X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=main.py;h=43d290049cf81c372821c6a13463a0b285d65397;hb=5fff2918fdcc35016195cd209afc864e9cd2ac32;hp=acecfdd311048fdbc1aedc49becae0b703783fbc;hpb=b003cc9f89b7c3356f7d1e6c0c10b3dea249ef96;p=picoclvr.git diff --git a/main.py b/main.py index acecfdd..43d2900 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 @@ -27,7 +27,8 @@ 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") @@ -40,7 +41,7 @@ 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=25) +parser.add_argument("--batch_size", type=int, default=None) parser.add_argument("--nb_train_samples", type=int, default=250000) @@ -128,6 +129,28 @@ if args.seed >= 0: ###################################################################### +default_args = { + "picoclvr": { + "batch_size": 25, + }, + "mnist": { + "batch_size": 10, + }, + "maze": { + "batch_size": 25, + }, + "snake": { + "batch_size": 20, + }, +} + +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) + +###################################################################### + def log_string(s): t = time.strftime("%Y%m%d-%H:%M:%S ", time.localtime()) @@ -149,7 +172,12 @@ for n in vars(args): 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)): + 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, + ): i = (ar_mask.sum(0) > 0).nonzero() if i.min() > 0: model( @@ -636,9 +664,11 @@ class TaskMaze(Task): def generate_snake_sequences( - nb, height, width, nb_colors, length, device=torch.device("cpu") + 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( ( @@ -649,6 +679,9 @@ def generate_snake_sequences( ) 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): @@ -680,7 +713,10 @@ def generate_snake_sequences( ), ).float() val = ( - torch.rand_like(val) * val * torch.tensor([[1.0, 4.0, 1.0]], device=device) + # The multiplicative factors bias toward moving forward + torch.rand_like(val) + * val + * torch.tensor([[1.0, 2.0, 1.0]], device=device) ) # nb @@ -688,18 +724,47 @@ def generate_snake_sequences( 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, worlds + 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=}') + + class TaskSnake(Task): def __init__( self, @@ -710,18 +775,32 @@ class TaskSnake(Task): 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 - self.train_input, self.train_worlds = generate_snake_sequences( - nb_train_samples, height, width, nb_colors, length, self.device + self.train_input, self.train_prior_visits = generate_snake_sequences( + nb_train_samples, + height, + width, + nb_colors, + length, + prompt_length, + 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_prior_visits = generate_snake_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 @@ -746,32 +825,44 @@ class TaskSnake(Task): t = model.training model.eval() - def compute_nb_correct(input): + def compute_nb_correct(input, prior_visits): result = input.clone() - i = torch.arange(result.size(1), device=result.device) - ar_mask = torch.logical_and(i >= i.size(0) // 2, i % 2 == 0)[ - None, : - ].long() + 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 + + # snake_solver(result,ar_mask) + masked_inplace_autoregression( model, self.batch_size, result, ar_mask, device=self.device ) - nb_total = ar_mask.sum() * input.size(0) - nb_correct = ((result == input).long() * ar_mask).sum() + nb_total = ((prior_visits > 0) * ar_mask).sum() + + 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() return nb_total, nb_correct - train_nb_total, train_nb_correct = compute_nb_correct(self.train_input) + # train_nb_total, train_nb_correct = compute_nb_correct( + # self.train_input, self.train_prior_visits + # ) - 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}%" - ) + # 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) + test_nb_total, test_nb_correct = compute_nb_correct( + self.test_input[:1000], self.test_prior_visits[: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}%" @@ -840,6 +931,7 @@ 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, )