prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  ImportError for get_rank function
  While running the **preprocess.py** I am getting an ImportError of get_rank function from Pytorch. What I have found on the internet that it is now deprecated and is inside the deprecated module. I can send a pull request for a fix of that?
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `onmt/utils/statistics.py`
  Content:
  ```
  1 """ Statistics calculation utility """
  2 from __future__ import division
  3 import time
  4 import math
  5 import sys
  6 
  7 from torch.distributed import get_rank
  8 from onmt.utils.distributed import all_gather_list
  9 from onmt.utils.logging import logger
  10 
  11 
  12 class Statistics(object):
  13     """
  14     Accumulator for loss statistics.
  15     Currently calculates:
  16 
  17     * accuracy
  18     * perplexity
  19     * elapsed time
  20     """
  21 
  22     def __init__(self, loss=0, n_words=0, n_correct=0):
  23         self.loss = loss
  24         self.n_words = n_words
  25         self.n_correct = n_correct
  26         self.n_src_words = 0
  27         self.start_time = time.time()
  28 
  29     @staticmethod
  30     def all_gather_stats(stat, max_size=4096):
  31         """
  32         Gather a `Statistics` object accross multiple process/nodes
  33 
  34         Args:
  35             stat(:obj:Statistics): the statistics object to gather
  36                 accross all processes/nodes
  37             max_size(int): max buffer size to use
  38 
  39         Returns:
  40             `Statistics`, the update stats object
  41         """
  42         stats = Statistics.all_gather_stats_list([stat], max_size=max_size)
  43         return stats[0]
  44 
  45     @staticmethod
  46     def all_gather_stats_list(stat_list, max_size=4096):
  47         """
  48         Gather a `Statistics` list accross all processes/nodes
  49 
  50         Args:
  51             stat_list(list([`Statistics`])): list of statistics objects to
  52                 gather accross all processes/nodes
  53             max_size(int): max buffer size to use
  54 
  55         Returns:
  56             our_stats(list([`Statistics`])): list of updated stats
  57         """
  58         # Get a list of world_size lists with len(stat_list) Statistics objects
  59         all_stats = all_gather_list(stat_list, max_size=max_size)
  60 
  61         our_rank = get_rank()
  62         our_stats = all_stats[our_rank]
  63         for other_rank, stats in enumerate(all_stats):
  64             if other_rank == our_rank:
  65                 continue
  66             for i, stat in enumerate(stats):
  67                 our_stats[i].update(stat, update_n_src_words=True)
  68         return our_stats
  69 
  70     def update(self, stat, update_n_src_words=False):
  71         """
  72         Update statistics by suming values with another `Statistics` object
  73 
  74         Args:
  75             stat: another statistic object
  76             update_n_src_words(bool): whether to update (sum) `n_src_words`
  77                 or not
  78 
  79         """
  80         self.loss += stat.loss
  81         self.n_words += stat.n_words
  82         self.n_correct += stat.n_correct
  83 
  84         if update_n_src_words:
  85             self.n_src_words += stat.n_src_words
  86 
  87     def accuracy(self):
  88         """ compute accuracy """
  89         return 100 * (self.n_correct / self.n_words)
  90 
  91     def xent(self):
  92         """ compute cross entropy """
  93         return self.loss / self.n_words
  94 
  95     def ppl(self):
  96         """ compute perplexity """
  97         return math.exp(min(self.loss / self.n_words, 100))
  98 
  99     def elapsed_time(self):
  100         """ compute elapsed time """
  101         return time.time() - self.start_time
  102 
  103     def output(self, step, num_steps, learning_rate, start):
  104         """Write out statistics to stdout.
  105 
  106         Args:
  107            step (int): current step
  108            n_batch (int): total batches
  109            start (int): start time of step.
  110         """
  111         t = self.elapsed_time()
  112         logger.info(
  113             ("Step %2d/%5d; acc: %6.2f; ppl: %5.2f; xent: %4.2f; " +
  114              "lr: %7.5f; %3.0f/%3.0f tok/s; %6.0f sec")
  115             % (step, num_steps,
  116                self.accuracy(),
  117                self.ppl(),
  118                self.xent(),
  119                learning_rate,
  120                self.n_src_words / (t + 1e-5),
  121                self.n_words / (t + 1e-5),
  122                time.time() - start))
  123         sys.stdout.flush()
  124 
  125     def log_tensorboard(self, prefix, writer, learning_rate, step):
  126         """ display statistics to tensorboard """
  127         t = self.elapsed_time()
  128         writer.add_scalar(prefix + "/xent", self.xent(), step)
  129         writer.add_scalar(prefix + "/ppl", self.ppl(), step)
  130         writer.add_scalar(prefix + "/accuracy", self.accuracy(), step)
  131         writer.add_scalar(prefix + "/tgtper", self.n_words / t, step)
  132         writer.add_scalar(prefix + "/lr", learning_rate, step)
  133
  ```
  --- 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/onmt/utils/statistics.py b/onmt/utils/statistics.py
  --- a/onmt/utils/statistics.py
  +++ b/onmt/utils/statistics.py
  @@ -4,8 +4,6 @@
   import math
   import sys
   
  -from torch.distributed import get_rank
  -from onmt.utils.distributed import all_gather_list
   from onmt.utils.logging import logger
   
   
  @@ -55,6 +53,9 @@
           Returns:
               our_stats(list([`Statistics`])): list of updated stats
           """
  +        from torch.distributed import get_rank
  +        from onmt.utils.distributed import all_gather_list
  +
           # Get a list of world_size lists with len(stat_list) Statistics objects
           all_stats = all_gather_list(stat_list, max_size=max_size)
  
verification_info: {"golden_diff": "diff --git a/onmt/utils/statistics.py b/onmt/utils/statistics.py\n--- a/onmt/utils/statistics.py\n+++ b/onmt/utils/statistics.py\n@@ -4,8 +4,6 @@\n import math\n import sys\n \n-from torch.distributed import get_rank\n-from onmt.utils.distributed import all_gather_list\n from onmt.utils.logging import logger\n \n \n@@ -55,6 +53,9 @@\n         Returns:\n             our_stats(list([`Statistics`])): list of updated stats\n         \"\"\"\n+        from torch.distributed import get_rank\n+        from onmt.utils.distributed import all_gather_list\n+\n         # Get a list of world_size lists with len(stat_list) Statistics objects\n         all_stats = all_gather_list(stat_list, max_size=max_size)\n", "issue": "ImportError for get_rank function\nWhile running the **preprocess.py** I am getting an ImportError of get_rank function from Pytorch. What I have found on the internet that it is now deprecated and is inside the deprecated module. I can send a pull request for a fix of that?\n", "before_files": [{"content": "\"\"\" Statistics calculation utility \"\"\"\nfrom __future__ import division\nimport time\nimport math\nimport sys\n\nfrom torch.distributed import get_rank\nfrom onmt.utils.distributed import all_gather_list\nfrom onmt.utils.logging import logger\n\n\nclass Statistics(object):\n    \"\"\"\n    Accumulator for loss statistics.\n    Currently calculates:\n\n    * accuracy\n    * perplexity\n    * elapsed time\n    \"\"\"\n\n    def __init__(self, loss=0, n_words=0, n_correct=0):\n        self.loss = loss\n        self.n_words = n_words\n        self.n_correct = n_correct\n        self.n_src_words = 0\n        self.start_time = time.time()\n\n    @staticmethod\n    def all_gather_stats(stat, max_size=4096):\n        \"\"\"\n        Gather a `Statistics` object accross multiple process/nodes\n\n        Args:\n            stat(:obj:Statistics): the statistics object to gather\n                accross all processes/nodes\n            max_size(int): max buffer size to use\n\n        Returns:\n            `Statistics`, the update stats object\n        \"\"\"\n        stats = Statistics.all_gather_stats_list([stat], max_size=max_size)\n        return stats[0]\n\n    @staticmethod\n    def all_gather_stats_list(stat_list, max_size=4096):\n        \"\"\"\n        Gather a `Statistics` list accross all processes/nodes\n\n        Args:\n            stat_list(list([`Statistics`])): list of statistics objects to\n                gather accross all processes/nodes\n            max_size(int): max buffer size to use\n\n        Returns:\n            our_stats(list([`Statistics`])): list of updated stats\n        \"\"\"\n        # Get a list of world_size lists with len(stat_list) Statistics objects\n        all_stats = all_gather_list(stat_list, max_size=max_size)\n\n        our_rank = get_rank()\n        our_stats = all_stats[our_rank]\n        for other_rank, stats in enumerate(all_stats):\n            if other_rank == our_rank:\n                continue\n            for i, stat in enumerate(stats):\n                our_stats[i].update(stat, update_n_src_words=True)\n        return our_stats\n\n    def update(self, stat, update_n_src_words=False):\n        \"\"\"\n        Update statistics by suming values with another `Statistics` object\n\n        Args:\n            stat: another statistic object\n            update_n_src_words(bool): whether to update (sum) `n_src_words`\n                or not\n\n        \"\"\"\n        self.loss += stat.loss\n        self.n_words += stat.n_words\n        self.n_correct += stat.n_correct\n\n        if update_n_src_words:\n            self.n_src_words += stat.n_src_words\n\n    def accuracy(self):\n        \"\"\" compute accuracy \"\"\"\n        return 100 * (self.n_correct / self.n_words)\n\n    def xent(self):\n        \"\"\" compute cross entropy \"\"\"\n        return self.loss / self.n_words\n\n    def ppl(self):\n        \"\"\" compute perplexity \"\"\"\n        return math.exp(min(self.loss / self.n_words, 100))\n\n    def elapsed_time(self):\n        \"\"\" compute elapsed time \"\"\"\n        return time.time() - self.start_time\n\n    def output(self, step, num_steps, learning_rate, start):\n        \"\"\"Write out statistics to stdout.\n\n        Args:\n           step (int): current step\n           n_batch (int): total batches\n           start (int): start time of step.\n        \"\"\"\n        t = self.elapsed_time()\n        logger.info(\n            (\"Step %2d/%5d; acc: %6.2f; ppl: %5.2f; xent: %4.2f; \" +\n             \"lr: %7.5f; %3.0f/%3.0f tok/s; %6.0f sec\")\n            % (step, num_steps,\n               self.accuracy(),\n               self.ppl(),\n               self.xent(),\n               learning_rate,\n               self.n_src_words / (t + 1e-5),\n               self.n_words / (t + 1e-5),\n               time.time() - start))\n        sys.stdout.flush()\n\n    def log_tensorboard(self, prefix, writer, learning_rate, step):\n        \"\"\" display statistics to tensorboard \"\"\"\n        t = self.elapsed_time()\n        writer.add_scalar(prefix + \"/xent\", self.xent(), step)\n        writer.add_scalar(prefix + \"/ppl\", self.ppl(), step)\n        writer.add_scalar(prefix + \"/accuracy\", self.accuracy(), step)\n        writer.add_scalar(prefix + \"/tgtper\", self.n_words / t, step)\n        writer.add_scalar(prefix + \"/lr\", learning_rate, step)\n", "path": "onmt/utils/statistics.py"}], "after_files": [{"content": "\"\"\" Statistics calculation utility \"\"\"\nfrom __future__ import division\nimport time\nimport math\nimport sys\n\nfrom onmt.utils.logging import logger\n\n\nclass Statistics(object):\n    \"\"\"\n    Accumulator for loss statistics.\n    Currently calculates:\n\n    * accuracy\n    * perplexity\n    * elapsed time\n    \"\"\"\n\n    def __init__(self, loss=0, n_words=0, n_correct=0):\n        self.loss = loss\n        self.n_words = n_words\n        self.n_correct = n_correct\n        self.n_src_words = 0\n        self.start_time = time.time()\n\n    @staticmethod\n    def all_gather_stats(stat, max_size=4096):\n        \"\"\"\n        Gather a `Statistics` object accross multiple process/nodes\n\n        Args:\n            stat(:obj:Statistics): the statistics object to gather\n                accross all processes/nodes\n            max_size(int): max buffer size to use\n\n        Returns:\n            `Statistics`, the update stats object\n        \"\"\"\n        stats = Statistics.all_gather_stats_list([stat], max_size=max_size)\n        return stats[0]\n\n    @staticmethod\n    def all_gather_stats_list(stat_list, max_size=4096):\n        \"\"\"\n        Gather a `Statistics` list accross all processes/nodes\n\n        Args:\n            stat_list(list([`Statistics`])): list of statistics objects to\n                gather accross all processes/nodes\n            max_size(int): max buffer size to use\n\n        Returns:\n            our_stats(list([`Statistics`])): list of updated stats\n        \"\"\"\n        from torch.distributed import get_rank\n        from onmt.utils.distributed import all_gather_list\n\n        # Get a list of world_size lists with len(stat_list) Statistics objects\n        all_stats = all_gather_list(stat_list, max_size=max_size)\n\n        our_rank = get_rank()\n        our_stats = all_stats[our_rank]\n        for other_rank, stats in enumerate(all_stats):\n            if other_rank == our_rank:\n                continue\n            for i, stat in enumerate(stats):\n                our_stats[i].update(stat, update_n_src_words=True)\n        return our_stats\n\n    def update(self, stat, update_n_src_words=False):\n        \"\"\"\n        Update statistics by suming values with another `Statistics` object\n\n        Args:\n            stat: another statistic object\n            update_n_src_words(bool): whether to update (sum) `n_src_words`\n                or not\n\n        \"\"\"\n        self.loss += stat.loss\n        self.n_words += stat.n_words\n        self.n_correct += stat.n_correct\n\n        if update_n_src_words:\n            self.n_src_words += stat.n_src_words\n\n    def accuracy(self):\n        \"\"\" compute accuracy \"\"\"\n        return 100 * (self.n_correct / self.n_words)\n\n    def xent(self):\n        \"\"\" compute cross entropy \"\"\"\n        return self.loss / self.n_words\n\n    def ppl(self):\n        \"\"\" compute perplexity \"\"\"\n        return math.exp(min(self.loss / self.n_words, 100))\n\n    def elapsed_time(self):\n        \"\"\" compute elapsed time \"\"\"\n        return time.time() - self.start_time\n\n    def output(self, step, num_steps, learning_rate, start):\n        \"\"\"Write out statistics to stdout.\n\n        Args:\n           step (int): current step\n           n_batch (int): total batches\n           start (int): start time of step.\n        \"\"\"\n        t = self.elapsed_time()\n        logger.info(\n            (\"Step %2d/%5d; acc: %6.2f; ppl: %5.2f; xent: %4.2f; \" +\n             \"lr: %7.5f; %3.0f/%3.0f tok/s; %6.0f sec\")\n            % (step, num_steps,\n               self.accuracy(),\n               self.ppl(),\n               self.xent(),\n               learning_rate,\n               self.n_src_words / (t + 1e-5),\n               self.n_words / (t + 1e-5),\n               time.time() - start))\n        sys.stdout.flush()\n\n    def log_tensorboard(self, prefix, writer, learning_rate, step):\n        \"\"\" display statistics to tensorboard \"\"\"\n        t = self.elapsed_time()\n        writer.add_scalar(prefix + \"/xent\", self.xent(), step)\n        writer.add_scalar(prefix + \"/ppl\", self.ppl(), step)\n        writer.add_scalar(prefix + \"/accuracy\", self.accuracy(), step)\n        writer.add_scalar(prefix + \"/tgtper\", self.n_words / t, step)\n        writer.add_scalar(prefix + \"/lr\", learning_rate, step)\n", "path": "onmt/utils/statistics.py"}]}