prompt: 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()
  
  ```
  
golden_diff: 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
  
verification_info: {"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"}]}