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chore(format): run black on dev

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github-actions[bot]
2024-01-26 08:09:56 +00:00
parent 850ec480d0
commit 738e55f05c
15 changed files with 266 additions and 148 deletions
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8
infer/lib/infer_pack/models.py
View File

@@ -400,13 +400,17 @@ class SineGen(torch.nn.Module):
f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
idx + 2
) # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
rad_values = (f0_buf / self.sampling_rate) % 1 ###%1意味着n_har的乘积无法后处理优化
rad_values = (
f0_buf / self.sampling_rate
) % 1 ###%1意味着n_har的乘积无法后处理优化
rand_ini = torch.rand(
f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
)
rand_ini[:, 0] = 0
rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
tmp_over_one = torch.cumsum(rad_values, 1) # % 1 #####%1意味着后面的cumsum无法再优化
tmp_over_one = torch.cumsum(
rad_values, 1
) # % 1 #####%1意味着后面的cumsum无法再优化
tmp_over_one *= upp
tmp_over_one = F.interpolate(
tmp_over_one.transpose(2, 1),

8
infer/lib/infer_pack/models_onnx.py
View File

@@ -333,13 +333,17 @@ class SineGen(torch.nn.Module):
f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (
idx + 2
) # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic
rad_values = (f0_buf / self.sampling_rate) % 1 ###%1意味着n_har的乘积无法后处理优化
rad_values = (
f0_buf / self.sampling_rate
) % 1 ###%1意味着n_har的乘积无法后处理优化
rand_ini = torch.rand(
f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
)
rand_ini[:, 0] = 0
rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
tmp_over_one = torch.cumsum(rad_values, 1) # % 1 #####%1意味着后面的cumsum无法再优化
tmp_over_one = torch.cumsum(
rad_values, 1
) # % 1 #####%1意味着后面的cumsum无法再优化
tmp_over_one *= upp
tmp_over_one = F.interpolate(
tmp_over_one.transpose(2, 1),

114
infer/modules/ipex/attention.py
View File

@@ -62,12 +62,12 @@ def torch_bmm(input, mat2, *, out=None):
): # pylint: disable=invalid-name
start_idx_2 = i2 * split_2_slice_size
end_idx_2 = (i2 + 1) * split_2_slice_size
hidden_states[
start_idx:end_idx, start_idx_2:end_idx_2
] = original_torch_bmm(
input[start_idx:end_idx, start_idx_2:end_idx_2],
mat2[start_idx:end_idx, start_idx_2:end_idx_2],
out=out,
hidden_states[start_idx:end_idx, start_idx_2:end_idx_2] = (
original_torch_bmm(
input[start_idx:end_idx, start_idx_2:end_idx_2],
mat2[start_idx:end_idx, start_idx_2:end_idx_2],
out=out,
)
)
else:
hidden_states[start_idx:end_idx] = original_torch_bmm(
@@ -138,61 +138,67 @@ def scaled_dot_product_attention(
start_idx_2 = i2 * split_2_slice_size
end_idx_2 = (i2 + 1) * split_2_slice_size
if no_shape_one:
hidden_states[
start_idx:end_idx, start_idx_2:end_idx_2
] = original_scaled_dot_product_attention(
query[start_idx:end_idx, start_idx_2:end_idx_2],
key[start_idx:end_idx, start_idx_2:end_idx_2],
value[start_idx:end_idx, start_idx_2:end_idx_2],
attn_mask=attn_mask[
start_idx:end_idx, start_idx_2:end_idx_2
]
if attn_mask is not None
else attn_mask,
dropout_p=dropout_p,
is_causal=is_causal,
hidden_states[start_idx:end_idx, start_idx_2:end_idx_2] = (
original_scaled_dot_product_attention(
query[start_idx:end_idx, start_idx_2:end_idx_2],
key[start_idx:end_idx, start_idx_2:end_idx_2],
value[start_idx:end_idx, start_idx_2:end_idx_2],
attn_mask=(
attn_mask[start_idx:end_idx, start_idx_2:end_idx_2]
if attn_mask is not None
else attn_mask
),
dropout_p=dropout_p,
is_causal=is_causal,
)
)
else:
hidden_states[
:, start_idx:end_idx, start_idx_2:end_idx_2
] = original_scaled_dot_product_attention(
query[:, start_idx:end_idx, start_idx_2:end_idx_2],
key[:, start_idx:end_idx, start_idx_2:end_idx_2],
value[:, start_idx:end_idx, start_idx_2:end_idx_2],
attn_mask=attn_mask[
:, start_idx:end_idx, start_idx_2:end_idx_2
]
if attn_mask is not None
else attn_mask,
dropout_p=dropout_p,
is_causal=is_causal,
hidden_states[:, start_idx:end_idx, start_idx_2:end_idx_2] = (
original_scaled_dot_product_attention(
query[:, start_idx:end_idx, start_idx_2:end_idx_2],
key[:, start_idx:end_idx, start_idx_2:end_idx_2],
value[:, start_idx:end_idx, start_idx_2:end_idx_2],
attn_mask=(
attn_mask[
:, start_idx:end_idx, start_idx_2:end_idx_2
]
if attn_mask is not None
else attn_mask
),
dropout_p=dropout_p,
is_causal=is_causal,
)
)
else:
if no_shape_one:
hidden_states[
start_idx:end_idx
] = original_scaled_dot_product_attention(
query[start_idx:end_idx],
key[start_idx:end_idx],
value[start_idx:end_idx],
attn_mask=attn_mask[start_idx:end_idx]
if attn_mask is not None
else attn_mask,
dropout_p=dropout_p,
is_causal=is_causal,
hidden_states[start_idx:end_idx] = (
original_scaled_dot_product_attention(
query[start_idx:end_idx],
key[start_idx:end_idx],
value[start_idx:end_idx],
attn_mask=(
attn_mask[start_idx:end_idx]
if attn_mask is not None
else attn_mask
),
dropout_p=dropout_p,
is_causal=is_causal,
)
)
else:
hidden_states[
:, start_idx:end_idx
] = original_scaled_dot_product_attention(
query[:, start_idx:end_idx],
key[:, start_idx:end_idx],
value[:, start_idx:end_idx],
attn_mask=attn_mask[:, start_idx:end_idx]
if attn_mask is not None
else attn_mask,
dropout_p=dropout_p,
is_causal=is_causal,
hidden_states[:, start_idx:end_idx] = (
original_scaled_dot_product_attention(
query[:, start_idx:end_idx],
key[:, start_idx:end_idx],
value[:, start_idx:end_idx],
attn_mask=(
attn_mask[:, start_idx:end_idx]
if attn_mask is not None
else attn_mask
),
dropout_p=dropout_p,
is_causal=is_causal,
)
)
else:
return original_scaled_dot_product_attention(

42
infer/modules/ipex/hijacks.py
View File

@@ -104,11 +104,11 @@ def return_xpu(device):
return (
f"xpu:{device[-1]}"
if isinstance(device, str) and ":" in device
else f"xpu:{device}"
if isinstance(device, int)
else torch.device("xpu")
if isinstance(device, torch.device)
else "xpu"
else (
f"xpu:{device}"
if isinstance(device, int)
else torch.device("xpu") if isinstance(device, torch.device) else "xpu"
)
)
@@ -271,12 +271,16 @@ def ipex_hijacks():
"torch.batch_norm",
lambda orig_func, input, weight, bias, *args, **kwargs: orig_func(
input,
weight
if weight is not None
else torch.ones(input.size()[1], device=input.device),
bias
if bias is not None
else torch.zeros(input.size()[1], device=input.device),
(
weight
if weight is not None
else torch.ones(input.size()[1], device=input.device)
),
(
bias
if bias is not None
else torch.zeros(input.size()[1], device=input.device)
),
*args,
**kwargs,
),
@@ -286,12 +290,16 @@ def ipex_hijacks():
"torch.instance_norm",
lambda orig_func, input, weight, bias, *args, **kwargs: orig_func(
input,
weight
if weight is not None
else torch.ones(input.size()[1], device=input.device),
bias
if bias is not None
else torch.zeros(input.size()[1], device=input.device),
(
weight
if weight is not None
else torch.ones(input.size()[1], device=input.device)
),
(
bias
if bias is not None
else torch.zeros(input.size()[1], device=input.device)
),
*args,
**kwargs,
),

8
infer/modules/train/extract_feature_print.py
View File

@@ -116,9 +116,11 @@ else:
feats = readwave(wav_path, normalize=saved_cfg.task.normalize)
padding_mask = torch.BoolTensor(feats.shape).fill_(False)
inputs = {
"source": feats.half().to(device)
if is_half and device not in ["mps", "cpu"]
else feats.to(device),
"source": (
feats.half().to(device)
if is_half and device not in ["mps", "cpu"]
else feats.to(device)
),
"padding_mask": padding_mask.to(device),
"output_layer": 9 if version == "v1" else 12, # layer 9
}

22
infer/modules/vc/modules.py
View File

@@ -38,26 +38,28 @@ class VC:
to_return_protect0 = {
"visible": self.if_f0 != 0,
"value": to_return_protect[0]
if self.if_f0 != 0 and to_return_protect
else 0.5,
"value": (
to_return_protect[0] if self.if_f0 != 0 and to_return_protect else 0.5
),
"__type__": "update",
}
to_return_protect1 = {
"visible": self.if_f0 != 0,
"value": to_return_protect[1]
if self.if_f0 != 0 and to_return_protect
else 0.33,
"value": (
to_return_protect[1] if self.if_f0 != 0 and to_return_protect else 0.33
),
"__type__": "update",
}
if sid == "" or sid == []:
if self.hubert_model is not None: # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的
if (
self.hubert_model is not None
): # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的
logger.info("Clean model cache")
del (self.net_g, self.n_spk, self.hubert_model, self.tgt_sr) # ,cpt
self.hubert_model = (
self.net_g
) = self.n_spk = self.hubert_model = self.tgt_sr = None
self.hubert_model = self.net_g = self.n_spk = self.hubert_model = (
self.tgt_sr
) = None
if torch.cuda.is_available():
torch.cuda.empty_cache()
###楼下不这么折腾清理不干净