damccorm commented on code in PR #30138:
URL: https://github.com/apache/beam/pull/30138#discussion_r1470022638
##########
sdks/python/apache_beam/ml/transforms/embeddings/huggingface_test.py:
##########
@@ -44,6 +45,7 @@
except ImportError:
tft = None
+_HF_TOKEN = os.environ.get('HF_INFERENCE_TOKEN')
Review Comment:
Can we add this to our actions yaml file so that we verify this runs?
##########
sdks/python/apache_beam/ml/transforms/embeddings/huggingface.py:
##########
@@ -130,6 +142,88 @@ def get_model_handler(self):
def get_ptransform_for_processing(self, **kwargs) -> beam.PTransform:
# wrap the model handler in a _TextEmbeddingHandler since
# the SentenceTransformerEmbeddings works on text input data.
- return RunInference(
- model_handler=_TextEmbeddingHandler(self),
- inference_args=self.inference_args)
+ return (
+ RunInference(
+ model_handler=_TextEmbeddingHandler(self),
+ inference_args=self.inference_args,
+ ))
+
+
+class _InferenceAPIHandler(ModelHandler):
+ def __init__(self, config: 'InferenceAPIEmbeddings'):
+ super().__init__()
+ self._config = config
+
+ def load_model(self):
+ session = requests.Session()
+ session.headers.update(self._config.authorization_token)
+ return session
+
+ def run_inference(
+ self, batch, session: requests.Session, inference_args=None):
+ response = session.post(
+ self._config.api_url,
+ headers=self._config.authorization_token,
+ json={
+ "inputs": batch, "options": inference_args
+ })
+ return response.json()
+
+
+class InferenceAPIEmbeddings(EmbeddingsManager):
+ def __init__(
+ self,
+ hf_token: str,
+ columns: List[str],
+ model_name: Optional[str] = None, # example:
"sentence-transformers/all-MiniLM-l6-v2" # pylint: disable=line-too-long
+ api_url: Optional[str] = None,
+ **kwargs,
+ ):
+ """
+ Feature extraction using HuggingFace's Inference API.
+ Intended to be used for feature-extraction. For other tasks, please
+ refer to https://huggingface.co/inference-api.
+ Args:
+ hf_token: HuggingFace token.
+ columns: List of columns to be embedded.
+ model_name: Model name used for feature extraction.
+ api_url: API url for feature extraction. If specified, model_name will be
+ ignored. If none, the default url
+
https://api-inference.huggingface.co/pipeline/feature-extraction/{model_name} #
pylint: disable=line-too-long
+ will be used.
+
+ """
+ super().__init__(columns, **kwargs)
+ self._api_url = api_url
+ self._authorization_token = {"Authorization": f"Bearer {hf_token}"}
+ self._model_name = model_name
Review Comment:
We should probably do some validation that exactly one of model_name and
api_url are set here instead of in api_url so that this throws at construction
time instead of runtime
##########
sdks/python/apache_beam/ml/transforms/embeddings/huggingface.py:
##########
@@ -130,6 +142,88 @@ def get_model_handler(self):
def get_ptransform_for_processing(self, **kwargs) -> beam.PTransform:
# wrap the model handler in a _TextEmbeddingHandler since
# the SentenceTransformerEmbeddings works on text input data.
- return RunInference(
- model_handler=_TextEmbeddingHandler(self),
- inference_args=self.inference_args)
+ return (
+ RunInference(
+ model_handler=_TextEmbeddingHandler(self),
+ inference_args=self.inference_args,
+ ))
+
+
+class _InferenceAPIHandler(ModelHandler):
+ def __init__(self, config: 'InferenceAPIEmbeddings'):
+ super().__init__()
+ self._config = config
+
+ def load_model(self):
+ session = requests.Session()
+ session.headers.update(self._config.authorization_token)
+ return session
+
+ def run_inference(
+ self, batch, session: requests.Session, inference_args=None):
+ response = session.post(
+ self._config.api_url,
+ headers=self._config.authorization_token,
+ json={
+ "inputs": batch, "options": inference_args
+ })
+ return response.json()
+
+
+class InferenceAPIEmbeddings(EmbeddingsManager):
+ def __init__(
+ self,
+ hf_token: str,
Review Comment:
Should we make this optional? It looks like this kind of authorization can
also be set in the environment -
https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables#hftoken
- we could try to pull the token using that env variable if this isn't set.
The downside is that it makes it a runtime error, but it would probably be a
more robust/secure approach if users have custom containers.
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