Update Gemma on Dataflow example to pass max_length as arg

dataflow/gemma/custom_model_gemma.py

@@ -74,13 +74,24 @@ def run_inference(
         Returns:
           An Iterable of type PredictionResult.
         """
+        if inference_args is None:
+            inference_args = {"max_length": 64}
         # Loop each text string, and use a tuple to store the inference results.
         predictions = []
         for one_text in batch:
-            result = model.generate(one_text, max_length=64)
+            result = model.generate(one_text, **inference_args)
             predictions.append(result)
         return utils._convert_to_result(batch, predictions, self._model_name)
 
+    def validate_inference_args(self, inference_args: Optional[dict[str,
+                                                                    Any]]):
+        if inference_args:
+            if len(inference_args
+                   ) > 1 or "max_length" not in inference_args.keys():
+                raise ValueError(
+                    "invalid inference args, only valid arg is max_length, got",
+                    inference_args)
+
 
 class FormatOutput(beam.DoFn):
     def process(self, element, *args, **kwargs):
@@ -123,8 +134,10 @@ def process(self, element, *args, **kwargs):
         beam.io.ReadFromPubSub(subscription=args.messages_subscription)
         | "Parse" >> beam.Map(lambda x: x.decode("utf-8"))
         | "RunInference-Gemma" >> RunInference(
-            GemmaModelHandler(args.model_path)
-        )  # Send the prompts to the model and get responses.
+            GemmaModelHandler(args.model_path),
+            inference_args={
+                "max_length": 32
+            })  # Send the prompts to the model and get responses.
         | "Format Output" >> beam.ParDo(FormatOutput())  # Format the output.
         | "Publish Result" >>
         beam.io.gcp.pubsub.WriteStringsToPubSub(topic=args.responses_topic))

dataflow/gemma/requirements.txt

@@ -1,4 +1,5 @@
 apache_beam[gcp]==2.54.0
 protobuf==4.25.0
 keras_nlp==0.8.2
-keras==3.0.5
+keras==3.0.5
+tensorflow==2.16.1