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("--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)
+ order = order.sort(1).indices
+ else:
+ order = (
+ torch.arange(x.size(1), device=x.device).unsqueeze(0).expand(x.size(0), -1)
+ )
+ return order
+
+
+def reorder(x, order, back=False): # x is NxTxD1x...xDk, order is NxT'
+ u = x.reshape(x.size()[:2] + (-1,))
+ order = order.unsqueeze(-1).expand(-1, -1, u.size(-1))
+ if back:
+ v = u.new(u.size())
+ v.scatter_(1, order, u)
+ else:
+ v = u.gather(1, order)
+ v = v.reshape(v.size()[:2] + x.size()[2:])
+ return v
+
+
+def shuffle(x, fixed_len):
+ order = generation_order(x, fixed_len)
+ return reorder(x, order), 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)
######################################################################
-def compute_perplexity(model, split="train"):
+def compute_perplexity(model, fixed_len, split="train"):
with torch.autograd.no_grad():
t = model.training
model.eval()
for input in task.batches(split=split):
input = input.to(device)
-
- output = model(mygpt.BracketedSequence(input)).x
+ x, order = shuffle(input, fixed_len)
+ x = model(mygpt.BracketedSequence(x), order=order).x
+ output = reorder(x, order, back=True)
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"):
- output_gpt = gpt(mygpt.BracketedSequence(mazes), mode=args.oneshot_input).x
+ x, order = shuffle(mazes, task.height * task.width)
+ x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
+ output_gpt = reorder(x, order, back=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
+ x, order = shuffle(mazes, task.height * task.width)
+ x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
+ output_gpt = reorder(x, order, back=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
+ x, order = shuffle(mazes, task.height * task.width)
+ x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
+ output_gpt = reorder(x, order, back=True)
output = model(output_gpt)
if args.oneshot_output == "policy":
targets = policies.permute(0, 2, 1)
scores = scores.reshape(-1, task.height, task.width)
mazes = mazes.reshape(-1, task.height, task.width)
targets = targets.reshape(-1, task.height, task.width)
+ filename = (
+ f"oneshot_{args.oneshot_input}_{args.oneshot_output}_{n_epoch:04d}.png"
+ )
maze.save_image(
- os.path.join(
- args.result_dir,
- f"oneshot_{args.oneshot_input}_{args.oneshot_output}_{n_epoch:04d}.png",
- ),
+ os.path.join(args.result_dir, filename),
mazes=mazes,
score_paths=scores,
score_truth=targets,
)
+ log_string(f"wrote {filename}")
+
# -------------------
gpt.train(t)
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)
+ x, order = shuffle(result, self.height * self.width)
+ masked_inplace_autoregression(
+ model, self.batch_size, x, ar_mask, order=order
+ )
+ result = reorder(x, order, back=True)
mazes, paths = self.seq2map(result)
nb_correct += maze.path_correctness(mazes, paths).long().sum()
nb_total += mazes.size(0)
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, f"result_{n_epoch:04d}.png"),
+ 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)
if nb_epochs_finished >= args.nb_epochs:
n_epoch = nb_epochs_finished
- train_perplexity = compute_perplexity(model, split="train")
- test_perplexity = compute_perplexity(model, split="test")
+ train_perplexity = compute_perplexity(
+ model, fixed_len=task.height * task.width, split="train"
+ )
+ test_perplexity = compute_perplexity(
+ model, fixed_len=task.height * task.width, split="test"
+ )
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, args.nb_epochs):
for input in task.batches(split="train"):
input = input.to(device)
- output = model(mygpt.BracketedSequence(input)).x
+ x, order = shuffle(input, task.height * task.width)
+ x = model(mygpt.BracketedSequence(x), order=order).x
+ output = reorder(x, order, back=True)
loss = F.cross_entropy(output.transpose(1, 2), input)
acc_train_loss += loss.item() * input.size(0)
nb_train_samples += input.size(0)
optimizer.step()
train_perplexity = math.exp(min(100, acc_train_loss / nb_train_samples))
- test_perplexity = compute_perplexity(model, split="test")
+ test_perplexity = compute_perplexity(
+ model, fixed_len=task.height * task.width, split="test"
+ )
log_string(
f"perplexity {n_epoch} train_set {train_set_perplexity} train_prediction {train_perplexity} test_prediction {test_perplexity}"
projects / beaver.git / blobdiff