TypeError: unsupported operand type(s) for *: 'Parameter' and 'NoneType'

[misonsky]

System Info

Adalora

def update_ipt(self, model):
    # Update the sensitivity and uncertainty for every weight
    for n, p in model.named_parameters():
        if "lora_" in n and self.adapter_name in n:
            if n not in self.ipt:
                self.ipt[n] = torch.zeros_like(p)
                self.exp_avg_ipt[n] = torch.zeros_like(p)
                self.exp_avg_unc[n] = torch.zeros_like(p)
            with torch.no_grad():
                self.ipt[n] = (p * p.grad).abs().detach()
                # Sensitivity smoothing
                self.exp_avg_ipt[n] = self.beta1 * self.exp_avg_ipt[n] + (1 - self.beta1) * self.ipt[n]
                # Uncertainty quantification
                self.exp_avg_unc[n] = (
                    self.beta2 * self.exp_avg_unc[n] + (1 - self.beta2) * (self.ipt[n] - self.exp_avg_ipt[n]).abs()
                )

When using adalora peft, the classification header layer includes:

base_model.model.classifier.original_module.dense.base_layer.weight
base_model.model.classifier.original_module.dense.base_layer.bias
base_model.model.classifier.original_module.dense.lora_A.default
base_model.model.classifier.original_module.dense.lora_B.default
base_model.model.classifier.original_module.dense.lora_E.default
base_model.model.classifier.original_module.dense.ranknum.default
base_model.model.classifier.original_module.out_proj.weight
base_model.model.classifier.original_module.out_proj.bias
base_model.model.classifier.modules_to_save.default.dense.base_layer.weight
base_model.model.classifier.modules_to_save.default.dense.base_layer.bias
base_model.model.classifier.modules_to_save.default.dense.lora_A.default
base_model.model.classifier.modules_to_save.default.dense.lora_B.default
base_model.model.classifier.modules_to_save.default.dense.lora_E.default
base_model.model.classifier.modules_to_save.default.dense.ranknum.default
base_model.model.classifier.modules_to_save.default.out_proj.weight
base_model.model.classifier.modules_to_save.default.out_proj.bias

But for layers

base_model.model.classifier.original_module.dense.lora_A.default
base_model.model.classifier.original_module.dense.lora_B.default
base_model.model.classifier.original_module.dense.lora_E.default

after checking, there is no gradient. In other words, the requires_grad attribute is False, but the inclulde "lora_" string. I think gradient checking should be added to the update_ipt function.

This error occurs when calling model.update_and_allocate(global_step).

Who can help?

No response

Information

• The official example scripts
• My own modified scripts

Tasks

• An officially supported task in the examples folder
• My own task or dataset (give details below)

Reproduction

This error occurs when calling model.update_and_allocate(global_step).

the config is:

peft_config = AdaLoraConfig(
            peft_type="ADALORA", 
            task_type="SEQ_CLS", 
            r=rank, 
            lora_alpha=32, 
            lora_dropout=0.01)

the model is RoBERTa.

Expected behavior

I think gradient checking should be added to the update_ipt function.

def update_ipt(self, model):
    # Update the sensitivity and uncertainty for every weight
    for n, p in model.named_parameters():
        if not p.requires_grad: continue
        if "lora_" in n and self.adapter_name in n:
            if n not in self.ipt:
                self.ipt[n] = torch.zeros_like(p)
                self.exp_avg_ipt[n] = torch.zeros_like(p)
                self.exp_avg_unc[n] = torch.zeros_like(p)
            with torch.no_grad():
                self.ipt[n] = (p * p.grad).abs().detach()
                # Sensitivity smoothing
                self.exp_avg_ipt[n] = self.beta1 * self.exp_avg_ipt[n] + (1 - self.beta1) * self.ipt[n]
                # Uncertainty quantification
                self.exp_avg_unc[n] = (
                    self.beta2 * self.exp_avg_unc[n] + (1 - self.beta2) * (self.ipt[n] - self.exp_avg_ipt[n]).abs()
                )

[BenjaminBossan]

Thanks for reporting. Could you please paste the full error message? Also, do you have a reproducer or are you using one of the examples from PEFT?