morpheus.messages.multi_inference_message.MultiInferenceNLPMessage

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class MultiInferenceNLPMessage(*args, **kwargs)[source]

Bases: morpheus.messages.multi_inference_message.MultiInferenceMessage

A stronger typed version of MultiInferenceMessage that is used for NLP workloads. Helps ensure the proper inputs are set and eases debugging.

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.
input_ids: Returns token-ids for each string padded with 0s to max_length.
input_mask: Returns mask for token-ids result where corresponding positions identify valid token-id values.
inputs: Get inputs stored in the TensorMemory container.
seq_ids: Returns sequence ids, which are used to keep track of which inference requests belong to each message.
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[, num_selected_rows])	Perform a copy of the current message instance for the given `ranges` of rows.
`get_input`(name)	Get input stored in the TensorMemory container.
`get_meta`([columns])	Return column values from `morpheus.pipeline.messages.MessageMeta.df`.
`get_meta_list`([col_name])	Return a column values from morpheus.messages.MessageMeta.df as a list.
`get_slice`(start, stop)	Returns sliced batches based on offsets supplied.
`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

rangestyping.List[typing.Tuple[int, int]]: 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)]
masktyping.Union[None, cupy.ndarray, numpy.ndarray]: 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, num_selected_rows=None)[source]

Perform a copy of the current message instance for the given ranges of rows.

Parameters

rangestyping.List[typing.Tuple[int, int]]: 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)]
num_selected_rowstyping.Union[None, int]: Optional specify the number of rows selected by ranges, otherwise this is computed by the result.

Returns

MultiMessage

get_input(name)[source]

Get input stored in the TensorMemory container.

Parameters

namestr: Input key name.

Returns

cupy.ndarray: Inference input.

get_meta(columns=None)[source]

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

Parameters

columnstyping.Union[None, str, typing.List[str]]: 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_list(col_name=None)[source]

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

Parameters

col_namestr: Column name in the dataframe.

Returns

List[str]: Column values from the dataframe.

get_slice(start, stop)[source]

Returns sliced batches based on offsets supplied. Automatically calculates the correct mess_offset and mess_count.

Parameters

startint: Start offset address.
stopint: Stop offset address.

Returns

MultiInferenceMessage: A new MultiInferenceMessage with sliced offset and count.

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.

property input_ids

Returns token-ids for each string padded with 0s to max_length.

Returns

cupy.ndarray: The token-ids for each string padded with 0s to max_length.

property input_mask

Returns mask for token-ids result where corresponding positions identify valid token-id values.

Returns

cupy.ndarray: The mask for token-ids result where corresponding positions identify valid token-id values.

property inputs

Get inputs stored in the TensorMemory container.

Returns

cupy.ndarray: Inference inputs.

property seq_ids

Returns sequence ids, which are used to keep track of which inference requests belong to each message.

Returns

cupy.ndarray: Ids used to index from an inference input to a message. Necessary since there can be more inference inputs than messages (i.e., if some messages get broken into multiple inference requests).

set_meta(columns, value)[source]

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

Parameters

columnstyping.Union[None, str, typing.List[str]]: 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.
valueAny: 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 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.