Fix typos with codespell (#126)

README.md

@@ -140,7 +140,7 @@ The automated tests use a more complex server configuration that can be found [h
 ### Code style
 
 We use [black](https://black.readthedocs.io/en/stable/the_black_code_style/current_style.html) and [isort](https://pycqa.github.io/isort/) for all pull requests.
-Before commiting your code, simply run `black . && isort .` and you will be fine.
+Before committing your code, simply run `black . && isort .` and you will be fine.
 
 --------------------------------------------------------------------------------
 

src/petals/bloom/ops.py

@@ -116,7 +116,7 @@ def dropout_add(x, residual, prob, training):
     Args:
         x (`torch.tensor`, *required*):
             input tensor
-        residual (`torch.tensor`, *rquired*):
+        residual (`torch.tensor`, *required*):
             esidual tensor
         prob (`float`, *required*):
             dropout probability

src/petals/client/inference_session.py

@@ -84,14 +84,14 @@ class _ServerInferenceSession:
         """
         Inference step: send a chunk of input tesors and receive a chunk of outputs
         :prompts: optional DEEP prompts, added to a prefix of each layer's outputs,
-          if specified, deep promts should have shape [num_layers, batch_size, prefix_len, hid_size]
+          if specified, deep prompts should have shape [num_layers, batch_size, prefix_len, hid_size]
         """
         if self.closed:
             raise Exception("Session is closed, cannot perform step")
         if prompts is None or is_dummy(prompts):
             prompts = DUMMY
         else:
-            assert prompts.ndim == 4, "deep promts should have shape [num_layers, batch_size, prefix_len, hid_size]"
+            assert prompts.ndim == 4, "deep prompts should have shape [num_layers, batch_size, prefix_len, hid_size]"
             assert prompts.shape[0] == self.num_blocks
             assert prompts.shape[1] in (new_hidden_states.shape[0], 1)
             assert prompts.shape[2] <= new_hidden_states.shape[1]

src/petals/client/routing/sequence_manager.py

@@ -35,7 +35,7 @@ class RemoteSequenceManager:
     :param block_uids: a sequence of DHT keys (strings) corresponding to remote layers
     :param p2p: an optional P2P replica (if not specified, create one via dht.replicate_p2p())
     :param update_period: by default, refresh DHT information once in this many seconds
-    :param request_timeout: float, in seconds, default timeout for RPC forwad/backward/inference requests
+    :param request_timeout: float, in seconds, default timeout for RPC forward/backward/inference requests
     :param min_backoff: after a repeated failure, sleep for this many seconds times 2 ^ (num_failures - 1)
     :param sequence_info: optionally, specify pre-generated sequence info. by default, create a new one using dht
     :param rpc_info: optionally, specify rpc info (communicated tensor shapes and compression) to save time
@@ -207,7 +207,7 @@ class RemoteSequenceManager:
     def get_request_metadata(self, protocol: str, *args, **kwargs) -> Optional[Dict[str, Any]]:
         """
         :param protocol: one of "rpc_forward", "rpc_backward" or "rpc_inference"
-        :param args: request-specific inputs, typicall block uids and input tensors
+        :param args: request-specific inputs, typically block uids and input tensors
         :param kwargs: additional request context, such as remote peer ID
         :returns: msgpack-serialized metadata dict that will be passed alongside a given request
         """

src/petals/dht_utils.py

@@ -33,7 +33,7 @@ def declare_active_modules(
     :param uids: a list of module ids to declare
     :param wait: if True, awaits for declaration to finish, otherwise runs in background
     :param throughput: specify your performance in terms of compute throughput
-    :param expiration_time: declated modules will be visible for this many seconds
+    :param expiration_time: declared modules will be visible for this many seconds
     :returns: if wait, returns store status for every key (True = store succeeded, False = store rejected)
     """
     if isinstance(uids, str):
@@ -107,7 +107,7 @@ def get_remote_module(
 ) -> Union[Union[petals.client.RemoteTransformerBlock, List[petals.client.RemoteTransformerBlock]], MPFuture]:
     """
     :param uid_or_uids: find one or more modules with these ids from across the DHT
-    :param config: model config, usualy taken by .from_pretrained(MODEL_NAME)
+    :param config: model config, usually taken by .from_pretrained(MODEL_NAME)
     :param return_future: if False (default), return when finished. Otherwise return MPFuture and run in background.
     :returns: a list of [RemoteTransformerBlock]
     """

src/petals/server/server.py

@@ -173,7 +173,7 @@ class Server:
     def _choose_num_blocks(self) -> int:
         assert (
             self.converted_model_name_or_path == "bigscience/bloom-petals"
-        ), "If you use a model other than bigscience/bloom-petals, please specify --num blocks manually"
+        ), "If you use a model other than bigscience/bloom-petals, please specify --num_blocks manually"
         assert self.device.type == "cuda", "If you run a non-GPU server, please specify --num_blocks manually"
 
         gib = 1024**3
@@ -497,7 +497,7 @@ class ModuleContainer(threading.Thread):
         logger.debug(f"Shutting down runtime")
         self.runtime.shutdown()
 
-        logger.info("Module container shut down succesfully")
+        logger.info("Module container shut down successfully")
 
 
 class ModuleAnnouncerThread(threading.Thread):

src/petals/server/task_prioritizer.py

@@ -4,11 +4,11 @@ import torch
 
 
 class TaskPrioritizerBase(ABC):
-    """Abstract class for TaskPrioritizer whose reponsibility is to evaluate task priority"""
+    """Abstract class for TaskPrioritizer whose responsibility is to evaluate task priority"""
 
     @abstractmethod
     def prioritize(self, *input: torch.Tensor, points: float = 0.0, **kwargs) -> float:
-        """Evaluates task value by the amout of points given, task input and additional kwargs. Lower priority is better"""
+        """Evaluates task value by the amount of points given, task input and additional kwargs. Lower priority is better"""
         pass
 
 

src/petals/utils/generation_algorithms.py

@@ -25,7 +25,7 @@ class DecodingAlgorithm(ABC):
 
 class GreedyAlgorithm(DecodingAlgorithm):
     """
-    The simpliest algorithm for decoding. It selects the most probable token.
+    The simplest algorithm for decoding. It selects the most probable token.
     """
 
     def __call__(self, logits: torch.Tensor) -> Tuple[TokenIds, HypoIds]:

src/petals/utils/generation_constraints.py

@@ -14,7 +14,7 @@ class ABCBloomConstraint(ABC):
     def __call__(self, tokens_id: torch.Tensor, logits: torch.Tensor, hypo_ids: torch.Tensor) -> torch.Tensor:
         """
         This method is called by the decoding algorithm to apply the constraint. It changes and returns new logits.
-        :param tokens_id: The token id of the last choosen token.
+        :param tokens_id: The token id of the last chosen token.
         :param logits: The logits from the Bloom model.
         :param hypo_ids: The hypothesis ids of the last tokens.
         """