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fix: support batched inference in solve_euler by removing hardcoded batch size #1827

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fix: support batched inference in solve_euler by removing hardcoded batch size #1827

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25 changes: 14 additions & 11 deletions cosyvoice/flow/flow_matching.py
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Original file line number Diff line number Diff line change
Expand Up @@ -92,20 +92,23 @@ 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)
bsz = x.size(0)
x_in = torch.zeros([2 * bsz, 80, x.size(2)], device=x.device, dtype=spks.dtype)
mask_in = torch.zeros([2 * bsz, 1, x.size(2)], device=x.device, dtype=spks.dtype)
mu_in = torch.zeros([2 * bsz, 80, x.size(2)], device=x.device, dtype=spks.dtype)
t_in = torch.zeros([2 * bsz], device=x.device, dtype=spks.dtype)
spks_in = torch.zeros([2 * bsz, 80], device=x.device, dtype=spks.dtype)
cond_in = torch.zeros([2 * bsz, 80, x.size(2)], device=x.device, dtype=spks.dtype)
for step in range(1, len(t_span)):
# Classifier-Free Guidance inference introduced in VoiceBox
x_in[:] = x
mask_in[:] = mask
mu_in[0] = mu
x_in[:bsz] = x
x_in[bsz:] = x
mask_in[:bsz] = mask
mask_in[bsz:] = mask
mu_in[:bsz] = mu
t_in[:] = t.unsqueeze(0)
spks_in[0] = spks
cond_in[0] = cond
spks_in[:bsz] = spks
cond_in[:bsz] = cond
dphi_dt = self.forward_estimator(
x_in, mask_in,
mu_in, t_in,
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