Eliaaazzz commented on code in PR #37945:
URL: https://github.com/apache/beam/pull/37945#discussion_r3010163107
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sdks/python/apache_beam/ml/inference/base_test.py:
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@@ -2319,6 +2322,264 @@ def test_batching_kwargs_none_values_omitted(self):
self.assertEqual(kwargs['min_batch_size'], 5)
+class PaddingReportingStringModelHandler(base.ModelHandler[str, str,
+ FakeModel]):
+ """Reports each element with the max length of the batch it ran in."""
+ def load_model(self):
+ return FakeModel()
+
+ def run_inference(self, batch, model, inference_args=None):
+ max_len = max(len(s) for s in batch)
+ return [f'{s}:{max_len}' for s in batch]
+
+
+class RunInferenceLengthAwareBatchingTest(unittest.TestCase):
+ """End-to-end tests for PR2 length-aware batching in RunInference."""
+ def test_run_inference_with_length_aware_batch_elements(self):
+ handler = PaddingReportingStringModelHandler(
+ min_batch_size=2,
+ max_batch_size=2,
+ max_batch_duration_secs=60,
+ batch_length_fn=len,
+ batch_bucket_boundaries=[5])
+
+ examples = ['a', 'cccccc', 'bb', 'ddddddd']
+ with TestPipeline('FnApiRunner') as p:
+ results = (
+ p
+ | beam.Create(examples, reshuffle=False)
+ | base.RunInference(handler))
+ assert_that(results, equal_to(['a:2', 'bb:2', 'cccccc:7', 'ddddddd:7']))
+
+
+class HandlerBucketingKwargsForwardingTest(unittest.TestCase):
Review Comment:
> Actually, I would probably just remove these tests actually; as it stands,
the only thing they protect us against is removing one of these arguments,
which almost certainly would need to be intentional or obvious from the diff.
And agreed on the kwargs forwarding tests as well. I’ll remove them since
they don’t add much protection beyond catching obvious or intentional argument
changes.
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