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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