yeandy commented on code in PR #17470:
URL: https://github.com/apache/beam/pull/17470#discussion_r878106500
##########
sdks/python/apache_beam/ml/inference/pytorch.py:
##########
@@ -39,25 +42,47 @@ class PytorchInferenceRunner(InferenceRunner):
def __init__(self, device: torch.device):
self._device = device
- def run_inference(self, batch: List[torch.Tensor],
- model: torch.nn.Module) -> Iterable[PredictionResult]:
+ def run_inference(
+ self,
+ batch: List[Union[torch.Tensor, Dict[str, torch.Tensor]]],
+ model: torch.nn.Module,
+ prediction_params: Optional[Dict[str, Any]] = None,
+ ) -> Iterable[PredictionResult]:
"""
Runs inferences on a batch of Tensors and returns an Iterable of
Tensor Predictions.
This method stacks the list of Tensors in a vectorized format to optimize
the inference call.
"""
+ if prediction_params is None:
+ prediction_params = {}
- torch_batch = torch.stack(batch)
- if torch_batch.device != self._device:
- torch_batch = torch_batch.to(self._device)
- predictions = model(torch_batch)
+ if isinstance(batch[0], dict):
+ result_dict = defaultdict(list)
+ for el in batch:
+ for k, v in el.items():
+ result_dict[k].append(v)
+ for k in result_dict:
+ batched_values = torch.stack(result_dict[k])
+ if batched_values.device != self._device:
+ batched_values = batched_values.to(self._device)
+ result_dict[k] = batched_values
+ predictions = model(**result_dict, **prediction_params)
+ else:
Review Comment:
Done
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