feat(mgb): add tensorboard tool python layer interface

GitOrigin-RevId: 065bc4d153d222c1cca7dac1902613847786155a

imperative/python/megengine/utils/tensorboard.py

+#!/usr/bin/env python
+# -*-coding=utf-8-*-
+
+from megengine.logger import get_logger
+
+logger = get_logger(__name__)
+
+try:
+    from tensorboardX import SummaryWriter
+    from tensorboardX.proto.attr_value_pb2 import AttrValue
+    from tensorboardX.proto.graph_pb2 import GraphDef
+    from tensorboardX.proto.node_def_pb2 import NodeDef
+    from tensorboardX.proto.plugin_text_pb2 import TextPluginData
+    from tensorboardX.proto.step_stats_pb2 import (
+        DeviceStepStats,
+        RunMetadata,
+        StepStats,
+    )
+    from tensorboardX.proto.summary_pb2 import Summary, SummaryMetadata
+    from tensorboardX.proto.tensor_pb2 import TensorProto
+    from tensorboardX.proto.tensor_shape_pb2 import TensorShapeProto
+    from tensorboardX.proto.versions_pb2 import VersionDef
+except ImportError:
+    logger.error(
+        "TensorBoard and TensorboardX are required for visualize.", exc_info=True,
+    )
+
+
+def tensor_shape_proto(shape):
+    """Creates an object matching
+    https://github.com/tensorflow/tensorboard/blob/master/tensorboard/compat/proto/tensor_shape.proto
+    """
+    return TensorShapeProto(dim=[TensorShapeProto.Dim(size=d) for d in shape])
+
+
+def attr_value_proto(shape, dtype, attr):
+    """Creates a dict of objects matching
+    https://github.com/tensorflow/tensorboard/blob/master/tensorboard/compat/proto/attr_value.proto
+    specifically designed for a NodeDef. The values have been
+    reverse engineered from standard TensorBoard logged data.
+    """
+    attr_proto = {}
+    if shape is not None:
+        shapeproto = tensor_shape_proto(shape)
+        attr_proto["_output_shapes"] = AttrValue(
+            list=AttrValue.ListValue(shape=[shapeproto])
+        )
+    if dtype is not None:
+        attr_proto["dtype"] = AttrValue(s=dtype.encode(encoding="utf-8"))
+    if attr is not None:
+        for key in attr.keys():
+            attr_proto[key] = AttrValue(s=attr[key].encode(encoding="utf-8"))
+
+    return attr_proto
+
+
+def node_proto(
+    name, op="UnSpecified", input=None, outputshape=None, dtype=None, attributes={}
+):
+    """Creates an object matching
+    https://github.com/tensorflow/tensorboard/blob/master/tensorboard/compat/proto/node_def.proto
+    """
+    if input is None:
+        input = []
+    if not isinstance(input, list):
+        input = [input]
+    return NodeDef(
+        name=name.encode(encoding="utf_8"),
+        op=op,
+        input=input,
+        attr=attr_value_proto(outputshape, dtype, attributes),
+    )
+
+
+def node(
+    name, op="UnSpecified", input=None, outputshape=None, dtype=None, attributes={}
+):
+    return node_proto(name, op, input, outputshape, dtype, attributes)
+
+
+def graph(node_list):
+    graph_def = GraphDef(node=node_list, versions=VersionDef(producer=22))
+    stepstats = RunMetadata(
+        step_stats=StepStats(dev_stats=[DeviceStepStats(device="/device:CPU:0")])
+    )
+    return graph_def, stepstats
+
+
+def text(tag, text):
+    plugin_data = SummaryMetadata.PluginData(
+        plugin_name="text", content=TextPluginData(version=0).SerializeToString()
+    )
+    smd = SummaryMetadata(plugin_data=plugin_data)
+    string_val = []
+    for item in text:
+        string_val.append(item.encode(encoding="utf_8"))
+    tensor = TensorProto(
+        dtype="DT_STRING",
+        string_val=string_val,
+        tensor_shape=TensorShapeProto(dim=[TensorShapeProto.Dim(size=len(text))]),
+    )
+
+    return Summary(value=[Summary.Value(tag=tag, metadata=smd, tensor=tensor)])
+
+
+class NodeRaw:
+    def __init__(self, name, op, input, outputshape, dtype, attributes):
+        self.name = name
+        self.op = op
+        self.input = input
+        self.outputshape = outputshape
+        self.dtype = dtype
+        self.attributes = attributes
+
+
+class SummaryWriterExtend(SummaryWriter):
+    def __init__(
+        self,
+        logdir=None,
+        comment="",
+        purge_step=None,
+        max_queue=10,
+        flush_secs=120,
+        filename_suffix="",
+        write_to_disk=True,
+        log_dir=None,
+        **kwargs
+    ):
+        self.node_raw_dict = {}
+        super().__init__(
+            logdir,
+            comment,
+            purge_step,
+            max_queue,
+            flush_secs,
+            filename_suffix,
+            write_to_disk,
+            log_dir,
+            **kwargs,
+        )
+
+    def add_text(self, tag, text_string_list, global_step=None, walltime=None):
+        """Add text data to summary.
+        Args:
+            tag (string): Data identifier
+            text_string_list (string list): String to save
+            global_step (int): Global step value to record
+            walltime (float): Optional override default walltime (time.time())
+                seconds after epoch of event
+        Examples::
+            # text can be divided into three levels by tag and global_step
+            from writer import SummaryWriterExtend
+            writer = SummaryWriterExtend()
+
+            writer.add_text('level1.0/level2.0', ['text0'], 0)
+            writer.add_text('level1.0/level2.0', ['text1'], 1)
+            writer.add_text('level1.0/level2.1', ['text2'])
+            writer.add_text('level1.1', ['text3'])
+        """
+
+        self._get_file_writer().add_summary(
+            text(tag, text_string_list), global_step, walltime
+        )
+
+    def add_node_raw(
+        self,
+        name,
+        op="UnSpecified",
+        input=[],
+        outputshape=None,
+        dtype=None,
+        attributes={},
+    ):
+        """Add node raw datas that can help build graph.After add all nodes, call
+           add_graph_by_node_raw_list() to build graph and add graph data to summary.
+        Args:
+            name (string): opr name.
+            op (string): opr class name.
+            input (string list): input opr name.
+            outputshape (list): output shape.
+            dtype (string): output data dtype.
+            attributes (dict): attributes info.
+        Examples::
+            from writer import SummaryWriterExtend
+            writer = SummaryWriterExtend()
+
+            writer.add_node_raw('node1', 'opr1', outputshape=[6, 2, 3], dtype="float32", attributes={
+                    "peak_size": "12MB", "mmory_alloc": "2MB, percent: 16.7%"})
+            writer.add_node_raw('node2', 'opr2', outputshape=[6, 2, 3], dtype="float32", input="node1",  attributes={
+                                "peak_size": "12MB", "mmory_alloc": "2MB, percent: 16.7%"})
+            writer.add_graph_by_node_raw_list()
+
+        """
+        # self.node_raw_list.append(
+        #     node(name, op, input, outputshape, dtype, attributes))
+        self.node_raw_dict[name] = NodeRaw(
+            name, op, input, outputshape, dtype, dict(attributes)
+        )
+
+    def add_node_raw_name_suffix(self, name, suffix):
+        """Give node name suffix in order to finding this node by 'search nodes'
+        Args:
+            name (string): opr name.
+            suffix (string): nam suffix.
+        """
+        old_name = self.node_raw_dict[name].name
+        new_name = old_name + suffix
+        # self.node_raw_dict[new_name] = self.node_raw_dict.pop(name)
+        self.node_raw_dict[name].name = new_name
+        for node_name, node in self.node_raw_dict.items():
+            node.input = [new_name if x == old_name else x for x in node.input]
+
+    def add_node_raw_attributes(self, name, attributes):
+        """
+        Args:
+            name (string): opr name.
+            attributes (dict): attributes info that need to be added.
+        """
+        for key, value in attributes.items():
+            self.node_raw_dict[name].attributes[key] = value
+
+    def add_graph_by_node_raw_list(self):
+        """Build graph and add graph data to summary."""
+        node_raw_list = []
+        for key, value in self.node_raw_dict.items():
+            node_raw_list.append(
+                node(
+                    value.name,
+                    value.op,
+                    value.input,
+                    value.outputshape,
+                    value.dtype,
+                    value.attributes,
+                )
+            )
+        self._get_file_writer().add_graph(graph(node_raw_list))