[GitHub] [beam] robertwb commented on a diff in pull request #16970: [BEAM-13982] A base class for run inference

[GitBox <gi...@apache.org>]

robertwb commented on code in PR #16970:
URL: https://github.com/apache/beam/pull/16970#discussion_r842194301


##########
sdks/python/apache_beam/ml/inference/base.py:
##########
@@ -0,0 +1,252 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""An extensible run inference transform."""
+
+import logging
+import os
+import pickle
+import platform
+import sys
+import time
+from typing import Any
+from typing import Iterable
+from typing import Tuple
+
+import apache_beam as beam
+from apache_beam.utils import shared
+
+try:
+  # pylint: disable=g-import-not-at-top
+  import resource
+except ImportError:
+  resource = None
+
+_MILLISECOND_TO_MICROSECOND = 1000
+_MICROSECOND_TO_NANOSECOND = 1000
+_SECOND_TO_MICROSECOND = 1000000
+
+
+class InferenceRunner():
+  """Implements running inferences for a framework."""
+  def run_inference(self, batch: Any, model: Any) -> Iterable[Any]:
+    """Runs inferences on a batch of examples and returns an Iterable of Predictions."""
+    raise NotImplementedError(type(self))
+
+  def get_num_bytes(self, batch: Any) -> int:
+    """Returns the number of bytes of data for a batch."""
+    return len(pickle.dumps(batch))
+
+  def get_metrics_namespace(self) -> str:
+    """Returns a namespace for metrics collected by the RunInference transform."""
+    return 'RunInference'
+
+
+class ModelLoader():
+  """Has the ability to load an ML model."""
+  def load_model(self) -> Any:
+    """Loads and initializes a model for processing."""
+    raise NotImplementedError(type(self))
+
+  def get_inference_runner(self) -> InferenceRunner:
+    """Returns an implementation of InferenceRunner for this model."""
+    raise NotImplementedError(type(self))
+
+
+def _unbatch(maybe_keyed_batches: Tuple[Any, Any]):
+  keys, results = maybe_keyed_batches
+  if keys:
+    return zip(keys, results)
+  else:
+    return results
+
+
+class RunInference(beam.PTransform):
+  """An extensible transform for running inferences."""
+  def __init__(self, model_loader: ModelLoader, clock=None):
+    self._model_loader = model_loader
+    self._clock = clock
+
+  # TODO(BEAM-14208): Add batch_size back off in the case there
+  # are functional reasons large batch sizes cannot be handled.
+  def expand(self, pcoll: beam.PCollection) -> beam.PCollection:
+    return (
+        pcoll
+        # TODO(BEAM-14044): Hook into the batching DoFn APIs.
+        | beam.BatchElements()
+        | beam.ParDo(
+            RunInferenceDoFn(shared.Shared(), self._model_loader, self._clock))

Review Comment:
   Passing it into the constructor vs. creating it in the constructor has no bearing on whether it's a side input (or its attributes are lazily initialized). It's just better practice for an argument that is not used or referenced elsewhere. (I think the docs on Shared should be updated.)



##########
sdks/python/apache_beam/ml/inference/base.py:
##########
@@ -0,0 +1,194 @@
+import logging
+import platform
+import resource
+import sys
+import time
+from typing import Any
+from typing import List
+
+import apache_beam as beam
+from apache_beam.utils import shared
+from apache_beam.ml.inference.apis import PredictionResult
+
+_MILLISECOND_TO_MICROSECOND = 1000
+_MICROSECOND_TO_NANOSECOND = 1000
+_SECOND_TO_MICROSECOND = 1000000
+
+
+def _unbatch(maybe_keyed_batches: Any):
+  keys, results = maybe_keyed_batches
+  if keys:
+    return zip(keys, results)
+  else:
+    return results
+
+
+class ModelLoader:
+  """Has the ability to load an ML model."""
+  def load_model(self):
+    """Loads an initializes a model for processing."""
+    raise NotImplementedError(type(self))
+
+
+class InferenceRunner:
+  """Implements running inferences for a framework."""
+  def run_inference(self, batch: Any, model: Any) -> List[PredictionResult]:
+    """Runs inferences on a batch of examples and returns a list of Predictions."""
+    raise NotImplementedError(type(self))
+
+
+class MetricsCollector:
+  """A metrics collector that tracks ML related performance and memory usage."""
+  def __init__(self, namespace: str):
+    # Metrics
+    self._inference_counter = beam.metrics.Metrics.counter(
+        namespace, 'num_inferences')
+    self._inference_request_batch_size = beam.metrics.Metrics.distribution(
+        namespace, 'inference_request_batch_size')
+    self._inference_request_batch_byte_size = (
+        beam.metrics.Metrics.distribution(
+            namespace, 'inference_request_batch_byte_size'))
+    # Batch inference latency in microseconds.
+    self._inference_batch_latency_micro_secs = (
+        beam.metrics.Metrics.distribution(
+            namespace, 'inference_batch_latency_micro_secs'))
+    self._model_byte_size = beam.metrics.Metrics.distribution(
+        namespace, 'model_byte_size')
+    # Model load latency in milliseconds.
+    self._load_model_latency_milli_secs = beam.metrics.Metrics.distribution(
+        namespace, 'load_model_latency_milli_secs')
+
+    # Metrics cache
+    self.load_model_latency_milli_secs_cache = None
+    self.model_byte_size_cache = None
+
+  def update_metrics_with_cache(self):
+    if self.load_model_latency_milli_secs_cache is not None:
+      self._load_model_latency_milli_secs.update(
+          self.load_model_latency_milli_secs_cache)
+      self.load_model_latency_milli_secs_cache = None
+    if self.model_byte_size_cache is not None:
+      self._model_byte_size.update(self.model_byte_size_cache)
+      self.model_byte_size_cache = None
+
+  def update(
+      self,
+      examples_count: int,
+      examples_byte_size: int,
+      latency_micro_secs: int):
+    self._inference_batch_latency_micro_secs.update(latency_micro_secs)
+    self._inference_counter.inc(examples_count)
+    self._inference_request_batch_size.update(examples_count)
+    self._inference_request_batch_byte_size.update(examples_byte_size)
+
+
+class RunInferenceDoFn(beam.DoFn):
+  def __init__(self, model_loader, inference_runner):
+    self._model_loader = model_loader
+    self._inference_runner = inference_runner
+    self._shared_model_handle = shared.Shared()
+    # TODO: Compute a good metrics namespace
+    self._metrics_collector = MetricsCollector('default_namespace')
+    self._clock = _ClockFactory.make_clock()
+    self._model = None
+
+  def _load_model(self):
+    def load():
+      """Function for constructing shared LoadedModel."""
+      memory_before = _get_current_process_memory_in_bytes()
+      start_time = self._clock.get_current_time_in_microseconds()
+      model = self._model_loader.load_model()
+      end_time = self._clock.get_current_time_in_microseconds()
+      memory_after = _get_current_process_memory_in_bytes()
+      self._metrics_collector.load_model_latency_milli_secs_cache = (
+          (end_time - start_time) / _MILLISECOND_TO_MICROSECOND)
+      self._metrics_collector.model_byte_size_cache = (
+          memory_after - memory_before)
+      return model
+
+    return self._shared_model_handle.acquire(load)
+
+  def setup(self):
+    super().setup()
+    self._model = self._load_model()
+
+  def process(self, batch):
+    has_keys = isinstance(batch[0], tuple)
+    start_time = self._clock.get_current_time_in_microseconds()
+    if has_keys:
+      examples = [example for _, example in batch]
+      keys = [key for key, _ in batch]
+    else:
+      examples = batch
+      keys = None
+    inferences = self._inference_runner.run_inference(examples, self._model)
+    inference_latency = self._clock.get_current_time_in_microseconds(
+    ) - start_time
+    num_bytes = sys.getsizeof(batch)
+    num_elements = len(batch)
+    self._metrics_collector.update(
+        num_elements, sys.getsizeof(batch), inference_latency)
+    yield keys, [PredictionResult(e, r) for e, r in zip(examples, inferences)]
+
+
+class RunInferenceImpl(beam.PTransform):
+  def __init__(self, model_loader, inference_runner):
+    self._model_loader = model_loader
+    self._inference_runner = inference_runner
+
+  def expand(self, pcoll: beam.PCollection) -> beam.PCollection:
+    return (
+        pcoll
+        # TODO: Hook into the batching DoFn APIs.
+        | beam.BatchElements()
+        | beam.ParDo(
+            RunInferenceDoFn(self._model_loader, self._inference_runner))
+        | beam.FlatMap(_unbatch))
+
+
+def _is_darwin() -> bool:
+  return sys.platform == 'darwin'
+
+
+def _get_current_process_memory_in_bytes():
+  """Returns memory usage in bytes."""
+
+  if resource is not None:
+    usage = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
+    if _is_darwin():
+      return usage
+    return usage * 1024
+  else:
+    logging.warning(
+        'Resource module is not available for current platform, '
+        'memory usage cannot be fetched.')
+  return 0
+
+
+def _is_windows() -> bool:
+  return platform.system() == 'Windows' or os.name == 'nt'
+
+
+def _is_cygwin() -> bool:
+  return platform.system().startswith('CYGWIN_NT')
+
+
+class _Clock(object):

Review Comment:
   I'm all for clock having the same API as time.time (i.e. a callable that returns the number of seconds as a floating point). We can have various implementations as needed for more precision but the API would be much simpler. 



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