# 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
######################################################################
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("--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)
parser.add_argument("--maze_nb_walls", type=int, default=15)
+##############################
+# Snake options
+
+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_length", type=int, default=400)
+
######################################################################
args = parser.parse_args()
######################################################################
+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())
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(
0,
)
- image_name = os.path.join(args.result_dir, f"result_{n_epoch:04d}.png")
+ 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
)
log_string(f"wrote {image_name}")
+######################################################################
+
+
+class TaskMNIST(Task):
+ def __init__(self, batch_size, device=torch.device("cpu")):
+ self.device = device
+ self.batch_size = batch_size
+
+ def batches(self, split="train"):
+ assert split in {"train", "test"}
+ data_set = torchvision.datasets.MNIST(
+ root="./data", train=(split == "train"), download=True
+ )
+ data_input = data_set.data.view(-1, 28 * 28).long()
+ if args.nb_train_samples is not None:
+ data_input = data_input[: args.nb_train_samples]
+ for batch in tqdm.tqdm(
+ data_input.split(self.batch_size), desc=f"epoch-{split}"
+ ):
+ yield batch
+
+ def vocabulary_size(self):
+ return 256
+
+ def produce_results(self, n_epoch, model):
+ results = torch.empty(64, 28 * 28, device=self.device, dtype=torch.int64)
+ ar_mask = torch.full_like(results, 1)
+ masked_inplace_autoregression(
+ model, self.batch_size, results, ar_mask, device=self.device
+ )
+ 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,
+ nrow=16,
+ pad_value=0.8,
+ )
+ log_string(f"wrote {image_name}")
+
+
######################################################################
import maze
self.width = width
self.device = device
- train_mazes, train_paths, train_policies = maze.create_maze_data(
+ train_mazes, train_paths, _ = maze.create_maze_data(
nb_train_samples,
height=height,
width=width,
progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-train"),
)
self.train_input = self.map2seq(train_mazes.to(device), train_paths.to(device))
- self.train_policies = train_policies.flatten(-2).to(device)
- test_mazes, test_paths, test_policies = maze.create_maze_data(
+ test_mazes, test_paths, _ = maze.create_maze_data(
nb_test_samples,
height=height,
width=width,
progress_bar=lambda x: tqdm.tqdm(x, dynamic_ncols=True, desc=f"data-test"),
)
self.test_input = self.map2seq(test_mazes.to(device), test_paths.to(device))
- self.test_policies = test_policies.flatten(-2).to(device)
- self.nb_codes = self.train_input.max() + 1
+ 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"}
):
yield batch
- def policy_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
- policies = self.train_policies if split == "train" else self.test_policies
- input = input[:, : self.height * self.width]
- policies = policies * (input != maze.v_wall)[:, None]
-
- if nb_to_use > 0:
- input = input[:nb_to_use]
- policies = policies[:nb_to_use]
-
- if desc is None:
- desc = f"epoch-{split}"
- for batch in tqdm.tqdm(
- zip(input.split(self.batch_size), policies.split(self.batch_size)),
- dynamic_ncols=True,
- desc=desc,
- ):
- yield batch
-
def vocabulary_size(self):
return self.nb_codes
mazes, paths = self.seq2map(input)
_, predicted_paths = self.seq2map(result)
- filename = f"result_{n_epoch:04d}.png"
+
+ filename = os.path.join(args.result_dir, f"maze_result_{n_epoch:04d}.png")
maze.save_image(
- os.path.join(args.result_dir, filename),
+ filename,
mazes=mazes,
target_paths=paths,
predicted_paths=predicted_paths,
######################################################################
+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=}')
+
+
+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
+
+ 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_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
+
+ 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, 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
+
+ # snake_solver(result,ar_mask)
+
+ masked_inplace_autoregression(
+ model, self.batch_size, result, ar_mask, device=self.device
+ )
+
+ 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, 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}%"
+ # )
+
+ 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}%"
+ )
+
+ model.train(t)
+
+
+######################################################################
+
+
def picoclvr_pruner_horizontal_green(p):
return not ("green" in p and ("left" in p or "right" in p))
pruner_eval=picoclvr_pruner_eval,
)
+elif args.task == "mnist":
+ task = TaskMNIST(
+ batch_size=args.batch_size,
+ device=device,
+ )
+
elif args.task == "maze":
task = TaskMaze(
nb_train_samples=args.nb_train_samples,
device=device,
)
+elif args.task == "snake":
+ task = TaskSnake(
+ nb_train_samples=args.nb_train_samples,
+ nb_test_samples=args.nb_test_samples,
+ batch_size=args.batch_size,
+ height=args.snake_height,
+ width=args.snake_width,
+ nb_colors=args.snake_nb_colors,
+ length=args.snake_length,
+ prompt_length=args.snake_length // 2,
+ device=device,
+ )
+
else:
raise ValueError(f"Unknown task {args.task}")