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7efaa76168
spaCy/spacy/syntax/_parser_model.pyx
Sofie Van Landeghem 06f0a8daa0
Default settings to configurations (#4995) 
* fix grad_clip naming

* cleaning up pretrained_vectors out of cfg

* further refactoring Model init's

* move Model building out of pipes

* further refactor to require a model config when creating a pipe

* small fixes

* making cfg in nn_parser more consistent

* fixing nr_class for parser

* fixing nn_parser's nO

* fix printing of loss

* architectures in own file per type, consistent naming

* convenience methods default_tagger_config and default_tok2vec_config

* let create_pipe access default config if available for that component

* default_parser_config

* move defaults to separate folder

* allow reading nlp from package or dir with argument 'name'

* architecture spacy.VocabVectors.v1 to read static vectors from file

* cleanup

* default configs for nel, textcat, morphologizer, tensorizer

* fix imports

* fixing unit tests

* fixes and clean up

* fixing defaults, nO, fix unit tests

* restore parser IO

* fix IO

* 'fix' serialization test

* add *.cfg to manifest

* fix example configs with additional arguments

* replace Morpohologizer with Tagger

* add IO bit when testing overfitting of tagger (currently failing)

* fix IO - don't initialize when reading from disk

* expand overfitting tests to also check IO goes OK

* remove dropout from HashEmbed to fix Tagger performance

* add defaults for sentrec

* update thinc

* always pass a Model instance to a Pipe

* fix piped_added statement

* remove obsolete W029

* remove obsolete errors

* restore byte checking tests (work again)

* clean up test

* further test cleanup

* convert from config to Model in create_pipe

* bring back error when component is not initialized

* cleanup

* remove calls for nlp2.begin_training

* use thinc.api in imports

* allow setting charembed's nM and nC

* fix for hardcoded nM/nC + unit test

* formatting fixes

* trigger build
2020-02-27 18:42:27 +01:00

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# cython: infer_types=True
# cython: cdivision=True
# cython: boundscheck=False
import numpy
cimport cython.parallel
import numpy.random
cimport numpy as np
from libc.math cimport exp
from libcpp.vector cimport vector
from libc.string cimport memset, memcpy
from libc.stdlib cimport calloc, free, realloc
from cymem.cymem cimport Pool
from thinc.extra.search cimport Beam
from thinc.api import Linear, Model, CupyOps, NumpyOps, use_ops
from thinc.backends.linalg cimport Vec, VecVec
cimport blis.cy
from ..typedefs cimport weight_t, class_t, hash_t
from ..compat import copy_array
from ..tokens.doc cimport Doc
from ..gold cimport GoldParse
from ..errors import Errors, TempErrors
from .. import util
from .stateclass cimport StateClass
from .transition_system cimport Transition
from . import _beam_utils
from . import nonproj
from ..util import link_vectors_to_models, create_default_optimizer
cdef WeightsC get_c_weights(model) except *:
cdef WeightsC output
cdef precompute_hiddens state2vec = model.state2vec
output.feat_weights = state2vec.get_feat_weights()
output.feat_bias = <const float*>state2vec.bias.data
cdef np.ndarray vec2scores_W
cdef np.ndarray vec2scores_b
if model.vec2scores is None:
output.hidden_weights = NULL
output.hidden_bias = NULL
else:
vec2scores_W = model.vec2scores.get_param("W")
vec2scores_b = model.vec2scores.get_param("b")
output.hidden_weights = <const float*>vec2scores_W.data
output.hidden_bias = <const float*>vec2scores_b.data
cdef np.ndarray class_mask = model._class_mask
output.seen_classes = <const float*>class_mask.data
return output
cdef SizesC get_c_sizes(model, int batch_size) except *:
cdef SizesC output
output.states = batch_size
if model.vec2scores is None:
output.classes = model.state2vec.get_dim("nO")
else:
output.classes = model.vec2scores.get_dim("nO")
output.hiddens = model.state2vec.get_dim("nO")
output.pieces = model.state2vec.get_dim("nP")
output.feats = model.state2vec.get_dim("nF")
output.embed_width = model.tokvecs.shape[1]
return output
cdef ActivationsC alloc_activations(SizesC n) nogil:
cdef ActivationsC A
memset(&A, 0, sizeof(A))
resize_activations(&A, n)
return A
cdef void free_activations(const ActivationsC* A) nogil:
free(A.token_ids)
free(A.scores)
free(A.unmaxed)
free(A.hiddens)
free(A.is_valid)
cdef void resize_activations(ActivationsC* A, SizesC n) nogil:
if n.states <= A._max_size:
A._curr_size = n.states
return
if A._max_size == 0:
A.token_ids = <int*>calloc(n.states * n.feats, sizeof(A.token_ids[0]))
A.scores = <float*>calloc(n.states * n.classes, sizeof(A.scores[0]))
A.unmaxed = <float*>calloc(n.states * n.hiddens * n.pieces, sizeof(A.unmaxed[0]))
A.hiddens = <float*>calloc(n.states * n.hiddens, sizeof(A.hiddens[0]))
A.is_valid = <int*>calloc(n.states * n.classes, sizeof(A.is_valid[0]))
A._max_size = n.states
else:
A.token_ids = <int*>realloc(A.token_ids,
n.states * n.feats * sizeof(A.token_ids[0]))
A.scores = <float*>realloc(A.scores,
n.states * n.classes * sizeof(A.scores[0]))
A.unmaxed = <float*>realloc(A.unmaxed,
n.states * n.hiddens * n.pieces * sizeof(A.unmaxed[0]))
A.hiddens = <float*>realloc(A.hiddens,
n.states * n.hiddens * sizeof(A.hiddens[0]))
A.is_valid = <int*>realloc(A.is_valid,
n.states * n.classes * sizeof(A.is_valid[0]))
A._max_size = n.states
A._curr_size = n.states
cdef void predict_states(ActivationsC* A, StateC** states,
const WeightsC* W, SizesC n) nogil:
cdef double one = 1.0
resize_activations(A, n)
for i in range(n.states):
states[i].set_context_tokens(&A.token_ids[i*n.feats], n.feats)
memset(A.unmaxed, 0, n.states * n.hiddens * n.pieces * sizeof(float))
memset(A.hiddens, 0, n.states * n.hiddens * sizeof(float))
sum_state_features(A.unmaxed,
W.feat_weights, A.token_ids, n.states, n.feats, n.hiddens * n.pieces)
for i in range(n.states):
VecVec.add_i(&A.unmaxed[i*n.hiddens*n.pieces],
W.feat_bias, 1., n.hiddens * n.pieces)
for j in range(n.hiddens):
index = i * n.hiddens * n.pieces + j * n.pieces
which = Vec.arg_max(&A.unmaxed[index], n.pieces)
A.hiddens[i*n.hiddens + j] = A.unmaxed[index + which]
memset(A.scores, 0, n.states * n.classes * sizeof(float))
if W.hidden_weights == NULL:
memcpy(A.scores, A.hiddens, n.states * n.classes * sizeof(float))
else:
# Compute hidden-to-output
blis.cy.gemm(blis.cy.NO_TRANSPOSE, blis.cy.TRANSPOSE,
n.states, n.classes, n.hiddens, one,
<float*>A.hiddens, n.hiddens, 1,
<float*>W.hidden_weights, n.hiddens, 1,
one,
<float*>A.scores, n.classes, 1)
# Add bias
for i in range(n.states):
VecVec.add_i(&A.scores[i*n.classes],
W.hidden_bias, 1., n.classes)
# Set unseen classes to minimum value
i = 0
min_ = A.scores[0]
for i in range(1, n.states * n.classes):
if A.scores[i] < min_:
min_ = A.scores[i]
for i in range(n.states):
for j in range(n.classes):
if not W.seen_classes[j]:
A.scores[i*n.classes+j] = min_
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
cdef int id_stride = F*O
cdef float one = 1.
for b in range(B):
for f in range(F):
if token_ids[f] < 0:
feature = &padding[f*O]
else:
idx = token_ids[f] * id_stride + f*O
feature = &cached[idx]
blis.cy.axpyv(blis.cy.NO_CONJUGATE, O, one,
<float*>feature, 1,
&output[b*O], 1)
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 = Vec.arg_max(scores, O)
if best == -1 or guess == -1:
# These shouldn't happen, but if they do, we want to make sure we don't
# cause an OOB access.
return
Z = 1e-10
gZ = 1e-10
max_ = scores[guess]
gmax = scores[best]
for i in range(O):
Z += exp(scores[i] - max_)
if costs[i] <= costs[best]:
gZ += exp(scores[i] - gmax)
for i in range(O):
if 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 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
class ParserModel(Model):
def __init__(self, tok2vec, lower_model, upper_model, unseen_classes=None):
# don't define nO for this object, because we can't dynamically change it
Model.__init__(self, name="parser_model", forward=forward, dims={"nI": None})
if tok2vec.has_dim("nI"):
self.set_dim("nI", tok2vec.get_dim("nI"))
self._layers = [tok2vec, lower_model]
if upper_model is not None:
self._layers.append(upper_model)
self.unseen_classes = set()
if unseen_classes:
for class_ in unseen_classes:
self.unseen_classes.add(class_)
self.set_ref("tok2vec", tok2vec)
def predict(self, docs):
step_model = ParserStepModel(docs, self._layers,
unseen_classes=self.unseen_classes, train=False)
return step_model
def resize_output(self, new_nO):
if len(self._layers) == 2:
return
if self.upper.has_dim("nO") and (new_nO == self.upper.get_dim("nO")):
return
smaller = self.upper
nI = None
if smaller.has_dim("nI"):
nI = smaller.get_dim("nI")
with use_ops('numpy'):
larger = Linear(nO=new_nO, nI=nI)
larger._init = smaller._init
# it could be that the model is not initialized yet, then skip this bit
if nI:
larger_W = larger.ops.alloc2f(new_nO, nI)
larger_b = larger.ops.alloc1f(new_nO)
smaller_W = smaller.get_param("W")
smaller_b = smaller.get_param("b")
# Weights are stored in (nr_out, nr_in) format, so we're basically
# just adding rows here.
if smaller.has_dim("nO"):
larger_W[:smaller.get_dim("nO")] = smaller_W
larger_b[:smaller.get_dim("nO")] = smaller_b
for i in range(smaller.get_dim("nO"), new_nO):
self.unseen_classes.add(i)
larger.set_param("W", larger_W)
larger.set_param("b", larger_b)
self._layers[-1] = larger
def initialize(self, X=None, Y=None):
self.tok2vec.initialize()
self.lower.initialize(X=X, Y=Y)
if self.upper is not None:
# In case we need to trigger the callbacks
statevecs = self.ops.alloc((2, self.lower.get_dim("nO")))
self.upper.initialize(X=statevecs)
def finish_update(self, optimizer):
self.tok2vec.finish_update(optimizer)
self.lower.finish_update(optimizer)
if self.upper is not None:
self.upper.finish_update(optimizer)
@property
def tok2vec(self):
return self._layers[0]
@property
def lower(self):
return self._layers[1]
@property
def upper(self):
return self._layers[2]
def forward(model:ParserModel, X, is_train):
step_model = ParserStepModel(X, model._layers, unseen_classes=model.unseen_classes,
train=is_train)
return step_model, step_model.finish_steps
class ParserStepModel(Model):
def __init__(self, docs, layers, unseen_classes=None, train=True):
Model.__init__(self, name="parser_step_model", forward=step_forward)
self.tokvecs, self.bp_tokvecs = layers[0](docs, is_train=train)
if layers[1].get_dim("nP") >= 2:
activation = "maxout"
elif len(layers) == 2:
activation = None
else:
activation = "relu"
self.state2vec = precompute_hiddens(len(docs), self.tokvecs, layers[1],
activation=activation, train=train)
if len(layers) == 3:
self.vec2scores = layers[-1]
else:
self.vec2scores = None
self.cuda_stream = util.get_cuda_stream(non_blocking=True)
self.backprops = []
if self.vec2scores is None:
self._class_mask = numpy.zeros((self.state2vec.nO,), dtype='f')
else:
self._class_mask = numpy.zeros((self.vec2scores.get_dim("nO"),), dtype='f')
self._class_mask.fill(1)
if unseen_classes is not None:
for class_ in unseen_classes:
self._class_mask[class_] = 0.
@property
def nO(self):
return self.state2vec.nO
def class_is_unseen(self, class_):
return self._class_mask[class_]
def mark_class_unseen(self, class_):
self._class_mask[class_] = 0
def mark_class_seen(self, class_):
self._class_mask[class_] = 1
def get_token_ids(self, batch):
states = _beam_utils.collect_states(batch)
cdef StateClass state
states = [state for state in states if not state.is_final()]
cdef np.ndarray ids = numpy.zeros((len(states), self.state2vec.nF),
dtype='i', order='C')
ids.fill(-1)
c_ids = <int*>ids.data
for state in states:
state.c.set_context_tokens(c_ids, ids.shape[1])
c_ids += ids.shape[1]
return ids
def finish_steps(self, golds):
# Add a padding vector to the d_tokvecs gradient, so that missing
# values don't affect the real gradient.
d_tokvecs = self.ops.alloc((self.tokvecs.shape[0]+1, self.tokvecs.shape[1]))
# Tells CUDA to block, so our async copies complete.
if self.cuda_stream is not None:
self.cuda_stream.synchronize()
for ids, d_vector, bp_vector in self.backprops:
d_state_features = bp_vector((d_vector, ids))
ids = ids.flatten()
d_state_features = d_state_features.reshape(
(ids.size, d_state_features.shape[2]))
self.ops.scatter_add(d_tokvecs, ids,
d_state_features)
# Padded -- see update()
if isinstance(self.ops, CupyOps):
d_tokvecs = self.ops.to_numpy(d_tokvecs)
self.bp_tokvecs(d_tokvecs[:-1])
return d_tokvecs
def step_forward(model: ParserStepModel, states, is_train):
token_ids = model.get_token_ids(states)
vector, get_d_tokvecs = model.state2vec(token_ids, is_train)
if model.vec2scores is not None:
scores, get_d_vector = model.vec2scores(vector, is_train)
else:
scores = NumpyOps().asarray(vector)
get_d_vector = lambda d_scores: d_scores
# If the class is unseen, make sure its score is minimum
scores[:, model._class_mask == 0] = numpy.nanmin(scores)
def backprop_parser_step(d_scores):
# Zero vectors for unseen classes
d_scores *= model._class_mask
d_vector = get_d_vector(d_scores)
if isinstance(model.state2vec.ops, CupyOps) \
and not isinstance(token_ids, model.state2vec.ops.xp.ndarray):
# Move token_ids and d_vector to GPU, asynchronously
model.backprops.append((
util.get_async(model.cuda_stream, token_ids),
util.get_async(model.cuda_stream, d_vector),
get_d_tokvecs
))
else:
model.backprops.append((token_ids, d_vector, get_d_tokvecs))
return None
return scores, backprop_parser_step
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 readonly 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
cdef object activation
def __init__(self, batch_size, tokvecs, lower_model, cuda_stream=None,
activation="maxout", train=False):
gpu_cached, bp_features = lower_model(tokvecs, train)
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.get_param("b"), numpy.ndarray):
# self.bias = lower_model.get_param("b").get(stream=cuda_stream) ???
self.bias = lower_model.get_param("b")
else:
self.bias = lower_model.get_param("b")
self.nF = cached.shape[1]
if lower_model.has_dim("nP"):
self.nP = lower_model.get_dim("nP")
else:
self.nP = 1
self.nO = cached.shape[2]
self.ops = lower_model.ops
assert activation in (None, "relu", "maxout")
self.activation = activation
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 has_dim(self, name):
if name == "nF":
return self.nF if self.nF is not None else True
elif name == "nP":
return self.nP if self.nP is not None else True
elif name == "nO":
return self.nO if self.nO is not None else True
else:
return False
def get_dim(self, name):
if name == "nF":
return self.nF
elif name == "nP":
return self.nP
elif name == "nO":
return self.nO
else:
raise ValueError(f"Dimension {name} invalid -- only nO, nF, nP")
def set_dim(self, name, value):
if name == "nF":
self.nF = value
elif name == "nP":
self.nP = value
elif name == "nO":
self.nO = value
else:
raise ValueError(f"Dimension {name} invalid -- only nO, nF, nP")
def __call__(self, X, bint is_train):
if is_train:
return self.begin_update(X)
else:
return self.predict(X), lambda X: X
def predict(self, X):
return self.begin_update(X)[0]
def begin_update(self, token_ids):
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 = state_vector + self.bias
state_vector, bp_nonlinearity = self._nonlinearity(state_vector)
def backward(d_state_vector_ids):
d_state_vector, token_ids = d_state_vector_ids
d_state_vector = bp_nonlinearity(d_state_vector)
d_tokens = bp_hiddens((d_state_vector, token_ids))
return d_tokens
return state_vector, backward
def _nonlinearity(self, state_vector):
if isinstance(state_vector, numpy.ndarray):
ops = NumpyOps()
else:
ops = CupyOps()
if self.activation == "maxout":
state_vector, mask = ops.maxout(state_vector)
else:
state_vector = state_vector.reshape(state_vector.shape[:-1])
if self.activation == "relu":
mask = state_vector >= 0.
state_vector *= mask
else:
mask = None
def backprop_nonlinearity(d_best):
if isinstance(d_best, numpy.ndarray):
ops = NumpyOps()
else:
ops = CupyOps()
if mask is not None:
mask_ = ops.asarray(mask)
# This will usually be on GPU
d_best = ops.asarray(d_best)
# Fix nans (which can occur from unseen classes.)
d_best[ops.xp.isnan(d_best)] = 0.
if self.activation == "maxout":
mask_ = ops.asarray(mask)
return ops.backprop_maxout(d_best, mask_, self.nP)
elif self.activation == "relu":
mask_ = ops.asarray(mask)
d_best *= mask_
d_best = d_best.reshape((d_best.shape + (1,)))
return d_best
else:
return d_best.reshape((d_best.shape + (1,)))
return state_vector, backprop_nonlinearity
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