From 037adb139441f40078421cd40f6aad1748c2724d Mon Sep 17 00:00:00 2001 From: =?utf8?q?Fran=C3=A7ois=20Fleuret?= Date: Sun, 7 Jan 2024 21:37:56 +0100 Subject: [PATCH] Update. --- fridge | 76 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ mygpt.py | 71 ---------------------------------------------------- pscan.py | 27 +++++++++++++++++--- 3 files changed, 99 insertions(+), 75 deletions(-) create mode 100644 fridge diff --git a/fridge b/fridge new file mode 100644 index 0000000..5dd85dd --- /dev/null +++ b/fridge @@ -0,0 +1,76 @@ + +###################################################################### + +2024 Jan 07 21:37:48 (from mygpt.py) + + +# 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) + + +###################################################################### diff --git a/mygpt.py b/mygpt.py index b5ac916..eda8685 100755 --- a/mygpt.py +++ b/mygpt.py @@ -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__( diff --git a/pscan.py b/pscan.py index a14f009..0bb0d14 100755 --- a/pscan.py +++ b/pscan.py @@ -122,6 +122,22 @@ def naive_pscan(A, X, Y_init): if __name__ == "__main__": import time, sys + ###################################################################### + + N, T, D = 16, 4096, 32 + + for r in range(timing.size(0)): + A = 0.9 + 0.1 * torch.rand(N, T, dtype=torch.float64).requires_grad_() + X = torch.randn(N, T, D, dtype=torch.float64).requires_grad_() + Y_init = torch.randn(N, D, dtype=torch.float64).requires_grad_() + + start_time = time.perf_counter() + for _ in range(1000): + Y = pscan(A, X, Y_init) + duration = time.perf_counter() - start_time + + ###################################################################### + # A = torch.rand(17, 12, 3) # X = torch.rand(17, 12, 3, 11) # Y_init = torch.rand(17, 3, 11) @@ -130,11 +146,12 @@ if __name__ == "__main__": # exit(0) err = 0 + timing = torch.empty(10) - for _ in range(100): - N, T, D = 2, 112, 3 + for r in range(timing.size(0)): + N, T, D = 2, 1120, 3 - T = torch.randint(10, (1,)).item() + 1 + # T = torch.randint(10, (1,)).item() + 1 A = 0.9 + 0.1 * torch.rand(N, T, dtype=torch.float64).requires_grad_() X = torch.randn(N, T, D, dtype=torch.float64).requires_grad_() @@ -161,7 +178,9 @@ if __name__ == "__main__": for _ in range(1000): Y = pscan(A, X, Y_init) duration = time.perf_counter() - start_time + print(f"duration {duration}") + timing[r] = duration s = Y.sum() @@ -181,4 +200,4 @@ if __name__ == "__main__": # print((Y - torch.cat([Y1, Y2], dim=1)).abs().max()) - print(f"{err=}") + print(f"err={err:.2e} duration={timing.mean():.2e} (+/- {timing.std():.2e})") -- 2.39.5