Source code for sparknlp.annotator.seq2seq.phi3_transformer
# Copyright 2017-2022 John Snow Labs
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"""Contains classes for the Phi3Transformer."""
from sparknlp.common import *
[docs]class Phi3Transformer(AnnotatorModel, HasBatchedAnnotate, HasEngine):
"""Phi-3
The Phi-3-Medium-4K-Instruct is a 14B parameters, lightweight, state-of-the-art open model trained with the Phi-3
datasets that includes both synthetic data and the filtered publicly available websites data with a focus on
high-quality and reasoning dense properties. The model belongs to the Phi-3 family with the Medium version in two
variants 4K and 128K which is the context length (in tokens) that it can support.
The model has underwent a post-training process that incorporates both supervised fine-tuning and direct preference
optimization for the instruction following and safety measures. When assessed against benchmarks testing common
sense, language understanding, math, code, long context and logical reasoning, Phi-3-Medium-4K-Instruct showcased
a robust and state-of-the-art performance among models of the same-size and next-size-up.
Pretrained models can be loaded with :meth:`.pretrained` of the companion
object:
>>> phi3 = Phi3Transformer.pretrained() \\
... .setInputCols(["document"]) \\
... .setOutputCol("generation")
The default model is ``"phi3"``, if no name is provided. For available
pretrained models please see the `Models Hub
<https://sparknlp.org/models?q=phi3>`__.
====================== ======================
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 module 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
----------
- `Phi-3: Small Language Models with Big Potential
<https://news.microsoft.com/source/features/ai/the-phi-3-small-language-models-with-big-potential//>`__
- https://huggingface.co/microsoft/phi-3
**Paper Abstract:**
*We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion
tokens, whose overall performance, as measured by both academic benchmarks and internal
testing, rivals that of models such as Mixtral 8x7B and GPT-3.5 (e.g., phi-3-mini achieves 69%
on MMLU and 8.38 on MT-bench), despite being small enough to be deployed on a phone. The
innovation lies entirely in our dataset for training, a scaled-up version of the one used for
phi-2, composed of heavily filtered publicly available web data and synthetic data. The model
is also further aligned for robustness, safety, and chat format. We also provide some initial
parameter-scaling results with a 7B and 14B models trained for 4.8T tokens, called phi-3-small
and phi-3-medium, both significantly more capable than phi-3-mini (e.g., respectively 75% and
78% on MMLU, and 8.7 and 8.9 on MT-bench). Moreover, we also introduce phi-3-vision, a 4.2
billion parameter model based on phi-3-mini with strong reasoning capabilities for image and
text prompts.*
Examples
--------
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \\
... .setInputCol("text") \\
... .setOutputCol("documents")
>>> phi3 = Phi3Transformer.pretrained("phi3") \\
... .setInputCols(["documents"]) \\
... .setMaxOutputLength(50) \\
... .setOutputCol("generation")
>>> pipeline = Pipeline().setStages([documentAssembler, phi3])
>>> data = spark.createDataFrame([["My name is Leonardo."]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.select("summaries.generation").show(truncate=False)
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|result |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|[My name is Leonardo . I am a student of the University of California, Berkeley. I am interested in the field of Artificial Intelligence and its applications in the real world. I have a strong |
| passion for learning and am always looking for ways to improve my knowledge and skills] |
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
"""
name = "Phi3Transformer"
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.Phi3Transformer", java_model=None):
super(Phi3Transformer, self).__init__(classname=classname, java_model=java_model)
self._setDefault(minOutputLength=0, maxOutputLength=20, doSample=False, temperature=1.0, topK=500, topP=1.0,
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
-------
Phi3Transformer
The restored model
"""
from sparknlp.internal import _Phi3Loader
jModel = _Phi3Loader(folder, spark_session._jsparkSession, use_openvino)._java_obj
return Phi3Transformer(java_model=jModel)
@staticmethod
[docs] def pretrained(name="phi3", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default "phi3"
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
-------
Phi3Transformer
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(Phi3Transformer, name, lang, remote_loc)