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[jit] Fix __constants__ for some nn modules #21071

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[jit] Fix __constants__ for some nn modules #21071

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[jit] Fix __constants__ for some nn modules
May 29, 2019
88f192a
fix
May 29, 2019
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14 changes: 10 additions & 4 deletions test/test_jit.py
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Expand Up @@ -14806,16 +14806,22 @@ class TheModule(torch.jit.ScriptModule):
def __init__(self):
super(TheModule, self).__init__()
self.submodule = nn_module(*constructor_args)

def make_module(script):
module = TheModule()
# check __repr__
str(module)
module.define(script)
return module

# module cannot be imported / exported
if module_name in EXCLUDE_MODULE_EXPORT_IMPORT:
with self.disableEmitHook():
module = TheModule()
module.define(script)
module = make_module(script)
create_script_module.last_graph = module.graph
mod = module(*args)
else:
module = TheModule()
module.define(script)
module = make_module(script)
self.assertExportImportModule(module, tensors)
create_script_module.last_graph = module.graph
mod = module(*args)
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1 change: 1 addition & 0 deletions torch/nn/modules/activation.py
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Expand Up @@ -829,6 +829,7 @@ class PReLU(Module):
>>> input = torch.randn(2)
>>> output = m(input)
"""
__constants__ = ['num_parameters']

def __init__(self, num_parameters=1, init=0.25):
self.num_parameters = num_parameters
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3 changes: 2 additions & 1 deletion torch/nn/modules/batchnorm.py
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Expand Up @@ -15,7 +15,8 @@
class _BatchNorm(Module):
_version = 2
__constants__ = ['track_running_stats', 'momentum', 'eps', 'weight', 'bias',
'running_mean', 'running_var', 'num_batches_tracked']
'running_mean', 'running_var', 'num_batches_tracked',
'num_features', 'affine']

def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True,
track_running_stats=True):
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8 changes: 3 additions & 5 deletions torch/nn/modules/conv.py
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Expand Up @@ -12,7 +12,9 @@
@weak_module
class _ConvNd(Module):

__constants__ = ['stride', 'padding', 'dilation', 'groups', 'bias', 'padding_mode']
__constants__ = ['stride', 'padding', 'dilation', 'groups', 'bias',
'padding_mode', 'output_padding', 'in_channels',
'out_channels', 'kernel_size']

def __init__(self, in_channels, out_channels, kernel_size, stride,
padding, dilation, transposed, output_padding,
Expand Down Expand Up @@ -478,10 +480,6 @@ def forward(self, input):

@weak_module
class _ConvTransposeMixin(object):
__constants__ = ['stride', 'padding', 'kernel_size', 'dim_size',
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@eellison eellison May 29, 2019

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same ?

'output_padding', 'groups', 'dilation', 'transposed',
'bias', 'padding_mode']

@weak_script_method
def forward(self, input, output_size=None):
# type(Tensor, Optional[List[int]]) -> Tensor
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3 changes: 0 additions & 3 deletions torch/nn/modules/instancenorm.py
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Expand Up @@ -4,9 +4,6 @@


class _InstanceNorm(_BatchNorm):
__constants__ = ['running_mean', 'running_var', 'weight', 'bias',
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@eellison eellison May 29, 2019

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why were these all removed ?

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@driazati driazati May 29, 2019

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They're on the parent class

'track_running_stats', 'momentum', 'eps']

def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=False,
track_running_stats=False):
super(_InstanceNorm, self).__init__(
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2 changes: 1 addition & 1 deletion torch/nn/modules/normalization.py
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Expand Up @@ -129,7 +129,7 @@ class LayerNorm(Module):

.. _`Layer Normalization`: https://arxiv.org/abs/1607.06450
"""
__constants__ = ['normalized_shape', 'weight', 'bias', 'eps']
__constants__ = ['normalized_shape', 'weight', 'bias', 'eps', 'elementwise_affine']

def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True):
super(LayerNorm, self).__init__()
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2 changes: 1 addition & 1 deletion torch/nn/modules/sparse.py
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Expand Up @@ -252,7 +252,7 @@ class EmbeddingBag(Module):
tensor([[-0.8861, -5.4350, -0.0523],
[ 1.1306, -2.5798, -1.0044]])
"""
__constants__ = ['num_embeddings, embedding_dim', 'max_norm', 'norm_type',
__constants__ = ['num_embeddings', 'embedding_dim', 'max_norm', 'norm_type',
'scale_grad_by_freq', 'mode', 'sparse']

def __init__(self, num_embeddings, embedding_dim,
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