morpheus.stages.preprocess.preprocess_nlp_stage.PreprocessNLPStage

(Latest Version)
class PreprocessNLPStage(c, vocab_hash_file='data/bert-base-cased-hash.txt', truncation=False, do_lower_case=False, add_special_tokens=False, stride=- 1, column='data')[source]

Bases: morpheus.stages.preprocess.preprocess_base_stage.PreprocessBaseStage

Prepare NLP input DataFrames for inference.

Parameters
cmorpheus.config.Config

Pipeline configuration instance.

vocab_hash_filestr

Path to hash file containing vocabulary of words with token-ids. This can be created from the raw vocabulary using the cudf.utils.hash_vocab_utils.hash_vocab function.

truncationbool

If set to true, strings will be truncated and padded to max_length. Each input string will result in exactly one output sequence. If set to false, there may be multiple output sequences when the max_length is smaller than generated tokens.

do_lower_casebool

If set to true, original text will be lowercased before encoding.

add_special_tokensbool

Whether or not to encode the sequences with the special tokens of the BERT classification model.

strideint

If truncation == False and the tokenized string is larger than max_length, the sequences containing the overflowing token-ids can contain duplicated token-ids from the main sequence. If max_length is equal to stride there are no duplicated-id tokens. If stride is 80% of max_length, 20% of the first sequence will be repeated on the second sequence and so on until the entire sentence is encoded.

columnstr

Name of the column containing the data that needs to be preprocessed.

Attributes
has_multi_input_ports

Indicates if this stage has multiple input ports.

has_multi_output_ports

Indicates if this stage has multiple output ports.

input_ports

Input ports to this stage.

is_built

Indicates if this stage has been built.

name

The name of the stage.

output_ports

Output ports from this stage.

unique_name

Unique name of stage.

Methods

accepted_types()

Returns accepted input types for this stage.

build(builder[, do_propagate])

Build this stage.

can_build([check_ports])

Determines if all inputs have been built allowing this node to be built.

get_all_input_stages()

Get all input stages to this stage.

get_all_inputs()

Get all input senders to this stage.

get_all_output_stages()

Get all output stages from this stage.

get_all_outputs()

Get all output receivers from this stage.

get_needed_columns()

Stages which need to have columns inserted into the dataframe, should populate the self._needed_columns dictionary with mapping of column names to morpheus.common.TypeId.

join()

Awaitable method that stages can implement this to perform cleanup steps when pipeline is stopped.

on_start()

This function can be overridden to add usecase-specific implementation at the start of any stage in the pipeline.

pre_process_batch(x, vocab_hash_file, ...)

For NLP category usecases, this function performs pre-processing.

start_async()

This function is called along with on_start during stage initialization.

stop()

Stages can implement this to perform cleanup steps when pipeline is stopped.

supports_cpp_node()

Specifies whether this Stage is capable of creating C++ nodes.

_build(builder, in_ports_streams)[source]

This function is responsible for constructing this stage’s internal mrc.SegmentObject object. The input of this function contains the returned value from the upstream stage.

The input values are the mrc.Builder for this stage and a StreamPair tuple which contain the input mrc.SegmentObject object and the message data type.

Parameters
buildermrc.Builder

mrc.Builder object for the pipeline. This should be used to construct/attach the internal mrc.SegmentObject.

in_ports_streamsmorpheus.pipeline.pipeline.StreamPair

List of tuples containing the input mrc.SegmentObject object and the message data type.

Returns
typing.List[morpheus.pipeline.pipeline.StreamPair]

List of tuples containing the output mrc.SegmentObject object from this stage and the message data type.

accepted_types()[source]

Returns accepted input types for this stage.

build(builder, do_propagate=True)[source]

Build this stage.

Parameters
buildermrc.Builder

MRC segment for this stage.

do_propagatebool, optional

Whether to propagate to build output stages, by default True.

can_build(check_ports=False)[source]

Determines if all inputs have been built allowing this node to be built.

Parameters
check_portsbool, optional

Check if we can build based on the input ports, by default False.

Returns
bool

True if we can build, False otherwise.

get_all_input_stages()[source]

Get all input stages to this stage.

Returns
typing.List[morpheus.pipeline.pipeline.StreamWrapper]

All input stages.

get_all_inputs()[source]

Get all input senders to this stage.

Returns
typing.List[morpheus.pipeline.pipeline.Sender]

All input senders.

get_all_output_stages()[source]

Get all output stages from this stage.

Returns
typing.List[morpheus.pipeline.pipeline.StreamWrapper]

All output stages.

get_all_outputs()[source]

Get all output receivers from this stage.

Returns
typing.List[morpheus.pipeline.pipeline.Receiver]

All output receivers.

get_needed_columns()[source]

Stages which need to have columns inserted into the dataframe, should populate the self._needed_columns dictionary with mapping of column names to morpheus.common.TypeId. This will ensure that the columns are allocated and populated with null values.

property has_multi_input_ports: bool

Indicates if this stage has multiple input ports.

Returns
bool

True if stage has multiple input ports, False otherwise.

property has_multi_output_ports: bool

Indicates if this stage has multiple output ports.

Returns
bool

True if stage has multiple output ports, False otherwise.

property input_ports: List[morpheus.pipeline.receiver.Receiver]

Input ports to this stage.

Returns
typing.List[morpheus.pipeline.pipeline.Receiver]

Input ports to this stage.

property is_built: bool

Indicates if this stage has been built.

Returns
bool

True if stage is built, False otherwise.

async join()[source]

Awaitable method that stages can implement this to perform cleanup steps when pipeline is stopped. Typically this is called after stop during a graceful shutdown, but may not be called if the pipeline is terminated.

property name: str

The name of the stage. Used in logging. Each derived class should override this property with a unique name.

Returns
str

Name of a stage.

on_start()[source]

This function can be overridden to add usecase-specific implementation at the start of any stage in the pipeline.

property output_ports: List[morpheus.pipeline.sender.Sender]

Output ports from this stage.

Returns
typing.List[morpheus.pipeline.pipeline.Sender]

Output ports from this stage.

static pre_process_batch(x, vocab_hash_file, do_lower_case, seq_len, stride, truncation, add_special_tokens, column)[source]

For NLP category usecases, this function performs pre-processing.

Parameters
xmorpheus.pipeline.messages.MultiMessage

Input rows received from Deserialized stage.

vocab_hashfilestr

Path to hash file containing vocabulary of words with token-ids. This can be created from the raw vocabulary using the cudf.utils.hash_vocab_utils.hash_vocab function.

do_lower_casebool

If set to true, original text will be lowercased before encoding.

seq_lenint

Limits the length of the sequence returned. If tokenized string is shorter than max_length, output will be padded with 0s. If the tokenized string is longer than max_length and do_truncate == False, there will be multiple returned sequences containing the overflowing token-ids.

strideint

If do_truncate == False and the tokenized string is larger than max_length, the sequences containing the overflowing token-ids can contain duplicated token-ids from the main sequence. If max_length is equal to stride there are no duplicated-id tokens. If stride is 80% of max_length, 20% of the first sequence will be repeated on the second sequence and so on until the entire sentence is encoded.

truncationbool

If set to true, strings will be truncated and padded to max_length. Each input string will result in exactly one output sequence. If set to false, there may be multiple output sequences when the max_length is smaller than generated tokens.

add_special_tokensbool

Whether or not to encode the sequences with the special tokens of the BERT classification model.

columnstr

Name of the column containing the data that needs to be preprocessed.

Returns
morpheus.pipeline.messages.MultiInferenceNLPMessage

NLP inference message.

async start_async()[source]

This function is called along with on_start during stage initialization. Allows stages to utilize the asyncio loop if needed.

stop()[source]

Stages can implement this to perform cleanup steps when pipeline is stopped.

supports_cpp_node()[source]

Specifies whether this Stage is capable of creating C++ nodes. During the build phase, this value will be combined with CppConfig.get_should_use_cpp() to determine whether or not a C++ node is created. This is an instance method to allow runtime decisions and derived classes to override base implementations.

property unique_name: str

Unique name of stage. Generated by appending stage id to stage name.

Returns
str

Unique name of stage.

© Copyright 2023, NVIDIA. Last updated on Apr 11, 2023.