# Written by Francois Fleuret <francois@fleuret.org>
+# torch.backends.cuda.matmul.allow_tf23
+# torch.autocast(torch.bfloat16)
+
import math, sys, argparse, time, tqdm, itertools, os
import torch, torchvision
######################################################################
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+if torch.cuda.is_available():
+ device = torch.device("cuda")
+ torch.backends.cuda.matmul.allow_tf32 = True
+else:
+ device = torch.device("cpu")
######################################################################
description="An implementation of GPT with cache to solve a toy geometric reasoning task."
)
+parser.add_argument("--task", type=str, default="picoclvr")
+
parser.add_argument("--log_filename", type=str, default="train.log")
parser.add_argument("--result_dir", type=str, default="results_default")
parser.add_argument("--nb_epochs", type=int, default=25)
-parser.add_argument("--batch_size", type=int, default=100)
+parser.add_argument("--batch_size", type=int, default=25)
-parser.add_argument("--data_size", type=int, default=-1)
+parser.add_argument("--nb_train_samples", type=int, default=250000)
+
+parser.add_argument("--nb_test_samples", type=int, default=10000)
parser.add_argument("--optim", type=str, default="adam")
-parser.add_argument("--learning_rate", type=float, default=1e-3)
+parser.add_argument("--learning_rate", type=float, default=1e-4)
-parser.add_argument(
- "--learning_rate_schedule", type=str, default="10: 2e-4,20: 4e-5,30: 8e-6"
-)
+parser.add_argument("--learning_rate_schedule", type=str, default="10: 2e-5,30: 4e-6")
parser.add_argument("--dim_model", type=int, default=512)
parser.add_argument("--dropout", type=float, default=0.1)
-parser.add_argument("--nb_oneshot_blocks", type=int, default=-1)
-
parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
parser.add_argument("--no_checkpoint", action="store_true", default=False)
##############################
# picoclvr options
-parser.add_argument("--nb_colors", type=int, default=5)
+parser.add_argument("--picoclvr_nb_colors", type=int, default=5)
-parser.add_argument("--height", type=int, default=12)
+parser.add_argument("--picoclvr_height", type=int, default=12)
-parser.add_argument("--width", type=int, default=16)
+parser.add_argument("--picoclvr_width", type=int, default=16)
-parser.add_argument("--prune_properties", type=str, default="none")
+parser.add_argument("--picocvlr_prune_properties", type=str, default="none")
+
+##############################
+# Maze options
+
+parser.add_argument("--maze_height", type=int, default=13)
+
+parser.add_argument("--maze_width", type=int, default=21)
+
+parser.add_argument("--maze_nb_walls", type=int, default=15)
######################################################################
args = parser.parse_args()
-assert args.prune_properties in {"none", "train+eval", "eval"}
+assert args.picocvlr_prune_properties in {"none", "train+eval", "eval"}
try:
os.mkdir(args.result_dir)
print(f"result directory {args.result_dir} already exists")
exit(1)
-log_file = open(os.path.join(args.result_dir, args.log_filename), "w")
+log_file = open(os.path.join(args.result_dir, args.log_filename), "a")
if args.seed >= 0:
# torch.backends.cudnn.deterministic = True
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:
class TaskPicoCLVR(Task):
-
# Make a tensor from a list of strings
def tensorize(self, descr):
token_descr = [s.strip().split(" ") for s in descr]
def __init__(
self,
+ nb_train_samples,
+ nb_test_samples,
batch_size,
height,
width,
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,
+ "nb_train_samples": nb_train_samples,
+ "nb_test_samples": nb_test_samples,
"height": height,
"width": width,
"nb_colors": nb_colors,
"rng_state": list(torch.get_rng_state()),
}
- log_string(f"generating {nb} samples (can take some time)")
+ log_string(
+ f"generating {nb_train_samples+nb_test_samples} samples (can take some time)"
+ )
self.train_descr = generate_descr(
- (nb * 4) // 5, "train", pruner=self.pruner_train
+ nb_train_samples, "train", pruner=self.pruner_train
)
- self.test_descr = generate_descr((nb * 1) // 5, "test", pruner=None)
+ self.test_descr = generate_descr(nb_test_samples, "test", pruner=None)
# Build the tokenizer
tokens = {"<nul>", "<img>"}
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
######################################################################
def produce_results(self, n_epoch, model):
-
self.compute_missing_properties(n_epoch, model)
if self.pruner_eval is not None:
######################################################################
-log_string(f"device {device}")
+import maze
+
+
+class TaskMaze(Task):
+ def map2seq(self, *m):
+ return torch.cat([x.flatten(1) for x in m], 1)
+
+ def seq2map(self, s):
+ s = s.reshape(s.size(0), -1, self.height, self.width)
+ return (s[:, k] for k in range(s.size(1)))
+
+ 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
+
+ train_mazes, train_paths, train_policies = maze.create_maze_data(
+ nb_train_samples,
+ height=height,
+ width=width,
+ nb_walls=nb_walls,
+ 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(
+ nb_test_samples,
+ height=height,
+ width=width,
+ nb_walls=nb_walls,
+ 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
+
+ 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 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]
-def pruner_horizontal_green(p):
+ 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):
+ 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
+ )
+ mazes, paths = self.seq2map(result)
+ nb_correct += maze.path_correctness(mazes, paths).long().sum()
+ nb_total += mazes.size(0)
+
+ return nb_total, nb_correct
+
+ 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(
+ 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[:48]
+ 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
+ )
+
+ mazes, paths = self.seq2map(input)
+ _, predicted_paths = self.seq2map(result)
+ filename = f"result_{n_epoch:04d}.png"
+ maze.save_image(
+ os.path.join(args.result_dir, filename),
+ mazes=mazes,
+ target_paths=paths,
+ predicted_paths=predicted_paths,
+ path_correct=maze.path_correctness(mazes, predicted_paths),
+ )
+ log_string(f"wrote {filename}")
+
+ model.train(t)
+
+
+######################################################################
+
+
+def picoclvr_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,
+picoclvr_pruner_train = (
+ picoclvr_pruner_horizontal_green
+ if args.picocvlr_prune_properties in {"train+eval"}
+ else None
+)
+
+picoclvr_pruner_eval = (
+ (lambda p: not picoclvr_pruner_horizontal_green(p))
+ if args.picocvlr_prune_properties in {"train+eval", "eval"}
+ else None
)
+######################################################################
+
+if args.task == "picoclvr":
+ task = TaskPicoCLVR(
+ nb_train_samples=args.nb_train_samples,
+ nb_test_samples=args.nb_test_samples,
+ batch_size=args.batch_size,
+ height=args.picoclvr_height,
+ width=args.picoclvr_width,
+ nb_colors=args.picoclvr_nb_colors,
+ device=device,
+ pruner_train=picoclvr_pruner_train,
+ pruner_eval=picoclvr_pruner_eval,
+ )
+
+elif args.task == "maze":
+ task = TaskMaze(
+ nb_train_samples=args.nb_train_samples,
+ nb_test_samples=args.nb_test_samples,
+ batch_size=args.batch_size,
+ height=args.maze_height,
+ width=args.maze_width,
+ nb_walls=args.maze_nb_walls,
+ device=device,
+ )
+
+else:
+ raise ValueError(f"Unknown task {args.task}")
+
+######################################################################
+
+log_string(f"device {device}")
+
vocabulary_size = task.vocabulary_size()
log_string(f"vocabulary_size {vocabulary_size}")
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}")
optimizer.step()
with torch.autograd.no_grad():
-
model.eval()
nb_test_samples, acc_test_loss = 0, 0.0
projects / picoclvr.git / blobdiff