Create modeling_minGRU.py

modeling_minGRU.py

@@ -0,0 +1,140 @@
+import torch
+from torch import nn
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import SequenceClassifierOutput
+from typing import Optional
+from .configuration_minGRU import MinGRUConfig
+from minGRU_pytorch.minGRU import minGRU
+
+class MinGRUWrapped(nn.Module):
+    def __init__(self, min_gru_model):
+        super().__init__()
+        self.min_gru_model = min_gru_model
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    def forward(self, *args, **kwargs):
+        args = [arg.to(self.device) if isinstance(arg, torch.Tensor) else arg for arg in args]
+        kwargs = {k: v.to(self.device) if isinstance(v, torch.Tensor) else v for k, v in kwargs.items()}
+        return self.min_gru_model(*args, **kwargs)
+
+    def to(self, device):
+        self.device = device
+        self.min_gru_model.to(device)
+        return self
+
+class MinGRUPreTrainedModel(PreTrainedModel):
+    config_class = MinGRUConfig
+    base_model_prefix = "model"
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+        for name, param in module.named_parameters():
+            if torch.isnan(param).any():
+                print(f"NaN detected in parameter {name}. Replacing with a safe number.")
+                param.data = torch.nan_to_num(param.data, nan=1e-6)
+
+class MinGRUForSequenceClassification(PreTrainedModel):
+    config_class = MinGRUConfig
+    base_model_prefix = "model"
+
+    def __init__(self, config: MinGRUConfig):
+        super().__init__(config)
+
+        self.embedding = nn.Embedding(config.vocab_size, config.d_model)
+
+        raw_min_gru = minGRU(
+            dim=config.d_model,
+            expansion_factor=config.ff_mult
+        )
+        self.model = MinGRUWrapped(raw_min_gru)
+
+        # Final linear layer for classification
+        self.classifier = nn.Linear(config.d_model, config.num_labels)
+
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        labels: Optional[torch.LongTensor] = None,
+        return_dict: Optional[bool] = True,
+        **kwargs
+    ):
+        embeddings = self.embedding(input_ids)
+
+        logits = self.model(embeddings)
+
+        pooled_output = logits.mean(dim=1)
+
+        logits = self.classifier(pooled_output)  # No need for additional layers here
+
+        loss = None
+        if labels is not None:
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
+
+        if not return_dict:
+            return (loss, logits) if loss is not None else (logits,)
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+        )
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
+        """
+        Load model from a pretrained checkpoint.
+        """
+        model = super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
+        
+        for name, param in model.named_parameters():
+            if name in ['embedding.weight', 'model.min_gru_model.to_hidden_and_gate.weight', 'model.min_gru_model.to_out.weight']:
+                if param is None or torch.isnan(param).any() or torch.isinf(param).any():
+                    nn.init.xavier_normal_(param)  # Başlatma işlemi
+                    print(f"Initialized parameter {name} manually.")
+    
+        return model
+
+    def save_pretrained(self, save_directory, safe_serialization: Optional[bool] = True, **kwargs):
+        """
+        Save the model and configuration to a directory.
+    
+        Args:
+            save_directory (str): Directory to save the model.
+            safe_serialization (bool, optional): Whether to use safe serialization. Defaults to True.
+            kwargs: Additional arguments like max_shard_size (ignored in this implementation).
+        """
+        import os
+        os.makedirs(save_directory, exist_ok=True)
+        
+        if safe_serialization:
+            print("Saving with safe serialization.")
+            
+            state_dict = {}
+    
+            for name, param in self.model.min_gru_model.named_parameters():
+                state_dict[f"model.{name}"] = param
+    
+            for name, param in self.classifier.named_parameters():
+                state_dict[f"classifier.{name}"] = param
+    
+            state_dict['config'] = self.config.__dict__
+            torch.save(state_dict, os.path.join(save_directory, "pytorch_model.bin"))
+            
+            self.config.save_pretrained(save_directory)
+        else:
+            print("Saving without safe serialization.")
+            super().save_pretrained(save_directory)