X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=mygpt.py;h=0414bb615435d610d2d7c1c0123763081462f330;hb=ec5bad2e7911bdf9b7851342a3ee007d41b80963;hp=a27b99e8dd47eb14696257fb1d814c8e33dd49cb;hpb=64dc96ddfa84511ba07d1929481e93e864735409;p=mygptrnn.git diff --git a/mygpt.py b/mygpt.py index a27b99e..0414bb6 100755 --- a/mygpt.py +++ b/mygpt.py @@ -202,7 +202,7 @@ class DumbRec(nn.Module): attention_dropout=0.0, len_max=1e5, logger=print, - **kwargs, + args=None, ): super().__init__() @@ -333,7 +333,7 @@ class KVRec(nn.Module): attention_dropout=0.0, len_max=1e5, logger=print, - **kwargs, + args=None, ): super().__init__() @@ -487,7 +487,7 @@ class Caterpillar(nn.Module): attention_dropout=0.0, len_max=1e5, logger=print, - **kwargs, + args=None, ): super().__init__() @@ -502,27 +502,12 @@ class Caterpillar(nn.Module): self.caterpillar_height = caterpillar_height self.attention_dropout = attention_dropout - ###################################################################### - # sup_args - - x = kwargs.get("gate_dropout") - if x is None: - self.proba_gate_dropout = 0.0 - else: - self.proba_gate_dropout = float(x) - - 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.gate_dropout_proba = args.gate_dropout_proba + self.gate_dropout_sync = args.gate_dropout_sync ###################################################################### + default_bg = -math.log(caterpillar_height - 1) self.w_G = randw(nb_heads, caterpillar_height, dim_model) self.b_G = nn.Parameter(torch.full((nb_heads, caterpillar_height), default_bg)) @@ -597,36 +582,14 @@ class Caterpillar(nn.Module): torch.einsum("ntc,hrc->nhrt", X, self.w_G) + self.b_G[None, :, :, None] ).sigmoid() - # warnings.warn("softmax gating", RuntimeWarning) + # Clip the gating to avoid values greater than 1 when several + # heads hit the same row - # G = ( - # torch.einsum("ntc,hrc->nhrt", X, self.w_G) + self.b_G[None, :, :, None] - # ).softmax(dim=2) + G = G / G.sum(1, keepdim=True).clamp(min=1) ###################################################################### - # 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) - - kill = ( - torch.rand(G.size(), device=G.device) <= self.proba_gate_dropout - ).float() - - alpha = G / (1 - self.proba_gate_dropout) - - G = alpha * (1 - kill) def recurrence(G, V, K): - # 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) @@ -639,8 +602,6 @@ class Caterpillar(nn.Module): 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-L, the parallel # scan operates with a period of L. To do so we split the @@ -663,6 +624,34 @@ class Caterpillar(nn.Module): next_V, next_K = recurrence(G, V, K) + if self.training and self.gate_dropout_proba > 0.0: + # G is NxHxRxT where r is the caterpillar's row. + + warnings.warn("gate dropout", RuntimeWarning) + + # Pick a point in each of the NxHxR timeline and set this + # entry and the following to 1 + kill = ( + torch.rand(N, H, R, 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(N, H, R, 1, device=G.device) <= self.gate_dropout_proba + ) + + # The coefficient to keep are the complementary + mask = 1 - kill + + masked_next_V, masked_next_K = recurrence(G * mask, V, K) + + next_V = next_V.detach() + (masked_next_V - masked_next_V.detach()) / ( + 1 - self.gate_dropout_proba + ) + next_K = next_K.detach() + (masked_next_K - masked_next_K.detach()) / ( + 1 - self.gate_dropout_proba + ) + self.rec_V[:, :, t0:t1] = next_V self.rec_K[:, :, t0:t1] = next_K @@ -671,8 +660,8 @@ class Caterpillar(nn.Module): 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( @@ -686,7 +675,7 @@ 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) @@ -726,7 +715,7 @@ class QKVAttention(nn.Module): causal=False, attention_dropout=0.0, logger=print, - **kwargs, + args=None, ): super().__init__() @@ -819,7 +808,7 @@ class MyGPT(nn.Module): len_max=1e5, attention_layer="kvrec", logger=print, - **kwargs, + args=None, ): super().__init__() @@ -857,7 +846,7 @@ class MyGPT(nn.Module): causal=causal, attention_dropout=dropout, logger=logger, - **kwargs, + args=args, ) elif attention_layer == "dumbrec": return DumbRec( @@ -868,7 +857,7 @@ class MyGPT(nn.Module): nb_lines=nb_lines, attention_dropout=dropout, logger=logger, - **kwargs, + args=args, ) elif attention_layer == "kvrec": return KVRec( @@ -879,7 +868,7 @@ class MyGPT(nn.Module): nb_lines=nb_lines, attention_dropout=dropout, logger=logger, - **kwargs, + args=args, ) elif attention_layer == "caterpillar": return Caterpillar( @@ -891,7 +880,7 @@ class MyGPT(nn.Module): caterpillar_height=self.caterpillar_height, attention_dropout=dropout, logger=logger, - **kwargs, + args=args, ) else: raise ValueError(f"Unknown attention type {attention_layer}.")