X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=mygpt.py;h=a27b99e8dd47eb14696257fb1d814c8e33dd49cb;hb=64dc96ddfa84511ba07d1929481e93e864735409;hp=7c9991f7d56fc3069a261b0642e4381c55bd02d9;hpb=73acbc986f9c386c001117581c4fc72d2f36803a;p=mygptrnn.git diff --git a/mygpt.py b/mygpt.py index 7c9991f..a27b99e 100755 --- a/mygpt.py +++ b/mygpt.py @@ -126,7 +126,6 @@ class AddPositionalEncoding(nn.Module): import pscan - # X is /.../xTxD A is /.../xT Y_init is /.../xD @@ -147,6 +146,18 @@ def pscan_dim(A, X, Y_init, dim=-2): return Y +def pscan_rgrad(grad_Y, A, X, Y_init, dim=-2, eps=1e-2): + with torch.no_grad(): + s_A, s_X = 0, 0 + for t in range(X.size(dim) - 1, 0, -1): + delta = (grad_Y[t] - s_A) / A[t].grad + s_A += A[t].grad * delta + A[t].grad = delta + delta = (grad_Y[t] - s_X) / X[t].grad + s_X += X[t].grad * delta + X[t].grad = delta + + def pscan_shape(A, X, Y_init): s = X.size() A = A.reshape(-1, s[-2]) @@ -491,16 +502,29 @@ class Caterpillar(nn.Module): self.caterpillar_height = caterpillar_height self.attention_dropout = attention_dropout - self.proba_gate_dropout = 0.0 + ###################################################################### + # sup_args - default_b_G = kwargs.get("default_b_G") - if default_b_G is None: - default_b_G = -math.log(caterpillar_height - 1) + x = kwargs.get("gate_dropout") + if x is None: + self.proba_gate_dropout = 0.0 + else: + self.proba_gate_dropout = float(x) - logger(f"default_b_G {default_b_G}") + logger(f"self.proba_gate_dropout {self.proba_gate_dropout}") + + x = kwargs.get("default_bg") + if x is None: + default_bg = -math.log(caterpillar_height - 1) + else: + default_bg = float(x) + + logger(f"default_bg {default_bg}") + + ###################################################################### self.w_G = randw(nb_heads, caterpillar_height, dim_model) - self.b_G = nn.Parameter(torch.full((nb_heads, caterpillar_height), default_b_G)) + self.b_G = nn.Parameter(torch.full((nb_heads, caterpillar_height), default_bg)) self.w_K = randw(nb_heads, dim_qk, dim_model) self.w_V = randw(nb_heads, dim_v, dim_model) @@ -552,8 +576,8 @@ class Caterpillar(nn.Module): 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 - L : t0] = self.init_V_rec[None, :, :, :] - self.rec_K[:, :, t0 - L : 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) @@ -573,20 +597,11 @@ class Caterpillar(nn.Module): torch.einsum("ntc,hrc->nhrt", X, self.w_G) + self.b_G[None, :, :, None] ).sigmoid() - # Clip the gating to avoid values greater than 1 when several - # heads hit the same row + # warnings.warn("softmax gating", RuntimeWarning) - G = G / G.sum(1, keepdim=True).clamp(min=1) - - ###################################################################### - # Roll the gating indexes - - # warnings.warn("rotating barrel", RuntimeWarning) - - # 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 - # G = G.gather(dim=2, index=r_barrel.expand_as(G)) + # G = ( + # torch.einsum("ntc,hrc->nhrt", X, self.w_G) + self.b_G[None, :, :, None] + # ).softmax(dim=2) ###################################################################### # The "flashbacks" @@ -597,66 +612,59 @@ class Caterpillar(nn.Module): # G is NxHxExT where e is the caterpillar's row. warnings.warn("gate dropout", RuntimeWarning) - epsilon = 0.5 - dropout_head = ( - (torch.rand(N, H, 1, t1 - t0, device=G.device).sort(dim=3).indices == 0) - .expand_as(G) - .float() - ) + kill = ( + torch.rand(G.size(), device=G.device) <= self.proba_gate_dropout + ).float() - dropout_tail = dropout_head.cumsum(dim=3) - dropout_head + alpha = G / (1 - self.proba_gate_dropout) - dropout_active = ( - torch.rand(N, 1, 1, 1, device=G.device) < self.proba_gate_dropout - ).long() + G = alpha * (1 - kill) - dropout_head *= dropout_active - dropout_tail *= dropout_active + def recurrence(G, V, K): + # Clip the gating to avoid values greater than 1 when several + # heads hit the same row - G = ( - G - + dropout_head * (1 - epsilon - G.detach()) - - dropout_tail * G.detach() - ) + G = G / G.sum(1, keepdim=True).clamp(min=1) - ###################################################################### + # We prepare the arguments for the parallel scan - # We prepare the arguments for the parallel scan + A = 1 - G.sum(1) - A = 1 - G.sum(1) + gated_V = torch.einsum("nhrt,nhtd->nrtd", G, V) + gated_K = torch.einsum("nhrt,nhtd->nrtd", G, K) - # warnings.warn("harmonic recurrence", RuntimeWarning) - # har = torch.arange(t0, t1, device = G.device).float() + 1 - # A = har / (har + 1) - # G = G / har + # We start from cached values, which matters in inference - gated_V = torch.einsum("nhrt,nhtd->nrtd", G, V) - gated_K = torch.einsum("nhrt,nhtd->nrtd", G, K) + init_rec_V = self.rec_V[:, :, t0 - L : t0] + init_rec_K = self.rec_K[:, :, t0 - L : t0] - # We start from cached values, which matters in inference + # Associative scan - init_rec_V = self.rec_V[:, :, t0 - L : t0] - init_rec_K = self.rec_K[:, :, t0 - L : t0] + # Here there is a trick: Since the stack at position t is + # 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. - ################################################################# - # Associative scan + 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) - # Here there is a trick: Since the stack at position t is - # 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. + next_V = next_V.flatten(2, 3) + next_K = next_K.flatten(2, 3) - A = A.unflatten(2, (-1, L)) - gated_V = gated_V.unflatten(2, (-1, L)) - gated_K = gated_K.unflatten(2, (-1, L)) + return next_V, next_K + + ################################################################# - next_V = pscan_dim(A, gated_V, init_rec_V, dim=2) - next_K = pscan_dim(A, gated_K, init_rec_K, dim=2) + next_V, next_K = recurrence(G, V, K) - self.rec_V[:, :, t0:t1] = next_V.flatten(2, 3) - self.rec_K[:, :, t0:t1] = next_K.flatten(2, 3) + self.rec_V[:, :, t0:t1] = next_V + self.rec_K[:, :, t0:t1] = next_K ###################################################################### # compute the readout