gemini-code-assist[bot] commented on code in PR #38110: URL: https://github.com/apache/beam/pull/38110#discussion_r3196069643
########## sdks/python/apache_beam/yaml/tests/runinference_huggingface.yaml: ########## @@ -0,0 +1,62 @@ +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You 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. + +pipelines: + - pipeline: + type: chain + transforms: + - type: Create + config: + elements: + - text: "I love Apache Beam!" + - text: "I hate this error." + - type: RunInference + config: + model_handler: + type: "HuggingFacePipeline" + config: + task: "text-classification" + inference_fn: + callable: | + def real_inference(batch, pipeline, inference_args): + predictions = pipeline(batch, **inference_args) + + # If it's a single dictionary (batch size of 1), wrap it in a list + if isinstance(predictions, dict): + predictions = [predictions] + + return { + 'label': [p['label'] for p in predictions], + 'score': [p['score'] for p in predictions] + } Review Comment:  The `real_inference` function is incorrectly implemented for use with `RunInference`. `RunInference` expects the inference function to return an iterable (e.g., a list) of the same length as the input `batch`, where each element is the prediction for the corresponding input. The current implementation returns a single dictionary for the entire batch, which will cause a length mismatch error or incorrect result mapping. Additionally, Hugging Face pipelines typically return a list of results when given a batch, where each result is a list of dictionaries (one per label). To match the expected output in `AssertEqual`, you should return a list of the top predictions for each element in the batch. ```yaml def real_inference(batch, pipeline, inference_args): predictions = pipeline(batch, **inference_args) return [p[0] if isinstance(p, list) else p for p in predictions] ``` ########## sdks/python/apache_beam/yaml/tests/runinference_huggingface.yaml: ########## @@ -0,0 +1,62 @@ +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You 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. + +pipelines: + - pipeline: + type: chain + transforms: + - type: Create + config: + elements: + - text: "I love Apache Beam!" + - text: "I hate this error." + - type: RunInference + config: + model_handler: + type: "HuggingFacePipeline" + config: + task: "text-classification" + inference_fn: + callable: | + def real_inference(batch, pipeline, inference_args): + predictions = pipeline(batch, **inference_args) + + # If it's a single dictionary (batch size of 1), wrap it in a list + if isinstance(predictions, dict): + predictions = [predictions] + + return { + 'label': [p['label'] for p in predictions], + 'score': [p['score'] for p in predictions] + } + preprocess: + callable: 'lambda x: x.text' + - type: MapToFields + config: + language: python + fields: + text: text + sentiment: + callable: 'lambda x: x.inference.inference["label"]' Review Comment:  The access pattern `x.inference.inference["label"]` is incorrect here. When a custom `inference_fn` is used, `x.inference` contains the direct output of that function for the current element. If the function is fixed to return a list of dictionaries (as suggested), you should access the label directly from the inference result. ```yaml callable: 'lambda x: x.inference["label"]' ``` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
