prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Bug in PMSNLoss
  Nice implementation of the [PMSNLoss](https://github.com/lightly-ai/lightly/blob/ddfed3c4dc03a8d2722df24bfa537d24ac80bde6/lightly/loss/pmsn_loss.py)! But the computation of Kullback-Leibler divergence missed `.log()` in Line 71&142.
  
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `lightly/loss/pmsn_loss.py`
  Content:
  ```
  1 from typing import Callable
  2 
  3 import torch
  4 import torch.nn.functional as F
  5 from torch import Tensor
  6 
  7 from lightly.loss.msn_loss import MSNLoss
  8 
  9 
  10 class PMSNLoss(MSNLoss):
  11     """Implementation of the loss function from PMSN [0] using a power law target
  12     distribution.
  13 
  14     - [0]: Prior Matching for Siamese Networks, 2022, https://arxiv.org/abs/2210.07277
  15 
  16     Attributes:
  17         temperature:
  18             Similarities between anchors and targets are scaled by the inverse of
  19             the temperature. Must be in (0, inf).
  20         sinkhorn_iterations:
  21             Number of sinkhorn normalization iterations on the targets.
  22         regularization_weight:
  23             Weight factor lambda by which the regularization loss is scaled. Set to 0
  24             to disable regularization.
  25         power_law_exponent:
  26             Exponent for power law distribution. Entry k of the distribution is
  27             proportional to (1 / k) ^ power_law_exponent, with k ranging from 1 to dim + 1.
  28         gather_distributed:
  29             If True, then target probabilities are gathered from all GPUs.
  30 
  31      Examples:
  32 
  33         >>> # initialize loss function
  34         >>> loss_fn = PMSNLoss()
  35         >>>
  36         >>> # generate anchors and targets of images
  37         >>> anchors = transforms(images)
  38         >>> targets = transforms(images)
  39         >>>
  40         >>> # feed through PMSN model
  41         >>> anchors_out = model(anchors)
  42         >>> targets_out = model.target(targets)
  43         >>>
  44         >>> # calculate loss
  45         >>> loss = loss_fn(anchors_out, targets_out, prototypes=model.prototypes)
  46     """
  47 
  48     def __init__(
  49         self,
  50         temperature: float = 0.1,
  51         sinkhorn_iterations: int = 3,
  52         regularization_weight: float = 1,
  53         power_law_exponent: float = 0.25,
  54         gather_distributed: bool = False,
  55     ):
  56         super().__init__(
  57             temperature=temperature,
  58             sinkhorn_iterations=sinkhorn_iterations,
  59             regularization_weight=regularization_weight,
  60             gather_distributed=gather_distributed,
  61         )
  62         self.power_law_exponent = power_law_exponent
  63 
  64     def regularization_loss(self, mean_anchor_probs: Tensor) -> Tensor:
  65         """Calculates regularization loss with a power law target distribution."""
  66         power_dist = _power_law_distribution(
  67             size=mean_anchor_probs.shape[0],
  68             exponent=self.power_law_exponent,
  69             device=mean_anchor_probs.device,
  70         )
  71         loss = F.kl_div(input=mean_anchor_probs, target=power_dist, reduction="sum")
  72         return loss
  73 
  74 
  75 class PMSNCustomLoss(MSNLoss):
  76     """Implementation of the loss function from PMSN [0] with a custom target
  77     distribution.
  78 
  79     - [0]: Prior Matching for Siamese Networks, 2022, https://arxiv.org/abs/2210.07277
  80 
  81     Attributes:
  82         target_distribution:
  83             A function that takes the mean anchor probabilities tensor with shape (dim,)
  84             as input and returns a target probability distribution tensor with the same
  85             shape. The returned distribution should sum up to one. The final
  86             regularization loss is calculated as KL(mean_anchor_probs, target_dist)
  87             where KL is the Kullback-Leibler divergence.
  88         temperature:
  89             Similarities between anchors and targets are scaled by the inverse of
  90             the temperature. Must be in (0, inf).
  91         sinkhorn_iterations:
  92             Number of sinkhorn normalization iterations on the targets.
  93         regularization_weight:
  94             Weight factor lambda by which the regularization loss is scaled. Set to 0
  95             to disable regularization.
  96         gather_distributed:
  97             If True, then target probabilities are gathered from all GPUs.
  98 
  99      Examples:
  100 
  101         >>> # define custom target distribution
  102         >>> def my_uniform_distribution(mean_anchor_probabilities: Tensor) -> Tensor:
  103         >>>     dim = mean_anchor_probabilities.shape[0]
  104         >>>     return mean_anchor_probabilities.new_ones(dim) / dim
  105         >>>
  106         >>> # initialize loss function
  107         >>> loss_fn = PMSNCustomLoss(target_distribution=my_uniform_distribution)
  108         >>>
  109         >>> # generate anchors and targets of images
  110         >>> anchors = transforms(images)
  111         >>> targets = transforms(images)
  112         >>>
  113         >>> # feed through PMSN model
  114         >>> anchors_out = model(anchors)
  115         >>> targets_out = model.target(targets)
  116         >>>
  117         >>> # calculate loss
  118         >>> loss = loss_fn(anchors_out, targets_out, prototypes=model.prototypes)
  119     """
  120 
  121     def __init__(
  122         self,
  123         target_distribution: Callable[[Tensor], Tensor],
  124         temperature: float = 0.1,
  125         sinkhorn_iterations: int = 3,
  126         regularization_weight: float = 1,
  127         gather_distributed: bool = False,
  128     ):
  129         super().__init__(
  130             temperature=temperature,
  131             sinkhorn_iterations=sinkhorn_iterations,
  132             regularization_weight=regularization_weight,
  133             gather_distributed=gather_distributed,
  134         )
  135         self.target_distribution = target_distribution
  136 
  137     def regularization_loss(self, mean_anchor_probs: Tensor) -> Tensor:
  138         """Calculates regularization loss with a custom target distribution."""
  139         target_dist = self.target_distribution(mean_anchor_probs).to(
  140             mean_anchor_probs.device
  141         )
  142         loss = F.kl_div(input=mean_anchor_probs, target=target_dist, reduction="sum")
  143         return loss
  144 
  145 
  146 def _power_law_distribution(size: int, exponent: float, device: torch.device) -> Tensor:
  147     """Returns a power law distribution summing up to 1."""
  148     k = torch.arange(1, size + 1, device=device)
  149     power_dist = k ** (-exponent)
  150     power_dist = power_dist / power_dist.sum()
  151     return power_dist
  152
  ```
  --- 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/lightly/loss/pmsn_loss.py b/lightly/loss/pmsn_loss.py
  --- a/lightly/loss/pmsn_loss.py
  +++ b/lightly/loss/pmsn_loss.py
  @@ -68,7 +68,9 @@
               exponent=self.power_law_exponent,
               device=mean_anchor_probs.device,
           )
  -        loss = F.kl_div(input=mean_anchor_probs, target=power_dist, reduction="sum")
  +        loss = F.kl_div(
  +            input=mean_anchor_probs.log(), target=power_dist, reduction="sum"
  +        )
           return loss
   
   
  @@ -139,7 +141,9 @@
           target_dist = self.target_distribution(mean_anchor_probs).to(
               mean_anchor_probs.device
           )
  -        loss = F.kl_div(input=mean_anchor_probs, target=target_dist, reduction="sum")
  +        loss = F.kl_div(
  +            input=mean_anchor_probs.log(), target=target_dist, reduction="sum"
  +        )
           return loss
  
verification_info: {"golden_diff": "diff --git a/lightly/loss/pmsn_loss.py b/lightly/loss/pmsn_loss.py\n--- a/lightly/loss/pmsn_loss.py\n+++ b/lightly/loss/pmsn_loss.py\n@@ -68,7 +68,9 @@\n             exponent=self.power_law_exponent,\n             device=mean_anchor_probs.device,\n         )\n-        loss = F.kl_div(input=mean_anchor_probs, target=power_dist, reduction=\"sum\")\n+        loss = F.kl_div(\n+            input=mean_anchor_probs.log(), target=power_dist, reduction=\"sum\"\n+        )\n         return loss\n \n \n@@ -139,7 +141,9 @@\n         target_dist = self.target_distribution(mean_anchor_probs).to(\n             mean_anchor_probs.device\n         )\n-        loss = F.kl_div(input=mean_anchor_probs, target=target_dist, reduction=\"sum\")\n+        loss = F.kl_div(\n+            input=mean_anchor_probs.log(), target=target_dist, reduction=\"sum\"\n+        )\n         return loss\n", "issue": "Bug in PMSNLoss\nNice implementation of the [PMSNLoss](https://github.com/lightly-ai/lightly/blob/ddfed3c4dc03a8d2722df24bfa537d24ac80bde6/lightly/loss/pmsn_loss.py)! But the computation of Kullback-Leibler divergence missed `.log()` in Line 71&142.\r\n\n", "before_files": [{"content": "from typing import Callable\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import Tensor\n\nfrom lightly.loss.msn_loss import MSNLoss\n\n\nclass PMSNLoss(MSNLoss):\n    \"\"\"Implementation of the loss function from PMSN [0] using a power law target\n    distribution.\n\n    - [0]: Prior Matching for Siamese Networks, 2022, https://arxiv.org/abs/2210.07277\n\n    Attributes:\n        temperature:\n            Similarities between anchors and targets are scaled by the inverse of\n            the temperature. Must be in (0, inf).\n        sinkhorn_iterations:\n            Number of sinkhorn normalization iterations on the targets.\n        regularization_weight:\n            Weight factor lambda by which the regularization loss is scaled. Set to 0\n            to disable regularization.\n        power_law_exponent:\n            Exponent for power law distribution. Entry k of the distribution is\n            proportional to (1 / k) ^ power_law_exponent, with k ranging from 1 to dim + 1.\n        gather_distributed:\n            If True, then target probabilities are gathered from all GPUs.\n\n     Examples:\n\n        >>> # initialize loss function\n        >>> loss_fn = PMSNLoss()\n        >>>\n        >>> # generate anchors and targets of images\n        >>> anchors = transforms(images)\n        >>> targets = transforms(images)\n        >>>\n        >>> # feed through PMSN model\n        >>> anchors_out = model(anchors)\n        >>> targets_out = model.target(targets)\n        >>>\n        >>> # calculate loss\n        >>> loss = loss_fn(anchors_out, targets_out, prototypes=model.prototypes)\n    \"\"\"\n\n    def __init__(\n        self,\n        temperature: float = 0.1,\n        sinkhorn_iterations: int = 3,\n        regularization_weight: float = 1,\n        power_law_exponent: float = 0.25,\n        gather_distributed: bool = False,\n    ):\n        super().__init__(\n            temperature=temperature,\n            sinkhorn_iterations=sinkhorn_iterations,\n            regularization_weight=regularization_weight,\n            gather_distributed=gather_distributed,\n        )\n        self.power_law_exponent = power_law_exponent\n\n    def regularization_loss(self, mean_anchor_probs: Tensor) -> Tensor:\n        \"\"\"Calculates regularization loss with a power law target distribution.\"\"\"\n        power_dist = _power_law_distribution(\n            size=mean_anchor_probs.shape[0],\n            exponent=self.power_law_exponent,\n            device=mean_anchor_probs.device,\n        )\n        loss = F.kl_div(input=mean_anchor_probs, target=power_dist, reduction=\"sum\")\n        return loss\n\n\nclass PMSNCustomLoss(MSNLoss):\n    \"\"\"Implementation of the loss function from PMSN [0] with a custom target\n    distribution.\n\n    - [0]: Prior Matching for Siamese Networks, 2022, https://arxiv.org/abs/2210.07277\n\n    Attributes:\n        target_distribution:\n            A function that takes the mean anchor probabilities tensor with shape (dim,)\n            as input and returns a target probability distribution tensor with the same\n            shape. The returned distribution should sum up to one. The final\n            regularization loss is calculated as KL(mean_anchor_probs, target_dist)\n            where KL is the Kullback-Leibler divergence.\n        temperature:\n            Similarities between anchors and targets are scaled by the inverse of\n            the temperature. Must be in (0, inf).\n        sinkhorn_iterations:\n            Number of sinkhorn normalization iterations on the targets.\n        regularization_weight:\n            Weight factor lambda by which the regularization loss is scaled. Set to 0\n            to disable regularization.\n        gather_distributed:\n            If True, then target probabilities are gathered from all GPUs.\n\n     Examples:\n\n        >>> # define custom target distribution\n        >>> def my_uniform_distribution(mean_anchor_probabilities: Tensor) -> Tensor:\n        >>>     dim = mean_anchor_probabilities.shape[0]\n        >>>     return mean_anchor_probabilities.new_ones(dim) / dim\n        >>>\n        >>> # initialize loss function\n        >>> loss_fn = PMSNCustomLoss(target_distribution=my_uniform_distribution)\n        >>>\n        >>> # generate anchors and targets of images\n        >>> anchors = transforms(images)\n        >>> targets = transforms(images)\n        >>>\n        >>> # feed through PMSN model\n        >>> anchors_out = model(anchors)\n        >>> targets_out = model.target(targets)\n        >>>\n        >>> # calculate loss\n        >>> loss = loss_fn(anchors_out, targets_out, prototypes=model.prototypes)\n    \"\"\"\n\n    def __init__(\n        self,\n        target_distribution: Callable[[Tensor], Tensor],\n        temperature: float = 0.1,\n        sinkhorn_iterations: int = 3,\n        regularization_weight: float = 1,\n        gather_distributed: bool = False,\n    ):\n        super().__init__(\n            temperature=temperature,\n            sinkhorn_iterations=sinkhorn_iterations,\n            regularization_weight=regularization_weight,\n            gather_distributed=gather_distributed,\n        )\n        self.target_distribution = target_distribution\n\n    def regularization_loss(self, mean_anchor_probs: Tensor) -> Tensor:\n        \"\"\"Calculates regularization loss with a custom target distribution.\"\"\"\n        target_dist = self.target_distribution(mean_anchor_probs).to(\n            mean_anchor_probs.device\n        )\n        loss = F.kl_div(input=mean_anchor_probs, target=target_dist, reduction=\"sum\")\n        return loss\n\n\ndef _power_law_distribution(size: int, exponent: float, device: torch.device) -> Tensor:\n    \"\"\"Returns a power law distribution summing up to 1.\"\"\"\n    k = torch.arange(1, size + 1, device=device)\n    power_dist = k ** (-exponent)\n    power_dist = power_dist / power_dist.sum()\n    return power_dist\n", "path": "lightly/loss/pmsn_loss.py"}], "after_files": [{"content": "from typing import Callable\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import Tensor\n\nfrom lightly.loss.msn_loss import MSNLoss\n\n\nclass PMSNLoss(MSNLoss):\n    \"\"\"Implementation of the loss function from PMSN [0] using a power law target\n    distribution.\n\n    - [0]: Prior Matching for Siamese Networks, 2022, https://arxiv.org/abs/2210.07277\n\n    Attributes:\n        temperature:\n            Similarities between anchors and targets are scaled by the inverse of\n            the temperature. Must be in (0, inf).\n        sinkhorn_iterations:\n            Number of sinkhorn normalization iterations on the targets.\n        regularization_weight:\n            Weight factor lambda by which the regularization loss is scaled. Set to 0\n            to disable regularization.\n        power_law_exponent:\n            Exponent for power law distribution. Entry k of the distribution is\n            proportional to (1 / k) ^ power_law_exponent, with k ranging from 1 to dim + 1.\n        gather_distributed:\n            If True, then target probabilities are gathered from all GPUs.\n\n     Examples:\n\n        >>> # initialize loss function\n        >>> loss_fn = PMSNLoss()\n        >>>\n        >>> # generate anchors and targets of images\n        >>> anchors = transforms(images)\n        >>> targets = transforms(images)\n        >>>\n        >>> # feed through PMSN model\n        >>> anchors_out = model(anchors)\n        >>> targets_out = model.target(targets)\n        >>>\n        >>> # calculate loss\n        >>> loss = loss_fn(anchors_out, targets_out, prototypes=model.prototypes)\n    \"\"\"\n\n    def __init__(\n        self,\n        temperature: float = 0.1,\n        sinkhorn_iterations: int = 3,\n        regularization_weight: float = 1,\n        power_law_exponent: float = 0.25,\n        gather_distributed: bool = False,\n    ):\n        super().__init__(\n            temperature=temperature,\n            sinkhorn_iterations=sinkhorn_iterations,\n            regularization_weight=regularization_weight,\n            gather_distributed=gather_distributed,\n        )\n        self.power_law_exponent = power_law_exponent\n\n    def regularization_loss(self, mean_anchor_probs: Tensor) -> Tensor:\n        \"\"\"Calculates regularization loss with a power law target distribution.\"\"\"\n        power_dist = _power_law_distribution(\n            size=mean_anchor_probs.shape[0],\n            exponent=self.power_law_exponent,\n            device=mean_anchor_probs.device,\n        )\n        loss = F.kl_div(\n            input=mean_anchor_probs.log(), target=power_dist, reduction=\"sum\"\n        )\n        return loss\n\n\nclass PMSNCustomLoss(MSNLoss):\n    \"\"\"Implementation of the loss function from PMSN [0] with a custom target\n    distribution.\n\n    - [0]: Prior Matching for Siamese Networks, 2022, https://arxiv.org/abs/2210.07277\n\n    Attributes:\n        target_distribution:\n            A function that takes the mean anchor probabilities tensor with shape (dim,)\n            as input and returns a target probability distribution tensor with the same\n            shape. The returned distribution should sum up to one. The final\n            regularization loss is calculated as KL(mean_anchor_probs, target_dist)\n            where KL is the Kullback-Leibler divergence.\n        temperature:\n            Similarities between anchors and targets are scaled by the inverse of\n            the temperature. Must be in (0, inf).\n        sinkhorn_iterations:\n            Number of sinkhorn normalization iterations on the targets.\n        regularization_weight:\n            Weight factor lambda by which the regularization loss is scaled. Set to 0\n            to disable regularization.\n        gather_distributed:\n            If True, then target probabilities are gathered from all GPUs.\n\n     Examples:\n\n        >>> # define custom target distribution\n        >>> def my_uniform_distribution(mean_anchor_probabilities: Tensor) -> Tensor:\n        >>>     dim = mean_anchor_probabilities.shape[0]\n        >>>     return mean_anchor_probabilities.new_ones(dim) / dim\n        >>>\n        >>> # initialize loss function\n        >>> loss_fn = PMSNCustomLoss(target_distribution=my_uniform_distribution)\n        >>>\n        >>> # generate anchors and targets of images\n        >>> anchors = transforms(images)\n        >>> targets = transforms(images)\n        >>>\n        >>> # feed through PMSN model\n        >>> anchors_out = model(anchors)\n        >>> targets_out = model.target(targets)\n        >>>\n        >>> # calculate loss\n        >>> loss = loss_fn(anchors_out, targets_out, prototypes=model.prototypes)\n    \"\"\"\n\n    def __init__(\n        self,\n        target_distribution: Callable[[Tensor], Tensor],\n        temperature: float = 0.1,\n        sinkhorn_iterations: int = 3,\n        regularization_weight: float = 1,\n        gather_distributed: bool = False,\n    ):\n        super().__init__(\n            temperature=temperature,\n            sinkhorn_iterations=sinkhorn_iterations,\n            regularization_weight=regularization_weight,\n            gather_distributed=gather_distributed,\n        )\n        self.target_distribution = target_distribution\n\n    def regularization_loss(self, mean_anchor_probs: Tensor) -> Tensor:\n        \"\"\"Calculates regularization loss with a custom target distribution.\"\"\"\n        target_dist = self.target_distribution(mean_anchor_probs).to(\n            mean_anchor_probs.device\n        )\n        loss = F.kl_div(\n            input=mean_anchor_probs.log(), target=target_dist, reduction=\"sum\"\n        )\n        return loss\n\n\ndef _power_law_distribution(size: int, exponent: float, device: torch.device) -> Tensor:\n    \"\"\"Returns a power law distribution summing up to 1.\"\"\"\n    k = torch.arange(1, size + 1, device=device)\n    power_dist = k ** (-exponent)\n    power_dist = power_dist / power_dist.sum()\n    return power_dist\n", "path": "lightly/loss/pmsn_loss.py"}]}