| import os |
| |
| |
| |
| import json |
| import time |
| import csv |
| import pathlib |
| import difflib |
| import re |
| from bleu import _bleu |
| from fuzzywuzzy import fuzz |
| import random |
| import numpy as np |
| from transformers import RobertaTokenizer |
| |
| import argparse |
|
|
| parser = argparse.ArgumentParser(description='Test') |
| parser.add_argument("--task", default=None, type=str, required=True, |
| help="Task Type: statement_level, next_statement" ) |
| args = parser.parse_args() |
|
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|
|
| folder = str(pathlib.Path(__file__).parent.resolve()) |
| isa_type_dir = folder+"/../../../Dataset" |
| src_dir = folder+f"/../../../Dataset/Code_Completion/{args.task}" |
| dst_dir = folder |
|
|
| train_lis = [] |
| valid_lis = [] |
| test_lis = [] |
|
|
| target_clf = {} |
| def get_target_clf_list(): |
| global target_clf |
| with open(isa_type_dir+"/comback_isa_type.csv","r",encoding="utf-8") as f: |
| reader = csv.reader(f) |
| for idx, l in enumerate(reader): |
| if l[1].lower() == "arc" or l[1].lower() == "riscv" or l[1].lower() == "nvptx": |
| continue |
| if l[0] + " " + l[2] not in target_clf.keys(): |
| target_clf[l[0] + " " + l[2]] = [l[1]] |
| else: |
| target_clf[l[0] + " " + l[2]] += [l[1]] |
|
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|
|
| def Calculate_Completion(): |
| get_target_clf_list() |
| print("############## Exp 2: Calculate ChatGPT Stmt Completion ################\n") |
| |
| test_lis = ["nvptx","arc","riscv"] |
|
|
|
|
| chatgpt_gcc_code = {} |
| chatgpt_llvm_code = {} |
|
|
| if args.task == "next_statement": |
| dst_file = dst_dir+"/Input/chatgpt_next_output_cleaned.csv" |
| else: |
| dst_file = dst_dir+"/Input/chatgpt_stmt_output_cleaned.csv" |
|
|
| |
|
|
| with open(dst_file, encoding="utf-8") as f: |
| reader = csv.reader(f) |
| for idx, row in enumerate(reader): |
| if row[0] == "GCC": |
| chatgpt_gcc_code[row[1] + " " + str(row[2])] = row[3] |
| else: |
| chatgpt_llvm_code[row[1] + " " + str(row[2])] = row[3] |
| avg_accuracy = {} |
| for comp_type in ["GCC", "LLVM"]: |
| for isa_type in ["GPU", "MPU", "CPU"]: |
| test_target_dic = {} |
| cnt_idx = 0 |
| if comp_type == "GCC": |
| if isa_type == "CPU": |
| cnt_idx = 0 |
| for line in open(src_dir + "/GCC/riscv.jsonl", 'r'): |
| dic = json.loads(line) |
| test_target_dic["riscv" + " " + str(cnt_idx)] = " ".join(dic["ground_truth"]) |
| |
| cnt_idx += 1 |
| total_EM = 0.0 |
| total_ED = 0.0 |
| for k in test_target_dic.keys(): |
| edit_dis = 0.0 |
| EM = 0.0 |
| src_code = test_target_dic[k] |
| |
| if k in chatgpt_gcc_code.keys(): |
| chat_code = chatgpt_gcc_code[k] |
| if chat_code.replace(" ", "") == src_code.replace(" ", ""): |
| EM = 1 |
| edit_dis = fuzz.ratio(chat_code.replace(" ", ""), src_code.replace(" ", "")) |
| total_ED += edit_dis |
| total_EM += EM |
| with open(dst_dir + '/result.csv', 'a', newline='') as file: |
| writer = csv.writer(file) |
| writer.writerow([comp_type, "RISCV", k.split(" ")[1], str(round(EM*100,2)), str(round(float(edit_dis),2))]) |
| else: |
| print(k) |
| with open(dst_dir + '/result.csv', 'a', newline='') as file: |
| writer = csv.writer(file) |
| writer.writerow([comp_type, "RISCV", "average", str(round((total_EM / cnt_idx)*100,2)), str(round(float(total_ED / cnt_idx),2))]) |
| avg_accuracy[comp_type + " " + "RISCV"] = [str(round((total_EM / cnt_idx)*100,2)), str(round(float(total_ED / cnt_idx),2))] |
| |
| if isa_type == "GPU": |
| cnt_idx = 0 |
| for line in open(src_dir + "/GCC/nvptx.jsonl", 'r'): |
| dic = json.loads(line) |
| test_target_dic["nvptx" + " " + str(cnt_idx)] = " ".join(dic["ground_truth"]) |
| cnt_idx += 1 |
| total_EM = 0.0 |
| total_ED = 0.0 |
| |
| for k in test_target_dic.keys(): |
| edit_dis = 0.0 |
| EM = 0.0 |
| src_code = test_target_dic[k] |
| if k in chatgpt_gcc_code.keys(): |
| chat_code = chatgpt_gcc_code[k] |
| if chat_code.replace(" ", "") == src_code.replace(" ", ""): |
| EM = 1 |
| edit_dis = fuzz.ratio(chat_code.replace(" ", ""), src_code.replace(" ", "")) |
| total_ED += edit_dis |
| total_EM += EM |
| with open(dst_dir + '/result.csv', 'a', newline='') as file: |
| writer = csv.writer(file) |
| writer.writerow([comp_type, "NVPTX", k.split(" ")[1], str(round(EM*100,2)), str(round(float(edit_dis),2))]) |
| else: |
| print(k) |
| with open(dst_dir + '/result.csv', 'a', newline='') as file: |
| writer = csv.writer(file) |
| writer.writerow([comp_type, "NVPTX", "average", str(round((total_EM / cnt_idx)*100,2)), str(round(float(total_ED / cnt_idx),2))]) |
| avg_accuracy[comp_type + " " + "NVPTX"] = [str(round((total_EM / cnt_idx)*100,2)), str(round(float(total_ED / cnt_idx),2))] |
| |
| if isa_type == "MPU": |
| cnt_idx = 0 |
| for line in open(src_dir + "/GCC/arc.jsonl", 'r'): |
| dic = json.loads(line) |
| test_target_dic["arc" + " " + str(cnt_idx)] = " ".join(dic["ground_truth"]) |
| cnt_idx += 1 |
| total_EM = 0.0 |
| total_ED = 0.0 |
| for k in test_target_dic.keys(): |
| edit_dis = 0.0 |
| EM = 0.0 |
| src_code = test_target_dic[k] |
| if k in chatgpt_gcc_code.keys(): |
| chat_code = chatgpt_gcc_code[k] |
| if chat_code.replace(" ", "") == src_code.replace(" ", ""): |
| EM = 1 |
| edit_dis = fuzz.ratio(chat_code.replace(" ", ""), src_code.replace(" ", "")) |
| total_ED += edit_dis |
| total_EM += EM |
| with open(dst_dir + '/result.csv', 'a', newline='') as file: |
| writer = csv.writer(file) |
| writer.writerow([comp_type, "ARC", k.split(" ")[1], str(round(EM*100,2)), str(round(float(edit_dis),2))]) |
| else: |
| print(k) |
| with open(dst_dir + '/result.csv', 'a', newline='') as file: |
| writer = csv.writer(file) |
| writer.writerow([comp_type, "ARC", "average", str(round((total_EM / cnt_idx)*100,2)), str(round(float(total_ED / cnt_idx),2))]) |
| avg_accuracy[comp_type + " " + "ARC"] = [str(round((total_EM / cnt_idx)*100,2)), str(round(float(total_ED / cnt_idx),2))] |
| |
|
|
| if comp_type == "LLVM": |
| if isa_type == "CPU": |
| cnt_idx = 0 |
| for line in open(src_dir + "/LLVM/RISCV.jsonl", 'r'): |
| dic = json.loads(line) |
| test_target_dic["RISCV" + " " + str(cnt_idx)] = " ".join(dic["ground_truth"]) |
| cnt_idx += 1 |
| total_EM = 0.0 |
| total_ED = 0.0 |
| for k in test_target_dic.keys(): |
| edit_dis = 0.0 |
| EM = 0.0 |
| src_code = test_target_dic[k] |
| if k in chatgpt_llvm_code.keys(): |
| chat_code = chatgpt_llvm_code[k] |
| if chat_code.replace(" ", "") == src_code.replace(" ", ""): |
| EM = 1 |
| edit_dis = fuzz.ratio(chat_code.replace(" ", ""), src_code.replace(" ", "")) |
| total_ED += edit_dis |
| total_EM += EM |
| with open(dst_dir + '/result.csv', 'a', newline='') as file: |
| writer = csv.writer(file) |
| writer.writerow([comp_type, "RISCV", k.split(" ")[1], str(round(EM*100,2)), str(round(float(edit_dis),2))]) |
| else: |
| print(k) |
| with open(dst_dir + '/result.csv', 'a', newline='') as file: |
| writer = csv.writer(file) |
| writer.writerow([comp_type, "RISCV", "average", str(round((total_EM / cnt_idx)*100,2)), str(round(float(total_ED / cnt_idx),2))]) |
| avg_accuracy[comp_type + " " + "RISCV"] = [str(round((total_EM / cnt_idx)*100,2)), str(round(float(total_ED / cnt_idx),2))] |
| if isa_type == "GPU": |
| cnt_idx = 0 |
| for line in open(src_dir + "/LLVM/NVPTX.jsonl", 'r'): |
| dic = json.loads(line) |
| test_target_dic["NVPTX" + " " + str(cnt_idx)] = " ".join(dic["ground_truth"]) |
| cnt_idx += 1 |
| total_EM = 0.0 |
| total_ED = 0.0 |
| for k in test_target_dic.keys(): |
| edit_dis = 0.0 |
| EM = 0.0 |
| src_code = test_target_dic[k] |
| if k in chatgpt_llvm_code.keys(): |
| chat_code = chatgpt_llvm_code[k] |
| if chat_code.replace(" ", "") == src_code.replace(" ", ""): |
| EM = 1 |
| edit_dis = fuzz.ratio(chat_code.replace(" ", ""), src_code.replace(" ", "")) |
| total_ED += edit_dis |
| total_EM += EM |
| with open(dst_dir + '/result.csv', 'a', newline='') as file: |
| writer = csv.writer(file) |
| writer.writerow([comp_type, "NVPTX", k.split(" ")[1], str(round(EM*100,2)), str(round(float(edit_dis),2))]) |
| else: |
| print(k) |
| with open(dst_dir + '/result.csv', 'a', newline='') as file: |
| writer = csv.writer(file) |
| writer.writerow([comp_type, "NVPTX", "average", str(round((total_EM / cnt_idx)*100,2)), str(round(float(total_ED / cnt_idx),2))]) |
| avg_accuracy[comp_type + " " + "NVPTX"] = [str(round((total_EM / cnt_idx)*100,2)), str(round(float(total_ED / cnt_idx),2))] |
| if isa_type == "MPU": |
| cnt_idx = 0 |
| for line in open(src_dir + "/LLVM/ARC.jsonl", 'r'): |
| dic = json.loads(line) |
| test_target_dic["ARC" + " " + str(cnt_idx)] = " ".join(dic["ground_truth"]) |
| cnt_idx += 1 |
| total_EM = 0.0 |
| total_ED = 0.0 |
| |
| for k in test_target_dic.keys(): |
| edit_dis = 0.0 |
| EM = 0.0 |
| src_code = test_target_dic[k] |
| if k in chatgpt_llvm_code.keys(): |
| chat_code = chatgpt_llvm_code[k] |
| if chat_code.replace(" ", "") == src_code.replace(" ", ""): |
| EM = 1 |
| edit_dis = fuzz.ratio(chat_code.replace(" ", ""), src_code.replace(" ", "")) |
| total_ED += edit_dis |
| total_EM += EM |
| with open(dst_dir + '/result.csv', 'a', newline='') as file: |
| writer = csv.writer(file) |
| writer.writerow([comp_type, "ARC", k.split(" ")[1], str(round(EM*100,2)), str(round(float(edit_dis),2))]) |
| else: |
| print(k) |
| with open(dst_dir + '/result.csv', 'a', newline='') as file: |
| writer = csv.writer(file) |
| writer.writerow([comp_type, "ARC", "average", str(round((total_EM / cnt_idx)*100,2)), str(round(float(total_ED / cnt_idx),2))]) |
| avg_accuracy[comp_type + " " + "ARC"] = [str(round((total_EM / cnt_idx)*100,2)), str(round(float(total_ED / cnt_idx),2))] |
|
|
| return avg_accuracy |
|
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|
|
|
| if __name__ == "__main__": |
| with open(dst_dir + '/result.csv', 'w', newline='') as file: |
| writer = csv.writer(file) |
| writer.writerow(["Compiler Type", "Target", "Idx", "Exact Match", "Edit Didtance"]) |
|
|
| avg_dic = Calculate_Completion() |
|
|
| for k in avg_dic: |
| print("########################") |
| |
| print(k) |
| print(" ".join(["Exact Match", "Edit Didtance"])) |
| print(" ".join(avg_dic[k])) |
|
|
