Phi3V python api and tests

prabod · prabod · commit 25580a9953e6 · 2024-11-08T06:35:32.000Z
Signed-off-by: Prabod Rathnayaka &lt;prabod@rathnayaka.me&gt;
diff --git a/python/sparknlp/annotator/cv/__init__.py b/python/sparknlp/annotator/cv/__init__.py
@@ -16,4 +16,4 @@
 from sparknlp.annotator.cv.convnext_for_image_classification import *
 from sparknlp.annotator.cv.vision_encoder_decoder_for_image_captioning import *
 from sparknlp.annotator.cv.clip_for_zero_shot_classification import *
-from sparknlp.annotator.cv.blip_for_question_answering import *
+from sparknlp.annotator.cv.phi3_vision_for_multimodal import *
diff --git a/python/sparknlp/annotator/cv/phi3_vision_for_multimodal.py b/python/sparknlp/annotator/cv/phi3_vision_for_multimodal.py
@@ -0,0 +1,329 @@
+#  Copyright 2017-2024 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.
+
+from sparknlp.common import *
+
+class Phi3Vision(AnnotatorModel,
+                               HasBatchedAnnotateImage,
+                               HasImageFeatureProperties,
+                               HasEngine,
+                               HasCandidateLabelsProperties,
+                               HasRescaleFactor):
+    """BLIPForQuestionAnswering can load BLIP models  for visual question answering.
+    The model consists of a vision encoder, a text encoder as well as a text decoder.
+    The vision encoder will encode the input image, the text encoder will encode the input question together
+    with the encoding of the image, and the text decoder will output the answer to the question.
+
+    Pretrained models can be loaded with :meth:`.pretrained` of the companion
+    object:
+
+    >>> visualQAClassifier = BLIPForQuestionAnswering.pretrained() \\
+    ...     .setInputCols(["image_assembler"]) \\
+    ...     .setOutputCol("answer")
+
+    The default model is ``"blip_vqa_base"``, if no name is
+    provided.
+
+    For available pretrained models please see the `Models Hub
+    <https://sparknlp.org/models?task=Question+Answering>`__.
+
+    To see which models are compatible and how to import them see
+    `Import Transformers into Spark NLP 🚀
+    <https://github.com/JohnSnowLabs/spark-nlp/discussions/5669>`_.
+
+    ====================== ======================
+    Input Annotation types Output Annotation type
+    ====================== ======================
+    ``IMAGE``              ``DOCUMENT``
+    ====================== ======================
+
+    Parameters
+    ----------
+    batchSize
+        Batch size. Large values allows faster processing but requires more
+        memory, by default 2
+    configProtoBytes
+        ConfigProto from tensorflow, serialized into byte array.
+    maxSentenceLength
+        Max sentence length to process, by default 50
+
+    Examples
+    --------
+    >>> import sparknlp
+    >>> from sparknlp.base import *
+    >>> from sparknlp.annotator import *
+    >>> from pyspark.ml import Pipeline
+    >>> image_df = SparkSessionForTest.spark.read.format("image").load(path=images_path)
+    >>> test_df = image_df.withColumn("text", lit("What's this picture about?"))
+    >>> imageAssembler = ImageAssembler() \\
+    ...     .setInputCol("image") \\
+    ...     .setOutputCol("image_assembler")
+    >>> visualQAClassifier = BLIPForQuestionAnswering.pretrained() \\
+    ...     .setInputCols("image_assembler") \\
+    ...     .setOutputCol("answer") \\
+    ...     .setSize(384)
+    >>> pipeline = Pipeline().setStages([
+    ...     imageAssembler,
+    ...     visualQAClassifier
+    ... ])
+    >>> result = pipeline.fit(test_df).transform(test_df)
+    >>> result.select("image_assembler.origin", "answer.result").show(false)
+    +--------------------------------------+------+
+    |origin                                |result|
+    +--------------------------------------+------+
+    |[file:///content/images/cat_image.jpg]|[cats]|
+    +--------------------------------------+------+
+    """
+
+    name = "Phi3Vision"
+
+    inputAnnotatorTypes = [AnnotatorType.IMAGE]
+
+    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)
+    beamSize = Param(Params._dummy(), "beamSize",
+                     "The Number of beams for beam search.",
+                     typeConverter=TypeConverters.toInt)
+
+    def setMaxSentenceSize(self, value):
+        """Sets Maximum sentence length that the annotator will process, by
+        default 50.
+
+        Parameters
+        ----------
+        value : int
+            Maximum sentence length that the annotator will process
+        """
+        return self._set(maxSentenceLength=value)
+
+    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)
+
+    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)
+
+    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)
+
+    def setMaxOutputLength(self, value):
+        """Sets maximum length of output text.
+
+        Parameters
+        ----------
+        value : int
+            Maximum length of output text
+        """
+        return self._set(maxOutputLength=value)
+
+    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)
+
+    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)
+
+    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)
+
+    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)
+
+    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)
+
+    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)
+
+    def setBeamSize(self, value):
+        """Sets the number of beam size for beam search, by default `4`.
+
+        Parameters
+        ----------
+        value : int
+            Number of beam size for beam search
+        """
+        return self._set(beamSize=value)
+    @keyword_only
+    def __init__(self, classname="com.johnsnowlabs.nlp.annotators.cv.Phi3Vision",
+                 java_model=None):
+        super(Phi3Vision, self).__init__(
+            classname=classname,
+            java_model=java_model
+        )
+        self._setDefault(
+            batchSize=2,
+            minOutputLength=0,
+            maxOutputLength=200,
+            doSample=False,
+            temperature=1,
+            topK=50,
+            topP=1,
+            repetitionPenalty=1.0,
+            noRepeatNgramSize=0,
+            ignoreTokenIds=[],
+            beamSize=1,
+        )
+
+    @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
+        -------
+        CLIPForZeroShotClassification
+            The restored model
+        """
+        from sparknlp.internal import _Phi3VisionLoader
+        jModel = _Phi3VisionLoader(folder, spark_session._jsparkSession, use_openvino)._java_obj
+        return Phi3Vision(java_model=jModel)
+
+    @staticmethod
+    def pretrained(name="phi3v", lang="en", remote_loc=None):
+        """Downloads and loads a pretrained model.
+
+        Parameters
+        ----------
+        name : str, optional
+            Name of the pretrained model, by default
+            "blip_vqa_tf"
+        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
+        -------
+        CLIPForZeroShotClassification
+            The restored model
+        """
+        from sparknlp.pretrained import ResourceDownloader
+        return ResourceDownloader.downloadModel(Phi3Vision, name, lang, remote_loc)
diff --git a/python/sparknlp/internal/__init__.py b/python/sparknlp/internal/__init__.py
@@ -363,6 +363,15 @@ def __init__(self, path, jspark, use_openvino=False):
             use_openvino,
         )
 
+class _Phi3VisionLoader(ExtendedJavaWrapper):
+    def __init__(self, path, jspark, use_openvino=False):
+        super(_Phi3VisionLoader, self).__init__(
+            "com.johnsnowlabs.nlp.annotators.cv.Phi3Vision.loadSavedModel",
+            path,
+            jspark,
+            use_openvino
+        )
+
 class _RoBertaLoader(ExtendedJavaWrapper):
     def __init__(self, path, jspark, use_openvino=False):
         super(_RoBertaLoader, self).__init__(
diff --git a/python/test/annotator/cv/phi3_vision_for_multimodal_test.py b/python/test/annotator/cv/phi3_vision_for_multimodal_test.py
