X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=mygpt.py;h=5754d557155b908e5952061fc72f33da3fdd84bb;hb=10c1ad582d28ec19465485d709c26ba9669d6369;hp=7105e97c351138b6dbb9e382d99c29293b1f594c;hpb=b458d5aa1f2ba736807e87b65ccad2f96b216a10;p=mygptrnn.git diff --git a/mygpt.py b/mygpt.py index 7105e97..5754d55 100755 --- a/mygpt.py +++ b/mygpt.py @@ -442,7 +442,8 @@ class KVRec(nn.Module): # Returns a tensor with an additional index at rank win_dim, that move -# along the same dimension as dim, on a domain {0...win_size-1} +# along the same dimension as dim, on a domain {0...win_size-1}, and +# dim is restricted on a domain reduced by win_size-1 values. def moving_window(x, dim, win_dim, win_size): @@ -456,6 +457,87 @@ 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].expand_as(rec_V[:, :, t0:t1]) + fbh_V = fbh[:, :, :, None].expand_as(rec_V[:, :, t0:t1]) + t = fbt_V.clamp(min=0) + n = torch.arange(V.size(0), device=V.device)[:, None, None, None].expand_as( + rec_V[:, :, t0:t1] + ) + d = torch.arange(V.size(3), device=V.device)[None, None, None, :].expand_as( + rec_V[:, :, t0:t1] + ) + 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].expand_as(rec_K[:, :, t0:t1]) + fbh_K = fbh[:, :, :, None].expand_as(rec_K[:, :, t0:t1]) + t = fbt_K.clamp(min=0) + n = torch.arange(K.size(0), device=K.device)[:, None, None, None].expand_as( + rec_K[:, :, t0:t1] + ) + d = torch.arange(K.size(3), device=K.device)[None, None, None, :].expand_as( + rec_K[:, :, t0:t1] + ) + q = K[:, :, t0:t1][n, fbh_K, t, d] + rec_K[:, :, t0:t1] = q * (fbt_K >= 0) + rec_K[:, :, t0:t1] * (fbt_K < 0) + + # print("SANITY", (fbt_K >=0).float().sum()/fbt_K.numel()) + + +###################################################################### + class Caterpillar(nn.Module): def __init__( @@ -480,6 +562,9 @@ class Caterpillar(nn.Module): self.caterpillar_height = caterpillar_height self.attention_dropout = attention_dropout + warnings.warn("flash back", RuntimeWarning) + self.proba_flashback = 0.1 + self.w_G = randw(nb_heads, caterpillar_height, dim_model) self.b_G = nn.Parameter( torch.full( @@ -511,9 +596,10 @@ class Caterpillar(nn.Module): N = bs.x.size(0) T = bs.x.size(1) + H = self.w_V.size(0) DV = self.w_V.size(1) DK = self.w_K.size(1) - Dout = self.w_O.size(1) + DM = self.w_O.size(1) CH = self.caterpillar_height CL = self.caterpillar_length @@ -521,6 +607,8 @@ class Caterpillar(nn.Module): t0 >= CL and (t1 - t0) % CL == 0 ), f"bs.first should be greater than caterpillar_length, and bs.nb should be a multiple of caterpillar_length" + # We cache values to deal efficiently with auto-regression + if bs.init_cache: self.rec_V = X.new_zeros(N, CH, T, DV) self.rec_K = X.new_zeros(N, CH, T, DK) @@ -529,7 +617,7 @@ class Caterpillar(nn.Module): self.rec_V[:, :, t0 - CL : t0] = self.init_V_rec[None, :, :, :] self.rec_K[:, :, t0 - CL : t0] = self.init_K_rec[None, :, :, :] - self.cache_Y = X.new_zeros(N, T, Dout) + self.cache_Y = X.new_zeros(N, T, DM) ###################################################################### # Compute the recurrent state @@ -537,13 +625,16 @@ class Caterpillar(nn.Module): # 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 all the pairs of the + # that the current K/V could 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) + V = torch.einsum("ntc,hdc->nhtd", X, self.w_V) K = torch.einsum("ntc,hdc->nhtd", X, self.w_K) @@ -560,7 +651,7 @@ class Caterpillar(nn.Module): # 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 alone as the sequence index. + # using the other as the sequence index. A = A.unflatten(2, (-1, CL)) gated_V = gated_V.unflatten(2, (-1, CL)) @@ -574,6 +665,38 @@ 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, + ) + + # 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, :] + + # 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) + + # 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] + + exit(0) + ###################################################################### # compute the readout