parser.add_argument("--deterministic_synthesis", action="store_true", default=False)
+parser.add_argument("--random_regression_order", action="store_true", default=False)
+
+parser.add_argument("--noncausal_prompt", 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("--one_shot", action="store_true", default=False)
-
parser.add_argument("--checkpoint_name", type=str, default="checkpoint.pth")
##############################
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="trace")
+
######################################################################
args = parser.parse_args()
######################################################################
+def generation_order(x, fixed_len=0):
+ if args.random_regression_order:
+ order = torch.rand(x.size(), device=x.device)
+ order[:, :fixed_len] = torch.arange(-fixed_len, 0, device=x.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, reverse=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 reverse:
+ v = u.new(u.size()).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
+
+
+def eval_mygpt(model, input, mode="standard", fixed_len=0):
+ x, order = shuffle(input, fixed_len)
+ x = model(mygpt.BracketedSequence(x), mode=mode, order=order).x
+ return reorder(x, order, reverse=True)
+
+
+######################################################################
+
# 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):
- for input, ar_mask in zip(input.split(batch_size), ar_mask.split(batch_size)):
+def masked_inplace_autoregression(model, batch_size, input, ar_mask, order=None):
+ for input, ar_mask, order in zip(
+ input.split(batch_size), ar_mask.split(batch_size), order.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, task, 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
+ output = eval_mygpt(model, input, fixed_len=fixed_len)
loss = F.cross_entropy(output.transpose(1, 2), input)
acc_loss += loss.item() * input.size(0)
nb_samples += input.size(0)
######################################################################
-def one_shot(gpt, task):
+def oneshot_policy_loss(mazes, output, policies, height, width):
+ masks = (mazes == maze.v_empty).unsqueeze(-1)
+ targets = policies.permute(0, 2, 1) * masks
+ output = output * masks
+ return -(output.log_softmax(-1) * targets).sum() / masks.sum()
+
+
+def oneshot_trace_loss(mazes, output, policies, height, width):
+ masks = mazes == maze.v_empty
+ targets = maze.stationary_densities(
+ mazes.view(-1, height, width), policies.view(-1, 4, height, width)
+ ).flatten(-2)
+ targets = targets * masks
+ output = output.squeeze(-1) * masks
+ return (output - targets).abs().sum() / masks.sum()
+
+
+def oneshot(gpt, task):
t = gpt.training
gpt.eval()
- for input, targets in task.policy_batches():
- output = gpt(mygpt.BracketedSequence(input), with_readout = False).x
+
+ if args.oneshot_input == "head":
+ dim_in = args.dim_model
+ elif args.oneshot_input == "deep":
+ dim_in = args.dim_model * args.nb_blocks * 2
+ else:
+ raise ValueError(f"{args.oneshot_input=}")
+
+ if args.oneshot_output == "policy":
+ dim_out = 4
+ compute_loss = oneshot_policy_loss
+ elif args.oneshot_output == "trace":
+ dim_out = 1
+ compute_loss = oneshot_trace_loss
+ else:
+ raise ValueError(f"{args.oneshot_output=}")
+
+ model = nn.Sequential(
+ nn.Linear(dim_in, args.dim_model),
+ nn.ReLU(),
+ nn.Linear(args.dim_model, args.dim_model),
+ nn.ReLU(),
+ nn.Linear(args.dim_model, dim_out),
+ ).to(device)
+
+ for n_epoch in range(args.nb_epochs):
+ learning_rate = learning_rate_schedule[n_epoch]
+ optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
+
+ acc_train_loss, nb_train_samples = 0, 0
+ for mazes, policies in task.policy_batches(split="train"):
+ output_gpt = eval_mygpt(
+ gpt, mazes, mode=args.oneshot_input, fixed_len=task.height * task.width
+ )
+ output = model(output_gpt)
+
+ loss = compute_loss(mazes, output, policies, task.height, task.width)
+ acc_train_loss += loss.item() * mazes.size(0)
+ nb_train_samples += mazes.size(0)
+
+ optimizer.zero_grad()
+ loss.backward()
+ optimizer.step()
+
+ acc_test_loss, nb_test_samples = 0, 0
+ for mazes, policies in task.policy_batches(split="test"):
+ output_gpt = eval_mygpt(
+ gpt, mazes, mode=args.oneshot_input, fixed_len=task.height * task.width
+ )
+ output = model(output_gpt)
+ loss = compute_loss(mazes, output, policies, task.height, task.width)
+ acc_test_loss += loss.item() * mazes.size(0)
+ nb_test_samples += mazes.size(0)
+
+ log_string(
+ f"diff_ce {n_epoch} train {acc_train_loss/nb_train_samples} test {acc_test_loss/nb_test_samples}"
+ )
+
+ # -------------------
+ mazes = task.test_input[:32, : task.height * task.width]
+ policies = task.test_policies[:32]
+ output_gpt = eval_mygpt(
+ gpt, mazes, mode=args.oneshot_input, fixed_len=task.height * task.width
+ )
+ output = model(output_gpt)
+ if args.oneshot_output == "policy":
+ targets = policies.permute(0, 2, 1)
+ scores = (
+ (F.one_hot(output.argmax(-1), num_classes=4) * targets).sum(-1) == 0
+ ).float()
+ elif args.oneshot_output == "trace":
+ targets = maze.stationary_densities(
+ mazes.view(-1, task.height, task.width),
+ policies.view(-1, 4, task.height, task.width),
+ ).flatten(-2)
+ scores = output
+ else:
+ raise ValueError(f"{args.oneshot_output=}")
+
+ 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, 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):
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.to(device)
+ self.train_policies = train_policies.flatten(-2).to(device)
test_mazes, test_paths, test_policies = maze.create_maze_data(
nb_test_samples,
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.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):
+ 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
- targets = self.train_policies if split == "train" else self.test_policies
+ policies = self.train_policies if split == "train" else self.test_policies
input = input[:, : self.height * self.width]
- targets = targets.flatten(-2) * (input != maze.v_wall)[:,None]
+ policies = policies * (input != maze.v_wall)[:, None]
if nb_to_use > 0:
input = input[:nb_to_use]
- targets = targets[: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), targets.split(self.batch_size)),
+ 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, reverse=True)
mazes, paths = self.seq2map(result)
nb_correct += maze.path_correctness(mazes, paths).long().sum()
nb_total += mazes.size(0)
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, reverse=True)
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"),
- mazes,
- paths,
- predicted_paths,
- maze.path_correctness(mazes, predicted_paths),
+ 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)
##############################
+amm_generator = None
+
+if args.noncausal_prompt:
+ amm_generator = lambda d: torch.logical_and(
+ torch.arange(d)[None, None, :, None] < torch.arange(d)[None, None, None, :],
+ torch.arange(d)[None, None, :, None] >= d // 2,
+ )
+
model = mygpt.MyGPT(
vocabulary_size=vocabulary_size,
dim_model=args.dim_model,
nb_blocks=args.nb_blocks,
causal=True,
dropout=args.dropout,
+ amm_generator=amm_generator,
)
model.to(device)
######################################################################
-nb_epochs = args.nb_epochs if args.nb_epochs > 0 else nb_epochs_default
-
token_count = 0
for input in task.batches(split="train"):
token_count += F.one_hot(input, num_classes=task.vocabulary_size()).sum((0, 1))
##############################
-if args.one_shot:
- one_shot(model, task)
- exit(0)
-
-##############################
-
-if nb_epochs_finished >= nb_epochs:
+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, task, fixed_len=task.height * task.width, split="train"
+ )
+ test_perplexity = compute_perplexity(
+ model, task, 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, nb_epochs):
+for n_epoch in range(nb_epochs_finished, args.nb_epochs):
learning_rate = learning_rate_schedule[n_epoch]
log_string(f"learning_rate {learning_rate}")
elif args.optim == "adamw":
optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)
else:
- raise ValueError(f"Unknown optimizer {args.optim}.")
+ raise ValueError(f"{args.optim=}")
model.train()
for input in task.batches(split="train"):
input = input.to(device)
- output = model(mygpt.BracketedSequence(input)).x
+ output = eval_mygpt(
+ model, input, mode=args.oneshot_input, fixed_len=task.height * task.width
+ )
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, task, 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}"
log_string(f"saved checkpoint {checkpoint_name}")
######################################################################
+
+if args.oneshot:
+ oneshot(model, task)
+
+######################################################################
projects / beaver.git / blobdiff