From 9ec709a2a08eb82dfc17ef1e24aa9a84751d63e0 Mon Sep 17 00:00:00 2001 From: =?utf8?q?Fran=C3=A7ois=20Fleuret?= Date: Tue, 9 Jul 2024 11:47:47 +0300 Subject: [PATCH] Update. --- grids.py | 56 ++++++++++++++++++++++++++++++++---------------------- problem.py | 26 ++++++++++++++++--------- 2 files changed, 50 insertions(+), 32 deletions(-) diff --git a/grids.py b/grids.py index a2e253e..47e5861 100755 --- a/grids.py +++ b/grids.py @@ -194,9 +194,9 @@ class Grids(problem.Problem): def nb_token_values(self): return len(self.colors) - @torch.compile + # @torch.compile def rec_coo_(self, nb_rec, min_height=3, min_width=3): - @torch.compile + # @torch.compile def overlap(ia, ja, ib, jb): return ( ia[1] >= ib[0] and ia[0] <= ib[1] and ja[1] >= jb[0] and ja[0] <= jb[1] @@ -226,7 +226,7 @@ class Grids(problem.Problem): # non-overlapping rectangles quickly, but made the generation of # 100k samples go from 1h50 with a lame pure python code to 3min30s # with this one. - @torch.compile + # @torch.compile def rec_coo(self, nb_rec, min_height=3, min_width=3): nb_trials = 200 @@ -288,7 +288,7 @@ class Grids(problem.Problem): ) ] - @torch.compile + # @torch.compile def rec_coo_(self, x, n, min_height=3, min_width=3): collision = x.new(x.size()) while True: @@ -313,7 +313,7 @@ class Grids(problem.Problem): ###################################################################### - @torch.compile + # @torch.compile def task_replace_color(self, A, f_A, B, f_B): nb_rec = 3 c = torch.randperm(len(self.colors) - 1)[: nb_rec + 1] + 1 @@ -324,7 +324,7 @@ class Grids(problem.Problem): X[i1:i2, j1:j2] = c[n] f_X[i1:i2, j1:j2] = c[n if n > 0 else -1] - @torch.compile + # @torch.compile def task_translate(self, A, f_A, B, f_B): di, dj = torch.randint(3, (2,)) - 1 nb_rec = 3 @@ -349,7 +349,7 @@ class Grids(problem.Problem): else: f_X[i1:i2, j1:j2] = c[n] - @torch.compile + # @torch.compile def task_grow(self, A, f_A, B, f_B): di, dj = torch.randint(2, (2,)) * 2 - 1 nb_rec = 3 @@ -375,7 +375,7 @@ class Grids(problem.Problem): X[i1:i2, j1:j2] = c[n] f_X[i1:i2, j1:j2] = c[n] - @torch.compile + # @torch.compile def task_color_grow(self, A, f_A, B, f_B): di, dj = torch.randint(2, (2,)) * 2 - 1 nb_rec = 3 @@ -417,7 +417,7 @@ class Grids(problem.Problem): else: f_X[i1:i2, j : j + 1] = c[2 * n + 1] - @torch.compile + # @torch.compile def task_frame(self, A, f_A, B, f_B): nb_rec = 3 c = torch.randperm(len(self.colors) - 1)[: nb_rec + 1] + 1 @@ -430,7 +430,7 @@ class Grids(problem.Problem): if n == nb_rec - 1: f_X[i1 + 1 : i2 - 1, j1 + 1 : j2 - 1] = 0 - @torch.compile + # @torch.compile def task_detect(self, A, f_A, B, f_B): nb_rec = 3 c = torch.randperm(len(self.colors) - 1)[: nb_rec + 1] + 1 @@ -442,7 +442,7 @@ class Grids(problem.Problem): if n < nb_rec - 1: f_X[i1, j1] = c[-1] - @torch.compile + # @torch.compile def contact(self, X, i, j, q): nq, nq_diag = 0, 0 no = 0 @@ -478,7 +478,7 @@ class Grids(problem.Problem): return no, nq, nq_diag - @torch.compile + # @torch.compile def task_count(self, A, f_A, B, f_B): N = (torch.randint(4, (1,)) + 2).item() c = torch.randperm(len(self.colors) - 1)[:N] + 1 @@ -502,7 +502,7 @@ class Grids(problem.Problem): for j in range(nb[n]): f_X[n, j] = c[n] - @torch.compile + # @torch.compile def task_trajectory(self, A, f_A, B, f_B): c = torch.randperm(len(self.colors) - 1)[:2] + 1 for X, f_X in [(A, f_A), (B, f_B)]: @@ -530,12 +530,11 @@ class Grids(problem.Problem): f_X[i + k * di, j + k * dj] = c[min(k, 1)] k += 1 - @torch.compile + # @torch.compile def task_bounce(self, A, f_A, B, f_B): c = torch.randperm(len(self.colors) - 1)[:3] + 1 for X, f_X in [(A, f_A), (B, f_B)]: - - @torch.compile + # @torch.compile def free(i, j): return ( i >= 0 @@ -595,7 +594,7 @@ class Grids(problem.Problem): if l > 3: break - @torch.compile + # @torch.compile def task_scale(self, A, f_A, B, f_B): c = torch.randperm(len(self.colors) - 1)[:2] + 1 @@ -620,7 +619,7 @@ class Grids(problem.Problem): X[i, j] = c[1] f_X[0:2, 0:2] = c[1] - @torch.compile + # @torch.compile def task_symbols(self, A, f_A, B, f_B): nb_rec = 4 c = torch.randperm(len(self.colors) - 1)[: nb_rec + 1] + 1 @@ -656,7 +655,7 @@ class Grids(problem.Problem): f_X[i[0] : i[0] + delta, j[0] : j[0] + delta] = c[q] - @torch.compile + # @torch.compile def task_ortho(self, A, f_A, B, f_B): nb_rec = 3 di, dj = torch.randint(3, (2,)) - 1 @@ -711,7 +710,7 @@ class Grids(problem.Problem): ): break - @torch.compile + # @torch.compile def task_islands(self, A, f_A, B, f_B): pass @@ -806,14 +805,25 @@ if __name__ == "__main__": grids = Grids() - if False: - nb = 8 + # nb = 1000 + # grids = problem.MultiThreadProblem( + # grids, max_nb_cached_chunks=50, chunk_size=100, nb_threads=1 + # ) + # time.sleep(10) + # start_time = time.perf_counter() + # prompts, answers = grids.generate_prompts_and_answers(nb) + # delay = time.perf_counter() - start_time + # print(f"{prompts.size(0)/delay:02f} seq/s") + # exit(0) + + if True: + nb = 72 for t in grids.all_tasks(): # for t in [grids.task_ortho]: print(t.__name__) prompts, answers = grids.generate_prompts_and_answers(nb, tasks=[t]) - grids.save_quizzes("/tmp", t.__name__, prompts[:nb], answers[:nb], nrow=2) + grids.save_quizzes("/tmp", t.__name__, prompts[:nb], answers[:nb], nrow=4) exit(0) diff --git a/problem.py b/problem.py index 7dd60dc..a49634d 100755 --- a/problem.py +++ b/problem.py @@ -5,7 +5,7 @@ # Written by Francois Fleuret -import threading, queue, torch +import threading, queue, torch, tqdm class Problem: @@ -33,11 +33,12 @@ class Problem: class MultiThreadProblem: - def __init__(self, problem, max_nb_cached_chunks, chunk_size): + def __init__(self, problem, max_nb_cached_chunks, chunk_size, nb_threads=1): self.problem = problem self.chunk_size = chunk_size self.queue = queue.Queue(maxsize=max_nb_cached_chunks) - threading.Thread(target=self.fill_cache, daemon=True).start() + for _ in range(nb_threads): + threading.Thread(target=self.fill_cache, daemon=True).start() self.rest = None def nb_token_values(self): @@ -67,7 +68,7 @@ class MultiThreadProblem: self.chunk_size ) - self.queue.put((prompts, answers), block=True) + self.queue.put((prompts.to("cpu"), answers.to("cpu")), block=True) def trivial_prompts_and_answers(self, prompts, answers): return self.problem.trivial_prompts_and_answers(prompts, answers) @@ -82,13 +83,20 @@ class MultiThreadProblem: n = sum([p.size(0) for p in prompts]) - while n < nb: - p, s = self.queue.get(block=True) - prompts.append(p) - answers.append(s) - n += p.size(0) + with tqdm.tqdm( + total=nb, + dynamic_ncols=True, + desc="world generation", + ) as pbar: + while n < nb: + p, s = self.queue.get(block=True) + prompts.append(p) + answers.append(s) + n += p.size(0) + pbar.update(p.size(0)) prompts, answers = torch.cat(prompts, dim=0), torch.cat(answers, dim=0) + assert n == prompts.size(0) k = n - nb -- 2.39.5