Parser: use C saxpy/sgemm provided by the Ops implementation

pyproject.toml

@@ -5,8 +5,7 @@ requires = [
     "cymem>=2.0.2,<2.1.0",
     "preshed>=3.0.2,<3.1.0",
     "murmurhash>=0.28.0,<1.1.0",
-    "thinc>=8.0.14,<8.1.0",
-    "blis>=0.4.0,<0.8.0",
+    "thinc>=8.1.0,<8.2.0",
     "pathy",
     "numpy>=1.15.0",
 ]

requirements.txt

@@ -3,8 +3,7 @@ spacy-legacy>=3.0.9,<3.1.0
 spacy-loggers>=1.0.0,<2.0.0
 cymem>=2.0.2,<2.1.0
 preshed>=3.0.2,<3.1.0
-thinc>=8.0.14,<8.1.0
-blis>=0.4.0,<0.8.0
+thinc>=8.1.0,<8.2.0
 ml_datasets>=0.2.0,<0.3.0
 murmurhash>=0.28.0,<1.1.0
 wasabi>=0.9.1,<1.1.0

setup.cfg

@@ -38,16 +38,15 @@ setup_requires =
     cymem>=2.0.2,<2.1.0
     preshed>=3.0.2,<3.1.0
     murmurhash>=0.28.0,<1.1.0
-    thinc>=8.0.14,<8.1.0
+    thinc>=8.1.0,<8.2.0
 install_requires =
     # Our libraries
     spacy-legacy>=3.0.9,<3.1.0
     spacy-loggers>=1.0.0,<2.0.0
     murmurhash>=0.28.0,<1.1.0
     cymem>=2.0.2,<2.1.0
     preshed>=3.0.2,<3.1.0
-    thinc>=8.0.14,<8.1.0
-    blis>=0.4.0,<0.8.0
+    thinc>=8.1.0,<8.2.0
     wasabi>=0.9.1,<1.1.0
     srsly>=2.4.3,<3.0.0
     catalogue>=2.0.6,<2.1.0

spacy/ml/parser_model.pxd

@@ -1,4 +1,5 @@
 from libc.string cimport memset, memcpy
+from thinc.backends.cblas cimport CBlas
 from ..typedefs cimport weight_t, hash_t
 from ..pipeline._parser_internals._state cimport StateC
 
@@ -38,7 +39,7 @@ cdef ActivationsC alloc_activations(SizesC n) nogil
 
 cdef void free_activations(const ActivationsC* A) nogil
 
-cdef void predict_states(ActivationsC* A, StateC** states,
+cdef void predict_states(CBlas cblas, ActivationsC* A, StateC** states,
         const WeightsC* W, SizesC n) nogil
 
 cdef int arg_max_if_valid(const weight_t* scores, const int* is_valid, int n) nogil

spacy/ml/parser_model.pyx

@@ -4,7 +4,6 @@ from libc.math cimport exp
 from libc.string cimport memset, memcpy
 from libc.stdlib cimport calloc, free, realloc
 from thinc.backends.linalg cimport Vec, VecVec
-cimport blis.cy
 
 import numpy
 import numpy.random
@@ -91,15 +90,15 @@ cdef void resize_activations(ActivationsC* A, SizesC n) nogil:
     A._curr_size = n.states
 
 
-cdef void predict_states(ActivationsC* A, StateC** states,
+cdef void predict_states(CBlas cblas, 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,
+    sum_state_features(cblas, 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],
@@ -113,12 +112,10 @@ cdef void predict_states(ActivationsC* A, StateC** states,
         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)
+        cblas.sgemm()(False, True, n.states, n.classes, n.hiddens,
+            1.0, <const float *>A.hiddens, n.hiddens,
+            <const float *>W.hidden_weights, n.hiddens,
+            0.0, A.scores, n.classes)
         # Add bias
         for i in range(n.states):
             VecVec.add_i(&A.scores[i*n.classes],
@@ -135,7 +132,7 @@ cdef void predict_states(ActivationsC* A, StateC** states,
                 A.scores[i*n.classes+j] = min_
 
 
-cdef void sum_state_features(float* output,
+cdef void sum_state_features(CBlas cblas, 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
@@ -150,9 +147,7 @@ cdef void sum_state_features(float* output,
             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)
+            cblas.saxpy()(O, one, <const float*>feature, 1, &output[b*O], 1)
         token_ids += F
 
 
@@ -443,9 +438,11 @@ cdef class precompute_hiddens:
         # - Output from backward on GPU
         bp_hiddens = self._bp_hiddens
 
+        cdef CBlas cblas = self.ops.cblas()
+
         feat_weights = self.get_feat_weights()
         cdef int[:, ::1] ids = token_ids
-        sum_state_features(<float*>state_vector.data,
+        sum_state_features(cblas, <float*>state_vector.data,
             feat_weights, &ids[0,0],
             token_ids.shape[0], self.nF, self.nO*self.nP)
         state_vector += self.bias

spacy/pipeline/transition_parser.pxd

@@ -1,4 +1,5 @@
 from cymem.cymem cimport Pool
+from thinc.backends.cblas cimport CBlas
 
 from ..vocab cimport Vocab
 from .trainable_pipe cimport TrainablePipe
@@ -12,7 +13,7 @@ cdef class Parser(TrainablePipe):
     cdef readonly TransitionSystem moves
     cdef public object _multitasks
 
-    cdef void _parseC(self, StateC** states,
+    cdef void _parseC(self, CBlas cblas, StateC** states,
             WeightsC weights, SizesC sizes) nogil
 
     cdef void c_transition_batch(self, StateC** states, const float* scores,

spacy/pipeline/transition_parser.pyx

@@ -259,6 +259,7 @@ cdef class Parser(TrainablePipe):
     def greedy_parse(self, docs, drop=0.):
         cdef vector[StateC*] states
         cdef StateClass state
+        cdef CBlas cblas = self.model.ops.cblas()
         self._ensure_labels_are_added(docs)
         set_dropout_rate(self.model, drop)
         batch = self.moves.init_batch(docs)
@@ -269,8 +270,7 @@ cdef class Parser(TrainablePipe):
                 states.push_back(state.c)
         sizes = get_c_sizes(model, states.size())
         with nogil:
-            self._parseC(&states[0],
-                weights, sizes)
+            self._parseC(cblas, &states[0], weights, sizes)
         model.clear_memory()
         del model
         return batch
@@ -297,14 +297,13 @@ cdef class Parser(TrainablePipe):
         del model
         return list(batch)
 
-    cdef void _parseC(self, StateC** states,
+    cdef void _parseC(self, CBlas cblas, StateC** states,
             WeightsC weights, SizesC sizes) nogil:
         cdef int i, j
         cdef vector[StateC*] unfinished
         cdef ActivationsC activations = alloc_activations(sizes)
         while sizes.states >= 1:
-            predict_states(&activations,
-                states, &weights, sizes)
+            predict_states(cblas, &activations, states, &weights, sizes)
             # Validate actions, argmax, take action.
             self.c_transition_batch(states,
                 activations.scores, sizes.classes, sizes.states)