Minor Performance Hits Elimination

examples/nlp/huggingface_model/text_generation/llama/quantization/weight_only/main.py

@@ -23,7 +23,6 @@
 import torch
 import logging
 import argparse
-import random
 import numpy as np
 import datasets
 import onnxruntime as ort

examples/nlp/huggingface_model/text_generation/llama/quantization/weight_only/run_benchmark.sh

@@ -35,7 +35,7 @@ function init_params {
 
 # run_benchmark
 function run_benchmark {
-    
+
     # Check if the input_model ends with the filename extension ".onnx"
     if [[ $input_model =~ \.onnx$ ]]; then
         # If the string ends with the filename extension, get the path of the file
@@ -50,7 +50,7 @@ function run_benchmark {
             --mode=${mode} \
             --intra_op_num_threads=${intra_op_num_threads-24} \
             --benchmark
-            
+
 }
 
 main "$@"

examples/nlp/huggingface_model/text_generation/llama/quantization/weight_only/run_quant.sh

@@ -52,18 +52,18 @@ function run_tuning {
     # Check if the directory exists
     if [ ! -d "$output_model" ]; then
         # If the directory doesn't exist, create it
-	mkdir -p "$output_model"
-	echo "Created directory $output_model"
+        mkdir -p "$output_model"
+        echo "Created directory $output_model"
     fi
 
     python main.py \
             --model_path ${input_model} \
-	        --tokenizer ${tokenizer-meta-llama/Llama-2-7b-hf} \
+            --tokenizer ${tokenizer-meta-llama/Llama-2-7b-hf} \
             --output_model ${output_model} \
             --batch_size ${batch_size-1} \
             --dataset ${dataset-NeelNanda/pile-10k} \
-	        --algorithm ${algorithm-WOQ_TUNE} \
-	        --tasks ${tasks-lambada_openai} \
+            --algorithm ${algorithm-WOQ_TUNE} \
+            --tasks ${tasks-lambada_openai} \
             --tune
 }
 

onnx_neural_compressor/config.py

@@ -277,7 +277,7 @@ def to_dict(self):
         return result
 
     def get_params_dict(self):
-        result = dict()
+        result = {}
         for param, value in self.__dict__.items():
             if param not in ["_global_config", "_local_config", "_white_list"]:
                 result[param] = value
@@ -448,8 +448,8 @@ def expand(self) -> List[BaseConfig]:
         return config_list
 
     def _get_op_name_op_type_config(self):
-        op_type_config_dict = dict()
-        op_name_config_dict = dict()
+        op_type_config_dict = {}
+        op_name_config_dict = {}
         for name, config in self.local_config.items():
             if self._is_op_type(name):
                 op_type_config_dict[name] = config
@@ -551,7 +551,7 @@ def get_config_set_for_tuning(cls) -> None:
         return None
 
     def get_model_info(self, model, *args, **kwargs):
-        model_info_dict = dict()
+        model_info_dict = {}
         for config in self.config_list:
             model_info_dict.update({config.name: config.get_model_info(model, *args, **kwargs)})
         return model_info_dict
@@ -650,7 +650,7 @@ def __init__(
         self._post_init()
 
     def get_model_params_dict(self):
-        result = dict()
+        result = {}
         for param in self.model_params_list:
             result[param] = getattr(self, param)
         return result
@@ -812,7 +812,7 @@ def __init__(
         self._post_init()
 
     def get_model_params_dict(self):
-        result = dict()
+        result = {}
         for param in self.model_params_list:
             result[param] = getattr(self, param)
         return result
@@ -964,7 +964,7 @@ def __init__(
         self._post_init()
 
     def get_model_params_dict(self):
-        result = dict()
+        result = {}
         for param in self.model_params_list:
             result[param] = getattr(self, param)
         return result

onnx_neural_compressor/onnx_model.py

@@ -984,8 +984,8 @@ def write_external_data_to_new_location(self, external_data_location="external.d
     def merge_split_models(self, to_merge_model):
         """Merge two split model into final model."""
         to_merge_model.write_external_data_to_new_location()
-        self.add_nodes([node for node in to_merge_model.nodes()])
-        self.add_initializers([init for init in to_merge_model.initializer()])
+        self.add_nodes(list(to_merge_model.nodes()))
+        self.add_initializers(list(to_merge_model.initializer()))
         self.update()
 
         # add new output