We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Bug: Segmentation fault in sqrl_pytorch-PyTorch CUDA
Just saw this while working on something else.   I haven't done a lot to debug it, but note that it's in copydown, on a fairly innocuous operation (aten::sum(Tensor 2) -> Float), so might be something to do with KS_ALLOCATOR not being defined?
Or could just be out of memory not caught?
![image](https://user-images.githubusercontent.com/128119/130364073-023976aa-9222-4d55-ad43-95a7ac530d76.png)


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `examples/dl-capsule/sqrl.py`
Content:
```
1 import torch
2 import ksc.torch_frontend as knossos
3 
4 # run-bench: Knossos source, and "nice" PyTorch implementation
5 # BEGINDOC
6 @knossos.register
7 def sqrl(x: torch.Tensor):
8     """
9     sqrl: Squared Leaky Relu
10     Like a capsule from /Stuck in a Rut/
11     Typically x is a 4x4 tensor, possibly
12     packed in a 4n x 4m array
13     """
14     y = torch.sum(x)
15     if y < 0.0:
16         t = -0.125 * x
17     else:
18         t = 1 / 2 * x ** 2
19     return torch.mean(torch.sin(t) * t)
20 
21 
22 # ENDDOC
23 
24 # run-bench: PyTorch "fast" implementation
25 def sqrl_pytorch(x: torch.Tensor):
26     return sqrl(x)
27 
28 
29 # run-bench: PyTorch "nice" implementation
30 def sqrl_pytorch_nice(x: torch.Tensor):
31     return sqrl(x)
32 
33 
34 # run-bench: Define a range of values at which to call the methods
35 def sqrl_bench_configs():
36     yield torch.randn((4, 4))
37     yield torch.randn((16, 16))
38 
39 
40 #################################
41 #
42 # vsqrl - vectorized sqrl
43 #
44 
45 vsqrl = knossos.vmap(sqrl)
46 
47 
48 # run-bench: Define a range of values at which to call the methods
49 def vsqrl_bench_configs():
50     yield torch.randn((10, 4, 4))
51     yield torch.randn((1000, 4, 4))
52     yield torch.randn((1000, 16, 16))
53
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/examples/dl-capsule/sqrl.py b/examples/dl-capsule/sqrl.py
--- a/examples/dl-capsule/sqrl.py
+++ b/examples/dl-capsule/sqrl.py
@@ -23,12 +23,12 @@
 
 # run-bench: PyTorch "fast" implementation
 def sqrl_pytorch(x: torch.Tensor):
-    return sqrl(x)
+    return sqrl.raw_f(x)
 
 
 # run-bench: PyTorch "nice" implementation
 def sqrl_pytorch_nice(x: torch.Tensor):
-    return sqrl(x)
+    return sqrl.raw_f(x)
 
 
 # run-bench: Define a range of values at which to call the methods

{"golden_diff": "diff --git a/examples/dl-capsule/sqrl.py b/examples/dl-capsule/sqrl.py\n--- a/examples/dl-capsule/sqrl.py\n+++ b/examples/dl-capsule/sqrl.py\n@@ -23,12 +23,12 @@\n \n # run-bench: PyTorch \"fast\" implementation\n def sqrl_pytorch(x: torch.Tensor):\n-    return sqrl(x)\n+    return sqrl.raw_f(x)\n \n \n # run-bench: PyTorch \"nice\" implementation\n def sqrl_pytorch_nice(x: torch.Tensor):\n-    return sqrl(x)\n+    return sqrl.raw_f(x)\n \n \n # run-bench: Define a range of values at which to call the methods\n", "issue": "Bug: Segmentation fault in sqrl_pytorch-PyTorch CUDA\nJust saw this while working on something else.   I haven't done a lot to debug it, but note that it's in copydown, on a fairly innocuous operation (aten::sum(Tensor 2) -> Float), so might be something to do with KS_ALLOCATOR not being defined?\r\nOr could just be out of memory not caught?\r\n![image](https://user-images.githubusercontent.com/128119/130364073-023976aa-9222-4d55-ad43-95a7ac530d76.png)\r\n\n", "before_files": [{"content": "import torch\nimport ksc.torch_frontend as knossos\n\n# run-bench: Knossos source, and \"nice\" PyTorch implementation\n# BEGINDOC\n@knossos.register\ndef sqrl(x: torch.Tensor):\n    \"\"\"\n    sqrl: Squared Leaky Relu\n    Like a capsule from /Stuck in a Rut/\n    Typically x is a 4x4 tensor, possibly\n    packed in a 4n x 4m array\n    \"\"\"\n    y = torch.sum(x)\n    if y < 0.0:\n        t = -0.125 * x\n    else:\n        t = 1 / 2 * x ** 2\n    return torch.mean(torch.sin(t) * t)\n\n\n# ENDDOC\n\n# run-bench: PyTorch \"fast\" implementation\ndef sqrl_pytorch(x: torch.Tensor):\n    return sqrl(x)\n\n\n# run-bench: PyTorch \"nice\" implementation\ndef sqrl_pytorch_nice(x: torch.Tensor):\n    return sqrl(x)\n\n\n# run-bench: Define a range of values at which to call the methods\ndef sqrl_bench_configs():\n    yield torch.randn((4, 4))\n    yield torch.randn((16, 16))\n\n\n#################################\n#\n# vsqrl - vectorized sqrl\n#\n\nvsqrl = knossos.vmap(sqrl)\n\n\n# run-bench: Define a range of values at which to call the methods\ndef vsqrl_bench_configs():\n    yield torch.randn((10, 4, 4))\n    yield torch.randn((1000, 4, 4))\n    yield torch.randn((1000, 16, 16))\n", "path": "examples/dl-capsule/sqrl.py"}], "after_files": [{"content": "import torch\nimport ksc.torch_frontend as knossos\n\n# run-bench: Knossos source, and \"nice\" PyTorch implementation\n# BEGINDOC\n@knossos.register\ndef sqrl(x: torch.Tensor):\n    \"\"\"\n    sqrl: Squared Leaky Relu\n    Like a capsule from /Stuck in a Rut/\n    Typically x is a 4x4 tensor, possibly\n    packed in a 4n x 4m array\n    \"\"\"\n    y = torch.sum(x)\n    if y < 0.0:\n        t = -0.125 * x\n    else:\n        t = 1 / 2 * x ** 2\n    return torch.mean(torch.sin(t) * t)\n\n\n# ENDDOC\n\n# run-bench: PyTorch \"fast\" implementation\ndef sqrl_pytorch(x: torch.Tensor):\n    return sqrl.raw_f(x)\n\n\n# run-bench: PyTorch \"nice\" implementation\ndef sqrl_pytorch_nice(x: torch.Tensor):\n    return sqrl.raw_f(x)\n\n\n# run-bench: Define a range of values at which to call the methods\ndef sqrl_bench_configs():\n    yield torch.randn((4, 4))\n    yield torch.randn((16, 16))\n\n\n#################################\n#\n# vsqrl - vectorized sqrl\n#\n\nvsqrl = knossos.vmap(sqrl)\n\n\n# run-bench: Define a range of values at which to call the methods\ndef vsqrl_bench_configs():\n    yield torch.randn((10, 4, 4))\n    yield torch.randn((1000, 4, 4))\n    yield torch.randn((1000, 16, 16))\n", "path": "examples/dl-capsule/sqrl.py"}]}