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Support variance ONNX export on PyTorch 2.x #293

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120 changes: 74 additions & 46 deletions deployment/exporters/variance_exporter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -388,33 +388,47 @@ def _torch_export_model(self):
noise = torch.randn(shape, device=self.device)
condition = torch.rand((1, hparams['hidden_size'], 15), device=self.device)

print(f'Tracing {self.pitch_backbone_class_name} backbone...')
pitch_predictor = self.model.view_as_pitch_predictor()
pitch_predictor.pitch_predictor.set_backbone(
torch.jit.trace(
pitch_predictor.pitch_predictor.backbone,
(
noise,
dummy_time,
condition

if torch.__version__.startswith('1.13.'):
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Copilot AI Mar 9, 2026

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The PyTorch version gate (torch.__version__.startswith('1.13.')) is duplicated in multiple blocks in this method. Consider computing a single boolean once (e.g., is_torch_113) near the top of _torch_export_model and reusing it to avoid repeating version parsing logic and to make future adjustments less error-prone.

Suggested change
if torch.__version__.startswith('1.13.'):
is_torch_113 = torch.__version__.startswith('1.13.')
if is_torch_113:

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print(f'Tracing {self.pitch_backbone_class_name} backbone...')
pitch_predictor.pitch_predictor.set_backbone(
torch.jit.trace(
pitch_predictor.pitch_predictor.backbone,
(
noise,
dummy_time,
condition
)
)
)
)

print(f'Scripting {self.pitch_predictor_class_name}...')
pitch_predictor = torch.jit.script(
pitch_predictor,
example_inputs=[
(
condition.transpose(1, 2),
1 # p_sample branch
),
(
condition.transpose(1, 2),
dummy_steps # p_sample_plms branch
)
]
)
print(f'Scripting {self.pitch_predictor_class_name}...')
pitch_predictor = torch.jit.script(
pitch_predictor,
example_inputs=[
(
condition.transpose(1, 2),
1 # p_sample branch
),
(
condition.transpose(1, 2),
dummy_steps # p_sample_plms branch
)
]
)
else:
print(f'Wrapping {self.pitch_predictor_class_name} for trace-based export...')

class _PitchPredWrapper(torch.nn.Module):
def __init__(self, model):
super().__init__()
self.pitch_predictor = model.pitch_predictor

def forward(self, pitch_cond, steps):
return self.pitch_predictor(pitch_cond, steps=steps)

pitch_predictor = _PitchPredWrapper(pitch_predictor)

print(f'Exporting {self.pitch_predictor_class_name}...')
torch.onnx.export(
Expand Down Expand Up @@ -535,33 +549,47 @@ def _torch_export_model(self):
condition = torch.rand((1, hparams['hidden_size'], 15), device=self.device)
step = (torch.rand((1,), device=self.device) * hparams['K_step']).long()

print(f'Tracing {self.variance_backbone_class_name} backbone...')
multi_var_predictor = self.model.view_as_variance_predictor()
multi_var_predictor.variance_predictor.set_backbone(
torch.jit.trace(
multi_var_predictor.variance_predictor.backbone,
(
noise,
step,
condition

if torch.__version__.startswith('1.13.'):
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Same version check duplication as above: this block repeats torch.__version__.startswith('1.13.'). To reduce maintenance overhead, reuse a single precomputed flag (e.g., is_torch_113) within _torch_export_model.

Suggested change
if torch.__version__.startswith('1.13.'):
if is_torch_113:

Copilot uses AI. Check for mistakes.
print(f'Tracing {self.variance_backbone_class_name} backbone...')
multi_var_predictor.variance_predictor.set_backbone(
torch.jit.trace(
multi_var_predictor.variance_predictor.backbone,
(
noise,
step,
condition
)
)
)
)

print(f'Scripting {self.multi_var_predictor_class_name}...')
multi_var_predictor = torch.jit.script(
multi_var_predictor,
example_inputs=[
(
condition.transpose(1, 2),
1 # p_sample branch
),
(
condition.transpose(1, 2),
dummy_steps # p_sample_plms branch
)
]
)
print(f'Scripting {self.multi_var_predictor_class_name}...')
multi_var_predictor = torch.jit.script(
multi_var_predictor,
example_inputs=[
(
condition.transpose(1, 2),
1 # p_sample branch
),
(
condition.transpose(1, 2),
dummy_steps # p_sample_plms branch
)
]
)
else:
print(f'Wrapping {self.multi_var_predictor_class_name} for trace-based export...')

class _VarPredWrapper(torch.nn.Module):
def __init__(self, model):
super().__init__()
self.variance_predictor = model.variance_predictor

def forward(self, variance_cond, steps):
return self.variance_predictor(variance_cond, steps=steps)

multi_var_predictor = _VarPredWrapper(multi_var_predictor)

print(f'Exporting {self.multi_var_predictor_class_name}...')
torch.onnx.export(
Expand Down
7 changes: 5 additions & 2 deletions scripts/export.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,10 +15,13 @@


def check_pytorch_version():
# Require PyTorch version to be exactly 1.13.x
import warnings
if torch.__version__.startswith('1.13.'):
return
raise RuntimeError('This script requires PyTorch 1.13.x. Please install the correct version.')
warnings.warn(
f'ONNX export is tested on PyTorch 1.13.x, but you have {torch.__version__}. '
f'Proceeding with trace-based fallback for variance models.'
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The warning text is misleading because check_pytorch_version() runs unconditionally for all export commands (acoustic/variance/vocoder), but the message claims it is "Proceeding with trace-based fallback for variance models." Consider either (1) making the warning generic (e.g., "Export is tested on 1.13.x...") or (2) moving this version warning into the variance export command / exporter so it only appears when the fallback is actually relevant.

Suggested change
f'Proceeding with trace-based fallback for variance models.'
f'Export may not behave as expected with this PyTorch version.'

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)


def find_exp(exp):
Expand Down
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