Adding files from hf_modeling_btm_log_prob_mixing

configuration.py

@@ -0,0 +1,62 @@
+from transformers import PretrainedConfig
+
+class MoLMConfig(PretrainedConfig):
+    model_type = "MoLM"
+
+    def __init__(
+        self,
+        vocab_size=50304,
+        n_embd=768,
+        n_layer=12,
+        n_head=12,
+        sequence_length=1024,
+        mlp_dim_exp_factor=1.0,
+        dropout=0.0,
+        bias=False,
+        num_experts=6,
+        expert_configs=None,
+        use_router=False,
+        top_k_experts=6,
+        architectures=["MoLM"],
+        auto_map={
+            "AutoConfig": "configuration.MoLMConfig",
+            "AutoModelForCausalLM": "modeling.MoLM",
+            "AutoTokenizer": "GPT2TokenizerFast"
+        },
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.vocab_size = vocab_size
+        self.n_embd = n_embd
+        self.n_layer = n_layer
+        self.n_head = n_head
+        self.sequence_length = sequence_length
+        self.mlp_dim_exp_factor = mlp_dim_exp_factor
+        self.dropout = dropout
+        self.bias = bias
+        self.num_experts = num_experts
+        self.expert_configs = expert_configs
+        self.use_router = use_router
+        self.architectures = architectures
+        self.auto_map = auto_map
+        self.top_k_experts = top_k_experts
+    def to_dict(self):
+        config_dict = super().to_dict()
+        config_dict.update({
+            "vocab_size": self.vocab_size,
+            "n_embd": self.n_embd,
+            "n_layer": self.n_layer,
+            "n_head": self.n_head,
+            "sequence_length": self.sequence_length,
+            "mlp_dim_exp_factor": self.mlp_dim_exp_factor,
+            "dropout": self.dropout,
+            "bias": self.bias,
+            "num_experts": self.num_experts,
+            "expert_configs": [
+                expert_config.to_dict() if not isinstance(expert_config, dict) else expert_config 
+                for expert_config in self.expert_configs
+            ] if self.expert_configs else None,  
+            "use_router": self.use_router,
+            "top_k_experts": self.top_k_experts,
+        })
+        return config_dict