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Tidy up parser and ML

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ines 2017-10-27 14:39:30 +02:00
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2 changed files with 94 additions and 260 deletions
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295
spacy/_ml.py
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@ -1,47 +1,42 @@
import ujson
from thinc.v2v import Model, Maxout, Softmax, Affine, ReLu, SELU
# 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, max_pool, mean_pool, sum_pool
from thinc.t2v import Pooling, sum_pool
from thinc.misc import Residual
from thinc.misc import BatchNorm as BN
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
from thinc.api import uniqued, wrap, flatten_add_lengths, noop
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
import random
import cytoolz
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, TAG, DEP, CLUSTER
from .tokens.doc import Doc
from .attrs import ID, ORTH, LOWER, NORM, PREFIX, SUFFIX, SHAPE
from . import util
import numpy
import io
# TODO: Unset this once we don't want to support models previous models.
import thinc.neural._classes.layernorm
thinc.neural._classes.layernorm.set_compat_six_eight(False)
VECTORS_KEY = 'spacy_pretrained_vectors'
@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
@ -55,33 +50,14 @@ def _logistic(X, drop=0.):
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
@layerize
def add_tuples(X, drop=0.):
"""Give inputs of sequence pairs, where each sequence is (vals, length),
sum the values, returning a single sequence.
If input is:
((vals1, length), (vals2, length)
Output is:
(vals1+vals2, length)
vals are a single tensor for the whole batch.
"""
(vals1, length1), (vals2, length2) = X
assert length1 == length2
def add_tuples_bwd(dY, sgd=None):
return (dY, dY)
return (vals1+vals2, length), add_tuples_bwd
def _zero_init(model):
def _zero_init_impl(self, X, y):
self.W.fill(0)
@ -115,13 +91,12 @@ def _init_for_precomputed(W, ops):
nF=Dimension("Number of features"),
nO=Dimension("Output size"),
W=Synapses("Weights matrix",
lambda obj: (obj.nF, obj.nO, obj.nI),
lambda W, ops: _init_for_precomputed(W, ops)),
lambda obj: (obj.nF, obj.nO, obj.nI),
lambda W, ops: _init_for_precomputed(W, ops)),
b=Biases("Bias vector",
lambda obj: (obj.nO,)),
lambda obj: (obj.nO,)),
d_W=Gradient("W"),
d_b=Gradient("b")
)
d_b=Gradient("b"))
class PrecomputableAffine(Model):
def __init__(self, nO=None, nI=None, nF=None, **kwargs):
Model.__init__(self, **kwargs)
@ -134,18 +109,19 @@ class PrecomputableAffine(Model):
# Yf: (b, f, i)
# dY: (b, o)
# dYf: (b, f, o)
#Yf = numpy.einsum('bi,foi->bfo', X, self.W)
# Yf = numpy.einsum('bi,foi->bfo', X, self.W)
Yf = self.ops.xp.tensordot(
X, self.W, axes=[[1], [2]])
Yf += self.b
def backward(dY_ids, sgd=None):
tensordot = self.ops.xp.tensordot
dY, ids = dY_ids
Xf = X[ids]
#dXf = numpy.einsum('bo,foi->bfi', dY, self.W)
# dXf = numpy.einsum('bo,foi->bfi', dY, self.W)
dXf = tensordot(dY, self.W, axes=[[1], [1]])
#dW = numpy.einsum('bo,bfi->ofi', dY, Xf)
# dW = numpy.einsum('bo,bfi->ofi', dY, Xf)
dW = tensordot(dY, Xf, axes=[[0], [0]])
# ofi -> foi
self.d_W += dW.transpose((1, 0, 2))
@ -154,6 +130,7 @@ class PrecomputableAffine(Model):
if sgd is not None:
sgd(self._mem.weights, self._mem.gradient, key=self.id)
return dXf
return Yf, backward
@ -164,13 +141,12 @@ class PrecomputableAffine(Model):
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)),
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)),
lambda obj: (obj.nO, obj.nP)),
d_W=Gradient("W"),
d_b=Gradient("b")
)
d_b=Gradient("b"))
class PrecomputableMaxouts(Model):
def __init__(self, nO=None, nI=None, nF=None, nP=3, **kwargs):
Model.__init__(self, **kwargs)
@ -186,114 +162,26 @@ class PrecomputableMaxouts(Model):
# dYp: (b, o, p)
# W: (f, o, p, i)
# b: (o, p)
# bi,opfi->bfop
# bop,fopi->bfi
# bop,fbi->opfi : fopi
tensordot = self.ops.xp.tensordot
ascontiguous = self.ops.xp.ascontiguousarray
Yfp = tensordot(X, self.W, axes=[[1], [3]])
Yfp += self.b
def backward(dYp_ids, sgd=None):
dYp, ids = dYp_ids
Xf = X[ids]
dXf = tensordot(dYp, self.W, axes=[[1, 2], [1,2]])
dXf = tensordot(dYp, self.W, axes=[[1, 2], [1, 2]])
dW = tensordot(dYp, Xf, axes=[[0], [0]])
self.d_W += dW.transpose((2, 0, 1, 3))
self.d_b += dYp.sum(axis=0)
if sgd is not None:
sgd(self._mem.weights, self._mem.gradient, key=self.id)
return dXf
return Yfp, backward
# Thinc's Embed class is a bit broken atm, so drop this here.
from thinc import describe
from thinc.neural._classes.embed import _uniform_init
@describe.attributes(
nV=describe.Dimension("Number of vectors"),
nO=describe.Dimension("Size of output"),
vectors=describe.Weights("Embedding table",
lambda obj: (obj.nV, obj.nO),
_uniform_init(-0.1, 0.1)
),
d_vectors=describe.Gradient("vectors")
)
class Embed(Model):
name = 'embed'
def __init__(self, nO, nV=None, **kwargs):
if nV is not None:
nV += 1
Model.__init__(self, **kwargs)
if 'name' in kwargs:
self.name = kwargs['name']
self.column = kwargs.get('column', 0)
self.nO = nO
self.nV = nV
def predict(self, ids):
if ids.ndim == 2:
ids = ids[:, self.column]
return self.ops.xp.ascontiguousarray(self.vectors[ids], dtype='f')
def begin_update(self, ids, drop=0.):
if ids.ndim == 2:
ids = ids[:, self.column]
vectors = self.ops.xp.ascontiguousarray(self.vectors[ids], dtype='f')
def backprop_embed(d_vectors, sgd=None):
n_vectors = d_vectors.shape[0]
self.ops.scatter_add(self.d_vectors, ids, d_vectors)
if sgd is not None:
sgd(self._mem.weights, self._mem.gradient, key=self.id)
return None
return vectors, backprop_embed
def HistoryFeatures(nr_class, hist_size=8, nr_dim=8):
'''Wrap a model, adding features representing action history.'''
if hist_size == 0:
return layerize(noop())
embed_tables = [Embed(nr_dim, nr_class, column=i, name='embed%d')
for i in range(hist_size)]
embed = chain(concatenate(*embed_tables),
LN(Maxout(hist_size*nr_dim, hist_size*nr_dim)))
ops = embed.ops
def add_history_fwd(vectors_hists, drop=0.):
vectors, hist_ids = vectors_hists
hist_feats, bp_hists = embed.begin_update(hist_ids, drop=drop)
outputs = ops.xp.hstack((vectors, hist_feats))
def add_history_bwd(d_outputs, sgd=None):
d_vectors = d_outputs[:, :vectors.shape[1]]
d_hists = d_outputs[:, vectors.shape[1]:]
bp_hists(d_hists, sgd=sgd)
return embed.ops.xp.ascontiguousarray(d_vectors)
return outputs, add_history_bwd
return wrap(add_history_fwd, embed)
def drop_layer(layer, factor=2.):
def drop_layer_fwd(X, drop=0.):
if drop <= 0.:
return layer.begin_update(X, drop=drop)
else:
coinflip = layer.ops.xp.random.random()
if (coinflip / factor) >= drop:
return layer.begin_update(X, drop=drop)
else:
return X, lambda dX, sgd=None: dX
model = wrap(drop_layer_fwd, layer)
model.predict = layer
return model
def link_vectors_to_models(vocab):
vectors = vocab.vectors
@ -308,16 +196,21 @@ def link_vectors_to_models(vocab):
# (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')
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))
@ -329,7 +222,6 @@ def Tok2Vec(width, embed_size, **kwargs):
(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))
@ -354,6 +246,7 @@ def reapply(layer, 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):
@ -363,39 +256,20 @@ def reapply(layer, n_times):
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 foreach(layer):
def forward(Xs, drop=0.):
results = []
backprops = []
for X in Xs:
result, bp = layer.begin_update(X, drop=drop)
results.append(result)
backprops.append(bp)
def backward(d_results, sgd=None):
dXs = []
for d_result, backprop in zip(d_results, backprops):
dXs.append(backprop(d_result, sgd))
return dXs
return results, backward
model = layerize(forward)
model._layers.append(layer)
return model
def rebatch(size, layer):
ops = layer.ops
def forward(X, drop=0.):
if X.shape[0] < size:
return layer.begin_update(X)
@ -403,6 +277,7 @@ def rebatch(size, layer):
results, bp_results = zip(*[layer.begin_update(p, drop=drop)
for p in parts])
y = ops.flatten(results)
def backward(dy, sgd=None):
d_parts = [bp(y, sgd=sgd) for bp, y in
zip(bp_results, _divide_array(dy, size))]
@ -413,6 +288,7 @@ def rebatch(size, layer):
except ValueError:
dX = None
return dX
return y, backward
model = layerize(forward)
model._layers.append(layer)
@ -423,13 +299,14 @@ def _divide_array(X, size):
parts = []
index = 0
while index < len(X):
parts.append(X[index : index + size])
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):
@ -437,30 +314,28 @@ def get_col(idx):
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 zero_init(model):
def _hook(self, X, y=None):
self.W.fill(0)
model.on_data_hooks.append(_hook)
return model
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
@ -474,28 +349,14 @@ def print_shape(prefix):
@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.):
if isinstance(seqs[0], numpy.ndarray):
ops = NumpyOps()
elif hasattr(CupyOps.xp, 'ndarray') and isinstance(seqs[0], CupyOps.xp.ndarray):
ops = CupyOps()
else:
raise ValueError("Unable to flatten sequence of type %s" % type(seqs[0]))
lengths = [len(seq) for seq in seqs]
def finish_update(d_X, sgd=None):
return ops.unflatten(d_X, lengths)
X = ops.xp.vstack(seqs)
return X, finish_update
@layerize
def logistic(X, drop=0.):
xp = get_array_module(X)
@ -505,9 +366,11 @@ def logistic(X, drop=0.):
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
@ -517,6 +380,7 @@ def zero_init(model):
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]
@ -526,11 +390,13 @@ def preprocess_doc(docs, drop=0.):
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:
@ -555,8 +421,6 @@ def build_tagger_model(nr_class, **cfg):
@layerize
def SpacyVectors(docs, drop=0.):
xp = get_array_module(docs[0].vocab.vectors.data)
width = docs[0].vocab.vectors.data.shape[1]
batch = []
for doc in docs:
indices = numpy.zeros((len(doc),), dtype='i')
@ -570,29 +434,6 @@ def SpacyVectors(docs, drop=0.):
return batch, None
def foreach(layer, drop_factor=1.0):
'''Map a layer across elements in a list'''
def foreach_fwd(Xs, drop=0.):
drop *= drop_factor
ys = []
backprops = []
for X in Xs:
y, bp_y = layer.begin_update(X, drop=drop)
ys.append(y)
backprops.append(bp_y)
def foreach_bwd(d_ys, sgd=None):
d_Xs = []
for d_y, bp_y in zip(d_ys, backprops):
if bp_y is not None and bp_y is not None:
d_Xs.append(d_y, sgd=sgd)
else:
d_Xs.append(None)
return d_Xs
return ys, foreach_bwd
model = wrap(foreach_fwd, layer)
return model
def build_text_classifier(nr_class, width=64, **cfg):
nr_vector = cfg.get('nr_vector', 5000)
pretrained_dims = cfg.get('pretrained_dims', 0)
@ -602,9 +443,7 @@ def build_text_classifier(nr_class, width=64, **cfg):
model = (
SpacyVectors
>> flatten_add_lengths
>> with_getitem(0,
Affine(width, pretrained_dims)
)
>> with_getitem(0, Affine(width, pretrained_dims))
>> ParametricAttention(width)
>> Pooling(sum_pool)
>> Residual(ReLu(width, width)) ** 2
@ -613,7 +452,6 @@ def build_text_classifier(nr_class, width=64, **cfg):
)
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)
@ -671,33 +509,40 @@ def build_text_classifier(nr_class, width=64, **cfg):
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
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

59
spacy/syntax/nn_parser.pyx
View File

@ -49,9 +49,8 @@ from .. import util
from ..util import get_async, get_cuda_stream
from .._ml import zero_init, PrecomputableAffine, PrecomputableMaxouts
from .._ml import Tok2Vec, doc2feats, rebatch
from .._ml import Residual, drop_layer, flatten
from .._ml import Residual, flatten
from .._ml import link_vectors_to_models
from .._ml import HistoryFeatures
from ..compat import json_dumps, copy_array
from .stateclass cimport StateClass
@ -77,7 +76,7 @@ def set_debug(val):
cdef class precompute_hiddens:
'''Allow a model to be "primed" by pre-computing input features in bulk.
"""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
@ -92,7 +91,7 @@ cdef class precompute_hiddens:
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.
'''
"""
cdef int nF, nO, nP
cdef bint _is_synchronized
cdef public object ops
@ -280,23 +279,19 @@ cdef class Parser:
return (tok2vec, lower, upper), cfg
def __init__(self, Vocab vocab, moves=True, model=True, **cfg):
"""
Create a Parser.
"""Create a Parser.
Arguments:
vocab (Vocab):
The vocabulary object. Must be shared with documents to be processed.
The value is set to the .vocab attribute.
moves (TransitionSystem):
Defines how the parse-state is created, updated and evaluated.
The value is set to the .moves attribute unless True (default),
in which case a new instance is created with Parser.Moves().
model (object):
Defines how the parse-state is created, updated and evaluated.
The value is set to the .model attribute unless True (default),
in which case a new instance is created with Parser.Model().
**cfg:
Arbitrary configuration parameters. Set to the .cfg attribute
vocab (Vocab): The vocabulary object. Must be shared with documents
to be processed. The value is set to the `.vocab` attribute.
moves (TransitionSystem): Defines how the parse-state is created,
updated and evaluated. The value is set to the .moves attribute
unless True (default), in which case a new instance is created with
`Parser.Moves()`.
model (object): Defines how the parse-state is created, updated and
evaluated. The value is set to the .model attribute unless True
(default), in which case a new instance is created with
`Parser.Model()`.
**cfg: Arbitrary configuration parameters. Set to the `.cfg` attribute
"""
self.vocab = vocab
if moves is True:
@ -322,13 +317,10 @@ cdef class Parser:
return (Parser, (self.vocab, self.moves, self.model), None, None)
def __call__(self, Doc doc, beam_width=None, beam_density=None):
"""
Apply the parser or entity recognizer, setting the annotations onto the Doc object.
"""Apply the parser or entity recognizer, setting the annotations onto
the `Doc` object.
Arguments:
doc (Doc): The document to be processed.
Returns:
None
doc (Doc): The document to be processed.
"""
if beam_width is None:
beam_width = self.cfg.get('beam_width', 1)
@ -350,16 +342,13 @@ cdef class Parser:
def pipe(self, docs, int batch_size=256, int n_threads=2,
beam_width=None, beam_density=None):
"""
Process a stream of documents.
"""Process a stream of documents.
Arguments:
stream: The sequence of documents to process.
batch_size (int):
The number of documents to accumulate into a working set.
n_threads (int):
The number of threads with which to work on the buffer in parallel.
Yields (Doc): Documents, in order.
stream: The sequence of documents to process.
batch_size (int): Number of documents to accumulate into a working set.
n_threads (int): The number of threads with which to work on the buffer
in parallel.
YIELDS (Doc): Documents, in order.
"""
if beam_width is None:
beam_width = self.cfg.get('beam_width', 1)