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Mkaglins/pruning masks applying #985

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Mkaglins/pruning masks applying #985

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230ba7c
POC of new mask applying algo
mkaglins Aug 26, 2021
1e7c220
Fix tests and change weights updating mechanism
mkaglins Oct 17, 2021
ae887b4
Merge branch 'develop' into mkaglins/pruning_masks_applying
mkaglins Oct 17, 2021
d235816
Fix mask type in apply_mask
mkaglins Oct 17, 2021
6e386d1
Small refactoring
mkaglins Oct 17, 2021
4effb86
Fix pylint
mkaglins Oct 17, 2021
64b35e0
UpdateWeightAndBias was refactored to use UpdateParameterList
mkaglins Oct 18, 2021
30da9d8
GroupNorm added
mkaglins Oct 18, 2021
dfd8b53
Removed zero_grad param and functionality, restoring weights in LeGR …
mkaglins Oct 19, 2021
351ccb8
Update nncf/torch/module_operations.py with docs
mkaglins Oct 19, 2021
0a4cab4
Update nncf/torch/module_operations.py
mkaglins Oct 19, 2021
e508faf
Fixed a lot of reerence graphs without BN + refactor
mkaglins Oct 21, 2021
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GroupNorm added
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@mkaglins
mkaglins committed Oct 18, 2021
commit 30da9d8e1a8ac67bf4ad67d8fe63f0e5c2ab4f2b
15 changes: 14 additions & 1 deletion nncf/torch/layers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -136,6 +136,18 @@ def from_module(module):
return nncf_bn


class NNCFGroupNorm(_NNCFModuleMixin, nn.GroupNorm):
op_func_name = "group_norm"

@staticmethod
def from_module(module):
assert module.__class__.__name__ == nn.GroupNorm.__name__

nncf_bn = NNCFGroupNorm(module.num_features)
dict_update(nncf_bn.__dict__, module.__dict__)
return nncf_bn


class NNCFConvTranspose2d(_NNCFModuleMixin, nn.ConvTranspose2d):
op_func_name = "conv_transpose2d"
target_weight_dim_for_compression = 1
Expand Down Expand Up @@ -222,7 +234,8 @@ def from_module(module):
NNCFConv2d: nn.Conv2d,
NNCFConv3d: nn.Conv3d,
NNCFLinear: nn.Linear,
NNCFBatchNorm:nn.BatchNorm2d,
NNCFBatchNorm : nn.BatchNorm2d,
NNCFGroupNorm : nn.GroupNorm,
NNCFConvTranspose2d: nn.ConvTranspose2d,
NNCFConvTranspose3d: nn.ConvTranspose3d,
NNCFEmbedding: nn.Embedding,
Expand Down
6 changes: 3 additions & 3 deletions nncf/torch/pruning/filter_pruning/algo.py
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Expand Up @@ -449,7 +449,7 @@ def set_pruning_rate(self, pruning_rate: Union[float, Dict[int, float]],
if self.zero_grad:
self.zero_grads_for_pruned_modules()

self._apply_masks()
self._propagate_masks()
if not groupwise_pruning_rates_set:
self._pruning_rate = passed_pruning_rate
else:
Expand Down Expand Up @@ -647,7 +647,7 @@ def _set_binary_masks_for_pruned_modules_globally_by_flops_target(self,
cur_num += 1
raise RuntimeError("Can't prune model to asked flops pruning rate")

def _apply_masks(self):
def _propagate_masks(self):
nncf_logger.debug("Propagating pruning masks")
# 1. Propagate masks for all modules
graph = self.model.get_original_graph()
Expand Down Expand Up @@ -676,7 +676,7 @@ def prepare_for_export(self):
"""
Applies pruning masks to layer weights before exporting the model to ONNX.
"""
self._apply_masks()
self._propagate_masks()

pruned_layers_stats = self.get_stats_for_pruned_modules()
nncf_logger.debug('Pruned layers statistics: \n%s', pruned_layers_stats.draw())
Expand Down