Check spelling in comments and names

examples/split_learning/llm_split_learning/split_learning_llm_model.py

@@ -1,7 +1,6 @@
 """
-Obtain LLM models from Huggingface, specifically designed for split learning
+Obtain LLM models from HuggingFace, specifically designed for split learning
 """
-
 import torch
 from transformers import AutoModelForCausalLM, AutoConfig
 from peft import get_peft_model, LoraConfig
@@ -18,8 +17,8 @@ def get_lora_model(model):
 
 def get_module(start_module: torch.nn.Module, module_names):
     """
-    Recursively get a pytorch module starting from the start module with
-        a given list of module names.
+    Recursively get a PyTorch module starting from the start module with
+    a given list of module names.
     """
     module = start_module
     for module_name in module_names:
@@ -29,7 +28,7 @@ def get_module(start_module: torch.nn.Module, module_names):
 
 class BaseModel(torch.nn.Module):
     """
-    The basic model loading hugginface model used for the server model and the client model
+    The basic model loading HuggingFace model used for the server model and the client model
     """
 
     def __init__(self, *args, **kwargs) -> None:
@@ -122,7 +121,7 @@ def __init__(self, *args, **kwargs) -> None:
         # In this design, we have two copies of the model
         # The first copy of the model is the whole model which is used for test.
         # The second copy of the model only contains the layers on the server
-        #   used for training.
+        # used for training.
         self.server_model = AutoModelForCausalLM.from_pretrained(
             self.model_name,
             config=self.config,

examples/split_learning/llm_split_learning/split_learning_main.py

@@ -1,5 +1,5 @@
 """
-Use Split learning to finetune Huggingface large language model.
+Finetune HuggingFace large language models using split learning.
 """
 import split_learning_trainer
 from split_learning_llm_model import ServerModel, ClientModel

examples/split_learning/llm_split_learning/split_learning_trainer.py

@@ -100,7 +100,7 @@ def __init__(self, model=None, callbacks=None):
         embedding_size = self.model.get_input_embeddings().weight.shape[0]
         if len(self.tokenizer) > embedding_size:
             self.model.resize_token_embeddings(len(self.tokenizer))
-        # self.training args for huggingface training
+        # self.training args for HuggingFace training
         parser = HfArgumentParser(TrainingArguments)
 
         (self.training_args,) = parser.parse_args_into_dataclasses(
@@ -139,7 +139,7 @@ def test_model_split_learning(self, batch_size, testset, sampler=None):
         return metrics["eval_accuracy"]
 
     # Redesign the training stage specific to Split Learning.
-    def process_training_samples_before_retreiving(self, training_samples):
+    def process_training_samples_before_retrieving(self, training_samples):
         inputs = training_samples["input_ids"]
         labels = training_samples["labels"]
         for index, input_item in enumerate(inputs):

plato/trainers/split_learning.py

@@ -13,7 +13,6 @@
 
 https://arxiv.org/pdf/2112.01637.pdf
 """
-
 import logging
 import os
 
@@ -99,7 +98,7 @@ def get_train_samples(self, batch_size, trainset, sampler):
         data_loader = iter(data_loader)
         self.training_samples = next(data_loader)
         # Wrap the training samples with datasource and sampler to be fed into Plato trainer
-        self.training_samples = self.process_training_samples_before_retreiving(
+        self.training_samples = self.process_training_samples_before_retrieving(
             self.training_samples
         )
         return self.training_samples
@@ -190,7 +189,7 @@ def test_model(self, config, testset, sampler=None, **kwargs):
         """
         Evaluates the model with the provided test dataset and test sampler.
 
-        Auguments:
+        Arguments:
         testset: the test dataset.
         sampler: the test sampler. The default is None.
         kwargs (optional): Additional keyword arguments.
@@ -200,19 +199,19 @@ def test_model(self, config, testset, sampler=None, **kwargs):
         return accuracy
 
     # API functions for split learning
-    def process_training_samples_before_retreiving(self, training_samples) -> ...:
-        """Process training samples before completing retreiving samples."""
+    def process_training_samples_before_retrieving(self, training_samples) -> ...:
+        """Process training samples before completing retrieving samples."""
         return training_samples
 
     def process_samples_before_client_forwarding(self, examples) -> ...:
-        """Process the examples befor client conducting forwarding."""
+        """Process the examples before client conducting forwarding."""
         return examples
 
     # pylint:disable=unused-argument
     def server_forward_from(self, batch, config) -> (..., ..., int):
         """
         The event for server completing training by forwarding from intermediate features.
-        Uses may override this function for training different models with split learning.
+        Users may override this function for training different models with split learning.
 
         Inputs:
             batch: the batch of inputs for forwarding.