Eliaaazzz commented on code in PR #37428:
URL: https://github.com/apache/beam/pull/37428#discussion_r2744081016
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sdks/python/apache_beam/ml/inference/base_test.py:
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@@ -2133,5 +2133,63 @@ def request(self, batch, model, inference_args=None):
model_handler.run_inference([1], FakeModel())
+class FakeModelHandlerForSizing(base.ModelHandler[int, int, FakeModel]):
+ """A ModelHandler used to test element sizing behavior."""
+ def __init__(
+ self,
+ max_batch_size: int = 10,
+ max_batch_weight: Optional[int] = None,
+ element_size_fn=None):
+ self._max_batch_size = max_batch_size
+ self._max_batch_weight = max_batch_weight
+ self._element_size_fn = element_size_fn
+
+ def load_model(self) -> FakeModel:
+ return FakeModel()
+
+ def run_inference(self, batch, model, inference_args=None):
+ return [model.predict(x) for x in batch]
+
+ def batch_elements_kwargs(self):
+ kwargs = {'max_batch_size': self._max_batch_size}
+ if self._max_batch_weight is not None:
+ kwargs['max_batch_weight'] = self._max_batch_weight
+ if self._element_size_fn:
+ kwargs['element_size_fn'] = self._element_size_fn
+ return kwargs
Review Comment:
> This is probably a good suggestion
Makes sense, I'll refactor this to use super().__init__ and rely on the base
class implementation.
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