zwestrick commented on code in PR #17527:
URL: https://github.com/apache/beam/pull/17527#discussion_r878354771
##########
sdks/python/apache_beam/ml/inference/base_test.py:
##########
@@ -72,6 +72,21 @@ def process(self, prediction_result):
yield prediction_result.inference
+class FakeInferenceRunnerNeedsBigBatch(FakeInferenceRunner):
+ def run_inference(self, batch, unused_model):
+ if len(batch) < 100:
+ raise ValueError('Unexpectedly small batch')
+ return batch
+
+
+class FakeLoaderWithBatchArgForwarding(FakeModelLoader):
+ def get_inference_runner(self):
+ return FakeInferenceRunnerNeedsBigBatch()
+
+ def batch_elements_kwargs(self):
+ return {'min_batch_size': 9999}
Review Comment:
It makes the test much more difficult to write, since the actual batch size
chosen in that case would be nondeterministic, so it's difficult to distinguish
between size 1 batches due to randomness and size 1 batches due to forwarding
the correct args.
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