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