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SyncBN: preserve qconfig if it exists #45317

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15 changes: 15 additions & 0 deletions test/quantization/test_workflow_module.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1536,6 +1536,21 @@ def forward(self, x):
isinstance(fused_model.conv.bn, nn.SyncBatchNorm),
"Expected BN to be converted to SyncBN")

def test_syncbn_preserves_qconfig(self):
"""
Makes sure that if a BatchNorm is not fused and a qconfig exists,
convering the module to SyncBatchNorm preserves the qconfig.
"""
m = nn.Sequential(
nn.Conv2d(1, 1, 1),
nn.BatchNorm2d(1),
)
m[1].qconfig = torch.quantization.default_qconfig
m = torch.nn.SyncBatchNorm.convert_sync_batchnorm(m)
self.assertTrue(
hasattr(m[1], "qconfig"),
"missing qconfig after SyncBatchNorm conversion")

@unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
@unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
@override_qengines
Expand Down
14 changes: 8 additions & 6 deletions torch/nn/modules/batchnorm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -114,7 +114,7 @@ def forward(self, input: Tensor) -> Tensor:
else: # use exponential moving average
exponential_average_factor = self.momentum

r"""
r"""
Decide whether the mini-batch stats should be used for normalization rather than the buffers.
Mini-batch stats are used in training mode, and in eval mode when buffers are None.
"""
Expand Down Expand Up @@ -185,7 +185,7 @@ class BatchNorm1d(_BatchNorm):
track_running_stats: a boolean value that when set to ``True``, this
module tracks the running mean and variance, and when set to ``False``,
this module does not track such statistics, and initializes statistics
buffers :attr:`running_mean` and :attr:`running_var` as ``None``.
buffers :attr:`running_mean` and :attr:`running_var` as ``None``.
When these buffers are ``None``, this module always uses batch statistics.
in both training and eval modes. Default: ``True``

Expand Down Expand Up @@ -258,7 +258,7 @@ class BatchNorm2d(_BatchNorm):
track_running_stats: a boolean value that when set to ``True``, this
module tracks the running mean and variance, and when set to ``False``,
this module does not track such statistics, and initializes statistics
buffers :attr:`running_mean` and :attr:`running_var` as ``None``.
buffers :attr:`running_mean` and :attr:`running_var` as ``None``.
When these buffers are ``None``, this module always uses batch statistics.
in both training and eval modes. Default: ``True``

Expand Down Expand Up @@ -332,7 +332,7 @@ class BatchNorm3d(_BatchNorm):
track_running_stats: a boolean value that when set to ``True``, this
module tracks the running mean and variance, and when set to ``False``,
this module does not track such statistics, and initializes statistics
buffers :attr:`running_mean` and :attr:`running_var` as ``None``.
buffers :attr:`running_mean` and :attr:`running_var` as ``None``.
When these buffers are ``None``, this module always uses batch statistics.
in both training and eval modes. Default: ``True``

Expand Down Expand Up @@ -414,7 +414,7 @@ class SyncBatchNorm(_BatchNorm):
track_running_stats: a boolean value that when set to ``True``, this
module tracks the running mean and variance, and when set to ``False``,
this module does not track such statistics, and initializes statistics
buffers :attr:`running_mean` and :attr:`running_var` as ``None``.
buffers :attr:`running_mean` and :attr:`running_var` as ``None``.
When these buffers are ``None``, this module always uses batch statistics.
in both training and eval modes. Default: ``True``
process_group: synchronization of stats happen within each process group
Expand Down Expand Up @@ -493,7 +493,7 @@ def forward(self, input: Tensor) -> Tensor:
else: # use exponential moving average
exponential_average_factor = self.momentum

r"""
r"""
Decide whether the mini-batch stats should be used for normalization rather than the buffers.
Mini-batch stats are used in training mode, and in eval mode when buffers are None.
"""
Expand Down Expand Up @@ -576,6 +576,8 @@ def convert_sync_batchnorm(cls, module, process_group=None):
module_output.running_mean = module.running_mean
module_output.running_var = module.running_var
module_output.num_batches_tracked = module.num_batches_tracked
if hasattr(module, "qconfig"):
module_output.qconfig = module.qconfig
for name, child in module.named_children():
module_output.add_module(name, cls.convert_sync_batchnorm(child, process_group))
del module
Expand Down