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Update
author
François Fleuret
<francois@fleuret.org>
Thu, 23 Mar 2023 08:24:59 +0000
(09:24 +0100)
committer
François Fleuret
<francois@fleuret.org>
Thu, 23 Mar 2023 08:24:59 +0000
(09:24 +0100)
beaver.py
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diff --git
a/beaver.py
b/beaver.py
index
8fe9a9b
..
69116ea
100755
(executable)
--- a/
beaver.py
+++ b/
beaver.py
@@
-135,24
+135,33
@@
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)
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
+
order =
order.sort(1).indices
else:
else:
-
return
(
+
order =
(
torch.arange(x.size(1), device=x.device).unsqueeze(0).expand(x.size(0), -1)
)
torch.arange(x.size(1), device=x.device).unsqueeze(0).expand(x.size(0), -1)
)
+ return order
-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
+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:
else:
- return x.gather(1, order)
+ 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
# ar_mask is a Boolean matrix of same shape as input, with 1s on the
# tokens that should be generated
@@
-186,8
+195,7
@@
def compute_perplexity(model, split="train"):
for input in task.batches(split=split):
input = input.to(device)
for input in task.batches(split=split):
input = input.to(device)
- order = generation_order(input, task.height * task.width)
- input = shuffle(input, order)
+ input, order = shuffle(input, task.height * task.width)
output = model(mygpt.BracketedSequence(input), order=order).x
loss = F.cross_entropy(output.transpose(1, 2), input)
acc_loss += loss.item() * input.size(0)
output = model(mygpt.BracketedSequence(input), order=order).x
loss = F.cross_entropy(output.transpose(1, 2), input)
acc_loss += loss.item() * input.size(0)
@@
-252,10
+260,9
@@
def oneshot(gpt, task):
acc_train_loss, nb_train_samples = 0, 0
for mazes, policies in task.policy_batches(split="train"):
acc_train_loss, nb_train_samples = 0, 0
for mazes, policies in task.policy_batches(split="train"):
- order = generation_order(mazes, task.height * task.width)
- x = shuffle(mazes, order)
+ x, order = shuffle(mazes, task.height * task.width)
x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
- output_gpt =
shuffle(x, order, reorder
=True)
+ output_gpt =
reorder(x, order, back
=True)
output = model(output_gpt)
loss = compute_loss(mazes, output, policies, task.height, task.width)
output = model(output_gpt)
loss = compute_loss(mazes, output, policies, task.height, task.width)
@@
-268,10
+275,9
@@
def oneshot(gpt, task):
acc_test_loss, nb_test_samples = 0, 0
for mazes, policies in task.policy_batches(split="test"):
acc_test_loss, nb_test_samples = 0, 0
for mazes, policies in task.policy_batches(split="test"):
- order = generation_order(mazes, task.height * task.width)
- x = shuffle(mazes, order)
+ x, order = shuffle(mazes, task.height * task.width)
x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
- output_gpt =
shuffle(x, order, reorder
=True)
+ 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)
output = model(output_gpt)
loss = compute_loss(mazes, output, policies, task.height, task.width)
acc_test_loss += loss.item() * mazes.size(0)
@@
-284,10
+290,9
@@
def oneshot(gpt, task):
# -------------------
mazes = task.test_input[:32, : task.height * task.width]
policies = task.test_policies[:32]
# -------------------
mazes = task.test_input[:32, : task.height * task.width]
policies = task.test_policies[:32]
- order = generation_order(mazes, task.height * task.width)
- x = shuffle(mazes, order)
+ x, order = shuffle(mazes, task.height * task.width)
x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
x = gpt(mygpt.BracketedSequence(x), mode=args.oneshot_input, order=order).x
- output_gpt =
shuffle(x, order, reorder
=True)
+ output_gpt =
reorder(x, order, back
=True)
output = model(output_gpt)
if args.oneshot_output == "policy":
targets = policies.permute(0, 2, 1)
output = model(output_gpt)
if args.oneshot_output == "policy":
targets = policies.permute(0, 2, 1)
@@
-426,11
+431,11
@@
class TaskMaze(Task):
ar_mask = result.new_zeros(result.size())
ar_mask[:, self.height * self.width :] = 1
result *= 1 - ar_mask
ar_mask = result.new_zeros(result.size())
ar_mask[:, self.height * self.width :] = 1
result *= 1 - ar_mask
-
order = generation_order
(result, self.height * self.width)
+
result, order = shuffle
(result, self.height * self.width)
masked_inplace_autoregression(
model, self.batch_size, result, ar_mask, order=order
)
masked_inplace_autoregression(
model, self.batch_size, result, ar_mask, order=order
)
- result =
shuffle(result, order, reorder
=True)
+ result =
reorder(result, 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(result)
nb_correct += maze.path_correctness(mazes, paths).long().sum()
nb_total += mazes.size(0)
@@
-608,8
+613,7
@@
for n_epoch in range(nb_epochs_finished, args.nb_epochs):
for input in task.batches(split="train"):
input = input.to(device)
for input in task.batches(split="train"):
input = input.to(device)
- order = generation_order(input, task.height * task.width)
- input = shuffle(input, order)
+ input, order = shuffle(input, task.height * task.width)
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)
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)