mirror of https://github.com/explosion/spaCy.git
Pseudocode for parser
This commit is contained in:
parent
6e1fad92a1
commit
ccaf26206b
|
@ -1,6 +1,4 @@
|
|||
from thinc.linear.avgtron cimport AveragedPerceptron
|
||||
from thinc.typedefs cimport atom_t
|
||||
from thinc.structs cimport FeatureC
|
||||
|
||||
from .stateclass cimport StateClass
|
||||
from .arc_eager cimport TransitionSystem
|
||||
|
@ -10,15 +8,10 @@ from ..structs cimport TokenC
|
|||
from ._state cimport StateC
|
||||
|
||||
|
||||
cdef class ParserModel(AveragedPerceptron):
|
||||
cdef int set_featuresC(self, atom_t* context, FeatureC* features,
|
||||
const StateC* state) nogil
|
||||
|
||||
|
||||
cdef class Parser:
|
||||
cdef readonly Vocab vocab
|
||||
cdef readonly ParserModel model
|
||||
cdef readonly object model
|
||||
cdef readonly TransitionSystem moves
|
||||
cdef readonly object cfg
|
||||
|
||||
cdef int parseC(self, TokenC* tokens, int length, int nr_feat) nogil
|
||||
#cdef int parseC(self, TokenC* tokens, int length, int nr_feat) nogil
|
||||
|
|
|
@ -49,78 +49,67 @@ def set_debug(val):
|
|||
DEBUG = val
|
||||
|
||||
|
||||
def get_templates(name):
|
||||
pf = _parse_features
|
||||
if name == 'ner':
|
||||
return pf.ner
|
||||
elif name == 'debug':
|
||||
return pf.unigrams
|
||||
elif name.startswith('embed'):
|
||||
return (pf.words, pf.tags, pf.labels)
|
||||
else:
|
||||
return (pf.unigrams + pf.s0_n0 + pf.s1_n0 + pf.s1_s0 + pf.s0_n1 + pf.n0_n1 + \
|
||||
pf.tree_shape + pf.trigrams)
|
||||
@layerize
|
||||
def get_context_tokens(states, drop=0.):
|
||||
for state in states:
|
||||
context[i, 0] = state.B(0)
|
||||
context[i, 1] = state.S(0)
|
||||
context[i, 2] = state.S(1)
|
||||
context[i, 3] = state.L(state.S(0), 1)
|
||||
context[i, 4] = state.L(state.S(0), 2)
|
||||
context[i, 5] = state.R(state.S(0), 1)
|
||||
context[i, 6] = state.R(state.S(0), 2)
|
||||
return (context, states), None
|
||||
|
||||
|
||||
cdef class ParserModel(AveragedPerceptron):
|
||||
cdef int set_featuresC(self, atom_t* context, FeatureC* features,
|
||||
const StateC* state) nogil:
|
||||
fill_context(context, state)
|
||||
nr_feat = self.extracter.set_features(features, context)
|
||||
return nr_feat
|
||||
def extract_features(attrs):
|
||||
def forward(contexts_states, drop=0.):
|
||||
contexts, states = contexts_states
|
||||
for i, state in enumerate(states):
|
||||
for j, tok_i in enumerate(contexts[i]):
|
||||
token = state.get_token(tok_i)
|
||||
for k, attr in enumerate(attrs):
|
||||
output[i, j, k] = getattr(token, attr)
|
||||
return output, None
|
||||
return layerize(forward)
|
||||
|
||||
def update(self, Example eg, itn=0):
|
||||
"""
|
||||
Does regression on negative cost. Sort of cute?
|
||||
"""
|
||||
self.time += 1
|
||||
cdef int best = arg_max_if_gold(eg.c.scores, eg.c.costs, eg.c.nr_class)
|
||||
cdef int guess = eg.guess
|
||||
if guess == best or best == -1:
|
||||
return 0.0
|
||||
cdef FeatureC feat
|
||||
cdef int clas
|
||||
cdef weight_t gradient
|
||||
if USE_FTRL:
|
||||
for feat in eg.c.features[:eg.c.nr_feat]:
|
||||
for clas in range(eg.c.nr_class):
|
||||
if eg.c.is_valid[clas] and eg.c.scores[clas] >= eg.c.scores[best]:
|
||||
gradient = eg.c.scores[clas] + eg.c.costs[clas]
|
||||
self.update_weight_ftrl(feat.key, clas, feat.value * gradient)
|
||||
else:
|
||||
for feat in eg.c.features[:eg.c.nr_feat]:
|
||||
self.update_weight(feat.key, guess, feat.value * eg.c.costs[guess])
|
||||
self.update_weight(feat.key, best, -feat.value * eg.c.costs[guess])
|
||||
return eg.c.costs[guess]
|
||||
|
||||
def update_from_histories(self, TransitionSystem moves, Doc doc, histories, weight_t min_grad=0.0):
|
||||
cdef Pool mem = Pool()
|
||||
features = <FeatureC*>mem.alloc(self.nr_feat, sizeof(FeatureC))
|
||||
def build_tok2vec(lang, width, depth, embed_size):
|
||||
cols = [LEX_ID, PREFIX, SUFFIX, SHAPE]
|
||||
static = StaticVectors('en', width, column=cols.index(LEX_ID))
|
||||
prefix = HashEmbed(width, embed_size, column=cols.index(PREFIX))
|
||||
suffix = HashEmbed(width, embed_size, column=cols.index(SUFFIX))
|
||||
shape = HashEmbed(width, embed_size, column=cols.index(SHAPE))
|
||||
with Model.overload_operaters('>>': chain, '|': concatenate, '+': add):
|
||||
tok2vec = (
|
||||
extract_features(cols)
|
||||
>> (static | prefix | suffix | shape)
|
||||
>> (ExtractWindow(nW=1) >> Maxout(width)) ** depth
|
||||
)
|
||||
return tok2vec
|
||||
|
||||
cdef StateClass stcls
|
||||
|
||||
cdef class_t clas
|
||||
self.time += 1
|
||||
cdef atom_t[CONTEXT_SIZE] atoms
|
||||
histories = [(grad, hist) for grad, hist in histories if abs(grad) >= min_grad and hist]
|
||||
if not histories:
|
||||
return None
|
||||
gradient = [Counter() for _ in range(max([max(h)+1 for _, h in histories]))]
|
||||
for d_loss, history in histories:
|
||||
stcls = StateClass.init(doc.c, doc.length)
|
||||
moves.initialize_state(stcls.c)
|
||||
for clas in history:
|
||||
nr_feat = self.set_featuresC(atoms, features, stcls.c)
|
||||
clas_grad = gradient[clas]
|
||||
for feat in features[:nr_feat]:
|
||||
clas_grad[feat.key] += d_loss * feat.value
|
||||
moves.c[clas].do(stcls.c, moves.c[clas].label)
|
||||
cdef feat_t key
|
||||
cdef weight_t d_feat
|
||||
for clas, clas_grad in enumerate(gradient):
|
||||
for key, d_feat in clas_grad.items():
|
||||
if d_feat != 0:
|
||||
self.update_weight_ftrl(key, clas, d_feat)
|
||||
def build_parse2vec(width, embed_size):
|
||||
cols = [TAG, DEP]
|
||||
tag_vector = HashEmbed(width, 1000, column=cols.index(TAG))
|
||||
dep_vector = HashEmbed(width, 1000, column=cols.index(DEP))
|
||||
with Model.overload_operaters('>>': chain):
|
||||
model = (
|
||||
extract_features([TAG, DEP])
|
||||
>> (tag_vector | dep_vector)
|
||||
)
|
||||
return model
|
||||
|
||||
|
||||
def build_model(get_contexts, tok2vec, parse2vec, width, depth, nr_class):
|
||||
with Model.overload_operaters('>>': chain):
|
||||
model = (
|
||||
get_contexts
|
||||
>> (tok2vec | parse2vec)
|
||||
>> Maxout(width) ** depth
|
||||
>> Softmax(nr_class)
|
||||
)
|
||||
return model
|
||||
|
||||
|
||||
cdef class Parser:
|
||||
|
@ -144,15 +133,6 @@ cdef class Parser:
|
|||
"""
|
||||
with (path / 'config.json').open() as file_:
|
||||
cfg = ujson.load(file_)
|
||||
# TODO: remove this shim when we don't have to support older data
|
||||
if 'labels' in cfg and 'actions' not in cfg:
|
||||
cfg['actions'] = cfg.pop('labels')
|
||||
# TODO: remove this shim when we don't have to support older data
|
||||
for action_name, labels in dict(cfg.get('actions', {})).items():
|
||||
# We need this to be sorted
|
||||
if isinstance(labels, dict):
|
||||
labels = list(sorted(labels.keys()))
|
||||
cfg['actions'][action_name] = labels
|
||||
self = cls(vocab, TransitionSystem=TransitionSystem, model=None, **cfg)
|
||||
if (path / 'model').exists():
|
||||
self.model.load(str(path / 'model'))
|
||||
|
@ -161,14 +141,14 @@ cdef class Parser:
|
|||
"Required file %s/model not found when loading" % str(path))
|
||||
return self
|
||||
|
||||
def __init__(self, Vocab vocab, TransitionSystem=None, ParserModel model=None, **cfg):
|
||||
def __init__(self, Vocab vocab, TransitionSystem=None, model=None, **cfg):
|
||||
"""
|
||||
Create a Parser.
|
||||
|
||||
Arguments:
|
||||
vocab (Vocab):
|
||||
The vocabulary object. Must be shared with documents to be processed.
|
||||
model (thinc.linear.AveragedPerceptron):
|
||||
model (thinc Model):
|
||||
The statistical model.
|
||||
Returns (Parser):
|
||||
The newly constructed object.
|
||||
|
@ -178,44 +158,40 @@ cdef class Parser:
|
|||
self.vocab = vocab
|
||||
cfg['actions'] = TransitionSystem.get_actions(**cfg)
|
||||
self.moves = TransitionSystem(vocab.strings, cfg['actions'])
|
||||
# TODO: Remove this when we no longer need to support old-style models
|
||||
if isinstance(cfg.get('features'), basestring):
|
||||
cfg['features'] = get_templates(cfg['features'])
|
||||
elif 'features' not in cfg:
|
||||
cfg['features'] = self.feature_templates
|
||||
|
||||
self.model = ParserModel(cfg['features'])
|
||||
self.model.l1_penalty = cfg.get('L1', 0.0)
|
||||
self.model.learn_rate = cfg.get('learn_rate', 0.001)
|
||||
|
||||
if model is None:
|
||||
model = self.build_model(**cfg)
|
||||
self.model = model
|
||||
self.cfg = cfg
|
||||
# TODO: This is a pretty hacky fix to the problem of adding more
|
||||
# labels. The issue is they come in out of order, if labels are
|
||||
# added during training
|
||||
for label in cfg.get('extra_labels', []):
|
||||
self.add_label(label)
|
||||
|
||||
|
||||
def __reduce__(self):
|
||||
return (Parser, (self.vocab, self.moves, self.model), None, None)
|
||||
|
||||
def __call__(self, Doc tokens):
|
||||
"""
|
||||
Apply the 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
|
||||
"""
|
||||
cdef int nr_feat = self.model.nr_feat
|
||||
with nogil:
|
||||
status = self.parseC(tokens.c, tokens.length, nr_feat)
|
||||
# Check for KeyboardInterrupt etc. Untested
|
||||
PyErr_CheckSignals()
|
||||
if status != 0:
|
||||
raise ParserStateError(tokens)
|
||||
self.parse_batch([tokens])
|
||||
self.moves.finalize_doc(tokens)
|
||||
|
||||
def parse_batch(self, docs):
|
||||
states = self._init_states(docs)
|
||||
todo = list(states)
|
||||
nr_class = self.moves.n_moves
|
||||
while todo:
|
||||
scores = self.model.predict(todo)
|
||||
self._validate_batch(is_valid, scores, states)
|
||||
for state, guess in zip(todo, scores.argmax(axis=1)):
|
||||
action = self.moves.c[guess]
|
||||
action.do(state, action.label)
|
||||
todo = [state for state in todo if not state.is_final()]
|
||||
for state, doc in zip(states, docs):
|
||||
self.moves.finalize_state(state, doc)
|
||||
|
||||
def pipe(self, stream, int batch_size=1000, int n_threads=2):
|
||||
"""
|
||||
Process a stream of documents.
|
||||
|
@ -229,7 +205,6 @@ cdef class Parser:
|
|||
Yields (Doc): Documents, in order.
|
||||
"""
|
||||
cdef Pool mem = Pool()
|
||||
cdef TokenC** doc_ptr = <TokenC**>mem.alloc(batch_size, sizeof(TokenC*))
|
||||
cdef int* lengths = <int*>mem.alloc(batch_size, sizeof(int))
|
||||
cdef Doc doc
|
||||
cdef int i
|
||||
|
@ -241,111 +216,71 @@ cdef class Parser:
|
|||
lengths[len(queue)] = doc.length
|
||||
queue.append(doc)
|
||||
if len(queue) == batch_size:
|
||||
with nogil:
|
||||
for i in cython.parallel.prange(batch_size, num_threads=n_threads):
|
||||
status = self.parseC(doc_ptr[i], lengths[i], nr_feat)
|
||||
if status != 0:
|
||||
with gil:
|
||||
raise ParserStateError(queue[i])
|
||||
PyErr_CheckSignals()
|
||||
self.parse_batch(queue)
|
||||
for doc in queue:
|
||||
self.moves.finalize_doc(doc)
|
||||
yield doc
|
||||
queue = []
|
||||
batch_size = len(queue)
|
||||
with nogil:
|
||||
for i in cython.parallel.prange(batch_size, num_threads=n_threads):
|
||||
status = self.parseC(doc_ptr[i], lengths[i], nr_feat)
|
||||
if status != 0:
|
||||
with gil:
|
||||
raise ParserStateError(queue[i])
|
||||
PyErr_CheckSignals()
|
||||
for doc in queue:
|
||||
self.moves.finalize_doc(doc)
|
||||
yield doc
|
||||
if queue:
|
||||
self.parse_batch(queue)
|
||||
for doc in queue:
|
||||
self.moves.finalize_doc(doc)
|
||||
yield doc
|
||||
|
||||
cdef int parseC(self, TokenC* tokens, int length, int nr_feat) nogil:
|
||||
state = new StateC(tokens, length)
|
||||
# NB: This can change self.moves.n_moves!
|
||||
# I think this causes memory errors if called by .pipe()
|
||||
self.moves.initialize_state(state)
|
||||
def update(self, docs, golds, drop=0., sgd=None):
|
||||
if isinstance(docs, Doc) and isinstance(golds, GoldParse):
|
||||
return self.update([docs], [golds], drop=drop)
|
||||
states = self._init_states(docs)
|
||||
nr_class = self.moves.n_moves
|
||||
|
||||
cdef ExampleC eg
|
||||
eg.nr_feat = nr_feat
|
||||
eg.nr_atom = CONTEXT_SIZE
|
||||
eg.nr_class = nr_class
|
||||
eg.features = <FeatureC*>calloc(sizeof(FeatureC), nr_feat)
|
||||
eg.atoms = <atom_t*>calloc(sizeof(atom_t), CONTEXT_SIZE)
|
||||
eg.scores = <weight_t*>calloc(sizeof(weight_t), nr_class)
|
||||
eg.is_valid = <int*>calloc(sizeof(int), nr_class)
|
||||
cdef int i
|
||||
while not state.is_final():
|
||||
eg.nr_feat = self.model.set_featuresC(eg.atoms, eg.features, state)
|
||||
self.moves.set_valid(eg.is_valid, state)
|
||||
self.model.set_scoresC(eg.scores, eg.features, eg.nr_feat)
|
||||
|
||||
guess = VecVec.arg_max_if_true(eg.scores, eg.is_valid, eg.nr_class)
|
||||
if guess < 0:
|
||||
return 1
|
||||
|
||||
action = self.moves.c[guess]
|
||||
|
||||
action.do(state, action.label)
|
||||
memset(eg.scores, 0, sizeof(eg.scores[0]) * eg.nr_class)
|
||||
for i in range(eg.nr_class):
|
||||
eg.is_valid[i] = 1
|
||||
self.moves.finalize_state(state)
|
||||
for i in range(length):
|
||||
tokens[i] = state._sent[i]
|
||||
del state
|
||||
free(eg.features)
|
||||
free(eg.atoms)
|
||||
free(eg.scores)
|
||||
free(eg.is_valid)
|
||||
while states:
|
||||
scores, finish_update = self.model.begin_update(states, drop=drop)
|
||||
self._validate_batch(is_valid, scores, states)
|
||||
for i, state in enumerate(states):
|
||||
self.moves.set_costs(costs[i], is_valid, state, golds[i])
|
||||
|
||||
self._transition_batch(states, scores)
|
||||
self._set_gradient(gradients, scores, costs)
|
||||
finish_update(gradients, sgd=sgd)
|
||||
gradients.fill(0)
|
||||
|
||||
states = [state for state in states if not state.is_final()]
|
||||
gradients = gradients[:len(states)]
|
||||
costs = costs[:len(states)]
|
||||
return 0
|
||||
|
||||
def update(self, Doc tokens, GoldParse gold, itn=0, double drop=0.0):
|
||||
"""
|
||||
Update the statistical model.
|
||||
|
||||
Arguments:
|
||||
doc (Doc):
|
||||
The example document for the update.
|
||||
gold (GoldParse):
|
||||
The gold-standard annotations, to calculate the loss.
|
||||
Returns (float):
|
||||
The loss on this example.
|
||||
"""
|
||||
self.moves.preprocess_gold(gold)
|
||||
cdef StateClass stcls = StateClass.init(tokens.c, tokens.length)
|
||||
self.moves.initialize_state(stcls.c)
|
||||
cdef Pool mem = Pool()
|
||||
cdef Example eg = Example(
|
||||
nr_class=self.moves.n_moves,
|
||||
nr_atom=CONTEXT_SIZE,
|
||||
nr_feat=self.model.nr_feat)
|
||||
cdef weight_t loss = 0
|
||||
cdef Transition action
|
||||
cdef double dropout_rate = self.cfg.get('dropout', drop)
|
||||
while not stcls.is_final():
|
||||
eg.c.nr_feat = self.model.set_featuresC(eg.c.atoms, eg.c.features,
|
||||
stcls.c)
|
||||
dropout(eg.c.features, eg.c.nr_feat, dropout_rate)
|
||||
self.moves.set_costs(eg.c.is_valid, eg.c.costs, stcls, gold)
|
||||
self.model.set_scoresC(eg.c.scores, eg.c.features, eg.c.nr_feat)
|
||||
guess = VecVec.arg_max_if_true(eg.c.scores, eg.c.is_valid, eg.c.nr_class)
|
||||
self.model.update(eg)
|
||||
def _validate_batch(self, is_valid, scores, states):
|
||||
for i, state in enumerate(states):
|
||||
self.moves.set_valid(is_valid, state)
|
||||
for j in range(self.moves.n_moves):
|
||||
if not is_valid[j]:
|
||||
scores[i, j] = 0
|
||||
|
||||
def _transition_batch(self, states, scores):
|
||||
for state, guess in zip(states, scores.argmax(axis=1)):
|
||||
action = self.moves.c[guess]
|
||||
action.do(stcls.c, action.label)
|
||||
loss += eg.costs[guess]
|
||||
eg.fill_scores(0, eg.c.nr_class)
|
||||
eg.fill_costs(0, eg.c.nr_class)
|
||||
eg.fill_is_valid(1, eg.c.nr_class)
|
||||
action.do(state, action.label)
|
||||
|
||||
self.moves.finalize_state(stcls.c)
|
||||
return loss
|
||||
def _init_states(self, docs):
|
||||
states = []
|
||||
cdef Doc doc
|
||||
for i, doc in enumerate(docs):
|
||||
state = StateClass.init(doc)
|
||||
self.moves.initialize_state(state)
|
||||
return states
|
||||
|
||||
def _set_gradient(self, gradients, scores, costs):
|
||||
"""Do multi-label log loss"""
|
||||
cdef double Z, gZ, max_, g_max
|
||||
maxes = scores.max(axis=1)
|
||||
g_maxes = (scores * costs <= 0).max(axis=1)
|
||||
exps = (scores-maxes).exp()
|
||||
g_exps = (g_scores-g_maxes).exp()
|
||||
|
||||
Zs = exps.sum(axis=1)
|
||||
gZs = g_exps.sum(axis=1)
|
||||
logprob = exps / Zs
|
||||
g_logprob = g_exps / gZs
|
||||
gradients[:] = logprob - g_logprob
|
||||
|
||||
def step_through(self, Doc doc, GoldParse gold=None):
|
||||
"""
|
||||
|
|
Loading…
Reference in New Issue