Skip to content

Make integer parameters and buffers immune to float(), double() and half() #3820

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
soumith merged 4 commits into from
Nov 22, 2017
Merged

Make integer parameters and buffers immune to float(), double() and half() #3820

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
449ce32
Avoid casting integer params and buffers to float(), double() and half()
lantiga Nov 21, 2017
5924bc4
Add test for immune integer buffers
lantiga Nov 21, 2017
fe1b657
Fix documentation for float(), double() and half()
lantiga Nov 21, 2017
3777533
Fix test
lantiga Nov 22, 2017
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
8 changes: 8 additions & 0 deletions test/test_nn.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -811,12 +811,20 @@ def test_type(self):
net.l = l
net.l2 = l
net.add_module('empty', None)
net.register_buffer('indices', torch.LongTensor(1))
net.float()
self.assertIsInstance(l.weight.data, torch.FloatTensor)
self.assertIsInstance(l.bias.data, torch.FloatTensor)
self.assertIsInstance(net.indices, torch.LongTensor)
net.double()
self.assertIsInstance(l.weight.data, torch.DoubleTensor)
self.assertIsInstance(l.bias.data, torch.DoubleTensor)
self.assertIsInstance(net.indices, torch.LongTensor)
if TEST_CUDA:
net.float().cuda()
self.assertIsInstance(l.weight.data, torch.cuda.FloatTensor)
self.assertIsInstance(l.bias.data, torch.cuda.FloatTensor)
self.assertIsInstance(net.indices, torch.cuda.LongTensor)
net.type(torch.FloatTensor)
self.assertIsInstance(l.weight.data, torch.FloatTensor)
self.assertIsInstance(l.bias.data, torch.FloatTensor)
Expand Down
3 changes: 3 additions & 0 deletions torch/__init__.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -266,6 +266,9 @@ def storage_type(cls):
CharTensor, ByteTensor, HalfTensor
}

_integer_tensor_classes = {
LongTensor, IntTensor, ShortTensor, CharTensor, ByteTensor
}

set_default_tensor_type('torch.FloatTensor')

Expand Down
6 changes: 6 additions & 0 deletions torch/cuda/__init__.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -522,6 +522,12 @@ def storage_type():
torch._tensor_classes.add(ByteTensor)
torch._tensor_classes.add(HalfTensor)

torch._integer_tensor_classes.add(LongTensor)
torch._integer_tensor_classes.add(IntTensor)
torch._integer_tensor_classes.add(ShortTensor)
torch._integer_tensor_classes.add(CharTensor)
torch._integer_tensor_classes.add(ByteTensor)

from . import sparse
from . import profiler
from . import nvtx
Expand Down
6 changes: 6 additions & 0 deletions torch/cuda/sparse.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -86,3 +86,9 @@ def is_signed(self):
_sparse_tensor_classes.add(ByteTensor)
_sparse_tensor_classes.add(HalfTensor)
torch._tensor_classes.update(_sparse_tensor_classes)

torch._integer_tensor_classes.add(LongTensor)
torch._integer_tensor_classes.add(IntTensor)
torch._integer_tensor_classes.add(ShortTensor)
torch._integer_tensor_classes.add(CharTensor)
torch._integer_tensor_classes.add(ByteTensor)
6 changes: 6 additions & 0 deletions torch/distributed/remote_types.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -129,6 +129,12 @@ def storage_type(cls):
torch._tensor_classes.add(CharTensor)
torch._tensor_classes.add(ByteTensor)

torch._integer_tensor_classes.add(LongTensor)
torch._integer_tensor_classes.add(IntTensor)
torch._integer_tensor_classes.add(ShortTensor)
torch._integer_tensor_classes.add(CharTensor)
torch._integer_tensor_classes.add(ByteTensor)

_type_names = ['Double', 'Float', 'Long', 'Int', 'Short', 'Char', 'Byte']
_locals = locals()
_tensors = [_locals[t + 'Tensor'] for t in _type_names]
Expand Down
12 changes: 6 additions & 6 deletions torch/nn/modules/module.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -235,28 +235,28 @@ def type(self, dst_type):
return self._apply(lambda t: t.type(dst_type))

def float(self):
"""Casts all parameters and buffers to float datatype.
"""Casts all floating point parameters and buffers to float datatype.

Returns:
Module: self
"""
return self._apply(lambda t: t.float())
return self._apply(lambda t: t.float() if not type(t) in torch._integer_tensor_classes else t)

def double(self):
"""Casts all parameters and buffers to double datatype.
"""Casts all floating point parameters and buffers to double datatype.

Returns:
Module: self
"""
return self._apply(lambda t: t.double())
return self._apply(lambda t: t.double() if not type(t) in torch._integer_tensor_classes else t)

def half(self):
"""Casts all parameters and buffers to half datatype.
"""Casts all floating point parameters and buffers to half datatype.

Returns:
Module: self
"""
return self._apply(lambda t: t.half())
return self._apply(lambda t: t.half() if not type(t) in torch._integer_tensor_classes else t)

def register_backward_hook(self, hook):
"""Registers a backward hook on the module.
Expand Down
6 changes: 6 additions & 0 deletions torch/sparse/__init__.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -178,4 +178,10 @@ def is_signed(self):
_sparse_tensor_classes.add(ByteTensor)
torch._tensor_classes.update(_sparse_tensor_classes)

torch._integer_tensor_classes.add(LongTensor)
torch._integer_tensor_classes.add(IntTensor)
torch._integer_tensor_classes.add(ShortTensor)
torch._integer_tensor_classes.add(CharTensor)
torch._integer_tensor_classes.add(ByteTensor)

_C._sparse_init()