X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=mygpt.py;h=040845ede9e4307f6e76c8a4a7faadd5bacd9974;hb=3d7db5b3c1304fdbd599c2a001b5c31df4df2599;hp=a62cf4908ba88622a3f567d082c7a94711887fde;hpb=4f5d03d3371b124121e8f9fc0ff583553fea1e38;p=mygptrnn.git diff --git a/mygpt.py b/mygpt.py index a62cf49..040845e 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]) @@ -190,6 +201,8 @@ class DumbRec(nn.Module): nb_lines, attention_dropout=0.0, len_max=1e5, + logger=print, + args=None, ): super().__init__() @@ -319,6 +332,8 @@ class KVRec(nn.Module): nb_lines, attention_dropout=0.0, len_max=1e5, + logger=print, + args=None, ): super().__init__() @@ -471,31 +486,31 @@ class Caterpillar(nn.Module): caterpillar_height, attention_dropout=0.0, len_max=1e5, + logger=print, + args=None, ): super().__init__() warnings.warn("Caterpillar", RuntimeWarning) - def randw(*d, amplitude=None): - if amplitude is None: - amplitude = 1 / math.sqrt(d[-1]) - return nn.Parameter(amplitude * torch.randn(*d)) + def randw(*d, factor=1): + return nn.Parameter(torch.randn(*d) * factor / math.sqrt(d[-1])) self.caterpillar_length = caterpillar_length self.caterpillar_height = caterpillar_height self.attention_dropout = attention_dropout - self.proba_gate_dropout = 0.0 + self.gate_dropout_proba = args.gate_dropout_proba + self.gate_dropout_sync = args.gate_dropout_sync + self.gate_dropout_replace = args.gate_dropout_replace - 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) - ) - ) + ###################################################################### + + self.w_G = randw(nb_heads, caterpillar_height, dim_model, factor=1.0) + self.b_G = nn.Parameter(torch.full((nb_heads, caterpillar_height), 0.0)) self.w_K = randw(nb_heads, dim_qk, dim_model) - self.w_V = randw(nb_heads, dim_v, dim_model) + self.w_V = randw(nb_heads, dim_v, dim_model, factor=1) self.w_Q = randw(nb_heads, dim_qk, dim_model) self.w_O = randw(dim_v * nb_heads, dim_model) @@ -510,14 +525,14 @@ class Caterpillar(nn.Module): dim_v, ) - def reset_inner_loss(self): - self.acc_attention = 0 - self.acc_nb = 0 + # def reset_inner_loss(self): + # self.acc_attention = 0 + # self.acc_nb = 0 - def get_inner_loss(self): - # warnings.warn("l2 regularization", RuntimeWarning) - # return (self.acc_attention / self.acc_nb).pow(2).sum() - return torch.tensor([0], device=self.w_Q.device) + # def get_inner_loss(self): + # warnings.warn("l2 regularization", RuntimeWarning) + # return (self.acc_attention / self.acc_nb).pow(2).sum() + # return torch.tensor([0], device=self.w_Q.device) def forward(self, bs): # Dimensions to make the source a bit clearer, that's needed @@ -544,8 +559,8 @@ 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) @@ -565,98 +580,97 @@ class Caterpillar(nn.Module): torch.einsum("ntc,hrc->nhrt", X, self.w_G) + self.b_G[None, :, :, None] ).sigmoid() - ###################################################################### - # Roll the gating indexes + # Clip the gating to avoid values greater than 1 when several + # heads hit the same row - warnings.warn("rotating barrel", RuntimeWarning) + # G = G / G.sum(1, keepdim=True).clamp(min=1) - 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)) + H = (1 - G).log().sum(1, keepdim=True).exp() ###################################################################### - # The "flashbacks" - if self.training and self.proba_gate_dropout > 0.0: - # This is a better implementation of "flashbacks". + def recurrence(G, V, K): + # We prepare the arguments for the parallel scan - # G is NxHxExT where e is the caterpillar's row. + A = H - warnings.warn("gate dropout", RuntimeWarning) - epsilon = 0.5 + gated_V = torch.einsum("nhrt,nhtd->nrtd", H * G / (1 - G), V) + gated_K = torch.einsum("nhrt,nhtd->nrtd", H * G / (1 - G), K) - dropout_head = ( - (torch.rand(N, H, 1, t1 - t0, device=G.device).sort(dim=3).indices == 0) - .expand_as(G) - .float() - ) + # We start from cached values, which matters in inference - dropout_tail = dropout_head.cumsum(dim=3) - dropout_head + init_rec_V = self.rec_V[:, :, t0 - L : t0] + init_rec_K = self.rec_K[:, :, t0 - L : t0] - dropout_active = ( - torch.rand(N, 1, 1, 1, device=G.device) < self.proba_gate_dropout - ).long() + # 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. - dropout_head *= dropout_active - dropout_tail *= dropout_active + A = A.unflatten(2, (-1, L)) + gated_V = gated_V.unflatten(2, (-1, L)) + gated_K = gated_K.unflatten(2, (-1, L)) - G = ( - G - + dropout_head * (1 - epsilon - G.detach()) - - dropout_tail * G.detach() - ) + next_V = pscan_dim(A, gated_V, init_rec_V, dim=2).flatten(2, 3) + next_K = pscan_dim(A, gated_K, init_rec_K, dim=2).flatten(2, 3) - ###################################################################### + return next_V, next_K - # 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) + ################################################################# - A = 1 - G.sum(1) + next_V, next_K = recurrence(G, V, K) - # warnings.warn("harmonic recurrence", RuntimeWarning) - # har = torch.arange(t0, t1, device = G.device).float() + 1 - # A = har / (har + 1) - # G = G / har + if self.training and self.gate_dropout_proba > 0.0: + # G is NxHxRxT where r is the caterpillar's row. - gated_V = torch.einsum("nhrt,nhtd->nrtd", G, V) - gated_K = torch.einsum("nhrt,nhtd->nrtd", G, K) + warnings.warn("gate dropout", RuntimeWarning) - # We start from cached values, which matters in inference + if self.gate_dropout_sync: + shape_kill = (N, 1, 1) + else: + shape_kill = (N, H, R) + + # Pick a point in each of the NxHxR timeline and set this + # entry and the following to 1 + kill = ( + torch.rand(*shape_kill, t1 - t0, device=G.device).sort(dim=3).indices + == 0 + ).cumsum(dim=3) + + # Keep these mask for only some of the NxHxR + kill = kill * ( + torch.rand(*shape_kill, 1, device=G.device) <= self.gate_dropout_proba + ) - init_rec_V = self.rec_V[:, :, t0 - L : t0] - init_rec_K = self.rec_K[:, :, t0 - L : t0] + # The coefficient to keep are the complementary + mask = 1 - kill - ################################################################# - # Associative scan + masked_next_V, masked_next_K = recurrence(G * mask, V, K) - # 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. + if self.gate_dropout_replace: + next_V = next_V.detach() + next_K = next_K.detach() - A = A.unflatten(2, (-1, L)) - gated_V = gated_V.unflatten(2, (-1, L)) - gated_K = gated_K.unflatten(2, (-1, L)) + warnings.warn("the rescaling is probably a bad idea", RuntimeWarning) - 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_V + (masked_next_V - masked_next_V.detach()) / ( + 1 - self.gate_dropout_proba + ) + next_K = next_K + (masked_next_K - masked_next_K.detach()) / ( + 1 - self.gate_dropout_proba + ) - 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 Q = torch.einsum("ntc,hdc->nhtd", X, self.w_Q) - # We build tensors NxHxTxFxL where N is the sample index, H - # the head, T the time, F the row in the caterpillar, and L + # We build tensors NxHxTxRxL where N is the sample index, H + # the head, T the time, R the row in the caterpillar, and L # the column in the caterpillar windowed_V = moving_window( @@ -670,7 +684,7 @@ class Caterpillar(nn.Module): # We have an attention score for each of the RxL values ar = torch.einsum( - "nhtd,nftld->nhtfl", + "nhtd,nrtld->nhtrl", Q, windowed_K, ) / math.sqrt(DK) @@ -709,6 +723,8 @@ class QKVAttention(nn.Module): nb_heads=1, causal=False, attention_dropout=0.0, + logger=print, + args=None, ): super().__init__() @@ -799,7 +815,9 @@ class MyGPT(nn.Module): causal=False, dropout=0.0, len_max=1e5, - attention_layer="kvrec", + attention_layer="caterpillar", + logger=print, + args=None, ): super().__init__() @@ -836,6 +854,8 @@ class MyGPT(nn.Module): nb_heads=nb_heads, causal=causal, attention_dropout=dropout, + logger=logger, + args=args, ) elif attention_layer == "dumbrec": return DumbRec( @@ -845,6 +865,8 @@ class MyGPT(nn.Module): nb_heads=nb_heads, nb_lines=nb_lines, attention_dropout=dropout, + logger=logger, + args=args, ) elif attention_layer == "kvrec": return KVRec( @@ -854,6 +876,8 @@ class MyGPT(nn.Module): nb_heads=nb_heads, nb_lines=nb_lines, attention_dropout=dropout, + logger=logger, + args=args, ) elif attention_layer == "caterpillar": return Caterpillar( @@ -864,6 +888,8 @@ class MyGPT(nn.Module): caterpillar_length=self.caterpillar_length, caterpillar_height=self.caterpillar_height, attention_dropout=dropout, + logger=logger, + args=args, ) else: raise ValueError(f"Unknown attention type {attention_layer}.") @@ -994,7 +1020,111 @@ class MyGPT(nn.Module): ###################################################################### if __name__ == "__main__": - print("Basic check.") + import argparse + + import numpy as np + import matplotlib.pyplot as plt + import matplotlib.collections as mc + + args = argparse.Namespace( + gate_dropout_proba=0.0, gate_dropout_sync=True, gate_dropout_replace=False + ) + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + dim_model, dim_keys, nb_heads = 512, 64, 1 + dropout = 0.1 + + caterpillar = Caterpillar( + dim_model=dim_model, + dim_qk=dim_keys, + dim_v=dim_model // nb_heads, + nb_heads=nb_heads, + caterpillar_length=16, + caterpillar_height=32, + attention_dropout=dropout, + args=args, + ).to(device) + + qkv = QKVAttention( + dim_model=dim_model, + dim_qk=dim_keys, + dim_v=dim_model // nb_heads, + nb_heads=nb_heads, + causal=True, + attention_dropout=dropout, + args=args, + ).to(device) + + linear = CacheWrapper(nn.Linear(512, 512)).to(device) + + x = torch.randn(1, 256, dim_model) + + x = x.to(device) + x.requires_grad_() + + ###################################################################### + + fig = plt.figure() + fig.set_figheight(6) + fig.set_figwidth(8) + + ax = fig.add_subplot(1, 1, 1) + + # ax.set_xlim(-1.5, 1.5) + # ax.set_ylim(-1.5, 1.5) + # ax.set(aspect=1) + # ax.spines.right.set_visible(False) + # ax.spines.top.set_visible(False) + + # dt = 0.01 + # t = np.arange(dt, 20.0, dt) + # ax.semilogx(t, np.exp(-t / 5.0)) + # ax.grid() + + ###################################################################### + + for label, model in [ + # ("nn.Linear", linear), + ("mygpy.QKVAttention", qkv), + ("mygpt.Caterpillar", caterpillar), + ]: + y = model(BracketedSequence(x, 32, x.size(1) - 32, init_cache=True)).x + + data = [] + for t in range(y.size(1)): + for d in torch.randperm(y.size(2))[:8]: + g = torch.autograd.grad(y[0, t, d], x, retain_graph=True)[0] + sg = g.pow(2).sum().item() + # sg = 0 + # for p in model.parameters(): + # g = torch.autograd.grad(y[0, t, d], p, retain_graph=True)[0] + # sg = sg + g.pow(2).sum().item() + data.append([t, sg]) + + data = torch.tensor(data) + ax.scatter( + data[:, 0], data[:, 1], s=1, label=label + ) # , color='gray', label='Input') + + # ax.legend(frameon=False, loc="top right") + + # Put a legend to the right of the current axis + box = ax.get_position() + ax.set_position([box.x0, box.y0, box.width * 0.8, box.height]) + ax.legend(loc="center left", bbox_to_anchor=(1, 0.5)) + + filename = "plot.pdf" + print(f"saving {filename}") + fig.savefig(filename, bbox_inches="tight") + + # if args.window and hasattr(plt.get_current_fig_manager(), 'window'): + # plt.get_current_fig_manager().window.setGeometry(2, 2, 1024, 768) + # plt.show() + + exit(0) + + ###################################################################### m = Caterpillar( dim_model=4, @@ -1016,8 +1146,6 @@ if __name__ == "__main__": print((y1 - torch.cat([y3a, y3b], dim=1)).abs().max()) exit(0) - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - vocabulary_size = 128 x = torch.randint(vocabulary_size, (6, 1024))