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

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

TheNeuralBit commented on code in PR #16970:
URL: https://github.com/apache/beam/pull/16970#discussion_r844397432


##########
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:
   Is it more complex than that they just wanted nanosecond precision?



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