feat(assets): SA3 音效补齐 radio 11+1 与 dungeon 13源+15pan（token 走 hf-mirror 实测可用）
- 模型 17/17 文件经镜像全量缓存（gated+token，直连本机不通；sfx.md/setup.md/asset-pipeline.md 镜像结论同步更新） - 修复迁移遗留：stable_audio_3/models/ 源码子包被 .gitignore models/ 规则误伤丢失，从上游恢复并加白名单 - radio：arg_muffled/brake_screech 等叙事件落地（接线原有 L111/L154 零改动）+ phone_ring SYN→SA3 升级；dungeon 13 源 A/B 对齐（RMS 偏差 ≤0.3dB）全量同名替换，SYN 原版备份 tools/asset_fix_backup/sa3_ab_20260902/ - 关键玩法语义达标：echo 距离档 0.83<1.43<2.18<2.98s 严格递增；one-shot 真峰值全 ≤-3dBFS（解码回读法） - check_all_assets 8 游戏全绿（radio 254/254、dungeon 303/303）；playtest radio 221/dungeon 1610 全绿
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import json
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import torch
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from safetensors import safe_open
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from safetensors.torch import save_file as _st_save_file
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from torch import nn
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from .model import LoRAParametrization, _expand
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from ...verbose import vprint
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def apply_to_lora(fn):
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"""apply a function to LoRAParametrization layers, designed to be used with model.apply"""
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def apply_fn(layer):
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if isinstance(layer, LoRAParametrization):
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fn(layer)
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return apply_fn
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def enable_lora(model, lora_index=None):
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"""Enable LoRA layers. If lora_index is None, enables all. If specified, enables only that index."""
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def _enable(layer):
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if isinstance(layer, LoRAParametrization):
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if lora_index is None or layer.lora_index == lora_index:
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layer.enable_lora()
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model.apply(_enable)
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def disable_lora(model, lora_index=None):
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"""Disable LoRA layers. If lora_index is None, disables all. If specified, disables only that index."""
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def _disable(layer):
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if isinstance(layer, LoRAParametrization):
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if lora_index is None or layer.lora_index == lora_index:
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layer.disable_lora()
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model.apply(_disable)
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def get_lora_layers(model):
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layers = []
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for name, m in model.named_modules():
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plist = getattr(getattr(m, "parametrizations", None), "weight", None)
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if plist is None:
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continue
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for p in plist:
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if isinstance(p, LoRAParametrization):
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layers.append((name, p))
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return layers
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def has_lora(model):
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"""Return True if the model has at least one LoRAParametrization on any weight."""
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return len(get_lora_layers(model))>=1
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def filter_lora_layers(model, lora_layer_filter, lora_index=None):
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"""Enable/disable LoRA layers by name filter. If lora_index is specified, only affects that index."""
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lora_layer_filter = (lora_layer_filter or "").strip()
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# lora_layer_filter: logical OR on comma separated substrings
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# e.g. ".transformer.layers, .to_global_embed."
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def is_filtered(layer_name):
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filters = [x.lower().strip() for x in lora_layer_filter.split(",") if len(x)]
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for f in filters:
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for _f in _expand(f):
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if _f in layer_name:
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return True
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return False
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for name, p in get_lora_layers(model):
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if lora_index is not None and p.lora_index != lora_index:
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continue
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if is_filtered(name):
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p.disable_lora()
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else:
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p.enable_lora()
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# ------------------- helper function for collecting parameters for training/saving -------------------
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def name_is_lora(name):
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return (
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len(name.split(".")) >= 4
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and (name.split(".")[-4]) == "parametrizations"
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and name.split(".")[-1] in ["lora_A", "lora_B", "M_xs", "magnitude", "magnitude_r", "magnitude_c"]
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)
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def name_is_bias(name):
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return name.split(".")[-1] == "bias"
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def get_params_by_name(model, print_shapes=False, name_filter=None):
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for n, p in model.named_parameters():
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if name_filter is None or name_filter(n):
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if print_shapes:
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print(n, p.shape)
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yield p
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def get_lora_params(model, print_shapes=False):
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return get_params_by_name(model, print_shapes=print_shapes, name_filter=name_is_lora)
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def get_bias_params(model, print_shapes=False):
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return get_params_by_name(model, print_shapes=print_shapes, name_filter=name_is_bias)
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def get_lora_state_dict(model):
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return {k: v for k, v in model.state_dict().items() if name_is_lora(k)}
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def cast_base_to_precision(model, precision):
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"""Cast frozen base weights to lower precision, keeping LoRA params in fp32.
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Args:
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model: Model with LoRA parametrizations applied.
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precision: One of "bf16", "bfloat16", "fp16", "float16".
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"""
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import torch
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dtype_map = {"bf16": torch.bfloat16, "bfloat16": torch.bfloat16,
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"fp16": torch.float16, "float16": torch.float16}
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target_dtype = dtype_map.get(precision)
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if target_dtype is None:
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print(f"Unknown base_precision={precision!r}, keeping fp32")
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return
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vprint(f"Casting frozen base weights to {target_dtype}")
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model.to(target_dtype)
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# Restore LoRA params to fp32 — optimizer needs full precision
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n_restored = 0
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for p in get_lora_params(model):
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p.data = p.data.to(torch.float32)
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n_restored += 1
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vprint(f" Restored {n_restored} LoRA params to fp32")
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# ------------------- helper function for loading LoRa at inference -------------------
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def infer_global_rank(lora_sd: dict) -> int:
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candidates = []
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for k, v in lora_sd.items():
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if k.endswith(".lora_A"):
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candidates.append(v.shape[0])
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elif k.endswith(".lora_B"):
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candidates.append(v.shape[1])
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elif k.endswith(".M_xs"):
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candidates.append(v.shape[0])
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if not candidates:
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raise ValueError("No LoRA/LoRA-XS tensors found to infer rank")
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r = candidates[0]
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# sanity check they all match (most checkpoints use one global r)
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if any(c != r for c in candidates):
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print(f"Multiple ranks detected in ckpt: {sorted(set(candidates))}. "
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"You’ll need per-layer ranks.")
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return r
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def detect_dora_variant(state_dict):
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"""Detect DoRA variant from a state dict.
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Returns "dora-cols" (dim=0, per-input-feature) or "dora-rows" (dim=1,
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per-output-neuron) based on the shape of the first .magnitude tensor
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found. Returns None if no magnitude tensors exist or axis is ambiguous.
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"""
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for key, val in state_dict.items():
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if not key.endswith(".magnitude"):
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continue
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if val.dim() == 2:
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if val.shape[0] == 1:
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return "dora-cols" # old format: (1, fan_in)
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elif val.shape[1] == 1:
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return "dora-rows" # (fan_out, 1)
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# 1D magnitude — can't determine axis without model context
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return None
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return None
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def prepare_dora_state_dict(state_dict):
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"""Squeeze 2D DoRA magnitude tensors to 1D for loading. Modifies in-place."""
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for key in list(state_dict.keys()):
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if key.endswith(".magnitude") and state_dict[key].dim() == 2:
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state_dict[key] = state_dict[key].squeeze()
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def resolve_adapter_type(adapter_type, state_dict=None):
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"""Resolve legacy "dora" adapter_type to the correct variant.
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If adapter_type is "dora" and a checkpoint state_dict is provided,
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attempts to detect dora-rows vs dora-cols from magnitude tensor shapes.
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Falls back to "dora-rows" (paper-correct default).
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All other adapter types are returned unchanged.
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"""
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if adapter_type != "dora":
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return adapter_type
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if state_dict is not None:
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detected = detect_dora_variant(state_dict)
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if detected:
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return detected
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return "dora-rows"
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# ------------------- helper functions for safetensors LoRA checkpoints -------------------
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def save_lora_safetensors(state_dict, lora_config, path):
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"""Save a LoRA checkpoint in safetensors format with config as metadata.
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The lora_config dict is JSON-serialized and stored under the metadata key
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``"lora_config"``, which safetensors stores in the file header as a string.
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Args:
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state_dict: Dict of LoRA tensors (from get_lora_state_dict).
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lora_config: Dict with keys like rank, alpha, adapter_type, include, exclude.
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path: Output file path (should end in .safetensors).
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"""
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metadata = {"lora_config": json.dumps(lora_config)}
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fp16_dict = {k: v.half() if v.is_floating_point() else v for k, v in state_dict.items()}
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_st_save_file(fp16_dict, str(path), metadata=metadata)
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def load_lora_checkpoint(path):
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"""Load a LoRA checkpoint from either .safetensors or .ckpt format.
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Returns:
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Tuple of (state_dict, lora_config) where lora_config may be an empty
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dict if the checkpoint has no embedded config.
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"""
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path = str(path)
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if path.endswith(".safetensors"):
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with safe_open(path, framework="pt", device="cpu") as f:
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state_dict = {key: f.get_tensor(key) for key in f.keys()}
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metadata = f.metadata()
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lora_config = {}
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if metadata and "lora_config" in metadata:
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lora_config = json.loads(metadata["lora_config"])
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return state_dict, lora_config
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else:
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ckpt = torch.load(path, map_location="cpu", weights_only=False)
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state_dict = ckpt["state_dict"]
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lora_config = ckpt.get("lora_config", {})
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return state_dict, lora_config
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def convert_lora_ckpt_to_safetensors(ckpt_path, output_path=None):
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"""Convert a .ckpt LoRA checkpoint to .safetensors format.
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Args:
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ckpt_path: Path to the input .ckpt file.
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output_path: Path for the output .safetensors file.
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If None, replaces .ckpt extension with .safetensors.
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Returns:
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The output path.
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"""
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ckpt_path = str(ckpt_path)
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if output_path is None:
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if ckpt_path.endswith(".ckpt"):
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output_path = ckpt_path[:-5] + ".safetensors"
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else:
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output_path = ckpt_path + ".safetensors"
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state_dict, lora_config = load_lora_checkpoint(ckpt_path)
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save_lora_safetensors(state_dict, lora_config, output_path)
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return output_path
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# ------------------- helper functions for multi-LoRA stacking -------------------
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def get_lora_count(model):
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"""Return the number of stacked LoRAs (distinct lora_index values)."""
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indices = set()
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for _, p in get_lora_layers(model):
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indices.add(p.lora_index)
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return len(indices)
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def remap_lora_state_dict(state_dict, target_index):
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"""Remap LoRA state dict keys from parametrization index 0 to target_index.
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Checkpoints always save keys with index 0 (e.g. *.parametrizations.weight.0.lora_A).
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When loading the Nth stacked LoRA, we need to remap to index N.
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"""
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if target_index == 0:
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return state_dict
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remapped = {}
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for k, v in state_dict.items():
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new_key = k.replace(
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".parametrizations.weight.0.",
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".parametrizations.weight.{}.".format(target_index)
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)
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remapped[new_key] = v
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return remapped
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# ------------------- helper function for inferencing with multiple lora -------------------
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def _get_adapter_param_names(lora_layer):
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"""Return the list of trainable parameter names for this adapter type."""
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if lora_layer.adapter_type == "lora":
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return ["lora_A", "lora_B"]
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elif lora_layer.adapter_type in ("dora", "dora-rows", "dora-cols"):
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return ["lora_A", "lora_B", "magnitude"]
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elif lora_layer.adapter_type == "bora":
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return ["lora_A", "lora_B", "magnitude_r", "magnitude_c"]
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elif lora_layer.adapter_type == "lora-xs":
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return ["M_xs"]
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elif lora_layer.adapter_type in ("dora-rows-xs", "dora-cols-xs"):
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return ["M_xs", "magnitude"]
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elif lora_layer.adapter_type == "bora-xs":
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return ["M_xs", "magnitude_r", "magnitude_c"]
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return []
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def _prepare_for_multiple_lora(lora_layer):
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lora_layer._multi_lora_slots = []
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def _append_lora(lora_layer):
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slot = {}
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for name in _get_adapter_param_names(lora_layer):
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slot[name] = nn.Parameter(getattr(lora_layer, name).clone())
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lora_layer._multi_lora_slots.append(slot)
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def load_multiple_lora(model, lora_state_dicts):
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model.apply(apply_to_lora(_prepare_for_multiple_lora))
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for state_dict in lora_state_dicts:
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_ = model.load_state_dict(state_dict, strict=False)
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model.apply(apply_to_lora(_append_lora))
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return model
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def _select_lora(lora_layer, index):
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slot = lora_layer._multi_lora_slots[index]
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for name, param in slot.items():
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setattr(lora_layer, name, param)
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def select_lora(model, index):
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model.apply(apply_to_lora(lambda x: _select_lora(x, index)))
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return model
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# ------------------- helper function for merging multiple LoRAs -------------------
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def merge_loras_into_base_model(model, lora_configurations):
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"""
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Merges multiple LoRAs with specified application weights directly into the base model's weights.
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Works with all adapter types (LoRA, DoRA, LoRA-XS) by using the parametrization's own forward.
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After merging, the LoRA parametrization on each layer is disabled.
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Args:
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model: The PyTorch model with LoRAParametrization already applied (via add_lora).
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lora_configurations: A list of dicts, each specifying a LoRA to merge:
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[
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{
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'name': 'style1',
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'state_dict': loaded_torch_state_dict,
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'application_weight': 0.7
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},
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...
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]
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"""
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import torch
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if not lora_configurations:
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return
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# Collect all parametrized layers with their LoRA instances
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parametrized_layers = []
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for name, module in model.named_modules():
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if not hasattr(module, "parametrizations"):
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continue
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for attr_name in list(module.parametrizations.keys()):
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plist = module.parametrizations[attr_name]
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for p in plist:
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if isinstance(p, LoRAParametrization):
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parametrized_layers.append((name, module, attr_name, p, plist.original))
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break
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# Initialize per-layer delta accumulators
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deltas = {id(orig): torch.zeros_like(orig.data) for _, _, _, _, orig in parametrized_layers}
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for lora_cfg in lora_configurations:
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lora_sd = lora_cfg['state_dict']
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app_weight = lora_cfg['application_weight']
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# Load this LoRA's adapter weights into the model's parametrizations
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model.load_state_dict(lora_sd, strict=False)
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# Compute each layer's delta using the parametrization's own forward
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with torch.no_grad():
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for name, module, attr_name, lora_p, original in parametrized_layers:
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merged = lora_p(original)
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deltas[id(original)] += app_weight * (merged - original)
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# Apply accumulated deltas to original weights and disable LoRA
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with torch.no_grad():
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for name, module, attr_name, lora_p, original in parametrized_layers:
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original.data.add_(deltas[id(original)])
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lora_p.disable_lora()
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# ------------------- helper function for tying and untieing weights -------------------
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|
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def tie_weights(linear: nn.Linear, embedding: nn.Embedding):
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"""tie the weights of the linear layer and the embedding layer both with the same lora"""
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lora_p = embedding.parametrizations.weight[0]
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if lora_p.adapter_type != "lora":
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raise NotImplementedError(f"tie_weights only supports standard LoRA, got adapter_type='{lora_p.adapter_type}'")
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# this line below is optional if the original is already tied
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embedding.parametrizations.weight.original = linear.parametrizations.weight.original
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embedding.parametrizations.weight[0].lora_A = linear.parametrizations.weight[0].lora_B
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embedding.parametrizations.weight[0].lora_B = linear.parametrizations.weight[0].lora_A
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def untie_weights(linear: nn.Linear, embedding: nn.Embedding):
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"""untie the weights of the linear layer and the embedding layer"""
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lora_p = embedding.parametrizations.weight[0]
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if lora_p.adapter_type != "lora":
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raise NotImplementedError(f"untie_weights only supports standard LoRA, got adapter_type='{lora_p.adapter_type}'")
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embedding.parametrizations.weight.original = nn.Parameter(embedding.weight.original.clone())
|
||||
embedding.parametrizations.weight[0].lora_A = nn.Parameter(embedding.parametrizations.weight[0].lora_A.clone())
|
||||
embedding.parametrizations.weight[0].lora_B = nn.Parameter(embedding.parametrizations.weight[0].lora_B.clone())
|
||||
Reference in New Issue
Block a user