# 1 for the successive tokens.
#
# Modules able to process brackets may implement a cache that is
-# resetted when the input bracket starts at t=0
+# resetted when init_cache is True
class BracketedSequence:
##############################
-# 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__(
self.attention_dropout = attention_dropout
warnings.warn("flash back", RuntimeWarning)
- self.proba_flashback = 0.1
+ self.proba_flashback = 1e-2
self.w_G = randw(nb_heads, caterpillar_height, dim_model)
self.b_G = nn.Parameter(
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, :]
+ 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
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) <= self.proba_flashback).long()
+ mask = (
+ 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 * V[n, src_head, src_time, dv]
+ + (1 - mask) * self.rec_V[:, :, t0:t1]
)
- mask_K = (torch.rand(N, CH, t1 - t0, DK) <= 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]
+ mask * K[n, src_head, src_time, dk]
+ + (1 - mask) * self.rec_K[:, :, t0:t1]
)
- exit(0)
-
######################################################################
# compute the readout
projects / mygptrnn.git / blobdiff