TheNeuralBit commented on code in PR #16970: URL: https://github.com/apache/beam/pull/16970#discussion_r844408711
########## 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]: Review Comment: The actual contract seems to be that batch is a `List`, but we put no constraints on the element type, right? ```suggestion def run_inference(self, batch: List[Any], model: Any) -> Iterable[Any]: ``` ########## 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: Could we remove the branching logic of `_unbatch` and just handle it here instead? ```suggestion if has_keys: yield from zip(keys, predictions) else: yield from predictions ``` ########## 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]: Review Comment: Also, shouldn't model be the same for every call to `run_inference`? Why pass it as an argument here? ########## 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: It would also be nice to use a `Generic` typehint for `model` in `ModelLoader` and `RunInference` instead of just using `Any` (implementors could always erase it with `Any` if they want to). ```suggestion class ModelLoader(Generic[T]): """Has the ability to load an ML model.""" def load_model(self) -> T: ``` -- This is an automated message from the Apache Git Service. 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