int) or isinstance(step, bool):
             raise ValueError("`step` should be int")
         # Set the current summary of train step
-        if not self._event_writer:
-            self._event_writer = self._init_event_writer()
-            logger.warning('SummaryRecord should be used as context manager for a with statement.')
-
         if self.network is not None and not self.has_graph:
             graph_proto = self.network.get_func_graph_proto()
             if graph_proto is None and train_network is not None:
@@ -174,39 +242,48 @@ class SummaryRecord:
             if graph_proto is None:
                 logger.error("Failed to get proto for graph")
             else:
-                self._event_writer.write(package_graph_event(graph_proto).SerializeToString())
+                self._event_writer.write({'graph': [{'step': step, 'value': graph_proto}]})
                 self.has_graph = True
                 if not _summary_tensor_cache:
                     return True
 
-        data = _get_summary_tensor_data()
-        if not data:
-            logger.info("The step(%r) does not have record data.", step)
-            return False
-        if self.queue_max_size > 0 and len(data) > self.queue_max_size:
-            logger.error("The size of data record is %r, which is greater than queue_max_size %r.", len(data),
-                         self.queue_max_size)
-
-        # process the data
-        result = self._data_convert(data)
-        if not result:
-            logger.error("The step(%r) summary data is invalid.", step)
-            return False
-        self._event_writer.write((result, step))
-        logger.debug("Send the summary data to scheduler for saving, step = %d", step)
+        if self._mode == 'train':
+            self._add_summary_tensor_data()
+
+        self._event_writer.write(self._consume_data_pool(step))
         return True
 
+    def _add_summary_tensor_data(self):
+        summary_data = _get_summary_tensor_data()
+        if not summary_data:
+            logger.debug(f'No summary data bubbled from the network.')
+        for name, tensor in summary_data.items():
+            tag, plugin = SummaryRecord._parse_from(name)
+            if (tag, plugin) == (None, None):
+                logger.warning("The name(%r) is invalid, expected 'TAG[:TYPE]'.", name)
+            else:
+                self.add_value(plugin.lower(), tag, tensor)
+
+    def _consume_data_pool(self, step):
+        try:
+            for values in self._data_pool.values():
+                for value in values:
+                    value['step'] = step
+            return self._data_pool
+        finally:
+            self._data_pool = _dictlist()
+
     @property
     def log_dir(self):
         """
         Get the full path of the log file.
 
+        Returns:
+            str, the full path of log file.
+
         Examples:
             >>> with SummaryRecord(log_dir="/opt/log", file_prefix="xxx_", file_suffix="_yyy") as summary_record:
             >>>     print(summary_record.log_dir)
-
-        Returns:
-            String, the full path of log file.
         """
         return self.full_file_name
 
@@ -235,46 +312,19 @@ class SummaryRecord:
         """
         if not self._closed and self._event_writer:
             # event writer flush and close
+            logger.info('Please wait it may take quite some time to finish writing and closing.')
             self._event_writer.close()
             self._closed = True
 
     def __del__(self) -> None:
         self.close()
 
-    def _data_convert(self, summary):
-        """Convert the data."""
-        # convert the summary to numpy
-        result = []
-        for name, data in summary.items():
-            # confirm the data is valid
-            summary_tag, summary_type = SummaryRecord._parse_from(name)
-            if summary_tag is None:
-                logger.error("The data type is invalid, name = %r, tensor = %r", name, data)
-                return None
-            if isinstance(data, Tensor):
-                result.append({'name': summary_tag, 'data': data.asnumpy(), '_type': summary_type})
-            else:
-                logger.error("The data type is invalid, name = %r, tensor = %r", name, data)
-                return None
-
-        return result
-
     @staticmethod
     def _parse_from(name: str = None):
-        """
-        Parse the tag and type from name.
-
-        Args:
-            name (str): Format: TAG[:TYPE].
-
-        Returns:
-            Tuple, (summary_tag, summary_type).
-        """
-        if name is None:
-            logger.error("The name is None")
+        """Parse the tag and type from name."""
+        if not isinstance(name, str):
             return None, None
         match = re.match(r'(.+)\[:(.+)\]', name)
         if match:
             return match.groups()
-        logger.error("The name(%r) format is invalid, expected 'TAG[:TYPE]'.", name)
         return None, None

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

@@ -84,21 +84,6 @@ def test_histogram_multi_summary():
                 event = reader.read_event()
                 assert event.summary.value[0].histogram.count == size
 
-
-def test_histogram_summary_scalar_tensor():
-    """Test histogram summary, input is a scalar tensor."""
-    with tempfile.TemporaryDirectory() as tmp_dir:
-        with SummaryRecord(tmp_dir, file_suffix="_MS_HISTOGRAM") as test_writer:
-            test_data = _wrap_test_data(Tensor(1))
-            _cache_summary_tensor_data(test_data)
-            test_writer.record(step=1)
-
-        file_name = os.path.join(tmp_dir, test_writer.event_file_name)
-        with SummaryReader(file_name) as reader:
-            event = reader.read_event()
-            assert event.summary.value[0].histogram.count == 1
-
-
 def test_histogram_summary_empty_tensor():
     """Test histogram summary, input is an empty tensor."""
     with tempfile.TemporaryDirectory() as tmp_dir: