Create modeling_minGRULM.py

modeling_minGRULM.py

@@ -0,0 +1,161 @@
+import torch
+from torch import nn
+from transformers import PreTrainedModel, AutoModelForCausalLM, AutoTokenizer
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from torch.nn import CrossEntropyLoss
+from typing import Optional
+import os
+from .configuration_minGRULM import MinGRULMConfig
+from minGRU_pytorch.minGRULM import minGRULM
+
+
+class MinGRULMWrapped(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 MinGRULMPreTrainedModel(PreTrainedModel):
+    config_class = MinGRULMConfig
+    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 MinGRULMForCausalLM(PreTrainedModel):
+    config_class = MinGRULMConfig
+    base_model_prefix = "model"
+
+    def __init__(self, config: MinGRULMConfig):
+        super().__init__(config)
+
+        raw_min_gru = minGRULM(
+            num_tokens=config.vocab_size,
+            dim=config.d_model,
+            depth=config.n_layer,
+            ff_mult=config.ff_mult,
+            min_gru_expansion=config.min_gru_expansion,
+            enable_conv=config.enable_conv,
+        )
+        self.model = MinGRULMWrapped(raw_min_gru)
+
+        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
+
+        self.post_init()
+
+    def post_init(self):
+        super().post_init()
+        self.tie_weights()
+
+    def tie_weights(self):
+        self.lm_head.weight = self.model.min_gru_model.token_emb.weight
+
+    def get_input_embeddings(self):
+        return self.model.min_gru_model.token_emb
+
+    def set_input_embeddings(self, value):
+        self.model.min_gru_model.token_emb = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def prepare_inputs_for_generation(self, input_ids: torch.LongTensor, **kwargs):
+        return {"input_ids": input_ids, "attention_mask": kwargs.get("attention_mask", None)}
+
+    def forward(self, input_ids: torch.LongTensor, labels: Optional[torch.LongTensor] = None, return_dict: Optional[bool] = True, **kwargs):
+        logits = self.model(input_ids)
+
+        if torch.isnan(logits).any():
+            print("NaN detected in logits! Replacing with a safe number.")
+            logits = torch.nan_to_num(logits, nan=1e-6)
+
+        loss = None
+        if labels is not None:
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(
+                shift_logits.view(-1, self.config.vocab_size),
+                shift_labels.view(-1),
+            )
+
+            if torch.isnan(loss).any():
+                print("NaN detected in loss! Replacing with a safe number.")
+                loss = torch.nan_to_num(loss, nan=1e-6)
+
+        if not return_dict:
+            return (loss, logits) if loss is not None else (logits,)
+
+        return CausalLMOutputWithPast(
+            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)
+        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)