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[BC-breaking] Use fn(param) instead of fn(param.data) in nn.Module._apply #21865

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[BC-breaking] Use fn(param) instead of fn(param.data) in nn.Module._apply #21865

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8df7cc6
Test fn(param)
Jun 17, 2019
fbb896a
add test
Jun 18, 2019
a1808b7
clean up comment
Jun 18, 2019
9af5732
change test
Jun 18, 2019
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25 changes: 25 additions & 0 deletions test/test_nn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1577,6 +1577,31 @@ def test_module_to_argparse(self):
with self.assertRaises(TypeError):
net.to(cpu, torch.tensor(3, dtype=torch.long), non_blocking=True)

def test_module_apply_inplace_op(self):
def add_one_inplace(t):
return t.add_(1.0)

# Test that applying an in-place operation to a module would bump
# the module's parameters' version counter.
m = nn.Linear(20, 10)
pvm = m.weight.mul(m.weight)
m_weight_version_saved = m.weight._version
m = m._apply(add_one_inplace)
self.assertGreater(m.weight._version, m_weight_version_saved)
with self.assertRaisesRegex(RuntimeError, "modified by an inplace operation"):
pvm.backward(torch.randn(10, 20))

# Test that applying an in-place operation to a module would bump
# the module's parameters' gradients' version counter.
m = nn.Linear(20, 10)
m.weight.grad = torch.randn(10, 20).requires_grad_()
pgm = m.weight.grad.mul(m.weight.grad)
m_weight_grad_version_saved = m.weight.grad._version
m = m._apply(add_one_inplace)
self.assertGreater(m.weight.grad._version, m_weight_grad_version_saved)
with self.assertRaisesRegex(RuntimeError, "modified by an inplace operation"):
pgm.backward(torch.randn(10, 20))

def test_type(self):
l = nn.Linear(10, 20)
net = nn.Module()
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10 changes: 6 additions & 4 deletions torch/nn/modules/module.py
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Original file line number Diff line number Diff line change
Expand Up @@ -195,11 +195,13 @@ def _apply(self, fn):

for param in self._parameters.values():
if param is not None:
# Tensors stored in modules are graph leaves, and we don't
# want to create copy nodes, so we have to unpack the data.
param.data = fn(param.data)
with torch.no_grad():
param_applied = fn(param)
param.data = param_applied
if param._grad is not None:
param._grad.data = fn(param._grad.data)
with torch.no_grad():
grad_applied = fn(param._grad)
param._grad.data = grad_applied

for key, buf in self._buffers.items():
if buf is not None:
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