# Deepcopy model errors

**URL:** <https://discuss.pytorch.org/t/deepcopy-model-errors/114566>\
**Category:** Uncategorized\
**Created:** [March 12, 2021, 9:22am UTC](https://discuss.pytorch.org/t/deepcopy-model-errors/114566 "2021-03-12T09:22:44Z")\
**Posts on this page:** 3\
**Page:** 1

<div class="post-metadata">

**Author:** ![samsja](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/samsja/32/35040_2.png) [@samsja](https://discuss.pytorch.org/u/samsja)\
**Post date:** [March 12, 2021, 9:22am UTC](https://discuss.pytorch.org/t/deepcopy-model-errors/114566/1 "2021-03-12T09:22:44Z")

</div>

Hi,

I have got a custom torch.nn.Module. I want to deepcopy it for checkpoint (according to this blog post [Can I deepcopy a model?](https://discuss.pytorch.org/t/can-i-deepcopy-a-model/52192))

However when I try:

```auto
import copy
model2 = copy.deepcopy(model)

```

I have the following error:

```auto
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-35-c0373e47f471> in <module>
      1 import copy
----> 2 model2 = copy.deepcopy(model)

/home/user/miniconda/lib/python3.8/copy.py in deepcopy(x, memo, _nil)
    170 y = x
    171 else:
--> 172 y = _reconstruct(x, memo, *rv)
    173 
    174 # If is its own copy, don't memoize.

/home/user/miniconda/lib/python3.8/copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
    268 if state is not None:
    269 if deep:
--> 270 state = deepcopy(state, memo)
    271 if hasattr(y, ' __setstate__'):
    272 y. __setstate__ (state)

/home/user/miniconda/lib/python3.8/copy.py in deepcopy(x, memo, _nil)
    144 copier = _deepcopy_dispatch.get(cls)
    145 if copier is not None:
--> 146 y = copier(x, memo)
    147 else:
    148 if issubclass(cls, type):

/home/user/miniconda/lib/python3.8/copy.py in _deepcopy_dict(x, memo, deepcopy)
    228 memo[id(x)] = y
    229 for key, value in x.items():
--> 230 y[deepcopy(key, memo)] = deepcopy(value, memo)
    231 return y
    232 d[dict] = _deepcopy_dict

/home/user/miniconda/lib/python3.8/copy.py in deepcopy(x, memo, _nil)
    170 y = x
    171 else:
--> 172 y = _reconstruct(x, memo, *rv)
    173 
    174 # If is its own copy, don't memoize.

/home/user/miniconda/lib/python3.8/copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
    294 for key, value in dictiter:
    295 key = deepcopy(key, memo)
--> 296 value = deepcopy(value, memo)
    297 y[key] = value
    298 else:

/home/user/miniconda/lib/python3.8/copy.py in deepcopy(x, memo, _nil)
    170 y = x
    171 else:
--> 172 y = _reconstruct(x, memo, *rv)
    173 
    174 # If is its own copy, don't memoize.

/home/user/miniconda/lib/python3.8/copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
    268 if state is not None:
    269 if deep:
--> 270 state = deepcopy(state, memo)
    271 if hasattr(y, ' __setstate__'):
    272 y. __setstate__ (state)

/home/user/miniconda/lib/python3.8/copy.py in deepcopy(x, memo, _nil)
    144 copier = _deepcopy_dispatch.get(cls)
    145 if copier is not None:
--> 146 y = copier(x, memo)
    147 else:
    148 if issubclass(cls, type):

/home/user/miniconda/lib/python3.8/copy.py in _deepcopy_dict(x, memo, deepcopy)
    228 memo[id(x)] = y
    229 for key, value in x.items():
--> 230 y[deepcopy(key, memo)] = deepcopy(value, memo)
    231 return y
    232 d[dict] = _deepcopy_dict

/home/user/miniconda/lib/python3.8/copy.py in deepcopy(x, memo, _nil)
    170 y = x
    171 else:
--> 172 y = _reconstruct(x, memo, *rv)
    173 
    174 # If is its own copy, don't memoize.

/home/user/miniconda/lib/python3.8/copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
    294 for key, value in dictiter:
    295 key = deepcopy(key, memo)
--> 296 value = deepcopy(value, memo)
    297 y[key] = value
    298 else:

/home/user/miniconda/lib/python3.8/copy.py in deepcopy(x, memo, _nil)
    170 y = x
    171 else:
--> 172 y = _reconstruct(x, memo, *rv)
    173 
    174 # If is its own copy, don't memoize.

/home/user/miniconda/lib/python3.8/copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
    268 if state is not None:
    269 if deep:
--> 270 state = deepcopy(state, memo)
    271 if hasattr(y, ' __setstate__'):
    272 y. __setstate__ (state)

/home/user/miniconda/lib/python3.8/copy.py in deepcopy(x, memo, _nil)
    144 copier = _deepcopy_dispatch.get(cls)
    145 if copier is not None:
--> 146 y = copier(x, memo)
    147 else:
    148 if issubclass(cls, type):

/home/user/miniconda/lib/python3.8/copy.py in _deepcopy_dict(x, memo, deepcopy)
    228 memo[id(x)] = y
    229 for key, value in x.items():
--> 230 y[deepcopy(key, memo)] = deepcopy(value, memo)
    231 return y
    232 d[dict] = _deepcopy_dict

/home/user/miniconda/lib/python3.8/copy.py in deepcopy(x, memo, _nil)
    170 y = x
    171 else:
--> 172 y = _reconstruct(x, memo, *rv)
    173 
    174 # If is its own copy, don't memoize.

/home/user/miniconda/lib/python3.8/copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
    294 for key, value in dictiter:
    295 key = deepcopy(key, memo)
--> 296 value = deepcopy(value, memo)
    297 y[key] = value
    298 else:

/home/user/miniconda/lib/python3.8/copy.py in deepcopy(x, memo, _nil)
    151 copier = getattr(x, " __deepcopy__", None)
    152 if copier is not None:
--> 153 y = copier(memo)
    154 else:
    155 reductor = dispatch_table.get(cls)

/home/user/miniconda/lib/python3.8/site-packages/torch/nn/parameter.py in __deepcopy__ (self, memo)
     30 return memo[id(self)]
     31 else:
---> 32 result = type(self)(self.data.clone(memory_format=torch.preserve_format), self.requires_grad)
     33 memo[id(self)] = result
     34 return result

TypeError: __new__ () takes from 1 to 2 positional arguments but 3 were given

```

I am not sure to understand where the issue is 🧐

my model:

```auto
RelationNet(
  (embedding): BasicEmbeddingModule(
    (conv_blocks): Sequential(
      (0): Sequential(
        (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1))
        (1): BatchNorm2d(64, eps=1e-05, momentum=1, affine=True, track_running_stats=True)
        (2): ReLU()
        (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
      )
      (1): Sequential(
        (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1))
        (1): BatchNorm2d(64, eps=1e-05, momentum=1, affine=True, track_running_stats=True)
        (2): ReLU()
        (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
      )
      (2): Sequential(
        (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (1): BatchNorm2d(64, eps=1e-05, momentum=1, affine=True, track_running_stats=True)
        (2): ReLU()
      )
      (3): Sequential(
        (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (1): BatchNorm2d(64, eps=1e-05, momentum=1, affine=True, track_running_stats=True)
        (2): ReLU()
      )
    )
  )
  (relation): BasicRelationModule(
    (conv1): Sequential(
      (0): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): BatchNorm2d(64, eps=1e-05, momentum=1, affine=True, track_running_stats=True)
      (2): ReLU()
      (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    )
    (conv2): Sequential(
      (0): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): BatchNorm2d(64, eps=1e-05, momentum=1, affine=True, track_running_stats=True)
      (2): ReLU()
      (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    )
    (linear1): LazyLinear(in_features=0, out_features=8, bias=True)
    (linear2): Linear(in_features=8, out_features=1, bias=True)
  )
)

```

pytorch version : 1.8.0  
python versions : 3.8.3

Thanks in advance 🙂

---

<div class="post-metadata">

**Author:** ![samsja](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/samsja/32/35040_2.png) [@samsja](https://discuss.pytorch.org/u/samsja)\
**Post date:** [March 15, 2021, 8:40am UTC](https://discuss.pytorch.org/t/deepcopy-model-errors/114566/2 "2021-03-15T08:40:15Z")

</div>

Hi,

I have found the particular module that can’t be deepcopied.

Turns out that it is the LaziLinear Module that create the errors.

```auto
import copy
import torch.nn as nn

a = nn.LazyLinear(12)
copy.deepcopy(a)

```

Errors :

```auto
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-38-62e92e6d76d5> in <module>
----> 1 copy.deepcopy(a)

/home/user/miniconda/lib/python3.8/copy.py in deepcopy(x, memo, _nil)
    170 y = x
    171 else:
--> 172 y = _reconstruct(x, memo, *rv)
    173 
    174 # If is its own copy, don't memoize.

/home/user/miniconda/lib/python3.8/copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
    268 if state is not None:
    269 if deep:
--> 270 state = deepcopy(state, memo)
    271 if hasattr(y, ' __setstate__'):
    272 y. __setstate__ (state)

/home/user/miniconda/lib/python3.8/copy.py in deepcopy(x, memo, _nil)
    144 copier = _deepcopy_dispatch.get(cls)
    145 if copier is not None:
--> 146 y = copier(x, memo)
    147 else:
    148 if issubclass(cls, type):

/home/user/miniconda/lib/python3.8/copy.py in _deepcopy_dict(x, memo, deepcopy)
    228 memo[id(x)] = y
    229 for key, value in x.items():
--> 230 y[deepcopy(key, memo)] = deepcopy(value, memo)
    231 return y
    232 d[dict] = _deepcopy_dict

/home/user/miniconda/lib/python3.8/copy.py in deepcopy(x, memo, _nil)
    170 y = x
    171 else:
--> 172 y = _reconstruct(x, memo, *rv)
    173 
    174 # If is its own copy, don't memoize.

/home/user/miniconda/lib/python3.8/copy.py in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy)
    294 for key, value in dictiter:
    295 key = deepcopy(key, memo)
--> 296 value = deepcopy(value, memo)
    297 y[key] = value
    298 else:

/home/user/miniconda/lib/python3.8/copy.py in deepcopy(x, memo, _nil)
    151 copier = getattr(x, " __deepcopy__", None)
    152 if copier is not None:
--> 153 y = copier(memo)
    154 else:
    155 reductor = dispatch_table.get(cls)

/home/user/miniconda/lib/python3.8/site-packages/torch/nn/parameter.py in __deepcopy__ (self, memo)
     30 return memo[id(self)]
     31 else:
---> 32 result = type(self)(self.data.clone(memory_format=torch.preserve_format), self.requires_grad)
     33 memo[id(self)] = result
     34 return result

TypeError: __new__ () takes from 1 to 2 positional arguments but 3 were given

```

If anyone has any thought …

Thanks in advance

---

<div class="post-metadata">

**Author:** ![samsja](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/samsja/32/35040_2.png) [@samsja](https://discuss.pytorch.org/u/samsja)\
**Post date:** [March 15, 2021, 9:07am UTC](https://discuss.pytorch.org/t/deepcopy-model-errors/114566/3 "2021-03-15T09:07:41Z")

</div>

Solution : wait for the first call to the model (and so the LazyLinear) before deepcopying it.

It would be great to have a warning when deepcopying a LazyLinear somewhere.