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alea/model.py

@@ -11,7 +11,7 @@ import torch.nn as nn
 from transformers import AutoModel
 
 from .heads import SetChoiceHead, NoulHead, RegressHead
-from .ngram import AuxEmbedding, NGramEncoder
+from .ngram import AuxEmbedding, NGramEncoder, NGramEncoderMH
 from .packing import build_additive_mask, SEG_OPT, SEG_ANS, SEG_QCTX, SEG_STATE
 
 
@@ -20,7 +20,7 @@ class AleaModel(nn.Module):
                  use_set_attn=True, attn_impl="eager",
                  use_aux_embed=False, use_ngram=False,
                  ngram_orders=(2, 3, 4), ngram_buckets=262144, d_ng=128,
-                 use_ngram2=False):
+                 use_ngram2=False, use_ngram3=False):
         super().__init__()
         self.backbone = AutoModel.from_pretrained(
             model_name, torch_dtype=torch.bfloat16,
@@ -38,6 +38,9 @@ class AleaModel(nn.Module):
         # second bank: longer orders + bigger hash space, separate gate
         self.ngram2 = (NGramEncoder(d, (5, 6, 7, 8), 524288, d_ng)
                        if use_ngram2 else None)
+        # third bank: multi-hash compositional tables (thin rows, K=2)
+        self.ngram3 = (NGramEncoderMH(d, (3, 4, 5, 6), 262144, 262144, 64)
+                       if use_ngram3 else None)
 
     def _input_embeds(self, input_ids):
         emb = self.backbone.embed_tokens(input_ids)
@@ -50,6 +53,9 @@ class AleaModel(nn.Module):
         if self.ngram2 is not None:
             ng2 = self.ngram2(input_ids)
             add = ng2 if add is None else add + ng2
+        if self.ngram3 is not None:
+            ng3 = self.ngram3(input_ids)
+            add = ng3 if add is None else add + ng3
         if add is not None:
             emb = emb + add.to(emb.dtype)
         return emb
@@ -60,7 +66,7 @@ class AleaModel(nn.Module):
         Returns list[list[dict]] — per sequence, per question:
           {'qtype','logits' (choice/score) or 'p_yes' (noul)}"""
         if (self.aux_embed is not None or self.ngram is not None
-                or self.ngram2 is not None):
+                or self.ngram2 is not None or self.ngram3 is not None):
             out = self.backbone(inputs_embeds=self._input_embeds(input_ids),
                                 attention_mask=attention_mask,
                                 position_ids=position_ids)