mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-26 09:56:28 +03:00
230 lines
8.1 KiB
Python
230 lines
8.1 KiB
Python
from thinc.api import add, layerize, chain, clone, concatenate, with_flatten
|
|
from thinc.neural import Model, Maxout, Softmax, Affine
|
|
from thinc.neural._classes.hash_embed import HashEmbed
|
|
|
|
from thinc.neural._classes.convolution import ExtractWindow
|
|
from thinc.neural._classes.static_vectors import StaticVectors
|
|
from thinc.neural._classes.batchnorm import BatchNorm
|
|
from thinc import describe
|
|
from thinc.describe import Dimension, Synapses, Biases, Gradient
|
|
from thinc.neural._classes.affine import _set_dimensions_if_needed
|
|
from .attrs import ID, LOWER, PREFIX, SUFFIX, SHAPE, TAG, DEP
|
|
|
|
import numpy
|
|
|
|
|
|
@describe.on_data(_set_dimensions_if_needed)
|
|
@describe.attributes(
|
|
nI=Dimension("Input size"),
|
|
nF=Dimension("Number of features"),
|
|
nO=Dimension("Output size"),
|
|
W=Synapses("Weights matrix",
|
|
lambda obj: (obj.nO, obj.nF, obj.nI),
|
|
lambda W, ops: ops.xavier_uniform_init(W)),
|
|
b=Biases("Bias vector",
|
|
lambda obj: (obj.nO,)),
|
|
d_W=Gradient("W"),
|
|
d_b=Gradient("b")
|
|
)
|
|
class PrecomputableAffine(Model):
|
|
def __init__(self, nO=None, nI=None, nF=None, **kwargs):
|
|
Model.__init__(self, **kwargs)
|
|
self.nO = nO
|
|
self.nI = nI
|
|
self.nF = nF
|
|
|
|
def begin_update(self, X, drop=0.):
|
|
# X: (b, i)
|
|
# Xf: (b, f, i)
|
|
# dY: (b, o)
|
|
# dYf: (b, f, o)
|
|
#Yf = numpy.einsum('bi,ofi->bfo', X, self.W)
|
|
Yf = self.ops.xp.tensordot(
|
|
X, self.W, axes=[[1], [2]]).transpose((0, 2, 1))
|
|
Yf += self.b
|
|
def backward(dY_ids, sgd=None):
|
|
dY, ids = dY_ids
|
|
Xf = X[ids]
|
|
|
|
#dW = numpy.einsum('bo,bfi->ofi', dY, Xf)
|
|
dW = self.ops.xp.tensordot(dY, Xf, axes=[[0], [0]])
|
|
db = dY.sum(axis=0)
|
|
#dXf = numpy.einsum('bo,ofi->bfi', dY, self.W)
|
|
dXf = self.ops.xp.tensordot(dY, self.W, axes=[[1], [0]])
|
|
|
|
self.d_W += dW
|
|
self.d_b += db
|
|
|
|
if sgd is not None:
|
|
sgd(self._mem.weights, self._mem.gradient, key=self.id)
|
|
return dXf
|
|
return Yf, backward
|
|
|
|
|
|
@describe.on_data(_set_dimensions_if_needed)
|
|
@describe.attributes(
|
|
nI=Dimension("Input size"),
|
|
nF=Dimension("Number of features"),
|
|
nP=Dimension("Number of pieces"),
|
|
nO=Dimension("Output size"),
|
|
W=Synapses("Weights matrix",
|
|
lambda obj: (obj.nF, obj.nO, obj.nP, obj.nI),
|
|
lambda W, ops: ops.xavier_uniform_init(W)),
|
|
b=Biases("Bias vector",
|
|
lambda obj: (obj.nO, obj.nP)),
|
|
d_W=Gradient("W"),
|
|
d_b=Gradient("b")
|
|
)
|
|
class PrecomputableMaxouts(Model):
|
|
def __init__(self, nO=None, nI=None, nF=None, pieces=2, **kwargs):
|
|
Model.__init__(self, **kwargs)
|
|
self.nO = nO
|
|
self.nP = pieces
|
|
self.nI = nI
|
|
self.nF = nF
|
|
|
|
def begin_update(self, X, drop=0.):
|
|
# X: (b, i)
|
|
# Yfp: (f, b, o, p)
|
|
# Yf: (f, b, o)
|
|
# Xf: (b, f, i)
|
|
# dY: (b, o)
|
|
# dYp: (b, o, p)
|
|
# W: (f, o, p, i)
|
|
# b: (o, p)
|
|
|
|
Yfp = numpy.einsum('bi,fopi->fbop', X, self.W)
|
|
Yfp += self.b
|
|
Yf = self.ops.allocate((self.nF, X.shape[0], self.nO))
|
|
which = self.ops.allocate((self.nF, X.shape[0], self.nO), dtype='i')
|
|
for i in range(self.nF):
|
|
Yf[i], which[i] = self.ops.maxout(Yfp[i])
|
|
def backward(dY_ids, sgd=None):
|
|
dY, ids = dY_ids
|
|
Xf = X[ids]
|
|
dYp = self.ops.allocate((dY.shape[0], self.nO, self.nP))
|
|
for i in range(self.nF):
|
|
dYp += self.ops.backprop_maxout(dY, which[i], self.nP)
|
|
|
|
dXf = numpy.einsum('bop,fopi->bfi', dYp, self.W)
|
|
dW = numpy.einsum('bop,bfi->fopi', dYp, Xf)
|
|
db = dYp.sum(axis=0)
|
|
|
|
self.d_W += dW
|
|
self.d_b += db
|
|
|
|
if sgd is not None:
|
|
sgd(self._mem.weights, self._mem.gradient, key=self.id)
|
|
return dXf
|
|
return Yf, backward
|
|
|
|
|
|
def get_col(idx):
|
|
def forward(X, drop=0.):
|
|
assert len(X.shape) <= 3
|
|
output = Model.ops.xp.ascontiguousarray(X[:, idx])
|
|
def backward(y, sgd=None):
|
|
dX = Model.ops.allocate(X.shape)
|
|
dX[:, idx] += y
|
|
return dX
|
|
return output, backward
|
|
return layerize(forward)
|
|
|
|
|
|
def build_tok2vec(lang, width, depth=2, embed_size=1000):
|
|
cols = [ID, LOWER, PREFIX, SUFFIX, SHAPE, TAG]
|
|
with Model.define_operators({'>>': chain, '|': concatenate, '**': clone}):
|
|
#static = get_col(cols.index(ID)) >> StaticVectors(lang, width)
|
|
lower = get_col(cols.index(LOWER)) >> HashEmbed(width, embed_size)
|
|
prefix = get_col(cols.index(PREFIX)) >> HashEmbed(width//4, embed_size)
|
|
suffix = get_col(cols.index(SUFFIX)) >> HashEmbed(width//4, embed_size)
|
|
shape = get_col(cols.index(SHAPE)) >> HashEmbed(width//4, embed_size)
|
|
tag = get_col(cols.index(TAG)) >> HashEmbed(width//2, embed_size)
|
|
tok2vec = (
|
|
doc2feats(cols)
|
|
>> with_flatten(
|
|
#(static | prefix | suffix | shape)
|
|
(lower | prefix | suffix | shape | tag)
|
|
>> Maxout(width)
|
|
>> (ExtractWindow(nW=1) >> Maxout(width, width*3))
|
|
>> (ExtractWindow(nW=1) >> Maxout(width, width*3))
|
|
)
|
|
)
|
|
return tok2vec
|
|
|
|
|
|
def doc2feats(cols):
|
|
def forward(docs, drop=0.):
|
|
feats = [doc.to_array(cols) for doc in docs]
|
|
feats = [model.ops.asarray(f, dtype='uint64') for f in feats]
|
|
return feats, None
|
|
model = layerize(forward)
|
|
return model
|
|
|
|
def print_shape(prefix):
|
|
def forward(X, drop=0.):
|
|
return X, lambda dX, **kwargs: dX
|
|
return layerize(forward)
|
|
|
|
|
|
@layerize
|
|
def get_token_vectors(tokens_attrs_vectors, drop=0.):
|
|
ops = Model.ops
|
|
tokens, attrs, vectors = tokens_attrs_vectors
|
|
def backward(d_output, sgd=None):
|
|
return (tokens, d_output)
|
|
return vectors, backward
|
|
|
|
|
|
@layerize
|
|
def flatten(seqs, drop=0.):
|
|
ops = Model.ops
|
|
def finish_update(d_X, sgd=None):
|
|
return d_X
|
|
X = ops.xp.concatenate([ops.asarray(seq) for seq in seqs])
|
|
return X, finish_update
|
|
|
|
|
|
|
|
#def build_feature_precomputer(model, feat_maps):
|
|
# '''Allow a model to be "primed" by pre-computing input features in bulk.
|
|
#
|
|
# This is used for the parser, where we want to take a batch of documents,
|
|
# and compute vectors for each (token, position) pair. These vectors can then
|
|
# be reused, especially for beam-search.
|
|
#
|
|
# Let's say we're using 12 features for each state, e.g. word at start of
|
|
# buffer, three words on stack, their children, etc. In the normal arc-eager
|
|
# system, a document of length N is processed in 2*N states. This means we'll
|
|
# create 2*N*12 feature vectors --- but if we pre-compute, we only need
|
|
# N*12 vector computations. The saving for beam-search is much better:
|
|
# if we have a beam of k, we'll normally make 2*N*12*K computations --
|
|
# so we can save the factor k. This also gives a nice CPU/GPU division:
|
|
# we can do all our hard maths up front, packed into large multiplications,
|
|
# and do the hard-to-program parsing on the CPU.
|
|
# '''
|
|
# def precompute(input_vectors):
|
|
# cached, backprops = zip(*[lyr.begin_update(input_vectors)
|
|
# for lyr in feat_maps)
|
|
# def forward(batch_token_ids, drop=0.):
|
|
# output = ops.allocate((batch_size, output_width))
|
|
# # i: batch index
|
|
# # j: position index (i.e. N0, S0, etc
|
|
# # tok_i: Index of the token within its document
|
|
# for i, token_ids in enumerate(batch_token_ids):
|
|
# for j, tok_i in enumerate(token_ids):
|
|
# output[i] += cached[j][tok_i]
|
|
# def backward(d_vector, sgd=None):
|
|
# d_inputs = ops.allocate((batch_size, n_feat, vec_width))
|
|
# for i, token_ids in enumerate(batch_token_ids):
|
|
# for j in range(len(token_ids)):
|
|
# d_inputs[i][j] = backprops[j](d_vector, sgd)
|
|
# # Return the IDs, so caller can associate to correct token
|
|
# return (batch_token_ids, d_inputs)
|
|
# return vector, backward
|
|
# return chain(layerize(forward), model)
|
|
# return precompute
|
|
#
|
|
#
|
|
|