Changes to support TNLRV3 fine-tuning #4639
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added test fixed type mismatch when calling cudnnreduce kernel fixed python frontend to remove redundant states to match pytorch state dict
Tixxx
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SherlockNoMad
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Jul 29, 2020
SherlockNoMad
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Jul 29, 2020
thiagocrepaldi
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Aug 31, 2020
#4639 changed the default behavior by removing optimizer state from state_dict/checkpoint APIs. The reason for the previous change was to allow models trained on ORT to be used for inference on PyTorch, which is an important feature. Due to the change aforementioned, when resuming training from a checkpoint, the optimizer would start with random weights, leading to a bad performance. This behavior would also cause reproducibility issues, as the optimizer wouldnt be able to resume from its previous state. This PR adds a boolean flag to state_dict/save_xheckpoint API that when True (default) it saves both model and optimizer state. When False, only the model state is kept.
thiagocrepaldi
pushed a commit
that referenced
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Sep 1, 2020
#4639 changed the default behavior by removing optimizer state from state_dict/checkpoint APIs. The reason for the previous change was to allow models trained on ORT to be used for inference on PyTorch, which is an important feature. Due to the change aforementioned, when resuming training from a checkpoint, the optimizer would start with random weights, leading to a bad performance. This behavior would also cause reproducibility issues, as the optimizer wouldnt be able to resume from its previous state. This PR adds a boolean flag to state_dict/save_xheckpoint API that when True (default) it saves both model and optimizer state. When False, only the model state is kept.
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Description: Describe your changes.
Changes to support tnlrv3 fine-tuning task
Motivation and Context
To support tnlrv3 fine-tuning