X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=mygpt.py;h=de69a755f9510daff3669c252fb14a8bec4b3148;hb=f3f490def0be8a3ea2b9a0ac60f5bb33c5c45fb5;hp=21ae73901d2525f9865ed48694f6a48dccb461a2;hpb=aad820c7d81e962b5f6459093fe558126198f1ed;p=mygptrnn.git

diff --git a/mygpt.py b/mygpt.py
index 21ae739..de69a75 100755
--- a/mygpt.py
+++ b/mygpt.py
@@ -457,77 +457,6 @@ def moving_window(x, dim, win_dim, win_size):
 
 ##############################
 
-# This is one order of magnitude more complicated than I expected, not
-# elegant, slow, hopefully not buggy
-
-
-def flash_back_time_src(N, H, t0, t1, CL, CH, proba, device):
-    # starting flash backs
-    fb_start = (torch.rand(N, CH, t1 - t0, device=device) <= proba).long()
-    fb_start[:, :, -CL:] = 0
-    fb_start[:, :, :CL] = 0
-
-    # Remove series longer than CL
-    fb_body = fb_start.clone()
-    fb_body[:, :, CL + 1 :] -= fb_start[:, :, : -(CL + 1)]
-    fb_body = fb_body.cumsum(dim=2)
-    fb_start = fb_start * (fb_body == 1)
-
-    # Set a origin source time (starting time of the chunck to copy
-    # here) We set it as the current time minus a multiple of CL to be
-    # consistent with the "rolling" caterpillar
-    t = torch.arange(fb_start.size(2), device=fb_start.device)[None, None, :]
-    src_time = fb_start * (
-        t
-        - CL
-        * (
-            1
-            + (
-                torch.rand(fb_start.size(), device=fb_start.device) * (t // CL - 1)
-            ).long()
-        )
-    )
-    src_time[:, :, CL:] -= src_time.clone()[:, :, :-CL]
-    src_time = src_time.cumsum(dim=2)
-
-    src_head = fb_start * torch.randint(H, fb_start.size(), device=fb_start.device)
-    src_head[:, :, CL:] -= src_head.clone()[:, :, :-CL]
-    src_head = src_head.cumsum(dim=2)
-
-    # combine
-    src_delta = fb_start.clone()
-    src_delta[:, :, CL:] -= fb_start[:, :, :-CL]
-    src_delta = src_delta.cumsum(dim=2)
-    src_delta[:, :, CL:] -= CL * fb_start[:, :, :-CL]
-    src_time += src_delta.cumsum(dim=2) - 1
-
-    return src_time, src_head
-
-
-def insert_flash_back(rec_V, V, rec_K, K, t0, t1, CL, proba):
-    N, H, CH = V.size(0), V.size(1), rec_V.size(1)
-
-    fbt, fbh = flash_back_time_src(N, H, t0, t1, CL, CH, proba, rec_V.device)
-
-    fbt_V = fbt[:, :, :, None]
-    fbh_V = fbh[:, :, :, None]
-    t = fbt_V.clamp(min=0)
-    n = torch.arange(V.size(0), device=V.device)[:, None, None, None]
-    d = torch.arange(V.size(3), device=V.device)[None, None, None, :]
-    q = V[:, :, t0:t1][n, fbh_V, t, d]
-    rec_V[:, :, t0:t1] = q * (fbt_V >= 0) + rec_V[:, :, t0:t1] * (fbt_V < 0)
-
-    fbt_K = fbt[:, :, :, None]
-    fbh_K = fbh[:, :, :, None]
-    t = fbt_K.clamp(min=0)
-    n = torch.arange(K.size(0), device=K.device)[:, None, None, None]
-    d = torch.arange(K.size(3), device=K.device)[None, None, None, :]
-    q = K[:, :, t0:t1][n, fbh_K, t, d]
-    rec_K[:, :, t0:t1] = q * (fbt_K >= 0) + rec_K[:, :, t0:t1] * (fbt_K < 0)
-
-
-######################################################################
-
 
 class Caterpillar(nn.Module):
     def __init__(
@@ -655,37 +584,40 @@ class Caterpillar(nn.Module):
         self.rec_V[:, :, t0:t1] = next_V.flatten(2, 3)
         self.rec_K[:, :, t0:t1] = next_K.flatten(2, 3)
 
-        if self.training and self.proba_flashback:
-            insert_flash_back(
-                self.rec_V,
-                V,
-                self.rec_K,
-                K,
-                t0,
-                t1,
-                CL,
-                proba=self.proba_flashback / CL,
-            )
+        if self.training and self.proba_flashback > 0.0:
+            # This piece of code makes the assumption that there is
+            # nothing informative before t0, otherwise we'd have to
+            # implement a cache for V and K too. This should not be
+            # too much of a problem since this is used only during
+            # train, where full sequence are available
 
-            # n = torch.arange(N, device=X.device)[:, None, None, None]
-            # t = torch.arange(t0, t1, device=X.device)[None, None, :, None]
-            # dv = torch.arange(DV)[None, None, None, :]
-            # dk = torch.arange(DK)[None, None, None, :]
+            n = torch.arange(N, device=X.device)[:, None, None, None]
+            t = torch.arange(t0, t1, device=X.device)[None, None, :, None]
+            dv = torch.arange(DV, device=X.device)[None, None, None, :]
+            dk = torch.arange(DK, device=X.device)[None, None, None, :]
 
-            # u = (
-            # torch.rand(N, CH, t1 - t0, 1, device=X.device).mul(t).long() // CL
-            # ) * CL
+            u = (
+                torch.rand(N, CH, t1 - t0, 1, device=X.device).mul(t).long() // CL
+            ) * CL
 
-            # src_time = t - u - t0
-            # src_head = torch.randint(H, (N, CH, t1 - t0, 1), device=X.device)
+            src_time = t - u - t0
+            src_head = torch.randint(H, (N, CH, t1 - t0, 1), device=X.device)
 
-            # mk = (
-            # torch.rand(self.rec_V[:, :, t0:t1].size()) <= self.proba_flashback
-            # ).long()
-            # self.rec_V[:, :, t0:t1] = V[n, src_head, src_time, dv]
-            # self.rec_K[:, :, t0:t1] = K[n, src_head, src_time, dk]
+            mask_V = (
+                torch.rand(N, CH, t1 - t0, DV, device=X.device) <= self.proba_flashback
+            ).long()
+            self.rec_V[:, :, t0:t1] = (
+                mask_V * V[n, src_head, src_time, dv]
+                + (1 - mask_V) * self.rec_V[:, :, t0:t1]
+            )
 
-        exit(0)
+            mask_K = (
+                torch.rand(N, CH, t1 - t0, DK, device=X.device) <= self.proba_flashback
+            ).long()
+            self.rec_K[:, :, t0:t1] = (
+                mask_K * K[n, src_head, src_time, dk]
+                + (1 - mask_K) * self.rec_K[:, :, t0:t1]
+            )
 
         ######################################################################
         # compute the readout