We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
SABlock parameters when using more heads
**Describe the bug**
The number of parameters in the SABlock should be increased when increasing the number of heads (_num_heads_). However, this is not the case and limits comparability to famous scaling like ViT-S or ViT-B.

**To Reproduce**
Steps to reproduce the behavior:
```
from monai.networks.nets import ViT

def count_trainable_parameters(model: nn.Module) -> int:
    return sum(p.numel() for p in model.parameters() if p.requires_grad)

# Create ViT models with different numbers of heads
vit_b = ViT(1, 224, 16, num_heads=12)
vit_s = ViT(1, 224, 16, num_heads=6)

print("ViT with 12 heads parameters:", count_trainable_parameters(vit_b))
print("ViT with 6 heads parameters:", count_trainable_parameters(vit_s))

>>> ViT with 12 heads parameters: 90282240
>>> ViT with 6 heads parameters: 90282240
```

**Expected behavior**
The number of trainable parameters should be increased with increasing number of heads.

**Environment**
```
================================
Printing MONAI config...
================================
MONAI version: 0.8.1rc4+1384.g139182ea
Numpy version: 1.26.4
Pytorch version: 2.2.2+cpu
MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False
MONAI rev id: 139182ea52725aa3c9214dc18082b9837e32f9a2
MONAI __file__: C:\Users\<username>\MONAI\monai\__init__.py

Optional dependencies:
Pytorch Ignite version: 0.4.11
ITK version: 5.3.0
Nibabel version: 5.2.1
scikit-image version: 0.23.1
scipy version: 1.13.0
Pillow version: 10.3.0
Tensorboard version: 2.16.2
gdown version: 4.7.3
TorchVision version: 0.17.2+cpu
tqdm version: 4.66.2
lmdb version: 1.4.1
psutil version: 5.9.8
pandas version: 2.2.2
einops version: 0.7.0
transformers version: 4.39.3
mlflow version: 2.12.1
pynrrd version: 1.0.0
clearml version: 1.15.1

For details about installing the optional dependencies, please visit:
    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies


================================
Printing system config...
================================
System: Windows
Win32 version: ('10', '10.0.22621', 'SP0', 'Multiprocessor Free')
Win32 edition: Professional
Platform: Windows-10-10.0.22621-SP0
Processor: Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
Machine: AMD64
Python version: 3.11.8
Process name: python.exe
Command: ['python', '-c', 'import monai; monai.config.print_debug_info()']
Open files: [popenfile(path='C:\\Windows\\System32\\de-DE\\KernelBase.dll.mui', fd=-1), popenfile(path='C:\\Windows\\System32\\de-DE\\kernel32.dll.mui', fd=-1), popenfile(path='C:\\Windows\\System32\\de-DE\\tzres.dll.mui', fd=-1)]
Num physical CPUs: 4
Num logical CPUs: 8
Num usable CPUs: 8
CPU usage (%): [3.9, 0.2, 3.7, 0.9, 3.9, 3.9, 2.8, 32.2]
CPU freq. (MHz): 1803
Load avg. in last 1, 5, 15 mins (%): [0.0, 0.0, 0.0]
Disk usage (%): 83.1
Avg. sensor temp. (Celsius): UNKNOWN for given OS
Total physical memory (GB): 15.8
Available memory (GB): 5.5
Used memory (GB): 10.2

================================
Printing GPU config...
================================
Num GPUs: 0
Has CUDA: False
cuDNN enabled: False
NVIDIA_TF32_OVERRIDE: None
TORCH_ALLOW_TF32_CUBLAS_OVERRIDE: None
```

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `monai/networks/blocks/selfattention.py`
Content:
```
1 # Copyright (c) MONAI Consortium
2 # Licensed under the Apache License, Version 2.0 (the "License");
3 # you may not use this file except in compliance with the License.
4 # You may obtain a copy of the License at
5 #     http://www.apache.org/licenses/LICENSE-2.0
6 # Unless required by applicable law or agreed to in writing, software
7 # distributed under the License is distributed on an "AS IS" BASIS,
8 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
9 # See the License for the specific language governing permissions and
10 # limitations under the License.
11 
12 from __future__ import annotations
13 
14 import torch
15 import torch.nn as nn
16 
17 from monai.utils import optional_import
18 
19 Rearrange, _ = optional_import("einops.layers.torch", name="Rearrange")
20 
21 
22 class SABlock(nn.Module):
23     """
24     A self-attention block, based on: "Dosovitskiy et al.,
25     An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>"
26     """
27 
28     def __init__(
29         self,
30         hidden_size: int,
31         num_heads: int,
32         dropout_rate: float = 0.0,
33         qkv_bias: bool = False,
34         save_attn: bool = False,
35     ) -> None:
36         """
37         Args:
38             hidden_size (int): dimension of hidden layer.
39             num_heads (int): number of attention heads.
40             dropout_rate (float, optional): fraction of the input units to drop. Defaults to 0.0.
41             qkv_bias (bool, optional): bias term for the qkv linear layer. Defaults to False.
42             save_attn (bool, optional): to make accessible the attention matrix. Defaults to False.
43 
44         """
45 
46         super().__init__()
47 
48         if not (0 <= dropout_rate <= 1):
49             raise ValueError("dropout_rate should be between 0 and 1.")
50 
51         if hidden_size % num_heads != 0:
52             raise ValueError("hidden size should be divisible by num_heads.")
53 
54         self.num_heads = num_heads
55         self.out_proj = nn.Linear(hidden_size, hidden_size)
56         self.qkv = nn.Linear(hidden_size, hidden_size * 3, bias=qkv_bias)
57         self.input_rearrange = Rearrange("b h (qkv l d) -> qkv b l h d", qkv=3, l=num_heads)
58         self.out_rearrange = Rearrange("b h l d -> b l (h d)")
59         self.drop_output = nn.Dropout(dropout_rate)
60         self.drop_weights = nn.Dropout(dropout_rate)
61         self.head_dim = hidden_size // num_heads
62         self.scale = self.head_dim**-0.5
63         self.save_attn = save_attn
64         self.att_mat = torch.Tensor()
65 
66     def forward(self, x):
67         output = self.input_rearrange(self.qkv(x))
68         q, k, v = output[0], output[1], output[2]
69         att_mat = (torch.einsum("blxd,blyd->blxy", q, k) * self.scale).softmax(dim=-1)
70         if self.save_attn:
71             # no gradients and new tensor;
72             # https://pytorch.org/docs/stable/generated/torch.Tensor.detach.html
73             self.att_mat = att_mat.detach()
74 
75         att_mat = self.drop_weights(att_mat)
76         x = torch.einsum("bhxy,bhyd->bhxd", att_mat, v)
77         x = self.out_rearrange(x)
78         x = self.out_proj(x)
79         x = self.drop_output(x)
80         return x
81
```
--- 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/monai/networks/blocks/selfattention.py b/monai/networks/blocks/selfattention.py
--- a/monai/networks/blocks/selfattention.py
+++ b/monai/networks/blocks/selfattention.py
@@ -32,6 +32,7 @@
         dropout_rate: float = 0.0,
         qkv_bias: bool = False,
         save_attn: bool = False,
+        dim_head: int | None = None,
     ) -> None:
         """
         Args:
@@ -40,6 +41,7 @@
             dropout_rate (float, optional): fraction of the input units to drop. Defaults to 0.0.
             qkv_bias (bool, optional): bias term for the qkv linear layer. Defaults to False.
             save_attn (bool, optional): to make accessible the attention matrix. Defaults to False.
+            dim_head (int, optional): dimension of each head. Defaults to hidden_size // num_heads.
 
         """
 
@@ -52,14 +54,16 @@
             raise ValueError("hidden size should be divisible by num_heads.")
 
         self.num_heads = num_heads
-        self.out_proj = nn.Linear(hidden_size, hidden_size)
-        self.qkv = nn.Linear(hidden_size, hidden_size * 3, bias=qkv_bias)
+        self.dim_head = hidden_size // num_heads if dim_head is None else dim_head
+        self.inner_dim = self.dim_head * num_heads
+
+        self.out_proj = nn.Linear(self.inner_dim, hidden_size)
+        self.qkv = nn.Linear(hidden_size, self.inner_dim * 3, bias=qkv_bias)
         self.input_rearrange = Rearrange("b h (qkv l d) -> qkv b l h d", qkv=3, l=num_heads)
         self.out_rearrange = Rearrange("b h l d -> b l (h d)")
         self.drop_output = nn.Dropout(dropout_rate)
         self.drop_weights = nn.Dropout(dropout_rate)
-        self.head_dim = hidden_size // num_heads
-        self.scale = self.head_dim**-0.5
+        self.scale = self.dim_head**-0.5
         self.save_attn = save_attn
         self.att_mat = torch.Tensor()

{"golden_diff": "diff --git a/monai/networks/blocks/selfattention.py b/monai/networks/blocks/selfattention.py\n--- a/monai/networks/blocks/selfattention.py\n+++ b/monai/networks/blocks/selfattention.py\n@@ -32,6 +32,7 @@\n         dropout_rate: float = 0.0,\n         qkv_bias: bool = False,\n         save_attn: bool = False,\n+        dim_head: int | None = None,\n     ) -> None:\n         \"\"\"\n         Args:\n@@ -40,6 +41,7 @@\n             dropout_rate (float, optional): fraction of the input units to drop. Defaults to 0.0.\n             qkv_bias (bool, optional): bias term for the qkv linear layer. Defaults to False.\n             save_attn (bool, optional): to make accessible the attention matrix. Defaults to False.\n+            dim_head (int, optional): dimension of each head. Defaults to hidden_size // num_heads.\n \n         \"\"\"\n \n@@ -52,14 +54,16 @@\n             raise ValueError(\"hidden size should be divisible by num_heads.\")\n \n         self.num_heads = num_heads\n-        self.out_proj = nn.Linear(hidden_size, hidden_size)\n-        self.qkv = nn.Linear(hidden_size, hidden_size * 3, bias=qkv_bias)\n+        self.dim_head = hidden_size // num_heads if dim_head is None else dim_head\n+        self.inner_dim = self.dim_head * num_heads\n+\n+        self.out_proj = nn.Linear(self.inner_dim, hidden_size)\n+        self.qkv = nn.Linear(hidden_size, self.inner_dim * 3, bias=qkv_bias)\n         self.input_rearrange = Rearrange(\"b h (qkv l d) -> qkv b l h d\", qkv=3, l=num_heads)\n         self.out_rearrange = Rearrange(\"b h l d -> b l (h d)\")\n         self.drop_output = nn.Dropout(dropout_rate)\n         self.drop_weights = nn.Dropout(dropout_rate)\n-        self.head_dim = hidden_size // num_heads\n-        self.scale = self.head_dim**-0.5\n+        self.scale = self.dim_head**-0.5\n         self.save_attn = save_attn\n         self.att_mat = torch.Tensor()\n", "issue": "SABlock parameters when using more heads\n**Describe the bug**\r\nThe number of parameters in the SABlock should be increased when increasing the number of heads (_num_heads_). However, this is not the case and limits comparability to famous scaling like ViT-S or ViT-B.\r\n\r\n**To Reproduce**\r\nSteps to reproduce the behavior:\r\n```\r\nfrom monai.networks.nets import ViT\r\n\r\ndef count_trainable_parameters(model: nn.Module) -> int:\r\n    return sum(p.numel() for p in model.parameters() if p.requires_grad)\r\n\r\n# Create ViT models with different numbers of heads\r\nvit_b = ViT(1, 224, 16, num_heads=12)\r\nvit_s = ViT(1, 224, 16, num_heads=6)\r\n\r\nprint(\"ViT with 12 heads parameters:\", count_trainable_parameters(vit_b))\r\nprint(\"ViT with 6 heads parameters:\", count_trainable_parameters(vit_s))\r\n\r\n>>> ViT with 12 heads parameters: 90282240\r\n>>> ViT with 6 heads parameters: 90282240\r\n```\r\n\r\n**Expected behavior**\r\nThe number of trainable parameters should be increased with increasing number of heads.\r\n\r\n**Environment**\r\n```\r\n================================\r\nPrinting MONAI config...\r\n================================\r\nMONAI version: 0.8.1rc4+1384.g139182ea\r\nNumpy version: 1.26.4\r\nPytorch version: 2.2.2+cpu\r\nMONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\r\nMONAI rev id: 139182ea52725aa3c9214dc18082b9837e32f9a2\r\nMONAI __file__: C:\\Users\\<username>\\MONAI\\monai\\__init__.py\r\n\r\nOptional dependencies:\r\nPytorch Ignite version: 0.4.11\r\nITK version: 5.3.0\r\nNibabel version: 5.2.1\r\nscikit-image version: 0.23.1\r\nscipy version: 1.13.0\r\nPillow version: 10.3.0\r\nTensorboard version: 2.16.2\r\ngdown version: 4.7.3\r\nTorchVision version: 0.17.2+cpu\r\ntqdm version: 4.66.2\r\nlmdb version: 1.4.1\r\npsutil version: 5.9.8\r\npandas version: 2.2.2\r\neinops version: 0.7.0\r\ntransformers version: 4.39.3\r\nmlflow version: 2.12.1\r\npynrrd version: 1.0.0\r\nclearml version: 1.15.1\r\n\r\nFor details about installing the optional dependencies, please visit:\r\n    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\r\n\r\n\r\n================================\r\nPrinting system config...\r\n================================\r\nSystem: Windows\r\nWin32 version: ('10', '10.0.22621', 'SP0', 'Multiprocessor Free')\r\nWin32 edition: Professional\r\nPlatform: Windows-10-10.0.22621-SP0\r\nProcessor: Intel64 Family 6 Model 142 Stepping 12, GenuineIntel\r\nMachine: AMD64\r\nPython version: 3.11.8\r\nProcess name: python.exe\r\nCommand: ['python', '-c', 'import monai; monai.config.print_debug_info()']\r\nOpen files: [popenfile(path='C:\\\\Windows\\\\System32\\\\de-DE\\\\KernelBase.dll.mui', fd=-1), popenfile(path='C:\\\\Windows\\\\System32\\\\de-DE\\\\kernel32.dll.mui', fd=-1), popenfile(path='C:\\\\Windows\\\\System32\\\\de-DE\\\\tzres.dll.mui', fd=-1)]\r\nNum physical CPUs: 4\r\nNum logical CPUs: 8\r\nNum usable CPUs: 8\r\nCPU usage (%): [3.9, 0.2, 3.7, 0.9, 3.9, 3.9, 2.8, 32.2]\r\nCPU freq. (MHz): 1803\r\nLoad avg. in last 1, 5, 15 mins (%): [0.0, 0.0, 0.0]\r\nDisk usage (%): 83.1\r\nAvg. sensor temp. (Celsius): UNKNOWN for given OS\r\nTotal physical memory (GB): 15.8\r\nAvailable memory (GB): 5.5\r\nUsed memory (GB): 10.2\r\n\r\n================================\r\nPrinting GPU config...\r\n================================\r\nNum GPUs: 0\r\nHas CUDA: False\r\ncuDNN enabled: False\r\nNVIDIA_TF32_OVERRIDE: None\r\nTORCH_ALLOW_TF32_CUBLAS_OVERRIDE: None\r\n```\n", "before_files": [{"content": "# Copyright (c) MONAI Consortium\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#     http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom __future__ import annotations\n\nimport torch\nimport torch.nn as nn\n\nfrom monai.utils import optional_import\n\nRearrange, _ = optional_import(\"einops.layers.torch\", name=\"Rearrange\")\n\n\nclass SABlock(nn.Module):\n    \"\"\"\n    A self-attention block, based on: \"Dosovitskiy et al.,\n    An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>\"\n    \"\"\"\n\n    def __init__(\n        self,\n        hidden_size: int,\n        num_heads: int,\n        dropout_rate: float = 0.0,\n        qkv_bias: bool = False,\n        save_attn: bool = False,\n    ) -> None:\n        \"\"\"\n        Args:\n            hidden_size (int): dimension of hidden layer.\n            num_heads (int): number of attention heads.\n            dropout_rate (float, optional): fraction of the input units to drop. Defaults to 0.0.\n            qkv_bias (bool, optional): bias term for the qkv linear layer. Defaults to False.\n            save_attn (bool, optional): to make accessible the attention matrix. Defaults to False.\n\n        \"\"\"\n\n        super().__init__()\n\n        if not (0 <= dropout_rate <= 1):\n            raise ValueError(\"dropout_rate should be between 0 and 1.\")\n\n        if hidden_size % num_heads != 0:\n            raise ValueError(\"hidden size should be divisible by num_heads.\")\n\n        self.num_heads = num_heads\n        self.out_proj = nn.Linear(hidden_size, hidden_size)\n        self.qkv = nn.Linear(hidden_size, hidden_size * 3, bias=qkv_bias)\n        self.input_rearrange = Rearrange(\"b h (qkv l d) -> qkv b l h d\", qkv=3, l=num_heads)\n        self.out_rearrange = Rearrange(\"b h l d -> b l (h d)\")\n        self.drop_output = nn.Dropout(dropout_rate)\n        self.drop_weights = nn.Dropout(dropout_rate)\n        self.head_dim = hidden_size // num_heads\n        self.scale = self.head_dim**-0.5\n        self.save_attn = save_attn\n        self.att_mat = torch.Tensor()\n\n    def forward(self, x):\n        output = self.input_rearrange(self.qkv(x))\n        q, k, v = output[0], output[1], output[2]\n        att_mat = (torch.einsum(\"blxd,blyd->blxy\", q, k) * self.scale).softmax(dim=-1)\n        if self.save_attn:\n            # no gradients and new tensor;\n            # https://pytorch.org/docs/stable/generated/torch.Tensor.detach.html\n            self.att_mat = att_mat.detach()\n\n        att_mat = self.drop_weights(att_mat)\n        x = torch.einsum(\"bhxy,bhyd->bhxd\", att_mat, v)\n        x = self.out_rearrange(x)\n        x = self.out_proj(x)\n        x = self.drop_output(x)\n        return x\n", "path": "monai/networks/blocks/selfattention.py"}], "after_files": [{"content": "# Copyright (c) MONAI Consortium\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#     http://www.apache.org/licenses/LICENSE-2.0\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom __future__ import annotations\n\nimport torch\nimport torch.nn as nn\n\nfrom monai.utils import optional_import\n\nRearrange, _ = optional_import(\"einops.layers.torch\", name=\"Rearrange\")\n\n\nclass SABlock(nn.Module):\n    \"\"\"\n    A self-attention block, based on: \"Dosovitskiy et al.,\n    An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>\"\n    \"\"\"\n\n    def __init__(\n        self,\n        hidden_size: int,\n        num_heads: int,\n        dropout_rate: float = 0.0,\n        qkv_bias: bool = False,\n        save_attn: bool = False,\n        dim_head: int | None = None,\n    ) -> None:\n        \"\"\"\n        Args:\n            hidden_size (int): dimension of hidden layer.\n            num_heads (int): number of attention heads.\n            dropout_rate (float, optional): fraction of the input units to drop. Defaults to 0.0.\n            qkv_bias (bool, optional): bias term for the qkv linear layer. Defaults to False.\n            save_attn (bool, optional): to make accessible the attention matrix. Defaults to False.\n            dim_head (int, optional): dimension of each head. Defaults to hidden_size // num_heads.\n\n        \"\"\"\n\n        super().__init__()\n\n        if not (0 <= dropout_rate <= 1):\n            raise ValueError(\"dropout_rate should be between 0 and 1.\")\n\n        if hidden_size % num_heads != 0:\n            raise ValueError(\"hidden size should be divisible by num_heads.\")\n\n        self.num_heads = num_heads\n        self.dim_head = hidden_size // num_heads if dim_head is None else dim_head\n        self.inner_dim = self.dim_head * num_heads\n\n        self.out_proj = nn.Linear(self.inner_dim, hidden_size)\n        self.qkv = nn.Linear(hidden_size, self.inner_dim * 3, bias=qkv_bias)\n        self.input_rearrange = Rearrange(\"b h (qkv l d) -> qkv b l h d\", qkv=3, l=num_heads)\n        self.out_rearrange = Rearrange(\"b h l d -> b l (h d)\")\n        self.drop_output = nn.Dropout(dropout_rate)\n        self.drop_weights = nn.Dropout(dropout_rate)\n        self.scale = self.dim_head**-0.5\n        self.save_attn = save_attn\n        self.att_mat = torch.Tensor()\n\n    def forward(self, x):\n        output = self.input_rearrange(self.qkv(x))\n        q, k, v = output[0], output[1], output[2]\n        att_mat = (torch.einsum(\"blxd,blyd->blxy\", q, k) * self.scale).softmax(dim=-1)\n        if self.save_attn:\n            # no gradients and new tensor;\n            # https://pytorch.org/docs/stable/generated/torch.Tensor.detach.html\n            self.att_mat = att_mat.detach()\n\n        att_mat = self.drop_weights(att_mat)\n        x = torch.einsum(\"bhxy,bhyd->bhxd\", att_mat, v)\n        x = self.out_rearrange(x)\n        x = self.out_proj(x)\n        x = self.drop_output(x)\n        return x\n", "path": "monai/networks/blocks/selfattention.py"}]}