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Update.
author
François Fleuret
<francois@fleuret.org>
Mon, 8 Jan 2024 07:02:21 +0000
(08:02 +0100)
committer
François Fleuret
<francois@fleuret.org>
Mon, 8 Jan 2024 07:02:21 +0000
(08:02 +0100)
mygpt.py
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diff --git
a/mygpt.py
b/mygpt.py
index
de69a75
..
f3c9a93
100755
(executable)
--- a/
mygpt.py
+++ b/
mygpt.py
@@
-37,7
+37,7
@@
import ffutils
# 1 for the successive tokens.
#
# Modules able to process brackets may implement a cache that is
# 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:
class BracketedSequence:
@@
-482,7
+482,7
@@
class Caterpillar(nn.Module):
self.attention_dropout = attention_dropout
warnings.warn("flash back", RuntimeWarning)
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.w_G = randw(nb_heads, caterpillar_height, dim_model)
self.b_G = nn.Parameter(
@@
-603,20
+603,18
@@
class Caterpillar(nn.Module):
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)
- mask
_V
= (
+ mask = (
torch.rand(N, CH, t1 - t0, DV, device=X.device) <= self.proba_flashback
).long()
torch.rand(N, CH, t1 - t0, DV, device=X.device) <= self.proba_flashback
).long()
+
self.rec_V[:, :, t0:t1] = (
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, device=X.device) <= self.proba_flashback
- ).long()
self.rec_K[:, :, t0:t1] = (
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]
)
######################################################################
)
######################################################################