Merge branch 'develop' of https://github.com/explosion/spaCy into develop

spacy/_ml.py

@@ -150,10 +150,10 @@ class PrecomputableAffine(Model):
 
     def _backprop_padding(self, dY, ids):
         # (1, nF, nO, nP) += (nN, nF, nO, nP) where IDs (nN, nF) < 0
-        for i in range(ids.shape[0]):
-            for j in range(ids.shape[1]):
-                if ids[i,j] < 0:
-                    self.d_pad[0,j] += dY[i, j]
+        mask = ids < 0.
+        mask = mask.sum(axis=1)
+        d_pad = dY * mask.reshape((ids.shape[0], 1, 1))
+        self.d_pad += d_pad.sum(axis=0)
         return dY, ids
 
     @staticmethod

spacy/cli/train.py

@@ -85,6 +85,7 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=30, n_sents=0,
     batch_sizes = util.compounding(util.env_opt('batch_from', 1),
                                    util.env_opt('batch_to', 16),
                                    util.env_opt('batch_compound', 1.001))
+    max_doc_len = util.env_opt('max_doc_len', 5000)
     corpus = GoldCorpus(train_path, dev_path, limit=n_sents)
     n_train_words = corpus.count_train()
 
@@ -108,6 +109,9 @@ def train(cmd, lang, output_dir, train_data, dev_data, n_iter=30, n_sents=0,
             with tqdm.tqdm(total=n_train_words, leave=False) as pbar:
                 losses = {}
                 for batch in minibatch(train_docs, size=batch_sizes):
+                    batch = [(d, g) for (d, g) in batch if len(d) < max_doc_len]
+                    if not batch:
+                        continue
                     docs, golds = zip(*batch)
                     nlp.update(docs, golds, sgd=optimizer,
                                drop=next(dropout_rates), losses=losses)

spacy/tests/test_misc.py

@@ -6,6 +6,7 @@ from .. import util
 from ..displacy import parse_deps, parse_ents
 from ..tokens import Span
 from .util import get_doc
+from .._ml import PrecomputableAffine
 
 from pathlib import Path
 import pytest
@@ -59,3 +60,19 @@ def test_displacy_parse_deps(en_vocab):
     assert deps['arcs'] == [{'start': 0, 'end': 1, 'label': 'nsubj', 'dir': 'left'},
                             {'start': 2, 'end': 3, 'label': 'det', 'dir': 'left'},
                             {'start': 1, 'end': 3, 'label': 'attr', 'dir': 'right'}]
+
+
+def test_PrecomputableAffine(nO=4, nI=5, nF=3, nP=2):
+    model = PrecomputableAffine(nO=nO, nI=nI, nF=nF, nP=nP)
+    assert model.W.shape == (nF, nO, nP, nI)
+    tensor = model.ops.allocate((10, nI))
+    Y, get_dX = model.begin_update(tensor)
+    assert Y.shape == (tensor.shape[0]+1, nF, nO, nP)
+    assert model.d_pad.shape == (1, nF, nO, nP)
+    dY = model.ops.allocate((15, nF, nO, nP))
+    ids = model.ops.allocate((15, nF))
+    ids[1,2] = -1
+    dY[1,2] = 1
+    assert model.d_pad[0, 2, 0, 0] == 0.
+    model._backprop_padding(dY, ids)
+    assert model.d_pad[0, 2, 0, 0] == 1.