Use version of NDArray split that always returns a list. (#454)

* Fix source factor splitting for single factor.

- Use version of ndarray split that always returns a list for uniform
handling.

* Unit test for factor splitting.

* Update version, changelog.

* Keep original split call for sym_gen.

CHANGELOG.md

@@ -11,6 +11,10 @@ Note that Sockeye has checks in place to not translate with an old model that wa
 Each version section may have have subsections for: _Added_, _Changed_, _Removed_, _Deprecated_, and _Fixed_.
 
 
+## [1.18.27]
+### Fixed
+- Fix silent failing of NDArray splits during inference by using a version that always returns a list. This was causing incorrect behavior when using lexicon restriction and batch inference with a single source factor.
+
 ## [1.18.26]
 ### Added
 - ROUGE score evaluation. It can be used as the stopping criterion for tasks such as summarization.

sockeye/__init__.py

@@ -11,4 +11,4 @@
 # express or implied. See the License for the specific language governing
 # permissions and limitations under the License.
 
-__version__ = '1.18.26'
+__version__ = '1.18.27'

sockeye/inference.py

@@ -1334,7 +1334,7 @@ def _beam_search(self,
         """
         Translates multiple sentences using beam search.
 
-        :param source: Source ids. Shape: (batch_size, bucket_key).
+        :param source: Source ids. Shape: (batch_size, bucket_key, num_factors).
         :param source_length: Max source length.
         :param raw_constraint_list: A list of optional lists containing phrases (as lists of target word IDs)
                that must appear in each output.
@@ -1383,9 +1383,9 @@ def _beam_search(self,
         pad_dist = self.pad_dist
         vocab_slice_ids = None  # type: mx.nd.NDArray
         if self.restrict_lexicon:
+            source_words = utils.split(source, num_outputs=self.num_source_factors, axis=2, squeeze_axis=True)[0]
             # TODO: See note in method about migrating to pure MXNet when set operations are supported.
             #       We currently convert source to NumPy and target ids back to NDArray.
-            source_words = source.split(num_outputs=self.num_source_factors, axis=2, squeeze_axis=True)[0]
             vocab_slice_ids = self.restrict_lexicon.get_trg_ids(source_words.astype("int32").asnumpy())
             if any(raw_constraint_list):
                 # Add the constraint IDs to the list of permissibled IDs, and then project them into the reduced space

sockeye/utils.py

@@ -860,3 +860,28 @@ def uncast_conditionally(data: mx.sym.Symbol, dtype: str) -> mx.sym.Symbol:
     if dtype != C.DTYPE_FP32:
         return mx.sym.cast(data=data, dtype=C.DTYPE_FP32)
     return data
+
+
+def split(data: mx.nd.NDArray,
+          num_outputs: int,
+          axis: int = 1,
+          squeeze_axis: bool = False) -> List[mx.nd.NDArray]:
+    """
+    Version of mxnet.ndarray.split that always returns a list.  The original
+    implementation only returns a list if num_outputs > 1:
+    https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html#mxnet.ndarray.split
+
+    Splits an array along a particular axis into multiple sub-arrays.
+
+    :param data: The input.
+    :param num_outputs: Number of splits. Note that this should evenly divide
+                        the length of the axis.
+    :param axis: Axis along which to split.
+    :param squeeze_axis: If true, Removes the axis with length 1 from the shapes
+                         of the output arrays.
+    :return: List of NDArrays resulting from the split.
+    """
+    ndarray_or_list = data.split(num_outputs=num_outputs, axis=axis, squeeze_axis=squeeze_axis)
+    if num_outputs == 1:
+        return [ndarray_or_list]
+    return ndarray_or_list

test/unit/test_utils.py

@@ -338,4 +338,12 @@ def test_metric_value_is_better(new, old, metric, result):
     assert utils.metric_value_is_better(new, old, metric) == result
 
 
-
+@pytest.mark.parametrize("num_factors", [1, 2, 3])
+def test_split(num_factors):
+    batch_size = 4
+    bucket_key = 10
+    # Simulates splitting factored input
+    data = mx.nd.random.normal(shape=(batch_size, bucket_key, num_factors))
+    result = utils.split(data, num_outputs=num_factors, axis=2, squeeze_axis=True)
+    assert isinstance(result, list)
+    assert result[0].shape == (batch_size, bucket_key)