######################################################################
-parser = argparse.ArgumentParser(
- description="An implementation of GPT with cache to solve a toy geometric reasoning task."
-)
+parser = argparse.ArgumentParser(description="A maze shortest path solving with a GPT.")
parser.add_argument("--log_filename", type=str, default="train.log")
parser.add_argument("--nb_epochs", type=int, default=25)
-parser.add_argument("--batch_size", type=int, default=100)
+parser.add_argument("--nb_train_samples", type=int, default=200000)
+
+parser.add_argument("--nb_test_samples", type=int, default=50000)
-parser.add_argument("--data_size", type=int, default=-1)
+parser.add_argument("--batch_size", type=int, default=25)
parser.add_argument("--optim", type=str, default="adam")
parser.add_argument("--overwrite_results", action="store_true", default=False)
+parser.add_argument("--one_shot", action="store_true", default=False)
+
parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
##############################
# maze options
-parser.add_argument("--world_height", type=int, default=13)
+parser.add_argument("--maze_height", type=int, default=13)
-parser.add_argument("--world_width", type=int, default=21)
+parser.add_argument("--maze_width", type=int, default=21)
-parser.add_argument("--world_nb_walls", type=int, default=15)
+parser.add_argument("--maze_nb_walls", type=int, default=15)
######################################################################
def masked_inplace_autoregression(model, batch_size, input, ar_mask):
-
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
+ # Needed to initialize the model's cache
+ model(mygpt.BracketedSequence(input, 0, i.min()))
for s in range(i.min(), i.max() + 1):
output = model(mygpt.BracketedSequence(input, s, 1)).x
logits = output[:, s]
######################################################################
+def compute_perplexity(model, split="train"):
+ with torch.autograd.no_grad():
+ t = model.training
+ model.eval()
+
+ nb_samples, acc_loss = 0, 0.0
+
+ for input in task.batches(split=split):
+ input = input.to(device)
+
+ output = model(mygpt.BracketedSequence(input)).x
+ loss = F.cross_entropy(output.transpose(1, 2), input)
+ acc_loss += loss.item() * input.size(0)
+ nb_samples += input.size(0)
+
+ model.train(t)
+
+ return math.exp(min(100, acc_loss / nb_samples))
+
+
+######################################################################
+
+
+def one_shot(gpt, task):
+ pass
+
+
+######################################################################
+
+
class Task:
def batches(self, split="train"):
pass
s = s.reshape(s.size(0), -1, self.height, self.width)
return (s[:, k] for k in range(s.size(1)))
- def __init__(self, batch_size, height, width, nb_walls, device=torch.device("cpu")):
+ def __init__(
+ self,
+ nb_train_samples,
+ nb_test_samples,
+ batch_size,
+ height,
+ width,
+ nb_walls,
+ device=torch.device("cpu"),
+ ):
self.batch_size = batch_size
self.height = height
self.width = width
self.device = device
- nb = args.data_size if args.data_size > 0 else 250000
-
mazes_train, paths_train = maze.create_maze_data(
- (4 * nb) // 5,
+ nb_train_samples,
height=height,
width=width,
nb_walls=nb_walls,
)
mazes_train, paths_train = mazes_train.to(device), paths_train.to(device)
self.train_input = self.map2seq(mazes_train, paths_train)
- self.nb_codes = self.train_input.max() + 1
mazes_test, paths_test = maze.create_maze_data(
- nb // 5,
+ nb_test_samples,
height=height,
width=width,
nb_walls=nb_walls,
mazes_test, paths_test = mazes_test.to(device), paths_test.to(device)
self.test_input = self.map2seq(mazes_test, paths_test)
- def batches(self, split="train"):
+ self.nb_codes = self.train_input.max() + 1
+
+ def batches(self, split="train", nb_to_use=-1):
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]
for batch in tqdm.tqdm(
input.split(self.batch_size), dynamic_ncols=True, desc=f"epoch-{split}"
):
def vocabulary_size(self):
return self.nb_codes
- def compute_error(self, model, split="train"):
+ def compute_error(self, model, split="train", nb_to_use=-1):
nb_total, nb_correct = 0, 0
- for input in task.batches(split):
+ for input in task.batches(split, nb_to_use):
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)
mazes, paths = self.seq2map(result)
nb_correct += maze.path_correctness(mazes, paths).long().sum()
return nb_total, nb_correct
def produce_results(self, n_epoch, model):
- train_nb_total, train_nb_correct = self.compute_error(model, "train")
- 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(model, "test")
- 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}%"
- )
-
- input = self.test_input[:32]
- result = input.clone()
- ar_mask = result.new_zeros(result.size())
+ with torch.autograd.no_grad():
+ t = model.training
+ model.eval()
+
+ train_nb_total, train_nb_correct = 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(
+ 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}%"
+ )
+
+ input = self.test_input[:32]
+ 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)
- ar_mask[:, self.height * self.width :] = 1
- masked_inplace_autoregression(model, self.batch_size, result, ar_mask)
+ mazes, paths = self.seq2map(input)
+ _, predicted_paths = self.seq2map(result)
+ maze.save_image(
+ os.path.join(args.result_dir, f"result_{n_epoch:04d}.png"),
+ mazes,
+ paths,
+ predicted_paths,
+ maze.path_correctness(mazes, predicted_paths),
+ )
- mazes, paths = self.seq2map(input)
- _, predicted_paths = self.seq2map(result)
- maze.save_image(f"result_{n_epoch:04d}.png", mazes, paths, predicted_paths)
+ model.train(t)
######################################################################
task = TaskMaze(
+ nb_train_samples=args.nb_train_samples,
+ nb_test_samples=args.nb_test_samples,
batch_size=args.batch_size,
- height=args.world_height,
- width=args.world_width,
- nb_walls=args.world_nb_walls,
+ height=args.maze_height,
+ width=args.maze_width,
+ nb_walls=args.maze_nb_walls,
device=device,
)
##############################
-nb_samples_seen = 0
+if args.one_shot:
+ one_shot(model, task)
+ exit(0)
+
+##############################
if nb_epochs_finished >= nb_epochs:
- task.produce_results(nb_epochs_finished, model)
+ n_epoch = nb_epochs_finished
+ train_perplexity = compute_perplexity(model, split="train")
+ test_perplexity = compute_perplexity(model, split="test")
-for n_epoch in range(nb_epochs_finished, nb_epochs):
+ 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)
+
+ exit(0)
+
+##############################
+for n_epoch in range(nb_epochs_finished, nb_epochs):
learning_rate = learning_rate_schedule[n_epoch]
log_string(f"learning_rate {learning_rate}")
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)
+ train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
+ test_perplexity = compute_perplexity(model, split="test")
- 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}"
- )
+ 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)
+ task.produce_results(n_epoch, model)
checkpoint = {
"nb_epochs_finished": n_epoch + 1,
projects / beaver.git / blobdiff