# # 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. # # TODO: https://github.com/apache/beam/issues/21822 # mypy: ignore-errors """An extensible run inference transform. Users of this module can extend the ModelHandler class for any machine learning framework. A ModelHandler implementation is a required parameter of RunInference. The transform handles standard inference functionality, like metric collection, sharing model between threads, and batching elements. """ import functools import logging import os import pickle import sys import threading import time import uuid from abc import ABC from abc import abstractmethod from collections import OrderedDict from collections import defaultdict from collections.abc import Callable from collections.abc import Iterable from collections.abc import Mapping from collections.abc import Sequence from copy import deepcopy from dataclasses import dataclass from datetime import datetime from datetime import timedelta from typing import Any from typing import Generic from typing import NamedTuple from typing import Optional from typing import TypeVar from typing import Union import apache_beam as beam from apache_beam.io.components.adaptive_throttler import AdaptiveThrottler from apache_beam.metrics.metric import Metrics from apache_beam.utils import multi_process_shared from apache_beam.utils import retry from apache_beam.utils import shared try: # pylint: disable=wrong-import-order, wrong-import-position import resource except ImportError: resource = None # type: ignore[assignment] _NANOSECOND_TO_MILLISECOND = 1_000_000 _NANOSECOND_TO_MICROSECOND = 1_000 _MILLISECOND_TO_SECOND = 1_000 ModelT = TypeVar('ModelT') ExampleT = TypeVar('ExampleT') PreProcessT = TypeVar('PreProcessT') PredictionT = TypeVar('PredictionT') PostProcessT = TypeVar('PostProcessT') _INPUT_TYPE = TypeVar('_INPUT_TYPE') _OUTPUT_TYPE = TypeVar('_OUTPUT_TYPE') KeyT = TypeVar('KeyT') # We use NamedTuple to define the structure of the PredictionResult, # however, as support for generic NamedTuples is not available in Python # versions prior to 3.11, we use the __new__ method to provide default # values for the fields while maintaining backwards compatibility. class PredictionResult(NamedTuple('PredictionResult', [('example', _INPUT_TYPE), ('inference', _OUTPUT_TYPE), ('model_id', Optional[str])])): __slots__ = () def __new__(cls, example, inference, model_id=None): return super().__new__(cls, example, inference, model_id) PredictionResult.__doc__ = """A NamedTuple containing both input and output from the inference.""" PredictionResult.example.__doc__ = """The input example.""" PredictionResult.inference.__doc__ = """Results for the inference on the model for the given example.""" PredictionResult.model_id.__doc__ = """Model ID used to run the prediction.""" class ModelMetadata(NamedTuple): model_id: str model_name: str class RunInferenceDLQ(NamedTuple): failed_inferences: beam.PCollection failed_preprocessing: Sequence[beam.PCollection] failed_postprocessing: Sequence[beam.PCollection] class _ModelLoadStats(NamedTuple): model_tag: str load_latency: Optional[int] byte_size: Optional[int] ModelMetadata.model_id.__doc__ = """Unique identifier for the model. This can be a file path or a URL where the model can be accessed. It is used to load the model for inference.""" ModelMetadata.model_name.__doc__ = """Human-readable name for the model. This can be used to identify the model in the metrics generated by the RunInference transform.""" def _to_milliseconds(time_ns: int) -> int: return int(time_ns / _NANOSECOND_TO_MILLISECOND) def _to_microseconds(time_ns: int) -> int: return int(time_ns / _NANOSECOND_TO_MICROSECOND) @dataclass(frozen=True) class KeyModelPathMapping(Generic[KeyT]): """ Dataclass for mapping 1 or more keys to 1 model path. This is used in conjunction with a KeyedModelHandler with many model handlers to update a set of keys' model handlers with the new path. Given `KeyModelPathMapping(keys: ['key1', 'key2'], update_path: 'updated/path', model_id: 'id1')`, all examples with keys `key1` or `key2` will have their corresponding model handler's update_model function called with 'updated/path' and their metrics will correspond with 'id1'. For more information see the KeyedModelHandler documentation https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.KeyedModelHandler documentation and the website section on model updates https://beam.apache.org/documentation/ml/about-ml/#automatic-model-refresh """ keys: list[KeyT] update_path: str model_id: str = '' class ModelHandler(Generic[ExampleT, PredictionT, ModelT]): """Has the ability to load and apply an ML model.""" def __init__(self): """Environment variables are set using a dict named 'env_vars' before loading the model. Child classes can accept this dict as a kwarg.""" self._env_vars = {} def load_model(self) -> ModelT: """Loads and initializes a model for processing.""" raise NotImplementedError(type(self)) def run_inference( self, batch: Sequence[ExampleT], model: ModelT, inference_args: Optional[dict[str, Any]] = None) -> Iterable[PredictionT]: """Runs inferences on a batch of examples. Args: batch: A sequence of examples or features. model: The model used to make inferences. inference_args: Extra arguments for models whose inference call requires extra parameters. Returns: An Iterable of Predictions. """ raise NotImplementedError(type(self)) def get_num_bytes(self, batch: Sequence[ExampleT]) -> 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' def get_resource_hints(self) -> dict: """ Returns: Resource hints for the transform. """ return {} def batch_elements_kwargs(self) -> Mapping[str, Any]: """ Returns: kwargs suitable for beam.BatchElements. """ return {} def validate_inference_args(self, inference_args: Optional[dict[str, Any]]): """Validates inference_args passed in the inference call. Because most frameworks do not need extra arguments in their predict() call, the default behavior is to error out if inference_args are present. """ if inference_args: raise ValueError( 'inference_args were provided, but should be None because this ' 'framework does not expect extra arguments on inferences.') def update_model_path(self, model_path: Optional[str] = None): """ Update the model path produced by side inputs. update_model_path should be used when a ModelHandler represents a single model, not multiple models. This will be true in most cases. For more information see the website section on model updates https://beam.apache.org/documentation/ml/about-ml/#automatic-model-refresh """ pass def update_model_paths( self, model: ModelT, model_paths: Optional[Union[str, list[KeyModelPathMapping]]] = None): """ Update the model paths produced by side inputs. update_model_paths should be used when updating multiple models at once (e.g. when using a KeyedModelHandler that holds multiple models). For more information see the KeyedModelHandler documentation https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.KeyedModelHandler documentation and the website section on model updates https://beam.apache.org/documentation/ml/about-ml/#automatic-model-refresh """ pass def get_preprocess_fns(self) -> Iterable[Callable[[Any], Any]]: """Gets all preprocessing functions to be run before batching/inference. Functions are in order that they should be applied.""" return [] def get_postprocess_fns(self) -> Iterable[Callable[[Any], Any]]: """Gets all postprocessing functions to be run after inference. Functions are in order that they should be applied.""" return [] def should_skip_batching(self) -> bool: """Whether RunInference's batching should be skipped. Can be flipped to True by using `with_no_batching`""" return False def set_environment_vars(self): """Sets environment variables using a dictionary provided via kwargs. Keys are the env variable name, and values are the env variable value. Child ModelHandler classes should set _env_vars via kwargs in __init__, or else call super().__init__().""" env_vars = getattr(self, '_env_vars', {}) for env_variable, env_value in env_vars.items(): os.environ[env_variable] = env_value def with_preprocess_fn( self, fn: Callable[[PreProcessT], ExampleT] ) -> 'ModelHandler[PreProcessT, PredictionT, ModelT]': """Returns a new ModelHandler with a preprocessing function associated with it. The preprocessing function will be run before batching/inference and should map your input PCollection to the base ModelHandler's input type. If you apply multiple preprocessing functions, they will be run on your original PCollection in order from last applied to first applied.""" return _PreProcessingModelHandler(self, fn) def with_postprocess_fn( self, fn: Callable[[PredictionT], PostProcessT] ) -> 'ModelHandler[ExampleT, PostProcessT, ModelT]': """Returns a new ModelHandler with a postprocessing function associated with it. The postprocessing function will be run after inference and should map the base ModelHandler's output type to your desired output type. If you apply multiple postprocessing functions, they will be run on your original inference result in order from first applied to last applied.""" return _PostProcessingModelHandler(self, fn) def with_no_batching( self ) -> """ModelHandler[Union[ ExampleT, Iterable[ExampleT]], PostProcessT, ModelT, PostProcessT]""": """Returns a new ModelHandler which does not require batching of inputs so that RunInference will skip this step. RunInference will expect the input to be pre-batched and passed in as an Iterable of records. If you skip batching, any preprocessing functions should accept a batch of data, not just a single record. This option is only recommended if you want to do custom batching yourself. If you just want to pass in records without a batching dimension, it is recommended to (1) add `max_batch_size=1` to `batch_elements_kwargs` and (2) remove the batching dimension as part of your inference call (by calling `record=batch[0]`)""" return _PrebatchedModelHandler(self) def share_model_across_processes(self) -> bool: """Returns a boolean representing whether or not a model should be shared across multiple processes instead of being loaded per process. This is primary useful for large models that can't fit multiple copies in memory. Multi-process support may vary by runner, but this will fallback to loading per process as necessary. See https://beam.apache.org/releases/pydoc/current/apache_beam.utils.multi_process_shared.html""" return False def model_copies(self) -> int: """Returns the maximum number of model copies that should be loaded at one time. This only impacts model handlers that are using share_model_across_processes to share their model across processes instead of being loaded per process.""" return 1 def override_metrics(self, metrics_namespace: str = '') -> bool: """Returns a boolean representing whether or not a model handler will override metrics reporting. If True, RunInference will not report any metrics.""" return False def should_garbage_collect_on_timeout(self) -> bool: """Whether the model should be garbage collected if model loading or inference timeout, or if it should be left for future calls. Usually should not be overriden unless the model handler implements other mechanisms for garbage collection.""" return self.share_model_across_processes() class RemoteModelHandler(ABC, ModelHandler[ExampleT, PredictionT, ModelT]): """Has the ability to call a model at a remote endpoint.""" def __init__( self, namespace: str = '', num_retries: int = 5, throttle_delay_secs: int = 5, retry_filter: Callable[[Exception], bool] = lambda x: True, *, window_ms: int = 1 * _MILLISECOND_TO_SECOND, bucket_ms: int = 1 * _MILLISECOND_TO_SECOND, overload_ratio: float = 2): """Initializes metrics tracking + an AdaptiveThrottler class for enabling client-side throttling for remote calls to an inference service. See https://s.apache.org/beam-client-side-throttling for more details on the configuration of the throttling and retry mechanics. Args: namespace: the metrics and logging namespace num_retries: the maximum number of times to retry a request on retriable errors before failing throttle_delay_secs: the amount of time to throttle when the client-side elects to throttle retry_filter: a function accepting an exception as an argument and returning a boolean. On a true return, the run_inference call will be retried. Defaults to always retrying. window_ms: length of history to consider, in ms, to set throttling. bucket_ms: granularity of time buckets that we store data in, in ms. overload_ratio: the target ratio between requests sent and successful requests. This is "K" in the formula in https://landing.google.com/sre/book/chapters/handling-overload.html. """ # Configure AdaptiveThrottler and throttling metrics for client-side # throttling behavior. self.throttled_secs = Metrics.counter( namespace, "cumulativeThrottlingSeconds") self.throttler = AdaptiveThrottler( window_ms=window_ms, bucket_ms=bucket_ms, overload_ratio=overload_ratio) self.logger = logging.getLogger(namespace) self.num_retries = num_retries self.throttle_delay_secs = throttle_delay_secs self.retry_filter = retry_filter def __init_subclass__(cls): if cls.load_model is not RemoteModelHandler.load_model: raise Exception( "Cannot override RemoteModelHandler.load_model, ", "implement create_client instead.") if cls.run_inference is not RemoteModelHandler.run_inference: raise Exception( "Cannot override RemoteModelHandler.run_inference, ", "implement request instead.") @abstractmethod def create_client(self) -> ModelT: """Creates the client that is used to make the remote inference request in request(). All relevant arguments should be passed to __init__(). """ raise NotImplementedError(type(self)) def load_model(self) -> ModelT: return self.create_client() def retry_on_exception(func): @functools.wraps(func) def wrapper(self, *args, **kwargs): return retry.with_exponential_backoff( num_retries=self.num_retries, retry_filter=self.retry_filter)(func)(self, *args, **kwargs) return wrapper @retry_on_exception def run_inference( self, batch: Sequence[ExampleT], model: ModelT, inference_args: Optional[dict[str, Any]] = None) -> Iterable[PredictionT]: """Runs inferences on a batch of examples. Calls a remote model for predictions and will retry if a retryable exception is raised. Args: batch: A sequence of examples or features. model: The model used to make inferences. inference_args: Extra arguments for models whose inference call requires extra parameters. Returns: An Iterable of Predictions. """ while self.throttler.throttle_request(time.time() * _MILLISECOND_TO_SECOND): self.logger.info( "Delaying request for %d seconds due to previous failures", self.throttle_delay_secs) time.sleep(self.throttle_delay_secs) self.throttled_secs.inc(self.throttle_delay_secs) try: req_time = time.time() predictions = self.request(batch, model, inference_args) self.throttler.successful_request(req_time * _MILLISECOND_TO_SECOND) return predictions except Exception as e: self.logger.error("exception raised as part of request, got %s", e) raise @abstractmethod def request( self, batch: Sequence[ExampleT], model: ModelT, inference_args: Optional[dict[str, Any]] = None) -> Iterable[PredictionT]: """Makes a request to a remote inference service and returns the response. Should raise an exception of some kind if there is an error to enable the retry and client-side throttling logic to work. Returns an iterable of the desired prediction type. This method should return the values directly, as handling return values as a generator can prevent the retry logic from functioning correctly. Args: batch: A sequence of examples or features. model: The model used to make inferences. inference_args: Extra arguments for models whose inference call requires extra parameters. Returns: An Iterable of Predictions. """ raise NotImplementedError(type(self)) class _ModelManager: """ A class for efficiently managing copies of multiple models. Will load a single copy of each model into a multi_process_shared object and then return a lookup key for that object. """ def __init__(self, mh_map: dict[str, ModelHandler]): """ Args: mh_map: A map from keys to model handlers which can be used to load a model. """ self._max_models = None # Map keys to model handlers self._mh_map: dict[str, ModelHandler] = mh_map # Map keys to the last updated model path for that key self._key_to_last_update: dict[str, str] = defaultdict(str) # Map key for a model to a unique tag that will persist for the life of # that model in memory. A new tag will be generated if a model is swapped # out of memory and reloaded. self._tag_map: dict[str, str] = OrderedDict() # Map a tag to a multiprocessshared model object for that tag. Each entry # of this map should last as long as the corresponding entry in _tag_map. self._proxy_map: dict[str, multi_process_shared.MultiProcessShared] = {} def load(self, key: str) -> _ModelLoadStats: """ Loads the appropriate model for the given key into memory. Args: key: the key associated with the model we'd like to load. Returns: _ModelLoadStats with tag, byte size, and latency to load the model. If the model was already loaded, byte size/latency will be None. """ # Map the key for a model to a unique tag that will persist until the model # is released. This needs to be unique between releasing/reacquiring th # model because otherwise the ProxyManager will try to reuse the model that # has been released and deleted. if key in self._tag_map: self._tag_map.move_to_end(key) return _ModelLoadStats(self._tag_map[key], None, None) else: self._tag_map[key] = uuid.uuid4().hex tag = self._tag_map[key] mh = self._mh_map[key] if self._max_models is not None and self._max_models < len(self._tag_map): # If we're about to exceed our LRU size, release the last used model. tag_to_remove = self._tag_map.popitem(last=False)[1] shared_handle, model_to_remove = self._proxy_map[tag_to_remove] shared_handle.release(model_to_remove) del self._proxy_map[tag_to_remove] # Load the new model memory_before = _get_current_process_memory_in_bytes() start_time = _to_milliseconds(time.time_ns()) shared_handle = multi_process_shared.MultiProcessShared( mh.load_model, tag=tag) model_reference = shared_handle.acquire() self._proxy_map[tag] = (shared_handle, model_reference) memory_after = _get_current_process_memory_in_bytes() end_time = _to_milliseconds(time.time_ns()) return _ModelLoadStats( tag, end_time - start_time, memory_after - memory_before) def increment_max_models(self, increment: int): """ Increments the number of models that this instance of a _ModelManager is able to hold. If it is never called, no limit is imposed. Args: increment: the amount by which we are incrementing the number of models. """ if self._max_models is None: self._max_models = 0 self._max_models += increment def update_model_handler(self, key: str, model_path: str, previous_key: str): """ Updates the model path of this model handler and removes it from memory so that it can be reloaded with the updated path. No-ops if no model update needs to be applied. Args: key: the key associated with the model we'd like to update. model_path: the new path to the model we'd like to load. previous_key: the key that is associated with the old version of this model. This will often be the same as the current key, but sometimes we will want to keep both the old and new models to serve different cohorts. In that case, the keys should be different. """ if self._key_to_last_update[key] == model_path: return self._key_to_last_update[key] = model_path if key not in self._mh_map: self._mh_map[key] = deepcopy(self._mh_map[previous_key]) self._mh_map[key].update_model_path(model_path) if key in self._tag_map: tag_to_remove = self._tag_map[key] shared_handle, model_to_remove = self._proxy_map[tag_to_remove] shared_handle.release(model_to_remove) del self._tag_map[key] del self._proxy_map[tag_to_remove] # Use a dataclass instead of named tuple because NamedTuples and generics don't # mix well across the board for all versions: # https://github.com/python/typing/issues/653 class KeyModelMapping(Generic[KeyT, ExampleT, PredictionT, ModelT]): """ Dataclass for mapping 1 or more keys to 1 model handler. Given `KeyModelMapping(['key1', 'key2'], myMh)`, all examples with keys `key1` or `key2` will be run against the model defined by the `myMh` ModelHandler. """ def __init__( self, keys: list[KeyT], mh: ModelHandler[ExampleT, PredictionT, ModelT]): self.keys = keys self.mh = mh class KeyedModelHandler(Generic[KeyT, ExampleT, PredictionT, ModelT], ModelHandler[tuple[KeyT, ExampleT], tuple[KeyT, PredictionT], Union[ModelT, _ModelManager]]): def __init__( self, unkeyed: Union[ModelHandler[ExampleT, PredictionT, ModelT], list[KeyModelMapping[KeyT, ExampleT, PredictionT, ModelT]]], max_models_per_worker_hint: Optional[int] = None): """A ModelHandler that takes keyed examples and returns keyed predictions. For example, if the original model is used with RunInference to take a PCollection[E] to a PCollection[P], this ModelHandler would take a PCollection[tuple[K, E]] to a PCollection[tuple[K, P]], making it possible to use the key to associate the outputs with the inputs. KeyedModelHandler is able to accept either a single unkeyed ModelHandler or many different model handlers corresponding to the keys for which that ModelHandler should be used. For example, the following configuration could be used to map keys 1-3 to ModelHandler1 and keys 4-5 to ModelHandler2: k1 = ['k1', 'k2', 'k3'] k2 = ['k4', 'k5'] KeyedModelHandler([KeyModelMapping(k1, mh1), KeyModelMapping(k2, mh2)]) Note that a single copy of each of these models may all be held in memory at the same time; be careful not to load too many large models or your pipeline may cause Out of Memory exceptions. KeyedModelHandlers support Automatic Model Refresh to update your model to a newer version without stopping your streaming pipeline. For an overview of this feature, see https://beam.apache.org/documentation/ml/about-ml/#automatic-model-refresh To use this feature with a KeyedModelHandler that has many models per key, you can pass in a list of KeyModelPathMapping objects to define your new model paths. For example, passing in the side input of [KeyModelPathMapping(keys=['k1', 'k2'], update_path='update/path/1'), KeyModelPathMapping(keys=['k3'], update_path='update/path/2')] will update the model corresponding to keys 'k1' and 'k2' with path 'update/path/1' and the model corresponding to 'k3' with 'update/path/2'. In order to do a side input update: (1) all restrictions mentioned in https://beam.apache.org/documentation/ml/about-ml/#automatic-model-refresh must be met, (2) all update_paths must be non-empty, even if they are not being updated from their original values, and (3) the set of keys originally defined cannot change. This means that if originally you have defined model handlers for 'key1', 'key2', and 'key3', all 3 of those keys must appear in your list of KeyModelPathMappings exactly once. No additional keys can be added. When using many models defined per key, metrics about inference and model loading will be gathered on an aggregate basis for all keys. These will be reported with no prefix. Metrics will also be gathered on a per key basis. Since some keys can share the same model, only one set of metrics will be reported per key 'cohort'. These will be reported in the form: `-`, where `` can be any key selected from the cohort. When model updates occur, the metrics will be reported in the form `--`. Loading multiple models at the same time can increase the risk of an out of memory (OOM) exception. To avoid this issue, use the parameter `max_models_per_worker_hint` to limit the number of models that are loaded at the same time. For more information about memory management, see `Use a keyed `ModelHandler _`. # pylint: disable=line-too-long Args: unkeyed: Either (a) an implementation of ModelHandler that does not require keys or (b) a list of KeyModelMappings mapping lists of keys to unkeyed ModelHandlers. max_models_per_worker_hint: A hint to the runner indicating how many models can be held in memory at one time per worker process. For example, if your worker has 8 GB of memory provisioned and your workers take up 1 GB each, you should set this to 7 to allow all models to sit in memory with some buffer. For more information about memory management, see `Use a keyed `ModelHandler _`. # pylint: disable=line-too-long """ self._metrics_collectors: dict[str, _MetricsCollector] = {} self._default_metrics_collector: _MetricsCollector = None self._metrics_namespace = '' self._single_model = not isinstance(unkeyed, list) if self._single_model: if len(unkeyed.get_preprocess_fns()) or len( unkeyed.get_postprocess_fns()): raise Exception( 'Cannot make make an unkeyed model handler with pre or ' 'postprocessing functions defined into a keyed model handler. All ' 'pre/postprocessing functions must be defined on the outer model' 'handler.') self._env_vars = getattr(unkeyed, '_env_vars', {}) self._unkeyed = unkeyed return self._max_models_per_worker_hint = max_models_per_worker_hint # To maintain an efficient representation, we will map all keys in a given # KeyModelMapping to a single id (the first key in the KeyModelMapping # list). We will then map that key to a ModelHandler. This will allow us to # quickly look up the appropriate ModelHandler for any given key. self._id_to_mh_map: dict[str, ModelHandler[ExampleT, PredictionT, ModelT]] = {} self._key_to_id_map: dict[str, str] = {} for mh_tuple in unkeyed: mh = mh_tuple.mh keys = mh_tuple.keys if len(mh.get_preprocess_fns()) or len(mh.get_postprocess_fns()): raise ValueError( 'Cannot use an unkeyed model handler with pre or ' 'postprocessing functions defined in a keyed model handler. All ' 'pre/postprocessing functions must be defined on the outer model' 'handler.') hints = mh.get_resource_hints() if len(hints) > 0: logging.warning( 'mh %s defines the following resource hints, which will be' 'ignored: %s. Resource hints are not respected when more than one ' 'model handler is used in a KeyedModelHandler. If you would like ' 'to specify resource hints, you can do so by overriding the ' 'KeyedModelHandler.get_resource_hints() method.', mh, hints) batch_kwargs = mh.batch_elements_kwargs() if len(batch_kwargs) > 0: logging.warning( 'mh %s defines the following batching kwargs which will be ' 'ignored %s. Batching kwargs are not respected when ' 'more than one model handler is used in a KeyedModelHandler. If ' 'you would like to specify resource hints, you can do so by ' 'overriding the KeyedModelHandler.batch_elements_kwargs() method.', hints, batch_kwargs) env_vars = getattr(mh, '_env_vars', {}) if len(env_vars) > 0: logging.warning( 'mh %s defines the following _env_vars which will be ignored %s. ' '_env_vars are not respected when more than one model handler is ' 'used in a KeyedModelHandler. If you need env vars set at ' 'inference time, you can do so with ' 'a custom inference function.', mh, env_vars) if len(keys) == 0: raise ValueError( f'Empty list maps to model handler {mh}. All model handlers must ' 'have one or more associated keys.') self._id_to_mh_map[keys[0]] = mh for key in keys: if key in self._key_to_id_map: raise ValueError( f'key {key} maps to multiple model handlers. All keys must map ' 'to exactly one model handler.') self._key_to_id_map[key] = keys[0] def load_model(self) -> Union[ModelT, _ModelManager]: if self._single_model: return self._unkeyed.load_model() return _ModelManager(self._id_to_mh_map) def run_inference( self, batch: Sequence[tuple[KeyT, ExampleT]], model: Union[ModelT, _ModelManager], inference_args: Optional[dict[str, Any]] = None ) -> Iterable[tuple[KeyT, PredictionT]]: if self._single_model: keys, unkeyed_batch = zip(*batch) return zip( keys, self._unkeyed.run_inference(unkeyed_batch, model, inference_args)) # The first time a MultiProcessShared ModelManager is used for inference # from this process, we should increment its max model count if self._max_models_per_worker_hint is not None: lock = threading.Lock() if lock.acquire(blocking=False): model.increment_max_models(self._max_models_per_worker_hint) self._max_models_per_worker_hint = None batch_by_key = defaultdict(list) key_by_id = defaultdict(set) for key, example in batch: batch_by_key[key].append(example) key_by_id[self._key_to_id_map[key]].add(key) predictions = [] for id, keys in key_by_id.items(): mh = self._id_to_mh_map[id] loaded_model = model.load(id) keyed_model_tag = loaded_model.model_tag if loaded_model.byte_size is not None: self._metrics_collectors[id].update_load_model_metrics( loaded_model.load_latency, loaded_model.byte_size) self._default_metrics_collector.update_load_model_metrics( loaded_model.load_latency, loaded_model.byte_size) keyed_model_shared_handle = multi_process_shared.MultiProcessShared( mh.load_model, tag=keyed_model_tag) keyed_model = keyed_model_shared_handle.acquire() start_time = _to_microseconds(time.time_ns()) num_bytes = 0 num_elements = 0 try: for key in keys: unkeyed_batches = batch_by_key[key] try: for inf in mh.run_inference(unkeyed_batches, keyed_model, inference_args): predictions.append((key, inf)) except BaseException as e: self._metrics_collectors[id].failed_batches_counter.inc() self._default_metrics_collector.failed_batches_counter.inc() raise e num_bytes += mh.get_num_bytes(unkeyed_batches) num_elements += len(unkeyed_batches) finally: keyed_model_shared_handle.release(keyed_model) end_time = _to_microseconds(time.time_ns()) inference_latency = end_time - start_time self._metrics_collectors[id].update( num_elements, num_bytes, inference_latency) self._default_metrics_collector.update( num_elements, num_bytes, inference_latency) return predictions def get_num_bytes(self, batch: Sequence[tuple[KeyT, ExampleT]]) -> int: keys, unkeyed_batch = zip(*batch) batch_bytes = len(pickle.dumps(keys)) if self._single_model: return batch_bytes + self._unkeyed.get_num_bytes(unkeyed_batch) batch_by_key = defaultdict(list) for key, examples in batch: batch_by_key[key].append(examples) for key, examples in batch_by_key.items(): mh_id = self._key_to_id_map[key] batch_bytes += self._id_to_mh_map[mh_id].get_num_bytes(examples) return batch_bytes def get_metrics_namespace(self) -> str: if self._single_model: return self._unkeyed.get_metrics_namespace() return 'BeamML_KeyedModels' def get_resource_hints(self): if self._single_model: return self._unkeyed.get_resource_hints() return {} def batch_elements_kwargs(self): if self._single_model: return self._unkeyed.batch_elements_kwargs() return {} def validate_inference_args(self, inference_args: Optional[dict[str, Any]]): if self._single_model: return self._unkeyed.validate_inference_args(inference_args) for mh in self._id_to_mh_map.values(): mh.validate_inference_args(inference_args) def update_model_paths( self, model: Union[ModelT, _ModelManager], model_paths: list[KeyModelPathMapping[KeyT]] = None): # When there are many models, the keyed model handler is responsible for # reorganizing the model handlers into cohorts and telling the model # manager to update every cohort's associated model handler. The model # manager is responsible for performing the updates and tracking which # updates have already been applied. if model_paths is None or len(model_paths) == 0 or model is None: return if self._single_model: raise RuntimeError( 'Invalid model update: sent many model paths to ' 'update, but KeyedModelHandler is wrapping a single ' 'model.') # Map cohort ids to a dictionary mapping new model paths to the keys that # were originally in that cohort. We will use this to construct our new # cohorts. # cohort_path_mapping will be structured as follows: # { # original_cohort_id: { # 'update/path/1': ['key1FromOriginalCohort', key2FromOriginalCohort'], # 'update/path/2': ['key3FromOriginalCohort', key4FromOriginalCohort'], # } # } cohort_path_mapping: dict[KeyT, dict[str, list[KeyT]]] = {} key_modelid_mapping: dict[KeyT, str] = {} seen_keys = set() for mp in model_paths: keys = mp.keys update_path = mp.update_path model_id = mp.model_id if len(update_path) == 0: raise ValueError(f'Invalid model update, path for {keys} is empty') for key in keys: if key in seen_keys: raise ValueError( f'Invalid model update: {key} appears in multiple ' 'update lists. A single model update must provide exactly one ' 'updated path per key.') seen_keys.add(key) if key not in self._key_to_id_map: raise ValueError( f'Invalid model update: {key} appears in ' 'update, but not in the original configuration.') key_modelid_mapping[key] = model_id cohort_id = self._key_to_id_map[key] if cohort_id not in cohort_path_mapping: cohort_path_mapping[cohort_id] = defaultdict(list) cohort_path_mapping[cohort_id][update_path].append(key) for key in self._key_to_id_map: if key not in seen_keys: raise ValueError( f'Invalid model update: {key} appears in the ' 'original configuration, but not the update.') # We now have our new set of cohorts. For each one, update our local model # handler configuration and send the results to the ModelManager for old_cohort_id, path_key_mapping in cohort_path_mapping.items(): for updated_path, keys in path_key_mapping.items(): cohort_id = old_cohort_id if old_cohort_id not in keys: # Create new cohort cohort_id = keys[0] for key in keys: self._key_to_id_map[key] = cohort_id mh = self._id_to_mh_map[old_cohort_id] self._id_to_mh_map[cohort_id] = deepcopy(mh) self._id_to_mh_map[cohort_id].update_model_path(updated_path) model.update_model_handler(cohort_id, updated_path, old_cohort_id) model_id = key_modelid_mapping[cohort_id] self._metrics_collectors[cohort_id] = _MetricsCollector( self._metrics_namespace, f'{cohort_id}-{model_id}-') def update_model_path(self, model_path: Optional[str] = None): if self._single_model: return self._unkeyed.update_model_path(model_path=model_path) if model_path is not None: raise RuntimeError( 'Model updates are currently not supported for ' + 'KeyedModelHandlers with multiple different per-key ' + 'ModelHandlers.') def share_model_across_processes(self) -> bool: if self._single_model: return self._unkeyed.share_model_across_processes() return True def model_copies(self) -> int: if self._single_model: return self._unkeyed.model_copies() for mh in self._id_to_mh_map.values(): if mh.model_copies() != 1: raise ValueError( 'KeyedModelHandler cannot map records to multiple ' 'models if one or more of its ModelHandlers ' 'require multiple model copies (set via ' 'model_copies). To fix, verify that each ' 'ModelHandler is not set to load multiple copies of ' 'its model.') return 1 def override_metrics(self, metrics_namespace: str = '') -> bool: if self._single_model: return self._unkeyed.override_metrics(metrics_namespace) self._metrics_namespace = metrics_namespace self._default_metrics_collector = _MetricsCollector(metrics_namespace) for cohort_id in self._id_to_mh_map: self._metrics_collectors[cohort_id] = _MetricsCollector( metrics_namespace, f'{cohort_id}-') return True def should_garbage_collect_on_timeout(self) -> bool: return self._single_model and self.share_model_across_processes() class MaybeKeyedModelHandler(Generic[KeyT, ExampleT, PredictionT, ModelT], ModelHandler[Union[ExampleT, tuple[KeyT, ExampleT]], Union[PredictionT, tuple[KeyT, PredictionT]], ModelT]): def __init__(self, unkeyed: ModelHandler[ExampleT, PredictionT, ModelT]): """A ModelHandler that takes examples that might have keys and returns predictions that might have keys. For example, if the original model is used with RunInference to take a PCollection[E] to a PCollection[P], this ModelHandler would take either PCollection[E] to a PCollection[P] or PCollection[tuple[K, E]] to a PCollection[tuple[K, P]], depending on the whether the elements are tuples. This pattern makes it possible to associate the outputs with the inputs based on the key. Note that you cannot use this ModelHandler if E is a tuple type. In addition, either all examples should be keyed, or none of them. Args: unkeyed: An implementation of ModelHandler that does not require keys. """ if len(unkeyed.get_preprocess_fns()) or len(unkeyed.get_postprocess_fns()): raise Exception( 'Cannot make make an unkeyed model handler with pre or ' 'postprocessing functions defined into a keyed model handler. All ' 'pre/postprocessing functions must be defined on the outer model' 'handler.') self._unkeyed = unkeyed self._env_vars = getattr(unkeyed, '_env_vars', {}) def load_model(self) -> ModelT: return self._unkeyed.load_model() def run_inference( self, batch: Sequence[Union[ExampleT, tuple[KeyT, ExampleT]]], model: ModelT, inference_args: Optional[dict[str, Any]] = None ) -> Union[Iterable[PredictionT], Iterable[tuple[KeyT, PredictionT]]]: # Really the input should be # Union[Sequence[ExampleT], Sequence[tuple[KeyT, ExampleT]]] # but there's not a good way to express (or check) that. if isinstance(batch[0], tuple): is_keyed = True keys, unkeyed_batch = zip(*batch) # type: ignore[arg-type] else: is_keyed = False unkeyed_batch = batch # type: ignore[assignment] unkeyed_results = self._unkeyed.run_inference( unkeyed_batch, model, inference_args) if is_keyed: return zip(keys, unkeyed_results) else: return unkeyed_results def get_num_bytes( self, batch: Sequence[Union[ExampleT, tuple[KeyT, ExampleT]]]) -> int: # MyPy can't follow the branching logic. if isinstance(batch[0], tuple): keys, unkeyed_batch = zip(*batch) # type: ignore[arg-type] return len( pickle.dumps(keys)) + self._unkeyed.get_num_bytes(unkeyed_batch) else: return self._unkeyed.get_num_bytes(batch) # type: ignore[arg-type] def get_metrics_namespace(self) -> str: return self._unkeyed.get_metrics_namespace() def get_resource_hints(self): return self._unkeyed.get_resource_hints() def batch_elements_kwargs(self): return self._unkeyed.batch_elements_kwargs() def validate_inference_args(self, inference_args: Optional[dict[str, Any]]): return self._unkeyed.validate_inference_args(inference_args) def update_model_path(self, model_path: Optional[str] = None): return self._unkeyed.update_model_path(model_path=model_path) def get_preprocess_fns(self) -> Iterable[Callable[[Any], Any]]: return self._unkeyed.get_preprocess_fns() def get_postprocess_fns(self) -> Iterable[Callable[[Any], Any]]: return self._unkeyed.get_postprocess_fns() def should_skip_batching(self) -> bool: return self._unkeyed.should_skip_batching() def share_model_across_processes(self) -> bool: return self._unkeyed.share_model_across_processes() def model_copies(self) -> int: return self._unkeyed.model_copies() class _PrebatchedModelHandler(Generic[ExampleT, PredictionT, ModelT], ModelHandler[Sequence[ExampleT], PredictionT, ModelT]): def __init__(self, base: ModelHandler[ExampleT, PredictionT, ModelT]): """A ModelHandler that skips batching in RunInference. Args: base: An implementation of the underlying model handler. """ self._base = base self._env_vars = getattr(base, '_env_vars', {}) def load_model(self) -> ModelT: return self._base.load_model() def run_inference( self, batch: Sequence[Union[ExampleT, tuple[KeyT, ExampleT]]], model: ModelT, inference_args: Optional[dict[str, Any]] = None ) -> Union[Iterable[PredictionT], Iterable[tuple[KeyT, PredictionT]]]: return self._base.run_inference(batch, model, inference_args) def get_num_bytes( self, batch: Sequence[Union[ExampleT, tuple[KeyT, ExampleT]]]) -> int: return self._base.get_num_bytes(batch) def get_metrics_namespace(self) -> str: return self._base.get_metrics_namespace() def get_resource_hints(self): return self._base.get_resource_hints() def batch_elements_kwargs(self): return self._base.batch_elements_kwargs() def validate_inference_args(self, inference_args: Optional[dict[str, Any]]): return self._base.validate_inference_args(inference_args) def update_model_path(self, model_path: Optional[str] = None): return self._base.update_model_path(model_path=model_path) def get_preprocess_fns(self) -> Iterable[Callable[[Any], Any]]: return self._base.get_preprocess_fns() def should_skip_batching(self) -> bool: return True def share_model_across_processes(self) -> bool: return self._base.share_model_across_processes() def model_copies(self) -> int: return self._base.model_copies() def get_postprocess_fns(self) -> Iterable[Callable[[Any], Any]]: return self._base.get_postprocess_fns() class _PreProcessingModelHandler(Generic[ExampleT, PredictionT, ModelT, PreProcessT], ModelHandler[PreProcessT, PredictionT, ModelT]): def __init__( self, base: ModelHandler[ExampleT, PredictionT, ModelT], preprocess_fn: Callable[[PreProcessT], ExampleT]): """A ModelHandler that has a preprocessing function associated with it. Args: base: An implementation of the underlying model handler. preprocess_fn: the preprocessing function to use. """ self._base = base self._env_vars = getattr(base, '_env_vars', {}) self._preprocess_fn = preprocess_fn def load_model(self) -> ModelT: return self._base.load_model() def run_inference( self, batch: Sequence[Union[ExampleT, tuple[KeyT, ExampleT]]], model: ModelT, inference_args: Optional[dict[str, Any]] = None ) -> Union[Iterable[PredictionT], Iterable[tuple[KeyT, PredictionT]]]: return self._base.run_inference(batch, model, inference_args) def get_num_bytes( self, batch: Sequence[Union[ExampleT, tuple[KeyT, ExampleT]]]) -> int: return self._base.get_num_bytes(batch) def get_metrics_namespace(self) -> str: return self._base.get_metrics_namespace() def get_resource_hints(self): return self._base.get_resource_hints() def batch_elements_kwargs(self): return self._base.batch_elements_kwargs() def validate_inference_args(self, inference_args: Optional[dict[str, Any]]): return self._base.validate_inference_args(inference_args) def update_model_path(self, model_path: Optional[str] = None): return self._base.update_model_path(model_path=model_path) def get_preprocess_fns(self) -> Iterable[Callable[[Any], Any]]: return [self._preprocess_fn] + self._base.get_preprocess_fns() def should_skip_batching(self) -> bool: return self._base.should_skip_batching() def share_model_across_processes(self) -> bool: return self._base.share_model_across_processes() def model_copies(self) -> int: return self._base.model_copies() def get_postprocess_fns(self) -> Iterable[Callable[[Any], Any]]: return self._base.get_postprocess_fns() class _PostProcessingModelHandler(Generic[ExampleT, PredictionT, ModelT, PostProcessT], ModelHandler[ExampleT, PostProcessT, ModelT]): def __init__( self, base: ModelHandler[ExampleT, PredictionT, ModelT], postprocess_fn: Callable[[PredictionT], PostProcessT]): """A ModelHandler that has a preprocessing function associated with it. Args: base: An implementation of the underlying model handler. postprocess_fn: the preprocessing function to use. """ self._base = base self._env_vars = getattr(base, '_env_vars', {}) self._postprocess_fn = postprocess_fn def load_model(self) -> ModelT: return self._base.load_model() def run_inference( self, batch: Sequence[Union[ExampleT, tuple[KeyT, ExampleT]]], model: ModelT, inference_args: Optional[dict[str, Any]] = None ) -> Union[Iterable[PredictionT], Iterable[tuple[KeyT, PredictionT]]]: return self._base.run_inference(batch, model, inference_args) def get_num_bytes( self, batch: Sequence[Union[ExampleT, tuple[KeyT, ExampleT]]]) -> int: return self._base.get_num_bytes(batch) def get_metrics_namespace(self) -> str: return self._base.get_metrics_namespace() def get_resource_hints(self): return self._base.get_resource_hints() def batch_elements_kwargs(self): return self._base.batch_elements_kwargs() def validate_inference_args(self, inference_args: Optional[dict[str, Any]]): return self._base.validate_inference_args(inference_args) def update_model_path(self, model_path: Optional[str] = None): return self._base.update_model_path(model_path=model_path) def get_preprocess_fns(self) -> Iterable[Callable[[Any], Any]]: return self._base.get_preprocess_fns() def should_skip_batching(self) -> bool: return self._base.should_skip_batching() def share_model_across_processes(self) -> bool: return self._base.share_model_across_processes() def model_copies(self) -> int: return self._base.model_copies() def get_postprocess_fns(self) -> Iterable[Callable[[Any], Any]]: return self._base.get_postprocess_fns() + [self._postprocess_fn] class RunInference(beam.PTransform[beam.PCollection[Union[ExampleT, Iterable[ExampleT]]], beam.PCollection[PredictionT]]): def __init__( self, model_handler: ModelHandler[ExampleT, PredictionT, Any], clock=time, inference_args: Optional[dict[str, Any]] = None, metrics_namespace: Optional[str] = None, *, model_metadata_pcoll: beam.PCollection[ModelMetadata] = None, watch_model_pattern: Optional[str] = None, model_identifier: Optional[str] = None, **kwargs): """ A transform that takes a PCollection of examples (or features) for use on an ML model. The transform then outputs inferences (or predictions) for those examples in a PCollection of PredictionResults that contains the input examples and the output inferences. Models for supported frameworks can be loaded using a URI. Supported services can also be used. This transform attempts to batch examples using the beam.BatchElements transform. Batching can be configured using the ModelHandler. Args: model_handler: An implementation of ModelHandler. clock: A clock implementing time_ns. *Used for unit testing.* inference_args: Extra arguments for models whose inference call requires extra parameters. metrics_namespace: Namespace of the transform to collect metrics. model_metadata_pcoll: PCollection that emits Singleton ModelMetadata containing model path and model name, that is used as a side input to the _RunInferenceDoFn. watch_model_pattern: A glob pattern used to watch a directory for automatic model refresh. model_identifier: A string used to identify the model being loaded. You can set this if you want to reuse the same model across multiple RunInference steps and don't want to reload it twice. Note that using the same tag for different models will lead to non-deterministic results, so exercise caution when using this parameter. This only impacts models which are already being shared across processes. """ self._model_handler = model_handler self._inference_args = inference_args self._clock = clock self._metrics_namespace = metrics_namespace self._model_metadata_pcoll = model_metadata_pcoll self._with_exception_handling = False self._exception_handling_timeout = None self._timeout = None self._watch_model_pattern = watch_model_pattern self._kwargs = kwargs # Generate a random tag to use for shared.py and multi_process_shared.py to # allow us to effectively disambiguate in multi-model settings. Only use # the same tag if the model being loaded across multiple steps is actually # the same. self._model_tag = model_identifier if model_identifier is None: self._model_tag = uuid.uuid4().hex def annotations(self): return { 'model_handler': str(self._model_handler), 'model_handler_type': ( f'{self._model_handler.__class__.__module__}' f'.{self._model_handler.__class__.__qualname__}'), **super().annotations() } def _get_model_metadata_pcoll(self, pipeline): # avoid circular imports. # pylint: disable=wrong-import-position from apache_beam.ml.inference.utils import WatchFilePattern extra_params = {} if 'interval' in self._kwargs: extra_params['interval'] = self._kwargs['interval'] if 'stop_timestamp' in self._kwargs: extra_params['stop_timestamp'] = self._kwargs['stop_timestamp'] return ( pipeline | WatchFilePattern( file_pattern=self._watch_model_pattern, **extra_params)) # TODO(BEAM-14046): Add and link to help documentation. @classmethod def from_callable(cls, model_handler_provider, **kwargs): """Multi-language friendly constructor. Use this constructor with fully_qualified_named_transform to initialize the RunInference transform from PythonCallableSource provided by foreign SDKs. Args: model_handler_provider: A callable object that returns ModelHandler. kwargs: Keyword arguments for model_handler_provider. """ return cls(model_handler_provider(**kwargs)) def _apply_fns( self, pcoll: beam.PCollection, fns: Iterable[Callable[[Any], Any]], step_prefix: str) -> tuple[beam.PCollection, Iterable[beam.PCollection]]: bad_preprocessed = [] for idx in range(len(fns)): fn = fns[idx] if self._with_exception_handling: pcoll, bad = (pcoll | f"{step_prefix}-{idx}" >> beam.Map( fn).with_exception_handling( exc_class=self._exc_class, use_subprocess=self._use_subprocess, threshold=self._threshold, timeout = self._timeout)) bad_preprocessed.append(bad) else: pcoll = pcoll | f"{step_prefix}-{idx}" >> beam.Map(fn) return pcoll, bad_preprocessed # TODO(https://github.com/apache/beam/issues/21447): Add batch_size back off # in the case there are functional reasons large batch sizes cannot be # handled. def expand( self, pcoll: beam.PCollection[ExampleT]) -> beam.PCollection[PredictionT]: self._model_handler.validate_inference_args(self._inference_args) # DLQ pcollections bad_preprocessed = [] bad_inference = None bad_postprocessed = [] preprocess_fns = self._model_handler.get_preprocess_fns() postprocess_fns = self._model_handler.get_postprocess_fns() pcoll, bad_preprocessed = self._apply_fns( pcoll, preprocess_fns, 'BeamML_RunInference_Preprocess') resource_hints = self._model_handler.get_resource_hints() # check for the side input if self._watch_model_pattern: self._model_metadata_pcoll = self._get_model_metadata_pcoll( pcoll.pipeline) if self._model_handler.should_skip_batching(): batched_elements_pcoll = pcoll else: batched_elements_pcoll = ( pcoll # TODO(https://github.com/apache/beam/issues/21440): Hook into the # batching DoFn APIs. | beam.BatchElements(**self._model_handler.batch_elements_kwargs())) # Skip loading in setup if we are dependent on side inputs or we want to # enforce a timeout since neither of these are available in a helpful way # in setup. load_model_at_runtime = ( self._model_metadata_pcoll is not None or self._timeout is not None) run_inference_pardo = beam.ParDo( _RunInferenceDoFn( self._model_handler, self._clock, self._metrics_namespace, load_model_at_runtime, self._model_tag), self._inference_args, beam.pvalue.AsSingleton( self._model_metadata_pcoll, ) if self._model_metadata_pcoll else None).with_resource_hints( **resource_hints) if self._with_exception_handling: # On timeouts, report back to the central model metadata # that the model is invalid model_tag = self._model_tag share_across_processes = self._model_handler.share_model_across_processes( ) timeout = self._timeout def failure_callback(exception: Exception, element: Any): if type(exception) is not TimeoutError: return model_metadata = load_model_status(model_tag, share_across_processes) model_metadata.try_mark_current_model_invalid(timeout) logging.warning( "Inference failed with a timeout, marking the current " + "model for garbage collection") callback = None if (self._timeout is not None and self._model_handler.should_garbage_collect_on_timeout()): callback = failure_callback results, bad_inference = ( batched_elements_pcoll | 'BeamML_RunInference' >> run_inference_pardo.with_exception_handling( exc_class=self._exc_class, use_subprocess=self._use_subprocess, threshold=self._threshold, timeout = self._timeout, on_failure_callback=callback)) else: results = ( batched_elements_pcoll | 'BeamML_RunInference' >> run_inference_pardo) results, bad_postprocessed = self._apply_fns( results, postprocess_fns, 'BeamML_RunInference_Postprocess') if self._with_exception_handling: dlq = RunInferenceDLQ(bad_inference, bad_preprocessed, bad_postprocessed) return results, dlq return results def with_exception_handling( self, *, exc_class=Exception, use_subprocess=False, threshold=1, timeout: Optional[int] = None): """Automatically provides a dead letter output for skipping bad records. This can allow a pipeline to continue successfully rather than fail or continuously throw errors on retry when bad elements are encountered. This returns a tagged output with two PCollections, the first being the results of successfully processing the input PCollection, and the second being the set of bad batches of records (those which threw exceptions during processing) along with information about the errors raised. For example, one would write:: main, other = RunInference( maybe_error_raising_model_handler ).with_exception_handling() and `main` will be a PCollection of PredictionResults and `other` will contain a `RunInferenceDLQ` object with PCollections containing failed records for each failed inference, preprocess operation, or postprocess operation. To access each collection of failed records, one would write: failed_inferences = other.failed_inferences failed_preprocessing = other.failed_preprocessing failed_postprocessing = other.failed_postprocessing failed_inferences is in the form PCollection[tuple[failed batch, exception]]. failed_preprocessing is in the form list[PCollection[tuple[failed record, exception]]]], where each element of the list corresponds to a preprocess function. These PCollections are in the same order that the preprocess functions are applied. failed_postprocessing is in the form list[PCollection[tuple[failed record, exception]]]], where each element of the list corresponds to a postprocess function. These PCollections are in the same order that the postprocess functions are applied. Args: exc_class: An exception class, or tuple of exception classes, to catch. Optional, defaults to 'Exception'. use_subprocess: Whether to execute the DoFn logic in a subprocess. This allows one to recover from errors that can crash the calling process (e.g. from an underlying library causing a segfault), but is slower as elements and results must cross a process boundary. Note that this starts up a long-running process that is used to handle all the elements (until hard failure, which should be rare) rather than a new process per element, so the overhead should be minimal (and can be amortized if there's any per-process or per-bundle initialization that needs to be done). Optional, defaults to False. threshold: An upper bound on the ratio of records that can be bad before aborting the entire pipeline. Optional, defaults to 1.0 (meaning up to 100% of records can be bad and the pipeline will still succeed). timeout: The maximum amount of time in seconds given to load a model, run inference on a batch of elements and perform and pre/postprocessing operations. Since the timeout applies in multiple places, it should be equal to the maximum possible timeout for any of these operations. Note in particular that model load and inference on a single batch count to the same timeout value. When an inference fails, all related resources, including the model, will be deleted and reloaded. As a result, it is recommended to leave significant buffer and set the timeout to at least `2 * (time to load model + time to run inference on a batch of data)`. """ self._with_exception_handling = True self._exc_class = exc_class self._use_subprocess = use_subprocess self._threshold = threshold self._timeout = timeout return self class _MetricsCollector: """ A metrics collector that tracks ML related performance and memory usage. """ def __init__(self, namespace: str, prefix: str = ''): """ Args: namespace: Namespace for the metrics. prefix: Unique identifier for metrics, used when models are updated using side input. """ # Metrics if prefix: prefix = f'{prefix}_' self._inference_counter = beam.metrics.Metrics.counter( namespace, prefix + 'num_inferences') self.failed_batches_counter = beam.metrics.Metrics.counter( namespace, prefix + 'failed_batches_counter') self._inference_request_batch_size = beam.metrics.Metrics.distribution( namespace, prefix + 'inference_request_batch_size') self._inference_request_batch_byte_size = ( beam.metrics.Metrics.distribution( namespace, prefix + 'inference_request_batch_byte_size')) # Batch inference latency in microseconds. self._inference_batch_latency_micro_secs = ( beam.metrics.Metrics.distribution( namespace, prefix + 'inference_batch_latency_micro_secs')) self._model_byte_size = beam.metrics.Metrics.distribution( namespace, prefix + 'model_byte_size') # Model load latency in milliseconds. self._load_model_latency_milli_secs = beam.metrics.Metrics.distribution( namespace, prefix + '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_load_model_metrics(self, load_model_latency_ms, model_byte_size): self._load_model_latency_milli_secs.update(load_model_latency_ms) self._model_byte_size.update(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 _ModelRoutingStrategy(): """A class meant to sit in a shared location for mapping incoming batches to different models. Currently only supports round-robin, but can be extended to support other protocols if needed. """ def __init__(self): self._cur_index = 0 def next_model_index(self, num_models): self._cur_index = (self._cur_index + 1) % num_models return self._cur_index class _ModelStatus(): """A class holding any metadata about a model required by RunInference. Currently, this only includes whether or not the model is valid. Uses the model tag to map models to metadata. """ def __init__(self, share_model_across_processes: bool): self._active_tags = set() self._invalid_tags = set() self._tag_mapping = {} self._model_first_seen = {} self._pending_hard_delete = [] self._share_model_across_process = share_model_across_processes def try_mark_current_model_invalid(self, min_model_life_seconds): """Mark the current model invalid. Since we don't have sufficient information to say which model is being marked invalid, but there may be multiple active models, we will mark all models currently in use as inactive so that they all get reloaded. To avoid thrashing, however, we will only mark models as invalid if they've been active at least min_model_life_seconds seconds. """ cutoff_time = datetime.now() - timedelta(seconds=min_model_life_seconds) for tag in list(self._active_tags): if cutoff_time >= self._model_first_seen[tag]: self._invalid_tags.add(tag) # Delete old models after a grace period of 2 * the model life. # This already happens automatically for shared.Shared models, so # cleanup is only necessary for multi_process_shared models. if self._share_model_across_process: self._pending_hard_delete.append(( tag, datetime.now() + 2 * timedelta(seconds=min_model_life_seconds))) self._active_tags.remove(tag) def get_valid_tag(self, tag: str) -> str: """Takes in a proposed valid tag and returns a valid one. Will always return a valid tag. If the passed in tag is valid, this function will simply return it, otherwise it will deterministically generate a new tag to use instead. The new tag will be the original tag with an incrementing suffix (e.g. `my_tag_reload_1`, `my_tag_reload_2`) for each reload """ if tag not in self._invalid_tags: if tag not in self._model_first_seen: self._model_first_seen[tag] = datetime.now() self._active_tags.add(tag) return tag if (tag in self._tag_mapping and self._tag_mapping[tag] not in self._invalid_tags): return self._tag_mapping[tag] i = 1 new_tag = f'{tag}_reload_{i}' while new_tag in self._invalid_tags: i += 1 new_tag = f'{tag}_reload_{i}' self._tag_mapping[tag] = new_tag self._model_first_seen[new_tag] = datetime.now() self._active_tags.add(new_tag) return new_tag def is_valid_tag(self, tag: str) -> bool: return tag == self.get_valid_tag(tag) def get_tags_for_garbage_collection(self) -> list[str]: # Since this function may be in multi_process_shared space, delegate model # deletion to the calling process which is not to avoid having a # multi_process_shared reference in multi_process_shared space, which # can create issues with python's multiprocessing module. # We will rely on the calling process to report back deleted models so that # we can confirm deletion. to_delete = [] cur_time = datetime.now() for i in range(len(self._pending_hard_delete)): delete_time = self._pending_hard_delete[i][1] tag = self._pending_hard_delete[i][0] if delete_time <= cur_time: to_delete.append(tag) else: # early return once we hit a model which was added later since models # are added in order. return to_delete return to_delete def mark_tags_deleted(self, deleted_tags: set[str]): while len(self._pending_hard_delete) > 0: tag = self._pending_hard_delete[0][0] if tag in deleted_tags: self._pending_hard_delete.pop(0) else: return def load_model_status( model_tag: str, share_across_processes: bool) -> _ModelStatus: tag = f'{model_tag}_model_status' if share_across_processes: return multi_process_shared.MultiProcessShared( lambda: _ModelStatus(True), tag=tag, always_proxy=True).acquire() return shared.Shared().acquire(lambda: _ModelStatus(False), tag=tag) class _SharedModelWrapper(): """A router class to map incoming calls to the correct model. This allows us to round robin calls to models sitting in different processes so that we can more efficiently use resources (e.g. GPUs). """ def __init__(self, models: list[Any], model_tag: str): self.models = models if len(models) > 1: self.model_router = multi_process_shared.MultiProcessShared( lambda: _ModelRoutingStrategy(), tag=f'{model_tag}_counter', always_proxy=True).acquire() def next_model(self): if len(self.models) == 1: # Short circuit if there's no routing strategy needed in order to # avoid the cross-process call return self.models[0] return self.models[self.model_router.next_model_index(len(self.models))] def all_models(self): return self.models class _RunInferenceDoFn(beam.DoFn, Generic[ExampleT, PredictionT]): def __init__( self, model_handler: ModelHandler[ExampleT, PredictionT, Any], clock, metrics_namespace, load_model_at_runtime: bool = False, model_tag: str = "RunInference"): """A DoFn implementation generic to frameworks. Args: model_handler: An implementation of ModelHandler. clock: A clock implementing time_ns. *Used for unit testing.* metrics_namespace: Namespace of the transform to collect metrics. load_model_at_runtime: Bool to indicate if model loading should be deferred to runtime - for example if we are depending on side inputs to get the model path or we want to enforce a timeout on model loading. model_tag: Tag to use to disambiguate models in multi-model settings. """ self._model_handler = model_handler self._shared_model_handle = shared.Shared() self._clock = clock self._model = None self._metrics_namespace = metrics_namespace self._load_model_at_runtime = load_model_at_runtime self._side_input_path = None # _model_tag is the original tag passed in. # _cur_tag is the tag of the actually loaded model self._model_tag = model_tag self._cur_tag = model_tag def _load_model( self, side_input_model_path: Optional[Union[str, list[KeyModelPathMapping]]] = None ) -> _SharedModelWrapper: def load(): """Function for constructing shared LoadedModel.""" memory_before = _get_current_process_memory_in_bytes() start_time = _to_milliseconds(self._clock.time_ns()) if isinstance(side_input_model_path, str): self._model_handler.update_model_path(side_input_model_path) else: if self._model is not None: models = self._model.all_models() for m in models: self._model_handler.update_model_paths(m, side_input_model_path) model = self._model_handler.load_model() end_time = _to_milliseconds(self._clock.time_ns()) memory_after = _get_current_process_memory_in_bytes() load_model_latency_ms = end_time - start_time model_byte_size = memory_after - memory_before if self._metrics_collector: self._metrics_collector.cache_load_model_metrics( load_model_latency_ms, model_byte_size) return model # TODO(https://github.com/apache/beam/issues/21443): Investigate releasing # model. model_tag = self._model_tag if isinstance(side_input_model_path, str) and side_input_model_path != '': model_tag = side_input_model_path # Ensure the tag we're loading is valid, if not replace it with a valid tag self._cur_tag = self._model_metadata.get_valid_tag(model_tag) if self._model_handler.share_model_across_processes(): models = [] for copy_tag in _get_tags_for_copies(self._cur_tag, self._model_handler.model_copies()): models.append( multi_process_shared.MultiProcessShared( load, tag=copy_tag, always_proxy=True).acquire()) model_wrapper = _SharedModelWrapper(models, self._cur_tag) else: model = self._shared_model_handle.acquire(load, tag=self._cur_tag) model_wrapper = _SharedModelWrapper([model], self._cur_tag) # since shared_model_handle is shared across threads, the model path # might not get updated in the model handler # because we directly get cached weak ref model from shared cache, instead # of calling load(). For sanity check, call update_model_path again. if isinstance(side_input_model_path, str): self._model_handler.update_model_path(side_input_model_path) else: if self._model is not None: models = self._model.all_models() for m in models: self._model_handler.update_model_paths(m, side_input_model_path) return model_wrapper def get_metrics_collector(self, prefix: str = ''): """ Args: prefix: Unique identifier for metrics, used when models are updated using side input. """ metrics_namespace = ( self._metrics_namespace) if self._metrics_namespace else ( self._model_handler.get_metrics_namespace()) if self._model_handler.override_metrics(metrics_namespace): return None return _MetricsCollector(metrics_namespace, prefix=prefix) def setup(self): self._metrics_collector = self.get_metrics_collector() self._model_handler.set_environment_vars() self._model_metadata = load_model_status( self._model_tag, self._model_handler.share_model_across_processes()) if not self._load_model_at_runtime: self._model = self._load_model() def update_model( self, side_input_model_path: Optional[Union[str, list[KeyModelPathMapping]]] = None): self._model = self._load_model(side_input_model_path=side_input_model_path) def _run_inference(self, batch, inference_args): start_time = _to_microseconds(self._clock.time_ns()) try: model = self._model.next_model() result_generator = self._model_handler.run_inference( batch, model, inference_args) except BaseException as e: if self._metrics_collector: self._metrics_collector.failed_batches_counter.inc() if (e is pickle.PickleError and self._model_handler.share_model_across_processes()): raise TypeError( 'Pickling error encountered while running inference. ' 'This may be caused by trying to send unpickleable ' 'data to a model which is shared across processes. ' 'For more information, see ' 'https://beam.apache.org/documentation/ml/large-language-modeling/#pickling-errors' # pylint: disable=line-too-long ) from e raise e predictions = list(result_generator) end_time = _to_microseconds(self._clock.time_ns()) inference_latency = end_time - start_time num_bytes = self._model_handler.get_num_bytes(batch) num_elements = len(batch) if self._metrics_collector: self._metrics_collector.update(num_elements, num_bytes, inference_latency) return predictions def process( self, batch, inference_args, si_model_metadata: Optional[Union[ModelMetadata, list[ModelMetadata], list[KeyModelPathMapping]]]): """ When side input is enabled: The method checks if the side input model has been updated, and if so, updates the model and runs inference on the batch of data. If the side input is empty or the model has not been updated, the method simply runs inference on the batch of data. """ if not si_model_metadata: if (not self._model_metadata.is_valid_tag(self._cur_tag) or self._model is None): self.update_model(side_input_model_path=None) return self._run_inference(batch, inference_args) if isinstance(si_model_metadata, beam.pvalue.EmptySideInput): self.update_model(side_input_model_path=None) elif isinstance(si_model_metadata, list) and hasattr(si_model_metadata[0], 'keys'): # TODO(https://github.com/apache/beam/issues/27628): Update metrics here self.update_model(si_model_metadata) elif self._side_input_path != si_model_metadata.model_id: self._side_input_path = si_model_metadata.model_id self._metrics_collector = self.get_metrics_collector( prefix=si_model_metadata.model_name) lock = threading.Lock() with lock: self.update_model(si_model_metadata.model_id) return self._run_inference(batch, inference_args) return self._run_inference(batch, inference_args) def finish_bundle(self): # TODO(https://github.com/apache/beam/issues/21435): Figure out why there # is a cache. if self._metrics_collector: self._metrics_collector.update_metrics_with_cache() # Do garbage collection of old models tags_to_gc = self._model_metadata.get_tags_for_garbage_collection() if len(tags_to_gc) > 0: for unprefixed_tag in tags_to_gc: for tag in _get_tags_for_copies(unprefixed_tag, self._model_handler.model_copies()): multi_process_shared.MultiProcessShared(lambda: None, tag).unsafe_hard_delete() self._model_metadata.mark_tags_deleted(tags_to_gc) 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 _get_tags_for_copies(base_tag, num_copies): tags = [] for i in range(num_copies): tags.append(f'{base_tag}{i}') return tags