diff --git a/README.md b/README.md
index efebd02..9f4fcad 100644
--- a/README.md
+++ b/README.md
@@ -72,9 +72,9 @@ $ python assert.py
 - [ ] allow for variable ring passes per layer, for <a href="https://arxiv.org/abs/2007.03356">local -> global attention</a> in ring transformer as one goes up the layers.
     - [ ] also allow for mixed striped layers, so one can do local strided attention (used in some image transformers but not language) would be a good fit for the initial local attention layers, in addition to classic lookback local
 - [ ] find a machine with 8 GPUs and test with a quarter million tokens first
-- [ ] figure out batch_isend_irecv
 - [ ] think about how to craft a special `Dataset` that shards across sequence length (take into account labels for cross entropy loss) for ring transformer training
 - [ ] add ring attention to Tri's flash attention implementation. find some cuda ring reduce impl
+- [ ] `batch_isend_irecv` in the presence of key padding mask needing ring exchange, but not a big priority
 
 ## Citations
 
diff --git a/ring_attention_pytorch/ring_flash_attention.py b/ring_attention_pytorch/ring_flash_attention.py
index bec219c..f13a5e1 100644
--- a/ring_attention_pytorch/ring_flash_attention.py
+++ b/ring_attention_pytorch/ring_flash_attention.py
@@ -91,7 +91,11 @@ def forward(
 
         num_tiles = math.ceil(per_machine_seq_size / bucket_size)
 
-        for ring_rank, (k, v, mask) in ring_pass_fn(k, v, mask, max_iters = max_ring_passes):
+        kv = torch.stack((k, v))
+
+        for ring_rank, (kv, mask) in ring_pass_fn(kv, mask, max_iters = max_ring_passes):
+
+            k, v = kv
 
             col_splits = zip(
                 k.split(bucket_size, dim = -2),
@@ -209,7 +213,11 @@ def backward(ctx, do):
             dq.split(bucket_size, dim = -2)
         )
 
-        for ring_rank, (k, v, mask, dk, dv) in ring_pass_fn(k, v, mask, dk, dv, max_iters = max_ring_passes):
+        kv_and_dkv = torch.stack((k, v, dk, dv))
+
+        for ring_rank, (kv_and_dkv, mask) in ring_pass_fn(kv_and_dkv, mask, max_iters = max_ring_passes):
+
+            k, v, dk, dv = kv_and_dkv
 
             col_splits = zip(
                 k.split(bucket_size, dim = -2),
@@ -257,8 +265,11 @@ def backward(ctx, do):
                     dkc.add_(dk_chunk)
                     dvc.add_(dv_chunk)
 
-            dk = one_ring_pass(dk)
-            dv = one_ring_pass(dv)
+            dkv = kv_and_dkv[2:]
+
+            dkv = one_ring_pass(dkv)
+
+            dk, dv = dkv
 
         return dq, dk, dv, None, None, None, None, None, None, None
 
diff --git a/setup.py b/setup.py
index 61aacd6..7cfdb96 100644
--- a/setup.py
+++ b/setup.py
@@ -3,7 +3,7 @@
 setup(
   name = 'ring-attention-pytorch',
   packages = find_packages(exclude=[]),
-  version = '0.1.12',
+  version = '0.1.14',
   license='MIT',
   description = 'Ring Attention - Pytorch',
   author = 'Phil Wang',