[GitHub] [beam] jrmccluskey commented on a diff in pull request #24039: [Draft] TensorRT Custom Inference Function Implementation

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

jrmccluskey commented on code in PR #24039:
URL: https://github.com/apache/beam/pull/24039#discussion_r1017054307


##########
sdks/python/apache_beam/ml/inference/tensorrt_inference.py:
##########
@@ -164,11 +165,63 @@ def get_engine_attrs(self):
         self.stream)
 
 
+TensorRTInferenceFn = Callable[
+    [Sequence[np.ndarray], TensorRTEngine, Optional[Dict[str, Any]]],
+    Iterable[PredictionResult]]
+
+
+def _default_tensorRT_inference_fn(
+    batch: Sequence[np.ndarray],
+    engine: TensorRTEngine,
+    inference_args: Optional[Dict[str,
+                                  Any]] = None) -> Iterable[PredictionResult]:
+  from cuda import cuda
+  (
+      engine,
+      context,
+      context_lock,
+      inputs,
+      outputs,
+      gpu_allocations,
+      cpu_allocations,
+      stream) = engine.get_engine_attrs()
+
+  # Process I/O and execute the network
+  with context_lock:
+    _assign_or_fail(
+        cuda.cuMemcpyHtoDAsync(
+            inputs[0]['allocation'],
+            np.ascontiguousarray(batch),
+            inputs[0]['size'],
+            stream))
+    context.execute_async_v2(gpu_allocations, stream)
+    for output in range(len(cpu_allocations)):
+      _assign_or_fail(
+          cuda.cuMemcpyDtoHAsync(
+              cpu_allocations[output],
+              outputs[output]['allocation'],
+              outputs[output]['size'],
+              stream))
+    _assign_or_fail(cuda.cuStreamSynchronize(stream))
+
+    return [
+        PredictionResult(
+            x, [prediction[idx] for prediction in cpu_allocations]) for idx,
+        x in enumerate(batch)
+    ]
+
+
 @experimental(extra_message="No backwards-compatibility guarantees.")
 class TensorRTEngineHandlerNumPy(ModelHandler[np.ndarray,
                                               PredictionResult,
                                               TensorRTEngine]):
-  def __init__(self, min_batch_size: int, max_batch_size: int, **kwargs):
+  def __init__(
+      self,
+      min_batch_size: int,
+      max_batch_size: int,
+      *,
+      inference_fn: TensorRTInferenceFn = _default_tensorRT_inference_fn,
+      **kwargs):

Review Comment:
   This pattern looks a little quirky here, but the keyword-only arg has to appear after varargs and before kwargs. This shouldn't change anything, fortunately. 



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