From 12c775dcbd3d3cd703f35c181faa6d2a680a0450 Mon Sep 17 00:00:00 2001 From: =?utf8?q?Fran=C3=A7ois=20Fleuret?= Date: Thu, 11 Jul 2024 22:43:35 +0200 Subject: [PATCH] Update. --- grids.py | 72 +++++++++++++++++++++++++++++++++++++++++++++++-- main.py | 22 +++++++-------- quiz_machine.py | 6 +---- 3 files changed, 81 insertions(+), 19 deletions(-) diff --git a/grids.py b/grids.py index 20a964b..7aec62c 100755 --- a/grids.py +++ b/grids.py @@ -869,6 +869,74 @@ class Grids(problem.Problem): # i,j=q%self.height,q//self.height # if + # @torch.compile + def task_puzzle(self, A, f_A, B, f_B): + S = 4 + i0, j0 = (self.height - S) // 2, (self.width - S) // 2 + c = torch.randperm(len(self.colors) - 1)[:4] + 1 + for X, f_X in [(A, f_A), (B, f_B)]: + while True: + f_X[...] = 0 + h = list(torch.randperm(c.size(0))) + n = torch.zeros(c.max() + 1) + for _ in range(2): + k = torch.randperm(S * S) + for q in k: + i, j = q % S + i0, q // S + j0 + if f_X[i, j] == 0: + r, s, t, u = ( + f_X[i - 1, j], + f_X[i, j - 1], + f_X[i + 1, j], + f_X[i, j + 1], + ) + r, s, t, u = torch.tensor([r, s, t, u])[torch.randperm(4)] + if r > 0 and n[r] < 6: + n[r] += 1 + f_X[i, j] = r + elif s > 0 and n[s] < 6: + n[s] += 1 + f_X[i, j] = s + elif t > 0 and n[t] < 6: + n[t] += 1 + f_X[i, j] = t + elif u > 0 and n[u] < 6: + n[u] += 1 + f_X[i, j] = u + else: + if len(h) > 0: + d = c[h.pop()] + n[d] += 1 + f_X[i, j] = d + + if n.sum() == S * S: + break + + k = 0 + for d in range(4): + while True: + ii, jj = torch.randint(self.height, (1,)), torch.randint( + self.width, (1,) + ) + e = 0 + for i in range(S): + for j in range(S): + if ( + ii + i >= self.height + or jj + j >= self.width + or ( + f_X[i + i0, j + j0] == c[d] + and X[ii + i, jj + j] > 0 + ) + ): + e = 1 + if e == 0: + break + for i in range(S): + for j in range(S): + if f_X[i + i0, j + j0] == c[d]: + X[ii + i, jj + j] = c[d] + ###################################################################### def all_tasks(self): @@ -976,12 +1044,12 @@ if __name__ == "__main__": # nb, nrow = 8, 2 # for t in grids.all_tasks(): - for t in [grids.task_path]: + for t in [grids.task_puzzle]: print(t.__name__) prompts, answers = grids.generate_prompts_and_answers_(nb, tasks=[t]) grids.save_quizzes("/tmp", t.__name__, prompts[:nb], answers[:nb], nrow=nrow) - # exit(0) + exit(0) nb = 1000 diff --git a/main.py b/main.py index a7338c7..8d18119 100755 --- a/main.py +++ b/main.py @@ -347,8 +347,6 @@ def one_epoch(model, quiz_machine, local_device=None): run_tests(model, quiz_machine, deterministic_synthesis=False) - model.TRAINING_LOCK.release() - ###################################################################### @@ -449,7 +447,6 @@ for k in range(args.nb_gpts): model.main_test_accuracy = 0.0 model.id = k - model.TRAINING_LOCK = threading.Lock() model.train_w_quizzes = quiz_machine.generate_token_sequences(args.nb_train_samples) quiz_machine.reverse_random_half_in_place(model.train_w_quizzes) @@ -547,20 +544,21 @@ for n_epoch in range(args.nb_epochs): weakest_models = ranked_models[: args.nb_gpus] + threads = [] + for gpu_id, model in enumerate(weakest_models): - model.TRAINING_LOCK.acquire() + log_string(f"training model {model.id}") - log_string( - f"training model {model.id} main_test_accuracy {model.main_test_accuracy}" + t = threading.Thread( + target=one_epoch, daemon=True, args=(model, quiz_machine, f"cuda:{gpu_id}") ) - threading.Thread( - target=one_epoch, daemon=True, args=(model, quiz_machine, f"cuda:{gpu_id}") - ).start() + threads.append(t) - for model in weakest_models: - model.TRAINING_LOCK.acquire() - model.TRAINING_LOCK.release() + t.start() + + for t in threads: + t.join() ################################################## # Replace a fraction of the w_quizzes with fresh ones diff --git a/quiz_machine.py b/quiz_machine.py index 8ab5696..4f704a0 100755 --- a/quiz_machine.py +++ b/quiz_machine.py @@ -368,11 +368,7 @@ class QuizMachine: backward_nb_total = correct[n_backward].size(0) self.logger( - f"{log_prefix}_forward_accuracy {n_epoch} model {model.id} nb_correct {forward_nb_correct} / {forward_nb_total} ({forward_nb_correct*100/forward_nb_total} %)" - ) - - self.logger( - f"{log_prefix}_backward_accuracy {n_epoch} model {model.id} nb_correct {backward_nb_correct} / {backward_nb_total} ({backward_nb_correct*100/backward_nb_total} %)" + f"{log_prefix}_accuracy {n_epoch} model {model.id} forward {forward_nb_correct} / {forward_nb_total} backward {backward_nb_correct} / {backward_nb_total}" ) return result, correct -- 2.39.5