X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=mygpt.py;h=633ad642c19a3045064ef858c0ee494a7c733425;hb=6e87fe0cb8bd8a0042bbf7b2ede9d8ed0372fb6b;hp=ba93851dc4c1ba5cdca1dff044f2da482df398a0;hpb=2bf045a277bb300dc851ffb8a93db3a6726faa60;p=mygptrnn.git diff --git a/mygpt.py b/mygpt.py index ba93851..633ad64 100755 --- a/mygpt.py +++ b/mygpt.py @@ -485,9 +485,9 @@ class Caterpillar(nn.Module): self.caterpillar_height = caterpillar_height self.attention_dropout = attention_dropout - self.proba_gate_dropout = 0.25 + self.proba_gate_dropout = 0.0 - self.w_G = randw(nb_heads, caterpillar_height, dim_model, amplitude=1e-5) + self.w_G = randw(nb_heads, caterpillar_height, dim_model) self.b_G = nn.Parameter( torch.full( (nb_heads, caterpillar_height), -math.log(caterpillar_height - 1) @@ -500,10 +500,14 @@ class Caterpillar(nn.Module): self.w_O = randw(dim_v * nb_heads, dim_model) self.init_K_rec = randw( - caterpillar_height, caterpillar_length, dim_qk, amplitude=1e-5 + caterpillar_height, + caterpillar_length, + dim_qk, ) self.init_V_rec = randw( - caterpillar_height, caterpillar_length, dim_v, amplitude=1e-5 + caterpillar_height, + caterpillar_length, + dim_v, ) def reset_inner_loss(self): @@ -526,22 +530,22 @@ class Caterpillar(nn.Module): DV = self.w_V.size(1) DK = self.w_K.size(1) DM = self.w_O.size(1) - CH = self.caterpillar_height - CL = self.caterpillar_length + R = self.caterpillar_height + L = self.caterpillar_length assert ( - t0 >= CL and (t1 - t0) % CL == 0 + t0 >= L and (t1 - t0) % L == 0 ), f"bs.first should be greater than caterpillar_length, and bs.nb should be a multiple of caterpillar_length" # We cache values to deal efficiently with auto-regression if bs.init_cache: - self.rec_V = X.new_zeros(N, CH, T, DV) - self.rec_K = X.new_zeros(N, CH, T, DK) + self.rec_V = X.new_zeros(N, R, T, DV) + self.rec_K = X.new_zeros(N, R, T, DK) # We start the recurrent sequences with optimizable # initial values. No idea if it helps. - self.rec_V[:, :, t0 - CL : t0] = self.init_V_rec[None, :, :, :] - self.rec_K[:, :, t0 - CL : t0] = self.init_K_rec[None, :, :, :] + self.rec_V[:, :, t0 - L : t0] = self.init_V_rec[None, :, :, :] + self.rec_K[:, :, t0 - L : t0] = self.init_K_rec[None, :, :, :] self.cache_Y = X.new_zeros(N, T, DM) @@ -552,8 +556,8 @@ class Caterpillar(nn.Module): # Compute the recurrent state # This is the Gating sequence that modulates the storing of - # the new key and value in the CH pairs of the current - # stack. There are CH independent gating values, which means + # the new key and value in the R pairs of the current + # stack. There are R independent gating values, which means # that the current K/V may be stored in multiple pairs of the # recurrent state, or not at all. @@ -561,6 +565,25 @@ class Caterpillar(nn.Module): torch.einsum("ntc,hrc->nhrt", X, self.w_G) + self.b_G[None, :, :, None] ).sigmoid() + ###################################################################### + # Roll the gating indexes + + warnings.warn("rotating barrel", RuntimeWarning) + + # print(f"SANITY2 {N=} {H=} {R=} {t0=} {t1=} {G.size()=}") + + n_barrel = torch.arange(N, device=G.device)[:, None, None, None] + h_barrel = torch.arange(H, device=G.device)[None, :, None, None] + r_barrel = torch.arange(R, device=G.device)[None, None, :, None] + t_barrel = torch.arange(t1 - t0, device=G.device)[None, None, None, :] + r_barrel = (r_barrel + (t_barrel + t0) // L) % R + + # GG = G.gather(dim=2,index=r_barrel) + G = G[n_barrel, h_barrel, r_barrel, t_barrel] + + # print("SANITY", (GG-G).abs()) + # exit(0) + ###################################################################### # The "flashbacks" @@ -573,14 +596,8 @@ class Caterpillar(nn.Module): epsilon = 0.5 dropout_head = ( - ( - torch.rand(G.size(), device=G.device) - .flatten(2, 3) - .sort(dim=2) - .indices - == 0 - ) - .unflatten(2, (CH, t1 - t0)) + (torch.rand(N, H, 1, t1 - t0, device=G.device).sort(dim=3).indices == 0) + .expand_as(G) .float() ) @@ -595,7 +612,7 @@ class Caterpillar(nn.Module): G = ( G - # + dropout_head * (1 - epsilon - G.detach()) + + dropout_head * (1 - epsilon - G.detach()) - dropout_tail * G.detach() ) @@ -609,26 +626,32 @@ class Caterpillar(nn.Module): G = G / G.sum(1, keepdim=True).clamp(min=1) A = 1 - G.sum(1) + + # warnings.warn("harmonic recurrence", RuntimeWarning) + # har = torch.arange(t0, t1, device = G.device).float() + 1 + # A = har / (har + 1) + # G = G / har + 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] + init_rec_V = self.rec_V[:, :, t0 - L : t0] + init_rec_K = self.rec_K[:, :, t0 - L : t0] ################################################################# # Associative scan # Here there is a trick: Since the stack at position t is - # computed by updating that at position t-CL, the parallel - # scan operates with a period of CL. To do so we split the - # sequence indexing in two axes, the second of size CL, and + # computed by updating that at position t-L, the parallel + # scan operates with a period of L. To do so we split the + # sequence indexing in two axes, the second of size L, and # run the parallel scan using the first as the sequence index. - A = A.unflatten(2, (-1, CL)) - gated_V = gated_V.unflatten(2, (-1, CL)) - gated_K = gated_K.unflatten(2, (-1, CL)) + A = A.unflatten(2, (-1, L)) + gated_V = gated_V.unflatten(2, (-1, L)) + gated_K = gated_K.unflatten(2, (-1, L)) next_V = pscan_dim(A, gated_V, init_rec_V, dim=2) next_K = pscan_dim(A, gated_K, init_rec_K, dim=2) @@ -646,14 +669,14 @@ class Caterpillar(nn.Module): # the column in the caterpillar windowed_V = moving_window( - self.rec_V[:, :, t0 - CL + 1 : t1], dim=2, win_dim=3, win_size=CL + self.rec_V[:, :, t0 - L + 1 : t1], dim=2, win_dim=3, win_size=L ) windowed_K = moving_window( - self.rec_K[:, :, t0 - CL + 1 : t1], dim=2, win_dim=3, win_size=CL + self.rec_K[:, :, t0 - L + 1 : t1], dim=2, win_dim=3, win_size=L ) - # We have an attention score for each of the CHxCL values + # We have an attention score for each of the RxL values ar = torch.einsum( "nhtd,nftld->nhtfl",