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Tmp GPU code

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This commit is contained in:
Matthew Honnibal 2017-05-07 11:04:24 -05:00
parent e420e5a809
commit 6782eedf9b
5 changed files with 50 additions and 25 deletions
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13
bin/parser/train_ud.py
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@ -26,6 +26,15 @@ try:
except ImportError:
pass
from thinc.neural import Model
try:
import cupy
from thinc.neural.ops import CupyOps
except:
cupy = None
def read_conllx(loc, n=0):
with io.open(loc, 'r', encoding='utf8') as file_:
@ -88,6 +97,8 @@ def organize_data(vocab, train_sents):
def main(lang_name, train_loc, dev_loc, model_dir, clusters_loc=None):
if cupy is not None:
Model.ops = CupyOps()
LangClass = spacy.util.get_lang_class(lang_name)
train_sents = list(read_conllx(train_loc))
dev_sents = list(read_conllx(dev_loc))
@ -163,7 +174,7 @@ def main(lang_name, train_loc, dev_loc, model_dir, clusters_loc=None):
print('%d:\t%.3f\t%.3f\t%.3f' % (itn, nn_loss[-1], scorer.uas, scorer.tags_acc))
nn_loss.append(0.)
trainer.each_epoch.append(track_progress)
trainer.batch_size = 12
trainer.batch_size = 24
trainer.nb_epoch = 10
for docs, golds in trainer.iterate(Xs, ys):
docs = [Doc(vocab, words=[w.text for w in doc]) for doc in docs]

10
spacy/pipeline.pyx
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@ -3,6 +3,7 @@ from __future__ import unicode_literals
from thinc.api import chain, layerize, with_getitem
from thinc.neural import Model, Softmax
import numpy
from .syntax.parser cimport Parser
#from .syntax.beam_parser cimport BeamParser
@ -39,16 +40,15 @@ class TokenVectorEncoder(object):
def update(self, docs, golds, drop=0., sgd=None):
scores, finish_update = self.tagger.begin_update(docs, drop=drop)
losses = scores.copy()
loss = 0.0
idx = 0
for i, gold in enumerate(golds):
ids = numpy.zeros((len(gold),), dtype='i')
start = idx
for j, tag in enumerate(gold.tags):
tag_id = docs[0].vocab.morphology.tag_names.index(tag)
losses[idx, tag_id] -= 1.0
loss += 1-scores[idx, tag_id]
ids[j] = docs[0].vocab.morphology.tag_names.index(tag)
idx += 1
self.tagger.ops.xp.scatter_add(losses[start:idx], ids, -1.0)
finish_update(losses, sgd)
return loss
cdef class EntityRecognizer(Parser):

46
spacy/syntax/parser.pyx
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@ -201,7 +201,7 @@ cdef class Parser:
costs = self.model.ops.allocate((len(docs), nr_class), dtype='f')
gradients = self.model.ops.allocate((len(docs), nr_class), dtype='f')
is_valid = self.model.ops.allocate((len(docs), nr_class), dtype='i')
attr_names = self.model.ops.allocate((2,), dtype='i')
attr_names = numpy.zeros((2,), dtype='i')
attr_names[0] = TAG
attr_names[1] = DEP
@ -228,10 +228,14 @@ cdef class Parser:
scores, finish_update = self._begin_update(states, tokvecs)
token_ids, batch_token_grads = finish_update(golds, sgd=sgd, losses=losses,
force_gold=False)
for i, tok_ids in enumerate(token_ids):
for j, tok_i in enumerate(tok_ids):
if tok_i >= 0:
d_tokens[i][tok_i] += batch_token_grads[i, j]
if hasattr(self.model.ops.xp, 'scatter_add'):
for i, tok_ids in enumerate(token_ids):
self.model.ops.xp.scatter_add(d_tokens[i],
tok_ids, batch_token_grads[i])
else:
for i, tok_ids in enumerate(token_ids):
self.model.ops.xp.add.at(d_tokens[i],
tok_ids, batch_token_grads[i])
self._transition_batch(states, scores)
@ -244,7 +248,7 @@ cdef class Parser:
def _begin_update(self, states, tokvecs, drop=0.):
nr_class = self.moves.n_moves
attr_names = self.model.ops.allocate((2,), dtype='i')
attr_names = numpy.zeros((2,), dtype='i')
attr_names[0] = TAG
attr_names[1] = DEP
@ -284,28 +288,38 @@ cdef class Parser:
nF=1, nB=0, nS=2, nL=2, nR=2):
n_tokens = states[0].nr_context_tokens(nF, nB, nS, nL, nR)
vector_length = all_tokvecs[0].shape[1]
tokens = self.model.ops.allocate((len(states), n_tokens), dtype='int32')
features = self.model.ops.allocate((len(states), n_tokens, attr_names.shape[0]), dtype='uint64')
cpu_tokens = numpy.zeros((len(states), n_tokens), dtype='int32')
features = numpy.zeros((len(states), n_tokens, attr_names.shape[0]), dtype='uint64')
tokvecs = self.model.ops.allocate((len(states), n_tokens, vector_length), dtype='f')
for i, state in enumerate(states):
state.set_context_tokens(tokens[i], nF, nB, nS, nL, nR)
state.set_attributes(features[i], tokens[i], attr_names)
state.set_token_vectors(tokvecs[i], all_tokvecs[i], tokens[i])
return (tokens, features, tokvecs)
state.set_context_tokens(cpu_tokens[i], nF, nB, nS, nL, nR)
#state.set_attributes(features[i], tokens[i], attr_names)
gpu_tokens = self.model.ops.xp.array(cpu_tokens)
for i in range(len(states)):
tokvecs[i] = all_tokvecs[i][gpu_tokens[i]]
tokvecs *= (gpu_tokens >= 0).reshape((gpu_tokens.shape[0], gpu_tokens.shape[1], 1))
return (gpu_tokens, self.model.ops.asarray(features), tokvecs)
def _validate_batch(self, int[:, ::1] is_valid, states):
def _validate_batch(self, is_valid, states):
cdef StateClass state
cdef int i
cdef int[:, :] is_valid_cpu = is_valid.get()
for i, state in enumerate(states):
self.moves.set_valid(&is_valid[i, 0], state.c)
self.moves.set_valid(&is_valid_cpu[i, 0], state.c)
is_valid.set(numpy.asarray(is_valid_cpu))
def _cost_batch(self, weight_t[:, ::1] costs, int[:, ::1] is_valid,
def _cost_batch(self, costs, is_valid,
states, golds):
cdef int i
cdef StateClass state
cdef GoldParse gold
cdef int[:, :] is_valid_cpu = is_valid.get()
cdef weight_t[:, :] costs_cpu = costs.get()
for i, (state, gold) in enumerate(zip(states, golds)):
self.moves.set_costs(&is_valid[i, 0], &costs[i, 0], state, gold)
self.moves.set_costs(&is_valid_cpu[i, 0], &costs_cpu[i, 0], state, gold)
is_valid.set(numpy.asarray(is_valid_cpu))
costs.set(numpy.asarray(costs_cpu))
def _transition_batch(self, states, scores):
cdef StateClass state

4
spacy/syntax/stateclass.pyx
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@ -76,8 +76,8 @@ cdef class StateClass:
else:
vals[i] = 0
def set_token_vectors(self, float[:, :] tokvecs,
float[:, :] all_tokvecs, int[:] indices):
def set_token_vectors(self, tokvecs,
all_tokvecs, int[:] indices):
for i in range(indices.shape[0]):
if indices[i] >= 0:
tokvecs[i] = all_tokvecs[indices[i]]

2
spacy/tokens/doc.pxd
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@ -32,7 +32,7 @@ cdef class Doc:
cdef public object _vector
cdef public object _vector_norm
cdef public np.ndarray tensor
cdef public object tensor
cdef public object user_data
cdef TokenC* c