bacow/GPT-SoVITS
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Refactor: Format Code with Ruff and Update Deprecated G2PW Link (#2255)

Browse Source 
* ruff check --fix

* ruff format --line-length 120 --target-version py39

* Change the link for G2PW Model

* update pytorch version and colab
This commit is contained in:
XXXXRT666
2025-04-07 09:42:47 +01:00
committed by GitHub
parent 9da7e17efe
commit 53cac93589
132 changed files with 8185 additions and 6648 deletions
Download Patch File Download Diff File Expand all files Collapse all files

52
GPT_SoVITS/AR/modules/activation_onnx.py
View File

@@ -1,17 +1,13 @@
# modified from https://github.com/lifeiteng/vall-e/blob/main/valle/modules/activation.py
from typing import Optional
from typing import Tuple
from typing import Optional, Tuple
import torch
from torch import Tensor
from torch.nn import Linear
from torch.nn import Module
from torch.nn.init import constant_
from torch.nn.init import xavier_normal_
from torch.nn.init import xavier_uniform_
from torch.nn import Linear, Module
from torch.nn.init import constant_, xavier_normal_, xavier_uniform_
from torch.nn.modules.linear import NonDynamicallyQuantizableLinear
from torch.nn.parameter import Parameter
from torch.nn import functional as F
from AR.modules.patched_mha_with_cache_onnx import multi_head_attention_forward_patched
@@ -47,9 +43,7 @@ class MultiheadAttention(Module):
self.dropout = dropout
self.batch_first = batch_first
self.head_dim = embed_dim // num_heads
assert (
self.head_dim * num_heads == self.embed_dim
), "embed_dim must be divisible by num_heads"
assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
if add_bias_kv:
self.bias_k = Parameter(torch.empty((1, 1, embed_dim), **factory_kwargs))
@@ -60,18 +54,30 @@ class MultiheadAttention(Module):
if linear1_cls == Linear:
if not self._qkv_same_embed_dim:
self.q_proj_weight = Parameter(
torch.empty((embed_dim, embed_dim), **factory_kwargs)
torch.empty(
(embed_dim, embed_dim),
**factory_kwargs,
)
)
self.k_proj_weight = Parameter(
torch.empty((embed_dim, self.kdim), **factory_kwargs)
torch.empty(
(embed_dim, self.kdim),
**factory_kwargs,
)
)
self.v_proj_weight = Parameter(
torch.empty((embed_dim, self.vdim), **factory_kwargs)
torch.empty(
(embed_dim, self.vdim),
**factory_kwargs,
)
)
self.register_parameter("in_proj_weight", None)
else:
self.in_proj_weight = Parameter(
torch.empty((3 * embed_dim, embed_dim), **factory_kwargs)
torch.empty(
(3 * embed_dim, embed_dim),
**factory_kwargs,
)
)
self.register_parameter("q_proj_weight", None)
self.register_parameter("k_proj_weight", None)
@@ -79,13 +85,11 @@ class MultiheadAttention(Module):
if bias:
self.in_proj_bias = Parameter(
torch.empty(3 * embed_dim, **factory_kwargs)
torch.empty(3 * embed_dim, **factory_kwargs),
)
else:
self.register_parameter("in_proj_bias", None)
self.out_proj = NonDynamicallyQuantizableLinear(
embed_dim, embed_dim, bias=bias, **factory_kwargs
)
self.out_proj = NonDynamicallyQuantizableLinear(embed_dim, embed_dim, bias=bias, **factory_kwargs)
self._reset_parameters()
else:
@@ -93,7 +97,10 @@ class MultiheadAttention(Module):
raise NotImplementedError
else:
self.in_proj_linear = linear1_cls(
embed_dim, 3 * embed_dim, bias=bias, **factory_kwargs
embed_dim,
3 * embed_dim,
bias=bias,
**factory_kwargs,
)
self.in_proj_weight = self.in_proj_linear.weight
@@ -107,7 +114,10 @@ class MultiheadAttention(Module):
self.register_parameter("in_proj_bias", None)
self.out_proj = linear2_cls(
embed_dim, embed_dim, bias=bias, **factory_kwargs
embed_dim,
embed_dim,
bias=bias,
**factory_kwargs,
)
if self.bias_k is not None: