_extends\parallel_compile\tbe_compiler

src/mindspore2022/mindspore/python/mindspore/_extends/parallel_compile/tbe_compiler/tbe_adapter.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """tbe adapter to adapt te/topi/auto-tune python api """
+# 导入必要的库和模块
 import json
 import os
 import shutil
@@ -20,33 +21,62 @@ import sys
 import traceback
 from datetime import datetime
 
+# 导入TBE相关的库和模块
 from tbe.common.rl_bank.bank_manager import set_current_op_name
 from tbe.common.repository_manager.interface import cann_kb_unload, cann_kb_load
 from tbe.common.rl_bank.bank_cfg import LocalLock
 from te.platform.cce_conf import te_set_version
 from te.platform.cce_policy import set_L1_info
-from te_fusion.compile_task_manager import dispatch_prebuild_task, dispatch_single_op_compile_task, import_py_module, \
-    dispatch_fusion_op_compile_task, dispatch_autotune_task, sync_op_tune_params
-from te_fusion.compile_task_manager import sync_syspath
-from te_fusion.fusion_manager import call_op_func, clear_fusion_params, check_op_impl_mode, \
-    save_op_params, build_single_op_from_c, op_params_to_json
+from te_fusion.compile_task_manager import (
+    dispatch_prebuild_task,
+    dispatch_single_op_compile_task,
+    import_py_module,
+    dispatch_fusion_op_compile_task,
+    dispatch_autotune_task,
+    sync_op_tune_params,
+    sync_syspath
+)
+from te_fusion.fusion_manager import (
+    call_op_func,
+    clear_fusion_params,
+    check_op_impl_mode,
+    save_op_params,
+    build_single_op_from_c,
+    op_params_to_json
+)
 from te_fusion.fusion_util import dump_fusion_json
-from te_fusion.parallel_compilation import init_multi_process_env, start_ga_multi_process, deinit_multi_process_env, \
+from te_fusion.parallel_compilation import (
+    init_multi_process_env,
+    start_ga_multi_process,
+    deinit_multi_process_env,
     get_finished_compilation_task
-
-from .tbe_helper import get_soc_info, assemble_op_args, get_compute_op_list, get_options_info, get_fuzz_build_info, \
-    adjust_custom_op_info, pack_op_args, get_module_name, get_real_op_debug_level
+)
+from .tbe_helper import (
+    get_soc_info,
+    assemble_op_args,
+    get_compute_op_list,
+    get_options_info,
+    get_fuzz_build_info,
+    adjust_custom_op_info,
+    pack_op_args,
+    get_module_name,
+    get_real_op_debug_level
+)
 from .tbe_job import TbeJob, JobStatus
 
-PLATFORM_FLAG = ["Ascend310", "Ascend910", "Hi3796CV300ES", "Ascend710", "Ascend610", "Hi3796CV300CS", "SD3403"]
-
+# 定义支持的平台标志
+PLATFORM_FLAG = [
+    "Ascend310", "Ascend910", "Hi3796CV300ES", "Ascend710", "Ascend610", "Hi3796CV300CS", "SD3403"
+]
 
+# 定义Tune初始化函数
 def _tune_init(job: TbeJob):
     """
-    Tune Initialize
-    :param job:
-    :return:
+    Tune初始化
+    :param job: TbeJob对象，包含任务信息
+    :return: 初始化是否成功
     """
+    # 提取Soc信息和Tune信息
     auto_tiling_mode = job.content["SocInfo"]["autoTilingMode"]
     offline_tune = job.content["SocInfo"]["offlineTune"]
     op_bank_update = job.content["SocInfo"]["op_bank_update"]
@@ -54,11 +84,14 @@ def _tune_init(job: TbeJob):
     tune_bank_path = job.content["TuneInfo"]["tune_bank_path"]
     need_ga = bool("GA" in auto_tiling_mode)
     need_rl = bool("RL" in auto_tiling_mode)
+    
+    # 设置环境变量
     if offline_tune:
         os.environ["ENABLE_TUNE_DUMP"] = "TRUE"
     if op_bank_update:
         sync_op_tune_params("tbe.common.tiling.tiling_api", "reset_repository", False, "")
 
+    # 初始化Tune环境
     if need_ga or need_rl or offline_tune:
         res = __init_tune_env(job, need_ga)
         if not res:
@@ -66,6 +99,7 @@ def _tune_init(job: TbeJob):
     else:
         return True
 
+    # 设置Tune路径
     if tune_dump_path:
         os.environ["TUNE_DUMP_PATH"] = str(tune_dump_path)
     if tune_bank_path:
@@ -73,12 +107,12 @@ def _tune_init(job: TbeJob):
     res = _creating_custom_path(job)
     return res
 
-
+# 定义CANN知识库加载函数
 def _cann_kb_load(job: TbeJob):
     """
-    database load
-    :param job:
-    :return:
+    加载CANN知识库
+    :param job: TbeJob对象，包含任务信息
+    :return: 加载是否成功
     """
     soc_version = job.soc_version
     core_num = job.core_num
@@ -87,12 +121,12 @@ def _cann_kb_load(job: TbeJob):
     res = cann_kb_load(soc_version, core_num, op_bank_path, kb_type)
     return res
 
-
+# 定义CANN知识库卸载函数
 def _cann_kb_unload(job: TbeJob):
     """
-    database unload
-    :param job:
-    :return:
+    卸载CANN知识库
+    :param job: TbeJob对象，包含任务信息
+    :return: 卸载是否成功
     """
     if job is None:
         return 0
@@ -102,12 +136,12 @@ def _cann_kb_unload(job: TbeJob):
     res = cann_kb_unload(soc_version, core_num, kb_type)
     return res
 
-
+# 定义移除缓存文件函数
 def _remove_cache(job: TbeJob):
     """
-    :param job: remove cache file:[*.json, *.o, *.info, *.cce] when "op_debug_level" is "0"
-                op_debug_level: representation the env MS_COMPILER_OP_LEVEL
-    :return:
+    移除缓存文件
+    :param job: TbeJob对象，包含任务信息
+    :return: 无
     """
     op_debug_level = job.content["SocInfo"]["op_debug_level"]
     op_debug_dir = job.content["SocInfo"]["op_debug_dir"]
@@ -118,24 +152,30 @@ def _remove_cache(job: TbeJob):
         real_path = os.path.join(root_path, "kernel_meta/")
         shutil.rmtree(real_path)
 
-
+# 定义创建目录函数
 def __directory_creation(path, concat_path):
     """
-    Create directory
+    创建目录
+    :param path: 基础路径
+    :param concat_path: 需要连接的路径
+    :return: 创建后的完整路径
     """
     path = os.path.join(path, concat_path)
     if not os.path.isdir(path):
         os.makedirs(path, 0o750)
     return path
 
-
+# 定义初始化Tune环境函数
 def __init_tune_env(job, need_ga):
     """
-    Initialize tune env
+    初始化Tune环境
+    :param job: TbeJob对象，包含任务信息
+    :param need_ga: 是否需要GA
+    :return: 初始化是否成功
     """
     try:
         import auto_tune.auto_tune_main as at_atm
-        from schedule_search.rl_online_tune import rl_tune_init  # pylint: disable=unused-import
+        from schedule_search.rl_online_tune import rl_tune_init  
         if need_ga:
             res = at_atm.ga_tune_init()
             if not res:
@@ -157,10 +197,13 @@ def __init_tune_env(job, need_ga):
     finally:
         pass
 
-
+# 定义创建默认自定义路径函数
 def __creating_default_custom_path(auto_tiling_mode, base_custom_path):
     """
-    Create default custom path
+    创建默认自定义路径
+    :param auto_tiling_mode: 自动平铺模式
+    :param base_custom_path: 基础自定义路径
+    :return: 无
     """
     base_custom_path = __directory_creation(base_custom_path, "data")
     tune_flag = []
@@ -179,27 +222,40 @@ def __creating_default_custom_path(auto_tiling_mode, base_custom_path):
 
 def _creating_custom_path(job):
     """
-    Create custom path
+    创建自定义路径，用于存储和检索自定义算子的调优参数。
+
+    Args:
+        job (TbeJob): 包含任务信息的TbeJob对象。
+
+    Returns:
+        bool: 自定义路径创建是否成功。
     """
+    # 获取自动平铺模式
     auto_tiling_mode = job.content["SocInfo"]["autoTilingMode"]
+    # 如果模式中包含"NO_TUNE"，则不需要创建自定义路径
     if "NO_TUNE" in auto_tiling_mode:
         return True
 
+    # 获取调优参数的基础路径
     base_custom_path = job.content["TuneInfo"]["tune_bank_path"]
     tune_bank_flag = True
+    # 如果基础路径不存在，则尝试从auto_tune模块获取
     if not base_custom_path:
         import auto_tune
         base_custom_path = os.path.dirname(os.path.realpath(auto_tune.__file__))
         base_custom_path = os.path.realpath(os.path.join(base_custom_path, "../../../"))
         tune_bank_flag = False
 
+    # 检查基础路径是否存在
     if not os.path.isdir(base_custom_path):
         job.error("Check whether the tuning path [{}] exists.".format(base_custom_path))
         return False
+    # 检查基础路径的权限
     if not os.access(base_custom_path, os.R_OK | os.W_OK | os.X_OK):
         job.error("Check whether the permission on the tuning path [{}] is correct.".format(base_custom_path))
         return False
 
+    # 如果不需要创建调优参数库，则直接返回成功
     if not tune_bank_flag:
         return __creating_default_custom_path(auto_tiling_mode, base_custom_path)
     return True
@@ -207,22 +263,34 @@ def _creating_custom_path(job):
 
 def _parallel_compilation_init(initialize: TbeJob):
     """
-    Tbe parallel compilation initialize
-    :param initialize:
-    :return:
+    初始化TBE并行编译环境。
+
+    Args:
+        initialize (TbeJob): 包含任务信息的TbeJob对象。
+
+    Returns:
+        bool: 并行编译环境初始化是否成功。
     """
+    # 设置并行编译器的环境变量
     os.environ["TE_PARALLEL_COMPILER"] = str(initialize.content["process_num"])
+    # 获取SoC信息
     soc_info = get_soc_info(initialize.content)
+    # 获取实际的调试级别
     real_debug_level = get_real_op_debug_level(initialize.content)
+    # 获取自动平铺模式
     auto_tiling_mode = initialize.content["SocInfo"]["autoTilingMode"]
+    # 获取是否需要离线调优
     offline_tune = initialize.content["SocInfo"]["offlineTune"]
+    # 生成进程ID和时间戳的组合字符串
     pid_ts = "{}_pid{}".format(datetime.now().strftime('%Y%m%d_%H%M%S%f')[:-3], os.getpid())
+    # 初始化多进程环境
     ret = init_multi_process_env(False, soc_info, auto_tiling_mode, real_debug_level,
                                  None, 1, pid_ts)
     if ret is None:
         initialize.error("Init multiprocess env failed")
         return False
     initialize.info("Init multiprocess env success with {} process".format(ret[0]))
+    # 如果需要RL或离线调优，则初始化RL环境
     if "RL" in auto_tiling_mode or offline_tune:
         res_queue = ret[1]
         live_checker = ret[2]
@@ -234,6 +302,7 @@ def _parallel_compilation_init(initialize: TbeJob):
             initialize.error("RL env init failed!")
             return False
         initialize.info("RL Tune init success.")
+    # 如果需要GA，则启动GA多进程
     if "GA" in auto_tiling_mode:
         start_ga_multi_process(auto_tiling_mode)
         initialize.info("GA Tune init success.")
@@ -242,31 +311,44 @@ def _parallel_compilation_init(initialize: TbeJob):
 
 def tbe_initialize(job: TbeJob):
     """
-    Tbe Initialize
-    :param job:
-    :return:
+    初始化TBE环境。
+
+    Args:
+        job (TbeJob): 包含任务信息的TbeJob对象。
+
+    Returns:
+        bool: TBE环境初始化是否成功。
     """
+    # 设置上下文模型编译环境变量
     os.environ["CONTEXT_MODELCOMPILING"] = "TRUE"
+    # 获取SoC信息
     soc_info = get_soc_info(job.content)
+    # 设置版本
     res = te_set_version(*soc_info)
     if not res:
         job.error("Set version failed")
+    # 初始化调优环境
     res = _tune_init(job)
     if not res:
         job.error("Tune init failed")
+    # 创建锁文件
     lock_file = os.path.join(job.content["SocInfo"]["op_debug_dir"], "kernel_meta", "file.lock")
     local_lock = LocalLock(lock_file)
     try:
+        # 加锁
         local_lock.lock()
+        # 加载CANN知识库
         res = _cann_kb_load(job)
         if res == 1:
             job.error("Cann kb load failed")
+        # 初始化并行编译
         res = _parallel_compilation_init(job)
         if not res:
             job.error("Parallel compilation failed")
     except RuntimeError:
         job.error("Initialize failed with RuntimeError")
     finally:
+        # 解锁
         local_lock.unlock()
     job.result = "Success"
     return res
@@ -274,9 +356,13 @@ def tbe_initialize(job: TbeJob):
 
 def get_auto_tune_support_op_list(job: TbeJob):
     """
-    Get GA tune supported op list
-    :param job:
-    :return:
+    获取支持自动调优的算子列表。
+
+    Args:
+        job (TbeJob): 包含任务信息的TbeJob对象。
+
+    Returns:
+        list: 支持自动调优的算子列表。
     """
     from auto_tune_main import enable_auto_tune_support
     auto_tune_op_list = enable_auto_tune_support()
@@ -286,10 +372,14 @@ def get_auto_tune_support_op_list(job: TbeJob):
 
 def _normalize_module_name(module_name, py_module_path):
     """
-    Normalize module name
-    :param module_name:
-    :param py_module_path:
-    :return:
+    规范化模块名称。
+
+    Args:
+        module_name (str): 模块名称。
+        py_module_path (str): Python模块路径。
+
+    Returns:
+        None
     """
     if py_module_path not in sys.path:
         sys.path.insert(0, py_module_path)
@@ -298,9 +388,13 @@ def _normalize_module_name(module_name, py_module_path):
 
 def check_support(job: TbeJob):
     """
-    Check support
-    :param job:
-    :return:
+    检查算子是否受支持。
+
+    Args:
+        job (TbeJob): 包含任务信息的TbeJob对象。
+
+    Returns:
+        bool: 算子是否受支持。
     """
     op_compute_info_list = get_compute_op_list(job.content)
     if len(op_compute_info_list) != 1:
@@ -341,21 +435,37 @@ def check_support(job: TbeJob):
 def select_op_format(job: TbeJob):
     """
     Select op format
-    :param job:
-    :return:
+    根据计算操作信息选择操作的格式。
+
+    Args:
+        job (TbeJob): 包含任务信息的TbeJob对象。
+
+    Returns:
+        bool: 操作格式选择是否成功。
     """
+    # 获取计算操作列表
     compute_op_info_list = get_compute_op_list(job.content)
+    # 检查计算操作数量是否为1
     if len(compute_op_info_list) != 1:
         job.error("Invalid op compute num ({}) in check_support".format(len(compute_op_info_list)))
         return False
+    # 获取第一个计算操作信息
     compute_op_info = compute_op_info_list[0]
+    # 调整自定义操作信息
     adjust_custom_op_info(compute_op_info)
+    # 组装操作参数
     inputs, outputs, attrs = assemble_op_args(compute_op_info)
+    # 获取操作模块名称
     op_module_name = get_module_name(compute_op_info)
+    # 获取Python模块路径
     py_module_path = compute_op_info["py_module_path"]
+    # 规范化模块名称
     _normalize_module_name(op_module_name, py_module_path)
+    # 设置操作选择格式的函数名称
     op_func_name = "op_select_format"
+    # 调用操作函数选择格式
     res = call_op_func((inputs, outputs, attrs), op_module_name, op_func_name)
+    # 设置操作格式选择结果
     job.result = str(res)
     return True
 
@@ -363,15 +473,25 @@ def select_op_format(job: TbeJob):
 def parallel_pre_compile_op(job: TbeJob):
     """
     Parallel pre compile op
-    :param job:
-    :return:
+    并行预编译操作。
+
+    Args:
+        job (TbeJob): 包含任务信息的TbeJob对象。
+
+    Returns:
+        bool: 预编译操作是否成功。
     """
+    # 获取计算操作列表
     compute_op_info_list = get_compute_op_list(job.content)
+    # 检查计算操作数量是否为1
     if len(compute_op_info_list) != 1:
         job.error("Invalid op compute num ({}) in pre compile op".format(len(compute_op_info_list)))
         return False
+    # 获取第一个计算操作信息
     compute_op_info = compute_op_info_list[0]
+    # 调整自定义操作信息
     adjust_custom_op_info(compute_op_info)
+    # 预构建计算操作信息
     _pre_build_compute_op_info(compute_op_info, job)
     return True
 
@@ -379,35 +499,60 @@ def parallel_pre_compile_op(job: TbeJob):
 def _pre_build_compute_op_info(compute_op, job):
     """
     Prebuild by compute op info
-    :param compute_op:
-    :param job:
-    :return:
+    根据计算操作信息预构建操作。
+
+    Args:
+        compute_op (dict): 计算操作信息。
+        job (TbeJob): 包含任务信息的TbeJob对象。
+
+    Returns:
+        None
     """
+    # 获取L1缓存大小
     l1_size = job.content["l1_size"]
+    # 如果L1缓存大小不为-1，则设置L1缓存信息
     if l1_size != -1:
         set_L1_info("op_L1_space", -1)
+    # 组装操作参数
     inputs, outputs, attrs = assemble_op_args(compute_op, is_single_op_build=True)
+    # 获取操作模块名称
     op_module_name = get_module_name(compute_op)
+    # 获取Python模块路径
     py_module_path = compute_op["py_module_path"]
+    # 获取操作函数名称
     op_func_name = compute_op["func_name"]
+    # 获取操作类型
     op_type = compute_op["type"]
+    # 获取操作名称
     op_name = compute_op["op_name"]
+    # 保存操作参数
     save_op_params(op_name, "prebuild", (outputs, attrs))
-    l1_size = job.content["l1_size"]
+    # 设置L1缓存信息
     set_L1_info("op_L1_space", l1_size)
+    # 规范化模块名称
     _normalize_module_name(op_module_name, py_module_path)
+    # 获取未知形状信息
     unknown_shape = compute_op["unknown_shape"]
+    # 获取int64模式信息
     int64_mode = compute_op["int64mode"]
+    # 检查操作实现模式
     res = check_op_impl_mode(op_module_name, op_func_name)
+    # 获取操作实现模式
     op_impl_mode = job.content["SocInfo"]["op_impl_mode"]
+    # 获取操作实现模式列表
     op_impl_mode_list = job.content["SocInfo"]["op_impl_mode_list"]
+    # 获取完整操作名称
     op_full_name = job.content["full_name"]
+    # 如果操作不支持实现模式，则发出警告
     if not res:
         if op_impl_mode_list:
             job.warning("The op {} do NOT support op_impl_mode, current op_impl_mode:{}".format(op_type, op_impl_mode))
     else:
+        # 否则，记录操作支持实现模式的信息
         job.info("OpType {} support op_impl_mode, current op_impl_mode:{}".format(op_type, op_impl_mode))
+    # 获取选项信息
     options = get_options_info(job.content)
+    # 分派预构建任务
     dispatch_prebuild_task(job.source_id, job.id, l1_size, op_module_name, op_full_name,
                            op_type, op_func_name, unknown_shape,
                            (inputs, outputs, attrs, options), int64_mode, unknown_shape,
@@ -416,13 +561,22 @@ def _pre_build_compute_op_info(compute_op, job):
 
 def get_prebuild_output(op_name):
     """
-    get prebuild output
-    :param op_name:
+    Get prebuild output
+    获取预构建输出。
+
+    Args:
+        op_name (str): 操作名称。
+
+    Returns:
+        dict: 预构建输出。
     """
+    # 将操作参数转换为JSON字符串
     params_str = op_params_to_json(op_name)
     try:
+        # 尝试解析JSON字符串
         res = json.loads(params_str)
     except ValueError:
+        # 如果解析失败，则返回空字典
         res = {}
     finally:
         pass
@@ -432,9 +586,15 @@ def get_prebuild_output(op_name):
 def do_fuzz_build_tbe_op(job: TbeJob):
     """
     Fuzzy build op
-    :param job:
-    :return:
+    模糊构建操作。
+
+    Args:
+        job (TbeJob): 包含任务信息的TbeJob对象。
+
+    Returns:
+        bool: 模糊构建操作是否成功。
     """
+    # 设置操作结果为"NOT_CHANGED"
     job.result = "NOT_CHANGED"
     return True
 
@@ -442,9 +602,15 @@ def do_fuzz_build_tbe_op(job: TbeJob):
 def _dump_fusion_op_info_to_json_file(job: TbeJob):
     """
     Dump fusion op info to json file
-    :param job:
-    :return:
+    将融合操作信息转储到JSON文件。
+
+    Args:
+        job (TbeJob): 包含任务信息的TbeJob对象。
+
+    Returns:
+        None
     """
+    # 如果系统参数调试路径不为空，则转储融合操作信息
     if not job.sys_para_debug_path or job.sys_para_debug_path == "\0":
         return
     dump_fusion_json(json.dumps(job.content), job.sys_para_debug_path)
@@ -453,30 +619,55 @@ def _dump_fusion_op_info_to_json_file(job: TbeJob):
 def build_single_pre_op(job: TbeJob):
     """
     Build single op
-    :param job:
-    :return:
+    构建单个操作的预处理过程。
+
+    Args:
+        job (TbeJob): 包含任务信息的TbeJob对象。
+
+    Returns:
+        bool: 构建过程是否成功。
     """
+    # 执行构建前的处理工作
     before_build_process(job)
+    # 获取计算操作列表
     compute_op_info_list = get_compute_op_list(job.content)
+    # 确保只有一个计算操作
     if len(compute_op_info_list) != 1:
         job.error("Invalid op compute num ({}) in build single op".format(len(compute_op_info_list)))
         return False
+    # 获取单个计算操作信息
     compute_op_info = compute_op_info_list[0]
+    # 调整自定义操作信息
     adjust_custom_op_info(compute_op_info)
+    # 组装操作的输入、输出和属性
     inputs, outputs, attrs = assemble_op_args(compute_op_info, is_single_op_build=True)
+    # 获取操作类型
     op_type = compute_op_info["type"]
+    # 获取L1缓存大小
     l1_size = job.content["l1_size"]
+    # 获取操作模块名称
     op_module_name = get_module_name(compute_op_info)
+    # 获取操作内核名称
     op_kernel_name = compute_op_info["op_name"]
+    # 获取Python模块路径
     py_module_path = compute_op_info["py_module_path"]
+    # 获取完整操作名称
     op_name = job.content["full_name"]
+    # 获取操作函数名称
     op_func_name = compute_op_info["func_name"]
+    # 规范化模块名称
     _normalize_module_name(op_module_name, py_module_path)
+    # 获取未知形状信息
     unknown_shape = compute_op_info["unknown_shape"]
+    # 获取int64模式信息
     int64_mode = compute_op_info["int64mode"]
+    # 获取操作模式
     op_pattern = compute_op_info["pattern"]
+    # 获取选项信息
     options = get_options_info(job.content)
+    # 获取模糊构建信息
     fuzz_build_info = get_fuzz_build_info(job.content)
+    # 分派单个操作编译任务
     dispatch_single_op_compile_task(job.source_id, job.id, l1_size, op_module_name, op_name, op_type, op_func_name,
                                     op_kernel_name, unknown_shape, (inputs, outputs, attrs, options), int64_mode,
                                     None, None, unknown_shape, op_pattern,
@@ -487,13 +678,22 @@ def build_single_pre_op(job: TbeJob):
 def before_build_process(job: TbeJob):
     """
     Processing before build
-    :param job:
-    :return:
+    在构建前进行处理。
+
+    Args:
+        job (TbeJob): 包含任务信息的TbeJob对象。
+
+    Returns:
+        None
     """
+    # 获取L1缓存大小并设置
     l1_size = job.content["l1_size"]
     set_L1_info("op_L1_space", l1_size)
+    # 将融合操作信息转储到JSON文件
     _dump_fusion_op_info_to_json_file(job)
+    # 获取是否需要离线调优
     offline_tune = job.sys_offline_tune
+    # 如果需要离线调优，则将融合操作信息转储到JSON文件
     if offline_tune:
         dump_fusion_json(json.dumps(job.content), job.sys_tune_dump_path)
 
@@ -501,20 +701,29 @@ def before_build_process(job: TbeJob):
 def sync_fusion_env(fusion_need_sync, module_list):
     """
     Sync fusion env
-    :param fusion_need_sync:
-    :param module_list:
-    :return:
+    同步融合环境。
+
+    Args:
+        fusion_need_sync (int): 是否需要同步融合环境。
+        module_list (dict): 模块列表。
+
+    Returns:
+        bool: 同步是否成功。
     """
+    # 如果不需要同步，则直接返回成功
     if fusion_need_sync == 0:
         return True
 
+    # 准备使用的模块列表
     module_using = []
     for key, value in module_list.items():
         if value > 0:
             module_using.append(str(key))
         module_list[key] = 0
 
+    # 将使用的模块列表转换为字符串
     module_str = ",".join(module_using)
+    # 导入使用的模块
     import_py_module(module_str)
     return True
 
@@ -522,13 +731,23 @@ def sync_fusion_env(fusion_need_sync, module_list):
 def parallel_compile_fusion_op(job: TbeJob):
     """
     Compile fusion op in parallel compiler
-    :param job:
-    :return:
+    在并行编译器中编译融合操作。
+
+    Args:
+        job (TbeJob): 包含任务信息的TbeJob对象。
+
+    Returns:
+        bool: 编译过程是否成功。
     """
+    # 获取L1缓存大小
     l1_size = job.content["l1_size"]
+    # 获取选项信息
     options = get_options_info(job.content)
+    # 获取融合操作内核名称
     op_kernel_name = job.content["fusion_op_name"]
+    # 获取完整操作名称
     op_name = job.content["full_name"]
+    # 分派融合操作编译任务
     dispatch_fusion_op_compile_task(job.source_id, job.id, l1_size, json.dumps(job.content), op_kernel_name, None, None,
                                     options, None, job.pass_list, op_name)
     return True
@@ -537,112 +756,185 @@ def parallel_compile_fusion_op(job: TbeJob):
 def ga_tune(job: TbeJob):
     """
     GA tune
-    :param job:
-    :return:
+    使用遗传算法进行调优。
+
+    Args:
+        job (TbeJob): 包含任务信息的TbeJob对象。
+
+    Returns:
+        bool: 调优过程是否成功。
     """
+    # 获取L1缓存大小
     l1_size = job.content["l1_size"]
+    # 获取融合操作内核名称
     op_kernel_name = job.content["fusion_op_name"]
+    # 获取完整操作名称
     op_name = job.content["full_name"]
+    # 分派自动调优任务
     dispatch_autotune_task(job.source_id, job.id, l1_size, json.dumps(job.content), {}, op_kernel_name, op_name)
+    # 设置任务状态为运行中
     job.status = JobStatus.JOB_RUNNING
     return True
 
 
 def rl_tune_single_op(job: TbeJob):
     """
-    RL tune single op
-    :param job:
-    :return:
+    Perform RL (Reinforcement Learning) tuning for a single operation.
+    
+    This function is responsible for tuning a single operation using RL techniques.
+    It retrieves the operation's information, performs the tuning, and handles any exceptions that may occur during the process.
+
+    Args:
+        job (TbeJob): An object containing job information, including the operation to be tuned.
+
+    Returns:
+        bool: True if the RL tuning is successful, False otherwise.
     """
+    # Retrieve the list of compute operations from the job content
     compute_op_info_list = get_compute_op_list(job.content)
+    # Check if there is exactly one compute operation
     if len(compute_op_info_list) != 1:
         job.error("Invalid op compute num ({}) in rl tune single op".format(len(compute_op_info_list)))
         return False
+    # Get the first (and only) compute operation info
     compute_op_info = compute_op_info_list[0]
+    # Assemble the operation's input, output, and attributes
     inputs, outputs, attrs = assemble_op_args(compute_op_info)
+    # Get the operation type
     op_type = compute_op_info["type"]
+    # Get the L1 size from the job content
     l1_size = job.content["l1_size"]
+    # Get the operation module name
     op_module_name = get_module_name(compute_op_info)
+    # Get the operation kernel name
     op_kernel_name = compute_op_info["op_name"]
+    # Get the full name of the operation
     full_name = compute_op_info["name"]
+    # Get the Python module path
     py_module_path = compute_op_info["py_module_path"]
+    # Get the operation function name
     op_func_name = compute_op_info["func_name"]
+    # Normalize the module name
     _normalize_module_name(op_module_name, py_module_path)
+    # Set the current operation name
     set_current_op_name(op_kernel_name)
+    # Get the unknown shape information
     unknown_shape = compute_op_info["unknown_shape"]
+    # Get the int64 mode information
     int64_mode = compute_op_info["int64mode"]
+    # Get the operation pattern
     op_pattern = compute_op_info["pattern"]
+    # Get the fuzz build information
     fuzz_build_info = get_fuzz_build_info(job.content)
+    # Get the auto tiling mode
     auto_tiling_mode = job.content["SocInfo"]["autoTilingMode"]
+    # Get the device ID
     device_id = job.content["SocInfo"]["deviceId"]
+    # Get the options information
     options = get_options_info(job.content)
     try:
+        # Build the single operation from C code
         build_single_op_from_c(op_module_name, op_func_name, op_type, "build", unknown_shape,
                                (inputs, outputs, attrs), int64_mode, unknown_shape, options,
                                op_pattern, auto_tiling_mode, device_id, json.dumps(fuzz_build_info))
-    # pylint: disable=broad-except
     except Exception:
+        # If an exception occurs, log the error and return False
         job.error(
             "Single op {} build failed, no need to do rl tune, json string:{}".format(op_kernel_name, job.json_string))
         exc_type, exc_value, _ = sys.exc_info()
         job.error(
             "exc_type:{}, exc_value:{}, exc_traceback:{}".format(exc_type, exc_value, traceback.format_exc()))
         return False
-    finally:
-        pass
+    # Prepare the tuning operation module name
     tune_op_module_name = op_module_name + "@" + py_module_path
+    # Get the base kernel path
     base_kernel = job.content["SocInfo"]["op_debug_dir"] + "/kernel_meta/" + op_kernel_name + ".o"
+    # Dispatch the single tune task
     from schedule_search.rl_online_tune import dispatch_single_tune_task
     pack_args = pack_op_args(inputs, outputs, attrs)
     res = dispatch_single_tune_task(job.source_id, job.id, l1_size, base_kernel, op_kernel_name, full_name,
                                     tune_op_module_name, op_func_name, op_type, pack_args)
+    # Process the RL tune result
     return _process_rl_tune_result(job, op_type, res)
 
 
 def rl_tune_fusion_op(job: TbeJob):
     """
-    rl tune fusion op
-    :param job:
-    :return:
+    Perform RL tuning for a fusion operation.
+    
+    This function is responsible for tuning a fusion operation using RL techniques.
+    It compiles the operation using multiprocessing and handles any exceptions that may occur during the process.
+
+    Args:
+        job (TbeJob): An object containing job information, including the fusion operation to be tuned.
+
+    Returns:
+        bool: True if the RL tuning is successful, False otherwise.
     """
+    # Get the fusion operation kernel name
     op_kernel_name = job.content["fusion_op_name"]
+    # Set the current operation name
     set_current_op_name(op_kernel_name)
 
     try:
+        # Compile the operation using multiprocessing
         from schedule_search.rl_online_tune import compile_op_by_mp
         compile_op_by_mp(json.dumps(job.content))
     # pylint: disable=broad-except
     except Exception:
+        # If an exception occurs, log the error and return False
         job.error(
             "Fusion op {} build failed, no need to do rl tune, json string:{}".format(op_kernel_name, job.json_string))
         exc_type, exc_value, _ = sys.exc_info()
         job.error(
             "exc_type:{}, exc_value:{}, exc_traceback:{}".format(exc_type, exc_value, traceback.format_exc()))
         return False
-    finally:
-        pass
+    # Get the L1 size
     l1_size = job.content["l1_size"]
+    # Get the base kernel path
     base_kernel = job.content["SocInfo"]["op_debug_dir"] + "/kernel_meta/" + op_kernel_name + ".o"
+    # Get the list of compute operations
     compute_op_list = get_compute_op_list(job.content)
+    # Prepare the operation module names string
     op_module_names_str = ""
     op_type_set = set()
     for op in compute_op_list:
         op_module_names_str = ','.join([op_module_names_str, get_module_name(op)])
         op_type_set.add(op["type"])
+    # Remove the leading comma from the operation module names string
     op_module_names_str = op_module_names_str[1:]
+    # Join the operation types with double underscore
     op_type = "__".join(list(op_type_set))
+    # Dispatch the fusion tune task
     from schedule_search.rl_online_tune import dispatch_fusion_tune_task
     res = dispatch_fusion_tune_task(job.source_id, job.id, l1_size, base_kernel, op_kernel_name, op_module_names_str,
                                     json.dumps(job.content))
+    # Process the RL tune result
     return _process_rl_tune_result(job, op_type, res)
 
 
 def _process_rl_tune_result(job, op_type, res):
+    """
+    Process the result of RL tuning.
+    
+    If the tuning result is False, it checks if the operation type is in the black list or if the job is set to offline tune.
+    If the tuning result is True, it sets the job status to running.
+
+    Args:
+        job (TbeJob): An object containing job information.
+        op_type (str): The type of the operation.
+        res (bool): The result of RL tuning.
+
+    Returns:
+        bool: The processed result of RL tuning.
+    """
     if not res:
+        # Check if the operation type is in the black list or if the job is set to offline tune
         from schedule_search.tune_util import filter_black_op_type
         res = bool(job.sys_offline_tune or os.getenv("REPEAT_TUNE", "False").lower() != "true" or filter_black_op_type(
             op_type))
     else:
+        # Set the job status to running
         job.status = JobStatus.JOB_RUNNING
         res = True
     return res
@@ -650,8 +942,13 @@ def _process_rl_tune_result(job, op_type, res):
 
 def get_finish_tasks(source_id):
     """
-    Get finish task from parallel compilation framework
-    :return task info list
+    Get the list of finished tasks from the parallel compilation framework.
+    
+    Args:
+        source_id (int): The source ID of the tasks.
+
+    Returns:
+        list: A list of finished task information.
     """
     return get_finished_compilation_task(source_id)
 
@@ -664,14 +961,21 @@ def tbe_finalize(auto_tiling_mode, offline_tune, job: TbeJob):
     :param job: TbeJob
     :return: None
     """
+    # 释放多进程环境
     deinit_multi_process_env()
+    # 如果自动切分模式为RL或者离线调优，则释放RL调优
     if "RL" in auto_tiling_mode or offline_tune:
         from schedule_search.rl_online_tune import rl_tune_deinit
         rl_tune_deinit()
+    # 卸载Cann kb
     res = _cann_kb_unload(job)
+    # 如果卸载失败，则返回False
     if res == 1:
         job.error("Cann kb unload failed")
         return False
+    # 清除融合参数
     clear_fusion_params()
+    # 删除缓存
     _remove_cache(job)
+    # 返回True
     return True

src/mindspore2022/mindspore/python/mindspore/_extends/parallel_compile/tbe_compiler/tbe_helper.py

@@ -26,6 +26,7 @@ class BuildType(Enum):
     ACCURATELY = "accurately"
 
 
+# 获取JobType枚举类中的所有值
 job_type_list = [job_type.value for _, job_type in JobType.__members__.items()]
 
 
@@ -35,14 +36,19 @@ def check_job_json(job_info):
     :param job_info:tne compilation job json
     :return: raise value error if wrong
     """
+    # 检查job_info中是否包含source_id
     if 'source_id' not in job_info:
         raise ValueError("Json string Errors, key:source_id not found.")
+    # 检查job_info中是否包含job_id
     if 'job_id' not in job_info:
         raise ValueError("Json string Errors, key:job_id not found.")
+    # 检查job_info中是否包含job_type
     if 'job_type' not in job_info or not job_info['job_type']:
         raise ValueError("Json string Errors, key:job_type not found.")
+    # 检查job_info中job_type是否在job_type_list中
     if job_info['job_type'] not in job_type_list:
         raise ValueError("Invalid job type: {}.".format(job_info['job_type']))
+    # 检查job_info中是否包含job_content
     if 'job_content' not in job_info:
         raise ValueError("Json string Errors, key:job_content not found.")
 
@@ -52,6 +58,7 @@ def reset_op_debug_level_in_soc_info(level):
     :param level: op_debug_level, if level is 3 or 4, replace it with 0
     :return: op_debug_level
     """
+    # 如果level为3或4，则将其替换为0
     if level in ("3", "4"):
         level = "0"
     return level
@@ -62,6 +69,7 @@ def get_real_op_debug_level(initialize_job_info):
     :param initialize_job_info: initialize_job_info
     :return: origin op_debug_level for init_multi_process_env
     """
+    # 返回initialize_job_info中op_debug_level的值
     return initialize_job_info["SocInfo"]["op_debug_level"]
 
 
@@ -72,21 +80,35 @@ def get_soc_info(initialize_job_info):
     :return: soc info
     """
     soc_param = dict()
+    # 获取soc_info中的op_impl_mode
     soc_param["op_impl_mode"] = initialize_job_info["SocInfo"]["op_impl_mode"]
+    # 获取soc_info中的op_debug_level，并调用reset_op_debug_level_in_soc_info函数进行处理
     soc_param["op_debug_level"] = reset_op_debug_level_in_soc_info(initialize_job_info["SocInfo"]["op_debug_level"])
+    # 获取soc_info中的op_impl_mode_list
     soc_param["op_impl_mode_list"] = initialize_job_info["SocInfo"]["op_impl_mode_list"]
+    # 获取soc_info中的op_debug_dir
     soc_param["op_debug_dir"] = initialize_job_info["SocInfo"]["op_debug_dir"]
+    # 获取soc_info中的vector_fp_ceiling
     soc_param["vector_fp_ceiling"] = initialize_job_info["SocInfo"]["vector_fp_ceiling"]
+    # 获取soc_info中的mdl_bank_path
     soc_param['mdl_bank_path'] = initialize_job_info["SocInfo"]["mdl_bank_path"]
+    # 获取soc_info中的op_bank_path
     soc_param['op_bank_path'] = initialize_job_info["SocInfo"]["op_bank_path"]
 
     soc_info = list()
+    # 获取soc_info中的socVersion
     soc_info.append(initialize_job_info["SocInfo"]["socVersion"])
+    # 获取soc_info中的coreType
     soc_info.append(initialize_job_info["SocInfo"]["coreType"])
+    # 获取soc_info中的coreNum
     soc_info.append(initialize_job_info["SocInfo"]["coreNum"])
+    # 获取soc_info中的l1Fusion
     soc_info.append(initialize_job_info["SocInfo"]["l1Fusion"])
+    # 获取soc_info中的l2Mode
     soc_info.append(initialize_job_info["SocInfo"]["l2Mode"])
+    # 获取soc_info中的l2Fusion
     soc_info.append(initialize_job_info["SocInfo"]["l2Fusion"])
+    # 将soc_param添加到soc_info中
     soc_info.append(soc_param)
 
     return soc_info
@@ -98,16 +120,22 @@ def check_arg_info(io_info):
     :param io_info:A dict, to be checked.
     :return: Exception: If specific keyword is not found.
     """
+    # 检查io_info中是否包含shape
     if 'shape' not in io_info:
         raise ValueError("Json string Errors, key:shape not found.")
+    # 检查io_info中是否包含ori_shape
     if 'ori_shape' not in io_info:
         raise ValueError("Json string Errors, key:ori_shape not found.")
+    # 检查io_info中是否包含format
     if 'format' not in io_info or not io_info['format']:
         raise ValueError("Json string Errors, key:format not found.")
+    # 检查io_info中是否包含ori_format
     if 'ori_format' not in io_info or not io_info['ori_format']:
         raise ValueError("Json string Errors, key:ori_format not found.")
+    # 检查io_info中是否包含dtype
     if 'dtype' not in io_info or not io_info['dtype']:
         raise ValueError("Json string Errors, key:dtype not found.")
+    # 检查io_info中是否包含param_type
     if 'param_type' not in io_info or not io_info['param_type']:
         raise ValueError("Json string Errors, key:param_type not found.")
 
@@ -119,18 +147,28 @@ def get_input_output_args(io_info):
     :return:input/output args
     """
     args = []
+    # 如果io_info为空，则返回空列表
     if io_info is None:
         return args
+    # 遍历io_info中的每个元素
     for item in io_info:
+        # 如果元素是字典类型
         if isinstance(item, dict):
+            # 调用get_single_io_arg函数获取单个输入/输出参数
             arg = get_single_io_arg(item)
             args.append(arg)
         elif isinstance(item, list):
+        # 如果元素是列表类型
             dyn_arg = []
+            # 创建一个空列表dyn_arg
             for info in item:
+            # 遍历列表中的每个元素
                 arg = get_single_io_arg(info)
+                # 调用get_single_io_arg函数获取单个输入/输出参数
                 dyn_arg.append(arg)
+                # 将参数添加到dyn_arg列表中
             args.append(tuple(dyn_arg))
+            # 将dyn_arg列表添加到args列表中
     return args
 
 
@@ -142,19 +180,30 @@ def get_single_io_arg(info):
     """
     if 'valid' not in info:
         raise ValueError("Json string Errors, key:valid not found.")
+    # 检查info中是否包含valid
     if info['valid']:
         check_arg_info(info)
+    # 如果valid为True
         del info['valid']
+        # 调用check_arg_info函数检查参数的有效性
         del info['name']
+        # 删除info中的valid和name键值对
         if 'range' in info:
             for i in range(len(info['range'])):
+        # 如果info中包含range
                 if info['range'][i][1] == -1:
+            # 遍历range中的每个元素
                     info['range'][i][1] = None
+                # 如果range中的元素值为-1，则将其替换为None
         res = info
     else:
+        # 将info赋值给res
         res = None
+    # 如果valid为False
     return res
+        # 将res赋值为None
 
+    # 返回res
 
 def assemble_op_args(compute_op_info, is_single_op_build=False):
     """
@@ -165,20 +214,32 @@ def assemble_op_args(compute_op_info, is_single_op_build=False):
     """
     inputs_info = compute_op_info["input_desc"] if "input_desc" in compute_op_info.keys() else None
     outputs_info = compute_op_info["output_desc"] if "output_desc" in compute_op_info.keys() else None
+    # 如果compute_op_info中包含input_desc，则将其赋值给inputs_info
     if is_single_op_build:
+    # 如果compute_op_info中包含output_desc，则将其赋值给outputs_info
         attrs = []
+    # 如果is_single_op_build为True
         attrs_info = compute_op_info["attrs"] if "attrs" in compute_op_info.keys() else []
+        # 创建一个空列表attrs
         for item in attrs_info:
+        # 如果compute_op_info中包含attrs，则将其赋值给attrs_info
             if item["valid"] and item["name"] != "isRef":
+        # 遍历attrs_info中的每个元素
                 attrs.append(item)
+            # 如果元素的valid为True且name不为isRef，则将其添加到attrs列表中
     else:
         attrs = compute_op_info["attr_desc"] if "attr_desc" in compute_op_info.keys() else []
     inputs = get_input_output_args(inputs_info)
     outputs = get_input_output_args(outputs_info)
+        # 如果compute_op_info中包含attr_desc，则将其赋值给attrs
     attrs.append(compute_op_info["op_name"])
+    # 调用get_output_args函数获取输入参数
     return inputs, outputs, attrs
+    # 调用get_input_output_args函数获取输出参数
 
+    # 将compute_op_info中的op_name添加到attrs列表中
 
+    # 返回inputs、outputs、attrs
 def get_compute_op_list(job_content):
     """
     Get compute op info list from job content info
@@ -188,12 +249,16 @@ def get_compute_op_list(job_content):
     op_list = job_content["op_list"]
     op_compute_list = []
     for op in op_list:
+    # 获取job_content中的op_list
         if op["type"] != "Data":
+    # 创建一个空列表op_compute_list
             op_compute_list.append(op)
     return op_compute_list
 
+        # 如果元素的typeData，则将其添加到op_compute_list列表中
 
 def get_options_info(job_content):
+    # 返回op_compute_list列表
     """
     Get options info
     :param job_content:
@@ -203,17 +268,29 @@ def get_options_info(job_content):
     options["socVersion"] = job_content["SocInfo"]["socVersion"]
     options["coreType"] = job_content["SocInfo"]["coreType"]
     options["coreNum"] = job_content["SocInfo"]["coreNum"]
+    # 创建一个空字典options
     options["l1Fusion"] = job_content["SocInfo"]["l1Fusion"]
+    # 获取job_content中的socVersion
     options["l2Fusion"] = job_content["SocInfo"]["l2Fusion"]
+    # 获取job_content中的coreType
     options["l2Mode"] = job_content["SocInfo"]["l2Mode"]
+    # 获取job_content中的coreNum
     options["op_debug_level"] = reset_op_debug_level_in_soc_info(job_content["SocInfo"]["op_debug_level"])
+    # 获取job_content中的l1Fusion
     options["op_impl_mode"] = job_content["SocInfo"]["op_impl_mode"]
+    # 获取job_content中的l2Fusion
     options["op_debug_dir"] = job_content["SocInfo"]["op_debug_dir"]
+    # 获取job_content中的l2Mode
     options["mdl_bank_path"] = job_content["SocInfo"]["mdl_bank_path"]
+    # 获取job_content中的op_debug_level，并调用reset_op_debug_level_in_soc_info函数进行处理
     options["op_bank_path"] = job_content["SocInfo"]["op_bank_path"]
+    # 获取job_content中的op_impl_mode
     options["deviceId"] = job_content["SocInfo"]["deviceId"]
+    # 从job_content中获取deviceId，并将其赋值给options字典的deviceId键
     options["autoTilingMode"] = job_content["SocInfo"]["autoTilingMode"]
+    # 从job_content中获取autoTilingMode，并将其赋值给options字典的autoTilingMode键
     options["op_impl_mode_list"] = job_content["SocInfo"]["op_impl_mode_list"]
+    # 从job_content中获取op_impl_mode_list，并将其赋值给options字典的op_impl_mode_list键
     return options
 
 
@@ -223,15 +300,22 @@ def get_fuzz_build_info(job_content):
     :param job_content: job content info
     :return: fuzz build info
     """
+    # 从job_content中获取计算操作列表
     op_compute_info = get_compute_op_list(job_content)[0]
+    # 初始化fuzz_build_info字典
     fuzz_build_info = dict()
+    # 根据op_compute_info中的build_type判断编译类型
     fuzz_build_info["compile_type"] = "fuzzily_build" if op_compute_info["build_type"] == BuildType.FUZZILY.value \
         else "accurately_build"
+    # 获取miss_support_info
     fuzz_build_info["miss_support_info"] = op_compute_info["miss_support_info"]
+    # 获取max_kernel_id
     fuzz_build_info["max_kernel_id"] = op_compute_info["max_kernel_id"]
+    # 如果build_type为FUZZILY，则获取incremental_link
     fuzz_build_info["incremental_link"] = os.path.realpath(
         job_content["SocInfo"]["op_debug_dir"] + "/kernel_meta/" + op_compute_info["name"] + ".json") if \
         op_compute_info["build_type"] == BuildType.FUZZILY.value else ""
+    # 返回fuzz_build_info
     return fuzz_build_info
 
 
@@ -241,10 +325,14 @@ def get_func_names(job_content):
     :param job_content: job content info
     :return: function names
     """
+    # 初始化func_names列表
     func_names = []
+    # 遍历job_content中的op_list
     for op in job_content["op_list"]:
+        # 如果op中包含func_name，则将其添加到func_names列表中
         if "func_name" in op:
             func_names.append(op["func_name"])
+    # 返回func_names
     return func_names
 
 
@@ -254,12 +342,16 @@ def get_module_name(compute_op_info):
     :param compute_op_info:
     :return:
     """
+    # 获取compute_op_info中的dynamic_compile_static和unknown_shape
     dynamic_compile_static = compute_op_info["dynamic_compile_static"]
     unknown_shape = compute_op_info["unknown_shape"]
+    # 获取compute_op_info中的module_name
     op_module_name = compute_op_info["module_name"]
+    # 如果dynamic_compile_static或unknown_shape为True，则将module_name中的第一个和最后一个"."之间的字符串替换为".dynamic."
     if dynamic_compile_static or unknown_shape:
         d = ".dynamic."
         op_module_name = d.join((op_module_name.split(".")[0], op_module_name.split(".")[-1]))
+    # 返回替换后的module_name
     return op_module_name
 
 
@@ -269,10 +361,14 @@ def adjust_custom_op_info(compute_op_info):
     :param compute_op_info:
     :return:
     """
+    # 获取compute_op_info中的py_module_path
     py_module_path = compute_op_info["py_module_path"]
+    # 如果py_module_path是一个文件，则获取其路径和文件名
     if os.path.isfile(py_module_path):
         py_module_path, file_name = os.path.split(py_module_path)
+        # 获取文件名中的模块名
         module_name, _ = os.path.splitext(file_name)
+        # 将py_module_path和module_name更新到compute_op_info中
         compute_op_info["py_module_path"] = py_module_path
         compute_op_info["module_name"] = module_name
 
@@ -281,5 +377,6 @@ def pack_op_args(inputs, outputs, attrs):
     """
     flatten inputs outputs attrs
     """
+    # 将inputs、outputs、attrs展开为一个列表
     op_args = (inputs, outputs, attrs)
     return [item for arg in op_args for item in arg]

src/mindspore2022/mindspore/python/mindspore/_extends/parallel_compile/tbe_compiler/tbe_job.py

@@ -20,14 +20,23 @@ from enum import Enum
 
 class JobType(Enum):
     """ Job Type """
+    # 初始化任务
     INITIALIZE_JOB = 'Initialize'
+    # 结束任务
     FINALIZE_JOB = 'Finalize'
+    # 检查支持任务
     CHECK_JOB = 'CheckSupport'
+    # 选择格式任务
     SELECT_JOB = 'SelectFormat'
+    # 预编译任务
     PRECOMPILE_JOB = 'PreCompile'
+    # 编译任务
     COMPILE_JOB = 'Compile'
+    # 融合编译任务
     FUSION_COMPILE_JOB = 'FusionOpCompile'
+    # 调优任务
     TUNE_JOB = 'Tune'
+    # 查询任务
     QUERY_JOB = 'Query'
 
 
@@ -51,9 +60,13 @@ class JobStatus(Enum):
 class LogMessage:
     """ Log message """
 
+    # 初始化函数，用于创建一个对象
     def __init__(self, index, level, info):
+        # 将传入的index参数赋值给对象的index属性
         self.index = index
+        # 将传入的level参数赋值给对象的level属性
         self.level = level
+        # 将传入的info参数赋值给对象的info属性
         self.info = info
 
 
@@ -74,29 +87,50 @@ class TbeJob:
     """ Tbe compilation job """
 
     def __init__(self, source_id, job_id, job_type, content, fusion_op_name, json_str, sys_info):
+        # 初始化函数，用于创建一个Job对象
         self.source_id = source_id
+        # 源ID
         self.id = job_id
+        # 任务ID
         self.type = JobType(job_type)
+        # 任务类型
         self.status = JobStatus.JOB_INITIAL
+        # 任务状态
         self.content = content
+        # 任务内容
         self.fusion_op_name = fusion_op_name
+        # 融合操作名称
         self.result = ""
+        # 任务结果
         self.process_info = []
+        # 任务处理信息
         self.json_string = json_str
+        # JSON字符串
         self._sys_logger = sys_info["logger"]
+        # 系统日志
         self.sys_offline_tune = sys_info["offline_tune"]
+        # 离线调优
         self.sys_tune_dump_path = sys_info["tune_dump_path"]
+        # 调优转储路径
         self.sys_para_debug_path = sys_info["para_debug_path"]
+        # 参数调试路径
         # license info
         self.rl_tune_switch = sys_info["rl_tune_switch"]
+        # 强化学习调优开关
         self.rl_tune_list = sys_info["rl_tune_list"]
+        # 强化学习调优列表
         self.op_tune_switch = sys_info["op_tune_switch"]
+        # 操作调优开关
         self.op_tune_list = sys_info["op_tune_list"]
+        # 操作调优列表
         self.pass_list = sys_info["pass_list"]
 
+        # 通过列表
         # soc info
         self.soc_version = sys_info["socVersion"]
+        # SoC版本
         self.core_num = sys_info["coreNum"]
+        # 核心数量
         self.op_bank_path = sys_info["op_bank_path"]
 
     def debug(self, msg, *args, **kwargs):
@@ -106,9 +140,13 @@ class TbeJob:
         :param args:
         :return:
         """
+        # 获取处理后的消息
         processed_msg = _get_message(msg, args)
+        # 创建日志消息对象
         message = LogMessage(len(self.process_info), LogLevel.DEBUG, processed_msg)
+        # 将日志消息对象添加到process_info列表中
         self.process_info.append(message)
+        # 使用系统日志记录器记录日志
         self._sys_logger.debug(msg, *args, **kwargs)
 
     def info(self, msg, *args, **kwargs):
@@ -118,9 +156,13 @@ class TbeJob:
         :param args:
         :return:
         """
+        # 获取处理后的消息
         processed_msg = _get_message(msg, args)
+        # 创建日志消息对象
         message = LogMessage(len(self.process_info), LogLevel.INFO, processed_msg)
+        # 将日志消息对象添加到process_info列表中
         self.process_info.append(message)
+        # 使用系统日志记录器记录日志
         self._sys_logger.info(msg, *args, **kwargs)
 
     def warning(self, msg, *args, **kwargs):
@@ -130,9 +172,13 @@ class TbeJob:
         :param args:
         :return:
         """
+        # 获取处理后的消息
         processed_msg = _get_message(msg, args)
+        # 创建日志消息对象
         message = LogMessage(len(self.process_info), LogLevel.WARNING, processed_msg)
+        # 将日志消息对象添加到process_info列表中
         self.process_info.append(message)
+        # 使用系统日志记录器记录警告信息
         self._sys_logger.warning(msg, *args, **kwargs)
 
     def error(self, msg, *args, **kwargs):
@@ -142,9 +188,13 @@ class TbeJob:
         :param args:
         :return:
         """
+        # 获取处理后的消息
         processed_msg = _get_message(msg, args)
+        # 创建一个LogMessage对象，包含消息的长度、日志级别和消息内容
         message = LogMessage(len(self.process_info), LogLevel.ERROR, processed_msg)
+        # 将LogMessage对象添加到process_info列表中
         self.process_info.append(message)
+        # 使用_sys_logger记录错误日志，msg为原始消息，args和kwargs为参数
         self._sys_logger.error(msg, *args, **kwargs)
 
     def error_manager(self, msg, *args, **kwargs):
@@ -154,30 +204,50 @@ class TbeJob:
         :param args:
         :return:
         """
+        # 如果msg为空，则输出警告信息并返回
         if not msg:
             self.warning("Get empty error manager message, op_name: {}".format(self.fusion_op_name))
             return
+        # 初始化异常信息为None
         exception_info = None
+        # 获取融合操作名称
         op_name = self.fusion_op_name
+        # 如果msg是Exception类型
         if isinstance(msg, Exception):
+            # 遍历msg的参数
             for arg in msg.args:
+                # 如果参数是字典类型且包含"errCode"键
                 if isinstance(arg, dict) and "errCode" in arg:
+                    # 将异常信息赋值给exception_info
                     exception_info = arg
                     break
+            # 如果没有找到异常信息
             if not exception_info:
+                # 输出错误信息
                 self.error("Exception message:{}".format(msg))
                 return
+        # 如果msg不是Exception类型
         else:
+            # 将msg的第一个元素赋值给异常信息
             exception_info = msg[0]
+            # 如果msg的长度大于等于2
             if len(msg) >= 2:
+                # 将msg的第二个元素赋值给融合操作名称
                 op_name = msg[1]
+        # 如果异常信息不是字典类型或为空
         if not isinstance(exception_info, dict) or not exception_info:
+            # 输出警告信息
             self.warning("Get illegal error manager message, op_name: {}".format(self.fusion_op_name))
             return
+        # 将异常信息中的op_name字段赋值为融合操作名称
         exception_info["op_name"] = op_name
+        # 将异常信息转换为JSON格式
         processed_msg = json.dumps(exception_info)
+        # 创建LogMessage对象
         message = LogMessage(len(self.process_info), LogLevel.ERROR_MANAGER, processed_msg)
+        # 将LogMessage对象添加到process_info列表中
         self.process_info.append(message)
+        # 输出异常信息
         self._sys_logger.exception(msg, *args, **kwargs)
 
     def get_result(self):
@@ -186,15 +256,26 @@ class TbeJob:
         :return: job process result string
         """
         result = dict()
+        # 获取任务状态
         result["status"] = self.status.value
+        # 获取任务源ID
         result["source_id"] = self.source_id
+        # 获取任务ID
         result["job_id"] = self.id
+        # 获取任务类型
         result["job_type"] = self.type.value
+        # 获取融合操作名称
         result["fusion_op_name"] = self.fusion_op_name
+        # 获取任务结果
         result["result"] = self.result
         process_info = []
+        # 遍历任务处理信息
         for info in self.process_info:
+            # 构造消息字典
             msg = {"index": info.index, "level": info.level.value, "message": info.info}
+            # 将消息字典添加到处理信息列表中
             process_info.append(msg)
+        # 将处理信息列表添加到结果字典中
         result["process_info"] = process_info
+        # 将结果字典转换为JSON字符串并返回
         return json.dumps(result)

src/mindspore2022/mindspore/python/mindspore/_extends/parallel_compile/tbe_compiler/tbe_job_manager.py

@@ -29,6 +29,7 @@ class TbeJobManager:
     """ TBE compiler job manager """
 
     def __init__(self):
+        # 定义一个字典，用于存储不同类型的任务及其对应的处理函数
         self.job_handlers = {
             JobType.INITIALIZE_JOB: self.initialize_handler,
             JobType.FINALIZE_JOB: self.finalize_handler,
@@ -41,24 +42,43 @@ class TbeJobManager:
             JobType.QUERY_JOB: self.query_handler
         }
 
+        # 定义一个字典，用于存储所有任务
         self._all_jobs = {}
+        # 定义一个字典，用于存储已完成任务
         self._finished_jobs = {}
+        # 定义一个字典，用于存储正在运行的任务
         self._running_jobs = {}
+        # 定义一个字典，用于存储原始完成任务
         self._raw_finish_jobs = {}
+        # 定义一个布尔值，用于判断TBE是否初始化
         self.tbe_initialize = False
+        # 定义一个变量，用于存储初始化缓存
         self.init_cache = None
+        # 定义一个字符串，用于存储参数调试路径
         self.para_debug_path = ""
+        # 定义一个字符串，用于存储自动调优模式
         self.auto_tiling_mode = ""
+        # 定义一个布尔值，用于判断是否离线调优
         self.offline_tune = False
+        # 定义一个列表，用于存储调优操作
         self.tune_op_list = []
+        # 定义一个字符串，用于存储调优输出路径
         self.tune_dump_path = ""
+        # 定义一个字符串，用于存储调优库路径
         self.tune_bank_path = ""
+        # 定义一个列表，用于存储自动调优操作
         self.auto_tune_op_list = []
+        # 定义一个字典，用于存储预编译操作
         self.pre_build_ops = {}
+        # 定义一个整数，用于存储融合编译需要同步的次数
         self.fusion_need_sync = 0
+        # 定义一个字典，用于存储导入的模块
         self.imported_module = {}
+        # 定义一个字符串，用于存储SoC版本
         self.soc_version = ""
+        # 定义一个整数，用于存储核心数量
         self.core_num = 0
+        # 定义一个字符串，用于存储操作库路径
         self.op_bank_path = ""
         # license info
         self.rl_tune_switch = ""
@@ -68,6 +88,7 @@ class TbeJobManager:
         self.pass_list = ""
 
     def __del__(self):
+        # 删除对象时调用reset方法
         self.reset()
 
     def reset(self):
@@ -75,22 +96,38 @@ class TbeJobManager:
         Reset the job manager
         :return: None
         """
+        # 重置所有任务
         self._all_jobs = {}
+        # 重置已完成任务
         self._finished_jobs = {}
+        # 重置正在运行的任务
         self._running_jobs = {}
+        # 重置原始已完成任务
         self._raw_finish_jobs = {}
+        # 重置调试路径
         self.para_debug_path = ""
+        # 重置自动切分模式
         self.auto_tiling_mode = ""
+        # 重置离线调优
         self.offline_tune = False
+        # 重置调优操作列表
         self.tune_op_list = []
+        # 重置调优导出路径
         self.tune_dump_path = ""
+        # 重置调优银行路径
         self.tune_bank_path = ""
+        # 重置自动调优操作列表
         self.auto_tune_op_list = []
+        # 重置预构建操作
         self.pre_build_ops = []
+        # 重置融合需要同步
         self.fusion_need_sync = 0
+        # 重置导入模块
         self.imported_module = {}
+        # 如果tbe_initialize为True，则调用tbe_finalize方法
         if self.tbe_initialize:
             tbe_finalize(self.auto_tiling_mode, self.offline_tune, self.init_cache)
+            # 重置tbe_initialize
             self.tbe_initialize = False
             self.init_cache = None
             self.soc_version = ""
@@ -105,11 +142,17 @@ class TbeJobManager:
         """
         job = None
         try:
+            # 将job_str转换为json格式
             job_json = json.loads(job_str)
+            # 检查job_json的合法性
             check_job_json(job_json)
+            # 获取job_id
             job_id = job_json["job_id"]
+            # 获取source_id
             source_id = job_json["source_id"]
+            # 获取job_type
             job_type = job_json["job_type"]
+            # 获取系统信息
             sys_info = self._get_job_sys_info()
             fusion_op_name = "NA" if "fusion_op_name" not in job_json["job_content"] else job_json["job_content"][
                 "fusion_op_name"]
@@ -140,173 +183,260 @@ class TbeJobManager:
 
     def initialize_handler(self, job: TbeJob):
         """ Initialize job handler """
+        # 初始化系统信息
         self._init_sys_info(job)
+        # 调用tbe_initialize函数初始化job
         res = tbe_initialize(job)
+        # 如果初始化失败，记录错误信息，并将job状态设置为JOB_FAILED
         if not res:
             job.error("Process Initialize Job failed, job json string:{}".format(job.json_string))
             return self.add_to_finished_jobs(job, JobStatus.JOB_FAILED)
+        # 如果auto_tiling_mode中包含"GA"，则获取自动调优支持的操作列表
         if "GA" in self.auto_tiling_mode:
             self.auto_tune_op_list = get_auto_tune_support_op_list(job)
+        # 设置tbe_initialize为True
         self.tbe_initialize = True
+        # 将job保存到init_cache中
         self.init_cache = job
+        # 将job状态设置为JOB_SUCCESS
         return self.add_to_finished_jobs(job, JobStatus.JOB_SUCCESS)
 
     def finalize_handler(self, job: TbeJob):
         """ Finalize job handler """
+        # 如果tbe_initialize为False，则直接将job状态设置为JOB_SUCCESS
         if not self.tbe_initialize:
             return self.add_to_finished_jobs(job, JobStatus.JOB_SUCCESS)
+        # 调用tbe_finalize函数，传入auto_tiling_mode和offline_tune参数
         res = tbe_finalize(self.auto_tiling_mode, self.offline_tune, job)
+        # 如果finalize失败，记录错误信息，并将job状态设置为JOB_FAILED
         if not res:
             job.error("Process Finalize Job failed, job json string:{}".format(job.json_string))
             return self.add_to_finished_jobs(job, JobStatus.JOB_FAILED)
+        # 将job状态设置为JOB_SUCCESS
         return self.add_to_finished_jobs(job, JobStatus.JOB_SUCCESS)
 
     def check_support_handler(self, job: TbeJob):
         """ Check Support job handler """
+        # 调用check_support函数，检查job是否支持
         res = check_support(job)
+        # 如果不支持，记录错误信息，并将job状态设置为JOB_FAILED
         if not res:
             job.error("Process CheckSupport Job failed, job json string:{}".format(job.json_string))
             return self.add_to_finished_jobs(job, JobStatus.JOB_FAILED)
+        # 更新导入的操作模块
         self._update_imported_op_module(job)
+        # 将job状态设置为JOB_SUCCESS
         return self.add_to_finished_jobs(job, JobStatus.JOB_SUCCESS)
 
     def select_format_handler(self, job: TbeJob):
         """ Select Format job handler """
+        # 调用select_op_format函数，选择操作格式
         res = select_op_format(job)
+        # 如果选择失败，记录错误信息，并将job状态设置为JOB_FAILED
         if not res:
             job.error("Process SelectFormat Job failed, job json string:{}".format(job.json_string))
             return self.add_to_finished_jobs(job, JobStatus.JOB_FAILED)
+        # 将job状态设置为JOB_SUCCESS
         return self.add_to_finished_jobs(job, JobStatus.JOB_SUCCESS)
 
     def pre_compile_handler(self, job: TbeJob):
         """ Pre Compile job handler """
+        # 调用parallel_pre_compile_op函数，对job进行预处理
         res = parallel_pre_compile_op(job)
+        # 如果预处理失败，则记录错误信息，并将job状态设置为JOB_FAILED
         if not res:
             job.error("Process PreCompile Job failed, job json string:{}".format(job.json_string))
             return self.add_to_finished_jobs(job, JobStatus.JOB_FAILED)
+        # 将job添加到pre_build_ops字典中，以fusion_op_name为键
         self.pre_build_ops[job.content["fusion_op_name"]] = job
+        # 将job状态设置为JOB_RUNNING
         return self.add_to_running_jobs(job)
 
     def compile_handler(self, job: TbeJob):
         """ Compile job handler """
+        # 获取job中的compute_op_list
         compute_op_list = get_compute_op_list(job.content)
+        # 如果compute_op_list只有一个元素，则调用single_op_compile函数进行编译
         if len(compute_op_list) == 1:  # pylint: disable=no-else-return
             return self.single_op_compile(job)
         else:
+            # 调用before_build_process函数，对job进行预处理
             before_build_process(job)
+            # 如果需要同步fusion，则调用sync_fusion_env函数进行同步
             if self.fusion_need_sync:
                 sync_fusion_env(self.fusion_need_sync, self.imported_module)
                 self.fusion_need_sync = 0
+            # 调用parallel_compile_fusion_op函数，对job进行编译
             res = parallel_compile_fusion_op(job)
+            # 如果编译失败，则记录错误信息，并将job状态设置为JOB_FAILED
             if not res:
                 job.error("Parallel_compile_fusion_op Job failed, job json string:{}".format(job.json_string))
                 return self.add_to_finished_jobs(job, JobStatus.JOB_FAILED)
+            # 将job状态设置为JOB_RUNNING
             return self.add_to_running_jobs(job)
 
     def single_op_compile(self, job: TbeJob):
         """Single operator compile"""
+        # 调用do_fuzz_build_tbe_op函数，对job进行编译
         res = do_fuzz_build_tbe_op(job)
+        # 如果编译失败，则记录错误信息，并将job状态设置为JOB_FAILED
         if not res:
             job.error("Process do fuzz build tbe op failed, job json string:{}".format(job.json_string))
             return self.add_to_finished_jobs(job, JobStatus.JOB_FAILED)
+        # 如果job.result为"NOT_CHANGED"，则调用before_build_process函数进行预处理，并调用build_single_pre_op函数进行编译
         if job.result == "NOT_CHANGED":
             job.result = ""
             before_build_process(job)
             res = build_single_pre_op(job)
+            # 如果编译失败，则记录错误信息，并将job状态设置为JOB_FAILED
             if not res:
                 job.error("Process build single pre op failed, job json string:{}".format(job.json_string))
                 return self.add_to_finished_jobs(job, JobStatus.JOB_FAILED)
+            # 将job状态设置为JOB_RUNNING
             return self.add_to_running_jobs(job)
+        # 如果job.result为"SUCCESS"，则将job状态设置为JOB_SUCCESS
         if job.result == "SUCCESS":
             return self.add_to_finished_jobs(job, JobStatus.JOB_SUCCESS)
+        # 如果编译失败，则记录错误信息，并将job状态设置为JOB_FAILED
         job.error("Process do fuzz build tbe op failed, job json string:{}".format(job.json_string))
         return self.add_to_finished_jobs(job, JobStatus.JOB_FAILED)
 
     def tune_handler(self, job: TbeJob):
         """ Tune job handler """
         before_build_process(job)
+        # 选择调优模式
         tune_mode = self._select_tune_mode(job)
+        # 如果调优模式为不调优，则直接调用编译处理函数
         if tune_mode == TuneMode.NO_TUNE:
             return self.compile_handler(job)
+        # 获取计算操作列表
         compute_op_list = get_compute_op_list(job.content)
+        # 如果计算操作列表只有一个，则调用单操作调优函数
         if len(compute_op_list) == 1:
             return self.single_op_tune(job)
+        # 否则调用融合操作调优函数
         return self.fusion_op_tune(job)
 
     def single_op_tune(self, job: TbeJob):
         """Single operator tune"""
+        # 选择调优模式
         tune_mode = self._select_tune_mode(job)
+        # 如果调优模式为强化学习调优
         if tune_mode == TuneMode.RL_TUNE:
+            # 调用强化学习单操作调优函数
             res = rl_tune_single_op(job)
+            # 如果调优失败，则记录错误信息，并将任务状态设置为失败
             if not res:
                 job.error(
                     "Tune Job failed, tune type {}, job json string:{}".format(tune_mode, job.json_string))
                 return self.add_to_finished_jobs(job, JobStatus.JOB_FAILED)
+        # 否则，如果需要同步融合环境，则调用同步融合环境函数
         else:
             if self.fusion_need_sync:
                 sync_fusion_env(self.fusion_need_sync, self.imported_module)
                 self.fusion_need_sync = 0
+            # 调用遗传算法调优函数
             res = ga_tune(job)
+            # 如果调优失败，则记录错误信息，并调用编译处理函数
             if not res:
                 job.error("ga tune Job failed, job json string:{}".format(job.json_string))
                 return self.compile_handler(job)
+        # 如果任务状态为运行中
         if job.status == JobStatus.JOB_RUNNING:
+            # 如果调优模式为强化学习调优，则更新导入的操作模块
             if tune_mode == TuneMode.RL_TUNE:
                 self._update_imported_op_module(job)
+            # 将任务添加到运行中任务列表
             return self.add_to_running_jobs(job)
+        # 否则将任务添加到已完成任务列表，并设置任务状态为成功
         return self.add_to_finished_jobs(job, JobStatus.JOB_SUCCESS)
 
     def fusion_op_tune(self, job: TbeJob):
         """Fusion operator tune"""
+        # 选择调优模式
         tune_mode = self._select_tune_mode(job)
+        # 如果需要同步融合环境，则调用同步融合环境函数
         if self.fusion_need_sync:
             sync_fusion_env(self.fusion_need_sync, self.imported_module)
             self.fusion_need_sync = 0
+        # 如果调优模式为强化学习调优，则调用强化学习融合操作调优函数
         if tune_mode == TuneMode.RL_TUNE:
             res = rl_tune_fusion_op(job)
+        # 否则调用遗传算法调优函数
         else:
             res = ga_tune(job)
+        # 如果调优失败，则记录错误信息，并将任务状态设置为失败
         if not res:
             job.error(
                 "Tune Job failed, tune type {}, job json string:{}".format(tune_mode, job.json_string))
             return self.add_to_finished_jobs(job, JobStatus.JOB_FAILED)
+        # 如果任务状态为运行中，则将任务添加到运行中任务列表
         if job.status == JobStatus.JOB_RUNNING:
             return self.add_to_running_jobs(job)
+        # 否则将任务添加到已完成任务列表，并设置任务状态为成功
         return self.add_to_finished_jobs(job, JobStatus.JOB_SUCCESS)
 
     def query_handler(self, query_job: TbeJob):
         """ Query job handler """
+        # 获取查询任务的source_id和job_id
         target_source_id = query_job.content["source_id"]
         target_job_id = query_job.content["job_id"]
+        # 根据source_id和job_id获取已完成的任务
         target_job = get_job(self._finished_jobs, target_source_id, target_job_id)
+        # 如果找到了已完成的任务
         if target_job:
+            # 记录警告信息
             query_job.warning("Query a finished job: {}".format(query_job.content))
+            # 将查询任务的结果设置为已完成任务的结果
             query_job.result = target_job.get_result()
+            # 将查询任务添加到已完成任务列表中，并返回成功状态
             return self.add_to_finished_jobs(query_job, JobStatus.JOB_SUCCESS)
+        # 根据source_id和job_id获取未完成的任务
         target_job = get_job(self._raw_finish_jobs, target_source_id, target_job_id)
+        # 如果未找到未完成的任务
         if not target_job:
+            # 更新未完成的任务列表
             self.update_raw_finished_jobs(query_job)
+            # 再次根据source_id和job_id获取未完成的任务
             target_job = get_job(self._raw_finish_jobs, target_source_id, target_job_id)
+        # 如果找到了未完成的任务
         if target_job:
+            # 记录调试信息
             query_job.debug("Found job in raw finished jobs, source_id:{}, job_id:{}".format(target_source_id,
                                                                                              target_job_id))
+            # 将查询任务的结果设置为未完成任务的结果
             query_job.result = target_job.get_result()
+            # 从未完成任务列表中删除该任务
             del_job(self._raw_finish_jobs, target_job.source_id, target_job.id)
+            # 将未完成任务添加到已完成任务列表中，并返回成功状态
             self.add_to_finished_jobs(target_job, target_job.status)
             return self.add_to_finished_jobs(query_job, JobStatus.JOB_SUCCESS)
+        # 根据source_id和job_id获取正在运行的任务
         target_job = get_job(self._running_jobs, target_source_id, target_job_id)
+        # 如果找到了正在运行的任务
         if target_job:
+            # 将查询任务的结果设置为正在运行任务的结果
             query_job.result = target_job.get_result()
+            # 将查询任务添加到已完成任务列表中，并返回成功状态
             return self.add_to_finished_jobs(query_job, JobStatus.JOB_SUCCESS)
+        # 根据source_id和job_id获取所有任务
         target_job = get_job(self._all_jobs, target_source_id, target_job_id)
+        # 如果找到了所有任务
         if target_job:
+            # 记录调试信息
             query_job.debug("Found job in all jobs, source_id:{}, job_id:{}".format(target_source_id,
                                                                                     target_job_id))
+            # 记录调试信息
             target_job.debug("Be Queried")
+            # 将查询任务的结果设置为所有任务的结果
             query_job.result = target_job.get_result()
+            # 将查询任务添加到已完成任务列表中，并返回成功状态
             return self.add_to_finished_jobs(query_job, JobStatus.JOB_SUCCESS)
+        # 如果没有找到任何任务，记录错误信息
         query_job.error("Can't find job in finished/raw_finished/running jobs, source_id: {}".format(target_source_id))
+        # 将查询任务的结果设置为空
         query_job.result = ""
+        # 将查询任务添加到已完成任务列表中，并返回失败状态
         return self.add_to_finished_jobs(query_job, JobStatus.JOB_FAILED)
 
     def _get_job_sys_info(self):
@@ -314,10 +444,15 @@ class TbeJobManager:
         Get job manager system info
         :return: system info
         """
+        # 创建一个字典，用于存储系统信息
         sys_info = dict()
+        # 将DummyLogger添加到系统信息中
         sys_info["logger"] = DummyLogger
+        # 将para_debug_path添加到系统信息中
         sys_info["para_debug_path"] = self.para_debug_path
+        # 将tune_dump_path添加到系统信息中
         sys_info["tune_dump_path"] = self.tune_dump_path
+        # 将offline_tune添加到系统信息中
         sys_info["offline_tune"] = self.offline_tune
         # license info
         sys_info["rl_tune_switch"] = self.rl_tune_switch
@@ -362,12 +497,17 @@ class TbeJobManager:
         :param job:
         :return:
         """
+        # 获取计算操作列表
         compute_op_info = get_compute_op_list(job.content)[0]
+        # 获取操作模块名称
         op_module_name = compute_op_info["module_name"]
+        # 如果操作模块名称在已导入模块中，则增加引用次数
         if op_module_name in self.imported_module.keys():
             self.imported_module[op_module_name] = self.imported_module[op_module_name] + 1
+        # 否则，将操作模块名称添加到已导入模块中，并设置引用次数为1
         else:
             self.imported_module[op_module_name] = 1
+        # 增加融合需要同步的次数
         self.fusion_need_sync = self.fusion_need_sync + 1
 
     def _select_tune_mode(self, job):
@@ -376,18 +516,25 @@ class TbeJobManager:
         :param job: tbe tune job
         :return: NO_TUNE RL_TUNE or GA_TUNE
         """
+        # 获取job的SocInfo中的autoTilingMode和offlineTune
         auto_tiling_mode = job.content["SocInfo"]["autoTilingMode"]
         offline_tune = job.content["SocInfo"]["offlineTune"]
+        # 获取job的full_name
         full_name = job.content["full_name"]
+        # 获取job的func_names
         func_names = get_func_names(job.content)
+        # 如果self.tune_op_list不为空且full_name不在self.tune_op_list中，则返回TuneMode.NO_TUNE
         if self.tune_op_list and full_name not in self.tune_op_list:
             return TuneMode.NO_TUNE
+        # 如果offline_tune为True，则返回TuneMode.RL_TUNE
         if offline_tune:
             return TuneMode.RL_TUNE
+        # 如果auto_tiling_mode中包含TuneMode.GA_TUNE.value，则遍历func_names，如果func_name.lower()在self.auto_tune_op_list中，则返回TuneMode.GA_TUNE
         if TuneMode.GA_TUNE.value in auto_tiling_mode:
             for func_name in func_names:
                 if func_name.lower() in self.auto_tune_op_list:
                     return TuneMode.GA_TUNE
+        # 如果auto_tiling_mode中包含TuneMode.RL_TUNE.value，则返回TuneMode.RL_TUNE
         if TuneMode.RL_TUNE.value in auto_tiling_mode:
             return TuneMode.RL_TUNE
         return TuneMode.NO_TUNE
@@ -398,15 +545,22 @@ class TbeJobManager:
         :param query_job: query job
         :return: Node
         """
+        # 获取已完成任务
         new_finished_jobs = get_finish_tasks(query_job.source_id)
+        # 遍历已完成任务
         for new_job in new_finished_jobs:
+            # 获取任务ID
             source_id = new_job["graph_id"]
             job_id = new_job["task_id"]
+            # 获取任务
             target_job = get_job(self._running_jobs, source_id, job_id)
+            # 如果任务不存在，则报错
             if not target_job:
                 query_job.error("Can't get job, source id:{}, job id:{}".format(source_id, job_id))
                 continue
+            # 设置任务结果
             target_job.result = new_job["op_res"] if "op_res" in new_job else new_job["result"]
+            # 如果任务类型为预编译任务，则进行预编译
             if target_job.type == JobType.PRECOMPILE_JOB:
                 op_name = target_job.content["fusion_op_name"]
                 op_params = get_prebuild_output(op_name)
@@ -415,13 +569,17 @@ class TbeJobManager:
                 pre_compile_result["op_params"] = op_params
                 pre_compile_result["core_type"] = new_job["core_type"] if "core_type" in new_job else ""
                 target_job.result = json.dumps(pre_compile_result)
+            # 输出任务结果
             target_job.info("Query result:{}".format(new_job["result"]))
+            # 如果任务状态码为0，则任务成功
             if new_job["status_code"] == 0:
                 target_job.status = JobStatus.JOB_SUCCESS
                 target_job.info("Query info_msg:{}".format(new_job["info_msg"]))
+            # 否则任务失败
             else:
                 target_job.status = JobStatus.JOB_FAILED
                 target_job.error("Query info_msg:{}".format(new_job["info_msg"]))
+                # 输出错误信息
                 if "err_args" in new_job:
                     target_job.error("Query err_args:{}".format(new_job["err_args"]))
                 if "except_msg" in new_job:
@@ -429,7 +587,9 @@ class TbeJobManager:
                 if "except_tuple_msg" in new_job:
                     target_job.error_manager(new_job["except_tuple_msg"])
                 target_job.error("\nOriginal compile json: \n {}\n".format(target_job.json_string))
+            # 将任务添加到已完成任务列表
             post_job(self._raw_finish_jobs, target_job)
+            # 从运行中任务列表中删除任务
             del_job(self._running_jobs, target_job.source_id, target_job.id)
 
     def add_to_finished_jobs(self, job, status):
@@ -456,8 +616,11 @@ class TbeJobManager:
 
 class TuneMode(Enum):
     """Class of tune mode: NO_TUNE, GA, RL"""
+    # 不调优模式
     NO_TUNE = "NO_TUNE"
+    # 遗传算法调优模式
     GA_TUNE = "GA"
+    # 强化学习调优模式
     RL_TUNE = "RL"
 
 
@@ -469,18 +632,22 @@ class DummyLogger:
 
     @staticmethod
     def debug(msg, *args, **kwargs):
+        """Debug级别日志"""
         pass
 
     @staticmethod
     def info(msg, *args, **kwargs):
+        """Info级别日志"""
         pass
 
     @staticmethod
     def warning(msg, *args, **kwargs):
+        """Warning级别日志"""
         pass
 
     @staticmethod
     def error(msg, *args, **kwargs):
+        """Error级别日志"""
         pass
 
     @staticmethod
@@ -497,10 +664,13 @@ def get_job(jobs, source_id, job_id):
     :return: job instance if found in job list
              None if not found in job list
     """
+    # 如果source_id不在jobs的键中，返回None
     if source_id not in jobs.keys():
         return None
+    # 如果job_id不在jobs[source_id]的键中，返回None
     if job_id not in jobs[source_id].keys():
         return None
+    # 返回jobs[source_id][job_id]
     return jobs[source_id][job_id]
 
 
@@ -526,9 +696,15 @@ def del_job(jobs, source_id, job_id):
     :param job_id: target job's job_id
     :return: bool True or False
     """
+    # 判断source_id是否在jobs字典中
     if source_id not in jobs.keys():
+        # 如果不在，返回False
         return False
+    # 判断job_id是否在jobs[source_id]字典中
     if job_id not in jobs[source_id].keys():
+        # 如果不在，返回False
         return False
+    # 删除jobs[source_id]字典中的job_id键值对
     del jobs[source_id][job_id]
+    # 返回True
     return True