mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-28 02:46:35 +03:00
412dbb1f38
* Remove dead and/or deprecated code * Remove n_threads Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
564 lines
23 KiB
Cython
564 lines
23 KiB
Cython
# cython: infer_types=True, cdivision=True, boundscheck=False
|
|
cimport cython.parallel
|
|
cimport numpy as np
|
|
from itertools import islice
|
|
from cpython.ref cimport PyObject, Py_XDECREF
|
|
from cpython.exc cimport PyErr_CheckSignals, PyErr_SetFromErrno
|
|
from libc.math cimport exp
|
|
from libcpp.vector cimport vector
|
|
from libc.string cimport memset, memcpy
|
|
from libc.stdlib cimport calloc, free
|
|
from cymem.cymem cimport Pool
|
|
from thinc.backends.linalg cimport Vec, VecVec
|
|
|
|
from thinc.api import chain, clone, Linear, list2array, NumpyOps, CupyOps, use_ops
|
|
from thinc.api import get_array_module, zero_init, set_dropout_rate
|
|
from itertools import islice
|
|
import srsly
|
|
import numpy.random
|
|
import numpy
|
|
import warnings
|
|
|
|
from ..tokens.doc cimport Doc
|
|
from ..typedefs cimport weight_t, class_t, hash_t
|
|
from ._parser_model cimport alloc_activations, free_activations
|
|
from ._parser_model cimport predict_states, arg_max_if_valid
|
|
from ._parser_model cimport WeightsC, ActivationsC, SizesC, cpu_log_loss
|
|
from ._parser_model cimport get_c_weights, get_c_sizes
|
|
from .stateclass cimport StateClass
|
|
from ._state cimport StateC
|
|
from .transition_system cimport Transition
|
|
from ..gold.example cimport Example
|
|
|
|
from ..util import link_vectors_to_models, create_default_optimizer, registry
|
|
from ..compat import copy_array
|
|
from ..errors import Errors, Warnings
|
|
from .. import util
|
|
from . import nonproj
|
|
|
|
|
|
cdef class Parser:
|
|
"""
|
|
Base class of the DependencyParser and EntityRecognizer.
|
|
"""
|
|
name = 'base_parser'
|
|
|
|
|
|
def __init__(self, Vocab vocab, model, **cfg):
|
|
"""Create a Parser.
|
|
|
|
vocab (Vocab): The vocabulary object. Must be shared with documents
|
|
to be processed. The value is set to the `.vocab` attribute.
|
|
**cfg: Configuration parameters. Set to the `.cfg` attribute.
|
|
If it doesn't include a value for 'moves', a new instance is
|
|
created with `self.TransitionSystem()`. This defines how the
|
|
parse-state is created, updated and evaluated.
|
|
"""
|
|
self.vocab = vocab
|
|
moves = cfg.get("moves", None)
|
|
if moves is None:
|
|
# defined by EntityRecognizer as a BiluoPushDown
|
|
moves = self.TransitionSystem(self.vocab.strings)
|
|
self.moves = moves
|
|
self.model = model
|
|
if self.moves.n_moves != 0:
|
|
self.set_output(self.moves.n_moves)
|
|
self.cfg = dict(cfg)
|
|
self.cfg.setdefault("update_with_oracle_cut_size", 100)
|
|
self.cfg.setdefault("normalize_gradients_with_batch_size", True)
|
|
self._multitasks = []
|
|
for multitask in cfg.get("multitasks", []):
|
|
self.add_multitask_objective(multitask)
|
|
|
|
self._rehearsal_model = None
|
|
|
|
@classmethod
|
|
def from_nlp(cls, nlp, model, **cfg):
|
|
return cls(nlp.vocab, model, **cfg)
|
|
|
|
def __reduce__(self):
|
|
return (Parser, (self.vocab, self.model), (self.moves, self.cfg))
|
|
|
|
def __getstate__(self):
|
|
return (self.moves, self.cfg)
|
|
|
|
def __setstate__(self, state):
|
|
moves, config = state
|
|
self.moves = moves
|
|
self.cfg = config
|
|
|
|
@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)
|
|
# Explicitly removing the internal "U-" token used for blocking entities
|
|
if name != "U-":
|
|
names.append(name)
|
|
return names
|
|
|
|
@property
|
|
def labels(self):
|
|
class_names = [self.moves.get_class_name(i) for i in range(self.moves.n_moves)]
|
|
return class_names
|
|
|
|
@property
|
|
def tok2vec(self):
|
|
'''Return the embedding and convolutional layer of the model.'''
|
|
return self.model.get_ref("tok2vec")
|
|
|
|
@property
|
|
def postprocesses(self):
|
|
# Available for subclasses, e.g. to deprojectivize
|
|
return []
|
|
|
|
def add_label(self, label):
|
|
resized = False
|
|
for action in self.moves.action_types:
|
|
added = self.moves.add_action(action, label)
|
|
if added:
|
|
resized = True
|
|
if resized:
|
|
self._resize()
|
|
|
|
def _resize(self):
|
|
self.model.attrs["resize_output"](self.model, self.moves.n_moves)
|
|
if self._rehearsal_model not in (True, False, None):
|
|
self._rehearsal_model.attrs["resize_output"](
|
|
self._rehearsal_model, self.moves.n_moves
|
|
)
|
|
|
|
def add_multitask_objective(self, target):
|
|
# Defined in subclasses, to avoid circular import
|
|
raise NotImplementedError
|
|
|
|
def init_multitask_objectives(self, get_examples, pipeline, **cfg):
|
|
'''Setup models for secondary objectives, to benefit from multi-task
|
|
learning. This method is intended to be overridden by subclasses.
|
|
|
|
For instance, the dependency parser can benefit from sharing
|
|
an input representation with a label prediction model. These auxiliary
|
|
models are discarded after training.
|
|
'''
|
|
pass
|
|
|
|
def use_params(self, params):
|
|
# Can't decorate cdef class :(. Workaround.
|
|
with self.model.use_params(params):
|
|
yield
|
|
|
|
def __call__(self, Doc doc):
|
|
"""Apply the parser or entity recognizer, setting the annotations onto
|
|
the `Doc` object.
|
|
|
|
doc (Doc): The document to be processed.
|
|
"""
|
|
states = self.predict([doc])
|
|
self.set_annotations([doc], states, tensors=None)
|
|
return doc
|
|
|
|
def pipe(self, docs, int batch_size=256):
|
|
"""Process a stream of documents.
|
|
|
|
stream: The sequence of documents to process.
|
|
batch_size (int): Number of documents to accumulate into a working set.
|
|
YIELDS (Doc): Documents, in order.
|
|
"""
|
|
cdef Doc doc
|
|
for batch in util.minibatch(docs, size=batch_size):
|
|
batch_in_order = list(batch)
|
|
by_length = sorted(batch, key=lambda doc: len(doc))
|
|
for subbatch in util.minibatch(by_length, size=max(batch_size//4, 2)):
|
|
subbatch = list(subbatch)
|
|
parse_states = self.predict(subbatch)
|
|
self.set_annotations(subbatch, parse_states, tensors=None)
|
|
yield from batch_in_order
|
|
|
|
def predict(self, docs):
|
|
if isinstance(docs, Doc):
|
|
docs = [docs]
|
|
if not any(len(doc) for doc in docs):
|
|
result = self.moves.init_batch(docs)
|
|
self._resize()
|
|
return result
|
|
return self.greedy_parse(docs, drop=0.0)
|
|
|
|
def greedy_parse(self, docs, drop=0.):
|
|
cdef vector[StateC*] states
|
|
cdef StateClass state
|
|
set_dropout_rate(self.model, drop)
|
|
batch = self.moves.init_batch(docs)
|
|
# This is pretty dirty, but the NER can resize itself in init_batch,
|
|
# if labels are missing. We therefore have to check whether we need to
|
|
# expand our model output.
|
|
self._resize()
|
|
model = self.model.predict(docs)
|
|
weights = get_c_weights(model)
|
|
for state in batch:
|
|
if not state.is_final():
|
|
states.push_back(state.c)
|
|
sizes = get_c_sizes(model, states.size())
|
|
with nogil:
|
|
self._parseC(&states[0],
|
|
weights, sizes)
|
|
return batch
|
|
|
|
cdef void _parseC(self, StateC** states,
|
|
WeightsC weights, SizesC sizes) nogil:
|
|
cdef int i, j
|
|
cdef vector[StateC*] unfinished
|
|
cdef ActivationsC activations = alloc_activations(sizes)
|
|
while sizes.states >= 1:
|
|
predict_states(&activations,
|
|
states, &weights, sizes)
|
|
# Validate actions, argmax, take action.
|
|
self.c_transition_batch(states,
|
|
activations.scores, sizes.classes, sizes.states)
|
|
for i in range(sizes.states):
|
|
if not states[i].is_final():
|
|
unfinished.push_back(states[i])
|
|
for i in range(unfinished.size()):
|
|
states[i] = unfinished[i]
|
|
sizes.states = unfinished.size()
|
|
unfinished.clear()
|
|
free_activations(&activations)
|
|
|
|
def set_annotations(self, docs, states, tensors=None):
|
|
cdef StateClass state
|
|
cdef Doc doc
|
|
for i, (state, doc) in enumerate(zip(states, docs)):
|
|
self.moves.finalize_state(state.c)
|
|
for j in range(doc.length):
|
|
doc.c[j] = state.c._sent[j]
|
|
self.moves.finalize_doc(doc)
|
|
for hook in self.postprocesses:
|
|
hook(doc)
|
|
|
|
def transition_states(self, states, float[:, ::1] scores):
|
|
cdef StateClass state
|
|
cdef float* c_scores = &scores[0, 0]
|
|
cdef vector[StateC*] c_states
|
|
for state in states:
|
|
c_states.push_back(state.c)
|
|
self.c_transition_batch(&c_states[0], c_scores, scores.shape[1], scores.shape[0])
|
|
return [state for state in states if not state.c.is_final()]
|
|
|
|
cdef void c_transition_batch(self, StateC** states, const float* scores,
|
|
int nr_class, int batch_size) nogil:
|
|
# n_moves should not be zero at this point, but make sure to avoid zero-length mem alloc
|
|
with gil:
|
|
assert self.moves.n_moves > 0
|
|
is_valid = <int*>calloc(self.moves.n_moves, sizeof(int))
|
|
cdef int i, guess
|
|
cdef Transition action
|
|
for i in range(batch_size):
|
|
self.moves.set_valid(is_valid, states[i])
|
|
guess = arg_max_if_valid(&scores[i*nr_class], is_valid, nr_class)
|
|
if guess == -1:
|
|
# This shouldn't happen, but it's hard to raise an error here,
|
|
# and we don't want to infinite loop. So, force to end state.
|
|
states[i].force_final()
|
|
else:
|
|
action = self.moves.c[guess]
|
|
action.do(states[i], action.label)
|
|
states[i].push_hist(guess)
|
|
free(is_valid)
|
|
|
|
def update(self, examples, drop=0., set_annotations=False, sgd=None, losses=None):
|
|
cdef StateClass state
|
|
if losses is None:
|
|
losses = {}
|
|
losses.setdefault(self.name, 0.)
|
|
for multitask in self._multitasks:
|
|
multitask.update(examples, drop=drop, sgd=sgd)
|
|
n_examples = len([eg for eg in examples if self.moves.has_gold(eg)])
|
|
if n_examples == 0:
|
|
return losses
|
|
set_dropout_rate(self.model, drop)
|
|
# Prepare the stepwise model, and get the callback for finishing the batch
|
|
model, backprop_tok2vec = self.model.begin_update(
|
|
[eg.predicted for eg in examples])
|
|
if self.cfg["update_with_oracle_cut_size"] >= 1:
|
|
# Chop sequences into lengths of this many transitions, to make the
|
|
# batch uniform length. We randomize this to overfit less.
|
|
cut_size = self.cfg["update_with_oracle_cut_size"]
|
|
states, golds, max_steps = self._init_gold_batch(
|
|
examples,
|
|
max_length=numpy.random.choice(range(5, cut_size))
|
|
)
|
|
else:
|
|
states, golds, _ = self.moves.init_gold_batch(examples)
|
|
max_steps = max([len(eg.x) for eg in examples])
|
|
if not states:
|
|
return losses
|
|
all_states = list(states)
|
|
states_golds = zip(states, golds)
|
|
for _ in range(max_steps):
|
|
if not states_golds:
|
|
break
|
|
states, golds = zip(*states_golds)
|
|
scores, backprop = model.begin_update(states)
|
|
d_scores = self.get_batch_loss(states, golds, scores, losses)
|
|
if self.cfg["normalize_gradients_with_batch_size"]:
|
|
# We have to be very careful how we do this, because of the way we
|
|
# cut up the batch. We subdivide long sequences. If we normalize
|
|
# naively, we end up normalizing by sequence length, which
|
|
# is bad: that would mean that states in long sequences
|
|
# consistently get smaller gradients. Imagine if we have two
|
|
# sequences, one length 1000, one length 20. If we cut up
|
|
# the 1k sequence so that we have a "batch" of 50 subsequences,
|
|
# we don't want the gradients to get 50 times smaller!
|
|
d_scores /= n_examples
|
|
|
|
backprop(d_scores)
|
|
# Follow the predicted action
|
|
self.transition_states(states, scores)
|
|
states_golds = [(s, g) for (s, g) in zip(states, golds) if not s.is_final()]
|
|
|
|
backprop_tok2vec(golds)
|
|
if sgd not in (None, False):
|
|
self.model.finish_update(sgd)
|
|
if set_annotations:
|
|
docs = [eg.predicted for eg in examples]
|
|
self.set_annotations(docs, all_states)
|
|
return losses
|
|
|
|
def rehearse(self, examples, sgd=None, losses=None, **cfg):
|
|
"""Perform a "rehearsal" update, to prevent catastrophic forgetting."""
|
|
if losses is None:
|
|
losses = {}
|
|
for multitask in self._multitasks:
|
|
if hasattr(multitask, 'rehearse'):
|
|
multitask.rehearse(examples, losses=losses, sgd=sgd)
|
|
if self._rehearsal_model is None:
|
|
return None
|
|
losses.setdefault(self.name, 0.)
|
|
|
|
docs = [eg.predicted for eg in examples]
|
|
states = self.moves.init_batch(docs)
|
|
# This is pretty dirty, but the NER can resize itself in init_batch,
|
|
# if labels are missing. We therefore have to check whether we need to
|
|
# expand our model output.
|
|
self._resize()
|
|
# Prepare the stepwise model, and get the callback for finishing the batch
|
|
set_dropout_rate(self._rehearsal_model, 0.0)
|
|
set_dropout_rate(self.model, 0.0)
|
|
tutor, _ = self._rehearsal_model.begin_update(docs)
|
|
model, finish_update = self.model.begin_update(docs)
|
|
n_scores = 0.
|
|
loss = 0.
|
|
while states:
|
|
targets, _ = tutor.begin_update(states)
|
|
guesses, backprop = model.begin_update(states)
|
|
d_scores = (guesses - targets) / targets.shape[0]
|
|
# If all weights for an output are 0 in the original model, don't
|
|
# supervise that output. This allows us to add classes.
|
|
loss += (d_scores**2).sum()
|
|
backprop(d_scores, sgd=sgd)
|
|
# Follow the predicted action
|
|
self.transition_states(states, guesses)
|
|
states = [state for state in states if not state.is_final()]
|
|
n_scores += d_scores.size
|
|
# Do the backprop
|
|
finish_update(docs)
|
|
if sgd is not None:
|
|
self.model.finish_update(sgd)
|
|
losses[self.name] += loss / n_scores
|
|
return losses
|
|
|
|
def get_gradients(self):
|
|
"""Get non-zero gradients of the model's parameters, as a dictionary
|
|
keyed by the parameter ID. The values are (weights, gradients) tuples.
|
|
"""
|
|
gradients = {}
|
|
queue = [self.model]
|
|
seen = set()
|
|
for node in queue:
|
|
if node.id in seen:
|
|
continue
|
|
seen.add(node.id)
|
|
if hasattr(node, "_mem") and node._mem.gradient.any():
|
|
gradients[node.id] = [node._mem.weights, node._mem.gradient]
|
|
if hasattr(node, "_layers"):
|
|
queue.extend(node._layers)
|
|
return gradients
|
|
|
|
def get_batch_loss(self, states, golds, float[:, ::1] scores, losses):
|
|
cdef StateClass state
|
|
cdef Pool mem = Pool()
|
|
cdef int i
|
|
|
|
# n_moves should not be zero at this point, but make sure to avoid zero-length mem alloc
|
|
assert self.moves.n_moves > 0
|
|
|
|
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
|
|
unseen_classes = self.model.attrs["unseen_classes"]
|
|
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)
|
|
for j in range(self.moves.n_moves):
|
|
if costs[j] <= 0.0 and j in unseen_classes:
|
|
unseen_classes.remove(j)
|
|
cpu_log_loss(c_d_scores,
|
|
costs, is_valid, &scores[i, 0], d_scores.shape[1])
|
|
c_d_scores += d_scores.shape[1]
|
|
if losses is not None:
|
|
losses.setdefault(self.name, 0.)
|
|
losses[self.name] += (d_scores**2).sum()
|
|
return d_scores
|
|
|
|
def create_optimizer(self):
|
|
return create_default_optimizer()
|
|
|
|
def set_output(self, nO):
|
|
self.model.attrs["resize_output"](self.model, nO)
|
|
|
|
def begin_training(self, get_examples, pipeline=None, sgd=None, **kwargs):
|
|
self.cfg.update(kwargs)
|
|
if len(self.vocab.lookups.get_table("lexeme_norm", {})) == 0:
|
|
warnings.warn(Warnings.W033.format(model="parser or NER"))
|
|
if not hasattr(get_examples, '__call__'):
|
|
gold_tuples = get_examples
|
|
get_examples = lambda: gold_tuples
|
|
actions = self.moves.get_actions(
|
|
examples=get_examples(),
|
|
min_freq=self.cfg['min_action_freq'],
|
|
learn_tokens=self.cfg["learn_tokens"]
|
|
)
|
|
for action, labels in self.moves.labels.items():
|
|
actions.setdefault(action, {})
|
|
for label, freq in labels.items():
|
|
if label not in actions[action]:
|
|
actions[action][label] = freq
|
|
self.moves.initialize_actions(actions)
|
|
# make sure we resize so we have an appropriate upper layer
|
|
self._resize()
|
|
if sgd is None:
|
|
sgd = self.create_optimizer()
|
|
doc_sample = []
|
|
for example in islice(get_examples(), 10):
|
|
doc_sample.append(example.predicted)
|
|
|
|
if pipeline is not None:
|
|
for name, component in pipeline:
|
|
if component is self:
|
|
break
|
|
if hasattr(component, "pipe"):
|
|
doc_sample = list(component.pipe(doc_sample, batch_size=8))
|
|
else:
|
|
doc_sample = [component(doc) for doc in doc_sample]
|
|
if doc_sample:
|
|
self.model.initialize(doc_sample)
|
|
else:
|
|
self.model.initialize()
|
|
if pipeline is not None:
|
|
self.init_multitask_objectives(get_examples, pipeline, sgd=sgd, **self.cfg)
|
|
link_vectors_to_models(self.vocab)
|
|
return sgd
|
|
|
|
def to_disk(self, path, exclude=tuple()):
|
|
serializers = {
|
|
'model': lambda p: (self.model.to_disk(p) if self.model is not True else True),
|
|
'vocab': lambda p: self.vocab.to_disk(p),
|
|
'moves': lambda p: self.moves.to_disk(p, exclude=["strings"]),
|
|
'cfg': lambda p: srsly.write_json(p, self.cfg)
|
|
}
|
|
util.to_disk(path, serializers, exclude)
|
|
|
|
def from_disk(self, path, exclude=tuple()):
|
|
deserializers = {
|
|
'vocab': lambda p: self.vocab.from_disk(p),
|
|
'moves': lambda p: self.moves.from_disk(p, exclude=["strings"]),
|
|
'cfg': lambda p: self.cfg.update(srsly.read_json(p)),
|
|
'model': lambda p: None,
|
|
}
|
|
util.from_disk(path, deserializers, exclude)
|
|
if 'model' not in exclude:
|
|
path = util.ensure_path(path)
|
|
with (path / 'model').open('rb') as file_:
|
|
bytes_data = file_.read()
|
|
try:
|
|
self._resize()
|
|
self.model.from_bytes(bytes_data)
|
|
except AttributeError:
|
|
raise ValueError(Errors.E149)
|
|
return self
|
|
|
|
def to_bytes(self, exclude=tuple()):
|
|
serializers = {
|
|
"model": lambda: (self.model.to_bytes()),
|
|
"vocab": lambda: self.vocab.to_bytes(),
|
|
"moves": lambda: self.moves.to_bytes(exclude=["strings"]),
|
|
"cfg": lambda: srsly.json_dumps(self.cfg, indent=2, sort_keys=True)
|
|
}
|
|
return util.to_bytes(serializers, exclude)
|
|
|
|
def from_bytes(self, bytes_data, exclude=tuple()):
|
|
deserializers = {
|
|
"vocab": lambda b: self.vocab.from_bytes(b),
|
|
"moves": lambda b: self.moves.from_bytes(b, exclude=["strings"]),
|
|
"cfg": lambda b: self.cfg.update(srsly.json_loads(b)),
|
|
"model": lambda b: None,
|
|
}
|
|
msg = util.from_bytes(bytes_data, deserializers, exclude)
|
|
if 'model' not in exclude:
|
|
if 'model' in msg:
|
|
try:
|
|
self.model.from_bytes(msg['model'])
|
|
except AttributeError:
|
|
raise ValueError(Errors.E149)
|
|
return self
|
|
|
|
def _init_gold_batch(self, examples, min_length=5, max_length=500):
|
|
"""Make a square batch, of length equal to the shortest transition
|
|
sequence or a cap. 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 start_state
|
|
StateClass state
|
|
Transition action
|
|
all_states = self.moves.init_batch([eg.predicted for eg in examples])
|
|
kept = []
|
|
max_length_seen = 0
|
|
for state, eg in zip(all_states, examples):
|
|
if self.moves.has_gold(eg) and not state.is_final():
|
|
gold = self.moves.init_gold(state, eg)
|
|
oracle_actions = self.moves.get_oracle_sequence_from_state(
|
|
state.copy(), gold)
|
|
kept.append((eg, state, gold, oracle_actions))
|
|
min_length = min(min_length, len(oracle_actions))
|
|
max_length_seen = max(max_length, len(oracle_actions))
|
|
if not kept:
|
|
return [], [], 0
|
|
max_length = max(min_length, min(max_length, max_length_seen))
|
|
states = []
|
|
golds = []
|
|
cdef int clas
|
|
max_moves = 0
|
|
for eg, state, gold, oracle_actions in kept:
|
|
for i in range(0, len(oracle_actions), max_length):
|
|
start_state = state.copy()
|
|
n_moves = 0
|
|
for clas in oracle_actions[i:i+max_length]:
|
|
action = self.moves.c[clas]
|
|
action.do(state.c, action.label)
|
|
state.c.push_hist(action.clas)
|
|
n_moves += 1
|
|
if state.is_final():
|
|
break
|
|
max_moves = max(max_moves, n_moves)
|
|
if self.moves.has_gold(eg, start_state.B(0), state.B(0)):
|
|
states.append(start_state)
|
|
golds.append(gold)
|
|
max_moves = max(max_moves, n_moves)
|
|
if state.is_final():
|
|
break
|
|
return states, golds, max_moves
|