parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
+parser.add_argument("--random_regression_order", action="store_true", default=False)
+
parser.add_argument("--no_checkpoint", action="store_true", default=False)
parser.add_argument("--overwrite_results", action="store_true", default=False)
parser.add_argument("--maze_nb_walls", type=int, default=15)
+##############################
+# one-shot prediction
+
parser.add_argument("--oneshot", action="store_true", default=False)
parser.add_argument("--oneshot_input", type=str, default="head")
-parser.add_argument("--oneshot_output", type=str, default="policy")
+parser.add_argument("--oneshot_output", type=str, default="trace")
######################################################################
######################################################################
+def generation_order(x, fixed_len):
+ if args.random_regression_order:
+ order = torch.rand(x.size(), device=x.device)
+ order[:, :fixed_len] = torch.linspace(-2, -1, fixed_len, device=order.device)
+ return order.sort(1).indices
+ else:
+ return (
+ torch.arange(x.size(1), device=x.device).unsqueeze(0).expand(x.size(0), -1)
+ )
+
+
+def shuffle(x, order, reorder=False):
+ if x.dim() == 3:
+ order = order.unsqueeze(-1).expand(-1, -1, x.size(-1))
+ if reorder:
+ y = x.new(x.size())
+ y.scatter_(1, order, x)
+ return y
+ else:
+ return x.gather(1, order)
+
+
# ar_mask is a Boolean matrix of same shape as input, with 1s on the
# tokens that should be generated
-def masked_inplace_autoregression(model, batch_size, input, ar_mask):
+def masked_inplace_autoregression(model, batch_size, input, ar_mask, order=None):
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:
# Needed to initialize the model's cache
- model(mygpt.BracketedSequence(input, 0, i.min()))
+ model(mygpt.BracketedSequence(input, 0, i.min()), order=order)
for s in range(i.min(), i.max() + 1):
- output = model(mygpt.BracketedSequence(input, s, 1)).x
+ output = model(mygpt.BracketedSequence(input, s, 1), order=order).x
logits = output[:, s]
if args.deterministic_synthesis:
t_next = logits.argmax(1)
for input in task.batches(split=split):
input = input.to(device)
-
- output = model(mygpt.BracketedSequence(input)).x
+ order = generation_order(input, task.height * task.width)
+ input = shuffle(input, order)
+ output = model(mygpt.BracketedSequence(input), order=order).x
loss = F.cross_entropy(output.transpose(1, 2), input)
acc_loss += loss.item() * input.size(0)
nb_samples += input.size(0)
acc_train_loss, nb_train_samples = 0, 0
for mazes, policies in task.policy_batches(split="train"):
- ####
- # print(f'{mazes.size()=} {policies.size()=}')
- # s = maze.stationary_densities(
- # exit(0)
- ####
- masks = mazes == maze.v_empty
- output_gpt = gpt(mygpt.BracketedSequence(mazes), mode=args.oneshot_input).x
+ order = generation_order(mazes, task.height * task.width)
+ x = shuffle(mazes, order)
+ x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
+ output_gpt = shuffle(x, order, reorder=True)
output = model(output_gpt)
loss = compute_loss(mazes, output, policies, task.height, task.width)
acc_test_loss, nb_test_samples = 0, 0
for mazes, policies in task.policy_batches(split="test"):
- output_gpt = gpt(mygpt.BracketedSequence(mazes), mode=args.oneshot_input).x
+ order = generation_order(mazes, task.height * task.width)
+ x = shuffle(mazes, order)
+ x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
+ output_gpt = shuffle(x, order, reorder=True)
output = model(output_gpt)
loss = compute_loss(mazes, output, policies, task.height, task.width)
acc_test_loss += loss.item() * mazes.size(0)
# -------------------
mazes = task.test_input[:32, : task.height * task.width]
policies = task.test_policies[:32]
- output_gpt = gpt(mygpt.BracketedSequence(mazes), mode=args.oneshot_input).x
+ order = generation_order(mazes, task.height * task.width)
+ x = shuffle(mazes, order)
+ x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
+ output_gpt = shuffle(x, order, reorder=True)
output = model(output_gpt)
if args.oneshot_output == "policy":
targets = policies.permute(0, 2, 1)
mazes.view(-1, task.height, task.width),
policies.view(-1, 4, task.height, task.width),
).flatten(-2)
- scores = output.flatten(-2)
+ scores = output
else:
raise ValueError(f"{args.oneshot_output=}")
class Task:
- def batches(self, split="train"):
+ def batches(self, split="train", nb_to_use=-1, desc=None):
pass
def vocabulary_size(self):
self.nb_codes = self.train_input.max() + 1
- def batches(self, split="train", nb_to_use=-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=f"epoch-{split}"
+ input.split(self.batch_size), dynamic_ncols=True, desc=desc
):
yield batch
- def policy_batches(self, split="train", nb_to_use=-1):
+ 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[: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=f"epoch-{split}",
+ desc=desc,
):
yield batch
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)
+ order = generation_order(result, self.height * self.width)
+ masked_inplace_autoregression(
+ model, self.batch_size, result, ar_mask, order=order
+ )
+ result = shuffle(result, order, reorder=True)
mazes, paths = self.seq2map(result)
nb_correct += maze.path_correctness(mazes, paths).long().sum()
nb_total += mazes.size(0)
##############################
-if args.oneshot:
- oneshot(model, task)
- exit(0)
-
-##############################
-
if nb_epochs_finished >= args.nb_epochs:
n_epoch = nb_epochs_finished
train_perplexity = compute_perplexity(model, split="train")
task.produce_results(n_epoch, model)
- exit(0)
-
##############################
for n_epoch in range(nb_epochs_finished, args.nb_epochs):
for input in task.batches(split="train"):
input = input.to(device)
- output = model(mygpt.BracketedSequence(input)).x
+ order = generation_order(input, task.height * task.width)
+ input = shuffle(input, order)
+ output = model(mygpt.BracketedSequence(input), order=order).x
loss = F.cross_entropy(output.transpose(1, 2), input)
acc_train_loss += loss.item() * input.size(0)
nb_train_samples += input.size(0)
log_string(f"saved checkpoint {checkpoint_name}")
######################################################################
+
+if args.oneshot:
+ oneshot(model, task)
+
+######################################################################
projects / beaver.git / blobdiff