mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-14 05:37:03 +03:00
526 lines
19 KiB
Cython
526 lines
19 KiB
Cython
# cython: infer_types=True
|
|
# cython: profile=True
|
|
# cython: cdivision=True
|
|
# cython: boundscheck=False
|
|
# coding: utf-8
|
|
from __future__ import unicode_literals, print_function
|
|
|
|
from collections import Counter
|
|
import ujson
|
|
import contextlib
|
|
|
|
from libc.math cimport exp
|
|
cimport cython
|
|
cimport cython.parallel
|
|
import cytoolz
|
|
import dill
|
|
|
|
import numpy.random
|
|
cimport numpy as np
|
|
|
|
from cpython.ref cimport PyObject, Py_INCREF, Py_XDECREF
|
|
from cpython.exc cimport PyErr_CheckSignals
|
|
from libc.stdint cimport uint32_t, uint64_t
|
|
from libc.string cimport memset, memcpy
|
|
from libc.stdlib cimport malloc, calloc, free
|
|
from thinc.typedefs cimport weight_t, class_t, feat_t, atom_t, hash_t
|
|
from thinc.linear.avgtron cimport AveragedPerceptron
|
|
from thinc.linalg cimport VecVec
|
|
from thinc.structs cimport SparseArrayC, FeatureC, ExampleC
|
|
from thinc.extra.eg cimport Example
|
|
from cymem.cymem cimport Pool, Address
|
|
from murmurhash.mrmr cimport hash64
|
|
from preshed.maps cimport MapStruct
|
|
from preshed.maps cimport map_get
|
|
|
|
from thinc.api import layerize, chain
|
|
from thinc.neural import Model, Affine, ELU, ReLu, Maxout
|
|
from thinc.neural.ops import NumpyOps, CupyOps
|
|
|
|
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 . import _parse_features
|
|
from ._parse_features cimport CONTEXT_SIZE
|
|
from ._parse_features cimport fill_context
|
|
from .stateclass cimport StateClass
|
|
from ._state cimport StateC
|
|
from .nonproj import PseudoProjectivity
|
|
from .transition_system import OracleError
|
|
from .transition_system cimport TransitionSystem, Transition
|
|
from ..structs cimport TokenC
|
|
from ..tokens.doc cimport Doc
|
|
from ..strings cimport StringStore
|
|
from ..gold cimport GoldParse
|
|
from ..attrs cimport TAG, DEP
|
|
|
|
|
|
def get_templates(*args, **kwargs):
|
|
return []
|
|
|
|
USE_FTRL = True
|
|
DEBUG = False
|
|
def set_debug(val):
|
|
global DEBUG
|
|
DEBUG = val
|
|
|
|
|
|
cdef class precompute_hiddens:
|
|
'''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.
|
|
'''
|
|
cdef int nF, nO, nP
|
|
cdef bint _is_synchronized
|
|
cdef public object ops
|
|
cdef np.ndarray _features
|
|
cdef np.ndarray _cached
|
|
cdef object _cuda_stream
|
|
cdef object _bp_hiddens
|
|
|
|
def __init__(self, batch_size, tokvecs, lower_model, cuda_stream=None, drop=0.):
|
|
gpu_cached, bp_features = lower_model.begin_update(tokvecs, drop=drop)
|
|
cdef np.ndarray cached
|
|
if not isinstance(gpu_cached, numpy.ndarray):
|
|
# Note the passing of cuda_stream here: it lets
|
|
# cupy make the copy asynchronously.
|
|
# We then have to block before first use.
|
|
cached = gpu_cached.get(stream=cuda_stream)
|
|
else:
|
|
cached = gpu_cached
|
|
self.nF = cached.shape[1]
|
|
self.nO = cached.shape[2]
|
|
self.nP = cached.shape[3]
|
|
self.ops = lower_model.ops
|
|
self._features = numpy.zeros((batch_size, self.nO, self.nP), dtype='f')
|
|
self._is_synchronized = False
|
|
self._cuda_stream = cuda_stream
|
|
self._cached = cached
|
|
self._bp_hiddens = bp_features
|
|
|
|
def __call__(self, X):
|
|
return self.begin_update(X)[0]
|
|
|
|
def begin_update(self, token_ids, drop=0.):
|
|
self._features.fill(0)
|
|
if not self._is_synchronized \
|
|
and self._cuda_stream is not None:
|
|
self._cuda_stream.synchronize()
|
|
self._is_synchronized = True
|
|
# This is tricky, but (assuming GPU available);
|
|
# - Input to forward on CPU
|
|
# - Output from forward on CPU
|
|
# - Input to backward on GPU!
|
|
# - Output from backward on GPU
|
|
cdef np.ndarray state_vector = self._features[:len(token_ids)]
|
|
cdef np.ndarray hiddens = self._cached
|
|
bp_hiddens = self._bp_hiddens
|
|
|
|
cdef int[:, ::1] ids = token_ids
|
|
self._sum_features(<float*>state_vector.data,
|
|
<float*>hiddens.data, &ids[0,0],
|
|
token_ids.shape[0], self.nF, self.nO*self.nP)
|
|
|
|
output, bp_output = self._apply_nonlinearity(state_vector)
|
|
|
|
def backward(d_output, sgd=None):
|
|
# This will usually be on GPU
|
|
if isinstance(d_output, numpy.ndarray):
|
|
d_output = self.ops.xp.array(d_output)
|
|
d_state_vector = bp_output(d_output, sgd)
|
|
d_tokens = bp_hiddens((d_state_vector, token_ids), sgd)
|
|
return d_tokens
|
|
return output, backward
|
|
|
|
def _apply_nonlinearity(self, X):
|
|
if self.nP < 2:
|
|
return X.reshape(X.shape[:2]), lambda dX, sgd=None: dX.reshape(X.shape)
|
|
best, which = self.ops.maxout(X)
|
|
return best, lambda dX, sgd=None: self.ops.backprop_maxout(dX, which, self.nP)
|
|
|
|
cdef void _sum_features(self, float* output,
|
|
const float* cached, const int* token_ids, int B, int F, int O) nogil:
|
|
cdef int idx, b, f, i
|
|
cdef const float* feature
|
|
for b in range(B):
|
|
for f in range(F):
|
|
if token_ids[f] < 0:
|
|
continue
|
|
idx = token_ids[f] * F * O + f*O
|
|
feature = &cached[idx]
|
|
for i in range(O):
|
|
output[i] += feature[i]
|
|
output += O
|
|
token_ids += F
|
|
|
|
|
|
cdef void cpu_log_loss(float* d_scores,
|
|
const float* costs, const int* is_valid, const float* scores,
|
|
int O) nogil:
|
|
"""Do multi-label log loss"""
|
|
cdef double max_, gmax, Z, gZ
|
|
best = arg_max_if_gold(scores, costs, is_valid, O)
|
|
guess = arg_max_if_valid(scores, is_valid, O)
|
|
Z = 1e-10
|
|
gZ = 1e-10
|
|
max_ = scores[guess]
|
|
gmax = scores[best]
|
|
for i in range(O):
|
|
if is_valid[i]:
|
|
Z += exp(scores[i] - max_)
|
|
if costs[i] <= costs[best]:
|
|
gZ += exp(scores[i] - gmax)
|
|
for i in range(O):
|
|
if not is_valid[i]:
|
|
d_scores[i] = 0.
|
|
elif costs[i] <= costs[best]:
|
|
d_scores[i] = (exp(scores[i]-max_) / Z) - (exp(scores[i]-gmax)/gZ)
|
|
else:
|
|
d_scores[i] = exp(scores[i]-max_) / Z
|
|
|
|
|
|
cdef void cpu_regression_loss(float* d_scores,
|
|
const float* costs, const int* is_valid, const float* scores,
|
|
int O) nogil:
|
|
cdef float eps = 2.
|
|
best = arg_max_if_gold(scores, costs, is_valid, O)
|
|
for i in range(O):
|
|
if not is_valid[i]:
|
|
d_scores[i] = 0.
|
|
elif scores[i] < scores[best]:
|
|
d_scores[i] = 0.
|
|
else:
|
|
# I doubt this is correct?
|
|
# Looking for something like Huber loss
|
|
diff = scores[i] - -costs[i]
|
|
if diff > eps:
|
|
d_scores[i] = eps
|
|
elif diff < -eps:
|
|
d_scores[i] = -eps
|
|
else:
|
|
d_scores[i] = diff
|
|
|
|
|
|
cdef class Parser:
|
|
"""
|
|
Base class of the DependencyParser and EntityRecognizer.
|
|
"""
|
|
@classmethod
|
|
def Model(cls, nr_class, token_vector_width=128, hidden_width=128, **cfg):
|
|
token_vector_width = util.env_opt('token_vector_width', token_vector_width)
|
|
hidden_width = util.env_opt('hidden_width', hidden_width)
|
|
maxout_pieces = util.env_opt('parser_maxout_pieces', 1)
|
|
lower = PrecomputableMaxouts(hidden_width,
|
|
nF=cls.nr_feature,
|
|
nI=token_vector_width,
|
|
pieces=maxout_pieces)
|
|
|
|
lower = rebatch(1024, lower)
|
|
|
|
with Model.use_device('cpu'):
|
|
upper = chain(
|
|
Maxout(hidden_width),
|
|
zero_init(Affine(nr_class))
|
|
)
|
|
# TODO: This is an unfortunate hack atm!
|
|
# Used to set input dimensions in network.
|
|
lower.begin_training(lower.ops.allocate((500, token_vector_width)))
|
|
upper.begin_training(upper.ops.allocate((500, hidden_width)))
|
|
return lower, upper
|
|
|
|
def __init__(self, Vocab vocab, moves=True, model=True, **cfg):
|
|
"""
|
|
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
|
|
"""
|
|
self.vocab = vocab
|
|
if moves is True:
|
|
self.moves = self.TransitionSystem(self.vocab.strings, {})
|
|
else:
|
|
self.moves = moves
|
|
self.cfg = cfg
|
|
if 'actions' in self.cfg:
|
|
for action, labels in self.cfg.get('actions', {}).items():
|
|
for label in labels:
|
|
self.moves.add_action(action, label)
|
|
self.model = model
|
|
|
|
def __reduce__(self):
|
|
return (Parser, (self.vocab, self.moves, self.model), None, None)
|
|
|
|
def __call__(self, Doc doc):
|
|
"""
|
|
Apply the parser or entity recognizer, setting the annotations onto the Doc object.
|
|
|
|
Arguments:
|
|
doc (Doc): The document to be processed.
|
|
Returns:
|
|
None
|
|
"""
|
|
self.parse_batch([doc], doc.tensor)
|
|
|
|
def pipe(self, docs, int batch_size=1000, int n_threads=2):
|
|
"""
|
|
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.
|
|
"""
|
|
cdef StateClass parse_state
|
|
cdef Doc doc
|
|
queue = []
|
|
for docs in cytoolz.partition_all(batch_size, docs):
|
|
tokvecs = self.model[0].ops.flatten([d.tensor for d in docs])
|
|
parse_states = self.parse_batch(docs, tokvecs)
|
|
self.set_annotations(docs, parse_states)
|
|
yield from docs
|
|
|
|
def parse_batch(self, docs, tokvecs):
|
|
cuda_stream = get_cuda_stream()
|
|
|
|
states = self.moves.init_batch(docs)
|
|
state2vec, vec2scores = self.get_batch_model(len(states), tokvecs,
|
|
cuda_stream, 0.0)
|
|
|
|
todo = [st for st in states if not st.is_final()]
|
|
while todo:
|
|
token_ids = self.get_token_ids(states)
|
|
vectors = state2vec(token_ids)
|
|
scores = vec2scores(vectors)
|
|
self.transition_batch(states, scores)
|
|
todo = [st for st in states if not st.is_final()]
|
|
return states
|
|
|
|
def update(self, docs_tokvecs, golds, drop=0., sgd=None):
|
|
docs, tokvecs = docs_tokvecs
|
|
if isinstance(docs, Doc) and isinstance(golds, GoldParse):
|
|
docs = [docs]
|
|
golds = [golds]
|
|
|
|
cuda_stream = get_cuda_stream()
|
|
for gold in golds:
|
|
self.moves.preprocess_gold(gold)
|
|
|
|
states = self.moves.init_batch(docs)
|
|
state2vec, vec2scores = self.get_batch_model(len(states), tokvecs, cuda_stream,
|
|
drop)
|
|
|
|
todo = [(s, g) for s, g in zip(states, golds) if not s.is_final()]
|
|
|
|
backprops = []
|
|
cdef float loss = 0.
|
|
cutoff = max(1, len(todo) // 10)
|
|
while len(todo) >= cutoff:
|
|
states, golds = zip(*todo)
|
|
|
|
token_ids = self.get_token_ids(states)
|
|
vector, bp_vector = state2vec.begin_update(token_ids, drop=drop)
|
|
scores, bp_scores = vec2scores.begin_update(vector, drop=drop)
|
|
|
|
d_scores = self.get_batch_loss(states, golds, scores)
|
|
d_vector = bp_scores(d_scores, sgd=sgd)
|
|
|
|
if isinstance(self.model[0].ops, CupyOps) \
|
|
and not isinstance(token_ids, state2vec.ops.xp.ndarray):
|
|
# Move token_ids and d_vector to CPU, asynchronously
|
|
backprops.append((
|
|
get_async(cuda_stream, token_ids),
|
|
get_async(cuda_stream, d_vector),
|
|
bp_vector
|
|
))
|
|
else:
|
|
backprops.append((token_ids, d_vector, bp_vector))
|
|
self.transition_batch(states, scores)
|
|
todo = [st for st in todo if not st[0].is_final()]
|
|
# Tells CUDA to block, so our async copies complete.
|
|
if cuda_stream is not None:
|
|
cuda_stream.synchronize()
|
|
d_tokvecs = state2vec.ops.allocate(tokvecs.shape)
|
|
xp = state2vec.ops.xp # Handle for numpy/cupy
|
|
for token_ids, d_vector, bp_vector in backprops:
|
|
d_state_features = bp_vector(d_vector, sgd=sgd)
|
|
active_feats = token_ids * (token_ids >= 0)
|
|
active_feats = active_feats.reshape((token_ids.shape[0], token_ids.shape[1], 1))
|
|
if hasattr(xp, 'scatter_add'):
|
|
xp.scatter_add(d_tokvecs,
|
|
token_ids, d_state_features * active_feats)
|
|
else:
|
|
xp.add.at(d_tokvecs,
|
|
token_ids, d_state_features * active_feats)
|
|
return d_tokvecs
|
|
|
|
def get_batch_model(self, batch_size, tokvecs, stream, dropout):
|
|
lower, upper = self.model
|
|
state2vec = precompute_hiddens(batch_size, tokvecs,
|
|
lower, stream, drop=dropout)
|
|
return state2vec, upper
|
|
|
|
nr_feature = 13
|
|
|
|
def get_token_ids(self, states):
|
|
cdef StateClass state
|
|
cdef int n_tokens = self.nr_feature
|
|
ids = numpy.zeros((len(states), n_tokens), dtype='i', order='C')
|
|
for i, state in enumerate(states):
|
|
state.set_context_tokens(ids[i])
|
|
return ids
|
|
|
|
def transition_batch(self, states, float[:, ::1] scores):
|
|
cdef StateClass state
|
|
cdef int[500] is_valid # TODO: Unhack
|
|
cdef float* c_scores = &scores[0, 0]
|
|
for state in states:
|
|
self.moves.set_valid(is_valid, state.c)
|
|
guess = arg_max_if_valid(c_scores, is_valid, scores.shape[1])
|
|
action = self.moves.c[guess]
|
|
action.do(state.c, action.label)
|
|
c_scores += scores.shape[1]
|
|
|
|
def get_batch_loss(self, states, golds, float[:, ::1] scores):
|
|
cdef StateClass state
|
|
cdef GoldParse gold
|
|
cdef Pool mem = Pool()
|
|
cdef int i
|
|
is_valid = <int*>mem.alloc(self.moves.n_moves, sizeof(int))
|
|
costs = <float*>mem.alloc(self.moves.n_moves, sizeof(float))
|
|
cdef np.ndarray d_scores = numpy.zeros((len(states), self.moves.n_moves),
|
|
dtype='f', order='C')
|
|
c_d_scores = <float*>d_scores.data
|
|
for i, (state, gold) in enumerate(zip(states, golds)):
|
|
memset(is_valid, 0, self.moves.n_moves * sizeof(int))
|
|
memset(costs, 0, self.moves.n_moves * sizeof(float))
|
|
self.moves.set_costs(is_valid, costs, state, gold)
|
|
cpu_log_loss(c_d_scores,
|
|
costs, is_valid, &scores[i, 0], d_scores.shape[1])
|
|
c_d_scores += d_scores.shape[1]
|
|
return d_scores
|
|
|
|
def set_annotations(self, docs, states):
|
|
cdef StateClass state
|
|
cdef Doc doc
|
|
for state, doc in zip(states, docs):
|
|
self.moves.finalize_state(state.c)
|
|
for i in range(doc.length):
|
|
doc.c[i] = state.c._sent[i]
|
|
self.moves.finalize_doc(doc)
|
|
|
|
def add_label(self, label):
|
|
for action in self.moves.action_types:
|
|
added = self.moves.add_action(action, label)
|
|
if added:
|
|
# Important that the labels be stored as a list! We need the
|
|
# order, or the model goes out of synch
|
|
self.cfg.setdefault('extra_labels', []).append(label)
|
|
|
|
def begin_training(self, gold_tuples, **cfg):
|
|
if 'model' in cfg:
|
|
self.model = cfg['model']
|
|
actions = self.moves.get_actions(gold_parses=gold_tuples)
|
|
for action, labels in actions.items():
|
|
for label in labels:
|
|
self.moves.add_action(action, label)
|
|
if self.model is True:
|
|
self.model = self.Model(self.moves.n_moves, **cfg)
|
|
|
|
def use_params(self, params):
|
|
# Can't decorate cdef class :(. Workaround.
|
|
with self.model[0].use_params(params):
|
|
with self.model[1].use_params(params):
|
|
yield
|
|
|
|
def to_disk(self, path):
|
|
path = util.ensure_path(path)
|
|
with (path / 'model.bin').open('wb') as file_:
|
|
dill.dump(self.model, file_)
|
|
|
|
def from_disk(self, path):
|
|
path = util.ensure_path(path)
|
|
with (path / 'model.bin').open('wb') as file_:
|
|
self.model = dill.load(file_)
|
|
|
|
def to_bytes(self):
|
|
pass
|
|
|
|
def from_bytes(self, data):
|
|
pass
|
|
|
|
|
|
class ParserStateError(ValueError):
|
|
def __init__(self, doc):
|
|
ValueError.__init__(self,
|
|
"Error analysing doc -- no valid actions available. This should "
|
|
"never happen, so please report the error on the issue tracker. "
|
|
"Here's the thread to do so --- reopen it if it's closed:\n"
|
|
"https://github.com/spacy-io/spaCy/issues/429\n"
|
|
"Please include the text that the parser failed on, which is:\n"
|
|
"%s" % repr(doc.text))
|
|
|
|
|
|
cdef int arg_max_if_gold(const weight_t* scores, const weight_t* costs, const int* is_valid, int n) nogil:
|
|
# Find minimum cost
|
|
cdef float cost = 1
|
|
for i in range(n):
|
|
if is_valid[i] and costs[i] < cost:
|
|
cost = costs[i]
|
|
# Now find best-scoring with that cost
|
|
cdef int best = -1
|
|
for i in range(n):
|
|
if costs[i] <= cost and is_valid[i]:
|
|
if best == -1 or scores[i] > scores[best]:
|
|
best = i
|
|
return best
|
|
|
|
|
|
cdef int arg_max_if_valid(const weight_t* scores, const int* is_valid, int n) nogil:
|
|
cdef int best = -1
|
|
for i in range(n):
|
|
if is_valid[i] >= 1:
|
|
if best == -1 or scores[i] > scores[best]:
|
|
best = i
|
|
return best
|
|
|
|
|
|
cdef int _arg_max_clas(const weight_t* scores, int move, const Transition* actions,
|
|
int nr_class) except -1:
|
|
cdef weight_t score = 0
|
|
cdef int mode = -1
|
|
cdef int i
|
|
for i in range(nr_class):
|
|
if actions[i].move == move and (mode == -1 or scores[i] >= score):
|
|
mode = i
|
|
score = scores[i]
|
|
return mode
|