ryanthompson591 commented on code in PR #22795:
URL: https://github.com/apache/beam/pull/22795#discussion_r959000211
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sdks/python/apache_beam/ml/inference/pytorch_inference.py:
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@@ -234,16 +266,17 @@ def run_inference(
# If elements in `batch` are provided as a dictionaries from key to
Tensors,
# then iterate through the batch list, and group Tensors to the same key
key_to_tensor_list = defaultdict(list)
- for example in batch:
- for key, tensor in example.items():
- key_to_tensor_list[key].append(tensor)
- key_to_batched_tensors = {}
- for key in key_to_tensor_list:
- batched_tensors = torch.stack(key_to_tensor_list[key])
- batched_tensors = _convert_to_device(batched_tensors, self._device)
- key_to_batched_tensors[key] = batched_tensors
- predictions = model(**key_to_batched_tensors, **inference_args)
- return [PredictionResult(x, y) for x, y in zip(batch, predictions)]
+ with torch.no_grad():
Review Comment:
I'm just thinking, if a future developer came in and said. Hey why is
torch.no_grad here? why do we need this check?
I wouldn't know, it wouldn't be apparent to me without a comment. But I
suppose it might also be discoverable through blame. I'll leave it up to you.
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