Fixing GPU for token-classification in a better way. #13856
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Co-authored-by: Pierre Snell <pierre.snell@botpress.com>
| @@ -216,7 +217,7 @@ def _forward(self, model_inputs): | |||
| } | |||
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| def postprocess(self, model_outputs, aggregation_strategy=AggregationStrategy.NONE): | |||
| outputs = model_outputs["outputs"] | |||
| outputs = model_outputs["outputs"].numpy() | |||
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I'm not as familiar as you with the pipeline but will the model_outputs["outputs"] can still be on GPU ?
Maybe all preprocessing is firstly casted to CPU but I would rather double check.
Else should we use model_outputs["outputs"].cpu().numpy() instead ?
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Hi @ierezell,
Just like this file does not ever mention .to(device) it shouldn't mention .cpu() as it's not the task of the pipeline to encapsulate that logic. Before it was done this way meaning many pipelines wouldn't actually support some features.
The logic is now in the Pipeline.forward method.
Rougly:
preprocess: (generic python objects) -> Tensors
_forward: Tensors -> Tensors
postprocess: Tensors -> Generic python objects
forward encapsulate classic logic for inference (inference mode, GPU tensor moving and other if needed, like batching)
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Ok, then everything will be at the good place at the good moment so that's perfect! Thanks for the clarification.
Co-authored-by: Pierre Snell <pierre.snell@botpress.com> Co-authored-by: Pierre Snell <pierre.snell@botpress.com>
What does this PR do?
We probably need a way to ensure this test is ran !
Superseeds #13819 (different fix for same issue)
Fixes # (issue)
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