Source code for sparknlp.annotator.seq2seq.starcoder_transformer
# Copyright 2017-2022 John Snow Labs
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"""Contains classes for the StarCoderTransformer."""
from sparknlp.common import *
[docs]class StarCoderTransformer(AnnotatorModel, HasBatchedAnnotate, HasEngine):
"""StarCoder2: The Versatile Code Companion.
StarCoder2 is a Transformer model designed specifically for code generation and understanding.
With 13 billion parameters, it builds upon the advancements of its predecessors and is trained
on a diverse dataset that includes multiple programming languages. This extensive training
allows StarCoder2 to support a wide array of coding tasks, from code completion to generation.
StarCoder2 was developed to assist developers in writing and understanding code more efficiently,
making it a valuable tool for various software development and data science tasks.
Pretrained models can be loaded with :meth:`.pretrained` of the companion
object:
>>> starcoder2 = StarCoder2Transformer.pretrained() \\
... .setInputCols(["document"]) \\
... .setOutputCol("generation")
The default model is ``"starcoder2-13b"``, if no name is provided. For available
pretrained models please see the `Models Hub
<https://sparknlp.org/models?q=starcoder2>`__.
====================== ======================
Input Annotation types Output Annotation type
====================== ======================
``DOCUMENT`` ``DOCUMENT``
====================== ======================
Parameters
----------
configProtoBytes
ConfigProto from tensorflow, serialized into byte array.
minOutputLength
Minimum length of the sequence to be generated, by default 0
maxOutputLength
Maximum length of output text, by default 20
doSample
Whether or not to use sampling; use greedy decoding otherwise, by default False
temperature
The value used to modulate the next token probabilities, by default 1.0
topK
The number of highest probability vocabulary tokens to keep for
top-k-filtering, by default 50
topP
Top cumulative probability for vocabulary tokens, by default 1.0
If set to float < 1, only the most probable tokens with probabilities
that add up to ``topP`` or higher are kept for generation.
repetitionPenalty
The parameter for repetition penalty, 1.0 means no penalty. , by default
1.0
noRepeatNgramSize
If set to int > 0, all ngrams of that size can only occur once, by
default 0
ignoreTokenIds
A list of token ids which are ignored in the decoder's output, by
default []
Notes
-----
This is a very computationally expensive module especially on larger
sequence. The use of an accelerator such as GPU is recommended.
References
----------
- `StarCoder2: The Versatile Code Companion.
<https://huggingface.co/blog/starcoder>`__
- https://github.com/bigcode-project/starcoder
**Paper Abstract:**
*The BigCode project, an open-scientific collaboration focused on the responsible
development of Large Language Models for Code (Code LLMs), introduces StarCoder2. In
partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons
of their source code archive. Alongside the SWH repositories spanning 619 programming
languages, we carefully select other high-quality data sources, such as GitHub pull requests,
Kaggle notebooks, and code documentation. This results in a training set that is 4× larger
than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters
on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM
benchmarks.*
*We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on
most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2-15B,
significantly outperforms other models of comparable size. In addition, it matches or
outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder-33B is
the best-performing model at code completion for high-resource languages, we find that
StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several
low-resource languages. We make the model weights available under an OpenRAIL license and
ensure full transparency regarding the training data by releasing the Software Heritage
persistent Identifiers (SWHIDs) of the source code data.*
Examples
--------
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \\
... .setInputCol("text") \\
... .setOutputCol("documents")
>>> starcoder2 = StarCoder2Transformer.pretrained("starcoder2") \\
... .setInputCols(["documents"]) \\
... .setMaxOutputLength(50) \\
... .setOutputCol("generation")
>>> pipeline = Pipeline().setStages([documentAssembler, starcoder2])
>>> data = spark.createDataFrame([["def add(a, b):"]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.select("generation.result").show(truncate=False)
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|result |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|[def add(a, b): return a + b] |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
"""
name = "StarCoderTransformer"
inputAnnotatorTypes = [AnnotatorType.DOCUMENT]
outputAnnotatorType = AnnotatorType.DOCUMENT
configProtoBytes = Param(Params._dummy(), "configProtoBytes",
"ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()",
TypeConverters.toListInt)
minOutputLength = Param(Params._dummy(), "minOutputLength", "Minimum length of the sequence to be generated",
typeConverter=TypeConverters.toInt)
maxOutputLength = Param(Params._dummy(), "maxOutputLength", "Maximum length of output text",
typeConverter=TypeConverters.toInt)
doSample = Param(Params._dummy(), "doSample", "Whether or not to use sampling; use greedy decoding otherwise",
typeConverter=TypeConverters.toBoolean)
temperature = Param(Params._dummy(), "temperature", "The value used to module the next token probabilities",
typeConverter=TypeConverters.toFloat)
topK = Param(Params._dummy(), "topK",
"The number of highest probability vocabulary tokens to keep for top-k-filtering",
typeConverter=TypeConverters.toInt)
topP = Param(Params._dummy(), "topP",
"If set to float < 1, only the most probable tokens with probabilities that add up to ``top_p`` or higher are kept for generation",
typeConverter=TypeConverters.toFloat)
repetitionPenalty = Param(Params._dummy(), "repetitionPenalty",
"The parameter for repetition penalty. 1.0 means no penalty. See `this paper <https://arxiv.org/pdf/1909.05858.pdf>`__ for more details",
typeConverter=TypeConverters.toFloat)
noRepeatNgramSize = Param(Params._dummy(), "noRepeatNgramSize",
"If set to int > 0, all ngrams of that size can only occur once",
typeConverter=TypeConverters.toInt)
ignoreTokenIds = Param(Params._dummy(), "ignoreTokenIds",
"A list of token ids which are ignored in the decoder's output",
typeConverter=TypeConverters.toListInt)
[docs] def setIgnoreTokenIds(self, value):
"""A list of token ids which are ignored in the decoder's output.
Parameters
----------
value : List[int]
The words to be filtered out
"""
return self._set(ignoreTokenIds=value)
[docs] def setConfigProtoBytes(self, b):
"""Sets configProto from tensorflow, serialized into byte array.
Parameters
----------
b : List[int]
ConfigProto from tensorflow, serialized into byte array
"""
return self._set(configProtoBytes=b)
[docs] def setMinOutputLength(self, value):
"""Sets minimum length of the sequence to be generated.
Parameters
----------
value : int
Minimum length of the sequence to be generated
"""
return self._set(minOutputLength=value)
[docs] def setMaxOutputLength(self, value):
"""Sets maximum length of output text.
Parameters
----------
value : int
Maximum length of output text
"""
return self._set(maxOutputLength=value)
[docs] def setDoSample(self, value):
"""Sets whether or not to use sampling, use greedy decoding otherwise.
Parameters
----------
value : bool
Whether or not to use sampling; use greedy decoding otherwise
"""
return self._set(doSample=value)
[docs] def setTemperature(self, value):
"""Sets the value used to module the next token probabilities.
Parameters
----------
value : float
The value used to module the next token probabilities
"""
return self._set(temperature=value)
[docs] def setTopK(self, value):
"""Sets the number of highest probability vocabulary tokens to keep for
top-k-filtering.
Parameters
----------
value : int
Number of highest probability vocabulary tokens to keep
"""
return self._set(topK=value)
[docs] def setTopP(self, value):
"""Sets the top cumulative probability for vocabulary tokens.
If set to float < 1, only the most probable tokens with probabilities
that add up to ``topP`` or higher are kept for generation.
Parameters
----------
value : float
Cumulative probability for vocabulary tokens
"""
return self._set(topP=value)
[docs] def setRepetitionPenalty(self, value):
"""Sets the parameter for repetition penalty. 1.0 means no penalty.
Parameters
----------
value : float
The repetition penalty
References
----------
See `Ctrl: A Conditional Transformer Language Model For Controllable
Generation <https://arxiv.org/pdf/1909.05858.pdf>`__ for more details.
"""
return self._set(repetitionPenalty=value)
[docs] def setNoRepeatNgramSize(self, value):
"""Sets size of n-grams that can only occur once.
If set to int > 0, all ngrams of that size can only occur once.
Parameters
----------
value : int
N-gram size can only occur once
"""
return self._set(noRepeatNgramSize=value)
@keyword_only
def __init__(self, classname="com.johnsnowlabs.nlp.annotators.seq2seq.StarCoderTransformer", java_model=None):
super(StarCoderTransformer, self).__init__(classname=classname, java_model=java_model)
self._setDefault(minOutputLength=0, maxOutputLength=20, doSample=False, temperature=0.6, topK=50, topP=0.9,
repetitionPenalty=1.0, noRepeatNgramSize=0, ignoreTokenIds=[], batchSize=1)
@staticmethod
[docs] def loadSavedModel(folder, spark_session, use_openvino=False):
"""Loads a locally saved model.
Parameters
----------
folder : str
Folder of the saved model
spark_session : pyspark.sql.SparkSession
The current SparkSession
Returns
-------
StarCoderTransformer
The restored model
"""
from sparknlp.internal import _StarCoderLoader
jModel = _StarCoderLoader(folder, spark_session._jsparkSession, use_openvino)._java_obj
return StarCoderTransformer(java_model=jModel)
@staticmethod
[docs] def pretrained(name="starcoder", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default "starcoder"
lang : str, optional
Language of the pretrained model, by default "en"
remote_loc : str, optional
Optional remote address of the resource, by default None. Will use
Spark NLPs repositories otherwise.
Returns
-------
StarCoderTransformer
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(StarCoderTransformer, name, lang, remote_loc)