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module 'thinc_gpu_ops' has no attribute 'hash'

See original GitHub issue

On Win10, thinc 6.12.0, spacy 2.0.16, cupy 4.1.0

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-5-2432a7701a48> in <module>()
      1 # defining doc
----> 2 doc = nlp("Jill laughed at John Johnson.")
      3 spacy.displacy.render(doc, style='dep', options={'distance' : 140}, jupyter=True)

E:\Anaconda3python\lib\site-packages\spacy\language.py in __call__(self, text, disable)
    344             if not hasattr(proc, '__call__'):
    345                 raise ValueError(Errors.E003.format(component=type(proc), name=name))
--> 346             doc = proc(doc)
    347             if doc is None:
    348                 raise ValueError(Errors.E005.format(name=name))

pipeline.pyx in spacy.pipeline.Tagger.__call__()

pipeline.pyx in spacy.pipeline.Tagger.predict()

E:\Anaconda3python\lib\site-packages\thinc\neural\_classes\model.py in __call__(self, x)
    159             Must match expected shape
    160         '''
--> 161         return self.predict(x)
    162 
    163     def pipe(self, stream, batch_size=128):

E:\Anaconda3python\lib\site-packages\thinc\api.py in predict(self, X)
     53     def predict(self, X):
     54         for layer in self._layers:
---> 55             X = layer(X)
     56         return X
     57 

E:\Anaconda3python\lib\site-packages\thinc\neural\_classes\model.py in __call__(self, x)
    159             Must match expected shape
    160         '''
--> 161         return self.predict(x)
    162 
    163     def pipe(self, stream, batch_size=128):

E:\Anaconda3python\lib\site-packages\thinc\api.py in predict(seqs_in)
    291     def predict(seqs_in):
    292         lengths = layer.ops.asarray([len(seq) for seq in seqs_in])
--> 293         X = layer(layer.ops.flatten(seqs_in, pad=pad))
    294         return layer.ops.unflatten(X, lengths, pad=pad)
    295 

E:\Anaconda3python\lib\site-packages\thinc\neural\_classes\model.py in __call__(self, x)
    159             Must match expected shape
    160         '''
--> 161         return self.predict(x)
    162 
    163     def pipe(self, stream, batch_size=128):

E:\Anaconda3python\lib\site-packages\thinc\api.py in predict(self, X)
     53     def predict(self, X):
     54         for layer in self._layers:
---> 55             X = layer(X)
     56         return X
     57 

E:\Anaconda3python\lib\site-packages\thinc\neural\_classes\model.py in __call__(self, x)
    159             Must match expected shape
    160         '''
--> 161         return self.predict(x)
    162 
    163     def pipe(self, stream, batch_size=128):

E:\Anaconda3python\lib\site-packages\thinc\neural\_classes\model.py in predict(self, X)
    123 
    124     def predict(self, X):
--> 125         y, _ = self.begin_update(X)
    126         return y
    127 

E:\Anaconda3python\lib\site-packages\thinc\api.py in uniqued_fwd(X, drop)
    372                                                     return_counts=True)
    373         X_uniq = layer.ops.xp.ascontiguousarray(X[ind])
--> 374         Y_uniq, bp_Y_uniq = layer.begin_update(X_uniq, drop=drop)
    375         Y = Y_uniq[inv].reshape((X.shape[0],) + Y_uniq.shape[1:])
    376         def uniqued_bwd(dY, sgd=None):

E:\Anaconda3python\lib\site-packages\thinc\api.py in begin_update(self, X, drop)
     59         callbacks = []
     60         for layer in self._layers:
---> 61             X, inc_layer_grad = layer.begin_update(X, drop=drop)
     62             callbacks.append(inc_layer_grad)
     63         def continue_update(gradient, sgd=None):

E:\Anaconda3python\lib\site-packages\thinc\api.py in begin_update(X, *a, **k)
    174     def begin_update(X, *a, **k):
    175         forward, backward = split_backward(layers)
--> 176         values = [fwd(X, *a, **k) for fwd in forward]
    177 
    178         output = ops.xp.hstack(values)

E:\Anaconda3python\lib\site-packages\thinc\api.py in <listcomp>(.0)
    174     def begin_update(X, *a, **k):
    175         forward, backward = split_backward(layers)
--> 176         values = [fwd(X, *a, **k) for fwd in forward]
    177 
    178         output = ops.xp.hstack(values)

E:\Anaconda3python\lib\site-packages\thinc\api.py in wrap(*args, **kwargs)
    256     '''
    257     def wrap(*args, **kwargs):
--> 258         output = func(*args, **kwargs)
    259         if splitter is None:
    260             to_keep, to_sink = output

E:\Anaconda3python\lib\site-packages\thinc\api.py in begin_update(X, *a, **k)
    174     def begin_update(X, *a, **k):
    175         forward, backward = split_backward(layers)
--> 176         values = [fwd(X, *a, **k) for fwd in forward]
    177 
    178         output = ops.xp.hstack(values)

E:\Anaconda3python\lib\site-packages\thinc\api.py in <listcomp>(.0)
    174     def begin_update(X, *a, **k):
    175         forward, backward = split_backward(layers)
--> 176         values = [fwd(X, *a, **k) for fwd in forward]
    177 
    178         output = ops.xp.hstack(values)

E:\Anaconda3python\lib\site-packages\thinc\api.py in wrap(*args, **kwargs)
    256     '''
    257     def wrap(*args, **kwargs):
--> 258         output = func(*args, **kwargs)
    259         if splitter is None:
    260             to_keep, to_sink = output

E:\Anaconda3python\lib\site-packages\thinc\api.py in begin_update(X, *a, **k)
    174     def begin_update(X, *a, **k):
    175         forward, backward = split_backward(layers)
--> 176         values = [fwd(X, *a, **k) for fwd in forward]
    177 
    178         output = ops.xp.hstack(values)

E:\Anaconda3python\lib\site-packages\thinc\api.py in <listcomp>(.0)
    174     def begin_update(X, *a, **k):
    175         forward, backward = split_backward(layers)
--> 176         values = [fwd(X, *a, **k) for fwd in forward]
    177 
    178         output = ops.xp.hstack(values)

E:\Anaconda3python\lib\site-packages\thinc\api.py in wrap(*args, **kwargs)
    256     '''
    257     def wrap(*args, **kwargs):
--> 258         output = func(*args, **kwargs)
    259         if splitter is None:
    260             to_keep, to_sink = output

E:\Anaconda3python\lib\site-packages\thinc\api.py in begin_update(X, *a, **k)
    174     def begin_update(X, *a, **k):
    175         forward, backward = split_backward(layers)
--> 176         values = [fwd(X, *a, **k) for fwd in forward]
    177 
    178         output = ops.xp.hstack(values)

E:\Anaconda3python\lib\site-packages\thinc\api.py in <listcomp>(.0)
    174     def begin_update(X, *a, **k):
    175         forward, backward = split_backward(layers)
--> 176         values = [fwd(X, *a, **k) for fwd in forward]
    177 
    178         output = ops.xp.hstack(values)

E:\Anaconda3python\lib\site-packages\thinc\api.py in wrap(*args, **kwargs)
    256     '''
    257     def wrap(*args, **kwargs):
--> 258         output = func(*args, **kwargs)
    259         if splitter is None:
    260             to_keep, to_sink = output

E:\Anaconda3python\lib\site-packages\thinc\neural\_classes\hash_embed.py in begin_update(self, ids, drop)
     49         if ids.ndim >= 2:
     50             ids = self.ops.xp.ascontiguousarray(ids[:, self.column], dtype='uint64')
---> 51         keys = self.ops.hash(ids, self.seed) % self.nV
     52         vectors = self.vectors[keys].sum(axis=1)
     53         mask = self.ops.get_dropout_mask((vectors.shape[1],), drop)

ops.pyx in thinc.neural.ops.CupyOps.hash()

AttributeError: module 'thinc_gpu_ops' has no attribute 'hash'

Issue Analytics

State:
Created 5 years ago
Reactions:4
Comments:32 (6 by maintainers)

Top GitHub Comments

5reactions

devforfucommented, May 6, 2019

Have the same error on Ubuntu with latest pip version of spaCy. There are some references on how to solve it but none of them helps to fix my case. Other GPU-dependant libraries work well (i.e. PyTorch, TensorFlow, Catboost, etc.).

Could you please create some README with the most recent recommendations on how to enable GPU training? Right now it is a bit difficult to make spaCy work with GPU.

1reaction

pythonBergcommented, Aug 7, 2019

Has anyone resolved this? I first filed this issue in October but this thread is more lively.

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