Fix typos in messages under torch (pytorch#89049)

This PR fixes typos of messages in `.py` files under torch directory.
Only in `torch/onnx/symbolic_opset16.py`, fix a typo in comment to make the operator name correct.

Pull Request resolved: pytorch#89049
Approved by: https://github.com/lezcano

Author: kiszk

torch/_refs/nn/functional/__init__.py

@@ -595,7 +595,7 @@ def _nll_loss_nd(
 ) -> TensorLikeType:
     utils.check(
         input.ndim > 0 and input.ndim <= 3,
-        lambda: f"Expected input dimension to be either [1, 2, 3] but recieved {input.ndim}.",
+        lambda: f"Expected input dimension to be either [1, 2, 3] but received {input.ndim}.",
     )
 
     utils.check(

torch/ao/nn/intrinsic/qat/modules/linear_fused.py

@@ -35,7 +35,7 @@ def __init__(self,
                  freeze_bn=False,
                  qconfig=None):
         nn.modules.linear.Linear.__init__(self, in_features, out_features, bias)
-        assert qconfig, 'qconfig must be provded for QAT module'
+        assert qconfig, 'qconfig must be provided for QAT module'
         self.qconfig = qconfig
         self.freeze_bn = freeze_bn if self.training else True
         self.bn = nn.BatchNorm1d(out_features, eps, momentum, True, True)

torch/ao/quantization/fx/_model_report/model_report.py

@@ -385,7 +385,7 @@ def _reformat_reports_for_visualizer(self) -> OrderedDict:
                         module_fqns_to_features[module_fqn] = {**new_info, **present_info}
                     else:
                         error_str = "You have the same key with different values across detectors. "
-                        error_str += "Someone incorrectly implemented a detector with conflicting keys to exisiting detectors."
+                        error_str += "Someone incorrectly implemented a detector with conflicting keys to existing detectors."
                         raise ValueError(error_str)
                 else:
                     # we just set it

torch/ao/quantization/observer.py

@@ -1019,7 +1019,7 @@ def _non_linear_param_search(self) -> Tuple[torch.Tensor, torch.Tensor]:
         This follows the implementation of NormMinimization::NonlinearQuantizationParamsSearch in
         caffe2/quantization/server/norm_minimization.cc
         """
-        assert self.histogram.size()[0] == self.bins, "bins mistmatch"
+        assert self.histogram.size()[0] == self.bins, "bins mismatch"
         bin_width = (self.max_val - self.min_val) / self.bins
 
         # cumulative sum

torch/backends/xeon/run_cpu.py

@@ -598,7 +598,7 @@ def create_args(parser=None):
     _add_multi_instance_params(parser)
     # positional
     parser.add_argument("program", type=str,
-                        help="The full path to the proram/script to be launched. "
+                        help="The full path to the program/script to be launched. "
                              "followed by all the arguments for the script")
 
     # rest from the training program

torch/cuda/memory.py

@@ -61,7 +61,7 @@ def caching_allocator_alloc(size, device: Union[Device, int] = None, stream=None
     if not isinstance(stream, int):
         raise TypeError('Invalid type for stream argument, must be '
                         '`torch.cuda.Stream` or `int` representing a pointer '
-                        'to a exisiting stream')
+                        'to a existing stream')
     with torch.cuda.device(device):
         return torch._C._cuda_cudaCachingAllocator_raw_alloc(size, stream)
 

torch/distributed/benchmarks/benchmark_ddp_rpc.py

@@ -335,7 +335,7 @@ def run_worker(rank, world_size):
         "--embedding-dim",
         type=int,
         default=EMBEDDING_DIM,
-        help="Number of embedding dimentions.",
+        help="Number of embedding dimensions.",
     )
     parser.add_argument(
         "--warmup-cycles",

torch/distributed/elastic/multiprocessing/api.py

@@ -537,7 +537,7 @@ def _close(self, death_sig: signal.Signals, timeout: int = 30) -> None:
         for proc in self._pc.processes:
             if proc.is_alive():
                 log.warning(
-                    f"Unable to shutdown process {proc.pid} via {death_sig}, forcefully exitting via {_get_kill_signal()}"
+                    f"Unable to shutdown process {proc.pid} via {death_sig}, forcefully exiting via {_get_kill_signal()}"
                 )
                 try:
                     os.kill(proc.pid, _get_kill_signal())
@@ -714,7 +714,7 @@ def _close(self, death_sig: signal.Signals, timeout: int = 30) -> None:
         for handler in self.subprocess_handlers.values():
             if handler.proc.poll() is None:
                 log.warning(
-                    f"Unable to shutdown process {handler.proc.pid} via {death_sig}, forcefully exitting via {_get_kill_signal()}"
+                    f"Unable to shutdown process {handler.proc.pid} via {death_sig}, forcefully exiting via {_get_kill_signal()}"
                 )
                 handler.close(death_sig=_get_kill_signal())
                 handler.proc.wait()

torch/distributed/elastic/rendezvous/etcd_rendezvous.py

@@ -293,7 +293,7 @@ def rendezvous_barrier(self):
                 time.sleep(1)
 
             except RendezvousTimeoutError:
-                log.info("Rendezvous timeout occured in EtcdRendezvousHandler")
+                log.info("Rendezvous timeout occurred in EtcdRendezvousHandler")
                 raise
 
             except RendezvousClosedError:

torch/distributions/mixture_same_family.py

@@ -60,7 +60,7 @@ def __init__(self,
 
         if not isinstance(self._mixture_distribution, Categorical):
             raise ValueError(" The Mixture distribution needs to be an "
-                             " instance of torch.distribtutions.Categorical")
+                             " instance of torch.distributions.Categorical")
 
         if not isinstance(self._component_distribution, Distribution):
             raise ValueError("The Component distribution need to be an "