diff --git a/deepspeed/runtime/pipe/engine.py b/deepspeed/runtime/pipe/engine.py
index 6b56f8c7ad18fa4329ce8f6c139bc083869587ea..8ecc0663fe952ec2ebcb5e93c816d0899ef7fcae 100644
--- a/deepspeed/runtime/pipe/engine.py
+++ b/deepspeed/runtime/pipe/engine.py
@@ -1060,7 +1060,7 @@ class PipelineEngine(DeepSpeedEngine):
                 local_part=outputs[1],
                 group=self.grid.get_slice_parallel_group())
             outputs[0].data = part_output.full()
-            outputs = ([outputs[0], *outputs[2:]])
+            outputs = (outputs[0], *outputs[2:])
             # save for backward
             self.pipe_buffers['outputs'][buffer_id] = outputs
 
@@ -1072,8 +1072,26 @@ class PipelineEngine(DeepSpeedEngine):
                                                         dtype=outputs.dtype,
                                                         num_buffers=1)[0]
             else:
-                sizes_and_dtypes = [(list(t.size()),
-                                     t.dtype) for t in outputs if t.is_floating_point()]
+                # XXX This is a HACK
+                # When we exchange activations/gradients, the two pipe stages
+                # need to issue the send/recv with the same buffer sizes or
+                # else there is a deadlock. The is_floating_point() filter is
+                # used to avoid sending gradients for tensors that do not
+                # produce gradients. When TP>1, we partition the first
+                # activations/gradients across TP ranks to save communication
+                # volume and memory. That partitioned tensor is represented as
+                # two tensors: a 1/TPth chunk of the original data and also a
+                # small LongTensor storing the metadata used to reconstruct on
+                # the other side. When combined, the floating point filter also
+                # filtered out the metadata tensor. A quick (hacky) fix just
+                # branches on is_grad_partitioned so we don't filter out the
+                # metadata tensor.
+                if self.is_grad_partitioned:
+                    sizes_and_dtypes = [(list(t.size()), t.dtype) for t in outputs]
+                else:
+                    sizes_and_dtypes = [(list(t.size()),
+                                         t.dtype) for t in outputs
+                                        if t.is_floating_point()]
                 self.grad_layer = self._allocate_buffers(sizes_and_dtypes,
                                                          num_buffers=1)[0]