# 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, OrderedDict
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 libcpp.vector cimport vector
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 thinc.extra.search cimport Beam
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, noop, clone
from thinc.neural import Model, Affine, ELU, ReLu, Maxout
from thinc.neural.ops import NumpyOps, CupyOps
from thinc.neural.util import get_array_module
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 ..compat import json_dumps
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 . import nonproj
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 = getattr(lower_model, 'nP', 1)
self.ops = lower_model.ops
self._is_synchronized = False
self._cuda_stream = cuda_stream
self._cached = cached
self._bp_hiddens = bp_features
cdef const float* get_feat_weights(self) except NULL:
if not self._is_synchronized \
and self._cuda_stream is not None:
self._cuda_stream.synchronize()
self._is_synchronized = True
return <float*>self._cached.data
def __call__(self, X):
return self.begin_update(X)[0]
def begin_update(self, token_ids, drop=0.):
cdef np.ndarray state_vector = numpy.zeros((token_ids.shape[0], self.nO*self.nP), dtype='f')
# 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
bp_hiddens = self._bp_hiddens
feat_weights = self.get_feat_weights()
cdef int[:, ::1] ids = token_ids
sum_state_features(<float*>state_vector.data,
feat_weights, &ids[0,0],
token_ids.shape[0], self.nF, self.nO*self.nP)
state_vector, bp_nonlinearity = self._nonlinearity(state_vector)
def backward(d_state_vector, sgd=None):
if bp_nonlinearity is not None:
d_state_vector = bp_nonlinearity(d_state_vector, sgd)
# This will usually be on GPU
if isinstance(d_state_vector, numpy.ndarray):
d_state_vector = self.ops.xp.array(d_state_vector)
d_tokens = bp_hiddens((d_state_vector, token_ids), sgd)
return d_tokens
return state_vector, backward
def _nonlinearity(self, state_vector):
if self.nP == 1:
return state_vector, None
state_vector = state_vector.reshape(
(state_vector.shape[0], state_vector.shape[1]//self.nP, self.nP))
best, which = self.ops.maxout(state_vector)
def backprop(d_best, sgd=None):
return self.ops.backprop_maxout(d_best, which, self.nP)
return best, backprop
cdef void sum_state_features(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, depth=1, **cfg):
depth = util.env_opt('parser_hidden_depth', depth)
token_vector_width = util.env_opt('token_vector_width', token_vector_width)
hidden_width = util.env_opt('hidden_width', hidden_width)
parser_maxout_pieces = util.env_opt('parser_maxout_pieces', 2)
if parser_maxout_pieces == 1:
lower = PrecomputableAffine(hidden_width if depth >= 1 else nr_class,
nF=cls.nr_feature,
nI=token_vector_width)
else:
lower = PrecomputableMaxouts(hidden_width if depth >= 1 else nr_class,
nF=cls.nr_feature,
nP=parser_maxout_pieces,
nI=token_vector_width)
with Model.use_device('cpu'):
upper = chain(
clone(Maxout(hidden_width), (depth-1)),
zero_init(Affine(nr_class, drop_factor=0.0))
)
# 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)))
cfg = {
'nr_class': nr_class,
'depth': depth,
'token_vector_width': token_vector_width,
'hidden_width': hidden_width,
'maxout_pieces': parser_maxout_pieces
}
return (lower, upper), cfg
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, beam_width=None, beam_density=None):
"""
Apply the parser or entity recognizer, setting the annotations onto the Doc object.
Arguments:
doc (Doc): The document to be processed.
Returns:
None
"""
if beam_width is None:
beam_width = self.cfg.get('beam_width', 1)
if beam_density is None:
beam_density = self.cfg.get('beam_density', 0.001)
cdef Beam beam
if beam_width == 1:
states = self.parse_batch([doc], [doc.tensor])
self.set_annotations([doc], states)
return doc
else:
beam = self.beam_parse([doc], [doc.tensor],
beam_width=beam_width, beam_density=beam_density)[0]
output = self.moves.get_beam_annot(beam)
state = <StateClass>beam.at(0)
self.set_annotations([doc], [state])
_cleanup(beam)
return output
def pipe(self, docs, int batch_size=1000, int n_threads=2,
beam_width=1, beam_density=0.001):
"""
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):
docs = list(docs)
tokvecs = [d.tensor for d in docs]
if beam_width == 1:
parse_states = self.parse_batch(docs, tokvecs)
else:
parse_states = self.beam_parse(docs, tokvecs,
beam_width=beam_width, beam_density=beam_density)
self.set_annotations(docs, parse_states)
yield from docs
def parse_batch(self, docs, tokvecses):
cdef:
precompute_hiddens state2vec
StateClass state
Pool mem
const float* feat_weights
StateC* st
vector[StateC*] next_step, this_step
int nr_class, nr_feat, nr_piece, nr_dim, nr_state
if isinstance(docs, Doc):
docs = [docs]
tokvecs = self.model[0].ops.flatten(tokvecses)
nr_state = len(docs)
nr_class = self.moves.n_moves
nr_dim = tokvecs.shape[1]
nr_feat = self.nr_feature
cuda_stream = get_cuda_stream()
state2vec, vec2scores = self.get_batch_model(nr_state, tokvecs,
cuda_stream, 0.0)
nr_piece = state2vec.nP
states = self.moves.init_batch(docs)
for state in states:
if not state.c.is_final():
next_step.push_back(state.c)
feat_weights = state2vec.get_feat_weights()
cdef int i
cdef np.ndarray token_ids = numpy.zeros((nr_state, nr_feat), dtype='i')
cdef np.ndarray is_valid = numpy.zeros((nr_state, nr_class), dtype='i')
cdef np.ndarray scores
c_token_ids = <int*>token_ids.data
c_is_valid = <int*>is_valid.data
while not next_step.empty():
for i in range(next_step.size()):
st = next_step[i]
st.set_context_tokens(&c_token_ids[i*nr_feat], nr_feat)
self.moves.set_valid(&c_is_valid[i*nr_class], st)
vectors = state2vec(token_ids[:next_step.size()])
scores = vec2scores(vectors)
c_scores = <float*>scores.data
for i in range(next_step.size()):
st = next_step[i]
guess = arg_max_if_valid(
&c_scores[i*nr_class], &c_is_valid[i*nr_class], nr_class)
action = self.moves.c[guess]
action.do(st, action.label)
this_step, next_step = next_step, this_step
next_step.clear()
for st in this_step:
if not st.is_final():
next_step.push_back(st)
return states
def beam_parse(self, docs, tokvecses, int beam_width=8, float beam_density=0.001):
cdef Beam beam
cdef np.ndarray scores
cdef Doc doc
cdef int nr_class = self.moves.n_moves
cdef StateClass stcls, output
tokvecs = self.model[0].ops.flatten(tokvecses)
cuda_stream = get_cuda_stream()
state2vec, vec2scores = self.get_batch_model(len(docs), tokvecs,
cuda_stream, 0.0)
beams = []
cdef int offset = 0
for doc in docs:
beam = Beam(nr_class, beam_width, min_density=beam_density)
beam.initialize(self.moves.init_beam_state, doc.length, doc.c)
for i in range(beam.width):
stcls = <StateClass>beam.at(i)
stcls.c.offset = offset
offset += len(doc)
beam.check_done(_check_final_state, NULL)
while not beam.is_done:
states = []
for i in range(beam.size):
stcls = <StateClass>beam.at(i)
states.append(stcls)
token_ids = self.get_token_ids(states)
vectors = state2vec(token_ids)
scores = vec2scores(vectors)
for i in range(beam.size):
stcls = <StateClass>beam.at(i)
if not stcls.is_final():
self.moves.set_valid(beam.is_valid[i], stcls.c)
for j in range(nr_class):
beam.scores[i][j] = scores[i, j]
beam.advance(_transition_state, _hash_state, <void*>self.moves.c)
beam.check_done(_check_final_state, NULL)
beams.append(beam)
return beams
def update(self, docs_tokvecs, golds, drop=0., sgd=None, losses=None):
if losses is not None and self.name not in losses:
losses[self.name] = 0.
docs, tokvec_lists = docs_tokvecs
tokvecs = self.model[0].ops.flatten(tokvec_lists)
if isinstance(docs, Doc) and isinstance(golds, GoldParse):
docs = [docs]
golds = [golds]
cuda_stream = get_cuda_stream()
states, golds, max_steps = self._init_gold_batch(docs, golds)
state2vec, vec2scores = self.get_batch_model(len(states), tokvecs, cuda_stream,
0.0)
todo = [(s, g) for (s, g) in zip(states, golds)
if not s.is_final() and g is not None]
if not todo:
return None
backprops = []
d_tokvecs = state2vec.ops.allocate(tokvecs.shape)
cdef float loss = 0.
n_steps = 0
while todo:
states, golds = zip(*todo)
token_ids = self.get_token_ids(states)
vector, bp_vector = state2vec.begin_update(token_ids, drop=0.0)
if drop != 0:
mask = vec2scores.ops.get_dropout_mask(vector.shape, drop)
vector *= mask
scores, bp_scores = vec2scores.begin_update(vector, drop=drop)
d_scores = self.get_batch_loss(states, golds, scores)
d_vector = bp_scores(d_scores / d_scores.shape[0], sgd=sgd)
if drop != 0:
d_vector *= mask
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()]
if losses is not None:
losses[self.name] += (d_scores**2).sum()
n_steps += 1
if n_steps >= max_steps:
break
self._make_updates(d_tokvecs,
backprops, sgd, cuda_stream)
return self.model[0].ops.unflatten(d_tokvecs, [len(d) for d in docs])
def _init_gold_batch(self, whole_docs, whole_golds):
"""Make a square batch, of length equal to the shortest doc. A long
doc will get multiple states. Let's say we have a doc of length 2*N,
where N is the shortest doc. We'll make two states, one representing
long_doc[:N], and another representing long_doc[N:]."""
cdef:
StateClass state
Transition action
whole_states = self.moves.init_batch(whole_docs)
max_length = max(5, min(50, min([len(doc) for doc in whole_docs])))
max_moves = 0
states = []
golds = []
for doc, state, gold in zip(whole_docs, whole_states, whole_golds):
gold = self.moves.preprocess_gold(gold)
if gold is None:
continue
oracle_actions = self.moves.get_oracle_sequence(doc, gold)
start = 0
while start < len(doc):
state = state.copy()
n_moves = 0
while state.B(0) < start and not state.is_final():
action = self.moves.c[oracle_actions.pop(0)]
action.do(state.c, action.label)
n_moves += 1
has_gold = self.moves.has_gold(gold, start=start,
end=start+max_length)
if not state.is_final() and has_gold:
states.append(state)
golds.append(gold)
max_moves = max(max_moves, n_moves)
start += min(max_length, len(doc)-start)
max_moves = max(max_moves, len(oracle_actions))
return states, golds, max_moves
def _make_updates(self, d_tokvecs, backprops, sgd, cuda_stream=None):
# Tells CUDA to block, so our async copies complete.
if cuda_stream is not None:
cuda_stream.synchronize()
xp = get_array_module(d_tokvecs)
for ids, d_vector, bp_vector in backprops:
d_state_features = bp_vector(d_vector, sgd=sgd)
active_feats = ids * (ids >= 0)
active_feats = active_feats.reshape((ids.shape[0], ids.shape[1], 1))
if hasattr(xp, 'scatter_add'):
xp.scatter_add(d_tokvecs,
ids, d_state_features * active_feats)
else:
xp.add.at(d_tokvecs,
ids, d_state_features * active_feats)
@property
def move_names(self):
names = []
for i in range(self.moves.n_moves):
name = self.moves.move_name(self.moves.c[i].move, self.moves.c[i].label)
names.append(name)
return names
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
cdef np.ndarray ids = numpy.zeros((len(states), n_tokens),
dtype='i', order='C')
c_ids = <int*>ids.data
for i, state in enumerate(states):
if not state.is_final():
state.c.set_context_tokens(c_ids, n_tokens)
c_ids += ids.shape[1]
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']
gold_tuples = nonproj.preprocess_training_data(gold_tuples)
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, cfg = self.Model(self.moves.n_moves, **cfg)
self.cfg.update(cfg)
def preprocess_gold(self, docs_golds):
for doc, gold in docs_golds:
yield doc, gold
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, **exclude):
serializers = {
'lower_model': lambda p: p.open('wb').write(
self.model[0].to_bytes()),
'upper_model': lambda p: p.open('wb').write(
self.model[1].to_bytes()),
'vocab': lambda p: self.vocab.to_disk(p),
'moves': lambda p: self.moves.to_disk(p, strings=False),
'cfg': lambda p: p.open('w').write(json_dumps(self.cfg))
}
util.to_disk(path, serializers, exclude)
def from_disk(self, path, **exclude):
deserializers = {
'vocab': lambda p: self.vocab.from_disk(p),
'moves': lambda p: self.moves.from_disk(p, strings=False),
'cfg': lambda p: self.cfg.update(ujson.load(p.open())),
'model': lambda p: None
}
util.from_disk(path, deserializers, exclude)
if 'model' not in exclude:
path = util.ensure_path(path)
if self.model is True:
self.model, cfg = self.Model(**self.cfg)
else:
cfg = {}
with (path / 'lower_model').open('rb') as file_:
bytes_data = file_.read()
self.model[0].from_bytes(bytes_data)
with (path / 'upper_model').open('rb') as file_:
bytes_data = file_.read()
self.model[1].from_bytes(bytes_data)
self.cfg.update(cfg)
return self
def to_bytes(self, **exclude):
serializers = OrderedDict((
('lower_model', lambda: self.model[0].to_bytes()),
('upper_model', lambda: self.model[1].to_bytes()),
('vocab', lambda: self.vocab.to_bytes()),
('moves', lambda: self.moves.to_bytes(strings=False)),
('cfg', lambda: ujson.dumps(self.cfg))
))
if 'model' in exclude:
exclude['lower_model'] = True
exclude['upper_model'] = True
exclude.pop('model')
return util.to_bytes(serializers, exclude)
def from_bytes(self, bytes_data, **exclude):
deserializers = OrderedDict((
('vocab', lambda b: self.vocab.from_bytes(b)),
('moves', lambda b: self.moves.from_bytes(b, strings=False)),
('cfg', lambda b: self.cfg.update(ujson.loads(b))),
('lower_model', lambda b: None),
('upper_model', lambda b: None)
))
msg = util.from_bytes(bytes_data, deserializers, exclude)
if 'model' not in exclude:
if self.model is True:
self.model, cfg = self.Model(self.moves.n_moves)
else:
cfg = {}
if 'lower_model' in msg:
self.model[0].from_bytes(msg['lower_model'])
if 'upper_model' in msg:
self.model[1].from_bytes(msg['upper_model'])
self.cfg.update(cfg)
return self
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_maxout_if_valid(const weight_t* scores, const int* is_valid,
int n, int nP) nogil:
cdef int best = -1
cdef float best_score = 0
for i in range(n):
if is_valid[i] >= 1:
for j in range(nP):
if best == -1 or scores[i*nP+j] > best_score:
best = i
best_score = scores[i*nP+j]
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
# These are passed as callbacks to thinc.search.Beam
cdef int _transition_state(void* _dest, void* _src, class_t clas, void* _moves) except -1:
dest = <StateClass>_dest
src = <StateClass>_src
moves = <const Transition*>_moves
dest.clone(src)
moves[clas].do(dest.c, moves[clas].label)
cdef int _check_final_state(void* _state, void* extra_args) except -1:
return (<StateClass>_state).is_final()
def _cleanup(Beam beam):
for i in range(beam.width):
Py_XDECREF(<PyObject*>beam._states[i].content)
Py_XDECREF(<PyObject*>beam._parents[i].content)
cdef hash_t _hash_state(void* _state, void* _) except 0:
state = <StateClass>_state
if state.c.is_final():
return 1
else:
return state.c.hash()