SparkNLP 1032: Introducing CoHere

python/sparknlp/annotator/seq2seq/__init__.py

@@ -28,3 +28,4 @@
 from sparknlp.annotator.seq2seq.qwen_transformer import *
 from sparknlp.annotator.seq2seq.starcoder_transformer import *
 from sparknlp.annotator.seq2seq.llama3_transformer import *
+from sparknlp.annotator.seq2seq.cohere_transformer import *

python/sparknlp/annotator/seq2seq/cohere_transformer.py

@@ -0,0 +1,357 @@
+#  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 CoHereTransformer."""
+
+from sparknlp.common import *
+
+
+class CoHereTransformer(AnnotatorModel, HasBatchedAnnotate, HasEngine):
+    """Cohere: Command-R Transformer
+
+        C4AI Command-R is a research release of a 35 billion parameter highly performant generative model.
+        Command-R is a large language model with open weights optimized for a variety of use cases including reasoning,
+        summarization, and question answering. Command-R has the capability for multilingual generation evaluated
+        in 10 languages and highly performant RAG capabilities.
+
+        Pretrained models can be loaded with :meth:`.pretrained` of the companion
+        object:
+
+        >>> CoHere = CoHereTransformer.pretrained() \\
+        ...     .setInputCols(["document"]) \\
+        ...     .setOutputCol("generation")
+
+
+        The default model is ``"CoHere-7b"``, if no name is provided. For available
+        pretrained models please see the `Models Hub
+        <https://sparknlp.org/models?q=CoHere>`__.
+
+        ====================== ======================
+        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 60
+        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 40
+        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
+        sequences. The use of an accelerator such as GPU is recommended.
+
+        References
+        ----------
+        - `Cohere <https://cohere.for.ai/>`__
+
+
+        Examples
+        --------
+        >>> import sparknlp
+        >>> from sparknlp.base import *
+        >>> from sparknlp.annotator import *
+        >>> from pyspark.ml import Pipeline
+        >>> documentAssembler = DocumentAssembler() \\
+        ...     .setInputCol("text") \\
+        ...     .setOutputCol("documents")
+        >>> CoHere = CoHereTransformer.pretrained() \\
+        ...     .setInputCols(["documents"]) \\
+        ...     .setMaxOutputLength(60) \\
+        ...     .setOutputCol("generation")
+        >>> pipeline = Pipeline().setStages([documentAssembler, CoHere])
+        >>> data = spark.createDataFrame([
+        ...     (
+        ...         1,
+        ...         "<BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello, how are you?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>"
+        ...         )
+        ... ]).toDF("id", "text")
+        >>> result = pipeline.fit(data).transform(data)
+        >>> result.select("generation.result").show(truncate=False)
+        +------------------------------------------------+
+        |result                                          |
+        +------------------------------------------------+
+        |[Hello! I'm doing well, thank you for asking! I'm excited to help you with whatever questions you have today. How can I assist you?]|
+        +------------------------------------------------+
+    """
+
+    name = "CoHereTransformer"
+
+    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)
+
+    beamSize = Param(Params._dummy(), "beamSize",
+                     "The number of beams to use for beam search",
+                     typeConverter=TypeConverters.toInt)
+
+    stopTokenIds = Param(Params._dummy(), "stopTokenIds",
+                         "A list of token ids which are considered as stop tokens in the decoder's output",
+                         typeConverter=TypeConverters.toListInt)
+
+    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 beams to use for beam search.
+
+        Parameters
+        ----------
+        value : int
+            The number of beams to use for beam search
+        """
+        return self._set(beamSize=value)
+
+    def setStopTokenIds(self, value):
+        """Sets a list of token ids which are considered as stop tokens in the decoder's output.
+
+        Parameters
+        ----------
+        value : List[int]
+            The words to be considered as stop tokens
+        """
+        return self._set(stopTokenIds=value)
+
+    @keyword_only
+    def __init__(self, classname="com.johnsnowlabs.nlp.annotators.seq2seq.CoHereTransformer", java_model=None):
+        super(CoHereTransformer, self).__init__(
+            classname=classname,
+            java_model=java_model
+        )
+        self._setDefault(
+            minOutputLength=0,
+            maxOutputLength=20,
+            doSample=False,
+            temperature=0.6,
+            topK=-1,
+            topP=0.9,
+            repetitionPenalty=1.0,
+            noRepeatNgramSize=3,
+            ignoreTokenIds=[],
+            batchSize=1,
+            beamSize=1,
+            stopTokenIds=[128001, ]
+        )
+
+    @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
+        -------
+        CoHereTransformer
+            The restored model
+        """
+        from sparknlp.internal import _CoHereLoader
+        jModel = _CoHereLoader(folder, spark_session._jsparkSession, use_openvino)._java_obj
+        return CoHereTransformer(java_model=jModel)
+
+    @staticmethod
+    def pretrained(name="cohere_35b_int4", lang="en", remote_loc=None):
+        """Downloads and loads a pretrained model.
+
+        Parameters
+        ----------
+        name : str, optional
+            Name of the pretrained model, by default "llama_2_7b_chat_hf_int4"
+        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
+        -------
+        CoHereTransformer
+            The restored model
+        """
+        from sparknlp.pretrained import ResourceDownloader
+        return ResourceDownloader.downloadModel(CoHereTransformer, name, lang, remote_loc)

python/sparknlp/internal/__init__.py

@@ -121,6 +121,15 @@ def __init__(self, path, jspark):
             jspark,
         )
 
+class _CoHereLoader(ExtendedJavaWrapper):
+    def __init__(self, path, jspark, use_openvino=False):
+        super(_CoHereLoader, self).__init__(
+            "com.johnsnowlabs.nlp.annotators.seq2seq.CoHereTransformer.loadSavedModel",
+            path,
+            jspark,
+            use_openvino,
+        )
+
 class _DeBERTaLoader(ExtendedJavaWrapper):
     def __init__(self, path, jspark):
         super(_DeBERTaLoader, self).__init__(