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fix: AttributeError when spks=None in ConditionalCFM.solve_euler (issue #1854) #1856

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fix: AttributeError when spks=None in ConditionalCFM.solve_euler (issue #1854) #1856

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b4c68e7
fix: DPO implementation bug in forward_dpo (issue #1449)
invalid-email-address Mar 18, 2026
1fe6d09
fix: AttributeError when spks=None in ConditionalCFM.solve_euler (iss…
invalid-email-address Mar 18, 2026
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14 changes: 8 additions & 6 deletions cosyvoice/flow/flow_matching.py
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Original file line number Diff line number Diff line change
Expand Up @@ -92,12 +92,14 @@ def solve_euler(self, x, t_span, mu, mask, spks, cond, streaming=False):

# Do not use concat, it may cause memory format changed and trt infer with wrong results!
# NOTE when flow run in amp mode, x.dtype is float32, which cause nan in trt fp16 inference, so set dtype=spks.dtype
x_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=spks.dtype)
mask_in = torch.zeros([2, 1, x.size(2)], device=x.device, dtype=spks.dtype)
mu_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=spks.dtype)
t_in = torch.zeros([2], device=x.device, dtype=spks.dtype)
spks_in = torch.zeros([2, 80], device=x.device, dtype=spks.dtype)
cond_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=spks.dtype)
# Fix: use x.dtype as fallback when spks is None to avoid AttributeError
dtype = spks.dtype if spks is not None else x.dtype
x_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=dtype)
mask_in = torch.zeros([2, 1, x.size(2)], device=x.device, dtype=dtype)
mu_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=dtype)
t_in = torch.zeros([2], device=x.device, dtype=dtype)
spks_in = torch.zeros([2, 80], device=x.device, dtype=dtype)
cond_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=dtype)
for step in range(1, len(t_span)):
# Classifier-Free Guidance inference introduced in VoiceBox
x_in[:] = x
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9 changes: 5 additions & 4 deletions cosyvoice/llm/llm.py
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Expand Up @@ -447,10 +447,11 @@ def forward_dpo(
acc = th_accuracy(chosen_logits.view(-1, self.speech_token_size + 3), chosen_lm_target, ignore_label=IGNORE_ID)

# 5. calculate dpo logits
chosen_lm_mask = chosen_lm_target == IGNORE_ID
rejected_lm_mask = rejected_lm_target == IGNORE_ID
chosen_logps = torch.gather(chosen_logits.log_softmax(dim=-1), dim=2, index=chosen_lm_target.masked_fill(chosen_lm_mask, 0).unsqueeze(dim=-1)).squeeze(dim=-1)
rejected_logps = torch.gather(rejected_logits.log_softmax(dim=-1), dim=2, index=rejected_lm_target.masked_fill(rejected_lm_mask, 0).unsqueeze(dim=-1)).squeeze(dim=-1)
# Fix: mask should select NON-IGNORE_ID positions (True for valid tokens)
chosen_lm_mask = chosen_lm_target != IGNORE_ID
rejected_lm_mask = rejected_lm_target != IGNORE_ID
chosen_logps = torch.gather(chosen_logits.log_softmax(dim=-1), dim=2, index=chosen_lm_target.masked_fill(~chosen_lm_mask, 0).unsqueeze(dim=-1)).squeeze(dim=-1)
rejected_logps = torch.gather(rejected_logits.log_softmax(dim=-1), dim=2, index=rejected_lm_target.masked_fill(~rejected_lm_mask, 0).unsqueeze(dim=-1)).squeeze(dim=-1)
chosen_logps = (chosen_logps * chosen_lm_mask).sum(dim=-1) / chosen_lm_mask.sum(dim=-1)
rejected_logps = (rejected_logps * rejected_lm_mask).sum(dim=-1) / rejected_lm_mask.sum(dim=-1)
return {'loss': loss, 'acc': acc, 'chosen_logps': chosen_logps, 'rejected_logps': rejected_logps}
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