X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=main.py;h=0323d0218ec587066817d5359044176cab99692b;hb=76671c582f029aa67fce2626764b02e8d9e2dbeb;hp=43d290049cf81c372821c6a13463a0b285d65397;hpb=5fff2918fdcc35016195cd209afc864e9cd2ac32;p=picoclvr.git diff --git a/main.py b/main.py index 43d2900..0323d02 100755 --- a/main.py +++ b/main.py @@ -31,15 +31,17 @@ parser = argparse.ArgumentParser( formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) -parser.add_argument("--task", type=str, default="picoclvr") +parser.add_argument( + "--task", type=str, default="picoclvr", help="picoclvr, mnist, maze, snake, stack" +) -parser.add_argument("--log_filename", type=str, default="train.log") +parser.add_argument("--log_filename", type=str, default="train.log", help=" ") parser.add_argument("--result_dir", type=str, default="results_default") parser.add_argument("--seed", type=int, default=0) -parser.add_argument("--nb_epochs", type=int, default=25) +parser.add_argument("--nb_epochs", type=int, default=None) parser.add_argument("--batch_size", type=int, default=None) @@ -100,9 +102,18 @@ parser.add_argument("--snake_height", type=int, default=6) parser.add_argument("--snake_width", type=int, default=8) -parser.add_argument("--snake_nb_colors", type=int, default=3) +parser.add_argument("--snake_nb_colors", type=int, default=5) + +parser.add_argument("--snake_length", type=int, default=200) + +############################## +# Snake options + +parser.add_argument("--stack_nb_steps", type=int, default=25) -parser.add_argument("--snake_length", type=int, default=400) +parser.add_argument("--stack_nb_stacks", type=int, default=1) + +parser.add_argument("--stack_nb_values", type=int, default=10) ###################################################################### @@ -131,16 +142,34 @@ if args.seed >= 0: default_args = { "picoclvr": { + "nb_epochs": 25, "batch_size": 25, + "nb_train_samples": 250000, + "nb_test_samples": 10000, }, "mnist": { + "nb_epochs": 25, "batch_size": 10, + "nb_train_samples": 250000, + "nb_test_samples": 10000, }, "maze": { + "nb_epochs": 25, "batch_size": 25, + "nb_train_samples": 250000, + "nb_test_samples": 10000, }, "snake": { - "batch_size": 20, + "nb_epochs": 5, + "batch_size": 25, + "nb_train_samples": 250000, + "nb_test_samples": 10000, + }, + "stack": { + "nb_epochs": 25, + "batch_size": 25, + "nb_train_samples": 10000, + "nb_test_samples": 1000, }, } @@ -169,15 +198,29 @@ for n in vars(args): ###################################################################### +# ra_mask is boolean, with 1s on the values to generate + + def masked_inplace_autoregression( - model, batch_size, input, ar_mask, forbidden_tokens=None, device=torch.device("cpu") + model, + batch_size, + input, + ar_mask, + forbidden_tokens=None, + progress_bar_desc="autoregression", + device=torch.device("cpu"), ): - for input, ar_mask in tqdm.tqdm( - zip(input.split(batch_size), ar_mask.split(batch_size)), - dynamic_ncols=True, - desc="autoregression", - total=input.size(0) // batch_size, - ): + # p = logits.softmax(1) + # entropy[:,s]= p.xlogy(p).sum(1) / math.log(2) + batches = zip(input.split(batch_size), ar_mask.split(batch_size)) + if progress_bar_desc is not None: + tqdm.tqdm( + batches, + dynamic_ncols=True, + desc=progress_bar_desc, + total=input.size(0) // batch_size, + ) + for input, ar_mask in batches: i = (ar_mask.sum(0) > 0).nonzero() if i.min() > 0: model( @@ -313,6 +356,7 @@ class TaskPicoCLVR(Task): input, ar_masks, forbidden_tokens, + progress_bar_desc=None, device=self.device, ) model.train(t) @@ -492,7 +536,7 @@ class TaskPicoCLVR(Task): image_name = os.path.join(args.result_dir, f"picoclvr_result_{n_epoch:04d}.png") torchvision.utils.save_image( - img / 255.0, image_name, nrow=nb_per_primer, padding=1, pad_value=1.0 + img / 255.0, image_name, nrow=nb_per_primer, padding=1, pad_value=0.0 ) log_string(f"wrote {image_name}") @@ -602,39 +646,83 @@ class TaskMaze(Task): def compute_error(self, model, split="train", nb_to_use=-1): nb_total, nb_correct = 0, 0 - for input in task.batches(split, nb_to_use): + count = torch.zeros( + self.width * self.height, + self.width * self.height, + device=self.device, + dtype=torch.int64, + ) + for input in tqdm.tqdm( + task.batches(split, nb_to_use), + dynamic_ncols=True, + desc=f"test-mazes", + ): result = input.clone() ar_mask = result.new_zeros(result.size()) ar_mask[:, self.height * self.width :] = 1 result *= 1 - ar_mask masked_inplace_autoregression( - model, self.batch_size, result, ar_mask, device=self.device + model, + self.batch_size, + result, + ar_mask, + progress_bar_desc=None, + device=self.device, ) mazes, paths = self.seq2map(result) - nb_correct += maze.path_correctness(mazes, paths).long().sum() + path_correctness = maze.path_correctness(mazes, paths) + nb_correct += path_correctness.long().sum() nb_total += mazes.size(0) - return nb_total, nb_correct + optimal_path_lengths = ( + (input[:, self.height * self.width :] == maze.v_path).long().sum(1) + ) + predicted_path_lengths = ( + (result[:, self.height * self.width :] == maze.v_path).long().sum(1) + ) + optimal_path_lengths = optimal_path_lengths[path_correctness] + predicted_path_lengths = predicted_path_lengths[path_correctness] + count[optimal_path_lengths, predicted_path_lengths] += 1 + + if count.max() == 0: + count = None + else: + count = count[ + : count.sum(1).nonzero().max() + 1, : count.sum(0).nonzero().max() + 1 + ] + + return nb_total, nb_correct, count def produce_results(self, n_epoch, model): with torch.autograd.no_grad(): t = model.training model.eval() - train_nb_total, train_nb_correct = self.compute_error( + train_nb_total, train_nb_correct, count = self.compute_error( model, "train", nb_to_use=1000 ) log_string( f"accuracy_train nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%" ) - test_nb_total, test_nb_correct = self.compute_error( + test_nb_total, test_nb_correct, count = self.compute_error( model, "test", nb_to_use=1000 ) log_string( f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%" ) + if count is not None: + proportion_optimal = count.diagonal().sum().float() / count.sum() + log_string(f"proportion_optimal_test {proportion_optimal*100:.02f}%") + with open( + os.path.join(args.result_dir, f"maze_result_{n_epoch:04d}.txt"), "w" + ) as f: + for i in range(count.size(0)): + for j in range(count.size(1)): + eol = " " if j < count.size(1) - 1 else "\n" + f.write(f"{count[i,j]}{eol}") + input = self.test_input[:48] result = input.clone() ar_mask = result.new_zeros(result.size()) @@ -654,6 +742,7 @@ class TaskMaze(Task): target_paths=paths, predicted_paths=predicted_paths, path_correct=maze.path_correctness(mazes, predicted_paths), + path_optimal=maze.path_optimality(paths, predicted_paths), ) log_string(f"wrote {filename}") @@ -663,106 +752,7 @@ class TaskMaze(Task): ###################################################################### -def generate_snake_sequences( - nb, height, width, nb_colors, length, prompt_length, device=torch.device("cpu") -): - worlds = torch.randint(nb_colors, (nb, height, width), device=device) - nb_prior_visits = torch.zeros(nb, height, width, device=device) - - # nb x 2 - snake_position = torch.cat( - ( - torch.randint(height, (nb, 1), device=device), - torch.randint(width, (nb, 1), device=device), - ), - 1, - ) - snake_direction = torch.randint(4, (nb,), device=device) - sequences = torch.empty(nb, 2 * length, device=device, dtype=torch.int64) - sequences_prior_visits = torch.zeros( - nb, 2 * length, device=device, dtype=torch.int64 - ) - i = torch.arange(nb, device=device) # [:,None] - - for l in range(length): - # nb x 3 - snake_next_direction = torch.cat( - ( - (snake_direction[:, None] - 1) % 4, - snake_direction[:, None], - (snake_direction[:, None] + 1) % 4, - ), - 1, - ) - - # nb x 3 - vh = (snake_next_direction + 1) % 2 * (snake_next_direction - 1) - vw = snake_next_direction % 2 * (snake_next_direction - 2) - - # nb x 3 x 2 - snake_next_speed = torch.cat((vh[:, :, None], vw[:, :, None]), 2) - snake_next_position = snake_position[:, None, :] + snake_next_speed - - # nb x 3 - val = torch.logical_and( - torch.logical_and( - snake_next_position[:, :, 0] >= 0, snake_next_position[:, :, 0] < height - ), - torch.logical_and( - snake_next_position[:, :, 1] >= 0, snake_next_position[:, :, 1] < width - ), - ).float() - val = ( - # The multiplicative factors bias toward moving forward - torch.rand_like(val) - * val - * torch.tensor([[1.0, 2.0, 1.0]], device=device) - ) - - # nb - j = val.argmax(1) - snake_direction = snake_next_direction[i, j] - - sequences[:, 2 * l] = worlds[i, snake_position[:, 0], snake_position[:, 1]] + 4 - sequences_prior_visits[:, 2 * l] = nb_prior_visits[ - i, snake_position[:, 0], snake_position[:, 1] - ] - if l < prompt_length: - nb_prior_visits[i, snake_position[:, 0], snake_position[:, 1]] += 1 - sequences[:, 2 * l + 1] = snake_direction - - # nb x 2 - snake_position = snake_next_position[i, j] - - return sequences, sequences_prior_visits - - -# generate_snake_sequences(nb=1, height=4, width=6, nb_colors=3, length=20) -# exit(0) - - -def snake_solver(input, ar_mask): - for n in range(input.size(0)): - i, j, memory = 0, 0, {} - # print(input[n]) - # print(ar_mask[n]) - for l in range(input.size(1) // 2): - if ar_mask[n, 2 * l] == 1: - if memory.get((i, j)) is None: - input[n, 2 * l] = -1 - else: - input[n, 2 * l] = memory[(i, j)] - else: - # print(f'@3 {memory=}') - if memory.get((i, j)) is None: - memory[(i, j)] = input[n, 2 * l] - else: - assert memory[(i, j)] == input[n, 2 * l], f"n={n} l={l}" - # print(f'@1 {i=} {j=}') - d = input[n, 2 * l + 1].item() - i += (d + 1) % 2 * (d - 1) - j += d % 2 * (d - 2) - # print(f'@2 {i=} {j=}') +import snake class TaskSnake(Task): @@ -784,7 +774,7 @@ class TaskSnake(Task): self.device = device self.prompt_length = prompt_length - self.train_input, self.train_prior_visits = generate_snake_sequences( + self.train_input, self.train_prior_visits, _, _ = snake.generate_sequences( nb_train_samples, height, width, @@ -793,7 +783,7 @@ class TaskSnake(Task): prompt_length, self.device, ) - self.test_input, self.test_prior_visits = generate_snake_sequences( + self.test_input, self.test_prior_visits, _, _ = snake.generate_sequences( nb_test_samples, height, width, @@ -835,7 +825,7 @@ class TaskSnake(Task): ) result *= 1 - ar_mask - # snake_solver(result,ar_mask) + # snake.solver(result,ar_mask) masked_inplace_autoregression( model, self.batch_size, result, ar_mask, device=self.device @@ -874,6 +864,86 @@ class TaskSnake(Task): ###################################################################### +import stack + + +class TaskStack(Task): + def __init__( + self, + nb_train_samples, + nb_test_samples, + batch_size, + nb_steps, + nb_stacks, + nb_values, + device=torch.device("cpu"), + ): + self.batch_size = batch_size + self.nb_steps = nb_steps + self.nb_stacks = nb_stacks + self.nb_values = nb_values + self.device = device + + self.train_input, self.train_stack_counts = stack.generate_sequences( + nb_train_samples, nb_steps, nb_stacks, nb_values, self.device + ) + + self.test_input, self.test_stack_counts = stack.generate_sequences( + nb_test_samples, nb_steps, nb_stacks, nb_values, self.device + ) + + self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1 + + def batches(self, split="train", nb_to_use=-1, desc=None): + assert split in {"train", "test"} + input = self.train_input if split == "train" else self.test_input + if nb_to_use > 0: + input = input[:nb_to_use] + if desc is None: + desc = f"epoch-{split}" + for batch in tqdm.tqdm( + input.split(self.batch_size), dynamic_ncols=True, desc=desc + ): + yield batch + + def vocabulary_size(self): + return self.nb_codes + + def produce_results(self, n_epoch, model): + with torch.autograd.no_grad(): + t = model.training + model.eval() + + def compute_nb_correct(input): + result = input.clone() + stack.remove_poped_values(result,self.nb_stacks) + ar_mask = (result != input).long() + result *= 1 - ar_mask + + masked_inplace_autoregression( + model, self.batch_size, result, ar_mask, device=self.device + ) + + nb_total = ar_mask.sum() + + nb_correct = ( + (result == input).long() * ar_mask + ).sum() + + return nb_total, nb_correct + + test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[:1000]) + + log_string( + f"accuracy_test nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%" + ) + + model.train(t) + + +###################################################################### + + def picoclvr_pruner_horizontal_green(p): return not ("green" in p and ("left" in p or "right" in p)) @@ -935,6 +1005,17 @@ elif args.task == "snake": device=device, ) +elif args.task == "stack": + task = TaskStack( + nb_train_samples=args.nb_train_samples, + nb_test_samples=args.nb_test_samples, + batch_size=args.batch_size, + nb_steps = args.stack_nb_steps, + nb_stacks = args.stack_nb_stacks, + nb_values = args.stack_nb_values, + device=device, + ) + else: raise ValueError(f"Unknown task {args.task}") @@ -1070,9 +1151,6 @@ for n_epoch in range(nb_epochs_finished, nb_epochs): for input in task.batches(split="test"): input = input.to(device) - # input, loss_masks, true_images = task.excise_last_image(input) - # input, loss_masks = task.add_true_image(input, true_images, loss_masks) - output = model(mygpt.BracketedSequence(input)).x loss = F.cross_entropy(output.transpose(1, 2), input) acc_test_loss += loss.item() * input.size(0)