X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=mygpt.py;h=a27b99e8dd47eb14696257fb1d814c8e33dd49cb;hb=64dc96ddfa84511ba07d1929481e93e864735409;hp=3a48cdbb793160ea9c88875d4f353b6a89555477;hpb=3dd98b99909b2bca323673263874e2abb39ac10c;p=mygptrnn.git

diff --git a/mygpt.py b/mygpt.py
index 3a48cdb..a27b99e 100755
--- a/mygpt.py
+++ b/mygpt.py
@@ -126,7 +126,6 @@ class AddPositionalEncoding(nn.Module):
 
 import pscan
 
-
 # X is /.../xTxD   A is /.../xT   Y_init is /.../xD
 
 
@@ -147,6 +146,18 @@ def pscan_dim(A, X, Y_init, dim=-2):
     return Y
 
 
+def pscan_rgrad(grad_Y, A, X, Y_init, dim=-2, eps=1e-2):
+    with torch.no_grad():
+        s_A, s_X = 0, 0
+        for t in range(X.size(dim) - 1, 0, -1):
+            delta = (grad_Y[t] - s_A) / A[t].grad
+            s_A += A[t].grad * delta
+            A[t].grad = delta
+            delta = (grad_Y[t] - s_X) / X[t].grad
+            s_X += X[t].grad * delta
+            X[t].grad = delta
+
+
 def pscan_shape(A, X, Y_init):
     s = X.size()
     A = A.reshape(-1, s[-2])
@@ -565,8 +576,8 @@ class Caterpillar(nn.Module):
             self.rec_K = X.new_zeros(N, R, T, DK)
             # We start the recurrent sequences with optimizable
             # initial values. No idea if it helps.
-            self.rec_V[:, :, t0 - L : t0] = self.init_V_rec[None, :, :, :]
-            self.rec_K[:, :, t0 - L : t0] = self.init_K_rec[None, :, :, :]
+            self.rec_V[:, :, t0 - L : t0, :] = self.init_V_rec[None, :, :, :]
+            self.rec_K[:, :, t0 - L : t0, :] = self.init_K_rec[None, :, :, :]
 
             self.cache_Y = X.new_zeros(N, T, DM)
 
@@ -610,57 +621,50 @@ class Caterpillar(nn.Module):
 
             G = alpha * (1 - kill)
 
-        ######################################################################
-        # Clip the gating to avoid values greater than 1 when several
-        # heads hit the same row
+        def recurrence(G, V, K):
+            # 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)
+            G = G / G.sum(1, keepdim=True).clamp(min=1)
 
-        ######################################################################
-        # Roll the gating indexes
+            # We prepare the arguments for the parallel scan
 
-        # warnings.warn("rotating barrel", RuntimeWarning)
+            A = 1 - G.sum(1)
 
-        # 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))
+            gated_V = torch.einsum("nhrt,nhtd->nrtd", G, V)
+            gated_K = torch.einsum("nhrt,nhtd->nrtd", G, K)
 
-        # We prepare the arguments for the parallel scan
+            # We start from cached values, which matters in inference
 
-        A = 1 - G.sum(1)
+            init_rec_V = self.rec_V[:, :, t0 - L : t0]
+            init_rec_K = self.rec_K[:, :, t0 - L : t0]
 
-        # warnings.warn("harmonic recurrence", RuntimeWarning)
-        # har = torch.arange(t0, t1, device = G.device).float() + 1
-        # A = har / (har + 1)
-        # G = G / har
+            # Associative scan
 
-        gated_V = torch.einsum("nhrt,nhtd->nrtd", G, V)
-        gated_K = torch.einsum("nhrt,nhtd->nrtd", G, K)
+            # Here there is a trick: Since the stack at position t is
+            # computed by updating that at position t-L, the parallel
+            # scan operates with a period of L. To do so we split the
+            # sequence indexing in two axes, the second of size L, and
+            # run the parallel scan using the first as the sequence index.
 
-        # We start from cached values, which matters in inference
+            A = A.unflatten(2, (-1, L))
+            gated_V = gated_V.unflatten(2, (-1, L))
+            gated_K = gated_K.unflatten(2, (-1, L))
 
-        init_rec_V = self.rec_V[:, :, t0 - L : t0]
-        init_rec_K = self.rec_K[:, :, t0 - L : t0]
+            next_V = pscan_dim(A, gated_V, init_rec_V, dim=2)
+            next_K = pscan_dim(A, gated_K, init_rec_K, dim=2)
 
-        #################################################################
-        # Associative scan
+            next_V = next_V.flatten(2, 3)
+            next_K = next_K.flatten(2, 3)
 
-        # Here there is a trick: Since the stack at position t is
-        # computed by updating that at position t-L, the parallel
-        # scan operates with a period of L. To do so we split the
-        # sequence indexing in two axes, the second of size L, and
-        # run the parallel scan using the first as the sequence index.
+            return next_V, next_K
 
-        A = A.unflatten(2, (-1, L))
-        gated_V = gated_V.unflatten(2, (-1, L))
-        gated_K = gated_K.unflatten(2, (-1, L))
+        #################################################################
 
-        next_V = pscan_dim(A, gated_V, init_rec_V, dim=2)
-        next_K = pscan_dim(A, gated_K, init_rec_K, dim=2)
+        next_V, next_K = recurrence(G, V, K)
 
-        self.rec_V[:, :, t0:t1] = next_V.flatten(2, 3)
-        self.rec_K[:, :, t0:t1] = next_K.flatten(2, 3)
+        self.rec_V[:, :, t0:t1] = next_V
+        self.rec_K[:, :, t0:t1] = next_K
 
         ######################################################################
         # compute the readout