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ftpo fixes

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sam-paech
2026-04-24 21:03:24 +10:00
parent 7b8e3217e1
commit adf87c2a0a
2 changed files with 24 additions and 26 deletions
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1
.gitignore vendored
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@@ -5,3 +5,4 @@ experiments
unsloth_compiled_cache unsloth_compiled_cache
results results
bct.txt bct.txt
.codex

41
core/ftpo_trainer.py
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@@ -232,27 +232,24 @@ class FTPOTrainer(DPOTrainer):
logp_bad = logp_all.gather(-1, rejected.unsqueeze(-1)).squeeze(-1) logp_bad = logp_all.gather(-1, rejected.unsqueeze(-1)).squeeze(-1)
batch_rows = torch.arange(B, device=logp_all.device).unsqueeze(1) batch_rows = torch.arange(B, device=logp_all.device).unsqueeze(1)
gathered = logits_last[batch_rows, ch_ids] delta_tok = logits_last[batch_rows, ch_ids] - logits_last.gather(
logit_bad = logits_last.gather(-1, rejected.unsqueeze(-1)) -1, rejected.unsqueeze(-1)
margin = gathered - logit_bad )
weights = torch.clamp((clip_epsilon_logits - margin) / clip_epsilon_logits, 0.0, 1.0) * ch_mask weights = (
torch.clamp(
(clip_epsilon_logits - delta_tok) / clip_epsilon_logits,
0.0,
1.0,
)
* ch_mask
)
zero_row = weights.sum(dim=-1, keepdim=True) < 1e-12 tau = 1.0
weights = torch.where(zero_row, ch_mask.float(), weights) gap = clip_epsilon_logits - delta_tok
weights_sum = weights.sum(dim=-1, keepdim=True)
batch_rows = torch.arange(B, device=ids.device).unsqueeze(1)
l_chosen = logits_last[batch_rows, ch_ids]
l_bad = logits_last.gather(-1, rejected.unsqueeze(-1))
delta_tok = l_chosen - l_bad
margin = clip_epsilon_logits
tau = 1.0
gap = margin - delta_tok
per_tok_loss = F.softplus(gap / tau) per_tok_loss = F.softplus(gap / tau)
pref_loss = (per_tok_loss * weights).sum() / weights_sum.sum() chosen_counts = ch_mask.sum(dim=-1).clamp(min=1)
pref_loss = ((per_tok_loss * weights).sum(dim=-1) / chosen_counts).mean()
extra_metrics = {} extra_metrics = {}
@@ -270,13 +267,13 @@ class FTPOTrainer(DPOTrainer):
use_cache=False, return_dict=True, use_cache=False, return_dict=True,
).logits[:, -1, :] ).logits[:, -1, :]
freeze_mask = torch.ones_like(logits_last, dtype=torch.bool) tether_mask = torch.ones_like(logits_last, dtype=torch.bool)
rows = torch.arange(B, device=ch_ids.device).unsqueeze(1).expand_as(ch_ids) rows = torch.arange(B, device=ch_ids.device).unsqueeze(1).expand_as(ch_ids)
freeze_mask[rows[ch_mask], ch_ids[ch_mask]] = False tether_mask[rows[ch_mask], ch_ids[ch_mask]] = False
freeze_mask.scatter_(1, rejected.unsqueeze(-1), False) tether_mask.scatter_(1, rejected.unsqueeze(-1), False)
diff = logits_last - ref_logits_last diff = logits_last - ref_logits_last
mse_elem_raw = (freeze_mask * diff.pow(2)).sum() / freeze_mask.sum() mse_elem_raw = (tether_mask * diff.pow(2)).sum() / tether_mask.sum()
tgt_mask = torch.zeros_like(logits_last, dtype=torch.bool) tgt_mask = torch.zeros_like(logits_last, dtype=torch.bool)
rows = torch.arange(B, device=ch_ids.device).unsqueeze(1).expand_as(ch_ids) rows = torch.arange(B, device=ch_ids.device).unsqueeze(1).expand_as(ch_ids)