# cython: infer_types=True, cdivision=True, boundscheck=False
from __future__ import print_function
from cymem.cymem cimport Pool
cimport numpy as np
from itertools import islice
from libcpp.vector cimport vector
from libc.string cimport memset
from libc.stdlib cimport calloc, free
import random
import srsly
from thinc.api import set_dropout_rate
import numpy.random
import numpy
import warnings
from ._parser_internals.stateclass cimport StateClass
from ..ml.parser_model cimport alloc_activations, free_activations
from ..ml.parser_model cimport predict_states, arg_max_if_valid
from ..ml.parser_model cimport WeightsC, ActivationsC, SizesC, cpu_log_loss
from ..ml.parser_model cimport get_c_weights, get_c_sizes
from ..tokens.doc cimport Doc
from ..training import validate_examples
from ..errors import Errors, Warnings
from .. import util
cdef class Parser(Pipe):
"""
Base class of the DependencyParser and EntityRecognizer.
"""
def __init__(
self,
Vocab vocab,
model,
name="base_parser",
moves=None,
*,
update_with_oracle_cut_size,
multitasks=tuple(),
min_action_freq,
learn_tokens,
):
"""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
self.name = name
cfg = {
"moves": moves,
"update_with_oracle_cut_size": update_with_oracle_cut_size,
"multitasks": list(multitasks),
"min_action_freq": min_action_freq,
"learn_tokens": learn_tokens
}
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 = cfg
self._multitasks = []
for multitask in cfg["multitasks"]:
self.add_multitask_objective(multitask)
self._rehearsal_model = None
def __getnewargs_ex__(self):
"""This allows pickling the Parser and its keyword-only init arguments"""
args = (self.vocab, self.model, self.name, self.moves)
return args, self.cfg
@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()
return 1
return 0
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)
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)
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)
model.clear_memory()
del model
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):
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, Errors.E924.format(name=self.name)
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.)
validate_examples(examples, "Parser.update")
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])
max_moves = self.cfg["update_with_oracle_cut_size"]
if max_moves >= 1:
# Chop sequences into lengths of this many words, to make the
# batch uniform length.
max_moves = int(random.uniform(max_moves // 2, max_moves * 2))
states, golds, _ = self._init_gold_batch(
examples,
max_length=max_moves
)
else:
states, golds, _ = self.moves.init_gold_batch(examples)
if not states:
return losses
all_states = list(states)
states_golds = list(zip(states, golds))
n_moves = 0
while states_golds:
states, golds = zip(*states_golds)
scores, backprop = model.begin_update(states)
d_scores = self.get_batch_loss(states, golds, scores, losses)
# Note that the gradient isn't normalized by the batch size
# here, because our "samples" are really the states...But we
# can't normalize by the number of states either, as then we'd
# be getting smaller gradients for states in long sequences.
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()]
if max_moves >= 1 and n_moves >= max_moves:
break
n_moves += 1
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)
# Ugh, this is annoying. If we're working on GPU, we want to free the
# memory ASAP. It seems that Python doesn't necessarily get around to
# removing these in time if we don't explicitly delete? It's confusing.
del backprop
del backprop_tok2vec
model.clear_memory()
del model
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.)
validate_examples(examples, "Parser.rehearse")
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, backprop_tok2vec = 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
backprop_tok2vec(docs)
if sgd is not None:
self.model.finish_update(sgd)
losses[self.name] += loss / n_scores
del backprop
del backprop_tok2vec
model.clear_memory()
tutor.clear_memory()
del model
del tutor
return losses
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, Errors.E924.format(name=self.name)
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]
# Note that we don't normalize this. See comment in update() for why.
if losses is not None:
losses.setdefault(self.name, 0.)
losses[self.name] += (d_scores**2).sum()
return d_scores
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._ensure_examples(get_examples)
self.cfg.update(kwargs)
lexeme_norms = self.vocab.lookups.get_table("lexeme_norm", {})
if len(lexeme_norms) == 0 and self.vocab.lang in util.LEXEME_NORM_LANGS:
langs = ", ".join(util.LEXEME_NORM_LANGS)
util.logger.debug(Warnings.W033.format(model="parser or NER", langs=langs))
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 = []
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 not doc_sample:
for example in islice(get_examples(), 10):
doc_sample.append(example.predicted)
assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
self.model.initialize(doc_sample)
if pipeline is not None:
self.init_multitask_objectives(get_examples, pipeline, sgd=sgd, **self.cfg)
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) from None
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) from None
return self
def _init_gold_batch(self, examples, max_length):
"""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])
states = []
golds = []
to_cut = []
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)
if len(eg.x) < max_length:
states.append(state)
golds.append(gold)
else:
oracle_actions = self.moves.get_oracle_sequence_from_state(
state.copy(), gold)
to_cut.append((eg, state, gold, oracle_actions))
if not to_cut:
return states, golds, 0
cdef int clas
for eg, state, gold, oracle_actions in to_cut:
for i in range(0, len(oracle_actions), max_length):
start_state = state.copy()
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)
if state.is_final():
break
if self.moves.has_gold(eg, start_state.B(0), state.B(0)):
states.append(start_state)
golds.append(gold)
if state.is_final():
break
return states, golds, max_length