# 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(
+ "--task", type=str, default="picoclvr", help="picoclvr, mnist, maze, snake, stack"
+)
-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("--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("--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=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("--stack_nb_values", type=int, default=10)
+
######################################################################
args = parser.parse_args()
######################################################################
+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,
+ },
+}
+
+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())
######################################################################
+# 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)):
+ # p = logits.softmax(1)
+ # entropy[:,s]= p.xlogy(p).sum(1) / math.log(2)
+ batches = zip(input.split(batch_size), ar_mask.split(batch_size))
+ if progress_bar_desc is not None:
+ 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(
input,
ar_masks,
forbidden_tokens,
+ progress_bar_desc=None,
device=self.device,
)
model.train(t)
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=0.0
+ )
+ 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(
- img / 255.0, image_name, nrow=nb_per_primer, padding=1, pad_value=1.0
+ 1 - results.reshape(-1, 1, 28, 28) / 255.0,
+ image_name,
+ nrow=16,
+ pad_value=0.8,
)
log_string(f"wrote {image_name}")
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
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}%"
)
- 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}%"
)
+ 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())
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,
path_correct=maze.path_correctness(mazes, predicted_paths),
+ path_optimal=maze.path_optimality(paths, predicted_paths),
)
log_string(f"wrote {filename}")
######################################################################
+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
+
+ 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()
+
+ 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)
+
+
+######################################################################
+
+
+import stack
+
+
+class TaskStack(Task):
+ def __init__(
+ self,
+ nb_train_samples,
+ nb_test_samples,
+ batch_size,
+ nb_steps,
+ nb_stacks,
+ nb_values,
+ device=torch.device("cpu"),
+ ):
+ self.batch_size = batch_size
+ self.nb_steps = nb_steps
+ self.nb_stacks = nb_stacks
+ self.nb_values = nb_values
+ self.device = device
+
+ self.train_input, self.train_stack_counts = stack.generate_sequences(
+ nb_train_samples, nb_steps, nb_stacks, nb_values, self.device
+ )
+
+ self.test_input, self.test_stack_counts = stack.generate_sequences(
+ nb_test_samples, nb_steps, nb_stacks, nb_values, self.device
+ )
+
+ mask = self.test_input.clone()
+ stack.remove_poped_values(mask,self.nb_stacks)
+ mask=(mask!=self.test_input)
+ counts = self.test_stack_counts.flatten()[mask.flatten()]
+ counts=F.one_hot(counts).sum(0)
+ log_string(f"stack_count {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_poped_values(result,self.nb_stacks)
+ ar_mask = (result != input).long()
+ result *= 1 - ar_mask
+
+ masked_inplace_autoregression(
+ model, self.batch_size, result, ar_mask, device=self.device
+ )
+
+ nb_total = ar_mask.sum()
+
+ nb_correct = (
+ (result == input).long() * ar_mask
+ ).sum()
+
+ return nb_total, nb_correct
+
+ test_nb_total, test_nb_correct = compute_nb_correct(self.test_input[: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,
+ )
+
+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_values = args.stack_nb_values,
+ device=device,
+ )
+
else:
raise ValueError(f"Unknown task {args.task}")
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)
projects / picoclvr.git / blobdiff