From 409b1747e380f1665c48c196622ba82106533c1a Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Sat, 21 Dec 2024 00:50:13 +0000
Subject: [PATCH] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 examples/llm/g_retriever.py | 31 ++++++++++++++++---------------
 1 file changed, 16 insertions(+), 15 deletions(-)

diff --git a/examples/llm/g_retriever.py b/examples/llm/g_retriever.py
index f3ec1e40c4f8..04d9adfb1c78 100644
--- a/examples/llm/g_retriever.py
+++ b/examples/llm/g_retriever.py
@@ -8,7 +8,6 @@
 
 import pandas as pd
 import torch
-from torch import Tensor
 from torch.nn.utils import clip_grad_norm_
 from tqdm import tqdm
 
@@ -18,10 +17,10 @@
 from torch_geometric.nn.models import GAT, GRetriever
 from torch_geometric.nn.nlp import LLM
 
+
 # Define a function to compute evaluation metrics
 def compute_metrics(eval_output):
-    """
-    Compute evaluation metrics (Hit, Precision, Recall, F1) from the output of the inference step.
+    """Compute evaluation metrics (Hit, Precision, Recall, F1) from the output of the inference step.
 
     Args:
         eval_output (list): List of dictionaries containing prediction and label information.
@@ -74,10 +73,10 @@ def compute_metrics(eval_output):
     print(f'Recall: {recall:.4f}')
     print(f'F1: {f1:.4f}')
 
+
 # Define a function to save model parameters to a file
 def save_params_dict(model, save_path):
-    """
-    Save model parameters to a file.
+    """Save model parameters to a file.
 
     Args:
         model (torch.nn.Module): Model to save.
@@ -93,10 +92,10 @@ def save_params_dict(model, save_path):
             del state_dict[k]  # Delete parameters that do not require gradient
     torch.save(state_dict, save_path)
 
+
 # Define a function to load model parameters from a file
 def load_params_dict(model, save_path):
-    """
-    Load model parameters from a file.
+    """Load model parameters from a file.
 
     Args:
         model (torch.nn.Module): Model to load.
@@ -106,10 +105,10 @@ def load_params_dict(model, save_path):
     model.load_state_dict(state_dict)
     return model
 
+
 # Define a function to compute the loss for a given model and batch
 def get_loss(model, batch, model_save_name):
-    """
-    Compute the loss for a given model and batch.
+    """Compute the loss for a given model and batch.
 
     Args:
         model (torch.nn.Module): Model to compute loss for.
@@ -125,10 +124,10 @@ def get_loss(model, batch, model_save_name):
         return model(batch.question, batch.x, batch.edge_index, batch.batch,
                      batch.label, batch.edge_attr, batch.desc)
 
+
 # Define a function to perform inference for a given model and batch
 def inference_step(model, batch, model_save_name):
-    """
-    Perform inference for a given model and batch.
+    """Perform inference for a given model and batch.
 
     Args:
         model (torch.nn.Module): Model to perform inference with.
@@ -144,6 +143,7 @@ def inference_step(model, batch, model_save_name):
         return model.inference(batch.question, batch.x, batch.edge_index,
                                batch.batch, batch.edge_attr, batch.desc)
 
+
 # Define the training loop
 def train(
     num_epochs,
@@ -155,8 +155,7 @@ def train(
     checkpointing=False,
     tiny_llama=False,
 ):
-    """
-    Train the model for a specified number of epochs.
+    """Train the model for a specified number of epochs.
 
     Args:
         num_epochs (int): Number of epochs to train for.
@@ -168,6 +167,7 @@ def train(
         checkpointing (bool): Whether to save model checkpoints. Default: False.
         tiny_llama (bool): Whether to use the tiny LLaMA model. Default: False.
     """
+
     # Define the learning rate schedule
     def adjust_learning_rate(param_group, LR, epoch):
         min_lr = 5e-6
@@ -276,7 +276,7 @@ def adjust_learning_rate(param_group, LR, epoch):
             # Update learning rate schedule
             if (step + 1) % grad_steps == 0:
                 lr = adjust_learning_rate(optimizer.param_groups[0], lr,
-                                         step / len(train_loader) + epoch)
+                                          step / len(train_loader) + epoch)
 
             # Update optimizer
             optimizer.step()
@@ -347,6 +347,7 @@ def adjust_learning_rate(param_group, LR, epoch):
     save_params_dict(model, f'{model_save_name}.pt')
     torch.save(eval_output, f'{model_save_name}_eval_outs.pt')
 
+
 if __name__ == '__main__':
     # Parse command-line arguments
     parser = argparse.ArgumentParser()
@@ -376,4 +377,4 @@ def adjust_learning_rate(param_group, LR, epoch):
     )
 
     # Print total time
-    print(f"Total Time: {time.time() - start_time:2f}s")
\ No newline at end of file
+    print(f"Total Time: {time.time() - start_time:2f}s")