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make fqn_matches_fqn_config public and fix module device loading #3302

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Merged
merged 1 commit into from
Nov 6, 2025
Merged

make fqn_matches_fqn_config public and fix module device loading #3302

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2 changes: 2 additions & 0 deletions torchao/quantization/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -69,6 +69,7 @@
float8_static_activation_float8_weight,
float8_weight_only,
fpx_weight_only,
fqn_matches_fqn_config,
gemlite_uintx_weight_only,
int4_dynamic_activation_int4_weight,
int4_weight_only,
Expand Down Expand Up @@ -142,6 +143,7 @@
"float8_static_activation_float8_weight",
"uintx_weight_only",
"fpx_weight_only",
"fqn_matches_fqn_config",
"gemlite_uintx_weight_only",
"swap_conv2d_1x1_to_linear",
"Int4DynamicActivationInt4WeightConfig",
Expand Down
15 changes: 6 additions & 9 deletions torchao/quantization/quant_api.py
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Original file line number Diff line number Diff line change
Expand Up @@ -480,15 +480,17 @@ def quantize_(

for module_fqn, module in model.named_modules():
if (
_fqn_matches_fqn_config(module_fqn, config)
fqn_matches_fqn_config(module_fqn, config)
or _module_param_matches_fqn_config(module, module_fqn, config)
or ("_default" in config.fqn_to_config and _is_linear(module))
):
module_name = (
module_fqn.rsplit(".", 1) if "." in module_fqn else module_fqn
)
# this replaces inplace, so no need to reassign
_fqn_to_config_handler(module, module_name, config, device)
_fqn_to_config_handler(module, module_name, config)
if device is not None:
module.to(device=device)
return
if isinstance(config, AOBaseConfig):
filter_fn = _is_linear if filter_fn is None else filter_fn
Expand Down Expand Up @@ -2470,7 +2472,6 @@ def _fqn_to_config_handler(
module: torch.nn.Module,
fqn: str,
config: FqnToConfig,
device: Optional[torch.device] = None,
):
"""This function expects a module that either is specified in FqnToConfig or has a parameter that is specified in FqnToConfig.

Expand All @@ -2479,17 +2480,13 @@ def _fqn_to_config_handler(
fqn (str): The fully qualified name of the module containing the parameters.
config (FqnToConfig): Configuration object containing regex patterns / fqn mapped
to quantization configurations.
device (Optional[torch.device]): The device to move the module to as part of quantization

Returns:
torch.nn.Module: The modified module with quantized parameters.

Raises:
NotImplementedError: If the quantization configuration is not yet supported for parameter quantization.
"""
if device is not None:
module = module.to(device)

parameter_config_found = False
top_level_params = []
for i, (parameter_name, param) in enumerate(list(module.named_parameters())):
Expand Down Expand Up @@ -2563,7 +2560,7 @@ def _fqn_to_config_handler(
return module


def _fqn_matches_fqn_config(
def fqn_matches_fqn_config(
fqn: str,
config: FqnToConfig,
):
Expand Down Expand Up @@ -2608,7 +2605,7 @@ def _module_param_matches_fqn_config(
for name, param in module.named_parameters():
if name in dir(module):
parameter_fqn = f"{fqn}.{name}" if len(fqn) > 0 else name
if _fqn_matches_fqn_config(parameter_fqn, config):
if fqn_matches_fqn_config(parameter_fqn, config):
return True

return False
Expand Down
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