From 0d25f8a86e80850cf6a6e27d419f7b043c6028f1 Mon Sep 17 00:00:00 2001 From: =?utf8?q?Fran=C3=A7ois=20Fleuret?= Date: Sun, 7 Jan 2024 11:37:05 +0100 Subject: [PATCH] Update. --- mygpt.py | 85 ++++++++++++++++++++++++++++++++++++++++++++++++++++++-- pscan.py | 2 +- 2 files changed, 84 insertions(+), 3 deletions(-) diff --git a/mygpt.py b/mygpt.py index 0e94672..f97af49 100755 --- a/mygpt.py +++ b/mygpt.py @@ -457,6 +457,82 @@ def moving_window(x, dim, win_dim, win_size): ############################## +# This is one order of magnitude more complicated than I expected + + +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) + + # pick past starting source times + src_time = ( + fb_start + * ( + torch.rand(fb_start.size(), device=fb_start.device) + * (torch.arange(fb_start.size(2), device=fb_start.device) - CL)[ + None, None, : + ] + ).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].expand_as(rec_V[:, :, t0:t1]) + fbh_V = fbh[:, :, :, None].expand_as(rec_V[:, :, t0:t1]) + 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] + ) + 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]) + 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] + ) + 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()) + + +###################################################################### + class Caterpillar(nn.Module): def __init__( @@ -540,14 +616,15 @@ class Caterpillar(nn.Module): # This is the Gating sequence that modulates the storing of # the new key and value in the CH pairs of the current # stack. The CH gating values are independent, which means - # that the current K/V could be stored in all the pairs of the + # that the current K/V could be stored in multiple pairs of the # recurrent state, or not at all. G = ( torch.einsum("ntc,hec->nhet", X, self.w_G) + self.b_G[None, :, :, None] ).sigmoid() - G = F.dropout(G, self.attention_dropout, self.training) + # That bas a bad idea + # G = F.dropout(G, self.attention_dropout, self.training) V = torch.einsum("ntc,hdc->nhtd", X, self.w_V) K = torch.einsum("ntc,hdc->nhtd", X, self.w_K) @@ -579,6 +656,10 @@ class Caterpillar(nn.Module): 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) + ###################################################################### # compute the readout diff --git a/pscan.py b/pscan.py index 88cb3d5..a14f009 100755 --- a/pscan.py +++ b/pscan.py @@ -181,4 +181,4 @@ if __name__ == "__main__": # print((Y - torch.cat([Y1, Y2], dim=1)).abs().max()) -print(f"{err=}") + print(f"{err=}") -- 2.39.5