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[Performance] Faster params and buffer registration in TensorDictParams #569

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merged 2 commits into from
Nov 23, 2023
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[Performance] Faster params and buffer registration in TensorDictParams #569

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vmoens Nov 23, 2023
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vmoens Nov 23, 2023
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29 changes: 21 additions & 8 deletions tensordict/nn/params.py
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Original file line number Diff line number Diff line change
Expand Up @@ -234,6 +234,11 @@ class TensorDictParams(TensorDictBase, nn.Module):
If ``no_convert`` is ``True`` and if non-parameters are present, they
will be registered as buffers.
Defaults to ``False``.
lock (bool): if ``True``, the tensordict hosted by TensorDictParams will
be locked. This can be useful to avoid unwanted modifications, but
also restricts the operations that can be done over the object (and
can have significant performance impact when `unlock_()` is required).
Defaults to ``False``.

Examples:
>>> from torch import nn
Expand Down Expand Up @@ -273,7 +278,9 @@ class TensorDictParams(TensorDictBase, nn.Module):

"""

def __init__(self, parameters: TensorDictBase, *, no_convert=False):
def __init__(
self, parameters: TensorDictBase, *, no_convert=False, lock: bool = False
):
super().__init__()
if isinstance(parameters, TensorDictParams):
parameters = parameters._param_td
Expand All @@ -283,7 +290,10 @@ def __init__(self, parameters: TensorDictBase, *, no_convert=False):
func = _maybe_make_param
else:
func = _maybe_make_param_or_buffer
self._param_td = _apply_leaves(self._param_td, lambda x: func(x)).lock_()
self._param_td = _apply_leaves(self._param_td, lambda x: func(x))
self._lock = lock
if lock:
self._param_td.lock_()
self._reset_params()
self._is_locked = False
self._locked_tensordicts = []
Expand All @@ -307,18 +317,21 @@ def _apply_get_post_hook(self, val):
def _reset_params(self):
parameters = self._param_td
param_keys = []
params = []
buffer_keys = []
buffers = []
for key, value in parameters.items(True, True):
# flatten key
if isinstance(key, tuple):
key = "_".join(key)
if isinstance(value, nn.Parameter):
param_keys.append(key)
params.append(value)
else:
buffer_keys.append(key)
self.__dict__["_parameters"] = (
parameters.select(*param_keys).flatten_keys("_").to_dict()
)
self.__dict__["_buffers"] = (
parameters.select(*buffer_keys).flatten_keys("_").to_dict()
)
buffers.append(value)
self.__dict__["_parameters"] = dict(zip(param_keys, params))
self.__dict__["_buffers"] = dict(zip(buffer_keys, buffers))

@classmethod
def __torch_function__(
Expand Down
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