From: François Fleuret Date: Wed, 10 Jan 2024 16:24:46 +0000 (+0100) Subject: Update. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=f0ea1f2375fa3a0be38970a58185cddee97dccef;p=mygptrnn.git Update. --- diff --git a/mygpt.py b/mygpt.py index 676b921..7c8e9f4 100755 --- a/mygpt.py +++ b/mygpt.py @@ -476,8 +476,10 @@ class Caterpillar(nn.Module): warnings.warn("Caterpillar", RuntimeWarning) - def randw(*d): - return nn.Parameter(torch.randn(*d) / math.sqrt(d[-1])) + def randw(*d, amplitude=None): + if amplitude is None: + amplitude = 1 / math.sqrt(d[-1]) + return nn.Parameter(amplitude * torch.randn(*d)) self.caterpillar_length = caterpillar_length self.caterpillar_height = caterpillar_height @@ -485,7 +487,7 @@ class Caterpillar(nn.Module): self.proba_gate_dropout = 0.0 - self.w_G = randw(nb_heads, caterpillar_height, dim_model) + self.w_G = randw(nb_heads, caterpillar_height, dim_model, amplitude=1e-5) self.b_G = nn.Parameter( torch.full( (nb_heads, caterpillar_height), -math.log(caterpillar_height - 1) @@ -497,8 +499,12 @@ class Caterpillar(nn.Module): self.w_Q = randw(nb_heads, dim_qk, dim_model) self.w_O = randw(dim_v * nb_heads, dim_model) - self.init_K_rec = randw(caterpillar_height, caterpillar_length, dim_qk) - self.init_V_rec = randw(caterpillar_height, caterpillar_length, dim_v) + self.init_K_rec = randw( + caterpillar_height, caterpillar_length, dim_qk, amplitude=1e-5 + ) + self.init_V_rec = randw( + caterpillar_height, caterpillar_length, dim_v, amplitude=1e-5 + ) def reset_inner_loss(self): self.acc_attention = 0 @@ -552,34 +558,65 @@ class Caterpillar(nn.Module): # recurrent state, or not at all. G = ( - torch.einsum("ntc,hec->nhet", X, self.w_G) + self.b_G[None, :, :, None] + torch.einsum("ntc,hrc->nhrt", X, self.w_G) + self.b_G[None, :, :, None] ).sigmoid() + ###################################################################### + # The "flashbacks" + + if self.training and self.proba_gate_dropout > 0.0: + # This is a better implementation of "flashbacks". + + # G is NxHxExT where e is the caterpillar's row. + + warnings.warn("gate dropout", RuntimeWarning) + epsilon = 0.5 + + dropout_start = ( + ( + torch.rand(G.size(), device=G.device) + .flatten(2, 3) + .sort(dim=2) + .indices + == 0 + ) + .unflatten(2, (CH, t1 - t0)) + .float() + ) + + dropout_tail = dropout_start.cumsum(dim=3) - dropout_start + + dropout_active = ( + torch.rand(N, 1, 1, 1, device=G.device) < self.proba_gate_dropout + ).long() + + dropout_start *= dropout_active + dropout_tail *= dropout_active + + G = ( + G + + dropout_start * (1 - epsilon - G.detach()) + - dropout_tail * G.detach() + ) + + ###################################################################### + + # We prepare the arguments for the parallel scan + # Clip the gating to avoid values greater than 1 when several # heads hit the same row G = G / G.sum(1, keepdim=True).clamp(min=1) - # We prepare the arguments for the parallel scan - A = 1 - G.sum(1) - gated_V = torch.einsum("nhet,nhtd->netd", G, V) - gated_K = torch.einsum("nhet,nhtd->netd", G, K) + gated_V = torch.einsum("nhrt,nhtd->nrtd", G, V) + gated_K = torch.einsum("nhrt,nhtd->nrtd", G, K) # We start from cached values, which matters in inference init_rec_V = self.rec_V[:, :, t0 - CL : t0] init_rec_K = self.rec_K[:, :, t0 - CL : t0] - ###################################################################### - - if self.training and self.proba_gate_dropout > 0.0: - # This is a better implementation of "flashbacks". A is - # NxExT where e is the caterpillar's row. - - warnings.warn("gate dropout", RuntimeWarning) - epsilon = 0.5 - ################################################################# # Associative scan