diff --git a/sharktank/sharktank/export_layer/export_paged_attention.py b/sharktank/sharktank/export_layer/export_paged_attention.py
index f87fae774..edd20ecf3 100644
--- a/sharktank/sharktank/export_layer/export_paged_attention.py
+++ b/sharktank/sharktank/export_layer/export_paged_attention.py
@@ -241,7 +241,7 @@ def main():
         head_dim=llama_config.hp.attn_head_dim,
         head_count_kv=llama_config.hp.attention_head_count_kv,
         rms_epsilon=llama_config.hp.attention_layer_norm_rms_epsilon,
-        attention_kernel=args.attention_kernel
+        attention_kernel=args.attention_kernel,
     )
 
     def generate_params_json(hp, prefill_bs: list[int], decode_bs: list[int]):
diff --git a/sharktank/sharktank/layers/paged_llama_attention_block.py b/sharktank/sharktank/layers/paged_llama_attention_block.py
index 774504501..ee767cf3e 100644
--- a/sharktank/sharktank/layers/paged_llama_attention_block.py
+++ b/sharktank/sharktank/layers/paged_llama_attention_block.py
@@ -113,12 +113,13 @@ def forward(
         # Full sequence length.
         kv_seq_len = seq_block_ids.shape[1] * self.cache.block_seq_stride
 
-        print("all",
-                start_positions,
-                cache_state,
-                xk_temp,
-                xv_temp,
-            )
+        print(
+            "all",
+            start_positions,
+            cache_state,
+            xk_temp,
+            xv_temp,
+        )
         if self.cache.is_paged:
             xk, xv = self.transact_cache_paged(
                 xk_cache_update=xk,
diff --git a/sharktank/tests/layers/paged_llama_attention_block_test.py b/sharktank/tests/layers/paged_llama_attention_block_test.py
index 7e859be90..6e737eccc 100644
--- a/sharktank/tests/layers/paged_llama_attention_block_test.py
+++ b/sharktank/tests/layers/paged_llama_attention_block_test.py
@@ -13,10 +13,15 @@
 import torch
 
 from iree.turbine import aot
-from sharktank.layers import PagedLlamaAttentionBlock, PagedKVCache, RotaryEmbeddingLayer
+from sharktank.layers import (
+    PagedLlamaAttentionBlock,
+    PagedKVCache,
+    RotaryEmbeddingLayer,
+)
 from sharktank.layers.testing import make_llama_attention_block_theta
 from sharktank.types.tensors import DefaultPrimitiveTensor
 
+
 class PagedLlamaAttentionBlockTest(unittest.TestCase):
     def setUp(self):
         torch.manual_seed(12345)
@@ -38,7 +43,6 @@ def setUp(self):
         self.batch_size = 3
         self.start_index = 0
 
-
     def testExportDecomposed(self):
         dtype = torch.float32
 
@@ -74,22 +78,32 @@ def testExportDecomposed(self):
         seq_block_ids = torch.arange(self.batch_size * self.block_seqlen).view(
             self.batch_size, -1
         )
-        
+
         embedding_module = RotaryEmbeddingLayer(
             rope_dimension_count=self.rope_dimension_count,
             max_seqlen=self.max_seqlen,
             rope_freq_base=self.rope_freq_base,
         )
-        
+
         class MyModule(torch.nn.Module):
             def forward(self, h, seq_block_ids, cache_state):
-                return attn.forward(h, seq_block_ids=seq_block_ids, embedding=embedding_module, start_index=0, cache_state=cache_state)
-        
+                return attn.forward(
+                    h,
+                    seq_block_ids=seq_block_ids,
+                    embedding=embedding_module,
+                    start_index=0,
+                    cache_state=cache_state,
+                )
+
         mod = MyModule()
-        h = torch.rand([self.batch_size, self.max_seqlen, self.attention_head_count * self.attention_head_dim])
-        mod.forward(h,
-                seq_block_ids,
-                cache_state)
+        h = torch.rand(
+            [
+                self.batch_size,
+                self.max_seqlen,
+                self.attention_head_count * self.attention_head_dim,
+            ]
+        )
+        mod.forward(h, seq_block_ids, cache_state)
         ep = torch.export.export(
             mod,
             args=(
@@ -104,8 +118,7 @@ def forward(self, h, seq_block_ids, cache_state):
         output.save_mlir("temp.mlir")
         self.assertNotIn("scaled_dot_product_attention", asm)
 
-
-    def testExportDecomposed(self):
+    def testExportNondecomposed(self):
         dtype = torch.float32
 
         cache = PagedKVCache(
@@ -140,22 +153,32 @@ def testExportDecomposed(self):
         seq_block_ids = torch.arange(self.batch_size * self.block_seqlen).view(
             self.batch_size, -1
         )
-        
+
         embedding_module = RotaryEmbeddingLayer(
             rope_dimension_count=self.rope_dimension_count,
             max_seqlen=self.max_seqlen,
             rope_freq_base=self.rope_freq_base,
         )
-        
+
         class MyModule(torch.nn.Module):
             def forward(self, h, seq_block_ids, cache_state):
-                return attn.forward(h, seq_block_ids=seq_block_ids, embedding=embedding_module, start_index=0, cache_state=cache_state)
-        
+                return attn.forward(
+                    h,
+                    seq_block_ids=seq_block_ids,
+                    embedding=embedding_module,
+                    start_index=0,
+                    cache_state=cache_state,
+                )
+
         mod = MyModule()
-        h = torch.rand([self.batch_size, self.max_seqlen, self.attention_head_count * self.attention_head_dim])
-        mod.forward(h,
-                seq_block_ids,
-                cache_state)
+        h = torch.rand(
+            [
+                self.batch_size,
+                self.max_seqlen,
+                self.attention_head_count * self.attention_head_dim,
+            ]
+        )
+        mod.forward(h, seq_block_ids, cache_state)
         ep = torch.export.export(
             mod,
             args=(