#
# 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 logging
import os
import pickle
import sys
import threading
import time
import uuid
from collections import OrderedDict
from collections import defaultdict
from copy import deepcopy
from dataclasses import dataclass
from datetime import datetime
from datetime import timedelta
from typing import Any
from typing import Callable
from typing import Dict
from typing import Generic
from typing import Iterable
from typing import List
from typing import Mapping
from typing import NamedTuple
from typing import Optional
from typing import Sequence
from typing import Set
from typing import Tuple
from typing import TypeVar
from typing import Union
import apache_beam as beam
from apache_beam.utils import multi_process_shared
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
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.
[docs]
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."""
[docs]
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)
[docs]
@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 = ''
[docs]
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 = {}
[docs]
def load_model(self) -> ModelT:
"""Loads and initializes a model for processing."""
raise NotImplementedError(type(self))
[docs]
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))
[docs]
def get_num_bytes(self, batch: Sequence[ExampleT]) -> int:
"""
Returns:
The number of bytes of data for a batch.
"""
return len(pickle.dumps(batch))
[docs]
def get_metrics_namespace(self) -> str:
"""
Returns:
A namespace for metrics collected by the RunInference transform.
"""
return 'RunInference'
[docs]
def get_resource_hints(self) -> dict:
"""
Returns:
Resource hints for the transform.
"""
return {}
[docs]
def batch_elements_kwargs(self) -> Mapping[str, Any]:
"""
Returns:
kwargs suitable for beam.BatchElements.
"""
return {}
[docs]
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.')
[docs]
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
[docs]
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
[docs]
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 []
[docs]
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 []
[docs]
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
[docs]
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
[docs]
def with_preprocess_fn(
self, fn: Callable[[PreProcessT], ExampleT]
) -> 'ModelHandler[PreProcessT, PredictionT, ModelT, PreProcessT]':
"""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)
[docs]
def with_postprocess_fn(
self, fn: Callable[[PredictionT], PostProcessT]
) -> 'ModelHandler[ExampleT, PostProcessT, ModelT, PostProcessT]':
"""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)
[docs]
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)
[docs]
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
[docs]
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
[docs]
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
[docs]
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 _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
[docs]
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
[docs]
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:
`<cohort_key>-<metric_name>`, where `<cohort_key>` can be any key selected
from the cohort. When model updates occur, the metrics will be reported in
the form `<cohort_key>-<model id>-<metric_name>`.
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 <https://beam.apache.org/documentation/ml/about-ml/#use-a-keyed-modelhandler-object>_`. # 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 <https://beam.apache.org/documentation/ml/about-ml/#use-a-keyed-modelhandler-object>_`. # 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]
[docs]
def load_model(self) -> Union[ModelT, _ModelManager]:
if self._single_model:
return self._unkeyed.load_model()
return _ModelManager(self._id_to_mh_map)
[docs]
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
[docs]
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
[docs]
def get_metrics_namespace(self) -> str:
if self._single_model:
return self._unkeyed.get_metrics_namespace()
return 'BeamML_KeyedModels'
[docs]
def get_resource_hints(self):
if self._single_model:
return self._unkeyed.get_resource_hints()
return {}
[docs]
def batch_elements_kwargs(self):
if self._single_model:
return self._unkeyed.batch_elements_kwargs()
return {}
[docs]
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)
[docs]
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}-')
[docs]
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.')
[docs]
def share_model_across_processes(self) -> bool:
if self._single_model:
return self._unkeyed.share_model_across_processes()
return True
[docs]
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
[docs]
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
[docs]
def should_garbage_collect_on_timeout(self) -> bool:
return self._single_model and self.share_model_across_processes()
[docs]
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', {})
[docs]
def load_model(self) -> ModelT:
return self._unkeyed.load_model()
[docs]
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
[docs]
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]
[docs]
def get_metrics_namespace(self) -> str:
return self._unkeyed.get_metrics_namespace()
[docs]
def get_resource_hints(self):
return self._unkeyed.get_resource_hints()
[docs]
def batch_elements_kwargs(self):
return self._unkeyed.batch_elements_kwargs()
[docs]
def validate_inference_args(self, inference_args: Optional[Dict[str, Any]]):
return self._unkeyed.validate_inference_args(inference_args)
[docs]
def update_model_path(self, model_path: Optional[str] = None):
return self._unkeyed.update_model_path(model_path=model_path)
[docs]
def get_preprocess_fns(self) -> Iterable[Callable[[Any], Any]]:
return self._unkeyed.get_preprocess_fns()
[docs]
def get_postprocess_fns(self) -> Iterable[Callable[[Any], Any]]:
return self._unkeyed.get_postprocess_fns()
[docs]
def should_skip_batching(self) -> bool:
return self._unkeyed.should_skip_batching()
[docs]
def share_model_across_processes(self) -> bool:
return self._unkeyed.share_model_across_processes()
[docs]
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]
[docs]
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
[docs]
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.
[docs]
@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.
[docs]
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
[docs]
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
[docs]
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