tvalentyn commented on a change in pull request #16970: URL: https://github.com/apache/beam/pull/16970#discussion_r821248609
########## File path: sdks/python/apache_beam/ml/inference/base.py ########## @@ -0,0 +1,232 @@ +# +# 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. +# + +import logging +import platform +import resource +import sys +import time +from typing import Any +from typing import Iterable + +import apache_beam as beam +from apache_beam.utils import shared +from apache_beam.ml.inference.api import PredictionResult + +_MILLISECOND_TO_MICROSECOND = 1000 +_MICROSECOND_TO_NANOSECOND = 1000 +_SECOND_TO_MICROSECOND = 1000000 + + +def _unbatch(maybe_keyed_batches: Any): Review comment: s/Any/Tuple[Any, Any] ? ########## File path: sdks/python/apache_beam/ml/inference/base.py ########## @@ -0,0 +1,232 @@ +# +# 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. +# + +import logging +import platform +import resource +import sys +import time +from typing import Any +from typing import Iterable + +import apache_beam as beam +from apache_beam.utils import shared +from apache_beam.ml.inference.api import PredictionResult + +_MILLISECOND_TO_MICROSECOND = 1000 +_MICROSECOND_TO_NANOSECOND = 1000 +_SECOND_TO_MICROSECOND = 1000000 + + +def _unbatch(maybe_keyed_batches: Any): + keys, results = maybe_keyed_batches + if keys: + return zip(keys, results) + else: + return results + + +class ModelLoader: + """Has the ability to load an ML model.""" + def load_model(self): + """Loads an initializes a model for processing.""" Review comment: ```suggestion """Loads and initializes a model for processing.""" ``` ########## File path: sdks/python/apache_beam/ml/inference/base_test.py ########## @@ -0,0 +1,134 @@ +# +# 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. +# + +from typing import Any +from typing import Iterable +import unittest + +import apache_beam as beam +import apache_beam.ml.inference.base as base +from apache_beam.metrics.metric import MetricsFilter +from apache_beam.ml.inference.api import PredictionResult +from apache_beam.testing.util import assert_that +from apache_beam.testing.util import equal_to +from apache_beam.testing.test_pipeline import TestPipeline + + +class MockModel: Review comment: nit: sounds like a FakeModel rather than MockModel, ditto below. ########## File path: sdks/python/apache_beam/ml/inference/base.py ########## @@ -0,0 +1,232 @@ +# +# 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. +# + +import logging +import platform +import resource +import sys +import time +from typing import Any +from typing import Iterable + +import apache_beam as beam +from apache_beam.utils import shared +from apache_beam.ml.inference.api import PredictionResult + +_MILLISECOND_TO_MICROSECOND = 1000 +_MICROSECOND_TO_NANOSECOND = 1000 +_SECOND_TO_MICROSECOND = 1000000 + + +def _unbatch(maybe_keyed_batches: Any): + keys, results = maybe_keyed_batches + if keys: + return zip(keys, results) + else: + return results + + +class ModelLoader: + """Has the ability to load an ML model.""" + def load_model(self): + """Loads an initializes a model for processing.""" + raise NotImplementedError(type(self)) + + def get_metrics_namespace(self) -> str: + """Returns a namespace for metrics collected by the RunInference transform.""" + return 'RunInference' + + +class InferenceRunner: + """Implements running inferences for a framework.""" + def run_inference(self, batch: Any, model: Any) -> Iterable[PredictionResult]: + """Runs inferences on a batch of examples and returns an Iterable of Predictions.""" + raise NotImplementedError(type(self)) + + +class MetricsCollector: + """A metrics collector that tracks ML related performance and memory usage.""" + def __init__(self, namespace: str): + # Metrics + self._inference_counter = beam.metrics.Metrics.counter( + namespace, 'num_inferences') + self._inference_request_batch_size = beam.metrics.Metrics.distribution( + namespace, 'inference_request_batch_size') Review comment: Is my understanding correct that batching / batch size will be encapsulated from user? If so, would it make sense to collect prorated performance metrics per example instead of per batch? ########## File path: sdks/python/apache_beam/ml/inference/base.py ########## @@ -0,0 +1,222 @@ +# +# 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. +# + +import logging +import platform +import resource +import sys +import time +from typing import Any +from typing import Iterable + +import apache_beam as beam +from apache_beam.utils import shared +from apache_beam.ml.inference.api import PredictionResult + +_MILLISECOND_TO_MICROSECOND = 1000 +_MICROSECOND_TO_NANOSECOND = 1000 +_SECOND_TO_MICROSECOND = 1000000 + + +def _unbatch(maybe_keyed_batches: Any): + keys, results = maybe_keyed_batches + if keys: + return zip(keys, results) + else: + return results + + +class ModelLoader: + """Has the ability to load an ML model.""" + def load_model(self): + """Loads an initializes a model for processing.""" + raise NotImplementedError(type(self)) + + +class InferenceRunner: + """Implements running inferences for a framework.""" + def run_inference(self, batch: Any, model: Any) -> Iterable[PredictionResult]: + """Runs inferences on a batch of examples and returns an Iterable of Predictions.""" + raise NotImplementedError(type(self)) + + +class MetricsCollector: + """A metrics collector that tracks ML related performance and memory usage.""" + def __init__(self, namespace: str): + # Metrics + self._inference_counter = beam.metrics.Metrics.counter( + namespace, 'num_inferences') + self._inference_request_batch_size = beam.metrics.Metrics.distribution( + namespace, 'inference_request_batch_size') + self._inference_request_batch_byte_size = ( + beam.metrics.Metrics.distribution( + namespace, 'inference_request_batch_byte_size')) + # Batch inference latency in microseconds. + self._inference_batch_latency_micro_secs = ( + beam.metrics.Metrics.distribution( + namespace, 'inference_batch_latency_micro_secs')) + self._model_byte_size = beam.metrics.Metrics.distribution( + namespace, 'model_byte_size') + # Model load latency in milliseconds. + self._load_model_latency_milli_secs = beam.metrics.Metrics.distribution( + namespace, 'load_model_latency_milli_secs') + + # Metrics cache + self.load_model_latency_milli_secs_cache = None + self.model_byte_size_cache = None + + def update_metrics_with_cache(self): + if self.load_model_latency_milli_secs_cache is not None: + self._load_model_latency_milli_secs.update( + self.load_model_latency_milli_secs_cache) + self.load_model_latency_milli_secs_cache = None + if self.model_byte_size_cache is not None: + self._model_byte_size.update(self.model_byte_size_cache) + self.model_byte_size_cache = None + + def update( + self, + examples_count: int, + examples_byte_size: int, + latency_micro_secs: int): + self._inference_batch_latency_micro_secs.update(latency_micro_secs) + self._inference_counter.inc(examples_count) + self._inference_request_batch_size.update(examples_count) + self._inference_request_batch_byte_size.update(examples_byte_size) + + +class RunInferenceDoFn(beam.DoFn): + def __init__(self, model_loader, inference_runner, clock=None): + self._model_loader = model_loader + self._inference_runner = inference_runner + self._shared_model_handle = shared.Shared() + # TODO: Compute a good metrics namespace + self._metrics_collector = MetricsCollector('default_namespace') + self._clock = clock + if not clock: + self._clock = _ClockFactory.make_clock() + self._model = None + + def _load_model(self): + def load(): + """Function for constructing shared LoadedModel.""" + memory_before = _get_current_process_memory_in_bytes() + start_time = self._clock.get_current_time_in_microseconds() + model = self._model_loader.load_model() + end_time = self._clock.get_current_time_in_microseconds() + memory_after = _get_current_process_memory_in_bytes() + self._metrics_collector.load_model_latency_milli_secs_cache = ( + (end_time - start_time) / _MILLISECOND_TO_MICROSECOND) + self._metrics_collector.model_byte_size_cache = ( + memory_after - memory_before) + return model + + return self._shared_model_handle.acquire(load) + + def setup(self): + super().setup() + self._model = self._load_model() + + def process(self, batch): + has_keys = isinstance(batch[0], tuple) + start_time = self._clock.get_current_time_in_microseconds() + if has_keys: + examples = [example for _, example in batch] + keys = [key for key, _ in batch] + else: + examples = batch + keys = None + inference_generator = self._inference_runner.run_inference( + examples, self._model) + predictions = [ + PredictionResult(e, r) for e, r in zip(examples, inference_generator) + ] + inference_latency = self._clock.get_current_time_in_microseconds( Review comment: +1, no need to add comments to explain what the code is doing (that's a sign of an unclear code). ########## File path: sdks/python/apache_beam/ml/inference/base.py ########## @@ -0,0 +1,232 @@ +# +# 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. +# + +import logging +import platform +import resource +import sys +import time +from typing import Any +from typing import Iterable + +import apache_beam as beam +from apache_beam.utils import shared +from apache_beam.ml.inference.api import PredictionResult + +_MILLISECOND_TO_MICROSECOND = 1000 +_MICROSECOND_TO_NANOSECOND = 1000 +_SECOND_TO_MICROSECOND = 1000000 + + +def _unbatch(maybe_keyed_batches: Any): + keys, results = maybe_keyed_batches + if keys: + return zip(keys, results) + else: + return results + + +class ModelLoader: + """Has the ability to load an ML model.""" + def load_model(self): + """Loads an initializes a model for processing.""" + raise NotImplementedError(type(self)) + + def get_metrics_namespace(self) -> str: + """Returns a namespace for metrics collected by the RunInference transform.""" + return 'RunInference' + + +class InferenceRunner: + """Implements running inferences for a framework.""" + def run_inference(self, batch: Any, model: Any) -> Iterable[PredictionResult]: + """Runs inferences on a batch of examples and returns an Iterable of Predictions.""" + raise NotImplementedError(type(self)) + + +class MetricsCollector: + """A metrics collector that tracks ML related performance and memory usage.""" + def __init__(self, namespace: str): + # Metrics + self._inference_counter = beam.metrics.Metrics.counter( + namespace, 'num_inferences') + self._inference_request_batch_size = beam.metrics.Metrics.distribution( + namespace, 'inference_request_batch_size') + self._inference_request_batch_byte_size = ( + beam.metrics.Metrics.distribution( + namespace, 'inference_request_batch_byte_size')) + # Batch inference latency in microseconds. + self._inference_batch_latency_micro_secs = ( + beam.metrics.Metrics.distribution( + namespace, 'inference_batch_latency_micro_secs')) + self._model_byte_size = beam.metrics.Metrics.distribution( + namespace, 'model_byte_size') + # Model load latency in milliseconds. + self._load_model_latency_milli_secs = beam.metrics.Metrics.distribution( + namespace, 'load_model_latency_milli_secs') + + # Metrics cache + self.load_model_latency_milli_secs_cache = None + self.model_byte_size_cache = None + + def update_metrics_with_cache(self): + if self.load_model_latency_milli_secs_cache is not None: + self._load_model_latency_milli_secs.update( + self.load_model_latency_milli_secs_cache) + self.load_model_latency_milli_secs_cache = None + if self.model_byte_size_cache is not None: + self._model_byte_size.update(self.model_byte_size_cache) + self.model_byte_size_cache = None + + def update( + self, + examples_count: int, + examples_byte_size: int, + latency_micro_secs: int): + self._inference_batch_latency_micro_secs.update(latency_micro_secs) + self._inference_counter.inc(examples_count) + self._inference_request_batch_size.update(examples_count) + self._inference_request_batch_byte_size.update(examples_byte_size) + + +class RunInferenceDoFn(beam.DoFn): + def __init__(self, model_loader, inference_runner, clock=None): + self._model_loader = model_loader + self._inference_runner = inference_runner + self._shared_model_handle = shared.Shared() + self._metrics_collector = MetricsCollector( + model_loader.get_metrics_namespace()) + self._clock = clock + if not clock: + self._clock = _ClockFactory.make_clock() + self._model = None + + def _load_model(self): + def load(): + """Function for constructing shared LoadedModel.""" + memory_before = _get_current_process_memory_in_bytes() + start_time = self._clock.get_current_time_in_microseconds() + model = self._model_loader.load_model() + end_time = self._clock.get_current_time_in_microseconds() + memory_after = _get_current_process_memory_in_bytes() + self._metrics_collector.load_model_latency_milli_secs_cache = ( + (end_time - start_time) / _MILLISECOND_TO_MICROSECOND) + self._metrics_collector.model_byte_size_cache = ( + memory_after - memory_before) + return model + + return self._shared_model_handle.acquire(load) + + def setup(self): + super().setup() Review comment: nit: it's a no-op. ########## File path: sdks/python/apache_beam/ml/inference/base.py ########## @@ -0,0 +1,194 @@ +import logging +import platform +import resource +import sys +import time +from typing import Any +from typing import List + +import apache_beam as beam +from apache_beam.utils import shared +from apache_beam.ml.inference.apis import PredictionResult + +_MILLISECOND_TO_MICROSECOND = 1000 +_MICROSECOND_TO_NANOSECOND = 1000 +_SECOND_TO_MICROSECOND = 1000000 + + +def _unbatch(maybe_keyed_batches: Any): + keys, results = maybe_keyed_batches + if keys: + return zip(keys, results) + else: + return results + + +class ModelLoader: + """Has the ability to load an ML model.""" + def load_model(self): + """Loads an initializes a model for processing.""" + raise NotImplementedError(type(self)) + + +class InferenceRunner: + """Implements running inferences for a framework.""" + def run_inference(self, batch: Any, model: Any) -> List[PredictionResult]: + """Runs inferences on a batch of examples and returns a list of Predictions.""" + raise NotImplementedError(type(self)) + + +class MetricsCollector: + """A metrics collector that tracks ML related performance and memory usage.""" + def __init__(self, namespace: str): + # Metrics + self._inference_counter = beam.metrics.Metrics.counter( + namespace, 'num_inferences') + self._inference_request_batch_size = beam.metrics.Metrics.distribution( + namespace, 'inference_request_batch_size') + self._inference_request_batch_byte_size = ( + beam.metrics.Metrics.distribution( + namespace, 'inference_request_batch_byte_size')) + # Batch inference latency in microseconds. + self._inference_batch_latency_micro_secs = ( + beam.metrics.Metrics.distribution( + namespace, 'inference_batch_latency_micro_secs')) + self._model_byte_size = beam.metrics.Metrics.distribution( + namespace, 'model_byte_size') + # Model load latency in milliseconds. + self._load_model_latency_milli_secs = beam.metrics.Metrics.distribution( + namespace, 'load_model_latency_milli_secs') + + # Metrics cache + self.load_model_latency_milli_secs_cache = None + self.model_byte_size_cache = None + + def update_metrics_with_cache(self): + if self.load_model_latency_milli_secs_cache is not None: + self._load_model_latency_milli_secs.update( + self.load_model_latency_milli_secs_cache) + self.load_model_latency_milli_secs_cache = None + if self.model_byte_size_cache is not None: + self._model_byte_size.update(self.model_byte_size_cache) + self.model_byte_size_cache = None + + def update( + self, + examples_count: int, + examples_byte_size: int, + latency_micro_secs: int): + self._inference_batch_latency_micro_secs.update(latency_micro_secs) + self._inference_counter.inc(examples_count) + self._inference_request_batch_size.update(examples_count) + self._inference_request_batch_byte_size.update(examples_byte_size) + + +class RunInferenceDoFn(beam.DoFn): + def __init__(self, model_loader, inference_runner): + self._model_loader = model_loader + self._inference_runner = inference_runner + self._shared_model_handle = shared.Shared() + # TODO: Compute a good metrics namespace + self._metrics_collector = MetricsCollector('default_namespace') + self._clock = _ClockFactory.make_clock() + self._model = None + + def _load_model(self): + def load(): + """Function for constructing shared LoadedModel.""" + memory_before = _get_current_process_memory_in_bytes() + start_time = self._clock.get_current_time_in_microseconds() + model = self._model_loader.load_model() + end_time = self._clock.get_current_time_in_microseconds() + memory_after = _get_current_process_memory_in_bytes() + self._metrics_collector.load_model_latency_milli_secs_cache = ( + (end_time - start_time) / _MILLISECOND_TO_MICROSECOND) + self._metrics_collector.model_byte_size_cache = ( + memory_after - memory_before) + return model + + return self._shared_model_handle.acquire(load) + + def setup(self): + super().setup() + self._model = self._load_model() + + def process(self, batch): + has_keys = isinstance(batch[0], tuple) + start_time = self._clock.get_current_time_in_microseconds() + if has_keys: + examples = [example for _, example in batch] + keys = [key for key, _ in batch] + else: + examples = batch + keys = None + inferences = self._inference_runner.run_inference(examples, self._model) + inference_latency = self._clock.get_current_time_in_microseconds( + ) - start_time + num_bytes = sys.getsizeof(batch) + num_elements = len(batch) + self._metrics_collector.update( + num_elements, sys.getsizeof(batch), inference_latency) + yield keys, [PredictionResult(e, r) for e, r in zip(examples, inferences)] + + +class RunInferenceImpl(beam.PTransform): + def __init__(self, model_loader, inference_runner): + self._model_loader = model_loader + self._inference_runner = inference_runner + + def expand(self, pcoll: beam.PCollection) -> beam.PCollection: + return ( + pcoll + # TODO: Hook into the batching DoFn APIs. + | beam.BatchElements() + | beam.ParDo( + RunInferenceDoFn(self._model_loader, self._inference_runner)) + | beam.FlatMap(_unbatch)) + + +def _is_darwin() -> bool: + return sys.platform == 'darwin' + + +def _get_current_process_memory_in_bytes(): + """Returns memory usage in bytes.""" + + if resource is not None: + usage = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss + if _is_darwin(): + return usage + return usage * 1024 + else: + logging.warning( + 'Resource module is not available for current platform, ' + 'memory usage cannot be fetched.') + return 0 + + +def _is_windows() -> bool: Review comment: we make our unit tests run on all major operating systems through github actions ########## File path: sdks/python/apache_beam/ml/inference/base.py ########## @@ -0,0 +1,232 @@ +# +# 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. +# + +import logging +import platform +import resource +import sys +import time +from typing import Any +from typing import Iterable + +import apache_beam as beam +from apache_beam.utils import shared +from apache_beam.ml.inference.api import PredictionResult + +_MILLISECOND_TO_MICROSECOND = 1000 +_MICROSECOND_TO_NANOSECOND = 1000 +_SECOND_TO_MICROSECOND = 1000000 + + +def _unbatch(maybe_keyed_batches: Any): + keys, results = maybe_keyed_batches + if keys: + return zip(keys, results) + else: + return results + + +class ModelLoader: + """Has the ability to load an ML model.""" + def load_model(self): + """Loads an initializes a model for processing.""" + raise NotImplementedError(type(self)) + + def get_metrics_namespace(self) -> str: + """Returns a namespace for metrics collected by the RunInference transform.""" + return 'RunInference' + + +class InferenceRunner: + """Implements running inferences for a framework.""" + def run_inference(self, batch: Any, model: Any) -> Iterable[PredictionResult]: + """Runs inferences on a batch of examples and returns an Iterable of Predictions.""" + raise NotImplementedError(type(self)) + + +class MetricsCollector: + """A metrics collector that tracks ML related performance and memory usage.""" + def __init__(self, namespace: str): + # Metrics + self._inference_counter = beam.metrics.Metrics.counter( + namespace, 'num_inferences') + self._inference_request_batch_size = beam.metrics.Metrics.distribution( + namespace, 'inference_request_batch_size') + self._inference_request_batch_byte_size = ( + beam.metrics.Metrics.distribution( + namespace, 'inference_request_batch_byte_size')) + # Batch inference latency in microseconds. + self._inference_batch_latency_micro_secs = ( + beam.metrics.Metrics.distribution( + namespace, 'inference_batch_latency_micro_secs')) + self._model_byte_size = beam.metrics.Metrics.distribution( + namespace, 'model_byte_size') + # Model load latency in milliseconds. + self._load_model_latency_milli_secs = beam.metrics.Metrics.distribution( + namespace, 'load_model_latency_milli_secs') + + # Metrics cache + self.load_model_latency_milli_secs_cache = None + self.model_byte_size_cache = None + + def update_metrics_with_cache(self): + if self.load_model_latency_milli_secs_cache is not None: + self._load_model_latency_milli_secs.update( + self.load_model_latency_milli_secs_cache) + self.load_model_latency_milli_secs_cache = None + if self.model_byte_size_cache is not None: + self._model_byte_size.update(self.model_byte_size_cache) + self.model_byte_size_cache = None + + def update( + self, + examples_count: int, + examples_byte_size: int, + latency_micro_secs: int): + self._inference_batch_latency_micro_secs.update(latency_micro_secs) + self._inference_counter.inc(examples_count) + self._inference_request_batch_size.update(examples_count) + self._inference_request_batch_byte_size.update(examples_byte_size) + + +class RunInferenceDoFn(beam.DoFn): + def __init__(self, model_loader, inference_runner, clock=None): + self._model_loader = model_loader + self._inference_runner = inference_runner + self._shared_model_handle = shared.Shared() + self._metrics_collector = MetricsCollector( + model_loader.get_metrics_namespace()) + self._clock = clock + if not clock: + self._clock = _ClockFactory.make_clock() + self._model = None + + def _load_model(self): + def load(): + """Function for constructing shared LoadedModel.""" + memory_before = _get_current_process_memory_in_bytes() + start_time = self._clock.get_current_time_in_microseconds() + model = self._model_loader.load_model() + end_time = self._clock.get_current_time_in_microseconds() + memory_after = _get_current_process_memory_in_bytes() + self._metrics_collector.load_model_latency_milli_secs_cache = ( + (end_time - start_time) / _MILLISECOND_TO_MICROSECOND) + self._metrics_collector.model_byte_size_cache = ( + memory_after - memory_before) + return model + + return self._shared_model_handle.acquire(load) + + def setup(self): + super().setup() + self._model = self._load_model() + + def process(self, batch): + # Process supports both keyed data, and example only data. + # First keys and samples are separated (if there are keys) + has_keys = isinstance(batch[0], tuple) + if has_keys: + examples = [example for _, example in batch] + keys = [key for key, _ in batch] + else: + examples = batch + keys = None + + start_time = self._clock.get_current_time_in_microseconds() + inference_generator = self._inference_runner.run_inference( + examples, self._model) + predictions = [ + PredictionResult(e, r) for e, r in zip(examples, inference_generator) + ] + + inference_latency = self._clock.get_current_time_in_microseconds( + ) - start_time + num_bytes = sys.getsizeof(batch) + num_elements = len(batch) + self._metrics_collector.update( + num_elements, sys.getsizeof(batch), inference_latency) + + # keys will be recombined with their predictions in the RunInferenceImpl PTransform. + yield keys, predictions + + def finish_bundle(self): + self._metrics_collector.update_metrics_with_cache() + + +class RunInferenceImpl(beam.PTransform): + def __init__(self, model_loader, inference_runner, clock=None): + self._model_loader = model_loader + self._inference_runner = inference_runner + self._clock = clock + + def expand(self, pcoll: beam.PCollection) -> beam.PCollection: + return ( + pcoll + # TODO(BEAM-14044): Hook into the batching DoFn APIs. + | beam.BatchElements(min_batch_size=2) + | beam.ParDo( + RunInferenceDoFn( + self._model_loader, self._inference_runner, clock=self._clock)) + | beam.FlatMap(_unbatch)) + + +def _is_darwin() -> bool: + return sys.platform == 'darwin' + + +def _get_current_process_memory_in_bytes(): + """Returns memory usage in bytes.""" + + if resource is not None: Review comment: You should have ``` try: import resource except ImportError: resource = None ``` to make this condition work -- This is an automated message from the Apache Git Service. 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