问题描述
我正在尝试评估Spacy最类似的方法（https://spacy.io/api/vectors#most_similar）的性能。我很好奇它是否可以在GPU上更快地工作。像这样的功能：
def spacy_most_similar(word,topn=10):
ms = nlp_ru.vocab.vectors.most_similar(nlp_ru(word).vector.reshape(1,100),n=topn)
words = [nlp_ru.vocab.strings[w] for w in ms[0][0]]
distances = ms[2]
return words,distances
spacy_most_similar("дерево",10)
在cpu版本上正常工作，但是在GPU（使用CuPy数组而不是NumPy）上，我收到错误消息：
TypeError Traceback (most recent call last)
<ipython-input-8-ea5e049ec55b> in <module>()
7 distances = ms[2]
8 return words,distances
----> 9 spacy_most_similar("дерево",10)
<ipython-input-8-ea5e049ec55b> in spacy_most_similar(word,topn)
3 print(nlp_ru(word).vector.reshape(1,100).shape)
4 ms = nlp_ru.vocab.vectors.most_similar(
----> 5 nlp_ru(word).vector.reshape(1,n=topn)
6 words = [nlp_ru.vocab.strings[w] for w in ms[0][0]]
7 distances = ms[2]
vectors.pyx in spacy.vectors.Vectors.most_similar()
TypeError: list indices must be integers or slices,not cupy.core.core.ndarray
我也尝试过这种方法：
def spacy_most_similar(word,topn=10):
ms = nlp_ru.vocab.vectors.most_similar(np.asarray([nlp_ru.vocab.vectors[nlp_ru.vocab.strings[word]]]),10)
在cpu上一切正常，但是对于GPU版本（我将np更改为cp）：
import cupy as cp
def spacy_most_similar(word,topn=10):
with cp.cuda.Device(0):
nlp_ru.vocab.vectors.data = cp.asarray(nlp_ru.vocab.vectors.data)
ms = nlp_ru.vocab.vectors.most_similar(cp.asarray([nlp_ru.vocab.vectors[nlp_ru.vocab.strings[word]]]),10)
我遇到这样的错误：
TypeError Traceback (most recent call last)
<ipython-input-6-876656d5f75d> in <module>()
7 distances = ms[2]
8 return words,10)
<ipython-input-6-876656d5f75d> in spacy_most_similar(word,topn)
3 with cp.cuda.Device(0):
4 nlp_ru.vocab.vectors.data = cp.asarray(nlp_ru.vocab.vectors.data)
----> 5 ms = nlp_ru.vocab.vectors.most_similar(cp.asarray([nlp_ru.vocab.vectors[nlp_ru.vocab.strings[word]]]),n=topn)
6 words = [nlp_ru.vocab.strings[w] for w in ms[0][0]]
7 distances = ms[2]
vectors.pyx in spacy.vectors.Vectors.most_similar()
TypeError: unhashable type: 'cupy.core.core.ndarray'
您能帮我为most_similar（）方法建立正确的CuPy输入吗？
解决方法
鉴于现有的source code，我怀疑您是否可以在GPU上进行most_similar：
def most_similar(self,queries,*,batch_size=1024,n=1,sort=True):
"""For each of the given vectors,find the n most similar entries
to it,by cosine.
Queries are by vector. Results are returned as a `(keys,best_rows,scores)` tuple. If `queries` is large,the calculations are performed in
chunks,to avoid consuming too much memory. You can set the `batch_size`
to control the size/space trade-off during the calculations.
queries (ndarray): An array with one or more vectors.
batch_size (int): The batch size to use.
n (int): The number of entries to return for each query.
sort (bool): Whether to sort the n entries returned by score.
RETURNS (tuple): The most similar entries as a `(keys,scores)`
tuple.
"""
filled = sorted(list({row for row in self.key2row.values()}))
if len(filled) < n:
raise ValueError(Errors.E198.format(n=n,n_rows=len(filled)))
xp = get_array_module(self.data)
norms = xp.linalg.norm(self.data[filled],axis=1,keepdims=True)
norms[norms == 0] = 1
vectors = self.data[filled] / norms
best_rows = xp.zeros((queries.shape[0],n),dtype='i')
scores = xp.zeros((queries.shape[0],dtype='f')
# Work in batches,to avoid memory problems.
for i in range(0,queries.shape[0],batch_size):
batch = queries[i : i+batch_size]
batch_norms = xp.linalg.norm(batch,keepdims=True)
batch_norms[batch_norms == 0] = 1
batch /= batch_norms
# batch e.g. (1024,300)
# vectors e.g. (10000,300)
# sims e.g. (1024,10000)
sims = xp.dot(batch,vectors.T)
best_rows[i:i+batch_size] = xp.argpartition(sims,-n,axis=1)[:,-n:]
scores[i:i+batch_size] = xp.partition(sims,-n:]
if sort and n >= 2:
sorted_index = xp.arange(scores.shape[0])[:,None][i:i+batch_size],xp.argsort(scores[i:i+batch_size],::-1]
scores[i:i+batch_size] = scores[sorted_index]
best_rows[i:i+batch_size] = best_rows[sorted_index]
for i,j in numpy.ndindex(best_rows.shape):
breakpoint()
best_rows[i,j] = filled[best_rows[i,j]]
# Round values really close to 1 or -1
scores = xp.around(scores,decimals=4,out=scores)
# Account for numerical error we want to return in range -1,1
scores = xp.clip(scores,a_min=-1,a_max=1,out=scores)
row2key = {row: key for key,row in self.key2row.items()}
keys = xp.asarray(
[[row2key[row] for row in best_rows[i] if row in row2key]
for i in range(len(queries)) ],dtype="uint64")
return (keys,scores)
请注意，filled已经是一个CPU对象，可以通过从numpy数组而不是cupy数组中获取的索引正确索引。错误TypeError: list indices must be integers or slices,not cupy.core.core.ndarray来自以下两行：
for i,j in numpy.ndindex(best_rows.shape):
best_rows[i,j]]
如果您认为在GPU上找到最相似的单词是有价值的，则可以在https://github.com/explosion/spaCy/issues上发布问题，也可以编写自己的most_similar（我认为这很简单）。