Public/automatic
1
0
Fork 0
mirror of https://github.com/vladmandic/automatic synced 2026-09-18 08:44:33 +02:00
Code Issues Packages Projects Releases Wiki Activity

cleanup

Browse Source
This commit is contained in:
Disty0
2026-02-02 20:30:52 +03:00
parent 90cf83b90e
commit ced81d630d
1 changed files with 10 additions and 9 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/sdnq/quantizer.py
+10 -9
View File
@@ -113,12 +113,12 @@ def prepare_svd_for_matmul(svd_up: torch.FloatTensor, svd_down: torch.FloatTenso
return svd_up, svd_down
def check_param_name_in(param_name: str, param_list: List[str]) -> bool:
def check_param_name_in(param_name: str, param_list: List[str]) -> str:
split_param_name = param_name.split(".")
for param in param_list:
if param.startswith("."):
if param_name.startswith(param[1:]):
return True
return param
else:
continue
if (
@@ -126,8 +126,8 @@ def check_param_name_in(param_name: str, param_list: List[str]) -> bool:
or param in split_param_name
or ("*" in param and re.match(param.replace(".*", "\\.*").replace("*", ".*"), param_name))
):
return True
return False
return param
return None
def get_quant_args_from_config(quantization_config: Union["SDNQConfig", dict]) -> dict:
@@ -156,7 +156,7 @@ def get_quant_args_from_config(quantization_config: Union["SDNQConfig", dict]) -
def get_minimum_dtype(weights_dtype: str, param_name: str, modules_dtype_dict: Dict[str, List[str]]):
if len(modules_dtype_dict.keys()) > 0:
for key, value in modules_dtype_dict.items():
if check_param_name_in(param_name, value):
if check_param_name_in(param_name, value) is not None:
key = key.lower()
if key in {"8bit", "8bits"}:
if dtype_dict[weights_dtype]["num_bits"] != 8:
@@ -181,8 +181,9 @@ def get_minimum_dtype(weights_dtype: str, param_name: str, modules_dtype_dict: D
def get_quant_kwargs(quant_kwargs: dict, modules_quant_config: Dict[str, dict]) -> dict:
if check_param_name_in(quant_kwargs["param_name"], modules_quant_config.keys()):
for key, value in modules_quant_config.items():
param_key = check_param_name_in(quant_kwargs["param_name"], modules_quant_config.keys())
if param_key is not None:
for key, value in modules_quant_config[param_key].items():
quant_kwargs[key] = value
quant_kwargs["weights_dtype"] = get_minimum_dtype(quant_kwargs["weights_dtype"], quant_kwargs["param_name"], quant_kwargs["modules_dtype_dict"])
return quant_kwargs
@@ -568,7 +569,7 @@ def apply_sdnq_to_module(model, weights_dtype="int8", quantized_matmul_dtype=Non
param_name = module_name
if hasattr(module, "weight") and module.weight is not None:
param_name = param_name + ".weight"
if check_param_name_in(param_name, modules_to_not_convert):
if check_param_name_in(param_name, modules_to_not_convert) is not None:
continue
layer_class_name = module.__class__.__name__
if layer_class_name in allowed_types and module.weight.dtype in {torch.float32, torch.float16, torch.bfloat16}:
@@ -779,7 +780,7 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
if hasattr(layer, "sdnq_dequantizer"):
return True
elif param_name.endswith(".weight"):
if not check_param_name_in(param_name, self.quantization_config.modules_to_not_convert):
if not check_param_name_in(param_name, self.quantization_config.modules_to_not_convert) is not None:
layer_class_name = get_module_from_name(model, param_name)[0].__class__.__name__
if layer_class_name in allowed_types:
if layer_class_name in conv_types or layer_class_name in conv_transpose_types: