X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=mygpt.py;h=0e94672777ad3df99016ffd0da855a927f8a6e6a;hb=597f3a01de92be1a4f05114df11d9e39b3608e29;hp=daaec016ee94326147c4bf00d5bfd6157ef2920d;hpb=b22210b3eb0940c9cb5f9f29af6ede69204d78cf;p=mygptrnn.git

diff --git a/mygpt.py b/mygpt.py
index daaec01..0e94672 100755
--- a/mygpt.py
+++ b/mygpt.py
@@ -181,7 +181,7 @@ def nsum_shape(X, Y_init):
 class DumbRec(nn.Module):
     def __init__(
         self,
-        dim_in,
+        dim_model,
         dim_qk,
         dim_v,
         nb_heads,
@@ -199,11 +199,11 @@ class DumbRec(nn.Module):
 
         self.k_star = randw(nb_lines, dim_qk)
 
-        self.w_qw = randw(nb_heads, dim_qk, dim_in)
-        self.w_qr = randw(nb_heads, dim_qk, dim_in)
-        # self.w_k = randw(nb_heads, dim_qk, dim_in)
-        self.w_v = randw(nb_heads, dim_v, dim_in)
-        self.w_o = randw(dim_v * nb_heads, dim_in)
+        self.w_qw = randw(nb_heads, dim_qk, dim_model)
+        self.w_qr = randw(nb_heads, dim_qk, dim_model)
+        # self.w_k = randw(nb_heads, dim_qk, dim_model)
+        self.w_v = randw(nb_heads, dim_v, dim_model)
+        self.w_o = randw(dim_v * nb_heads, dim_model)
 
     def reset_inner_loss(self):
         self.acc_attention = 0
@@ -310,7 +310,7 @@ class DumbRec(nn.Module):
 class KVRec(nn.Module):
     def __init__(
         self,
-        dim_in,
+        dim_model,
         dim_qk,
         dim_v,
         nb_heads,
@@ -328,11 +328,11 @@ class KVRec(nn.Module):
 
         self.k_star = randw(nb_lines, dim_qk)
 
-        self.w_qw = randw(nb_heads, dim_qk, dim_in)
-        self.w_qr = randw(nb_heads, dim_qk, dim_in)
-        self.w_k = randw(nb_heads, dim_qk, dim_in)
-        self.w_v = randw(nb_heads, dim_v, dim_in)
-        self.w_o = randw(dim_v * nb_heads, dim_in)
+        self.w_qw = randw(nb_heads, dim_qk, dim_model)
+        self.w_qr = randw(nb_heads, dim_qk, dim_model)
+        self.w_k = randw(nb_heads, dim_qk, dim_model)
+        self.w_v = randw(nb_heads, dim_v, dim_model)
+        self.w_o = randw(dim_v * nb_heads, dim_model)
 
     def reset_inner_loss(self):
         self.acc_attention = 0
@@ -441,6 +441,11 @@ class KVRec(nn.Module):
 ##############################
 
 
+# Returns a tensor with an additional index at rank win_dim, that move
+# along the same dimension as dim, on a domain {0...win_size-1}, and
+# dim is restricted on a domain reduced by win_size-1 values.
+
+
 def moving_window(x, dim, win_dim, win_size):
     size, stride = x.size(), x.stride()
     size = size[:dim] + (size[dim] - win_size + 1,) + size[dim + 1 :]
@@ -456,7 +461,7 @@ def moving_window(x, dim, win_dim, win_size):
 class Caterpillar(nn.Module):
     def __init__(
         self,
-        dim_in,
+        dim_model,
         dim_qk,
         dim_v,
         nb_heads,
@@ -476,17 +481,17 @@ class Caterpillar(nn.Module):
         self.caterpillar_height = caterpillar_height
         self.attention_dropout = attention_dropout
 
-        self.w_G = randw(nb_heads, caterpillar_height, dim_in)
+        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.w_K = randw(nb_heads, dim_qk, dim_in)
-        self.w_V = randw(nb_heads, dim_v, dim_in)
-        self.w_Q = randw(nb_heads, dim_qk, dim_in)
-        self.w_O = randw(dim_v * nb_heads, dim_in)
+        self.w_K = randw(nb_heads, dim_qk, dim_model)
+        self.w_V = randw(nb_heads, dim_v, dim_model)
+        self.w_Q = randw(nb_heads, dim_qk, dim_model)
+        self.w_O = randw(dim_v * nb_heads, dim_model)
 
         self.init_K_rec = randw(caterpillar_height, caterpillar_length, dim_qk)
         self.init_V_rec = randw(caterpillar_height, caterpillar_length, dim_v)
@@ -509,7 +514,7 @@ class Caterpillar(nn.Module):
         T = bs.x.size(1)
         DV = self.w_V.size(1)
         DK = self.w_K.size(1)
-        Dout = self.w_O.size(1)
+        DM = self.w_O.size(1)
         CH = self.caterpillar_height
         CL = self.caterpillar_length
 
@@ -517,6 +522,8 @@ class Caterpillar(nn.Module):
             t0 >= CL and (t1 - t0) % CL == 0
         ), f"bs.first should be greater than caterpillar_length, and bs.nb should be a multiple of caterpillar_length"
 
+        # We cache values to deal efficiently with auto-regression
+
         if bs.init_cache:
             self.rec_V = X.new_zeros(N, CH, T, DV)
             self.rec_K = X.new_zeros(N, CH, T, DK)
@@ -525,21 +532,23 @@ class Caterpillar(nn.Module):
             self.rec_V[:, :, t0 - CL : t0] = self.init_V_rec[None, :, :, :]
             self.rec_K[:, :, t0 - CL : t0] = self.init_K_rec[None, :, :, :]
 
-            self.cache_Y = X.new_zeros(N, T, Dout)
+            self.cache_Y = X.new_zeros(N, T, DM)
 
         ######################################################################
         # Compute the recurrent state
 
-        # This is the Gating sequence that modulates if they key and
-        # values should be stored in one of the CH pairs of the
-        # current stack. The CH gating values are independent, which
-        # means that the same thing could be stored multiple times or
-        # not at all
+        # This is the Gating sequence that modulates the storing of
+        # the new key and value in the CH pairs of the current
+        # stack. The CH gating values are independent, which means
+        # that the current K/V could be stored in all the pairs of the
+        # recurrent state, or not at all.
 
         G = (
             torch.einsum("ntc,hec->nhet", X, self.w_G) + self.b_G[None, :, :, None]
         ).sigmoid()
 
+        G = F.dropout(G, self.attention_dropout, self.training)
+
         V = torch.einsum("ntc,hdc->nhtd", X, self.w_V)
         K = torch.einsum("ntc,hdc->nhtd", X, self.w_K)
 
@@ -552,10 +561,11 @@ class Caterpillar(nn.Module):
         init_rec_V = self.rec_V[:, :, t0 - CL : t0]
         init_rec_K = self.rec_K[:, :, t0 - CL : t0]
 
-        # Here there is a trick: The parallel scan operates with a
-        # period of L, so we split the sequence indexing in two axes,
-        # the second of size CL, and run the parallel scan using the
-        # other alone as the sequence index.
+        # Here there is a trick: Since the stack at time t is computed
+        # by updating that at time t-L, the parallel scan operates
+        # with a period of L. To do so we split the time indexing in
+        # two axes, the second of size CL, and run the parallel scan
+        # using the other as the sequence index.
 
         A = A.unflatten(2, (-1, CL))
         gated_V = gated_V.unflatten(2, (-1, CL))
@@ -586,7 +596,7 @@ class Caterpillar(nn.Module):
             self.rec_K[:, :, t0 - CL + 1 : t1], dim=2, win_dim=3, win_size=CL
         )
 
-        # We have an attention score for each of the CHxCL value
+        # We have an attention score for each of the CHxCL values
 
         ar = torch.einsum(
             "nhtd,nftld->nhtfl",
@@ -622,7 +632,7 @@ class Caterpillar(nn.Module):
 class QKVAttention(nn.Module):
     def __init__(
         self,
-        dim_in,
+        dim_model,
         dim_qk,
         dim_v,
         nb_heads=1,
@@ -638,10 +648,10 @@ class QKVAttention(nn.Module):
         self.attention_dropout = attention_dropout
         self.record_attention = False
 
-        self.w_q = randw(nb_heads, dim_qk, dim_in)
-        self.w_k = randw(nb_heads, dim_qk, dim_in)
-        self.w_v = randw(nb_heads, dim_v, dim_in)
-        self.w_o = randw(dim_v * nb_heads, dim_in)
+        self.w_q = randw(nb_heads, dim_qk, dim_model)
+        self.w_k = randw(nb_heads, dim_qk, dim_model)
+        self.w_v = randw(nb_heads, dim_v, dim_model)
+        self.w_o = randw(dim_v * nb_heads, dim_model)
 
     def forward(self, bs):
         x_q = bs.x
@@ -745,7 +755,7 @@ class MyGPT(nn.Module):
         def attlayer():
             if attention_layer == "mha":
                 return QKVAttention(
-                    dim_in=dim_model,
+                    dim_model=dim_model,
                     dim_qk=dim_keys,
                     dim_v=dim_model // nb_heads,
                     nb_heads=nb_heads,
@@ -754,7 +764,7 @@ class MyGPT(nn.Module):
                 )
             elif attention_layer == "dumbrec":
                 return DumbRec(
-                    dim_in=dim_model,
+                    dim_model=dim_model,
                     dim_qk=dim_keys,
                     dim_v=dim_rec_v,
                     nb_heads=nb_heads,
@@ -763,7 +773,7 @@ class MyGPT(nn.Module):
                 )
             elif attention_layer == "kvrec":
                 return KVRec(
-                    dim_in=dim_model,
+                    dim_model=dim_model,
                     dim_qk=dim_keys,
                     dim_v=dim_rec_v,
                     nb_heads=nb_heads,
@@ -772,7 +782,7 @@ class MyGPT(nn.Module):
                 )
             elif attention_layer == "caterpillar":
                 return Caterpillar(
-                    dim_in=dim_model,
+                    dim_model=dim_model,
                     dim_qk=dim_keys,
                     dim_v=dim_rec_v,
                     nb_heads=nb_heads,
@@ -912,7 +922,7 @@ if __name__ == "__main__":
     print("Basic check.")
 
     m = Caterpillar(
-        dim_in=4,
+        dim_model=4,
         dim_qk=3,
         dim_v=7,
         nb_heads=1,