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* Added Phi3 with openvino and onnx support * removed changes to phi2 and changed fasttest to slowtest --------- Co-authored-by: Maziyar Panahi <maziyar.panahi@iscpif.fr>
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# Copyright 2017-2022 John Snow Labs | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
"""Contains classes for the Phi3Transformer.""" | ||
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from sparknlp.common import * | ||
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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] | | ||
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+ | ||
""" | ||
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name = "Phi3Transformer" | ||
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inputAnnotatorTypes = [AnnotatorType.DOCUMENT] | ||
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outputAnnotatorType = AnnotatorType.DOCUMENT | ||
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configProtoBytes = Param(Params._dummy(), "configProtoBytes", | ||
"ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()", | ||
TypeConverters.toListInt) | ||
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minOutputLength = Param(Params._dummy(), "minOutputLength", "Minimum length of the sequence to be generated", | ||
typeConverter=TypeConverters.toInt) | ||
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maxOutputLength = Param(Params._dummy(), "maxOutputLength", "Maximum length of output text", | ||
typeConverter=TypeConverters.toInt) | ||
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doSample = Param(Params._dummy(), "doSample", "Whether or not to use sampling; use greedy decoding otherwise", | ||
typeConverter=TypeConverters.toBoolean) | ||
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temperature = Param(Params._dummy(), "temperature", "The value used to module the next token probabilities", | ||
typeConverter=TypeConverters.toFloat) | ||
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topK = Param(Params._dummy(), "topK", | ||
"The number of highest probability vocabulary tokens to keep for top-k-filtering", | ||
typeConverter=TypeConverters.toInt) | ||
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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) | ||
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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) | ||
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noRepeatNgramSize = Param(Params._dummy(), "noRepeatNgramSize", | ||
"If set to int > 0, all ngrams of that size can only occur once", | ||
typeConverter=TypeConverters.toInt) | ||
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ignoreTokenIds = Param(Params._dummy(), "ignoreTokenIds", | ||
"A list of token ids which are ignored in the decoder's output", | ||
typeConverter=TypeConverters.toListInt) | ||
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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) | ||
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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) | ||
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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) | ||
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def setMaxOutputLength(self, value): | ||
"""Sets maximum length of output text. | ||
Parameters | ||
---------- | ||
value : int | ||
Maximum length of output text | ||
""" | ||
return self._set(maxOutputLength=value) | ||
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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) | ||
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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) | ||
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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) | ||
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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) | ||
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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) | ||
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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) | ||
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@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) | ||
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@staticmethod | ||
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) | ||
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@staticmethod | ||
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) |
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