X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=mygpt.py;h=d8fd227f63c39a70dded3c55f3c230c3a9d58862;hb=3e4af6d54fb3d7bd6794035cb79e30ecdcadeb6f;hp=f3c9a933bf66b9663eb90fe9e5620d5b5d6ce08c;hpb=195d05199b5203c79694702756921d10b7d03ddc;p=mygptrnn.git diff --git a/mygpt.py b/mygpt.py index f3c9a93..d8fd227 100755 --- a/mygpt.py +++ b/mygpt.py @@ -10,6 +10,8 @@ # with a caching mechanism for keys and values to avoid a O(N^3) cost # for auto-regression. +# This implementation is equipped with RNN layers to replace the MHA + import math, warnings import torch, einops @@ -481,8 +483,7 @@ class Caterpillar(nn.Module): self.caterpillar_height = caterpillar_height self.attention_dropout = attention_dropout - warnings.warn("flash back", RuntimeWarning) - self.proba_flashback = 1e-2 + self.proba_gate_dropout = 0.0 self.w_G = randw(nb_heads, caterpillar_height, dim_model) self.b_G = nn.Parameter( @@ -538,24 +539,26 @@ class Caterpillar(nn.Module): self.cache_Y = X.new_zeros(N, T, DM) + V = torch.einsum("ntc,hdc->nhtd", X, self.w_V) + K = torch.einsum("ntc,hdc->nhtd", X, self.w_K) + ###################################################################### # 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. The CH gating values are independent, which means - # that the current K/V could be stored in multiple pairs of the + # stack. There are CH independent gating values, which means + # that the current K/V may be stored in multiple pairs of the # recurrent state, or not at all. G = ( torch.einsum("ntc,hec->nhet", X, self.w_G) + self.b_G[None, :, :, None] ).sigmoid() - # That bas a bad idea - # G = F.dropout(G, self.attention_dropout, self.training) + # Clip the gating to avoid values greater than 1 when several + # heads hit the same row - V = torch.einsum("ntc,hdc->nhtd", X, self.w_V) - K = torch.einsum("ntc,hdc->nhtd", X, self.w_K) + G = G / G.sum(1, keepdim=True).clamp(min=1) # We prepare the arguments for the parallel scan @@ -563,14 +566,25 @@ class Caterpillar(nn.Module): gated_V = torch.einsum("nhet,nhtd->netd", G, V) gated_K = torch.einsum("nhet,nhtd->netd", 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] - # Here there is a trick: Since the stack at time t is computed - # by updating that at time t-L, the parallel scan operates - # with a period of L. To do so we split the time indexing in - # two axes, the second of size CL, and run the parallel scan - # using the other as the sequence index. + ###################################################################### + + if self.training and self.proba_gate_dropout > 0.0: + warnings.warn("gate dropout", RuntimeWarning) + epsilon = 0.5 + + ################################################################# + # 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 + # 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)) @@ -579,44 +593,9 @@ class Caterpillar(nn.Module): next_V = pscan_dim(A, gated_V, init_rec_V, dim=2) next_K = pscan_dim(A, gated_K, init_rec_K, dim=2) - # Put back the sequence index - self.rec_V[:, :, t0:t1] = next_V.flatten(2, 3) self.rec_K[:, :, t0:t1] = next_K.flatten(2, 3) - if self.training and self.proba_flashback > 0.0: - # This piece of code makes the assumption that there is - # nothing informative before t0, otherwise we'd have to - # implement a cache for V and K too. This should not be - # too much of a problem since this is used only during - # train, where full sequence are available - - n = torch.arange(N, device=X.device)[:, None, None, None] - t = torch.arange(t0, t1, device=X.device)[None, None, :, None] - dv = torch.arange(DV, device=X.device)[None, None, None, :] - dk = torch.arange(DK, device=X.device)[None, None, None, :] - - u = ( - torch.rand(N, CH, t1 - t0, 1, device=X.device).mul(t).long() // CL - ) * CL - - src_time = t - u - t0 - src_head = torch.randint(H, (N, CH, t1 - t0, 1), device=X.device) - - mask = ( - torch.rand(N, CH, t1 - t0, DV, device=X.device) <= self.proba_flashback - ).long() - - self.rec_V[:, :, t0:t1] = ( - mask * V[n, src_head, src_time, dv] - + (1 - mask) * self.rec_V[:, :, t0:t1] - ) - - self.rec_K[:, :, t0:t1] = ( - mask * K[n, src_head, src_time, dk] - + (1 - mask) * self.rec_K[:, :, t0:t1] - ) - ###################################################################### # compute the readout @@ -763,7 +742,6 @@ class MyGPT(nn.Module): nb_blocks, nb_lines=None, caterpillar_height=None, - dim_rec_v=-1, causal=False, dropout=0.0, len_max=1e5, @@ -771,7 +749,12 @@ class MyGPT(nn.Module): ): super().__init__() - assert attention_layer in {"mha", "dumbrec", "kvrec", "caterpillar"} + assert attention_layer in { + "mha", + "dumbrec", + "kvrec", + "caterpillar", + }, f"Unknown attention operator {attention_layer}." if attention_layer == "caterpillar": assert nb_lines % caterpillar_height == 0 @@ -804,7 +787,7 @@ class MyGPT(nn.Module): return DumbRec( dim_model=dim_model, dim_qk=dim_keys, - dim_v=dim_rec_v, + dim_v=dim_model // nb_heads, nb_heads=nb_heads, nb_lines=nb_lines, attention_dropout=dropout, @@ -813,7 +796,7 @@ class MyGPT(nn.Module): return KVRec( dim_model=dim_model, dim_qk=dim_keys, - dim_v=dim_rec_v, + dim_v=dim_model // nb_heads, nb_heads=nb_heads, nb_lines=nb_lines, attention_dropout=dropout, @@ -822,7 +805,7 @@ class MyGPT(nn.Module): return Caterpillar( dim_model=dim_model, dim_qk=dim_keys, - dim_v=dim_rec_v, + dim_v=dim_model // nb_heads, nb_heads=nb_heads, caterpillar_length=self.caterpillar_length, caterpillar_height=self.caterpillar_height,