Don't assume blas methods are present
@@ -28,7 +28,7 @@ from thinc.neural.ops import CupyOps
from thinc.neural.util import get_array_module
from thinc.linalg cimport Vec, VecVec
-from thinc.openblas cimport simple_gemm, simple_axpy
+from thinc.linalg cimport MatVec, VecVec
from .._ml import zero_init, PrecomputableAffine, Tok2Vec, flatten
from .._ml import link_vectors_to_models, create_default_optimizer
@@ -172,9 +172,8 @@ cdef void sum_state_features(float* output,
else:
idx = token_ids[f] * F * O + f*O
feature = &cached[idx]
- simple_axpy(output, O, feature, 1.)
- #for i in range(O):
- # output[i] += feature[i]
+ VecVec.add_i(output,
+ feature, 1., O)
output += O
token_ids += F
@@ -267,7 +266,7 @@ cdef class Parser:
with Model.use_device('cpu'):
upper = chain(
- clone(LayerNorm(Maxout(hidden_width, hidden_width)), depth-1),
+ clone(Maxout(hidden_width, hidden_width), depth-1),
zero_init(Affine(nr_class, hidden_width, drop_factor=0.0))
)
@@ -456,19 +455,20 @@ cdef class Parser:
memset(unmaxed, 0, nr_hidden * nr_piece * sizeof(float))
sum_state_features(unmaxed,
feat_weights, token_ids, 1, nr_feat, nr_hidden * nr_piece)
- simple_axpy(unmaxed, nr_hidden*nr_piece, bias, 1.0)
+ 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
- simple_gemm(scores, nr_todo, nr_class,
- vectors, nr_todo, nr_hidden,
- hW, nr_hidden, nr_class, 0, 0)
+ MatVec.batch_dot(scores,
+ hW, vectors, nr_class, nr_hidden, nr_todo)
# Add bias
for i in range(nr_todo):
- simple_axpy(&scores[i*nr_class], nr_class, hb, 1.0)
+ 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]
