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config.py

@@ -0,0 +1,47 @@
+"""
+sx.Config — Model architecture configuration for SocrateX.
+Contains only Transformer + SocratePool parameters.
+"""
+
+class Config:
+    """
+    Unified architecture config for SOCRATE.
+    Covers the Transformer layers and SocratePool.
+    
+    Pass to sx.init() to build a fully custom model:
+        config = sx.Config(d_model=256, nhead=4, num_layers=3)
+        model = sx.init(config=config, tokenizer=tokenizer)
+    """
+
+    def __init__(
+        self,
+        # ─── Transformer ────────────────────────────────────────────
+        d_model: int = 640,
+        max_len: int = 512,
+        nhead: int = 10,
+        dim_feedforward: int = 2560,
+        activation: str = "gelu",
+        norm_first: bool = True,
+        num_layers: int = 12,
+
+        # ─── SocratePool ────────────────────────────────────────────
+        pool_height: int = 4,   # target_height in nn.AdaptiveMaxPool2d((pool_height, None))
+    ):
+        self.d_model = d_model
+        self.max_len = max_len
+        self.nhead = nhead
+        self.dim_feedforward = dim_feedforward
+        self.activation = activation
+        self.norm_first = norm_first
+        self.num_layers = num_layers
+        self.pool_height = pool_height
+
+    def __repr__(self):
+        return (
+            f"sx.Config(\n"
+            f"  d_model={self.d_model}, nhead={self.nhead}, num_layers={self.num_layers},\n"
+            f"  dim_feedforward={self.dim_feedforward}, activation='{self.activation}',\n"
+            f"  norm_first={self.norm_first}, max_len={self.max_len},\n"
+            f"  pool_height={self.pool_height}  # AdaptiveMaxPool2d((pool_height, None))\n"
+            f")"
+        )