# cython: infer_types=True
# cython: cdivision=True
# cython: boundscheck=False
# coding: utf-8
from __future__ import unicode_literals, print_function
from collections import OrderedDict
import ujson
import json
import numpy
cimport cython.parallel
import cytoolz
import numpy.random
cimport numpy as np
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.typedefs cimport weight_t, class_t, hash_t
from thinc.extra.search cimport Beam
from thinc.api import chain, clone
from thinc.v2v import Model, Maxout, Affine
from thinc.misc import LayerNorm
from thinc.neural.ops import CupyOps
from thinc.neural.util import get_array_module
from thinc.linalg cimport Vec, VecVec
from thinc cimport openblas
from .._ml import zero_init, PrecomputableAffine, Tok2Vec, flatten
from .._ml import link_vectors_to_models, create_default_optimizer
from ..compat import json_dumps, copy_array
from ..tokens.doc cimport Doc
from ..gold cimport GoldParse
from .. import util
from .stateclass cimport StateClass
from ._state cimport StateC
from .transition_system cimport Transition
from . import _beam_utils, nonproj
def get_templates(*args, **kwargs):
return []
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 np.ndarray bias
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
if not isinstance(lower_model.b, numpy.ndarray):
self.bias = lower_model.b.get()
else:
self.bias = lower_model.b
self.nF = cached.shape[1]
self.nP = getattr(lower_model, 'nP', 1)
self.nO = cached.shape[2]
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 += self.bias
state_vector, bp_nonlinearity = self._nonlinearity(state_vector)
def backward(d_state_vector_ids, sgd=None):
d_state_vector, token_ids = d_state_vector_ids
d_state_vector = bp_nonlinearity(d_state_vector, sgd)
# This will usually be on GPU
if not isinstance(d_state_vector, self.ops.xp.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:
state_vector = state_vector.reshape(state_vector.shape[:-1])
mask = state_vector >= 0.
state_vector *= mask
else:
state_vector, mask = self.ops.maxout(state_vector)
def backprop_nonlinearity(d_best, sgd=None):
if self.nP == 1:
d_best *= mask
d_best = d_best.reshape((d_best.shape + (1,)))
return d_best
else:
return self.ops.backprop_maxout(d_best, mask, self.nP)
return state_vector, backprop_nonlinearity
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
padding = cached
cached += F * O
for b in range(B):
for f in range(F):
if token_ids[f] < 0:
feature = &padding[f*O]
else:
idx = token_ids[f] * F * O + f*O
feature = &cached[idx]
VecVec.add_i(output,
feature, 1., O)
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
def _collect_states(beams):
cdef StateClass state
cdef Beam beam
states = []
for beam in beams:
state = StateClass.borrow(<StateC*>beam.at(0))
states.append(state)
return states
cdef class Parser:
"""
Base class of the DependencyParser and EntityRecognizer.
"""
@classmethod
def Model(cls, nr_class, **cfg):
depth = util.env_opt('parser_hidden_depth', cfg.get('hidden_depth', 1))
if depth != 1:
raise ValueError("Currently parser depth is hard-coded to 1.")
parser_maxout_pieces = util.env_opt('parser_maxout_pieces',
cfg.get('maxout_pieces', 2))
token_vector_width = util.env_opt('token_vector_width',
cfg.get('token_vector_width', 128))
hidden_width = util.env_opt('hidden_width', cfg.get('hidden_width', 200))
embed_size = util.env_opt('embed_size', cfg.get('embed_size', 7000))
hist_size = util.env_opt('history_feats', cfg.get('hist_size', 0))
hist_width = util.env_opt('history_width', cfg.get('hist_width', 0))
if hist_size != 0:
raise ValueError("Currently history size is hard-coded to 0")
if hist_width != 0:
raise ValueError("Currently history width is hard-coded to 0")
tok2vec = Tok2Vec(token_vector_width, embed_size,
pretrained_dims=cfg.get('pretrained_dims', 0))
tok2vec = chain(tok2vec, flatten)
lower = PrecomputableAffine(hidden_width,
nF=cls.nr_feature, nI=token_vector_width,
nP=parser_maxout_pieces)
lower.nP = parser_maxout_pieces
with Model.use_device('cpu'):
upper = chain(
clone(Maxout(hidden_width, hidden_width), depth-1),
zero_init(Affine(nr_class, hidden_width, drop_factor=0.0))
)
cfg = {
'nr_class': nr_class,
'hidden_depth': depth,
'token_vector_width': token_vector_width,
'hidden_width': hidden_width,
'maxout_pieces': parser_maxout_pieces,
'hist_size': hist_size,
'hist_width': hist_width
}
return (tok2vec, lower, upper), cfg
def create_optimizer(self):
return create_default_optimizer(self.model[0].ops,
**self.cfg.get('optimizer', {}))
def __init__(self, Vocab vocab, moves=True, model=True, **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.
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
if 'beam_width' not in cfg:
cfg['beam_width'] = util.env_opt('beam_width', 1)
if 'beam_density' not in cfg:
cfg['beam_density'] = util.env_opt('beam_density', 0.0)
if 'pretrained_dims' not in cfg:
cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
self.cfg = cfg
self.model = model
self._multitasks = []
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.
doc (Doc): The document to be processed.
"""
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', 0.0)
cdef Beam beam
if beam_width == 1:
states, tokvecs = self.parse_batch([doc])
self.set_annotations([doc], states, tensors=tokvecs)
return doc
else:
beams, tokvecs = self.beam_parse([doc],
beam_width=beam_width,
beam_density=beam_density)
beam = beams[0]
output = self.moves.get_beam_annot(beam)
state = StateClass.borrow(<StateC*>beam.at(0))
self.set_annotations([doc], [state], tensors=tokvecs)
_cleanup(beam)
return output
def pipe(self, docs, int batch_size=256, int n_threads=2,
beam_width=None, beam_density=None):
"""Process a stream of documents.
stream: The sequence of documents to process.
batch_size (int): 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.
"""
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', 0.0)
cdef Doc doc
for batch in cytoolz.partition_all(batch_size, docs):
batch_in_order = list(batch)
by_length = sorted(batch_in_order, key=lambda doc: len(doc))
batch_beams = []
for subbatch in cytoolz.partition_all(8, by_length):
subbatch = list(subbatch)
if beam_width == 1:
parse_states, tokvecs = self.parse_batch(subbatch)
beams = []
else:
beams, tokvecs = self.beam_parse(subbatch,
beam_width=beam_width,
beam_density=beam_density)
parse_states = _collect_states(beams)
self.set_annotations(subbatch, parse_states, tensors=None)
for beam in beams:
_cleanup(beam)
for doc in batch_in_order:
yield doc
def parse_batch(self, docs):
cdef:
precompute_hiddens state2vec
Pool mem
const float* feat_weights
StateC* st
StateClass stcls
vector[StateC*] states
int guess, nr_class, nr_feat, nr_piece, nr_dim, nr_state, nr_step
int j
if isinstance(docs, Doc):
docs = [docs]
cuda_stream = util.get_cuda_stream()
(tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(
docs, cuda_stream, 0.0)
nr_state = len(docs)
nr_class = self.moves.n_moves
nr_dim = tokvecs.shape[1]
nr_feat = self.nr_feature
nr_piece = state2vec.nP
state_objs = self.moves.init_batch(docs)
for stcls in state_objs:
if not stcls.c.is_final():
states.push_back(stcls.c)
feat_weights = state2vec.get_feat_weights()
cdef int i
cdef np.ndarray hidden_weights = numpy.ascontiguousarray(
vec2scores._layers[-1].W.T)
cdef np.ndarray hidden_bias = vec2scores._layers[-1].b
hW = <float*>hidden_weights.data
hb = <float*>hidden_bias.data
bias = <float*>state2vec.bias.data
cdef int nr_hidden = hidden_weights.shape[0]
cdef int nr_task = states.size()
with nogil:
self._parseC(&states[0], nr_task, feat_weights, bias, hW, hb,
nr_class, nr_hidden, nr_feat, nr_piece)
PyErr_CheckSignals()
tokvecs = self.model[0].ops.unflatten(tokvecs,
[len(doc) for doc in docs])
return state_objs, tokvecs
cdef void _parseC(self, StateC** states, int nr_task,
const float* feat_weights, const float* bias,
const float* hW, const float* hb,
int nr_class, int nr_hidden, int nr_feat, int nr_piece) nogil:
token_ids = <int*>calloc(nr_feat, sizeof(int))
is_valid = <int*>calloc(nr_class, sizeof(int))
vectors = <float*>calloc(nr_hidden * nr_task, sizeof(float))
unmaxed = <float*>calloc(nr_hidden * nr_piece, sizeof(float))
scores = <float*>calloc(nr_class*nr_task, sizeof(float))
if not (token_ids and is_valid and vectors and scores):
with gil:
PyErr_SetFromErrno(MemoryError)
PyErr_CheckSignals()
cdef int nr_todo = nr_task
cdef int i, j
cdef vector[StateC*] unfinished
while nr_todo >= 1:
memset(vectors, 0, nr_todo * nr_hidden * sizeof(float))
memset(scores, 0, nr_todo * nr_class * sizeof(float))
for i in range(nr_todo):
state = states[i]
state.set_context_tokens(token_ids, nr_feat)
memset(unmaxed, 0, nr_hidden * nr_piece * sizeof(float))
sum_state_features(unmaxed,
feat_weights, token_ids, 1, nr_feat, nr_hidden * nr_piece)
VecVec.add_i(unmaxed,
bias, 1., nr_hidden*nr_piece)
state_vector = &vectors[i*nr_hidden]
for j in range(nr_hidden):
index = j * nr_piece
which = Vec.arg_max(&unmaxed[index], nr_piece)
state_vector[j] = unmaxed[index + which]
# Compute hidden-to-output
openblas.simple_gemm(scores, nr_todo, nr_class,
vectors, nr_todo, nr_hidden, hW, nr_hidden, nr_class, 0, 0)
# Add bias
for i in range(nr_todo):
VecVec.add_i(&scores[i*nr_class],
hb, 1., nr_class)
# Validate actions, argmax, take action.
for i in range(nr_todo):
state = states[i]
self.moves.set_valid(is_valid, state)
guess = arg_max_if_valid(&scores[i*nr_class], is_valid, nr_class)
action = self.moves.c[guess]
action.do(state, action.label)
state.push_hist(guess)
if not state.is_final():
unfinished.push_back(state)
for i in range(unfinished.size()):
states[i] = unfinished[i]
nr_todo = unfinished.size()
unfinished.clear()
free(token_ids)
free(is_valid)
free(vectors)
free(unmaxed)
free(scores)
def beam_parse(self, docs, int beam_width=3, float beam_density=0.001,
float drop=0.):
cdef Beam beam
cdef np.ndarray scores
cdef Doc doc
cdef int nr_class = self.moves.n_moves
cuda_stream = util.get_cuda_stream()
(tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(
docs, cuda_stream, drop)
cdef int offset = 0
cdef int j = 0
cdef int k
beams = []
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):
state = <StateC*>beam.at(i)
state.offset = offset
offset += len(doc)
beam.check_done(_check_final_state, NULL)
beams.append(beam)
cdef np.ndarray token_ids
token_ids = numpy.zeros((len(docs) * beam_width, self.nr_feature),
dtype='i', order='C')
todo = [beam for beam in beams if not beam.is_done]
cdef int* c_ids
cdef int nr_feature = self.nr_feature
cdef int n_states
while todo:
todo = [beam for beam in beams if not beam.is_done]
token_ids.fill(-1)
c_ids = <int*>token_ids.data
n_states = 0
for beam in todo:
for i in range(beam.size):
state = <StateC*>beam.at(i)
# This way we avoid having to score finalized states
# We do have to take care to keep indexes aligned, though
if not state.is_final():
state.set_context_tokens(c_ids, nr_feature)
c_ids += nr_feature
n_states += 1
if n_states == 0:
break
vectors, _ = state2vec.begin_update(token_ids[:n_states], drop)
scores, _ = vec2scores.begin_update(vectors, drop=drop)
c_scores = <float*>scores.data
for beam in todo:
for i in range(beam.size):
state = <StateC*>beam.at(i)
if not state.is_final():
self.moves.set_valid(beam.is_valid[i], state)
memcpy(beam.scores[i], c_scores, nr_class * sizeof(float))
c_scores += nr_class
beam.advance(_transition_state, NULL, <void*>self.moves.c)
beam.check_done(_check_final_state, NULL)
tokvecs = self.model[0].ops.unflatten(tokvecs,
[len(doc) for doc in docs])
return beams, tokvecs
def update(self, docs, golds, drop=0., sgd=None, losses=None):
if not any(self.moves.has_gold(gold) for gold in golds):
return None
assert len(docs) == len(golds)
if self.cfg.get('beam_width', 1) >= 2 and numpy.random.random() >= 0.0:
return self.update_beam(docs, golds,
self.cfg['beam_width'], self.cfg['beam_density'],
drop=drop, sgd=sgd, losses=losses)
if losses is not None and self.name not in losses:
losses[self.name] = 0.
if isinstance(docs, Doc) and isinstance(golds, GoldParse):
docs = [docs]
golds = [golds]
for multitask in self._multitasks:
multitask.update(docs, golds, drop=drop, sgd=sgd)
cuda_stream = util.get_cuda_stream()
# Chop sequences into lengths of this many transitions, to make the
# batch uniform length.
cut_gold = numpy.random.choice(range(20, 100))
states, golds, max_steps = self._init_gold_batch(docs, golds, max_length=cut_gold)
(tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(docs, cuda_stream,
drop)
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 = []
# Add a padding vector to the d_tokvecs gradient, so that missing
# values don't affect the real gradient.
d_tokvecs = state2vec.ops.allocate((tokvecs.shape[0]+1, tokvecs.shape[1]))
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
hists = numpy.asarray([st.history for st in states], dtype='i')
if self.cfg.get('hist_size', 0):
scores, bp_scores = vec2scores.begin_update((vector, hists), drop=drop)
else:
scores, bp_scores = vec2scores.begin_update(vector, drop=drop)
d_scores = self.get_batch_loss(states, golds, scores)
d_scores /= len(docs)
d_vector = bp_scores(d_scores, 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 GPU, asynchronously
backprops.append((
util.get_async(cuda_stream, token_ids),
util.get_async(cuda_stream, d_vector),
bp_vector
))
else:
backprops.append((token_ids, d_vector, bp_vector))
self.transition_batch(states, scores)
todo = [(st, gold) for (st, gold) in todo
if not st.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,
bp_tokvecs, backprops, sgd, cuda_stream)
def update_beam(self, docs, golds, width=None, density=None,
drop=0., sgd=None, losses=None):
if not any(self.moves.has_gold(gold) for gold in golds):
return None
if not golds:
return None
if width is None:
width = self.cfg.get('beam_width', 2)
if density is None:
density = self.cfg.get('beam_density', 0.0)
if losses is not None and self.name not in losses:
losses[self.name] = 0.
lengths = [len(d) for d in docs]
assert min(lengths) >= 1
states = self.moves.init_batch(docs)
for gold in golds:
self.moves.preprocess_gold(gold)
cuda_stream = util.get_cuda_stream()
(tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(
docs, cuda_stream, drop)
states_d_scores, backprops, beams = _beam_utils.update_beam(
self.moves, self.nr_feature, 500, states, golds, state2vec,
vec2scores, width, density, self.cfg.get('hist_size', 0),
drop=drop, losses=losses)
backprop_lower = []
cdef float batch_size = len(docs)
for i, d_scores in enumerate(states_d_scores):
d_scores /= batch_size
if losses is not None:
losses[self.name] += (d_scores**2).sum()
ids, bp_vectors, bp_scores = backprops[i]
d_vector = bp_scores(d_scores, sgd=sgd)
if isinstance(self.model[0].ops, CupyOps) \
and not isinstance(ids, state2vec.ops.xp.ndarray):
backprop_lower.append((
util.get_async(cuda_stream, ids),
util.get_async(cuda_stream, d_vector),
bp_vectors))
else:
backprop_lower.append((ids, d_vector, bp_vectors))
# Add a padding vector to the d_tokvecs gradient, so that missing
# values don't affect the real gradient.
d_tokvecs = state2vec.ops.allocate((tokvecs.shape[0]+1, tokvecs.shape[1]))
self._make_updates(d_tokvecs, bp_tokvecs, backprop_lower, sgd,
cuda_stream)
cdef Beam beam
for beam in beams:
_cleanup(beam)
def _init_gold_batch(self, whole_docs, whole_golds, min_length=5, max_length=500):
"""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(min_length, min(max_length, 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)
state.c.push_hist(action.clas)
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, bp_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, ids), sgd=sgd)
ids = ids.flatten()
d_state_features = d_state_features.reshape(
(ids.size, d_state_features.shape[2]))
self.model[0].ops.scatter_add(d_tokvecs, ids,
d_state_features)
# Padded -- see update()
bp_tokvecs(d_tokvecs[:-1], sgd=sgd)
@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, docs, stream, dropout):
tok2vec, lower, upper = self.model
tokvecs, bp_tokvecs = tok2vec.begin_update(docs, drop=dropout)
state2vec = precompute_hiddens(len(docs), tokvecs,
lower, stream, drop=0.0)
return (tokvecs, bp_tokvecs), 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]
state.c.push_hist(guess)
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, 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]
if tensors is not None:
if isinstance(doc.tensor, numpy.ndarray) \
and not isinstance(tensors[i], numpy.ndarray):
doc.extend_tensor(tensors[i].get())
else:
doc.extend_tensor(tensors[i])
self.moves.finalize_doc(doc)
for hook in self.postprocesses:
for doc in docs:
hook(doc)
@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.'''
if self.model in (None, True, False):
return None
else:
return self.model[0]
@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 self.model not in (True, False, None) and resized:
# Weights are stored in (nr_out, nr_in) format, so we're basically
# just adding rows here.
smaller = self.model[-1]._layers[-1]
larger = Affine(self.moves.n_moves, smaller.nI)
copy_array(larger.W[:smaller.nO], smaller.W)
copy_array(larger.b[:smaller.nO], smaller.b)
self.model[-1]._layers[-1] = larger
def begin_training(self, gold_tuples, pipeline=None, sgd=None, **cfg):
if 'model' in cfg:
self.model = cfg['model']
cfg.setdefault('min_action_freq', 30)
actions = self.moves.get_actions(gold_parses=gold_tuples,
min_freq=cfg.get('min_action_freq', 30))
self.moves.initialize_actions(actions)
cfg.setdefault('token_vector_width', 128)
if self.model is True:
cfg['pretrained_dims'] = self.vocab.vectors_length
self.model, cfg = self.Model(self.moves.n_moves, **cfg)
if sgd is None:
sgd = self.create_optimizer()
self.model[1].begin_training(
self.model[1].ops.allocate((5, cfg['token_vector_width'])))
if pipeline is not None:
self.init_multitask_objectives(gold_tuples, pipeline, sgd=sgd, **cfg)
link_vectors_to_models(self.vocab)
else:
if sgd is None:
sgd = self.create_optimizer()
self.model[1].begin_training(
self.model[1].ops.allocate((5, cfg['token_vector_width'])))
self.cfg.update(cfg)
return sgd
def add_multitask_objective(self, target):
# Defined in subclasses, to avoid circular import
raise NotImplementedError
def init_multitask_objectives(self, gold_tuples, 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 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 = {
'tok2vec_model': lambda p: p.open('wb').write(
self.model[0].to_bytes()),
'lower_model': lambda p: p.open('wb').write(
self.model[1].to_bytes()),
'upper_model': lambda p: p.open('wb').write(
self.model[2].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(util.read_json(p)),
'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.cfg.setdefault('pretrained_dims', self.vocab.vectors_length)
self.model, cfg = self.Model(**self.cfg)
else:
cfg = {}
with (path / 'tok2vec_model').open('rb') as file_:
bytes_data = file_.read()
self.model[0].from_bytes(bytes_data)
with (path / 'lower_model').open('rb') as file_:
bytes_data = file_.read()
self.model[1].from_bytes(bytes_data)
with (path / 'upper_model').open('rb') as file_:
bytes_data = file_.read()
self.model[2].from_bytes(bytes_data)
self.cfg.update(cfg)
return self
def to_bytes(self, **exclude):
serializers = OrderedDict((
('tok2vec_model', lambda: self.model[0].to_bytes()),
('lower_model', lambda: self.model[1].to_bytes()),
('upper_model', lambda: self.model[2].to_bytes()),
('vocab', lambda: self.vocab.to_bytes()),
('moves', lambda: self.moves.to_bytes(strings=False)),
('cfg', lambda: json.dumps(self.cfg, indent=2, sort_keys=True))
))
if 'model' in exclude:
exclude['tok2vec_model'] = True
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(json.loads(b))),
('tok2vec_model', lambda b: None),
('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.cfg)
cfg['pretrained_dims'] = self.vocab.vectors_length
else:
cfg = {}
cfg['pretrained_dims'] = self.vocab.vectors_length
if 'tok2vec_model' in msg:
self.model[0].from_bytes(msg['tok2vec_model'])
if 'lower_model' in msg:
self.model[1].from_bytes(msg['lower_model'])
if 'upper_model' in msg:
self.model[2].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
# 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 = <StateC*>_dest
src = <StateC*>_src
moves = <const Transition*>_moves
dest.clone(src)
moves[clas].do(dest, moves[clas].label)
dest.push_hist(clas)
cdef int _check_final_state(void* _state, void* extra_args) except -1:
state = <StateC*>_state
return state.is_final()
def _cleanup(Beam beam):
cdef StateC* state
# Once parsing has finished, states in beam may not be unique. Is this
# correct?
seen = set()
for i in range(beam.width):
addr = <size_t>beam._parents[i].content
if addr not in seen:
state = <StateC*>addr
del state
seen.add(addr)
else:
print(i, addr)
print(seen)
raise Exception
addr = <size_t>beam._states[i].content
if addr not in seen:
state = <StateC*>addr
del state
seen.add(addr)
else:
print(i, addr)
print(seen)
raise Exception