Create modeling_minGRULM.py

modeling_minGRULM.py

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+import torch
+from torch import nn
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from torch.nn import CrossEntropyLoss
+from typing import Optional
+from .configuration_minGRULM import MinGRULMConfig
+from minGRU_pytorch.minGRULM import minGRULM
+
+
+class MinGRULMPreTrainedModel(PreTrainedModel):
+    config_class = MinGRULMConfig
+    base_model_prefix = "model"
+
+    def _init_weights(self, module):
+        std = 0.02
+        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_()
+
+
+class MinGRULMForCausalLM(MinGRULMPreTrainedModel):
+    def __init__(self, config: MinGRULMConfig):
+        super().__init__(config)
+
+        # Load model from minGRULM library
+        self.model = minGRULM(
+            num_tokens=config.vocab_size,
+            dim=config.d_model,
+            depth=config.n_layer,
+            ff_mult=config.ff_mult,
+            min_gru_expansion=config.expand,
+            enable_conv=config.enable_conv,
+        )
+
+        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.token_emb
+
+    def set_input_embeddings(self, value):
+        self.model.token_emb = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        labels: Optional[torch.LongTensor] = None,
+        return_dict: Optional[bool] = True,
+    ):
+        # Forward pass through the model
+        logits = self.model(input_ids)
+
+        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 not return_dict:
+            return (loss, logits) if loss is not None else (logits,)
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+        )