Make Pre-Commit-Hook Exit 0 While Using Ruff Check (#2427)

Modified gradio Layout Refactor WebUI half-precision and GPU detection logic
2 months ago · 05d44215f1
parent 2ff2cf5ba1
commit 05d44215f1
3 changed files with 604 additions and 528 deletions
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@ -8,7 +8,7 @@ repos:
    # Run the linter.
    - id: ruff
      types_or: [ python, pyi ]
-      args: [ --fix ]
+      args: [ --fix , "--exit-zero" ]
    # Run the formatter.
    - id: ruff-format
      types_or: [ python, pyi ]
--- a/config.py
+++ b/config.py
@ -1,30 +1,32 @@
-import sys
 import os
+import re
+import sys

-import torch,re
+import torch
+
+from tools.i18n.i18n import I18nAuto

-from tools.i18n.i18n import I18nAuto, scan_language_list
 i18n = I18nAuto(language=os.environ.get("language", "Auto"))


 pretrained_sovits_name = {
-    "v1":"GPT_SoVITS/pretrained_models/s2G488k.pth",
-    "v2":"GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth",
-    "v3":"GPT_SoVITS/pretrained_models/s2Gv3.pth",###v3v4还要检查vocoder，算了。。。
-    "v4":"GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth",
-    "v2Pro":"GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro.pth",
-    "v2ProPlus":"GPT_SoVITS/pretrained_models/v2Pro/s2Gv2ProPlus.pth",
+    "v1": "GPT_SoVITS/pretrained_models/s2G488k.pth",
+    "v2": "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth",
+    "v3": "GPT_SoVITS/pretrained_models/s2Gv3.pth",  ###v3v4还要检查vocoder，算了。。。
+    "v4": "GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth",
+    "v2Pro": "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro.pth",
+    "v2ProPlus": "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2ProPlus.pth",
 }

 pretrained_gpt_name = {
-    "v1":"GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
-    "v2":"GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt",
-    "v3":"GPT_SoVITS/pretrained_models/s1v3.ckpt",
-    "v4":"GPT_SoVITS/pretrained_models/s1v3.ckpt",
-    "v2Pro":"GPT_SoVITS/pretrained_models/s1v3.ckpt",
-    "v2ProPlus":"GPT_SoVITS/pretrained_models/s1v3.ckpt",
+    "v1": "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
+    "v2": "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt",
+    "v3": "GPT_SoVITS/pretrained_models/s1v3.ckpt",
+    "v4": "GPT_SoVITS/pretrained_models/s1v3.ckpt",
+    "v2Pro": "GPT_SoVITS/pretrained_models/s1v3.ckpt",
+    "v2ProPlus": "GPT_SoVITS/pretrained_models/s1v3.ckpt",
 }
-name2sovits_path={
+name2sovits_path = {
    # i18n("不训练直接推v1底模！"): "GPT_SoVITS/pretrained_models/s2G488k.pth",
    i18n("不训练直接推v2底模！"): "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth",
    # i18n("不训练直接推v3底模！"): "GPT_SoVITS/pretrained_models/s2Gv3.pth",
@ -32,29 +34,47 @@ name2sovits_path={
    i18n("不训练直接推v2Pro底模！"): "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro.pth",
    i18n("不训练直接推v2ProPlus底模！"): "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2ProPlus.pth",
 }
-name2gpt_path={
+name2gpt_path = {
    # i18n("不训练直接推v1底模！"):"GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
-    i18n("不训练直接推v2底模！"):"GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt",
-    i18n("不训练直接推v3底模！"):"GPT_SoVITS/pretrained_models/s1v3.ckpt",
+    i18n(
+        "不训练直接推v2底模！"
+    ): "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt",
+    i18n("不训练直接推v3底模！"): "GPT_SoVITS/pretrained_models/s1v3.ckpt",
 }
-SoVITS_weight_root = ["SoVITS_weights", "SoVITS_weights_v2", "SoVITS_weights_v3", "SoVITS_weights_v4", "SoVITS_weights_v2Pro", "SoVITS_weights_v2ProPlus"]
-GPT_weight_root = ["GPT_weights", "GPT_weights_v2", "GPT_weights_v3", "GPT_weights_v4", "GPT_weights_v2Pro", "GPT_weights_v2ProPlus"]
-SoVITS_weight_version2root={
-    "v1":"SoVITS_weights",
-    "v2":"SoVITS_weights_v2",
-    "v3":"SoVITS_weights_v3",
-    "v4":"SoVITS_weights_v4",
-    "v2Pro":"SoVITS_weights_v2Pro",
-    "v2ProPlus":"SoVITS_weights_v2ProPlus",
+SoVITS_weight_root = [
+    "SoVITS_weights",
+    "SoVITS_weights_v2",
+    "SoVITS_weights_v3",
+    "SoVITS_weights_v4",
+    "SoVITS_weights_v2Pro",
+    "SoVITS_weights_v2ProPlus",
+]
+GPT_weight_root = [
+    "GPT_weights",
+    "GPT_weights_v2",
+    "GPT_weights_v3",
+    "GPT_weights_v4",
+    "GPT_weights_v2Pro",
+    "GPT_weights_v2ProPlus",
+]
+SoVITS_weight_version2root = {
+    "v1": "SoVITS_weights",
+    "v2": "SoVITS_weights_v2",
+    "v3": "SoVITS_weights_v3",
+    "v4": "SoVITS_weights_v4",
+    "v2Pro": "SoVITS_weights_v2Pro",
+    "v2ProPlus": "SoVITS_weights_v2ProPlus",
 }
-GPT_weight_version2root={
-    "v1":"GPT_weights",
-    "v2":"GPT_weights_v2",
-    "v3":"GPT_weights_v3",
-    "v4":"GPT_weights_v4",
-    "v2Pro":"GPT_weights_v2Pro",
-    "v2ProPlus":"GPT_weights_v2ProPlus",
+GPT_weight_version2root = {
+    "v1": "GPT_weights",
+    "v2": "GPT_weights_v2",
+    "v3": "GPT_weights_v3",
+    "v4": "GPT_weights_v4",
+    "v2Pro": "GPT_weights_v2Pro",
+    "v2ProPlus": "GPT_weights_v2ProPlus",
 }
+
+
 def custom_sort_key(s):
    # 使用正则表达式提取字符串中的数字部分和非数字部分
    parts = re.split("(\d+)", s)
@ -62,27 +82,37 @@ def custom_sort_key(s):
    parts = [int(part) if part.isdigit() else part for part in parts]
    return parts

+
 def get_weights_names():
    SoVITS_names = []
    for key in name2sovits_path:
-        if os.path.exists(name2sovits_path[key]):SoVITS_names.append(key)
+        if os.path.exists(name2sovits_path[key]):
+            SoVITS_names.append(key)
    for path in SoVITS_weight_root:
-        if not os.path.exists(path):continue
+        if not os.path.exists(path):
+            continue
        for name in os.listdir(path):
            if name.endswith(".pth"):
                SoVITS_names.append("%s/%s" % (path, name))
+    if not SoVITS_names:
+        SoVITS_names = [""]
    GPT_names = []
    for key in name2gpt_path:
-        if os.path.exists(name2gpt_path[key]):GPT_names.append(key)
+        if os.path.exists(name2gpt_path[key]):
+            GPT_names.append(key)
    for path in GPT_weight_root:
-        if not os.path.exists(path):continue
+        if not os.path.exists(path):
+            continue
        for name in os.listdir(path):
            if name.endswith(".ckpt"):
                GPT_names.append("%s/%s" % (path, name))
-    SoVITS_names=sorted(SoVITS_names, key=custom_sort_key)
-    GPT_names=sorted(GPT_names, key=custom_sort_key)
+    SoVITS_names = sorted(SoVITS_names, key=custom_sort_key)
+    GPT_names = sorted(GPT_names, key=custom_sort_key)
+    if not GPT_names:
+        GPT_names = [""]
    return SoVITS_names, GPT_names

+
 def change_choices():
    SoVITS_names, GPT_names = get_weights_names()
    return {"choices": SoVITS_names, "__type__": "update"}, {
@ -106,10 +136,6 @@ pretrained_gpt_path = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=

 exp_root = "logs"
 python_exec = sys.executable or "python"
-if torch.cuda.is_available():
-    infer_device = "cuda"
-else:
-    infer_device = "cpu"

 webui_port_main = 9874
 webui_port_uvr5 = 9873
@ -118,20 +144,55 @@ webui_port_subfix = 9871

 api_port = 9880

-if infer_device == "cuda":
-    gpu_name = torch.cuda.get_device_name(0)
-    if (
-        ("16" in gpu_name and "V100" not in gpu_name.upper())
-        or "P40" in gpu_name.upper()
-        or "P10" in gpu_name.upper()
-        or "1060" in gpu_name
-        or "1070" in gpu_name
-        or "1080" in gpu_name
-    ):
-        is_half = False
-
-if infer_device == "cpu":
-    is_half = False
+
+def get_device_dtype_sm(idx: int) -> tuple[torch.device, torch.dtype, float, float]:
+    cpu = torch.device("cpu")
+    cuda = torch.device(f"cuda:{idx}")
+    if not torch.cuda.is_available():
+        return cpu, torch.float32, 0.0, 0.0
+    device_idx = idx
+    capability = torch.cuda.get_device_capability(device_idx)
+    name = torch.cuda.get_device_name(device_idx)
+    mem_bytes = torch.cuda.get_device_properties(device_idx).total_memory
+    mem_gb = mem_bytes / (1024**3) + 0.4
+    major, minor = capability
+    sm_version = major + minor / 10.0
+    is_16_series = bool(re.search(r"16\d{2}", name))
+    if mem_gb < 4:
+        return cpu, torch.float32, 0.0, 0.0
+    if (sm_version >= 7.0 and sm_version != 7.5) or (5.3 <= sm_version <= 6.0):
+        if is_16_series and sm_version == 7.5:
+            return cuda, torch.float32, sm_version, mem_gb  # 16系卡除外
+        else:
+            return cuda, torch.float16, sm_version, mem_gb
+    return cpu, torch.float32, 0.0, 0.0
+
+
+IS_GPU = True
+GPU_INFOS: list[str] = []
+GPU_INDEX: set[int] = set()
+GPU_COUNT = torch.cuda.device_count()
+CPU_INFO: str = "0\tCPU " + i18n("CPU训练,较慢")
+tmp: list[tuple[torch.device, torch.dtype, float, float]] = []
+memset: set[float] = set()
+
+for i in range(max(GPU_COUNT, 1)):
+    tmp.append(get_device_dtype_sm(i))
+
+for j in tmp:
+    device = j[0]
+    memset.add(j[3])
+    if device.type != "cpu":
+        GPU_INFOS.append(f"{device.index}\t{torch.cuda.get_device_name(device.index)}")
+        GPU_INDEX.add(device.index)
+
+if not GPU_INFOS:
+    IS_GPU = False
+    GPU_INFOS.append(CPU_INFO)
+    GPU_INDEX.add(0)
+
+infer_device = max(tmp, key=lambda x: (x[2], x[3]))[0]
+is_half = any(dtype == torch.float16 for _, dtype, _, _ in tmp)


 class Config:
--- a/webui.py
+++ b/webui.py