We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Tensor.__eq__() with two bool tensors raises error on Torch backend
This was introduced from #1493

To reproduce:
```
In [24]: (Tensor([1], dtype=dtypes.bool, device="TORCH") == Tensor([1], dtype=dtypes.bool, device="TORCH")).realize()
RuntimeError: Subtraction, the `-` operator, with a bool tensor is not supported. If you are trying to invert a mask, use the `~` or `logical_not()` operator instead.
```
RuntimeError is from pytorch



--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `tinygrad/runtime/ops_torch.py`
Content:
```
1 import torch
2 from typing import Dict, Callable, Optional
3 from tinygrad.ops import UnaryOps, BinaryOps, MovementOps, TernaryOps, Op, Interpreted
4 from tinygrad.helpers import getenv, dtypes, prod, DType
5 from tinygrad.runtime.ops_cpu import base_fxn_for_op, einsum_mulacc
6 from tinygrad.runtime.lib import RawBuffer
7 
8 device = torch.device("cuda:0" if torch.cuda.is_available() else ("mps" if getenv("MPS", 0) else "cpu"))
9 type_map = {torch.float64: dtypes.float64, torch.float16: dtypes.float16, torch.float32: dtypes.float32, torch.int8: dtypes.int8, torch.int32: dtypes.int32, torch.int64: dtypes.int64, torch.uint8: dtypes.uint8, torch.bool: dtypes.bool}
10 inverse_type_map = {v:k for k,v in type_map.items()}
11 
12 def as_strided(x, arg):
13   if any(i < 0 for i in arg[1]):
14     return torch.as_strided(x.contiguous(), arg[0], tuple(abs(i) for i in arg[1]),
15       arg[2] + sum((s-1)*a if a < 0 else 0 for (s,a) in zip(arg[0], arg[1]))).flip([i for i,a in enumerate(arg[1]) if a < 0])
16   return torch.as_strided(x.contiguous(), arg[0], arg[1], arg[2])
17 
18 torch_fxn_for_op: Dict[Op, Callable] = {**base_fxn_for_op, **{
19   UnaryOps.NOOP: lambda x: x.contiguous(), UnaryOps.SQRT: lambda x: x.sqrt(), UnaryOps.EXP2: lambda x: x.exp2(), UnaryOps.LOG2: lambda x: x.log2(), UnaryOps.SIN: torch.sin,
20   UnaryOps.CAST: lambda x,y: (x.view if y[1] else x.type)(next(k for k,v in type_map.items() if v==y[0])),
21   BinaryOps.MAX: torch.maximum, BinaryOps.CMPLT: lambda x,y: (x<y).type(torch.promote_types(x.dtype, y.dtype)),
22   MovementOps.PAD: lambda x, padding: torch.nn.functional.pad(x, [item for sublist in padding[::-1] for item in sublist]),
23   TernaryOps.MULACC: einsum_mulacc(lambda s,a,b: torch.einsum(s, a.float(), b.float()).type(torch.promote_types(a.dtype, b.dtype)), lambda x: x.stride(), lambda x,s: x.expand(s)),
24   TernaryOps.WHERE: lambda x, y, z: torch.where(x != 0, y, z),
25   MovementOps.STRIDE: lambda x, arg: x[tuple(slice(None, None, abs(i)) for i in arg)].flip([i for i,a in enumerate(arg) if a < 0]),
26   MovementOps.EXPAND: lambda x, arg: x.expand(arg), MovementOps.PERMUTE: lambda x, arg: x.permute(arg),
27   MovementOps.AS_STRIDED: as_strided
28 }}
29 
30 class RawTorchBuffer(RawBuffer):
31   def __init__(self, size:int, dtype:DType, buf:Optional[torch.Tensor]=None): super().__init__(size, dtype, buf if buf is not None else torch.empty([size], dtype=inverse_type_map[dtype]))
32   @classmethod
33   def fromCPU(cls, x):
34     buf = torch.from_numpy(x if all(s>=0 for s in x.strides) else x.copy()).requires_grad_(False).to(device)
35     return cls(prod(x.shape), type_map[buf.dtype], buf)
36   def toCPU(self): return self._buf.cpu().numpy()
37 TorchBuffer = Interpreted(RawTorchBuffer, torch_fxn_for_op, from_underlying=lambda x: RawTorchBuffer(prod(x.shape), type_map[x.dtype], x))
38
```
--- 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/tinygrad/runtime/ops_torch.py b/tinygrad/runtime/ops_torch.py
--- a/tinygrad/runtime/ops_torch.py
+++ b/tinygrad/runtime/ops_torch.py
@@ -18,7 +18,7 @@
 torch_fxn_for_op: Dict[Op, Callable] = {**base_fxn_for_op, **{
   UnaryOps.NOOP: lambda x: x.contiguous(), UnaryOps.SQRT: lambda x: x.sqrt(), UnaryOps.EXP2: lambda x: x.exp2(), UnaryOps.LOG2: lambda x: x.log2(), UnaryOps.SIN: torch.sin,
   UnaryOps.CAST: lambda x,y: (x.view if y[1] else x.type)(next(k for k,v in type_map.items() if v==y[0])),
-  BinaryOps.MAX: torch.maximum, BinaryOps.CMPLT: lambda x,y: (x<y).type(torch.promote_types(x.dtype, y.dtype)),
+  BinaryOps.MAX: torch.maximum, BinaryOps.CMPLT: lambda x,y: (x<y).type(torch.promote_types(x.dtype, y.dtype)), BinaryOps.SUB: lambda x,y: torch.logical_xor(x, y) if y.dtype is torch.bool else torch.sub(x, y),
   MovementOps.PAD: lambda x, padding: torch.nn.functional.pad(x, [item for sublist in padding[::-1] for item in sublist]),
   TernaryOps.MULACC: einsum_mulacc(lambda s,a,b: torch.einsum(s, a.float(), b.float()).type(torch.promote_types(a.dtype, b.dtype)), lambda x: x.stride(), lambda x,s: x.expand(s)),
   TernaryOps.WHERE: lambda x, y, z: torch.where(x != 0, y, z),

{"golden_diff": "diff --git a/tinygrad/runtime/ops_torch.py b/tinygrad/runtime/ops_torch.py\n--- a/tinygrad/runtime/ops_torch.py\n+++ b/tinygrad/runtime/ops_torch.py\n@@ -18,7 +18,7 @@\n torch_fxn_for_op: Dict[Op, Callable] = {**base_fxn_for_op, **{\n   UnaryOps.NOOP: lambda x: x.contiguous(), UnaryOps.SQRT: lambda x: x.sqrt(), UnaryOps.EXP2: lambda x: x.exp2(), UnaryOps.LOG2: lambda x: x.log2(), UnaryOps.SIN: torch.sin,\n   UnaryOps.CAST: lambda x,y: (x.view if y[1] else x.type)(next(k for k,v in type_map.items() if v==y[0])),\n-  BinaryOps.MAX: torch.maximum, BinaryOps.CMPLT: lambda x,y: (x<y).type(torch.promote_types(x.dtype, y.dtype)),\n+  BinaryOps.MAX: torch.maximum, BinaryOps.CMPLT: lambda x,y: (x<y).type(torch.promote_types(x.dtype, y.dtype)), BinaryOps.SUB: lambda x,y: torch.logical_xor(x, y) if y.dtype is torch.bool else torch.sub(x, y),\n   MovementOps.PAD: lambda x, padding: torch.nn.functional.pad(x, [item for sublist in padding[::-1] for item in sublist]),\n   TernaryOps.MULACC: einsum_mulacc(lambda s,a,b: torch.einsum(s, a.float(), b.float()).type(torch.promote_types(a.dtype, b.dtype)), lambda x: x.stride(), lambda x,s: x.expand(s)),\n   TernaryOps.WHERE: lambda x, y, z: torch.where(x != 0, y, z),\n", "issue": "Tensor.__eq__() with two bool tensors raises error on Torch backend\nThis was introduced from #1493\r\n\r\nTo reproduce:\r\n```\r\nIn [24]: (Tensor([1], dtype=dtypes.bool, device=\"TORCH\") == Tensor([1], dtype=dtypes.bool, device=\"TORCH\")).realize()\r\nRuntimeError: Subtraction, the `-` operator, with a bool tensor is not supported. If you are trying to invert a mask, use the `~` or `logical_not()` operator instead.\r\n```\r\nRuntimeError is from pytorch\r\n\r\n\n", "before_files": [{"content": "import torch\nfrom typing import Dict, Callable, Optional\nfrom tinygrad.ops import UnaryOps, BinaryOps, MovementOps, TernaryOps, Op, Interpreted\nfrom tinygrad.helpers import getenv, dtypes, prod, DType\nfrom tinygrad.runtime.ops_cpu import base_fxn_for_op, einsum_mulacc\nfrom tinygrad.runtime.lib import RawBuffer\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else (\"mps\" if getenv(\"MPS\", 0) else \"cpu\"))\ntype_map = {torch.float64: dtypes.float64, torch.float16: dtypes.float16, torch.float32: dtypes.float32, torch.int8: dtypes.int8, torch.int32: dtypes.int32, torch.int64: dtypes.int64, torch.uint8: dtypes.uint8, torch.bool: dtypes.bool}\ninverse_type_map = {v:k for k,v in type_map.items()}\n\ndef as_strided(x, arg):\n  if any(i < 0 for i in arg[1]):\n    return torch.as_strided(x.contiguous(), arg[0], tuple(abs(i) for i in arg[1]),\n      arg[2] + sum((s-1)*a if a < 0 else 0 for (s,a) in zip(arg[0], arg[1]))).flip([i for i,a in enumerate(arg[1]) if a < 0])\n  return torch.as_strided(x.contiguous(), arg[0], arg[1], arg[2])\n\ntorch_fxn_for_op: Dict[Op, Callable] = {**base_fxn_for_op, **{\n  UnaryOps.NOOP: lambda x: x.contiguous(), UnaryOps.SQRT: lambda x: x.sqrt(), UnaryOps.EXP2: lambda x: x.exp2(), UnaryOps.LOG2: lambda x: x.log2(), UnaryOps.SIN: torch.sin,\n  UnaryOps.CAST: lambda x,y: (x.view if y[1] else x.type)(next(k for k,v in type_map.items() if v==y[0])),\n  BinaryOps.MAX: torch.maximum, BinaryOps.CMPLT: lambda x,y: (x<y).type(torch.promote_types(x.dtype, y.dtype)),\n  MovementOps.PAD: lambda x, padding: torch.nn.functional.pad(x, [item for sublist in padding[::-1] for item in sublist]),\n  TernaryOps.MULACC: einsum_mulacc(lambda s,a,b: torch.einsum(s, a.float(), b.float()).type(torch.promote_types(a.dtype, b.dtype)), lambda x: x.stride(), lambda x,s: x.expand(s)),\n  TernaryOps.WHERE: lambda x, y, z: torch.where(x != 0, y, z),\n  MovementOps.STRIDE: lambda x, arg: x[tuple(slice(None, None, abs(i)) for i in arg)].flip([i for i,a in enumerate(arg) if a < 0]),\n  MovementOps.EXPAND: lambda x, arg: x.expand(arg), MovementOps.PERMUTE: lambda x, arg: x.permute(arg),\n  MovementOps.AS_STRIDED: as_strided\n}}\n\nclass RawTorchBuffer(RawBuffer):\n  def __init__(self, size:int, dtype:DType, buf:Optional[torch.Tensor]=None): super().__init__(size, dtype, buf if buf is not None else torch.empty([size], dtype=inverse_type_map[dtype]))\n  @classmethod\n  def fromCPU(cls, x):\n    buf = torch.from_numpy(x if all(s>=0 for s in x.strides) else x.copy()).requires_grad_(False).to(device)\n    return cls(prod(x.shape), type_map[buf.dtype], buf)\n  def toCPU(self): return self._buf.cpu().numpy()\nTorchBuffer = Interpreted(RawTorchBuffer, torch_fxn_for_op, from_underlying=lambda x: RawTorchBuffer(prod(x.shape), type_map[x.dtype], x))\n", "path": "tinygrad/runtime/ops_torch.py"}], "after_files": [{"content": "import torch\nfrom typing import Dict, Callable, Optional\nfrom tinygrad.ops import UnaryOps, BinaryOps, MovementOps, TernaryOps, Op, Interpreted\nfrom tinygrad.helpers import getenv, dtypes, prod, DType\nfrom tinygrad.runtime.ops_cpu import base_fxn_for_op, einsum_mulacc\nfrom tinygrad.runtime.lib import RawBuffer\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else (\"mps\" if getenv(\"MPS\", 0) else \"cpu\"))\ntype_map = {torch.float64: dtypes.float64, torch.float16: dtypes.float16, torch.float32: dtypes.float32, torch.int8: dtypes.int8, torch.int32: dtypes.int32, torch.int64: dtypes.int64, torch.uint8: dtypes.uint8, torch.bool: dtypes.bool}\ninverse_type_map = {v:k for k,v in type_map.items()}\n\ndef as_strided(x, arg):\n  if any(i < 0 for i in arg[1]):\n    return torch.as_strided(x.contiguous(), arg[0], tuple(abs(i) for i in arg[1]),\n      arg[2] + sum((s-1)*a if a < 0 else 0 for (s,a) in zip(arg[0], arg[1]))).flip([i for i,a in enumerate(arg[1]) if a < 0])\n  return torch.as_strided(x.contiguous(), arg[0], arg[1], arg[2])\n\ntorch_fxn_for_op: Dict[Op, Callable] = {**base_fxn_for_op, **{\n  UnaryOps.NOOP: lambda x: x.contiguous(), UnaryOps.SQRT: lambda x: x.sqrt(), UnaryOps.EXP2: lambda x: x.exp2(), UnaryOps.LOG2: lambda x: x.log2(), UnaryOps.SIN: torch.sin,\n  UnaryOps.CAST: lambda x,y: (x.view if y[1] else x.type)(next(k for k,v in type_map.items() if v==y[0])),\n  BinaryOps.MAX: torch.maximum, BinaryOps.CMPLT: lambda x,y: (x<y).type(torch.promote_types(x.dtype, y.dtype)), BinaryOps.SUB: lambda x,y: torch.logical_xor(x, y) if y.dtype is torch.bool else torch.sub(x, y),\n  MovementOps.PAD: lambda x, padding: torch.nn.functional.pad(x, [item for sublist in padding[::-1] for item in sublist]),\n  TernaryOps.MULACC: einsum_mulacc(lambda s,a,b: torch.einsum(s, a.float(), b.float()).type(torch.promote_types(a.dtype, b.dtype)), lambda x: x.stride(), lambda x,s: x.expand(s)),\n  TernaryOps.WHERE: lambda x, y, z: torch.where(x != 0, y, z),\n  MovementOps.STRIDE: lambda x, arg: x[tuple(slice(None, None, abs(i)) for i in arg)].flip([i for i,a in enumerate(arg) if a < 0]),\n  MovementOps.EXPAND: lambda x, arg: x.expand(arg), MovementOps.PERMUTE: lambda x, arg: x.permute(arg),\n  MovementOps.AS_STRIDED: as_strided\n}}\n\nclass RawTorchBuffer(RawBuffer):\n  def __init__(self, size:int, dtype:DType, buf:Optional[torch.Tensor]=None): super().__init__(size, dtype, buf if buf is not None else torch.empty([size], dtype=inverse_type_map[dtype]))\n  @classmethod\n  def fromCPU(cls, x):\n    buf = torch.from_numpy(x if all(s>=0 for s in x.strides) else x.copy()).requires_grad_(False).to(device)\n    return cls(prod(x.shape), type_map[buf.dtype], buf)\n  def toCPU(self): return self._buf.cpu().numpy()\nTorchBuffer = Interpreted(RawTorchBuffer, torch_fxn_for_op, from_underlying=lambda x: RawTorchBuffer(prod(x.shape), type_map[x.dtype], x))\n", "path": "tinygrad/runtime/ops_torch.py"}]}