Add bos_eos function for tok2vec

spacy/_ml.py

@@ -25,6 +25,7 @@ from thinc.describe import Dimension, Synapses, Biases, Gradient
 from thinc.neural._classes.affine import _set_dimensions_if_needed
 import thinc.extra.load_nlp
 
+from .strings import get_string_id
 from .attrs import ID, ORTH, LOWER, NORM, PREFIX, SUFFIX, SHAPE
 from .errors import Errors, user_warning, Warnings
 from . import util
@@ -468,11 +469,13 @@ def Tok2Vec(width, embed_size, **kwargs):
 
         tok2vec = (
             FeatureExtracter(cols)
-            >> with_flatten(
+            >> with_bos_eos(
+              with_flatten(
                 embed
                 >> CNN(width, conv_depth, cnn_maxout_pieces, nW=conv_window),
               pad=conv_depth * conv_window)
             )
+        )
 
         if bilstm_depth >= 1:
             tok2vec = (
@@ -488,6 +491,42 @@ def Tok2Vec(width, embed_size, **kwargs):
     return tok2vec
 
 
+def with_bos_eos(layer):
+    bos = get_string_id("-bos-")
+    eos = get_string_id("-eos-")
+    def bos_eos_forward(feats, drop=0.):
+        if not feats:
+            return layer.begin_update(feats, drop=drop)
+        xp = get_array_module(feats[0])
+        size = feats[0].shape[1]
+        dtype = feats[0].dtype
+        bos_row = xp.array([bos] * size, dtype=dtype)
+        eos_row = xp.array([eos] * size, dtype=dtype)
+        
+        # Add the bos and eos rows
+        feats = [xp.vstack((bos_row, f, eos_row)) for f in feats]
+        # Pass through the network
+        outputs, backprop_output = layer.begin_update(feats, drop=drop)
+        # Remove the rows for the bos and eos
+        outputs = [out[1:-1] for out in outputs]
+
+        def bos_eos_backward(d_outputs, sgd=None):
+            if not d_outputs:
+                return backprop_output(d_outputs, sgd=sgd)
+            # Get empty row
+            xp = get_array_module(d_outputs[0])
+            empty = xp.zeros((d_outputs[0].shape[1],), dtype=d_outputs[0].dtype)
+            d_outputs = [xp.vstack((empty, d_o, empty)) for d_o in d_outputs]
+            d_feats = backprop_output(d_outputs, sgd=sgd)
+            if d_feats is None:
+                return None
+            else:
+                return [d_f[1:-1] for d_f in d_feats]
+        return outputs, bos_eos_backward
+    return wrap(bos_eos_forward, layer)
+
+
+
 def reapply(layer, n_times):
     def reapply_fwd(X, drop=0.0):
         backprops = []