Two fixes to SampleK (#1086)

* Fix: set device for best_hyp_indices in SampleK.

* Fix: Take top-k values.

* Changelog.

CHANGELOG.md

@@ -11,6 +11,11 @@ Note that Sockeye has checks in place to not translate with an old model that wa
 
 Each version section may have subsections for: _Added_, _Changed_, _Removed_, _Deprecated_, and _Fixed_.
 
+## [3.1.33]
+
+### Fixed 
+- Two small fixes to SampleK. Before the device was not set correctly leading to issues when running sampling on GPUs. Furthermore, SampleK did not return the top-k values correctly.
+
 ## [3.1.32]
 
 ### Added

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__ = '3.1.32'
+__version__ = '3.1.33'

sockeye/beam_search.py

@@ -475,9 +475,9 @@ def forward(self, scores, target_dists, finished):
         # n == 0 means sample from the full vocabulary. Otherwise, we sample from the top n.
         if self.n != 0:
             # select the top n in each row, via a mask
-            _, indices = pt.topk(target_dists, k=self.n, dim=1, largest=True, sorted=True)
+            values, indices = pt.topk(target_dists, k=self.n, dim=1, largest=True, sorted=True)
             # set items not chosen by topk to 0
-            target_dists = pt.scatter(pt.zeros_like(target_dists), 1, indices, target_dists)
+            target_dists = pt.scatter(pt.zeros_like(target_dists), 1, indices, values)
             # renormalize
             target_dists = target_dists / target_dists.sum(1, keepdim=True)
 
@@ -489,7 +489,7 @@ def forward(self, scores, target_dists, finished):
         # (batch, 1)
         values = scores.gather(dim=1, index=best_word_indices.long().unsqueeze(1))
         # (batch,)
-        best_hyp_indices = pt.arange(0, best_word_indices.size()[0])
+        best_hyp_indices = pt.arange(0, best_word_indices.size()[0], device=best_word_indices.device)
 
         return best_hyp_indices, best_word_indices, values