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morpheus.messages.multi_tensor_message.MultiTensorMessage

class MultiTensorMessage(*, meta, mess_offset=0, mess_count=- 1, memory, offset=0, count=- 1, id_tensor_name='seq_ids')[source]

Bases: morpheus.messages.multi_message.MultiMessage

This class contains several inference responses as well as the corresponding message metadata.

Parameters
memory : TensorMemory

Container holding generic tensor data in cupy arrays
offset

Offset of each message into the TensorMemory block.
count

Number of rows in the TensorMemory block.
Attributes
id

Returns ID column values from morpheus.pipeline.messages.MessageMeta.df as list.
id_col

Returns ID column values from morpheus.pipeline.messages.MessageMeta.df.
tensors

Get tensors stored in the TensorMemory container sliced according to offset and count.
timestamp

Returns timestamp column values from morpheus.messages.MessageMeta.df as list.

Methods

copy_meta_ranges(ranges[, mask]) Perform a copy of the underlying dataframe for the given ranges of rows.
copy_ranges(ranges) Perform a copy of the current message, dataframe and tensors for the given ranges of rows.
copy_tensor_ranges(ranges[, mask]) Perform a copy of the underlying tensor tensors for the given ranges of rows.
from_message(message, *[, meta, ...]) Creates a new instance of a derived class from MultiMessage using an existing message as the template.
get_id_tensor() Get the tensor that holds message ID information.
get_meta([columns]) Return column values from morpheus.pipeline.messages.MessageMeta.df.
get_meta_column_names() Return column names available in the underlying DataFrame.
get_meta_list([col_name]) Return a column values from morpheus.messages.MessageMeta.df as a list.
get_slice(start, stop) Perform a slice of the current message from start:stop (excluding stop)
get_tensor(name) Get tensor stored in the TensorMemory container.
set_meta(columns, value) Set column values to morpheus.pipelines.messages.MessageMeta.df.
copy_meta_ranges(ranges, mask=None)[source]

Perform a copy of the underlying dataframe for the given ranges of rows.

Parameters
ranges

Rows to include in the copy in the form of [(`start_row, stop_row),…]` The stop_row isn’t included. For example to copy rows 1-2 & 5-7 ranges=[(1, 3), (5, 8)]
mask

Optionally specify rows as a cupy array (when using cudf Dataframes) or a numpy array (when using pandas Dataframes) of booleans. When not-None ranges will be ignored. This is useful as an optimization as this avoids needing to generate the mask on it’s own.
Returns
Dataframe

copy_ranges(ranges)[source]

Perform a copy of the current message, dataframe and tensors for the given ranges of rows.

Parameters
ranges

Rows to include in the copy in the form of [(`start_row, stop_row),…]` The stop_row isn’t included. For example to copy rows 1-2 & 5-7 ranges=[(1, 3), (5, 8)]
——-

`MultiTensorMessage`

copy_tensor_ranges(ranges, mask=None)[source]

Perform a copy of the underlying tensor tensors for the given ranges of rows.

Parameters
ranges

Rows to include in the copy in the form of [(`start_row, stop_row),…]` The stop_row isn’t included. For example to copy rows 1-2 & 5-7 ranges=[(1, 3), (5, 8)]
mask

Optionally specify rows as a cupy array (when using cudf Dataframes) or a numpy array (when using pandas Dataframes) of booleans. When not-None ranges will be ignored. This is useful as an optimization as this avoids needing to generate the mask on it’s own.
Returns
typing.Dict[str, cupy.ndarray]

classmethod from_message(message, *, meta=None, mess_offset=- 1, mess_count=- 1, memory=None, offset=- 1, count=- 1, **kwargs)[source]

Creates a new instance of a derived class from MultiMessage using an existing message as the template. This is very useful when a new message needs to be created with a single change to an existing MessageMeta.

When creating the new message, all required arguments for the class specified by cls will be pulled from message unless otherwise specified in the kwargs. Special handling is performed depending on whether or not a new meta object is supplied. If one is supplied, the offset and count defaults will be 0 and meta.count respectively. Otherwise offset and count will be pulled from the input message.

Parameters
cls

The class to create
message

An existing message to use as a template. Can be a base or derived from cls as long as all arguments can be pulled from message or proveded in kwargs
meta

A new MessageMeta to use, by default None
mess_offset

A new mess_offset to use, by default -1
mess_count

A new mess_count to use, by default -1
memory

A new TensorMemory to use. If supplied, offset and count default to 0 and memory.count respectively. By default None
offset

A new offset to use, by default -1
count

A new count to use, by default -1
**kwargs : dict

Keyword arguments to use when creating the new instance.
Returns
Self

A new instance of type cls
Raises
ValueError

If the incoming message is None
get_id_tensor()[source]

Get the tensor that holds message ID information. Equivalent to get_tensor(id_tensor_name)

Returns
cupy.ndarray

Array containing the ID information
Raises
KeyError

If self.id_tensor_name is not found in the tensors
get_meta(columns=None)[source]

Return column values from morpheus.pipeline.messages.MessageMeta.df.

Parameters
columns

Input column names. Returns all columns if None is specified. When a string is passed, a Series is returned. Otherwise, a Dataframe is returned.
Returns
Series or Dataframe

Column values from the dataframe.
get_meta_column_names()[source]

Return column names available in the underlying DataFrame.

Returns
list[str]

Column names from the dataframe.
get_meta_list(col_name=None)[source]

Return a column values from morpheus.messages.MessageMeta.df as a list.

Parameters
col_name

Column name in the dataframe.
Returns
List[str]

Column values from the dataframe.
get_slice(start, stop)[source]

Perform a slice of the current message from start:stop (excluding stop)

For example to slice from rows 1-3 use m.get_slice(1, 4). The returned MultiTensorMessage will contain references to the same underlying Dataframe and tensor tensors, and this calling this method is reletively low cost compared to MultiTensorMessage.copy_ranges

Parameters
start

Starting row of the slice
stop

Stop of the slice
——-

`MultiTensorMessage`

get_tensor(name)[source]

Get tensor stored in the TensorMemory container.

Parameters
name

tensor key name.
Returns
cupy.ndarray

Inference tensor.
property id: List[int]

Returns ID column values from morpheus.pipeline.messages.MessageMeta.df as list.

Returns
List[int]

ID column values from the dataframe as list.
property id_col

Returns ID column values from morpheus.pipeline.messages.MessageMeta.df.

Returns
pandas.Series

ID column values from the dataframe.
id_tensor_name: ClassVar[str] = 'seq_ids'

Name of the tensor that correlates tensor rows to message IDs
required_tensors: ClassVar[List[str]] = []

The tensor names that are required for instantiation
set_meta(columns, value)[source]

Set column values to morpheus.pipelines.messages.MessageMeta.df.

Parameters
columns

Input column names. Sets the value for the corresponding column names. If None is specified, all columns will be used. If the column does not exist, a new one will be created.
value

Value to apply to the specified columns. If a single value is passed, it will be broadcast to all rows. If a Series or Dataframe is passed, rows will be matched by index.
property tensors

Get tensors stored in the TensorMemory container sliced according to offset and count.

Returns
cupy.ndarray

Inference tensors.
property timestamp: List[int]

Returns timestamp column values from morpheus.messages.MessageMeta.df as list.

Returns
List[int]

Timestamp column values from the dataframe as list.
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