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Speedup supports channel pruning #2906

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Speedup support channel pruning
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Merge branch 'master' into speedup-channel-pruning
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fix conv groups
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auto detect prune dim
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Merge branch 'master' into speedup-channel-pruning
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chicm-ms committed Sep 25, 2020
commit 891feb222b9506e8c1c7f5bbc456b1cf9891a86a
15 changes: 11 additions & 4 deletions src/sdk/pynni/nni/compression/torch/utils/mask_conflict.py
Original file line number Diff line number Diff line change
Expand Up @@ -237,6 +237,7 @@ def fix_mask(self):
channel_depen = InputChannelDependency(self.model, self.dummy_input, self.traced)
depen_sets = channel_depen.dependency_sets
sum_idx = (1, 2, 3) if self.conv_prune_dim == 0 else (0, 2, 3)
fine_grained = False
for dset in depen_sets:
if len(dset) <= 1:
continue
Expand All @@ -248,18 +249,24 @@ def fix_mask(self):
if name in self.masks:
_, m = get_module_by_name(self.model, name)
assert m is not None
mask = self.masks[name]['weight']
if type(m).__name__ == 'Conv2d':
channel_masks.append((self.masks[name]['weight'].abs().sum(sum_idx) != 0).int())
channel_mask = (mask.abs().sum(sum_idx) != 0).int()
channel_masks.append(channel_mask)
if (channel_mask.sum() * (mask.numel() / mask.shape[self.conv_prune_dim])).item() != (mask > 0).sum().item():
fine_grained = True
elif type(m).__name__ == 'Linear':
channel_masks.append((self.masks[name]['weight'].abs().sum(0) != 0).int())
channel_masks.append((mask.abs().sum(0) != 0).int())
elif type(m).__name__ == 'BatchNorm2d':
channel_masks.append((self.masks[name]['weight']).int())
channel_masks.append(mask.int())
else:
raise RuntimeError(f'unsupported module type: {type(m).__name__}')
else:
# no mask means not pruned, equivlent to full masks
channel_masks.append(None)

if fine_grained:
_logger.info('fine-grained mask detected, no mask conflict need to be fixed!')
break
if all(x is None for x in channel_masks):
continue
num_channels_list = [len(x) for x in channel_masks if x is not None]
Expand Down