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spaCy/spacy/_ml.py

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# 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, mean_pool
from thinc.misc import Residual
from thinc.misc import LayerNorm as LN
from thinc.misc import FeatureExtracter
from thinc.api import add, layerize, chain, clone, concatenate, with_flatten
from thinc.api import with_getitem, flatten_add_lengths
from thinc.api import uniqued, wrap, noop
from thinc.api import with_square_sequences
from thinc.linear.linear import LinearModel
from thinc.neural.ops import NumpyOps, CupyOps
from thinc.neural.util import get_array_module
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 .errors import Errors
from . import util
try:
import torch.nn
from thinc.extra.wrappers import PyTorchWrapperRNN
except ImportError:
torch = None
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 == 0.0 or norm2 == 0.0:
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", 0.8)
beta2 = util.env_opt("optimizer_B2", 0.8)
eps = util.env_opt("optimizer_eps", 0.00001)
L2 = util.env_opt("L2_penalty", 1e-6)
max_grad_norm = util.env_opt("grad_norm_clip", 5.0)
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=0.0):
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(0.0)
return model
@layerize
def _preprocess_doc(docs, drop=0.0):
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) + 1.0
return (keys, vals, lengths), None
@layerize
def _preprocess_doc_bigrams(docs, drop=0.0):
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, 1.0),
),
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=0.0):
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(0.0)
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 < 0.0
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() != 0.0:
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=0.0, scale=1.0, size=tokvecs.size).reshape(
tokvecs.shape
)
def predict(ids, tokvecs):
# nS ids. nW tokvecs. Exclude the padding array.
hiddens = model(tokvecs[:-1]) # (nW, f, o, p)
vectors = model.ops.allocate((ids.shape[0], model.nO * model.nP), dtype="f")
# need nS vectors
hiddens = hiddens.reshape(
(hiddens.shape[0] * model.nF, model.nO * model.nP)
)
model.ops.scatter_add(vectors, ids.flatten(), hiddens)
vectors = vectors.reshape((vectors.shape[0], model.nO, model.nP))
vectors += model.b
vectors = model.ops.asarray(vectors)
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
if vectors.name is None:
vectors.name = VECTORS_KEY
if vectors.data.size != 0:
print(
"Warning: Unnamed vectors -- this won't allow multiple vectors "
"models to be loaded. (Shape: (%d, %d))" % vectors.data.shape
)
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.name)] = data
def PyTorchBiLSTM(nO, nI, depth, dropout=0.2):
if depth == 0:
return layerize(noop())
model = torch.nn.LSTM(nI, nO // 2, depth, bidirectional=True, dropout=dropout)
return with_square_sequences(PyTorchWrapperRNN(model))
def Tok2Vec(width, embed_size, **kwargs):
pretrained_vectors = kwargs.get("pretrained_vectors", None)
cnn_maxout_pieces = kwargs.get("cnn_maxout_pieces", 3)
subword_features = kwargs.get("subword_features", True)
conv_depth = kwargs.get("conv_depth", 4)
bilstm_depth = kwargs.get("bilstm_depth", 0)
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")
if subword_features:
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"
)
else:
prefix, suffix, shape = (None, None, None)
if pretrained_vectors is not None:
glove = StaticVectors(pretrained_vectors, width, column=cols.index(ID))
if subword_features:
embed = uniqued(
(glove | norm | prefix | suffix | shape)
>> LN(Maxout(width, width * 5, pieces=3)),
column=cols.index(ORTH),
)
else:
embed = uniqued(
(glove | norm) >> LN(Maxout(width, width * 2, pieces=3)),
column=cols.index(ORTH),
)
elif subword_features:
embed = uniqued(
(norm | prefix | suffix | shape)
>> LN(Maxout(width, width * 4, pieces=3)),
column=cols.index(ORTH),
)
else:
embed = norm
convolution = Residual(
ExtractWindow(nW=1)
>> LN(Maxout(width, width * 3, pieces=cnn_maxout_pieces))
)
tok2vec = FeatureExtracter(cols) >> with_flatten(
embed >> convolution ** conv_depth, pad=conv_depth
)
if bilstm_depth >= 1:
tok2vec = tok2vec >> PyTorchBiLSTM(width, width, bilstm_depth)
# 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=0.0):
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=0.0):
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):
if idx < 0:
raise IndexError(Errors.E066.format(value=idx))
def forward(X, drop=0.0):
if isinstance(X, numpy.ndarray):
ops = NumpyOps()
else:
ops = CupyOps()
output = ops.xp.ascontiguousarray(X[:, idx], dtype=X.dtype)
def backward(y, sgd=None):
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=0.0):
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=0.0):
return X, lambda dX, **kwargs: dX
return layerize(forward)
@layerize
def get_token_vectors(tokens_attrs_vectors, drop=0.0):
tokens, attrs, vectors = tokens_attrs_vectors
def backward(d_output, sgd=None):
return (tokens, d_output)
return vectors, backward
@layerize
def logistic(X, drop=0.0):
xp = get_array_module(X)
if not isinstance(X, xp.ndarray):
X = xp.asarray(X)
# Clip to range (-10, 10)
X = xp.minimum(X, 10.0, X)
X = xp.maximum(X, -10.0, X)
Y = 1.0 / (1.0 + 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=0.0):
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=0.0):
return X[i], None
return layerize(getitem_fwd)
def build_tagger_model(nr_class, **cfg):
embed_size = util.env_opt("embed_size", 2000)
if "token_vector_width" in cfg:
token_vector_width = cfg["token_vector_width"]
else:
token_vector_width = util.env_opt("token_vector_width", 96)
pretrained_vectors = cfg.get("pretrained_vectors")
subword_features = cfg.get("subword_features", True)
with Model.define_operators({">>": chain, "+": add}):
if "tok2vec" in cfg:
tok2vec = cfg["tok2vec"]
else:
tok2vec = Tok2Vec(
token_vector_width,
embed_size,
subword_features=subword_features,
pretrained_vectors=pretrained_vectors,
)
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=0.0):
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
tok2vec = vectors >> with_flatten(
LN(Maxout(width, vectors_width))
>> Residual((ExtractWindow(nW=1) >> LN(Maxout(width, width * 3)))) ** depth,
pad=depth,
)
cnn_model = (
tok2vec
>> 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.tok2vec = tok2vec
model.nO = nr_class
model.lsuv = False
return model
def build_simple_cnn_text_classifier(tok2vec, nr_class, exclusive_classes=True, **cfg):
"""
Build a simple CNN text classifier, given a token-to-vector model as inputs.
If exclusive_classes=True, a softmax non-linearity is applied, so that the
outputs sum to 1. If exclusive_classes=False, a logistic non-linearity
is applied instead, so that outputs are in the range [0, 1].
"""
with Model.define_operators({">>": chain}):
if exclusive_classes:
output_layer = Softmax(nr_class, tok2vec.nO)
else:
output_layer = (
zero_init(Affine(nr_class, tok2vec.nO))
>> logistic
)
model = (
tok2vec
>> flatten_add_lengths
>> Pooling(mean_pool)
>> output_layer
)
model.tok2vec = chain(tok2vec, flatten)
model.nO = nr_class
return model
@layerize
def flatten(seqs, drop=0.0):
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=0.0):
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
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