mirror of https://github.com/explosion/spaCy.git
Don't assume blas methods are present
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e85dd038fe
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@ -28,7 +28,7 @@ from thinc.neural.ops import CupyOps
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from thinc.neural.util import get_array_module
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from thinc.linalg cimport Vec, VecVec
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from thinc.openblas cimport simple_gemm, simple_axpy
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from thinc.linalg cimport MatVec, VecVec
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from .._ml import zero_init, PrecomputableAffine, Tok2Vec, flatten
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from .._ml import link_vectors_to_models, create_default_optimizer
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@ -172,9 +172,8 @@ cdef void sum_state_features(float* output,
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else:
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idx = token_ids[f] * F * O + f*O
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feature = &cached[idx]
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simple_axpy(output, O, feature, 1.)
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#for i in range(O):
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# output[i] += feature[i]
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VecVec.add_i(output,
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feature, 1., O)
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output += O
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token_ids += F
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@ -267,7 +266,7 @@ cdef class Parser:
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with Model.use_device('cpu'):
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upper = chain(
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clone(LayerNorm(Maxout(hidden_width, hidden_width)), depth-1),
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clone(Maxout(hidden_width, hidden_width), depth-1),
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zero_init(Affine(nr_class, hidden_width, drop_factor=0.0))
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)
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@ -456,19 +455,20 @@ cdef class Parser:
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memset(unmaxed, 0, nr_hidden * nr_piece * sizeof(float))
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sum_state_features(unmaxed,
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feat_weights, token_ids, 1, nr_feat, nr_hidden * nr_piece)
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simple_axpy(unmaxed, nr_hidden*nr_piece, bias, 1.0)
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VecVec.add_i(unmaxed,
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bias, 1., nr_hidden*nr_piece)
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state_vector = &vectors[i*nr_hidden]
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for j in range(nr_hidden):
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index = j * nr_piece
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which = Vec.arg_max(&unmaxed[index], nr_piece)
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state_vector[j] = unmaxed[index + which]
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# Compute hidden-to-output
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simple_gemm(scores, nr_todo, nr_class,
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vectors, nr_todo, nr_hidden,
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hW, nr_hidden, nr_class, 0, 0)
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MatVec.batch_dot(scores,
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hW, vectors, nr_class, nr_hidden, nr_todo)
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# Add bias
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for i in range(nr_todo):
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simple_axpy(&scores[i*nr_class], nr_class, hb, 1.0)
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VecVec.add_i(&scores[i*nr_class],
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hb, 1., nr_class)
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# Validate actions, argmax, take action.
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for i in range(nr_todo):
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state = states[i]
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