Log chosen/rejected entropy #1159
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jacklanchantin
commented
May 1, 2025
| @@ -221,7 +221,7 @@ def rollout_from_model(self, prompt_list, sampling_params=None): | |||
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| return outputs | |||
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| def reward_from_model(self, prompt_list, batch_size=64): | |||
| def reward_from_model(self, prompt_list, batch_size=16): | |||
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was getting some vllm cuda OOM with batch_size=64 because we have custom vllm model
jacklanchantin
commented
May 1, 2025
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| register("chosen_logit_entropy", "Chosen Logit Entropy", 51, format_as_float) | ||
| register("rejected_logit_entropy","Rejected Logit Entropy", 51, format_as_float) |
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these are the only two added. rest are formatted
jacklanchantin
commented
May 1, 2025
| per_seq_loss = ( | ||
| (per_token_loss * target_mask).sum(dim=-1) | ||
| ).mean(dim=1) | ||
| per_seq_loss = ((per_token_loss * target_mask).sum(dim=-1)).mean(dim=1) |
jacklanchantin
commented
May 1, 2025
| ) # [Batch x Rollouts, 1] | ||
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| # entropy for all N rollouts | ||
| logit_entropy = self.get_all_rollouts_entropy(rollouts) |
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not sure if we want this. previously logit_entropy was computed for the chosen sequences. here i'm computing it for all rollouts
jacklanchantin
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