From cebc20b3608a41bfd27b2ab9d950c082f9b7ea89 Mon Sep 17 00:00:00 2001 From: =?utf8?q?Fran=C3=A7ois=20Fleuret?= Date: Thu, 11 Jan 2024 18:47:49 +0100 Subject: [PATCH] Update. --- main.py | 27 ++++++++++++------------ mygpt.py | 64 +++++++++++++++++++++++++++++++++++++------------------- 2 files changed, 57 insertions(+), 34 deletions(-) diff --git a/main.py b/main.py index c51035c..969b47f 100755 --- a/main.py +++ b/main.py @@ -16,14 +16,6 @@ import mygpt, tasks, problems ###################################################################### -if torch.cuda.is_available(): - device = torch.device("cuda") - torch.backends.cuda.matmul.allow_tf32 = True -else: - device = torch.device("cpu") - -###################################################################### - def str2bool(x): x = x.lower() @@ -55,6 +47,8 @@ parser.add_argument("--seed", type=int, default=0) parser.add_argument("--max_percents_of_test_in_train", type=int, default=1) +parser.add_argument("--force_cpu", type=str2bool, default=False) + ######################################## parser.add_argument("--nb_epochs", type=int, default=50) @@ -217,6 +211,14 @@ if args.result_dir is None: ###################################################################### +if not args.force_cpu and torch.cuda.is_available(): + device = torch.device("cuda") + torch.backends.cuda.matmul.allow_tf32 = True +else: + device = torch.device("cpu") + +###################################################################### + default_task_args = { "addition": { "model": "352M", @@ -832,7 +834,7 @@ if nb_epochs_finished >= nb_epochs: deterministic_synthesis=args.deterministic_synthesis, ) -time_pred_result = None +time_pred_result = datetime.datetime.now() it = 0 @@ -910,10 +912,9 @@ for n_epoch in range(nb_epochs_finished, nb_epochs): ) time_current_result = datetime.datetime.now() - if time_pred_result is not None: - log_string( - f"next_result {time_current_result + (time_current_result - time_pred_result)}" - ) + log_string( + f"next_result {time_current_result + (time_current_result - time_pred_result)}" + ) time_pred_result = time_current_result checkpoint = { diff --git a/mygpt.py b/mygpt.py index 185df38..9d3abb6 100755 --- a/mygpt.py +++ b/mygpt.py @@ -530,22 +530,22 @@ class Caterpillar(nn.Module): DV = self.w_V.size(1) DK = self.w_K.size(1) DM = self.w_O.size(1) - CH = self.caterpillar_height - CL = self.caterpillar_length + R = self.caterpillar_height + L = self.caterpillar_length assert ( - t0 >= CL and (t1 - t0) % CL == 0 + t0 >= L and (t1 - t0) % L == 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) + self.rec_V = X.new_zeros(N, R, T, DV) + 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 - CL : t0] = self.init_V_rec[None, :, :, :] - self.rec_K[:, :, t0 - CL : 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) @@ -556,8 +556,8 @@ class Caterpillar(nn.Module): # 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. There are CH independent gating values, which means + # the new key and value in the R pairs of the current + # stack. There are R independent gating values, which means # that the current K/V may be stored in multiple pairs of the # recurrent state, or not at all. @@ -565,6 +565,22 @@ class Caterpillar(nn.Module): torch.einsum("ntc,hrc->nhrt", X, self.w_G) + self.b_G[None, :, :, None] ).sigmoid() + ###################################################################### + # Roll the gating indexes + + warnings.warn("rotating barrel", RuntimeWarning) + n_barrel = torch.arange(N, device=G.device)[:, None, None, None] + h_barrel = torch.arange(H, device=G.device)[None, :, None, None] + 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) % R + + # print(f"({N}, {H}, {R}, {t1-t0}) {G.size()=}") + + G = G[n_barrel, h_barrel, r_barrel, t_barrel] + + # print(G.sum()) + ###################################################################### # The "flashbacks" @@ -593,7 +609,7 @@ class Caterpillar(nn.Module): G = ( G - # + dropout_head * (1 - epsilon - G.detach()) + + dropout_head * (1 - epsilon - G.detach()) - dropout_tail * G.detach() ) @@ -607,26 +623,32 @@ class Caterpillar(nn.Module): G = G / G.sum(1, keepdim=True).clamp(min=1) A = 1 - G.sum(1) + + # warnings.warn("harmonic recurrence", RuntimeWarning) + # har = torch.arange(t0, t1, device = G.device).float() + 1 + # A = har / (har + 1) + # G = G / har + gated_V = torch.einsum("nhrt,nhtd->nrtd", G, V) gated_K = torch.einsum("nhrt,nhtd->nrtd", 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] + 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-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 + # 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. - A = A.unflatten(2, (-1, CL)) - gated_V = gated_V.unflatten(2, (-1, CL)) - gated_K = gated_K.unflatten(2, (-1, CL)) + A = A.unflatten(2, (-1, L)) + gated_V = gated_V.unflatten(2, (-1, L)) + gated_K = gated_K.unflatten(2, (-1, L)) next_V = pscan_dim(A, gated_V, init_rec_V, dim=2) next_K = pscan_dim(A, gated_K, init_rec_K, dim=2) @@ -644,14 +666,14 @@ class Caterpillar(nn.Module): # the column in the caterpillar windowed_V = moving_window( - self.rec_V[:, :, t0 - CL + 1 : t1], dim=2, win_dim=3, win_size=CL + self.rec_V[:, :, t0 - L + 1 : t1], dim=2, win_dim=3, win_size=L ) windowed_K = moving_window( - self.rec_K[:, :, t0 - CL + 1 : t1], dim=2, win_dim=3, win_size=CL + self.rec_K[:, :, t0 - L + 1 : t1], dim=2, win_dim=3, win_size=L ) - # We have an attention score for each of the CHxCL values + # We have an attention score for each of the RxL values ar = torch.einsum( "nhtd,nftld->nhtfl", -- 2.39.5