Log KL Divergence in GRPO Loss function #323
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casteryh
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Oct 7, 2025
| def forward_backward( | ||
| self, inputs: dict[str, Tensor], targets: dict[str, Tensor] | ||
| ) -> Tensor: | ||
| ) -> Tensor | LossMetrics: |
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shouldn't this be tuple[..,..]?
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@casteryh let's merge |
joecummings
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Oct 8, 2025
| self.beta = beta | ||
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| def forward(self, logprobs, ref_logprobs, advantages, padding_mask): | ||
| kl = torch.exp(ref_logprobs - logprobs) - (ref_logprobs - logprobs) - 1 |
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Can we log the KL divergence minus padding tokens? May have to move that op up in the loss function.
| import torch | ||
| from torch import nn | ||
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| from forge.data_models.loss_metrics import LossMetrics |
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I'm not sure this is a fully fleshed out data model we want to use.
For now could we just define a loose type in this file and shove the metrics in that?
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I've done it with data_model, because we might want to log some other things from different losses in future (margins from DPO loss for instance).
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Resolves #298