Delete utils.py

utils.py

@@ -1,122 +0,0 @@
-from transformers import OlmoModel, OlmoPreTrainedModel, GenerationMixin, AutoConfig, AutoModelForSequenceClassification
-from transformers.modeling_outputs import SequenceClassifierOutputWithPast
-import torch
-
-from peft import PeftModel, PeftConfig
-
-from transformers import AutoConfig
-
-# The custom model for using Olmo with a sequence classification task
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-class OlmoForSequenceClassification(OlmoPreTrainedModel, GenerationMixin):
-    def __init__(self, config):
-        # Check OlmoForCausalLM.__init__
-        super().__init__(config)
-        self.model = OlmoModel(config)
-        self.num_labels = config.num_labels
-        self.classifier = torch.nn.Linear(config.hidden_size, config.num_labels)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        attention_mask: torch.Tensor | None = None,
-        labels: torch.LongTensor | None = None,
-        **kwargs,
-    ) -> SequenceClassifierOutputWithPast:
-        outputs = self.model(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            **kwargs,
-        )
-        logits = self.classifier(outputs.last_hidden_state)  # [B, N, H] => [B, N, C]
-        pooled_logits = logits[:, -1]   # NOTE: tokenizer.padding_side must be 'left'
-
-        loss = None
-        if labels is not None:
-            loss = self.loss_function(
-                logits=logits,
-                labels=labels,
-                pooled_logits=pooled_logits,
-                config=self.config,
-            )
-
-        return SequenceClassifierOutputWithPast(
-            loss=loss,
-            logits=pooled_logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-# The function for loading a fulltuning model
-
-def get_fulltuning_model(model_path, model_type="olmo"):
-    if model_type == "olmo":
-        model = OlmoForSequenceClassification.from_pretrained(
-            model_path,
-            trust_remote_code=True,
-            torch_dtype=torch.float32,
-        ).to("cuda" if torch.cuda.is_available() else "cpu")
-        model.eval()
-    elif model_type == "pythia":
-        cfg = AutoConfig.from_pretrained(model_path, num_labels=3)
-        model = AutoModelForSequenceClassification.from_pretrained(
-            model_path,
-            config=cfg,
-            torch_dtype=torch.float32,
-        ).to(device)
-    else:
-        raise ValueError(f"Unsupported model_type: {model_type}")
-
-    return model
-
-# The function for loading a softprompt model
-
-# NOTE: The "missing or unexpected params" warning is no reason for concern. It stems from the 
-# fact that the model is first loaded without a classifier head, which is added afterwards.
-
-def get_peft_model(model_path, model_type="olmo"):
-    peft_config = PeftConfig.from_pretrained(model_path)
-    
-    if model_type == "olmo":
-        config = AutoConfig.from_pretrained(
-            peft_config.base_model_name_or_path, 
-            trust_remote_code=True, 
-            num_labels=2
-        )
-
-        base = OlmoForSequenceClassification.from_pretrained(
-            peft_config.base_model_name_or_path,
-            trust_remote_code=True,
-            torch_dtype=torch.float32,
-            config=config,
-        ).to(device)
-
-    elif model_type == "pythia":
-        config = AutoConfig.from_pretrained(
-            peft_config.base_model_name_or_path, 
-            num_labels=2
-        )
-
-        base = AutoModelForSequenceClassification.from_pretrained(
-            peft_config.base_model_name_or_path,
-            config=config,
-            torch_dtype=torch.float32,
-        ).to(device)
-    
-    else:
-        raise ValueError(f"Unsupported model_type: {model_type}")
-
-    model = PeftModel.from_pretrained(
-        base,
-        model_path,
-    ).to("cuda" if torch.cuda.is_available() else "cpu")
-
-    model.eval() 
-
-    return model