mirror of https://github.com/explosion/spaCy.git
564 lines
18 KiB
Python
564 lines
18 KiB
Python
# coding: utf8
|
|
from __future__ import unicode_literals
|
|
|
|
import numpy
|
|
from thinc.v2v import Model, Maxout, Softmax, Affine, ReLu
|
|
from thinc.i2v import HashEmbed, StaticVectors
|
|
from thinc.t2t import ExtractWindow, ParametricAttention
|
|
from thinc.t2v import Pooling, sum_pool
|
|
from thinc.misc import Residual
|
|
from thinc.misc import LayerNorm as LN
|
|
from thinc.api import add, layerize, chain, clone, concatenate, with_flatten
|
|
from thinc.api import FeatureExtracter, with_getitem, flatten_add_lengths
|
|
from thinc.api import uniqued, wrap, noop
|
|
from thinc.linear.linear import LinearModel
|
|
from thinc.neural.ops import NumpyOps, CupyOps
|
|
from thinc.neural.util import get_array_module, copy_array
|
|
from thinc.neural._lsuv import svd_orthonormal
|
|
from thinc.neural.optimizers import Adam
|
|
|
|
from thinc import describe
|
|
from thinc.describe import Dimension, Synapses, Biases, Gradient
|
|
from thinc.neural._classes.affine import _set_dimensions_if_needed
|
|
import thinc.extra.load_nlp
|
|
|
|
from .attrs import ID, ORTH, LOWER, NORM, PREFIX, SUFFIX, SHAPE
|
|
from . import util
|
|
|
|
|
|
VECTORS_KEY = 'spacy_pretrained_vectors'
|
|
|
|
|
|
def cosine(vec1, vec2):
|
|
xp = get_array_module(vec1)
|
|
norm1 = xp.linalg.norm(vec1)
|
|
norm2 = xp.linalg.norm(vec2)
|
|
if norm1 == 0. or norm2 == 0.:
|
|
return 0
|
|
else:
|
|
return vec1.dot(vec2) / (norm1 * norm2)
|
|
|
|
|
|
def create_default_optimizer(ops, **cfg):
|
|
learn_rate = util.env_opt('learn_rate', 0.001)
|
|
beta1 = util.env_opt('optimizer_B1', 0.9)
|
|
beta2 = util.env_opt('optimizer_B2', 0.999)
|
|
eps = util.env_opt('optimizer_eps', 1e-08)
|
|
L2 = util.env_opt('L2_penalty', 1e-6)
|
|
max_grad_norm = util.env_opt('grad_norm_clip', 1.)
|
|
optimizer = Adam(ops, learn_rate, L2=L2, beta1=beta1,
|
|
beta2=beta2, eps=eps)
|
|
optimizer.max_grad_norm = max_grad_norm
|
|
optimizer.device = ops.device
|
|
return optimizer
|
|
|
|
@layerize
|
|
def _flatten_add_lengths(seqs, pad=0, drop=0.):
|
|
ops = Model.ops
|
|
lengths = ops.asarray([len(seq) for seq in seqs], dtype='i')
|
|
|
|
def finish_update(d_X, sgd=None):
|
|
return ops.unflatten(d_X, lengths, pad=pad)
|
|
|
|
X = ops.flatten(seqs, pad=pad)
|
|
return (X, lengths), finish_update
|
|
|
|
|
|
def _zero_init(model):
|
|
def _zero_init_impl(self, X, y):
|
|
self.W.fill(0)
|
|
model.on_data_hooks.append(_zero_init_impl)
|
|
if model.W is not None:
|
|
model.W.fill(0.)
|
|
return model
|
|
|
|
|
|
@layerize
|
|
def _preprocess_doc(docs, drop=0.):
|
|
keys = [doc.to_array(LOWER) for doc in docs]
|
|
ops = Model.ops
|
|
# The dtype here matches what thinc is expecting -- which differs per
|
|
# platform (by int definition). This should be fixed once the problem
|
|
# is fixed on Thinc's side.
|
|
lengths = ops.asarray([arr.shape[0] for arr in keys], dtype=numpy.int_)
|
|
keys = ops.xp.concatenate(keys)
|
|
vals = ops.allocate(keys.shape) + 1.
|
|
return (keys, vals, lengths), None
|
|
|
|
@layerize
|
|
def _preprocess_doc_bigrams(docs, drop=0.):
|
|
unigrams = [doc.to_array(LOWER) for doc in docs]
|
|
ops = Model.ops
|
|
bigrams = [ops.ngrams(2, doc_unis) for doc_unis in unigrams]
|
|
keys = [ops.xp.concatenate(feats) for feats in zip(unigrams, bigrams)]
|
|
keys, vals = zip(*[ops.xp.unique(k, return_counts=True) for k in keys])
|
|
# The dtype here matches what thinc is expecting -- which differs per
|
|
# platform (by int definition). This should be fixed once the problem
|
|
# is fixed on Thinc's side.
|
|
lengths = ops.asarray([arr.shape[0] for arr in keys], dtype=numpy.int_)
|
|
keys = ops.xp.concatenate(keys)
|
|
vals = ops.asarray(ops.xp.concatenate(vals), dtype='f')
|
|
return (keys, vals, lengths), None
|
|
|
|
|
|
@describe.on_data(_set_dimensions_if_needed,
|
|
lambda model, X, y: model.init_weights(model))
|
|
@describe.attributes(
|
|
nI=Dimension("Input size"),
|
|
nF=Dimension("Number of features"),
|
|
nO=Dimension("Output size"),
|
|
nP=Dimension("Maxout pieces"),
|
|
W=Synapses("Weights matrix",
|
|
lambda obj: (obj.nF, obj.nO, obj.nP, obj.nI)),
|
|
b=Biases("Bias vector",
|
|
lambda obj: (obj.nO, obj.nP)),
|
|
pad=Synapses("Pad",
|
|
lambda obj: (1, obj.nF, obj.nO, obj.nP),
|
|
lambda M, ops: ops.normal_init(M, 1.)),
|
|
d_W=Gradient("W"),
|
|
d_pad=Gradient("pad"),
|
|
d_b=Gradient("b"))
|
|
class PrecomputableAffine(Model):
|
|
def __init__(self, nO=None, nI=None, nF=None, nP=None, **kwargs):
|
|
Model.__init__(self, **kwargs)
|
|
self.nO = nO
|
|
self.nP = nP
|
|
self.nI = nI
|
|
self.nF = nF
|
|
|
|
def begin_update(self, X, drop=0.):
|
|
Yf = self.ops.gemm(X,
|
|
self.W.reshape((self.nF*self.nO*self.nP, self.nI)), trans2=True)
|
|
Yf = Yf.reshape((Yf.shape[0], self.nF, self.nO, self.nP))
|
|
Yf = self._add_padding(Yf)
|
|
|
|
def backward(dY_ids, sgd=None):
|
|
dY, ids = dY_ids
|
|
dY, ids = self._backprop_padding(dY, ids)
|
|
Xf = X[ids]
|
|
Xf = Xf.reshape((Xf.shape[0], self.nF * self.nI))
|
|
|
|
self.d_b += dY.sum(axis=0)
|
|
dY = dY.reshape((dY.shape[0], self.nO*self.nP))
|
|
|
|
Wopfi = self.W.transpose((1, 2, 0, 3))
|
|
Wopfi = self.ops.xp.ascontiguousarray(Wopfi)
|
|
Wopfi = Wopfi.reshape((self.nO*self.nP, self.nF * self.nI))
|
|
dXf = self.ops.gemm(dY.reshape((dY.shape[0], self.nO*self.nP)), Wopfi)
|
|
|
|
# Reuse the buffer
|
|
dWopfi = Wopfi; dWopfi.fill(0.)
|
|
self.ops.gemm(dY, Xf, out=dWopfi, trans1=True)
|
|
dWopfi = dWopfi.reshape((self.nO, self.nP, self.nF, self.nI))
|
|
# (o, p, f, i) --> (f, o, p, i)
|
|
self.d_W += dWopfi.transpose((2, 0, 1, 3))
|
|
|
|
if sgd is not None:
|
|
sgd(self._mem.weights, self._mem.gradient, key=self.id)
|
|
return dXf.reshape((dXf.shape[0], self.nF, self.nI))
|
|
return Yf, backward
|
|
|
|
def _add_padding(self, Yf):
|
|
Yf_padded = self.ops.xp.vstack((self.pad, Yf))
|
|
return Yf_padded
|
|
|
|
def _backprop_padding(self, dY, ids):
|
|
# (1, nF, nO, nP) += (nN, nF, nO, nP) where IDs (nN, nF) < 0
|
|
mask = ids < 0.
|
|
mask = mask.sum(axis=1)
|
|
d_pad = dY * mask.reshape((ids.shape[0], 1, 1))
|
|
self.d_pad += d_pad.sum(axis=0)
|
|
return dY, ids
|
|
|
|
@staticmethod
|
|
def init_weights(model):
|
|
'''This is like the 'layer sequential unit variance', but instead
|
|
of taking the actual inputs, we randomly generate whitened data.
|
|
|
|
Why's this all so complicated? We have a huge number of inputs,
|
|
and the maxout unit makes guessing the dynamics tricky. Instead
|
|
we set the maxout weights to values that empirically result in
|
|
whitened outputs given whitened inputs.
|
|
'''
|
|
if (model.W**2).sum() != 0.:
|
|
return
|
|
ops = model.ops
|
|
xp = ops.xp
|
|
ops.normal_init(model.W, model.nF * model.nI, inplace=True)
|
|
|
|
ids = ops.allocate((5000, model.nF), dtype='f')
|
|
ids += xp.random.uniform(0, 1000, ids.shape)
|
|
ids = ops.asarray(ids, dtype='i')
|
|
tokvecs = ops.allocate((5000, model.nI), dtype='f')
|
|
tokvecs += xp.random.normal(loc=0., scale=1.,
|
|
size=tokvecs.size).reshape(tokvecs.shape)
|
|
|
|
def predict(ids, tokvecs):
|
|
# nS ids. nW tokvecs
|
|
hiddens = model(tokvecs) # (nW, f, o, p)
|
|
# need nS vectors
|
|
vectors = model.ops.allocate((ids.shape[0], model.nO, model.nP))
|
|
for i, feats in enumerate(ids):
|
|
for j, id_ in enumerate(feats):
|
|
vectors[i] += hiddens[id_, j]
|
|
vectors += model.b
|
|
if model.nP >= 2:
|
|
return model.ops.maxout(vectors)[0]
|
|
else:
|
|
return vectors * (vectors >= 0)
|
|
|
|
tol_var = 0.01
|
|
tol_mean = 0.01
|
|
t_max = 10
|
|
t_i = 0
|
|
for t_i in range(t_max):
|
|
acts1 = predict(ids, tokvecs)
|
|
var = model.ops.xp.var(acts1)
|
|
mean = model.ops.xp.mean(acts1)
|
|
if abs(var - 1.0) >= tol_var:
|
|
model.W /= model.ops.xp.sqrt(var)
|
|
elif abs(mean) >= tol_mean:
|
|
model.b -= mean
|
|
else:
|
|
break
|
|
|
|
|
|
def link_vectors_to_models(vocab):
|
|
vectors = vocab.vectors
|
|
ops = Model.ops
|
|
for word in vocab:
|
|
if word.orth in vectors.key2row:
|
|
word.rank = vectors.key2row[word.orth]
|
|
else:
|
|
word.rank = 0
|
|
data = ops.asarray(vectors.data)
|
|
# Set an entry here, so that vectors are accessed by StaticVectors
|
|
# (unideal, I know)
|
|
thinc.extra.load_nlp.VECTORS[(ops.device, VECTORS_KEY)] = data
|
|
|
|
|
|
def Tok2Vec(width, embed_size, **kwargs):
|
|
pretrained_dims = kwargs.get('pretrained_dims', 0)
|
|
cnn_maxout_pieces = kwargs.get('cnn_maxout_pieces', 2)
|
|
cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
|
|
with Model.define_operators({'>>': chain, '|': concatenate, '**': clone,
|
|
'+': add, '*': reapply}):
|
|
norm = HashEmbed(width, embed_size, column=cols.index(NORM),
|
|
name='embed_norm')
|
|
prefix = HashEmbed(width, embed_size//2, column=cols.index(PREFIX),
|
|
name='embed_prefix')
|
|
suffix = HashEmbed(width, embed_size//2, column=cols.index(SUFFIX),
|
|
name='embed_suffix')
|
|
shape = HashEmbed(width, embed_size//2, column=cols.index(SHAPE),
|
|
name='embed_shape')
|
|
if pretrained_dims is not None and pretrained_dims >= 1:
|
|
glove = StaticVectors(VECTORS_KEY, width, column=cols.index(ID))
|
|
|
|
embed = uniqued(
|
|
(glove | norm | prefix | suffix | shape)
|
|
>> LN(Maxout(width, width*5, pieces=3)), column=5)
|
|
else:
|
|
embed = uniqued(
|
|
(norm | prefix | suffix | shape)
|
|
>> LN(Maxout(width, width*4, pieces=3)), column=5)
|
|
|
|
convolution = Residual(
|
|
ExtractWindow(nW=1)
|
|
>> LN(Maxout(width, width*3, pieces=cnn_maxout_pieces))
|
|
)
|
|
|
|
tok2vec = (
|
|
FeatureExtracter(cols)
|
|
>> with_flatten(
|
|
embed
|
|
>> convolution ** 4, pad=4
|
|
)
|
|
)
|
|
# Work around thinc API limitations :(. TODO: Revise in Thinc 7
|
|
tok2vec.nO = width
|
|
tok2vec.embed = embed
|
|
return tok2vec
|
|
|
|
|
|
def reapply(layer, n_times):
|
|
def reapply_fwd(X, drop=0.):
|
|
backprops = []
|
|
for i in range(n_times):
|
|
Y, backprop = layer.begin_update(X, drop=drop)
|
|
X = Y
|
|
backprops.append(backprop)
|
|
|
|
def reapply_bwd(dY, sgd=None):
|
|
dX = None
|
|
for backprop in reversed(backprops):
|
|
dY = backprop(dY, sgd=sgd)
|
|
if dX is None:
|
|
dX = dY
|
|
else:
|
|
dX += dY
|
|
return dX
|
|
|
|
return Y, reapply_bwd
|
|
return wrap(reapply_fwd, layer)
|
|
|
|
|
|
def asarray(ops, dtype):
|
|
def forward(X, drop=0.):
|
|
return ops.asarray(X, dtype=dtype), None
|
|
return layerize(forward)
|
|
|
|
|
|
def _divide_array(X, size):
|
|
parts = []
|
|
index = 0
|
|
while index < len(X):
|
|
parts.append(X[index:index + size])
|
|
index += size
|
|
return parts
|
|
|
|
|
|
def get_col(idx):
|
|
assert idx >= 0, idx
|
|
|
|
def forward(X, drop=0.):
|
|
assert idx >= 0, idx
|
|
if isinstance(X, numpy.ndarray):
|
|
ops = NumpyOps()
|
|
else:
|
|
ops = CupyOps()
|
|
output = ops.xp.ascontiguousarray(X[:, idx], dtype=X.dtype)
|
|
|
|
def backward(y, sgd=None):
|
|
assert idx >= 0, idx
|
|
dX = ops.allocate(X.shape)
|
|
dX[:, idx] += y
|
|
return dX
|
|
|
|
return output, backward
|
|
|
|
return layerize(forward)
|
|
|
|
|
|
def doc2feats(cols=None):
|
|
if cols is None:
|
|
cols = [ID, NORM, PREFIX, SUFFIX, SHAPE, ORTH]
|
|
|
|
def forward(docs, drop=0.):
|
|
feats = []
|
|
for doc in docs:
|
|
feats.append(doc.to_array(cols))
|
|
return feats, None
|
|
|
|
model = layerize(forward)
|
|
model.cols = cols
|
|
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.):
|
|
tokens, attrs, vectors = tokens_attrs_vectors
|
|
|
|
def backward(d_output, sgd=None):
|
|
return (tokens, d_output)
|
|
|
|
return vectors, backward
|
|
|
|
|
|
@layerize
|
|
def logistic(X, drop=0.):
|
|
xp = get_array_module(X)
|
|
if not isinstance(X, xp.ndarray):
|
|
X = xp.asarray(X)
|
|
# Clip to range (-10, 10)
|
|
X = xp.minimum(X, 10., X)
|
|
X = xp.maximum(X, -10., X)
|
|
Y = 1. / (1. + xp.exp(-X))
|
|
|
|
def logistic_bwd(dY, sgd=None):
|
|
dX = dY * (Y * (1-Y))
|
|
return dX
|
|
|
|
return Y, logistic_bwd
|
|
|
|
|
|
def zero_init(model):
|
|
def _zero_init_impl(self, X, y):
|
|
self.W.fill(0)
|
|
model.on_data_hooks.append(_zero_init_impl)
|
|
return model
|
|
|
|
|
|
@layerize
|
|
def preprocess_doc(docs, drop=0.):
|
|
keys = [doc.to_array([LOWER]) for doc in docs]
|
|
ops = Model.ops
|
|
lengths = ops.asarray([arr.shape[0] for arr in keys])
|
|
keys = ops.xp.concatenate(keys)
|
|
vals = ops.allocate(keys.shape[0]) + 1
|
|
return (keys, vals, lengths), None
|
|
|
|
|
|
def getitem(i):
|
|
def getitem_fwd(X, drop=0.):
|
|
return X[i], None
|
|
return layerize(getitem_fwd)
|
|
|
|
|
|
def build_tagger_model(nr_class, **cfg):
|
|
embed_size = util.env_opt('embed_size', 7000)
|
|
if 'token_vector_width' in cfg:
|
|
token_vector_width = cfg['token_vector_width']
|
|
else:
|
|
token_vector_width = util.env_opt('token_vector_width', 128)
|
|
pretrained_dims = cfg.get('pretrained_dims', 0)
|
|
with Model.define_operators({'>>': chain, '+': add}):
|
|
if 'tok2vec' in cfg:
|
|
tok2vec = cfg['tok2vec']
|
|
else:
|
|
tok2vec = Tok2Vec(token_vector_width, embed_size,
|
|
pretrained_dims=pretrained_dims)
|
|
softmax = with_flatten(Softmax(nr_class, token_vector_width))
|
|
model = (
|
|
tok2vec
|
|
>> softmax
|
|
)
|
|
model.nI = None
|
|
model.tok2vec = tok2vec
|
|
model.softmax = softmax
|
|
return model
|
|
|
|
|
|
@layerize
|
|
def SpacyVectors(docs, drop=0.):
|
|
batch = []
|
|
for doc in docs:
|
|
indices = numpy.zeros((len(doc),), dtype='i')
|
|
for i, word in enumerate(doc):
|
|
if word.orth in doc.vocab.vectors.key2row:
|
|
indices[i] = doc.vocab.vectors.key2row[word.orth]
|
|
else:
|
|
indices[i] = 0
|
|
vectors = doc.vocab.vectors.data[indices]
|
|
batch.append(vectors)
|
|
return batch, None
|
|
|
|
|
|
def build_text_classifier(nr_class, width=64, **cfg):
|
|
depth = cfg.get('depth', 2)
|
|
nr_vector = cfg.get('nr_vector', 5000)
|
|
pretrained_dims = cfg.get('pretrained_dims', 0)
|
|
with Model.define_operators({'>>': chain, '+': add, '|': concatenate,
|
|
'**': clone}):
|
|
if cfg.get('low_data') and pretrained_dims:
|
|
model = (
|
|
SpacyVectors
|
|
>> flatten_add_lengths
|
|
>> with_getitem(0, Affine(width, pretrained_dims))
|
|
>> ParametricAttention(width)
|
|
>> Pooling(sum_pool)
|
|
>> Residual(ReLu(width, width)) ** 2
|
|
>> zero_init(Affine(nr_class, width, drop_factor=0.0))
|
|
>> logistic
|
|
)
|
|
return model
|
|
|
|
lower = HashEmbed(width, nr_vector, column=1)
|
|
prefix = HashEmbed(width//2, nr_vector, column=2)
|
|
suffix = HashEmbed(width//2, nr_vector, column=3)
|
|
shape = HashEmbed(width//2, nr_vector, column=4)
|
|
|
|
trained_vectors = (
|
|
FeatureExtracter([ORTH, LOWER, PREFIX, SUFFIX, SHAPE, ID])
|
|
>> with_flatten(
|
|
uniqued(
|
|
(lower | prefix | suffix | shape)
|
|
>> LN(Maxout(width, width+(width//2)*3)),
|
|
column=0
|
|
)
|
|
)
|
|
)
|
|
|
|
if pretrained_dims:
|
|
static_vectors = (
|
|
SpacyVectors
|
|
>> with_flatten(Affine(width, pretrained_dims))
|
|
)
|
|
# TODO Make concatenate support lists
|
|
vectors = concatenate_lists(trained_vectors, static_vectors)
|
|
vectors_width = width*2
|
|
else:
|
|
vectors = trained_vectors
|
|
vectors_width = width
|
|
static_vectors = None
|
|
cnn_model = (
|
|
vectors
|
|
>> with_flatten(
|
|
LN(Maxout(width, vectors_width))
|
|
>> Residual(
|
|
(ExtractWindow(nW=1) >> LN(Maxout(width, width*3)))
|
|
) ** depth, pad=depth
|
|
)
|
|
>> flatten_add_lengths
|
|
>> ParametricAttention(width)
|
|
>> Pooling(sum_pool)
|
|
>> Residual(zero_init(Maxout(width, width)))
|
|
>> zero_init(Affine(nr_class, width, drop_factor=0.0))
|
|
)
|
|
|
|
linear_model = (
|
|
_preprocess_doc
|
|
>> LinearModel(nr_class)
|
|
)
|
|
model = (
|
|
(linear_model | cnn_model)
|
|
>> zero_init(Affine(nr_class, nr_class*2, drop_factor=0.0))
|
|
>> logistic
|
|
)
|
|
model.nO = nr_class
|
|
model.lsuv = False
|
|
return model
|
|
|
|
|
|
@layerize
|
|
def flatten(seqs, drop=0.):
|
|
ops = Model.ops
|
|
lengths = ops.asarray([len(seq) for seq in seqs], dtype='i')
|
|
|
|
def finish_update(d_X, sgd=None):
|
|
return ops.unflatten(d_X, lengths, pad=0)
|
|
|
|
X = ops.flatten(seqs, pad=0)
|
|
return X, finish_update
|
|
|
|
|
|
def concatenate_lists(*layers, **kwargs): # pragma: no cover
|
|
"""Compose two or more models `f`, `g`, etc, such that their outputs are
|
|
concatenated, i.e. `concatenate(f, g)(x)` computes `hstack(f(x), g(x))`
|
|
"""
|
|
if not layers:
|
|
return noop()
|
|
drop_factor = kwargs.get('drop_factor', 1.0)
|
|
ops = layers[0].ops
|
|
layers = [chain(layer, flatten) for layer in layers]
|
|
concat = concatenate(*layers)
|
|
|
|
def concatenate_lists_fwd(Xs, drop=0.):
|
|
drop *= drop_factor
|
|
lengths = ops.asarray([len(X) for X in Xs], dtype='i')
|
|
flat_y, bp_flat_y = concat.begin_update(Xs, drop=drop)
|
|
ys = ops.unflatten(flat_y, lengths)
|
|
|
|
def concatenate_lists_bwd(d_ys, sgd=None):
|
|
return bp_flat_y(ops.flatten(d_ys), sgd=sgd)
|
|
|
|
return ys, concatenate_lists_bwd
|
|
|
|
model = wrap(concatenate_lists_fwd, concat)
|
|
return model
|