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Small vectorization improvements #336

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merged 3 commits into from
Oct 28, 2022
Merged

Small vectorization improvements #336

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e33ca64
Vectorization doesn't expand if objective bs = 1.
luisenp Oct 22, 2022
65a51d9
Retract all variables now updates vectorization.
luisenp Oct 22, 2022
cd7d722
Fix typo.
luisenp Oct 27, 2022
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9 changes: 6 additions & 3 deletions theseus/core/objective.py
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Original file line number Diff line number Diff line change
Expand Up @@ -509,9 +509,7 @@ def _get_batch_size(batch_sizes: Sequence[int]) -> int:
self._batch_size = _get_batch_size(batch_sizes)

def _vectorization_needs_update(self):
num_updates = dict(
(name, v._num_updates) for name, v in self._all_variables.items()
)
num_updates = {name: v._num_updates for name, v in self._all_variables.items()}
needs = False
if num_updates != self._num_updates_variables:
self._num_updates_variables = num_updates
Expand Down Expand Up @@ -575,6 +573,11 @@ def retract_optim_vars(
self._retract_method(
delta, ordering, ignore_mask=ignore_mask, force_update=force_update
)
# Updating immediately is useful, since it will keep grad history if
# needed. Otherwise, with lazy waitng we can be in a situation where
# vectorization is updated with torch.no_grad() (e.g., for error logging),
# and then it has to be run again later when grad is back on.
self.update_vectorization_if_needed()

def _enable_vectorization(
self,
Expand Down
6 changes: 5 additions & 1 deletion theseus/core/vectorizer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -228,7 +228,11 @@ def _update_all_cost_fns_var_tensors(
# when updating the vectorized variable containers.
tensor = (
var.tensor
if (var_batch_size > 1 or Vectorize._SHARED_TOKEN in name)
if (
var_batch_size > 1
or objective_batch_size == 1
or Vectorize._SHARED_TOKEN in name
)
else Vectorize._expand(var.tensor, objective_batch_size)
)
names_to_tensors[name].append(tensor)
Expand Down