damccorm commented on code in PR #25200:
URL: https://github.com/apache/beam/pull/25200#discussion_r1092132299
##########
sdks/python/apache_beam/ml/inference/base_test.py:
##########
@@ -339,6 +375,79 @@ def validate_inference_args(
third_party_model_handler.batch_elements_kwargs()
third_party_model_handler.validate_inference_args({})
+ def test_run_inference_prediction_result_with_model_id(self):
+ examples = [1, 5, 3, 10]
+ expected = [
+ base.PredictionResult(
+ example=example,
+ inference=example + 1,
+ model_id='fake_model_id_default') for example in examples
+ ]
+ with TestPipeline() as pipeline:
+ pcoll = pipeline | 'start' >> beam.Create(examples)
+ actual = pcoll | base.RunInference(
+ FakeModelHandlerReturnsPredictionResult())
+ assert_that(actual, equal_to(expected), label='assert:inferences')
+
+ @pytest.mark.it_postcommit
+ def test_run_inference_prediction_result_with_side_input(self):
+ test_pipeline = TestPipeline(is_integration_test=True)
Review Comment:
LGTM
##########
sdks/python/apache_beam/examples/inference/run_inference_side_inputs.py:
##########
@@ -0,0 +1,165 @@
+#
+# 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.
+#
+
+"""
+Used for internal testing. No backwards compatibility.
+"""
+
+import argparse
+import logging
+import time
+from typing import Iterable
+from typing import Optional
+from typing import Sequence
+
+import apache_beam as beam
+from apache_beam.ml.inference import base
+from apache_beam.options.pipeline_options import PipelineOptions
+from apache_beam.options.pipeline_options import SetupOptions
+from apache_beam.transforms import trigger
+from apache_beam.transforms import window
+from apache_beam.transforms.periodicsequence import PeriodicImpulse
+from apache_beam.transforms.userstate import CombiningValueStateSpec
+
+
+# create some fake models which returns different inference results.
+class FakeModelDefault:
+ def predict(self, example: int) -> int:
+ return example
+
+
+class FakeModelAdd(FakeModelDefault):
+ def predict(self, example: int) -> int:
+ return example + 1
+
+
+class FakeModelSub(FakeModelDefault):
+ def predict(self, example: int) -> int:
+ return example - 1
+
+
+class FakeModelHandlerReturnsPredictionResult(
+ base.ModelHandler[int, base.PredictionResult, FakeModelDefault]):
+ def __init__(self, clock=None, model_id='fake_model_id_default'):
+ self.model_id = model_id
+ self._fake_clock = clock
+
+ def load_model(self):
+ if self._fake_clock:
+ self._fake_clock.current_time_ns += 500_000_000 # 500ms
+ if self.model_id == 'model_add.pkl':
+ return FakeModelAdd()
+ elif self.model_id == 'model_sub.pkl':
+ return FakeModelSub()
+ return FakeModelDefault()
+
+ def run_inference(
+ self,
+ batch: Sequence[int],
+ model: FakeModelDefault,
+ inference_args=None) -> Iterable[base.PredictionResult]:
+ for example in batch:
+ yield base.PredictionResult(
+ model_id=self.model_id,
+ example=example,
+ inference=model.predict(example))
+
+ def update_model_path(self, model_path: Optional[str] = None):
+ self.model_id = model_path if model_path else self.model_id
+
+
+def run(argv=None, save_main_session=True):
+ parser = argparse.ArgumentParser()
+ first_ts = time.time()
+ side_input_interval = 60
+ main_input_interval = 20
+ # give some time for dataflow to start.
+ last_ts = first_ts + 1200
+ mid_ts = (first_ts + last_ts) / 2
+
+ _, pipeline_args = parser.parse_known_args(argv)
+ options = PipelineOptions(pipeline_args)
+ options.view_as(SetupOptions).save_main_session = save_main_session
+
+ test_pipeline = beam.Pipeline(options=options)
+
+ class GetModel(beam.DoFn):
+ def process(self, element) -> Iterable[base.ModelMetdata]:
+ if time.time() > mid_ts:
+ yield base.ModelMetdata(
+ model_id='model_add.pkl', model_name='model_add')
+ else:
+ yield base.ModelMetdata(
+ model_id='model_sub.pkl', model_name='model_sub')
+
+ class _EmitSingletonSideInput(beam.DoFn):
+ COUNT_STATE = CombiningValueStateSpec('count', combine_fn=sum)
+
+ def process(self, element, count_state=beam.DoFn.StateParam(COUNT_STATE)):
+ _, path = element
+ counter = count_state.read()
+ if counter == 0:
+ count_state.add(1)
+ yield path
+
+ def validate_prediction_result(x: base.PredictionResult):
+ model_id = x.model_id
+ if model_id == 'model_sub.pkl':
+ assert (x.example == 1 and x.inference == 0)
+
+ if model_id == 'model_add.pkl':
+ assert (x.example == 1 and x.inference == 2)
Review Comment:
Nit: Please add an if/assert for the default model
##########
sdks/python/apache_beam/pipeline.py:
##########
@@ -525,6 +525,14 @@ def run(self, test_runner_api='AUTO'):
self.contains_external_transforms = (
ExternalTransformFinder.contains_external_transforms(self))
+ self.contains_run_inference_transform = (
+ RunInferenceSideInputFinder.contains_run_inference_transform(self))
+
+ if (self.contains_run_inference_transform and
+ not self._options.view_as(StandardOptions).streaming):
+ raise RuntimeError(
Review Comment:
I think I like the error that you have
##########
sdks/python/apache_beam/pipeline.py:
##########
@@ -525,14 +525,31 @@ def run(self, test_runner_api='AUTO'):
self.contains_external_transforms = (
ExternalTransformFinder.contains_external_transforms(self))
- self.contains_run_inference_transform = (
- RunInferenceSideInputFinder.contains_run_inference_transform(self))
+ # Finds if RunInference has side inputs enables.
+ # also, checks for the side input window is global and has non default
+ # triggers.
+ run_inference_visitor = RunInferenceVisitor().visit_run_inference(self)
+ self._run_inference_contains_side_input = (
+ run_inference_visitor.contains_run_inference_side_inputs)
- if (self.contains_run_inference_transform and
+ self.run_inference_global_window_non_default_trigger = (
+ run_inference_visitor.contains_global_windows_non_default_trigger)
+
+ if (self._run_inference_contains_side_input and
not self._options.view_as(StandardOptions).streaming):
raise RuntimeError(
"SideInputs to RunInference PTransform is only supported "
- "in streaming mode.")
+ "in streaming mode. To run in streaming mode, add the"
+ " --streaming pipeline option")
+
+ if (self._run_inference_contains_side_input and
+ not self.run_inference_global_window_non_default_trigger):
+ raise RuntimeError(
+ "The RunInference PTransform's SideInput is either using "
+ "GlobalWindows with a default trigger or no Windowing, which "
Review Comment:
```suggestion
"GlobalWindows with a default trigger, non-global windowing, or no
Windowing, which "
```
(probably needs formatting + corresponding test changes)
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