X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=mygpt.py;h=b885e218be6704cd86afed18966e5609e9873369;hb=42831bd654d030b71bca88578d041279018f836c;hp=f10f1fe2013c4755a8ea7f2eef86a6b932059f25;hpb=6183291906184569c2206c34588d118cc77f74bb;p=mygptrnn.git diff --git a/mygpt.py b/mygpt.py index f10f1fe..b885e21 100755 --- 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 -# resetted when the input bracket starts at t=0 +# resetted when init_cache is True class BracketedSequence: @@ -457,85 +457,6 @@ 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) - - # t_s = t0-(t0//L * R)*L - - 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].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__( @@ -560,6 +481,9 @@ class Caterpillar(nn.Module): self.caterpillar_height = caterpillar_height self.attention_dropout = attention_dropout + self.proba_flashback = 0.0 + self.proba_gate_dropout = 0.0 + self.w_G = randw(nb_heads, caterpillar_height, dim_model) self.b_G = nn.Parameter( torch.full( @@ -591,6 +515,7 @@ class Caterpillar(nn.Module): 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) @@ -626,7 +551,11 @@ class Caterpillar(nn.Module): torch.einsum("ntc,hec->nhet", X, self.w_G) + self.b_G[None, :, :, None] ).sigmoid() - # That bas a bad idea + if self.training and self.proba_gate_dropout > 0.0: + warnings.warn("gate droupout", RuntimeWarning) + epsilon = 0.5 + + # That was a bad idea # G = F.dropout(G, self.attention_dropout, self.training) V = torch.einsum("ntc,hdc->nhtd", X, self.w_V) @@ -634,6 +563,10 @@ class Caterpillar(nn.Module): # We prepare the arguments for the parallel scan + # Clip the gating + warnings.warn("gating clipping", RuntimeWarning) + G = G / G.sum(1, keepdim=True).clamp(min=1) + A = 1 - G.sum(1) gated_V = torch.einsum("nhet,nhtd->netd", G, V) gated_K = torch.einsum("nhet,nhtd->netd", G, K) @@ -659,9 +592,39 @@ 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) + if self.training and self.proba_flashback > 0.0: + warnings.warn("flash back", RuntimeWarning) + # 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, 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 = ( + torch.rand(N, CH, t1 - t0, DV, device=X.device) <= self.proba_flashback + ).long() + + self.rec_V[:, :, t0:t1] = ( + mask * V[n, src_head, src_time, dv] + + (1 - mask) * self.rec_V[:, :, t0:t1] + ) + + self.rec_K[:, :, t0:t1] = ( + mask * K[n, src_head, src_time, dk] + + (1 - mask) * self.rec_K[:, :, t0:t1] + ) ###################################################################### # compute the readout @@ -817,7 +780,12 @@ class MyGPT(nn.Module): ): super().__init__() - assert attention_layer in {"mha", "dumbrec", "kvrec", "caterpillar"} + assert attention_layer in { + "mha", + "dumbrec", + "kvrec", + "caterpillar", + }, f"Unknown attention operator {attention_layer}." if attention_layer == "caterpillar": assert nb_lines % caterpillar_height == 0