##############################
-# This is one order of magnitude more complicated than I expected
+# 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):
fb_body = fb_body.cumsum(dim=2)
fb_start = fb_start * (fb_body == 1)
- # pick past starting source times
- src_time = (
- fb_start
+ # 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
* (
- torch.rand(fb_start.size(), device=fb_start.device)
- * (torch.arange(fb_start.size(2), device=fb_start.device) - CL)[
- None, None, :
- ]
- ).long()
+ 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)
fbt, fbh = flash_back_time_src(N, H, t0, t1, CL, CH, proba, rec_V.device)
- fbt_V = fbt[:, :, :, None].expand_as(rec_V[:, :, t0:t1])
- fbh_V = fbh[:, :, :, None].expand_as(rec_V[:, :, t0:t1])
+ 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].expand_as(
- rec_V[:, :, t0:t1]
- )
- d = torch.arange(V.size(3), device=V.device)[None, None, None, :].expand_as(
- rec_V[:, :, t0:t1]
- )
+ 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].expand_as(rec_K[:, :, t0:t1])
- fbh_K = fbh[:, :, :, None].expand_as(rec_K[:, :, t0:t1])
+ 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].expand_as(
- rec_K[:, :, t0:t1]
- )
- d = torch.arange(K.size(3), device=K.device)[None, None, None, :].expand_as(
- rec_K[:, :, t0:t1]
- )
+ 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)
- # print("SANITY", (fbt_K >=0).float().sum()/fbt_K.numel())
-
######################################################################
self.caterpillar_height = caterpillar_height
self.attention_dropout = attention_dropout
+ warnings.warn("flash back", RuntimeWarning)
+ self.proba_flashback = 0.1
+
self.w_G = randw(nb_heads, caterpillar_height, dim_model)
self.b_G = nn.Parameter(
torch.full(
N = bs.x.size(0)
T = bs.x.size(1)
+ H = self.w_V.size(0)
DV = self.w_V.size(1)
DK = self.w_K.size(1)
DM = self.w_O.size(1)
self.rec_V[:, :, t0:t1] = next_V.flatten(2, 3)
self.rec_K[:, :, t0:t1] = next_K.flatten(2, 3)
- warnings.warn("flash back", RuntimeWarning)
- if self.training:
- insert_flash_back(self.rec_V, V, self.rec_K, K, t0, t1, CL, proba=1e-2 / CL)
+ if self.training and self.proba_flashback:
+ # 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
+
+ # insert_flash_back(
+ # self.rec_V,
+ # V,
+ # self.rec_K,
+ # K,
+ # t0,
+ # t1,
+ # CL,
+ # proba=self.proba_flashback / CL,
+ # )
+
+ 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
+
+ src_time = t - u - t0
+ src_head = torch.randint(H, (N, CH, t1 - t0, 1), device=X.device)
+
+ 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]
+ )
+
+ 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