robertwb commented on code in PR #16970: URL: https://github.com/apache/beam/pull/16970#discussion_r844531031
########## sdks/python/apache_beam/ml/inference/base.py: ########## @@ -0,0 +1,262 @@ +# +# 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. +# + +"""An extensible run inference transform. + +Users of this module can extend the ModelLoader class for any MLframework. Then +pass their extended ModelLoader object into RunInference to create a +RunInference Beam transform for that framework. + +The transform will handle standard inference functionality like metric +collection, sharing model between threads and batching elements. +""" + +import logging +import os +import pickle +import platform +import sys +import time +from typing import Any +from typing import Iterable +from typing import Tuple + +import apache_beam as beam +from apache_beam.utils import shared + +try: + # pylint: disable=g-import-not-at-top + import resource +except ImportError: + resource = None + +_MICROSECOND_TO_MILLISECOND = 1000 +_NANOSECOND_TO_MICROSECOND = 1000 +_SECOND_TO_MICROSECOND = 1_000_000 + + +class InferenceRunner(): + """Implements running inferences for a framework.""" + def run_inference(self, batch: Any, model: Any) -> Iterable[Any]: + """Runs inferences on a batch of examples and returns an Iterable of Predictions.""" + raise NotImplementedError(type(self)) + + def get_num_bytes(self, batch: Any) -> int: + """Returns the number of bytes of data for a batch.""" + return len(pickle.dumps(batch)) + + def get_metrics_namespace(self) -> str: + """Returns a namespace for metrics collected by the RunInference transform.""" + return 'RunInference' + + +class ModelLoader(): + """Has the ability to load an ML model.""" + def load_model(self) -> Any: + """Loads and initializes a model for processing.""" + raise NotImplementedError(type(self)) + + def get_inference_runner(self) -> InferenceRunner: + """Returns an implementation of InferenceRunner for this model.""" + raise NotImplementedError(type(self)) + + +def _unbatch(maybe_keyed_batches: Tuple[Any, Any]): + keys, results = maybe_keyed_batches + if keys: + return zip(keys, results) + else: + return results + + +class RunInference(beam.PTransform): + """An extensible transform for running inferences.""" + def __init__(self, model_loader: ModelLoader, clock=None): + self._model_loader = model_loader + self._clock = clock + + # TODO(BEAM-14208): Add batch_size back off in the case there + # are functional reasons large batch sizes cannot be handled. + def expand(self, pcoll: beam.PCollection) -> beam.PCollection: + return ( + pcoll + # TODO(BEAM-14044): Hook into the batching DoFn APIs. + | beam.BatchElements() + | beam.ParDo(_RunInferenceDoFn(self._model_loader, self._clock)) + | beam.FlatMap(_unbatch)) + + +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 cache_load_model_metrics(self, load_model_latency_ms, model_byte_size): + self._load_model_latency_milli_secs_cache = load_model_latency_ms + self._model_byte_size_cache = model_byte_size + + 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): + """A DoFn implementation generic to frameworks.""" + def __init__(self, model_loader: ModelLoader, clock=None): + self._model_loader = model_loader + self._inference_runner = model_loader.get_inference_runner() + self._shared_model_handle = shared.Shared() + self._metrics_collector = _MetricsCollector( + self._inference_runner.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() + load_model_latency_ms = ((end_time - start_time) / + _MICROSECOND_TO_MILLISECOND) + model_byte_size = memory_after - memory_before + self._metrics_collector.cache_load_model_metrics( + load_model_latency_ms, model_byte_size) + return model + + # TODO(BEAM-14207): Investigate releasing model. + return self._shared_model_handle.acquire(load) + + def setup(self): + 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() + result_generator = self._inference_runner.run_inference( + examples, self._model) + predictions = list(result_generator) + + inference_latency = self._clock.get_current_time_in_microseconds( + ) - start_time + num_bytes = self._inference_runner.get_num_bytes(examples) + num_elements = len(batch) + self._metrics_collector.update(num_elements, num_bytes, inference_latency) + + # Keys are recombined with predictions in the RunInference PTransform. + yield keys, predictions Review Comment: +1 ########## sdks/python/apache_beam/ml/inference/base.py: ########## @@ -0,0 +1,262 @@ +# +# 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. +# + +"""An extensible run inference transform. + +Users of this module can extend the ModelLoader class for any MLframework. Then +pass their extended ModelLoader object into RunInference to create a +RunInference Beam transform for that framework. + +The transform will handle standard inference functionality like metric +collection, sharing model between threads and batching elements. +""" + +import logging +import os +import pickle +import platform +import sys +import time +from typing import Any +from typing import Iterable +from typing import Tuple + +import apache_beam as beam +from apache_beam.utils import shared + +try: + # pylint: disable=g-import-not-at-top + import resource +except ImportError: + resource = None + +_MICROSECOND_TO_MILLISECOND = 1000 +_NANOSECOND_TO_MICROSECOND = 1000 +_SECOND_TO_MICROSECOND = 1_000_000 + + +class InferenceRunner(): + """Implements running inferences for a framework.""" + def run_inference(self, batch: Any, model: Any) -> Iterable[Any]: + """Runs inferences on a batch of examples and returns an Iterable of Predictions.""" + raise NotImplementedError(type(self)) + + def get_num_bytes(self, batch: Any) -> int: + """Returns the number of bytes of data for a batch.""" + return len(pickle.dumps(batch)) + + def get_metrics_namespace(self) -> str: + """Returns a namespace for metrics collected by the RunInference transform.""" + return 'RunInference' + + +class ModelLoader(): + """Has the ability to load an ML model.""" + def load_model(self) -> Any: Review Comment: +1, though this may fall under the typing improvements that were deferred to another JIRA. (Frankly, there's no need to return a model that is then passed to the InferenceRunner on every call, instead the InferenceRunner could encapsulate the model itself on instantiation.) -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
