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configuration_tiny_recursive.py

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+
+from transformers import PretrainedConfig
+
+class TinyRecursiveConfig(PretrainedConfig):
+    model_type = "tiny_recursive"
+
+    def __init__(
+        self,
+        vocab_size=50257,
+        n_positions=1024,
+        n_embd=512,
+        n_head=8,
+        n_physical_layers=2,
+        n_loops=6,
+        activation_function="gelu_new",
+        resid_pdrop=0.1,
+        embd_pdrop=0.1,
+        attn_pdrop=0.1,
+        layer_norm_epsilon=1e-5,
+        scale_attn_weights=True,
+        scale_attn_by_inverse_layer_idx=False,
+        reorder_and_upcast_attn=False,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        # Standard config
+        self.vocab_size = vocab_size
+        self.n_positions = n_positions
+        self.n_embd = n_embd
+        self.n_head = n_head
+        self.n_physical_layers = n_physical_layers
+        self.n_loops = n_loops
+        self.activation_function = activation_function
+        self.resid_pdrop = resid_pdrop
+        self.embd_pdrop = embd_pdrop
+        self.attn_pdrop = attn_pdrop
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.scale_attn_weights = scale_attn_weights
+        self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx
+        self.reorder_and_upcast_attn = reorder_and_upcast_attn
+
+        # CRITICAL FIXES FOR COMPATIBILITY
+        self.max_position_embeddings = n_positions 
+        self.hidden_size = n_embd
+        self.num_attention_heads = n_head
+        self.num_hidden_layers = n_physical_layers
+        self.n_inner = None