X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;ds=sidebyside;f=mygpt.py;h=7c9991f7d56fc3069a261b0642e4381c55bd02d9;hb=73acbc986f9c386c001117581c4fc72d2f36803a;hp=a62cf4908ba88622a3f567d082c7a94711887fde;hpb=4f5d03d3371b124121e8f9fc0ff583553fea1e38;p=mygptrnn.git

diff --git a/mygpt.py b/mygpt.py
index a62cf49..7c9991f 100755
--- a/mygpt.py
+++ b/mygpt.py
@@ -190,6 +190,8 @@ class DumbRec(nn.Module):
         nb_lines,
         attention_dropout=0.0,
         len_max=1e5,
+        logger=print,
+        **kwargs,
     ):
         super().__init__()
 
@@ -319,6 +321,8 @@ class KVRec(nn.Module):
         nb_lines,
         attention_dropout=0.0,
         len_max=1e5,
+        logger=print,
+        **kwargs,
     ):
         super().__init__()
 
@@ -471,6 +475,8 @@ class Caterpillar(nn.Module):
         caterpillar_height,
         attention_dropout=0.0,
         len_max=1e5,
+        logger=print,
+        **kwargs,
     ):
         super().__init__()
 
@@ -487,12 +493,14 @@ class Caterpillar(nn.Module):
 
         self.proba_gate_dropout = 0.0
 
+        default_b_G = kwargs.get("default_b_G")
+        if default_b_G is None:
+            default_b_G = -math.log(caterpillar_height - 1)
+
+        logger(f"default_b_G {default_b_G}")
+
         self.w_G = randw(nb_heads, caterpillar_height, dim_model)
-        self.b_G = nn.Parameter(
-            torch.full(
-                (nb_heads, caterpillar_height), -math.log(caterpillar_height - 1)
-            )
-        )
+        self.b_G = nn.Parameter(torch.full((nb_heads, caterpillar_height), default_b_G))
 
         self.w_K = randw(nb_heads, dim_qk, dim_model)
         self.w_V = randw(nb_heads, dim_v, dim_model)
@@ -565,15 +573,20 @@ class Caterpillar(nn.Module):
             torch.einsum("ntc,hrc->nhrt", X, self.w_G) + self.b_G[None, :, :, None]
         ).sigmoid()
 
+        # Clip the gating to avoid values greater than 1 when several
+        # heads hit the same row
+
+        G = G / G.sum(1, keepdim=True).clamp(min=1)
+
         ######################################################################
         # Roll the gating indexes
 
-        warnings.warn("rotating barrel", RuntimeWarning)
+        # warnings.warn("rotating barrel", RuntimeWarning)
 
-        r_barrel = torch.arange(R, device=G.device)[None, None, :, None]
-        t_barrel = torch.arange(t1 - t0, device=G.device)[None, None, None, :]
-        r_barrel = (r_barrel + (t_barrel + t0) // L) % R
-        G = G.gather(dim=2, index=r_barrel.expand_as(G))
+        # r_barrel = torch.arange(R, device=G.device)[None, None, :, None]
+        # t_barrel = torch.arange(t1 - t0, device=G.device)[None, None, None, :]
+        # r_barrel = (r_barrel + (t_barrel + t0) // L) % R
+        # G = G.gather(dim=2, index=r_barrel.expand_as(G))
 
         ######################################################################
         # The "flashbacks"
@@ -611,11 +624,6 @@ class Caterpillar(nn.Module):
 
         # We prepare the arguments for the parallel scan
 
-        # Clip the gating to avoid values greater than 1 when several
-        # heads hit the same row
-
-        G = G / G.sum(1, keepdim=True).clamp(min=1)
-
         A = 1 - G.sum(1)
 
         # warnings.warn("harmonic recurrence", RuntimeWarning)
@@ -709,6 +717,8 @@ class QKVAttention(nn.Module):
         nb_heads=1,
         causal=False,
         attention_dropout=0.0,
+        logger=print,
+        **kwargs,
     ):
         super().__init__()
 
@@ -800,6 +810,8 @@ class MyGPT(nn.Module):
         dropout=0.0,
         len_max=1e5,
         attention_layer="kvrec",
+        logger=print,
+        **kwargs,
     ):
         super().__init__()
 
@@ -836,6 +848,8 @@ class MyGPT(nn.Module):
                     nb_heads=nb_heads,
                     causal=causal,
                     attention_dropout=dropout,
+                    logger=logger,
+                    **kwargs,
                 )
             elif attention_layer == "dumbrec":
                 return DumbRec(
@@ -845,6 +859,8 @@ class MyGPT(nn.Module):
                     nb_heads=nb_heads,
                     nb_lines=nb_lines,
                     attention_dropout=dropout,
+                    logger=logger,
+                    **kwargs,
                 )
             elif attention_layer == "kvrec":
                 return KVRec(
@@ -854,6 +870,8 @@ class MyGPT(nn.Module):
                     nb_heads=nb_heads,
                     nb_lines=nb_lines,
                     attention_dropout=dropout,
+                    logger=logger,
+                    **kwargs,
                 )
             elif attention_layer == "caterpillar":
                 return Caterpillar(
@@ -864,6 +882,8 @@ class MyGPT(nn.Module):
                     caterpillar_length=self.caterpillar_length,
                     caterpillar_height=self.caterpillar_height,
                     attention_dropout=dropout,
+                    logger=logger,
+                    **kwargs,
                 )
             else:
                 raise ValueError(f"Unknown attention type {attention_layer}.")