X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=mygpt.py;h=e7362b749210dcce6c6b92934bd34744d95b770d;hb=6833683bd343fd687d093d6c47cca8f1909e8b03;hp=21ae73901d2525f9865ed48694f6a48dccb461a2;hpb=aad820c7d81e962b5f6459093fe558126198f1ed;p=mygptrnn.git diff --git a/mygpt.py b/mygpt.py index 21ae739..e7362b7 100755 --- a/mygpt.py +++ b/mygpt.py @@ -37,7 +37,7 @@ import ffutils # 1 for the successive tokens. # # Modules able to process brackets may implement a cache that is -# resetted when the input bracket starts at t=0 +# resetted when init_cache is True class BracketedSequence: @@ -457,77 +457,6 @@ def moving_window(x, dim, win_dim, win_size): ############################## -# This is one order of magnitude more complicated than I expected, not -# elegant, slow, hopefully not buggy - - -def flash_back_time_src(N, H, t0, t1, CL, CH, proba, device): - # starting flash backs - fb_start = (torch.rand(N, CH, t1 - t0, device=device) <= proba).long() - fb_start[:, :, -CL:] = 0 - fb_start[:, :, :CL] = 0 - - # Remove series longer than CL - fb_body = fb_start.clone() - fb_body[:, :, CL + 1 :] -= fb_start[:, :, : -(CL + 1)] - fb_body = fb_body.cumsum(dim=2) - fb_start = fb_start * (fb_body == 1) - - # Set a origin source time (starting time of the chunck to copy - # here) We set it as the current time minus a multiple of CL to be - # consistent with the "rolling" caterpillar - t = torch.arange(fb_start.size(2), device=fb_start.device)[None, None, :] - src_time = fb_start * ( - t - - CL - * ( - 1 - + ( - torch.rand(fb_start.size(), device=fb_start.device) * (t // CL - 1) - ).long() - ) - ) - src_time[:, :, CL:] -= src_time.clone()[:, :, :-CL] - src_time = src_time.cumsum(dim=2) - - src_head = fb_start * torch.randint(H, fb_start.size(), device=fb_start.device) - src_head[:, :, CL:] -= src_head.clone()[:, :, :-CL] - src_head = src_head.cumsum(dim=2) - - # combine - src_delta = fb_start.clone() - src_delta[:, :, CL:] -= fb_start[:, :, :-CL] - src_delta = src_delta.cumsum(dim=2) - src_delta[:, :, CL:] -= CL * fb_start[:, :, :-CL] - src_time += src_delta.cumsum(dim=2) - 1 - - return src_time, src_head - - -def insert_flash_back(rec_V, V, rec_K, K, t0, t1, CL, proba): - N, H, CH = V.size(0), V.size(1), rec_V.size(1) - - fbt, fbh = flash_back_time_src(N, H, t0, t1, CL, CH, proba, rec_V.device) - - fbt_V = fbt[:, :, :, None] - fbh_V = fbh[:, :, :, None] - t = fbt_V.clamp(min=0) - n = torch.arange(V.size(0), device=V.device)[:, None, None, None] - d = torch.arange(V.size(3), device=V.device)[None, None, None, :] - q = V[:, :, t0:t1][n, fbh_V, t, d] - rec_V[:, :, t0:t1] = q * (fbt_V >= 0) + rec_V[:, :, t0:t1] * (fbt_V < 0) - - fbt_K = fbt[:, :, :, None] - fbh_K = fbh[:, :, :, None] - t = fbt_K.clamp(min=0) - n = torch.arange(K.size(0), device=K.device)[:, None, None, None] - d = torch.arange(K.size(3), device=K.device)[None, None, None, :] - q = K[:, :, t0:t1][n, fbh_K, t, d] - rec_K[:, :, t0:t1] = q * (fbt_K >= 0) + rec_K[:, :, t0:t1] * (fbt_K < 0) - - -###################################################################### - class Caterpillar(nn.Module): def __init__( @@ -553,7 +482,7 @@ class Caterpillar(nn.Module): self.attention_dropout = attention_dropout warnings.warn("flash back", RuntimeWarning) - self.proba_flashback = 0.1 + self.proba_flashback = 1e-2 self.w_G = randw(nb_heads, caterpillar_height, dim_model) self.b_G = nn.Parameter( @@ -655,37 +584,38 @@ class Caterpillar(nn.Module): 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: - insert_flash_back( - self.rec_V, - V, - self.rec_K, - K, - t0, - t1, - CL, - proba=self.proba_flashback / CL, - ) + 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)[None, None, None, :] - # dk = torch.arange(DK)[None, None, None, :] + 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 + 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) + src_time = t - u - t0 + src_head = torch.randint(H, (N, CH, t1 - t0, 1), device=X.device) - # mk = ( - # torch.rand(self.rec_V[:, :, t0:t1].size()) <= self.proba_flashback - # ).long() - # self.rec_V[:, :, t0:t1] = V[n, src_head, src_time, dv] - # self.rec_K[:, :, t0:t1] = K[n, src_head, src_time, dk] + mask = ( + torch.rand(N, CH, t1 - t0, DV, device=X.device) <= self.proba_flashback + ).long() - exit(0) + 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 @@ -841,7 +771,12 @@ 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