From: François Fleuret Date: Sat, 11 Mar 2023 12:58:56 +0000 (+0100) Subject: Initial commit X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=3602bfe2c4e1cd513759bf45cb83f8c2d914674b;p=beaver.git Initial commit --- 3602bfe2c4e1cd513759bf45cb83f8c2d914674b diff --git a/beaver.py b/beaver.py new file mode 100755 index 0000000..b0fa03c --- /dev/null +++ b/beaver.py @@ -0,0 +1,631 @@ +#!/usr/bin/env python + +# Any copyright is dedicated to the Public Domain. +# https://creativecommons.org/publicdomain/zero/1.0/ + +# Written by Francois Fleuret + +# torch.backends.cuda.matmul.allow_tf23 +# torch.autocast(torch.bfloat16) + +import math, sys, argparse, time, tqdm, itertools, os + +import torch, torchvision +from torch import nn +from torch.nn import functional as F + +import mygpt, tensorstack + +###################################################################### + +if torch.cuda.is_available(): + device = torch.device("cuda") + torch.backends.cuda.matmul.allow_tf32 = True +else: + device = torch.device("cpu") + +###################################################################### + +parser = argparse.ArgumentParser( + description="An implementation of GPT with cache to solve a toy geometric reasoning task." +) + +parser.add_argument("--log_filename", type=str, default="train.log") + +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("--batch_size", type=int, default=100) + +parser.add_argument("--data_size", type=int, default=-1) + +parser.add_argument("--optim", type=str, default="adam") + +parser.add_argument("--learning_rate", type=float, default=1e-3) + +parser.add_argument( + "--learning_rate_schedule", type=str, default="10: 2e-4,20: 4e-5,30: 8e-6" +) + +parser.add_argument("--dim_model", type=int, default=512) + +parser.add_argument("--dim_keys", type=int, default=64) + +parser.add_argument("--dim_hidden", type=int, default=2048) + +parser.add_argument("--nb_heads", type=int, default=8) + +parser.add_argument("--nb_blocks", type=int, default=12) + +parser.add_argument("--dropout", type=float, default=0.1) + +parser.add_argument("--deterministic_synthesis", action="store_true", default=False) + +parser.add_argument("--no_checkpoint", action="store_true", default=False) + +parser.add_argument("--overwrite_results", action="store_true", default=False) + +parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth") + +############################## +# picoclvr options + +parser.add_argument("--world_height", type=int, default=23) + +parser.add_argument("--world_width", type=int, default=31) + +parser.add_argument("--world_nb_walls", type=int, default=15) + +###################################################################### + +args = parser.parse_args() + +assert args.prune_properties in {"none", "train+eval", "eval"} + +try: + os.mkdir(args.result_dir) +except FileExistsError: + if not args.overwrite_results: + print(f"result directory {args.result_dir} already exists") + exit(1) + +log_file = open(os.path.join(args.result_dir, args.log_filename), "a") + +if args.seed >= 0: + # torch.backends.cudnn.deterministic = True + # torch.backends.cudnn.benchmark = False + # torch.use_deterministic_algorithms(True) + torch.manual_seed(args.seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(args.seed) + +###################################################################### + + +def log_string(s): + t = time.strftime("%Y%m%d-%H:%M:%S ", time.localtime()) + + if log_file is not None: + log_file.write(t + s + "\n") + log_file.flush() + + print(t + s) + sys.stdout.flush() + + +for n in vars(args): + log_string(f"args.{n} {getattr(args, n)}") + +###################################################################### + + +def masked_inplace_autoregression( + model, batch_size, input, ar_mask, forbidden_tokens=None, device=torch.device("cpu") +): + + for input, ar_mask in zip(input.split(batch_size), ar_mask.split(batch_size)): + i = (ar_mask.sum(0) > 0).nonzero() + if i.min() > 0: + model( + mygpt.BracketedSequence(input, 0, i.min()) + ) # Needed to initialize the model's cache + for s in range(i.min(), i.max() + 1): + output = model(mygpt.BracketedSequence(input, s, 1)).x + logits = output[:, s] + if forbidden_tokens is not None: + logits = logits.masked_fill(forbidden_tokens, float("-inf")) + if args.deterministic_synthesis: + t_next = logits.argmax(1) + else: + dist = torch.distributions.categorical.Categorical(logits=logits) + t_next = dist.sample() + input[:, s] = ar_mask[:, s] * t_next + (1 - ar_mask[:, s]) * input[:, s] + + +###################################################################### + + +class Task: + def batches(self, split="train"): + pass + + def vocabulary_size(self): + pass + + def produce_results(self, n_epoch, model): + pass + + +###################################################################### + +import picoclvr + + +class TaskPicoCLVR(Task): + + # Make a tensor from a list of strings + def tensorize(self, descr): + token_descr = [s.strip().split(" ") for s in descr] + l = max([len(s) for s in token_descr]) + token_descr = [s + [""] * (l - len(s)) for s in token_descr] + id_descr = [[self.token2id[u] for u in s] for s in token_descr] + return torch.tensor(id_descr, device=self.device) + + # Make a list of strings from a tensor + def detensorize(self, x): + return [" ".join([self.id2token[t.item()] for t in r]) for r in x] + + # trim all the tensors in the tuple z to remove as much token from + # left and right in the first tensor. If z is a tuple, all its + # elements are trimed according to the triming for the first + def trim(self, z, token=""): + n = self.token2id[token] + if type(z) == tuple: + x = z[0] + i = (1 - (F.pad(x, (1, 1), value=n) == n).min(0).values.long()).cumsum(0) + a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min() + return tuple([t[:, a:b] for t in z]) + else: + i = (1 - (F.pad(z, (1, 1), value=n) == n).min(0).values.long()).cumsum(0) + a, b = (i == 0).nonzero().max(), (i == i.max()).nonzero().min() + return z[:, a:b] + + ###################### + # Not the cleanest part of the code + + # Extract the last image of each sequence, from the last + # included, and set to all the tokens from the beginning of + # that image to the end + def excise_last_image(self, input): + t_img, t_nul = self.token2id[""], self.token2id[""] + nb_img_tokens = self.height * self.width + 1 + + input = input.clone() + t = (input == t_img).long() + tail_masks = (t.cumsum(dim=1) == t.sum(dim=1, keepdim=True)).long() + i = (t * tail_masks).nonzero(as_tuple=True) + j = ( + i[0][:, None], + i[1][:, None] + torch.arange(nb_img_tokens, device=input.device)[None, :], + ) + images = self.trim(input[j]) + input[j] = t_nul + loss_masks = 1 - tail_masks + input, loss_masks = self.trim((input, loss_masks)) + return input, loss_masks, images + + def add_true_image(self, input, images, loss_masks): + t_nul = self.token2id[""] + nb_img_tokens = self.height * self.width + 1 + input = F.pad(input, (0, nb_img_tokens), value=t_nul) + loss_masks = F.pad(loss_masks, (0, nb_img_tokens), value=0) + t = (input == t_nul).long() + i = (t.cumsum(dim=1) == 1).nonzero(as_tuple=True) + j = ( + i[0][:, None], + i[1][:, None] + torch.arange(nb_img_tokens, device=input.device)[None, :], + ) + input[j] = images + loss_masks[j] = 1 + input, loss_masks = self.trim((input, loss_masks)) + return input, loss_masks + + def add_generated_image(self, input, loss_masks, model): + t_img, t_nul = self.token2id[""], self.token2id[""] + nb_img_tokens = self.height * self.width + 1 + + input = F.pad(input, (0, nb_img_tokens), value=t_nul) + loss_masks = F.pad(loss_masks, (0, nb_img_tokens), value=0) + t = (input == t_nul).long() + i = (t.cumsum(dim=1) == 1).nonzero(as_tuple=True) + input[i] = t_img + + j = ( + i[0][:, None], + i[1][:, None] + + 1 + + torch.arange(nb_img_tokens - 1, device=input.device)[None, :], + ) + ar_masks = input.new_zeros(input.size(), dtype=torch.int64) + ar_masks[j] = 1 + forbidden_tokens = ( + torch.arange(self.vocabulary_size(), device=input.device) == t_nul + ) + with torch.autograd.no_grad(): + t = model.training + model.eval() + masked_inplace_autoregression( + model, + self.batch_size, + input, + ar_masks, + forbidden_tokens, + device=self.device, + ) + model.train(t) + + input, loss_masks = self.trim((input, loss_masks)) + + return input, loss_masks + + ###################### + + def __init__( + self, + batch_size, + height, + width, + nb_colors=5, + device=torch.device("cpu"), + pruner_train=None, + pruner_eval=None, + ): + def generate_descr(nb, cache_suffix, pruner): + return picoclvr.generate( + nb, + height=self.height, + width=self.width, + nb_colors=nb_colors, + pruner=pruner, + ) + + self.height = height + self.width = width + self.batch_size = batch_size + self.device = device + nb = args.data_size if args.data_size > 0 else 250000 + self.pruner_train = pruner_train + self.pruner_eval = pruner_eval + + param = { + "nb": nb, + "height": height, + "width": width, + "nb_colors": nb_colors, + "batch_size": batch_size, + "rng_state": list(torch.get_rng_state()), + } + + log_string(f"generating {nb} samples (can take some time)") + self.train_descr = generate_descr( + (nb * 4) // 5, "train", pruner=self.pruner_train + ) + self.test_descr = generate_descr((nb * 1) // 5, "test", pruner=None) + + # Build the tokenizer + tokens = {"", ""} + for d in [self.train_descr, self.test_descr]: + for s in d: + for t in s.strip().split(" "): + tokens.add(t) + # make this set a sorted list to get the same tensors given + # the same descr + tokens = list(tokens) + tokens.sort() + self.token2id = dict([(t, n) for n, t in enumerate(tokens)]) + self.id2token = dict([(n, t) for n, t in enumerate(tokens)]) + + # Tokenize the train and test sets + self.train_input = self.tensorize(self.train_descr) + self.test_input = self.tensorize(self.test_descr) + + def batches(self, split="train"): + assert split in {"train", "test"} + input = self.train_input if split == "train" else self.test_input + for batch in tqdm.tqdm( + input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}" + ): + yield self.trim(batch) + + def vocabulary_size(self): + return len(self.token2id) + + def compute_missing_properties(self, n_epoch, model, pruner=None): + + acc_nb_requested_properties = [] + acc_nb_missing_properties = [] + acc_nb_results = 0 + + for input in tqdm.tqdm( + self.test_input.split(self.batch_size), + dynamic_ncols=True, + desc=f"test-properties", + ): + tape, loss_masks, _ = self.excise_last_image(input) + tape, loss_masks = self.add_generated_image(tape, loss_masks, model) + result_descr = self.detensorize(tape) + np = picoclvr.nb_properties( + result_descr, + height=self.height, + width=self.width, + pruner=pruner, + ) + nb_requested_properties, _, nb_missing_properties = zip(*np) + acc_nb_requested_properties += nb_requested_properties + acc_nb_missing_properties += nb_missing_properties + acc_nb_results += len(result_descr) + + nb_requested_properties = sum(acc_nb_requested_properties) + nb_missing_properties = sum(acc_nb_missing_properties) + + prefix = "" if pruner is None else "pruned_" + log_string(f"nb_{prefix}samples {n_epoch} {acc_nb_results}") + log_string( + f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}" + ) + log_string( + f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%" + ) + + ###################################################################### + + def produce_results(self, n_epoch, model): + + self.compute_missing_properties(n_epoch, model) + + if self.pruner_eval is not None: + self.compute_missing_properties(n_epoch, model, self.pruner_eval) + + nb_tokens_to_generate = self.height * self.width + 3 + result_descr = [] + nb_per_primer = 8 + primer = [] + + for primer_descr in [ + "red above green green top blue right of red", + "there is red there is yellow there is blue", + "red below yellow yellow below green green below blue red right yellow left green right blue left", + "green bottom yellow bottom green left of blue yellow right of blue blue top", + ]: + primer += [primer_descr] * nb_per_primer + + tape = self.tensorize(primer) + loss_masks = 1 - (tape == self.token2id[""]).long() + tape, loss_masks = self.add_generated_image(tape, loss_masks, model) + result_descr = self.detensorize(tape) + + np = picoclvr.nb_properties(result_descr, height=self.height, width=self.width) + + acc_nb_requested_properties, _, acc_nb_missing_properties = zip(*np) + acc_nb_results = len(result_descr) + + nb_requested_properties = sum(acc_nb_requested_properties) + nb_missing_properties = sum(acc_nb_missing_properties) + + prefix = "demo_" + log_string(f"nb_{prefix}samples {n_epoch} {acc_nb_results}") + log_string( + f"property_{prefix}nb {n_epoch} requested {sum(acc_nb_requested_properties)} missing {sum(acc_nb_missing_properties)}" + ) + log_string( + f"property_{prefix}miss {n_epoch} {100*nb_missing_properties/nb_requested_properties:.02f}%" + ) + + img = picoclvr.descr2img(result_descr, height=self.height, width=self.width) + + if img.dim() == 5: + if img.size(1) == 1: + img = F.pad(img.squeeze(1), pad=(1, 1, 1, 1), value=64) + else: + img = torch.cat( + [ + torchvision.utils.make_grid(x, padding=1, pad_value=64)[None] + for x in img + ], + 0, + ) + + image_name = os.path.join(args.result_dir, f"result_{n_epoch:04d}.png") + torchvision.utils.save_image( + img / 255.0, image_name, nrow=nb_per_primer, padding=1, pad_value=1.0 + ) + log_string(f"wrote {image_name}") + + +###################################################################### + +log_string(f"device {device}") + + +def pruner_horizontal_green(p): + return not ("green" in p and ("left" in p or "right" in p)) + + +task = TaskPicoCLVR( + batch_size=args.batch_size, + height=args.height, + width=args.width, + nb_colors=args.nb_colors, + device=device, + pruner_train=pruner_horizontal_green + if args.prune_properties in {"train+eval"} + else None, + pruner_eval=(lambda p: not pruner_horizontal_green(p)) + if args.prune_properties in {"train+eval", "eval"} + else None, +) + +vocabulary_size = task.vocabulary_size() + +log_string(f"vocabulary_size {vocabulary_size}") + +############################## + +model = mygpt.MyGPT( + vocabulary_size=vocabulary_size, + dim_model=args.dim_model, + dim_keys=args.dim_keys, + dim_hidden=args.dim_hidden, + nb_heads=args.nb_heads, + nb_blocks=args.nb_blocks, + causal=True, + dropout=args.dropout, +) + +model.to(device) + +nb_parameters = sum(p.numel() for p in model.parameters()) +log_string(f"nb_parameters {nb_parameters} ({int(nb_parameters/1e6)}M)") + +###################################################################### + +nb_epochs_finished = 0 + +if args.no_checkpoint: + log_string(f"not trying to load checkpoint.") + +else: + try: + checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name) + checkpoint = torch.load(checkpoint_name) + nb_epochs_finished = checkpoint["nb_epochs_finished"] + model.load_state_dict(checkpoint["model_state"]) + torch.set_rng_state(checkpoint["rng_state"]) + if torch.cuda.is_available(): + torch.cuda.set_rng_state(checkpoint["cuda_rng_state"]) + + log_string(f"checkpoint loaded with {nb_epochs_finished} epochs finished.") + + except FileNotFoundError: + log_string("starting from scratch.") + + except: + log_string("error when loading the checkpoint.") + exit(1) + +###################################################################### + +nb_epochs = args.nb_epochs if args.nb_epochs > 0 else nb_epochs_default + +token_count = 0 +for input in task.batches(split="train"): + token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1)) +token_probas = token_count / token_count.sum() +entropy = -torch.xlogy(token_probas, token_probas).sum() +train_set_perplexity = math.exp(entropy) + +############################## + +if args.learning_rate_schedule == "cos": + learning_rate_schedule = {} + for n_epoch in range(args.nb_epochs): + u = n_epoch / args.nb_epochs * math.pi + learning_rate_schedule[n_epoch] = args.learning_rate * 0.5 * (1 + math.cos(u)) +else: + u = { + int(k): float(v) + for k, v in [ + tuple(x.split(":")) for x in args.learning_rate_schedule.split(",") + ] + } + + learning_rate_schedule = {} + learning_rate = args.learning_rate + for n_epoch in range(args.nb_epochs): + if n_epoch in u: + learning_rate = u[n_epoch] + learning_rate_schedule[n_epoch] = learning_rate + +log_string(f"learning_rate_schedule {learning_rate_schedule}") + +############################## + +nb_samples_seen = 0 + +if nb_epochs_finished >= nb_epochs: + task.produce_results(nb_epochs_finished, model) + +for n_epoch in range(nb_epochs_finished, nb_epochs): + + learning_rate = learning_rate_schedule[n_epoch] + + log_string(f"learning_rate {learning_rate}") + + if args.optim == "sgd": + optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate) + elif args.optim == "adam": + optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) + elif args.optim == "adamw": + optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate) + else: + raise ValueError(f"Unknown optimizer {args.optim}.") + + model.train() + + nb_train_samples, acc_train_loss = 0, 0.0 + + for input in task.batches(split="train"): + input = input.to(device) + output = model(mygpt.BracketedSequence(input)).x + loss = F.cross_entropy(output.transpose(1, 2), input) + acc_train_loss += loss.item() * input.size(0) + nb_train_samples += input.size(0) + nb_samples_seen += input.size(0) + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + with torch.autograd.no_grad(): + + model.eval() + + nb_test_samples, acc_test_loss = 0, 0.0 + + 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) + nb_test_samples += input.size(0) + + train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples)) + test_perplexity = math.exp(min(100, acc_test_loss / nb_test_samples)) + + log_string( + f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}" + ) + + task.produce_results(n_epoch, model) + + checkpoint = { + "nb_epochs_finished": n_epoch + 1, + "model_state": model.state_dict(), + "rng_state": torch.get_rng_state(), + } + + if torch.cuda.is_available(): + checkpoint["cuda_rng_state"] = torch.cuda.get_rng_state() + + checkpoint_name = os.path.join(args.result_dir, args.checkpoint_name) + torch.save(checkpoint, checkpoint_name) + log_string(f"saved checkpoint {checkpoint_name}") + +###################################################################### diff --git a/maze.py b/maze.py new file mode 100755 index 0000000..2c44319 --- /dev/null +++ b/maze.py @@ -0,0 +1,218 @@ +#!/usr/bin/env python + +# Any copyright is dedicated to the Public Domain. +# https://creativecommons.org/publicdomain/zero/1.0/ + +# Written by Francois Fleuret + +import torch, torchvision + +###################################################################### + +v_empty, v_wall, v_start, v_goal, v_path = 0, 1, 2, 3, 4 + + +def create_maze(h=11, w=17, nb_walls=8): + a, k = 0, 0 + + while k < nb_walls: + while True: + if a == 0: + m = torch.zeros(h, w, dtype=torch.int64) + m[0, :] = 1 + m[-1, :] = 1 + m[:, 0] = 1 + m[:, -1] = 1 + + r = torch.rand(4) + + if r[0] <= 0.5: + i1, i2, j = ( + int((r[1] * h).item()), + int((r[2] * h).item()), + int((r[3] * w).item()), + ) + i1, i2, j = i1 - i1 % 2, i2 - i2 % 2, j - j % 2 + i1, i2 = min(i1, i2), max(i1, i2) + if i2 - i1 > 1 and i2 - i1 <= h / 2 and m[i1 : i2 + 1, j].sum() <= 1: + m[i1 : i2 + 1, j] = 1 + break + else: + i, j1, j2 = ( + int((r[1] * h).item()), + int((r[2] * w).item()), + int((r[3] * w).item()), + ) + i, j1, j2 = i - i % 2, j1 - j1 % 2, j2 - j2 % 2 + j1, j2 = min(j1, j2), max(j1, j2) + if j2 - j1 > 1 and j2 - j1 <= w / 2 and m[i, j1 : j2 + 1].sum() <= 1: + m[i, j1 : j2 + 1] = 1 + break + a += 1 + + if a > 10 * nb_walls: + a, k = 0, 0 + + k += 1 + + return m + + +###################################################################### + + +def compute_distance(walls, i, j): + max_length = walls.numel() + dist = torch.full_like(walls, max_length) + + dist[i, j] = 0 + pred_dist = torch.empty_like(dist) + + while True: + pred_dist.copy_(dist) + d = ( + torch.cat( + ( + dist[None, 1:-1, 0:-2], + dist[None, 2:, 1:-1], + dist[None, 1:-1, 2:], + dist[None, 0:-2, 1:-1], + ), + 0, + ).min(dim=0)[0] + + 1 + ) + + dist[1:-1, 1:-1] = torch.min(dist[1:-1, 1:-1], d) + dist = walls * max_length + (1 - walls) * dist + + if dist.equal(pred_dist): + return dist * (1 - walls) + + +###################################################################### + + +def compute_policy(walls, i, j): + distance = compute_distance(walls, i, j) + distance = distance + walls.numel() * walls + + value = distance.new_full((4,) + distance.size(), walls.numel()) + value[0, :, 1:] = distance[:, :-1] + value[1, :, :-1] = distance[:, 1:] + value[2, 1:, :] = distance[:-1, :] + value[3, :-1, :] = distance[1:, :] + + proba = (value.min(dim=0)[0][None] == value).float() + proba = proba / proba.sum(dim=0)[None] + proba = proba * (1 - walls) + walls.float() / 4 + + return proba + + +###################################################################### + + +def mark_path(walls, i, j, goal_i, goal_j): + policy = compute_policy(walls, goal_i, goal_j) + action = torch.distributions.categorical.Categorical( + policy.permute(1, 2, 0) + ).sample() + walls[i, j] = 4 + n, nmax = 0, walls.numel() + while i != goal_i or j != goal_j: + di, dj = [(0, -1), (0, 1), (-1, 0), (1, 0)][action[i, j]] + i, j = i + di, j + dj + assert walls[i, j] == 0 + walls[i, j] = 4 + n += 1 + assert n < nmax + + +def valid_paths(mazes, paths): + still_ok = (mazes - (paths * (paths < 4))).view(mazes.size(0), -1).abs().sum(1) == 0 + reached = still_ok.new_zeros(still_ok.size()) + current, pred_current = paths.clone(), paths.new_zeros(paths.size()) + goal = (mazes == v_goal).long() + while not pred_current.equal(current): + # print(current) + # print(f'{still_ok=} {reached=}') + pred_current.copy_(current) + u = (current == v_start).long() + possible_next = ( + u[:, 2:, 1:-1] + u[:, 0:-2, 1:-1] + u[:, 1:-1, 2:] + u[:, 1:-1, 0:-2] > 0 + ).long() + u = u[:, 1:-1, 1:-1] + reached += ((goal[:, 1:-1, 1:-1] * possible_next).sum((1, 2)) == 1) * ( + (current == v_path).sum((1, 2)) == 0 + ) + current[:, 1:-1, 1:-1] = (1 - u) * current[:, 1:-1, 1:-1] + ( + v_start - v_path + ) * (possible_next * (current[:, 1:-1, 1:-1] == v_path)) + still_ok *= (current == v_start).sum((1, 2)) <= 1 + + return still_ok * reached + + +###################################################################### + + +def create_maze_data(nb, h=11, w=17, nb_walls=8, dist_min=-1): + mazes = torch.empty(nb, h, w, dtype=torch.int64) + paths = torch.empty(nb, h, w, dtype=torch.int64) + + for n in range(nb): + maze = create_maze(h, w, nb_walls) + i = (1 - maze).nonzero() + while True: + start, goal = i[torch.randperm(i.size(0))[:2]] + if (start - goal).abs().sum() >= dist_min: + break + + path = maze.clone() + mark_path(path, start[0], start[1], goal[0], goal[1]) + maze[start[0], start[1]] = v_start + maze[goal[0], goal[1]] = v_goal + path[start[0], start[1]] = v_start + path[goal[0], goal[1]] = v_goal + + mazes[n] = maze + paths[n] = path + + return mazes, paths + + +###################################################################### + + +def save_image(name, mazes, paths): + mazes, paths = mazes.cpu(), paths.cpu() + + colors = torch.tensor( + [ + [255, 255, 255], # empty + [0, 0, 0], # wall + [0, 255, 0], # start + [0, 0, 255], # goal + [255, 0, 0], # path + ] + ) + + mazes = colors[mazes.reshape(-1)].reshape(mazes.size() + (-1,)).permute(0, 3, 1, 2) + paths = colors[paths.reshape(-1)].reshape(paths.size() + (-1,)).permute(0, 3, 1, 2) + + img = torch.cat((mazes.unsqueeze(1), paths.unsqueeze(1)), 1) + img = img.reshape((-1,) + img.size()[2:]).float() / 255.0 + + torchvision.utils.save_image(img, name, padding=1, pad_value=0.5, nrow=8) + + +###################################################################### + +if __name__ == "__main__": + + mazes, paths = create_maze_data(32, dist_min=10) + save_image("test.png", mazes, paths) + print(valid_paths(mazes, paths)) + +###################################################################### diff --git a/mygpt.py b/mygpt.py new file mode 100755 index 0000000..5ea4668 --- /dev/null +++ b/mygpt.py @@ -0,0 +1,290 @@ +#!/usr/bin/env python + +# Any copyright is dedicated to the Public Domain. +# https://creativecommons.org/publicdomain/zero/1.0/ + +# Written by Francois Fleuret + +import math + +import torch + +from torch import nn +from torch.nn import functional as F + +###################################################################### + + +class WithResidual(nn.Module): + def __init__(self, *f): + super().__init__() + self.f = f[0] if len(f) == 1 else nn.Sequential(*f) + + def forward(self, bs): + bs.x = bs.x + self.f(bs).x + return bs + + +###################################################################### + +# A BracketedSequence is a BxTx... tensor with a first and a nb time +# steps to compute. + +# Modules able to process it expect that they will have to process a +# first bracket starting at t=0, followed by a succession of brackets +# that move forward in time, do not overlap, and cover the axis T with +# no holes. +# +# Although it is more general, for a classical prompt-conditioned +# auto-regressive process it will be a first bracket starting at 0 and +# of arbitrary length for the "prompt", followed by brackets of length +# 1 for the successive tokens. +# +# Modules able to process brackets may implement a cache that is +# resetted when the input bracket starts at t=0 + + +class BracketedSequence: + def __init__(self, x, first=None, nb=None): + self.x = x + self.first = 0 if first is None else first + self.nb = x.size(1) if nb is None else nb + + def slice(self): + return self.x[:, self.first : self.first + self.nb] + + +###################################################################### + + +class CacheWrapper(nn.Module): + def __init__(self, *f): + super().__init__() + self.f = f[0] if len(f) == 1 else nn.Sequential(*f) + + def forward(self, bs): + if bs.first == 0: + y = self.f(bs.slice()) + self.cache_y = y.new(*((y.size(0), bs.x.size(1)) + y.size()[2:])) + self.cache_y[:, bs.first : bs.first + bs.nb] = y + else: + self.cache_y[:, bs.first : bs.first + bs.nb] = self.f(bs.slice()) + + bs.x = self.cache_y + + return bs + + +############################## + + +class AddPositionalEncoding(nn.Module): + def __init__(self, len_max): + super().__init__() + self.len_max = len_max + + # [Vaswani et al 2018] PE_{t,2i} = sin(t/(L^{2i/D})), PE_{t,2i+1} = cos(t/(L^{2i/D})) + + def forward(self, bs): + if bs.first == 0: + t = torch.arange(bs.x.size(1), dtype=bs.x.dtype, device=bs.x.device)[ + :, None + ] + j = torch.arange(bs.x.size(2), dtype=bs.x.dtype, device=bs.x.device)[ + None, : + ] + k = j % 2 + self.pe = torch.sin( + t / (self.len_max ** ((j - k) / bs.x.size(2))) + math.pi / 2 * k + ) + self.cache_y = bs.x.new(bs.x.size()) + + self.cache_y[:, bs.first : bs.first + bs.nb] = ( + bs.slice() + self.pe[bs.first : bs.first + bs.nb] + ) + + bs.x = self.cache_y + + return bs + + +############################## + + +class QKVAttention(nn.Module): + def __init__( + self, dim_in, dim_qk, dim_v, nb_heads=1, causal=False, attention_dropout=0.0 + ): + super().__init__() + + def randw(*d): + return nn.Parameter(torch.randn(*d) / math.sqrt(d[-1])) + + self.causal = causal + self.attention_dropout = attention_dropout + + self.w_q = randw(nb_heads, dim_qk, dim_in) + self.w_k = randw(nb_heads, dim_qk, dim_in) + self.w_v = randw(nb_heads, dim_v, dim_in) + self.w_o = randw(dim_v * nb_heads, dim_in) + + def forward(self, bs_q): + x_q = bs_q.x + + if bs_q.first == 0: + self.cache_k = x_q.new_zeros( + x_q.size(0), self.w_k.size(0), x_q.size(1), self.w_k.size(1) + ) + self.cache_v = x_q.new_zeros( + x_q.size(0), self.w_v.size(0), x_q.size(1), self.w_v.size(1) + ) + self.cache_y = x_q.new_zeros(x_q.size(0), x_q.size(1), self.w_o.size(1)) + + q = torch.einsum( + "ntc,hdc->nhtd", x_q[:, bs_q.first : bs_q.first + bs_q.nb], self.w_q + ) + self.cache_k[:, :, bs_q.first : bs_q.first + bs_q.nb] = torch.einsum( + "ntc,hdc->nhtd", x_q[:, bs_q.first : bs_q.first + bs_q.nb], self.w_k + ) + self.cache_v[:, :, bs_q.first : bs_q.first + bs_q.nb] = torch.einsum( + "ntc,hdc->nhtd", x_q[:, bs_q.first : bs_q.first + bs_q.nb], self.w_v + ) + + a = torch.einsum( + "nhtd,nhsd->nhts", q, self.cache_k[:, :, : bs_q.first + bs_q.nb] + ) / math.sqrt(self.w_q.size(1)) + + if self.causal: + if bs_q.first == 0: + self.cache_attzero = ( + torch.arange(x_q.size(1), device=q.device)[None, None, :, None] + < torch.arange(x_q.size(1), device=q.device)[None, None, None, :] + ) + a = a.masked_fill( + self.cache_attzero[ + :, :, bs_q.first : bs_q.first + bs_q.nb, : bs_q.first + bs_q.nb + ], + float("-inf"), + ) + + a = a.softmax(dim=3) + a = F.dropout(a, self.attention_dropout, self.training) + + y = torch.einsum( + "nhts,nhsd->nthd", a, self.cache_v[:, :, : bs_q.first + bs_q.nb] + ).flatten(2) + + self.cache_y[:, bs_q.first : bs_q.first + bs_q.nb] = y @ self.w_o + + bs_q.x = self.cache_y + + return bs_q + + +############################## + + +class MyGPT(nn.Module): + def __init__( + self, + vocabulary_size, + dim_model, + dim_keys, + dim_hidden, + nb_heads, + nb_blocks, + causal=False, + dropout=0.0, + len_max=1e5, + ): + + super().__init__() + + assert dim_model % nb_heads == 0 + + self.embedding = nn.Sequential( + CacheWrapper(nn.Embedding(vocabulary_size, dim_model), nn.Dropout(dropout)), + AddPositionalEncoding(len_max), + ) + + trunk_blocks = [] + + for b in range(nb_blocks): + trunk_blocks += [ + WithResidual( + CacheWrapper(nn.LayerNorm((dim_model,))), + QKVAttention( + dim_in=dim_model, + dim_qk=dim_keys, + dim_v=dim_model // nb_heads, + nb_heads=nb_heads, + causal=causal, + attention_dropout=dropout, + ), + ), + WithResidual( + CacheWrapper( + nn.LayerNorm((dim_model,)), + nn.Linear(in_features=dim_model, out_features=dim_hidden), + nn.ReLU(), + nn.Linear(in_features=dim_hidden, out_features=dim_model), + nn.Dropout(dropout), + ), + ), + ] + + self.trunk = nn.Sequential(*trunk_blocks) + + self.readout = CacheWrapper( + nn.Linear(in_features=dim_model, out_features=vocabulary_size) + ) + + with torch.no_grad(): + for m in self.modules(): + if isinstance(m, nn.Embedding): + m.weight.normal_(mean=0, std=2e-2) + elif isinstance(m, nn.LayerNorm): + m.bias.zero_() + m.weight.fill_(1.0) + + def forward(self, bs): + bs.x = F.pad(bs.x, (1, -1)) + bs = self.embedding(bs) + bs = self.trunk(bs) + bs = self.readout(bs) + return bs + + +###################################################################### + +if __name__ == "__main__": + + print("Basic check.") + + vocabulary_size = 10 + x = torch.randint(vocabulary_size, (9, 7)) + + model = MyGPT( + vocabulary_size=vocabulary_size, + dim_model=18, + dim_keys=50, + dim_hidden=100, + nb_heads=2, + nb_blocks=1, + dropout=0.1, + ) + + model.eval() + + y1 = model(BracketedSequence(x)).x + + y2 = torch.randn_like(y1) + for s in range(x.size(1)): + z = model(BracketedSequence(x, s, 1)) + y2[:, s] = z.x[:, s] + + # print(y1.max(dim = 2).values) + # print(y2.max(dim = 2).values) + print(f"error={((y1 - y2).norm() / (y1.norm() + y2.norm())).item()}") + +######################################################################