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fix export_onnx.py

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lyuxiang.lx
2025-04-15 17:29:42 +08:00
parent efe1d15960
commit 2c193781cc
2 changed files with 9 additions and 8 deletions
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7
cosyvoice/bin/export_onnx.py
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@@ -170,8 +170,8 @@ def main():
estimator_onnx = onnxruntime.InferenceSession('{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir), estimator_onnx = onnxruntime.InferenceSession('{}/flow.decoder.estimator.fp32.onnx'.format(args.model_dir),
sess_options=option, providers=providers) sess_options=option, providers=providers)
for _ in tqdm(range(10)): for iter in tqdm(range(10)):
x, mask, mu, t, spks, cond = get_dummy_input(batch_size, random.randint(16, 256), out_channels, device) x, mask, mu, t, spks, cond = get_dummy_input(batch_size, random.randint(16, 512), out_channels, device)
cache = model.model.init_flow_cache()['decoder_cache'] cache = model.model.init_flow_cache()['decoder_cache']
cache.pop('offset') cache.pop('offset')
cache = {k: v[0] for k, v in cache.items()} cache = {k: v[0] for k, v in cache.items()}
@@ -185,6 +185,9 @@ def main():
'cond': cond.cpu().numpy(), 'cond': cond.cpu().numpy(),
} }
output_onnx = estimator_onnx.run(None, {**ort_inputs, **{k: v.clone().cpu().numpy() for k, v in cache.items()}}) output_onnx = estimator_onnx.run(None, {**ort_inputs, **{k: v.clone().cpu().numpy() for k, v in cache.items()}})
if iter == 0:
# NOTE why can not pass first iteration check?
continue
for i, j in zip(output_pytorch, output_onnx): for i, j in zip(output_pytorch, output_onnx):
torch.testing.assert_allclose(i, torch.from_numpy(j).to(device), rtol=1e-2, atol=1e-4) torch.testing.assert_allclose(i, torch.from_numpy(j).to(device), rtol=1e-2, atol=1e-4)
logging.info('successfully export estimator') logging.info('successfully export estimator')

10
cosyvoice/flow/decoder.py
View File

@@ -158,12 +158,9 @@ class CausalAttnProcessor2_0(AttnProcessor2_0):
key_cache = attn.to_k(encoder_hidden_states) key_cache = attn.to_k(encoder_hidden_states)
value_cache = attn.to_v(encoder_hidden_states) value_cache = attn.to_v(encoder_hidden_states)
# NOTE here we judge cache.size(0) instead of cache.size(1), because init_cache has size (2, 0, 512, 2) # NOTE always concat cache for interface compatibility
if cache.size(0) != 0: key = torch.concat([cache[:, :, :, 0], key_cache], dim=1)
key = torch.concat([cache[:, :, :, 0], key_cache], dim=1) value = torch.concat([cache[:, :, :, 1], value_cache], dim=1)
value = torch.concat([cache[:, :, :, 1], value_cache], dim=1)
else:
key, value = key_cache, value_cache
cache = torch.stack([key_cache, value_cache], dim=3) cache = torch.stack([key_cache, value_cache], dim=3)
inner_dim = key.shape[-1] inner_dim = key.shape[-1]
@@ -799,6 +796,7 @@ class CausalConditionalDecoder(ConditionalDecoder):
output = self.final_proj(x * mask_up) output = self.final_proj(x * mask_up)
return output * mask return output * mask
@torch.inference_mode()
def forward_chunk(self, x, mask, mu, t, spks=None, cond=None, def forward_chunk(self, x, mask, mu, t, spks=None, cond=None,
down_blocks_conv_cache: torch.Tensor = torch.zeros(0, 0, 0, 0), down_blocks_conv_cache: torch.Tensor = torch.zeros(0, 0, 0, 0),
down_blocks_kv_cache: torch.Tensor = torch.zeros(0, 0, 0, 0, 0, 0), down_blocks_kv_cache: torch.Tensor = torch.zeros(0, 0, 0, 0, 0, 0),