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Feat (nn): cache modules that require subtensor slicing #628

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Jun 15, 2023
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Feat (nn): cache modules that require subtensor slicing #628

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22 changes: 16 additions & 6 deletions src/brevitas/nn/mixin/parameter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,6 +34,7 @@ def __init__(self, weight_quant: Optional[WeightQuantType], **kwargs):
kwargs_prefix='weight_',
proxy_prefix='weight_',
**kwargs)
self._cached_sub_tensor_slice_list_modules = None

@property
@abstractmethod
Expand Down Expand Up @@ -64,9 +65,17 @@ def quant_weight(
# prepare the quantizer for a subtensor input, if any modifications are required
# we set a list of tuples rather than a list of slices so that it's jit friendly
# slices generation is handled by each module internally
for m in self.weight_quant.modules():
if hasattr(m, 'subtensor_slice_list'):
m.setattr('subtensor_slice_list', subtensor_slice_list)

# we cache which modules require the attribute
if self._cached_sub_tensor_slice_list_modules is not None:
for m in self._cached_sub_tensor_slice_list_modules:
m.subtensor_slice_list = subtensor_slice_list
else:
self._cached_sub_tensor_slice_list_modules = []
for m in self.weight_quant.modules():
if hasattr(m, 'subtensor_slice_list'):
self._cached_sub_tensor_slice_list_modules.append(m)
m.subtensor_slice_list = subtensor_slice_list
# generate slices for the weight tensor based on the list passed in
weight_slice_tuple = tuple(
slice(*s) if s is not None else slice(s) for s in subtensor_slice_list)
Expand All @@ -91,9 +100,10 @@ def quant_weight(
out = self.weight_quant(self.weight[weight_slice_tuple])
if subtensor_slice_list is not None:
# Restore the quantizer behaviour to full tensor quantization
for m in self.weight_quant.modules():
if hasattr(m, 'subtensor_slice_list'):
m.setattr('subtensor_slice_list', None)
# The modules to slice should have been cached already at this point
assert self._cached_sub_tensor_slice_list_modules is not None, "Missing cache of modules to slice."
for m in self._cached_sub_tensor_slice_list_modules:
m.subtensor_slice_list = None
return out

def int_weight(self, float_datatype=False):
Expand Down