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Support making updates periodically during training

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This commit is contained in:
Matthew Honnibal 2017-05-23 04:23:29 -05:00
parent 3f725ff7b3
commit 6b918cc58e
1 changed files with 19 additions and 8 deletions
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27
spacy/syntax/nn_parser.pyx
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@ -29,6 +29,7 @@ from thinc.linear.avgtron cimport AveragedPerceptron
from thinc.linalg cimport VecVec
from thinc.structs cimport SparseArrayC, FeatureC, ExampleC
from thinc.extra.eg cimport Example
from cymem.cymem cimport Pool, Address
from murmurhash.mrmr cimport hash64
from preshed.maps cimport MapStruct
@ -37,6 +38,7 @@ from preshed.maps cimport map_get
from thinc.api import layerize, chain, noop, clone
from thinc.neural import Model, Affine, ELU, ReLu, Maxout
from thinc.neural.ops import NumpyOps, CupyOps
from thinc.neural.util import get_array_module
from .. import util
from ..util import get_async, get_cuda_stream
@ -381,6 +383,7 @@ cdef class Parser:
if not s.is_final() and g is not None]
backprops = []
d_tokvecs = state2vec.ops.allocate(tokvecs.shape)
cdef float loss = 0.
while len(todo) >= 3:
states, golds = zip(*todo)
@ -404,22 +407,30 @@ cdef class Parser:
backprops.append((token_ids, d_vector, bp_vector))
self.transition_batch(states, scores)
todo = [st for st in todo if not st[0].is_final()]
if len(backprops) >= 50:
self._make_updates(d_tokvecs,
backprops, sgd, cuda_stream)
backprops = []
if backprops:
self._make_updates(d_tokvecs,
backprops, sgd, cuda_stream)
return self.model[0].ops.unflatten(d_tokvecs, [len(d) for d in docs])
def _make_updates(self, d_tokvecs, backprops, sgd, cuda_stream=None):
# Tells CUDA to block, so our async copies complete.
if cuda_stream is not None:
cuda_stream.synchronize()
d_tokvecs = state2vec.ops.allocate(tokvecs.shape)
xp = state2vec.ops.xp # Handle for numpy/cupy
for token_ids, d_vector, bp_vector in backprops:
xp = get_array_module(d_tokvecs)
for ids, d_vector, bp_vector in backprops:
d_state_features = bp_vector(d_vector, sgd=sgd)
active_feats = token_ids * (token_ids >= 0)
active_feats = active_feats.reshape((token_ids.shape[0], token_ids.shape[1], 1))
active_feats = ids * (ids >= 0)
active_feats = active_feats.reshape((ids.shape[0], ids.shape[1], 1))
if hasattr(xp, 'scatter_add'):
xp.scatter_add(d_tokvecs,
token_ids, d_state_features * active_feats)
ids, d_state_features * active_feats)
else:
xp.add.at(d_tokvecs,
token_ids, d_state_features * active_feats)
return self.model[0].ops.unflatten(d_tokvecs, [len(d) for d in docs])
ids, d_state_features * active_feats)
def get_batch_model(self, batch_size, tokvecs, stream, dropout):
lower, upper = self.model