X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=main.py;h=b774fce4aba98521e6b7fc3069f4cd4d3c063c6b;hb=38d3035f027881bb2baffdaffc8cd666d3df5dba;hp=6e8ebff6ad9bd449e1e8f5b144ac6eab4016b328;hpb=a35b7a5ebee0b58fc76b64c13d7550eb71bc4567;p=picoclvr.git diff --git a/main.py b/main.py index 6e8ebff..b774fce 100755 --- a/main.py +++ b/main.py @@ -8,7 +8,7 @@ # torch.backends.cuda.matmul.allow_tf23 # torch.autocast(torch.bfloat16) -import math, sys, argparse, time, tqdm, itertools, os +import math, sys, argparse, time, tqdm, os import torch, torchvision from torch import nn @@ -27,24 +27,30 @@ else: ###################################################################### parser = argparse.ArgumentParser( - description="An implementation of GPT with cache to solve a toy geometric reasoning task." + description="An implementation of GPT with cache.", + 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, expr", +) -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("--result_dir", type=str, default=None) 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=25) +parser.add_argument("--batch_size", type=int, default=None) -parser.add_argument("--nb_train_samples", type=int, default=250000) +parser.add_argument("--nb_train_samples", type=int, default=None) -parser.add_argument("--nb_test_samples", type=int, default=10000) +parser.add_argument("--nb_test_samples", type=int, default=None) parser.add_argument("--optim", type=str, default="adam") @@ -99,9 +105,20 @@ 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=100) + +parser.add_argument("--stack_nb_stacks", type=int, default=1) -parser.add_argument("--snake_length", type=int, default=100) +parser.add_argument("--stack_nb_digits", type=int, default=3) + +parser.add_argument("--stack_fraction_values_for_train", type=float, default=None) ###################################################################### @@ -109,6 +126,57 @@ args = parser.parse_args() assert args.picocvlr_prune_properties in {"none", "train+eval", "eval"} +if args.result_dir is None: + args.result_dir = f"results_{args.task}" + +###################################################################### + +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": { + "nb_epochs": 5, + "batch_size": 25, + "nb_train_samples": 250000, + "nb_test_samples": 10000, + }, + "stack": { + "nb_epochs": 5, + "batch_size": 25, + "nb_train_samples": 100000, + "nb_test_samples": 1000, + }, + "expr": { + "nb_epochs": 5, + "batch_size": 25, + "nb_train_samples": 100000, + "nb_test_samples": 1000, + }, +} + +if args.task in default_args: + for k, v in default_args[args.task].items(): + if getattr(args, k) is None: + setattr(args, k, v) + +###################################################################### + try: os.mkdir(args.result_dir) except FileExistsError: @@ -146,10 +214,29 @@ 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 zip(input.split(batch_size), ar_mask.split(batch_size)): + batches = zip(input.split(batch_size), ar_mask.split(batch_size)) + + if progress_bar_desc is not None: + batches = 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( @@ -285,6 +372,7 @@ class TaskPicoCLVR(Task): input, ar_masks, forbidden_tokens, + progress_bar_desc=None, device=self.device, ) model.train(t) @@ -464,7 +552,7 @@ 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}") @@ -499,7 +587,7 @@ class TaskMNIST(Task): masked_inplace_autoregression( model, self.batch_size, results, ar_mask, device=self.device ) - image_name = os.path.join(args.result_dir, f"result_mnist_{n_epoch:04d}.png") + image_name = os.path.join(args.result_dir, f"mnist_result_{n_epoch:04d}.png") torchvision.utils.save_image( 1 - results.reshape(-1, 1, 28, 28) / 255.0, image_name, @@ -574,39 +662,83 @@ 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}%" + f"accuracy_train {n_epoch} 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}%" + f"accuracy_test {n_epoch} 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()) @@ -619,13 +751,14 @@ class TaskMaze(Task): mazes, paths = self.seq2map(input) _, predicted_paths = self.seq2map(result) - filename = os.path.join(args.result_dir, f"result_{n_epoch:04d}.png") + filename = os.path.join(args.result_dir, f"maze_result_{n_epoch:04d}.png") maze.save_image( filename, mazes=mazes, 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}") @@ -635,98 +768,284 @@ class TaskMaze(Task): ###################################################################### -def generate_snake_sequences( - nb, height, width, nb_colors, length, device=torch.device("cpu") -): - worlds = torch.randint(nb_colors, (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) - count = 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, - ) +import snake + + +class TaskSnake(Task): + def __init__( + self, + nb_train_samples, + nb_test_samples, + batch_size, + height, + width, + nb_colors, + length, + prompt_length, + device=torch.device("cpu"), + ): + self.batch_size = batch_size + self.height = height + self.width = width + self.device = device + self.prompt_length = prompt_length - # 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 = ( - torch.rand_like(val) * val * torch.tensor([[1.0, 4.0, 1.0]], device=device) + self.train_input, self.train_prior_visits, _, _ = snake.generate_sequences( + nb_train_samples, + height, + width, + nb_colors, + length, + prompt_length, + self.device, ) + self.test_input, self.test_prior_visits, _, _ = snake.generate_sequences( + nb_test_samples, + height, + width, + nb_colors, + length, + prompt_length, + 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() - # nb - i = torch.arange(val.size(0), device=device) - j = val.argmax(1) - snake_direction = snake_next_direction[i, j] + def compute_nb_correct(input, prior_visits): + result = input.clone() + i = torch.arange(result.size(1), device=result.device)[None, :] + ar_mask = ( + torch.logical_and(i >= self.prompt_length * 2, i % 2 == 0) + .long() + .expand_as(result) + ) + result *= 1 - ar_mask - sequences[:, 2 * l] = worlds[count, snake_position[:, 0], snake_position[:, 1]] - sequences[:, 2 * l + 1] = snake_direction + # snake.solver(result,ar_mask) - # nb x 2 - snake_position = snake_next_position[i, j] + masked_inplace_autoregression( + model, self.batch_size, result, ar_mask, device=self.device + ) - return sequences, worlds + nb_total = ((prior_visits > 0) * ar_mask).sum() - # print(snake_position) + nb_correct = ( + (result == input).long() * (prior_visits > 0) * ar_mask + ).sum() + # nb_total = result.size(0) + # nb_correct = ((result - input).abs().sum(1) == 0).sum() -# generate_snake_sequences(nb=1, height=4, width=6, nb_colors=3, length=20) -# exit(0) + return nb_total, nb_correct + # train_nb_total, train_nb_correct = compute_nb_correct( + # self.train_input, self.train_prior_visits + # ) -class TaskSnake(Task): + # 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 = compute_nb_correct( + self.test_input[:1000], self.test_prior_visits[:1000] + ) + + log_string( + f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%" + ) + + model.train(t) + + +###################################################################### + + +import stack + + +class TaskStack(Task): def __init__( self, nb_train_samples, nb_test_samples, batch_size, - height, - width, - nb_colors, - length, + nb_steps, + nb_stacks, + nb_digits, + fraction_values_for_train=None, device=torch.device("cpu"), ): self.batch_size = batch_size - self.height = height - self.width = width + self.nb_steps = nb_steps + self.nb_stacks = nb_stacks + self.nb_digits = nb_digits self.device = device - self.train_input, self.train_worlds = generate_snake_sequences( - nb_train_samples, height, width, nb_colors, length, self.device + if fraction_values_for_train is None: + values_for_train = None + values_for_test = None + else: + all = torch.randperm(10**nb_digits) + nb_for_train = int(all.size(0) * fraction_values_for_train) + values_for_train = all[:nb_for_train] + values_for_test = all[nb_for_train:] + + self.train_input, self.train_stack_counts = stack.generate_sequences( + nb_train_samples, + nb_steps, + nb_stacks, + nb_digits, + values_for_train, + self.device, ) - self.test_input, self.test_worlds = generate_snake_sequences( - nb_test_samples, height, width, nb_colors, length, self.device + + self.test_input, self.test_stack_counts = stack.generate_sequences( + nb_test_samples, + nb_steps, + nb_stacks, + nb_digits, + values_for_test, + self.device, ) + i = torch.logical_and(self.test_input % 2 == 1, self.test_input < 2 * nb_stacks) + counts = self.test_stack_counts.flatten()[i.flatten()] + counts = F.one_hot(counts).sum(0) + log_string(f"test_pop_stack_counts {counts}") + + 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_popped_values(result, self.nb_stacks, self.nb_digits) + ar_mask = (result != input).long() + masked_inplace_autoregression( + model, self.batch_size, result, ar_mask, device=self.device + ) + + errors = ((result != input).long() * ar_mask).reshape( + -1, 1 + self.nb_digits + ) + ar_mask = ar_mask.reshape(-1, 1 + self.nb_digits) + + nb_total = ar_mask.max(1).values.sum() + nb_correct = nb_total - errors.max(1).values.sum() + + return nb_total, nb_correct + + test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[:1000]) + + log_string( + f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%" + ) + + ############################################################## + # Log a few generated sequences + input = self.test_input[:10, : 12 * (1 + self.nb_digits)] + result = input.clone() + stack.remove_popped_values(result, self.nb_stacks, self.nb_digits) + ar_mask = (result != input).long() + for n in range(result.size(0)): + log_string( + f"test_before {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}" + ) + masked_inplace_autoregression( + model, self.batch_size, result, ar_mask, device=self.device + ) + for n in range(result.size(0)): + log_string( + f"test_after {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}" + ) + ############################################################## + + model.train(t) + + +###################################################################### + + +import expr + + +class TaskExpr(Task): + def __init__( + self, + nb_train_samples, + nb_test_samples, + batch_size, + device=torch.device("cpu"), + ): + self.batch_size = batch_size + self.device = device + + train_sequences = expr.generate_sequences(nb_train_samples) + test_sequences = expr.generate_sequences(nb_test_samples) + self.char2id = dict( + [ + (c, n) + for n, c in enumerate(set("".join(train_sequences + test_sequences))) + ] + ) + self.id2char = dict([(n, c) for n, c in self.char2id.items()]) + len_max = max([len(x) for x in train_sequences + test_sequences]) + self.train_input = torch.cat( + [ + torch.tensor( + [char2id(c) for c in s + " " * (len_max - len(s))] + for s in train_sequences + ) + ], + 0, + ) + self.test_input = torch.cat( + [ + torch.tensor( + [char2id(c) for c in s + " " * (len_max - len(s))] + for s in test_sequences + ) + ], + 0, + ) self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1 def batches(self, split="train", nb_to_use=-1, desc=None): @@ -744,6 +1063,57 @@ class TaskSnake(Task): 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_popped_values(result, self.nb_stacks, self.nb_digits) + ar_mask = (result != input).long() + masked_inplace_autoregression( + model, self.batch_size, result, ar_mask, device=self.device + ) + + errors = ((result != input).long() * ar_mask).reshape( + -1, 1 + self.nb_digits + ) + ar_mask = ar_mask.reshape(-1, 1 + self.nb_digits) + + nb_total = ar_mask.max(1).values.sum() + nb_correct = nb_total - errors.max(1).values.sum() + + return nb_total, nb_correct + + test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[:1000]) + + log_string( + f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%" + ) + + ############################################################## + # Log a few generated sequences + input = self.test_input[:10, : 12 * (1 + self.nb_digits)] + result = input.clone() + stack.remove_popped_values(result, self.nb_stacks, self.nb_digits) + ar_mask = (result != input).long() + for n in range(result.size(0)): + log_string( + f"test_before {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}" + ) + masked_inplace_autoregression( + model, self.batch_size, result, ar_mask, device=self.device + ) + for n in range(result.size(0)): + log_string( + f"test_after {stack.seq_to_str(result[n],nb_stacks=self.nb_stacks,nb_digits=self.nb_digits)}" + ) + ############################################################## + + model.train(t) + ###################################################################### @@ -805,6 +1175,27 @@ elif args.task == "snake": width=args.snake_width, nb_colors=args.snake_nb_colors, length=args.snake_length, + prompt_length=args.snake_length // 2, + 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_digits=args.stack_nb_digits, + fraction_values_for_train=args.stack_fraction_values_for_train, + device=device, + ) + +elif args.task == "expr": + task = TaskExpr( + nb_train_samples=args.nb_train_samples, + nb_test_samples=args.nb_test_samples, + batch_size=args.batch_size, device=device, ) @@ -943,9 +1334,6 @@ 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)