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SDQN add OpenVINO FP16 MM and add update_torch_dtype to SDNQQuantizer

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Disty0
2026-06-20 21:41:34 +03:00
parent f40a83352f
commit d2d0efbc48
4 changed files with 138 additions and 49 deletions
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modules/sdnq/quantizer.py
+38 -28
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@@ -589,18 +589,6 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
self.quantization_config.modules_to_not_convert.append(param_name)
return False
def check_quantized_param(self, *args, **kwargs) -> bool:
"""
needed for transformers compatibility, returns self.check_if_quantized_param
"""
return self.check_if_quantized_param(*args, **kwargs)
def param_needs_quantization(self, model, param_name: str, *args, **kwargs) -> bool:
"""
needed for transformers compatibility, returns self.check_if_quantized_param
"""
return self.check_if_quantized_param(model, None, param_name, *args, **kwargs)
@devices.inference_context()
def create_quantized_param( # pylint: disable=arguments-differ
self,
@@ -656,16 +644,6 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
parent_module, tensor_name = get_module_from_name(model, param_name.removesuffix(tensor_name).removesuffix("."))
setattr(parent_module, tensor_name, layer)
def get_quantize_ops(self):
return SDNQQuantize(self)
def adjust_max_memory(self, max_memory: dict[str, int | str]) -> dict[str, int | str]:
max_memory = {key: val * 0.80 for key, val in max_memory.items()}
return max_memory
def adjust_target_dtype(self, target_dtype: torch.dtype) -> torch.dtype: # pylint: disable=unused-argument,arguments-renamed
return dtype_dict[self.quantization_config.weights_dtype]["target_dtype"]
def _process_model_before_weight_loading( # pylint: disable=arguments-differ
self,
model: torch.nn.Module,
@@ -726,6 +704,20 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
devices.torch_gc(force=True, reason="sdnq")
return model
def get_quantize_ops(self):
return SDNQQuantize(self)
def adjust_max_memory(self, max_memory: dict[str, int | str]) -> dict[str, int | str]:
max_memory = {key: val * 0.80 for key, val in max_memory.items()}
return max_memory
def adjust_target_dtype(self, target_dtype: torch.dtype) -> torch.dtype: # pylint: disable=unused-argument,arguments-renamed
return dtype_dict[self.quantization_config.weights_dtype]["target_dtype"]
def update_torch_dtype(self, torch_dtype: torch.dtype) -> torch.dtype:
self.torch_dtype = torch_dtype
return torch_dtype
def get_state_dict_and_metadata(self, state_dict: dict | torch.nn.Module, **kwargs) -> tuple[dict | None, dict]: # pylint: disable=unused-argument, arguments-differ
# transformers
if isinstance(state_dict, torch.nn.Module):
@@ -736,12 +728,6 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
def get_accelerator_warm_up_factor(self):
return 32 // dtype_dict[self.quantization_config.weights_dtype]["num_bits"]
def get_cuda_warm_up_factor(self):
"""
needed for transformers compatibility, returns self.get_accelerator_warm_up_factor
"""
return self.get_accelerator_warm_up_factor()
def _dequantize(self, model):
return dequantize_sdnq_model(model)
@@ -764,6 +750,30 @@ class SDNQQuantizer(DiffusersQuantizer, HfQuantizer):
def is_compileable(self):
return True
def check_quantized_param(self, *args, **kwargs) -> bool:
"""
needed for transformers compatibility, returns self.check_if_quantized_param
"""
return self.check_if_quantized_param(*args, **kwargs)
def param_needs_quantization(self, model, param_name: str, *args, **kwargs) -> bool:
"""
needed for transformers compatibility, returns self.check_if_quantized_param
"""
return self.check_if_quantized_param(model, None, param_name, *args, **kwargs)
def get_cuda_warm_up_factor(self):
"""
needed for transformers compatibility, returns self.get_accelerator_warm_up_factor
"""
return self.get_accelerator_warm_up_factor()
def update_dtype(self, dtype: torch.dtype) -> torch.dtype:
"""
needed for transformers compatibility, returns self.update_torch_dtype
"""
return self.update_torch_dtype(dtype)
@dataclass
class SDNQConfig(QuantizationConfigMixin):