mirror of
https://github.com/explosion/spaCy.git
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213 lines
6.8 KiB
Python
213 lines
6.8 KiB
Python
from thinc.api import add, layerize, chain, clone, concatenate, with_flatten
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from thinc.neural import Model, Maxout, Softmax, Affine
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from thinc.neural._classes.hash_embed import HashEmbed
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from thinc.neural.ops import NumpyOps, CupyOps
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from thinc.neural._classes.convolution import ExtractWindow
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from thinc.neural._classes.static_vectors import StaticVectors
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from thinc.neural._classes.batchnorm import BatchNorm
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from thinc.neural._classes.resnet import Residual
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from thinc import describe
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from thinc.describe import Dimension, Synapses, Biases, Gradient
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from thinc.neural._classes.affine import _set_dimensions_if_needed
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from .attrs import ID, LOWER, PREFIX, SUFFIX, SHAPE, TAG, DEP
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from .tokens.doc import Doc
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import numpy
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@describe.on_data(_set_dimensions_if_needed)
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@describe.attributes(
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nI=Dimension("Input size"),
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nF=Dimension("Number of features"),
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nO=Dimension("Output size"),
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W=Synapses("Weights matrix",
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lambda obj: (obj.nO, obj.nF, obj.nI),
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lambda W, ops: ops.xavier_uniform_init(W)),
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b=Biases("Bias vector",
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lambda obj: (obj.nO,)),
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d_W=Gradient("W"),
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d_b=Gradient("b")
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)
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class PrecomputableAffine(Model):
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def __init__(self, nO=None, nI=None, nF=None, **kwargs):
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Model.__init__(self, **kwargs)
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self.nO = nO
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self.nI = nI
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self.nF = nF
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def begin_update(self, X, drop=0.):
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# X: (b, i)
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# Xf: (b, f, i)
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# dY: (b, o)
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# dYf: (b, f, o)
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#Yf = numpy.einsum('bi,ofi->bfo', X, self.W)
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Yf = self.ops.xp.tensordot(
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X, self.W, axes=[[1], [2]]).transpose((0, 2, 1))
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Yf += self.b
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def backward(dY_ids, sgd=None):
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dY, ids = dY_ids
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Xf = X[ids]
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#dW = numpy.einsum('bo,bfi->ofi', dY, Xf)
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dW = self.ops.xp.tensordot(dY, Xf, axes=[[0], [0]])
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db = dY.sum(axis=0)
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#dXf = numpy.einsum('bo,ofi->bfi', dY, self.W)
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dXf = self.ops.xp.tensordot(dY, self.W, axes=[[1], [0]])
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self.d_W += dW
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self.d_b += db
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if sgd is not None:
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sgd(self._mem.weights, self._mem.gradient, key=self.id)
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return dXf
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return Yf, backward
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@describe.on_data(_set_dimensions_if_needed)
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@describe.attributes(
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nI=Dimension("Input size"),
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nF=Dimension("Number of features"),
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nP=Dimension("Number of pieces"),
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nO=Dimension("Output size"),
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W=Synapses("Weights matrix",
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lambda obj: (obj.nF, obj.nO, obj.nP, obj.nI),
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lambda W, ops: ops.xavier_uniform_init(W)),
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b=Biases("Bias vector",
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lambda obj: (obj.nO, obj.nP)),
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d_W=Gradient("W"),
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d_b=Gradient("b")
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)
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class PrecomputableMaxouts(Model):
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def __init__(self, nO=None, nI=None, nF=None, pieces=3, **kwargs):
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Model.__init__(self, **kwargs)
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self.nO = nO
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self.nP = pieces
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self.nI = nI
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self.nF = nF
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def begin_update(self, X, drop=0.):
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# X: (b, i)
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# Yfp: (b, f, o, p)
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# Xf: (f, b, i)
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# dYp: (b, o, p)
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# W: (f, o, p, i)
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# b: (o, p)
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# bi,opfi->bfop
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# bop,fopi->bfi
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# bop,fbi->opfi : fopi
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tensordot = self.ops.xp.tensordot
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ascontiguous = self.ops.xp.ascontiguousarray
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Yfp = tensordot(X, self.W, axes=[[1], [3]])
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Yfp += self.b
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def backward(dYp_ids, sgd=None):
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dYp, ids = dYp_ids
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Xf = X[ids]
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dXf = tensordot(dYp, self.W, axes=[[1, 2], [1,2]])
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dW = tensordot(dYp, Xf, axes=[[0], [0]])
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self.d_W += dW.transpose((2, 0, 1, 3))
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self.d_b += dYp.sum(axis=0)
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if sgd is not None:
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sgd(self._mem.weights, self._mem.gradient, key=self.id)
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return dXf
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return Yfp, backward
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def Tok2Vec(width, embed_size, preprocess=None):
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cols = [ID, LOWER, PREFIX, SUFFIX, SHAPE]
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with Model.define_operators({'>>': chain, '|': concatenate, '**': clone, '+': add}):
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lower = get_col(cols.index(LOWER)) >> HashEmbed(width, embed_size)
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prefix = get_col(cols.index(PREFIX)) >> HashEmbed(width, embed_size//2)
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suffix = get_col(cols.index(SUFFIX)) >> HashEmbed(width, embed_size//2)
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shape = get_col(cols.index(SHAPE)) >> HashEmbed(width, embed_size//2)
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tok2vec = (
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flatten
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>> (lower | prefix | suffix | shape )
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>> Maxout(width, width*4, pieces=3)
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>> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3))
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>> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3))
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>> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3))
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>> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3))
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)
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if preprocess not in (False, None):
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tok2vec = preprocess >> tok2vec
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# Work around thinc API limitations :(. TODO: Revise in Thinc 7
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tok2vec.nO = width
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return tok2vec
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def get_col(idx):
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def forward(X, drop=0.):
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if isinstance(X, numpy.ndarray):
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ops = NumpyOps()
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else:
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ops = CupyOps()
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output = ops.xp.ascontiguousarray(X[:, idx], dtype=X.dtype)
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def backward(y, sgd=None):
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dX = ops.allocate(X.shape)
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dX[:, idx] += y
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return dX
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return output, backward
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return layerize(forward)
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def zero_init(model):
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def _hook(self, X, y=None):
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self.W.fill(0)
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model.on_data_hooks.append(_hook)
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return model
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def doc2feats(cols=None):
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cols = [ID, LOWER, PREFIX, SUFFIX, SHAPE]
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def forward(docs, drop=0.):
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feats = []
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for doc in docs:
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if 'cached_feats' not in doc.user_data:
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doc.user_data['cached_feats'] = model.ops.asarray(
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doc.to_array(cols),
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dtype='uint64')
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feats.append(doc.user_data['cached_feats'])
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assert feats[-1].dtype == 'uint64'
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return feats, None
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model = layerize(forward)
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model.cols = cols
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return model
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def print_shape(prefix):
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def forward(X, drop=0.):
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return X, lambda dX, **kwargs: dX
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return layerize(forward)
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@layerize
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def get_token_vectors(tokens_attrs_vectors, drop=0.):
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ops = Model.ops
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tokens, attrs, vectors = tokens_attrs_vectors
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def backward(d_output, sgd=None):
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return (tokens, d_output)
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return vectors, backward
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@layerize
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def flatten(seqs, drop=0.):
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if isinstance(seqs[0], numpy.ndarray):
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ops = NumpyOps()
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elif hasattr(CupyOps.xp, 'ndarray') and isinstance(seqs[0], CupyOps.xp.ndarray):
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ops = CupyOps()
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else:
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raise ValueError("Unable to flatten sequence of type %s" % type(seqs[0]))
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lengths = [len(seq) for seq in seqs]
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def finish_update(d_X, sgd=None):
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return ops.unflatten(d_X, lengths)
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X = ops.xp.vstack(seqs)
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return X, finish_update
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