branch-xg

src/mindspore2022/mindspore/python/mindspore/dataset/engine/datasets_user_defined.py

@@ -452,11 +452,14 @@ class _GeneratorWorkerMp(multiprocessing.Process):
     """
 
     def __init__(self, dataset, eof, max_rowsize, queue_size, ppid):
+        # 初始化一个多进程队列，用于存储索引
         self.idx_queue = multiprocessing.Queue(queue_size)
+        # 如果启用了共享内存，则初始化一个共享队列，否则初始化一个多进程队列
         if get_enable_shared_mem():
             self.res_queue = _SharedQueue(queue_size, max_rowsize=max_rowsize)
         else:
             self.res_queue = multiprocessing.Queue(queue_size)
+        # 设置队列的_joincancelled属性为True，表示在进程退出时，队列不会阻塞
         self.idx_queue._joincancelled = True  # pylint: disable=W0212
         self.res_queue._joincancelled = True  # pylint: disable=W0212
         super().__init__(target=_generator_worker_loop, args=(dataset, self.idx_queue, self.res_queue, eof, True, ppid))
@@ -465,6 +468,7 @@ class _GeneratorWorkerMp(multiprocessing.Process):
         """
         Put function for worker index queue. Never block. Raise queue.Full on failure.
         """
+        # 将item放入idx_queue队列中，不阻塞，如果失败则抛出queue.Full异常
         self.idx_queue.put_nowait(item)
 
     def get(self):
@@ -476,12 +480,19 @@ class _GeneratorWorkerMp(multiprocessing.Process):
         return self.res_queue.get(timeout=30)
 
     def queue_empty(self):
+        # 检查idx_queue是否为空
         if not self.idx_queue.empty():
+            # 如果不为空，记录警告日志
             logger.warning("idx_queue is not empty.")
+            # 返回False
             return False
+        # 检查res_queue是否为空
         if not self.res_queue.empty():
+            # 如果不为空，记录警告日志
             logger.warning("res_queue is not empty.")
+            # 返回False
             return False
+        # 如果两个队列都为空，返回True
         return True
 
     def __del__(self):
@@ -632,14 +643,17 @@ class GeneratorDataset(MappableDataset, UnionBaseDataset):
     def __init__(self, source, column_names=None, column_types=None, schema=None, num_samples=None,
                  num_parallel_workers=1, shuffle=None, sampler=None, num_shards=None, shard_id=None,
                  python_multiprocessing=True, max_rowsize=6):
+        # 调用父类的初始化方法
         super().__init__(num_parallel_workers=num_parallel_workers, sampler=sampler, num_samples=num_samples,
                          shuffle=shuffle, num_shards=num_shards, shard_id=shard_id)
+        # 如果source是zip类型，则将其转换为列表
         if isinstance(source, builtins.zip):
             # Although zip is iteratable, it does not have the feature of repeated iteration, so pass it to the array.
             self.source = [item for item in source]
         else:
             self.source = source
         self.prepared_source = None  # source to be sent to C++
+        # 如果self.operator_mixed属性为True，则将num_parallel_workers设置为1
         if hasattr(self, 'operator_mixed') and getattr(self, 'operator_mixed') is True:
             self.num_parallel_workers = 1
             logger.warning(
@@ -650,56 +664,78 @@ class GeneratorDataset(MappableDataset, UnionBaseDataset):
 
         self.python_multiprocessing = python_multiprocessing
 
+        # 将column_names转换为列表
         self.column_names = to_list(column_names)
 
+        # 如果column_types不为空，则将其转换为detypelist类型
         if column_types is not None:
             self.column_types = mstypelist_to_detypelist(column_types)
         else:
             self.column_types = []
 
         self.schema = schema
+        # 如果schema不为空，则将其转换为Schema类型
         if schema is not None:
+            # 如果schema不为空，则将其赋值给self.schema
             self.schema = schema
+            # 如果schema不是Schema类型，则将其转换为Schema类型
             if not isinstance(schema, Schema):
                 self.schema = Schema(schema)
         # Move get dataset_size by len from parse to here, because self.source will
         # lose attribution of '__len__' after deepcopy.
         self.source_len = -1  # unknown
+        # 如果self.source有__len__属性，则获取self.source的长度
         if hasattr(self.source, "__len__"):
             self.source_len = len(self.source)
 
+        # 设置最大行大小
         self.max_rowsize = max_rowsize
+        # 设置采样函数为None
         self.sample_fn = None
 
     def __deepcopy__(self, memodict):
+        # 深度复制当前对象，并传入一个字典memodict，用于存储已经复制的对象
         if id(self) in memodict:
+            # 如果当前对象的id已经在memodict中，则直接返回该对象
             return memodict[id(self)]
+        # 否则，调用__safe_deepcopy__方法进行深度复制，并传入memodict和exclude参数
         new_op = self.__safe_deepcopy__(memodict, exclude=("source", "__transfer_dataset__"))
 
         sample_fn = None
+        # 如果新对象的sampler属性不为空，并且self.source对象具有__getitem__方法
         if new_op.sampler is not None and hasattr(self.source, "__getitem__"):
             # The reason why there is a try catch here is because when the new op is being constructed with shared
             # memory enabled, there will be an exception thrown if there is not enough shared memory available
+            # 如果self.source_len为-1，则抛出RuntimeError异常，因为尝试构造一个随机访问的数据集，需要__len__方法
             if self.source_len == -1:
                 raise RuntimeError("Attempt to construct a random access dataset, '__len__' method is required!")
             try:
+                # 如果新对象的num_parallel_workers大于1，则调用__validate_memory_usage方法进行内存使用验证
                 if new_op.num_parallel_workers > 1:
                     self.__validate_memory_usage()
 
+                    # 创建一个SamplerFn对象，用于并行采样
                     sample_fn = SamplerFn(self.source, new_op.num_parallel_workers, self.python_multiprocessing,
                                           self.max_rowsize)
+                    # 将新对象的prepared_source属性设置为_cpp_sampler_fn_mp函数，用于并行采样
                     new_op.prepared_source = (lambda sample_ids: _cpp_sampler_fn_mp(sample_ids, sample_fn))
                 else:
+                    # 否则，将新对象的prepared_source属性设置为_cpp_sampler_fn函数，用于单线程采样
                     new_op.prepared_source = (lambda sample_ids: _cpp_sampler_fn(sample_ids, self.source))
+                # 将新对象的sample_fn属性设置为sample_fn
                 new_op.sample_fn = sample_fn
             except RuntimeError as e:
+                # 如果抛出RuntimeError异常，则抛出Exception异常，并传入异常信息
                 raise Exception(str(e))
         else:
             try:
+                # 否则，将新对象的sampler属性设置为None，sample_fn属性设置为sample_fn
                 new_op.sampler = None
                 new_op.sample_fn = sample_fn
+                # 将新对象的source_len属性设置为min(new_op.source_len, new_op.num_samples)，如果new_op.num_samples不为0，否则设置为new_op.source_len
                 new_op.source_len = min(new_op.source_len,
                                         new_op.num_samples) if new_op.num_samples != 0 else new_op.source_len
+                # 遍历self.source对象
                 iter(self.source)
             except TypeError:
                 # Use generator function if input callable
@@ -711,19 +747,26 @@ class GeneratorDataset(MappableDataset, UnionBaseDataset):
 
         return new_op
 
+    # 判断是否被洗牌
     def is_shuffled(self):
         return self.sampler.is_shuffled()
 
+    # 判断是否被分片
     def is_sharded(self):
         return self.sampler.is_sharded()
 
+    # 解析
     def parse(self, children=None):
+        # 如果schema为空，则返回GeneratorNode对象
         if self.schema is None:
             return cde.GeneratorNode(self.prepared_source, self.column_names, self.column_types, self.source_len,
                                      self.sampler, self.num_parallel_workers)
+        # 获取schema
         schema = self.schema
+        # 如果schema是Schema类型，则获取cpp_schema
         if isinstance(schema, Schema):
             schema = self.schema.cpp_schema
+        # 返回GeneratorNode对象
         return cde.GeneratorNode(self.prepared_source, schema, self.source_len, self.sampler,
                                  self.num_parallel_workers)
 
@@ -735,24 +778,37 @@ class GeneratorDataset(MappableDataset, UnionBaseDataset):
             # if use num_parallel_workers is to large when python_multiprocessing=True which would cause
             # OOM error get the num_shards
             valid_num_shards = 1
+            # 判断self.sampler是否为samplers.DistributedSampler类型
             if isinstance(self.sampler, samplers.DistributedSampler):
+                # 如果是，则将self.sampler的num_shards赋值给valid_num_shards
                 valid_num_shards = self.sampler.num_shards
+            # 否则，判断self.num_shards是否为None
             elif self.num_shards is not None:
+                # 如果不是，则将self.num_shards赋值给valid_num_shards
                 valid_num_shards = self.num_shards
 
             # get process memory usage
+            # 获取当前进程
             process = psutil.Process(os.getpid())
+            # 获取当前进程的内存信息
             process_memory = process.memory_info().rss
+            # 获取系统内存的空闲量
             sys_memory_free = psutil.virtual_memory().free
 
+            # 计算可能使用的总内存量
             total_memory_maybe_used = process_memory * self.num_parallel_workers * valid_num_shards
+            # 如果总内存可能使用的内存量除以系统可用内存大于0.85
             if total_memory_maybe_used / sys_memory_free > 0.85:
+                # 计算有效的worker数量，即系统可用内存乘以0.85除以有效的shards数量再除以每个进程的内存
                 valid_num_worker = math.floor(sys_memory_free * 0.85 / valid_num_shards / process_memory)
+                # 如果有效的worker数量小于等于0，则将其设置为1
                 valid_num_worker = 1 if valid_num_worker <= 0 else valid_num_worker
+                # 构造警告信息，提示用户num_parallel_workers设置过大，可能会导致内存占用过高或OOM，建议将其减小到valid_num_worker或更小
                 info = "GeneratorDataset's num_parallel_workers: {} is too large which may cause a lot of memory " \
                        "occupation (>85%) or out of memory(OOM) during multiprocessing. Therefore, it is recommended " \
                        "to reduce num_parallel_workers to {} or smaller.".format(self.num_parallel_workers,
                                                                                  valid_num_worker)
+                # 打印警告信息
                 logger.warning(info)
 
 
@@ -764,37 +820,55 @@ class _NumpySlicesDataset:
     def __init__(self, data, column_list=None):
         self.column_list = None
         # Convert dict data into tuple
+        # 判断data是否为字典类型
         if isinstance(data, dict):
+            # 如果是字典类型，则调用process_dict方法处理
             data = self.process_dict(data)
 
+        # 判断data是否为元组类型
         if isinstance(data, tuple):
+            # 如果是元组类型，则将self.data初始化为空元组
             self.data = ()
+            # 获取data的长度
             data_len = len(data)
+            # 遍历data中的每个元素
             for i in range(data_len):
+                # 将data中的每个元素转换为numpy数组，并添加到self.data中
                 self.data = self.data + (np.array(data[i]),)
         else:
+            # 如果data不是元组类型，则将data转换为numpy数组，并添加到self.data中
             self.data = (np.array(data),)
 
         # check whether the data length in each column is equal
+        # 获取每个data_item的长度
         data_len = [len(data_item) for data_item in self.data]
+        # 如果每个data_item的长度不相等，则抛出ValueError异常
         if data_len[1:] != data_len[:-1]:
             raise ValueError("Data length in each column is not equal.")
 
         # Init column_name
+        # 如果column_list不为空，则将self.column_list赋值为column_list
         if column_list is not None:
             self.column_list = column_list
+        # 如果self.column_list为空，则将self.column_list赋值为空列表
         elif self.column_list is None:
             self.column_list = []
+            # 获取data的列数
             column_num = len(self.data)
+            # 遍历列数，将"column_" + str(i)添加到self.column_list中
             for i in range(column_num):
                 self.column_list.append("column_" + str(i))
 
     def __getitem__(self, index):
+        # 获取指定索引的数据行
         data_row = [d[index, ...] for d in self.data]
+        # 将数据行转换为元组
         data_res = tuple(data_row)
+        # 返回数据行
         return data_res
 
     def __len__(self):
+        # 返回data的第一个元素的长度
         return len(self.data[0])
 
     def process_dict(self, input_data):
@@ -802,24 +876,29 @@ class _NumpySlicesDataset:
         Convert the dict like data into tuple format, when input is a tuple of dicts then compose it into a dict first.
         """
         # Convert pandas like dict(has "values" column) into General dict
+        # 将pandas样式的字典（有"values"列）转换为通用字典
         data_keys = list(input_data.keys())
+        # 获取字典的第一个键对应的值
         data_col = input_data[data_keys[0]]
+        # 如果值有values属性，则将其转换为通用字典
         if hasattr(data_col, "values"):
             new_dict = {}
             for key in data_keys:
+                # 将字典中的键对应的值转换为列表
                 item1 = input_data.pop(key)
                 new_dict[key] = item1.values
+            # 将转换后的字典赋值给input_data
             input_data = new_dict
 
         # Convert the data in dict into tuple
-        data = ()
+        data = ()  # 初始化一个空元组
-        keys = list(input_data.keys())
+        keys = list(input_data.keys())  # 将输入数据的键转换为列表
-        self.column_list = keys
+        self.column_list = keys  # 将键列表赋值给实例变量column_list
-        for key in keys:
+        for key in keys:  # 遍历键列表
-            value = input_data[key]
+            value = input_data[key]  # 获取键对应的值
-            data = data + (list(value),)
+            data = data + (list(value),)  # 将值转换为列表，并添加到元组中
 
-        return data
+        return data  # 返回元组
 
 
 class NumpySlicesDataset(GeneratorDataset):
@@ -909,7 +988,9 @@ class NumpySlicesDataset(GeneratorDataset):
     @check_numpyslicesdataset
     def __init__(self, data, column_names=None, num_samples=None, num_parallel_workers=1, shuffle=None, sampler=None,
                  num_shards=None, shard_id=None):
+        # 创建一个_NumpySlicesDataset对象，传入data和column_names参数
         dataset = _NumpySlicesDataset(data, column_names)
+        # 调用父类的__init__方法，传入dataset、column_names、num_samples、num_parallel_workers、shuffle、sampler、num_shards和shard_id参数
         super().__init__(dataset, column_names=dataset.column_list, num_samples=num_samples,
                          num_parallel_workers=num_parallel_workers, shuffle=shuffle, sampler=sampler,
                          num_shards=num_shards, shard_id=shard_id)

src/mindspore2022/mindspore/python/mindspore/train/dataset_helper.py

@@ -256,36 +256,53 @@ class DatasetHelper:
     """
 
     def __init__(self, dataset, dataset_sink_mode=True, sink_size=-1, epoch_num=1):
+        # 检查dataset_sink_mode是否为布尔值
         dataset_sink_mode = Validator.check_bool(dataset_sink_mode)
+        # 检查sink_size是否为整数
         Validator.check_is_int(sink_size)
+        # 如果sink_size小于-1或者等于0，抛出异常
         if sink_size < -1 or sink_size == 0:
             raise ValueError("The 'sink_size' must be -1 or positive, but got sink_size {}.".format(sink_size))
+        # 如果sink_size等于-1，则将其设置为dataset的dataset_size
         if sink_size == -1:
             sink_size = dataset.get_dataset_size()
 
+        # 如果dataset_sink_mode为True，则根据不同的设备类型选择不同的迭代器
         if dataset_sink_mode:
+            # 如果启用了GE，则使用GE的迭代器
             if context.get_context("enable_ge"):
                 iterclass = _DatasetIterGE
             else:
+                # 如果当前模式为GRAPH_MODE，则根据角色选择不同的迭代器
                 if context.get_context("mode") == context.GRAPH_MODE:
+                    # 如果当前角色为调度器或者参数服务器，则使用参数服务器的迭代器
                     if _is_role_sched() or _is_role_pserver():
                         iterclass = _DatasetIterPSServer
+                    # 如果当前角色为工作节点并且是参数服务器模式，则使用参数服务器工作节点的迭代器
                     elif _is_role_worker() and _is_ps_mode():
                         iterclass = _DatasetIterPSWork
+                    # 如果当前设备类型为Ascend或者GPU，则使用多线程循环的迭代器
                     elif (context.get_context("device_target") == "Ascend") or \
                          (context.get_context("device_target") == "GPU"):
                         iterclass = _DatasetIterMSLoopSink
+                    # 如果当前设备类型为CPU，则抛出异常，因为CPU不支持数据集下沉模式
                     elif context.get_context("device_target") == "CPU":
                         raise RuntimeError("Currently dataset sink mode is not supported when the device "
                                            "target is CPU, please set dataset sink mode to False.")
+                # 如果当前模式不是GRAPH_MODE，则使用PyNative的迭代器
                 else:
                     iterclass = _DatasetIterPyNative
+            # 创建迭代器
             self.iter = iterclass(dataset, sink_size, epoch_num)
+        # 如果dataset_sink_mode为False，则使用普通的迭代器
         else:
+            # 如果不是分布式训练，则使用_DatasetIterNormal类
             iterclass = _DatasetIterNormal
+            # 初始化迭代器
             self.iter = iterclass(dataset, epoch_num=epoch_num)
 
     def __iter__(self):
+        # 返回self.iter的迭代器
         return self.iter.__iter__()
 
     # A temp solution for loop sink. Delete later
@@ -301,6 +318,7 @@ class DatasetHelper:
             >>>
             >>> types, shapes = dataset_helper.types_shapes()
         """
+        # 从当前配置的dataset中获取类型和形状
         return self.iter.types_shapes()
 
     def sink_size(self):
@@ -316,18 +334,22 @@ class DatasetHelper:
             >>> # if sink_size==-1, then will return the full size of source dataset.
             >>> sink_size = dataset_helper.sink_size()
         """
+        # 返回迭代器的接收缓冲区大小
         return self.iter.get_sink_size()
 
     def stop_send(self):
         """Stop send data about data sink."""
+        # 停止发送关于数据接收器的数据
         self.iter.stop_send()
 
     def release(self):
         """Free up resources about data sink."""
+        # 释放数据接收器的资源
         self.iter.release()
 
     def continue_send(self):
         """Continue to send data to device at the beginning of epoch."""
+        # 在每个epoch的开始处继续向设备发送数据
         self.iter.continue_send()
 
     def _reset(self, step):
@@ -339,6 +361,7 @@ class DatasetHelper:
         In sink mode, it returns the types and shapes of the current data.
         Generally, it works in dynamic shape scenarios.
         """
+        # 返回迭代器的数据信息
         return self.iter.get_data_info()
 
     def dynamic_min_max_shapes(self):
@@ -355,6 +378,7 @@ class DatasetHelper:
             >>>
             >>> min_shapes, max_shapes = dataset_helper.dynamic_min_max_shapes()
         """
+        # 返回self.iter的dynamic_min_max_shapes方法
         return self.iter.dynamic_min_max_shapes()
 
 
@@ -362,20 +386,27 @@ class _DatasetIter:
     """Base iter for dataset helper"""
 
     def __init__(self, dataset, sink_size, epoch_num):
+        # 初始化函数，传入数据集、sink大小和epoch数量
         self.dataset = dataset
         self.sink_size = sink_size
         self.sink_count = self.get_sink_count(dataset)
 
+        # 如果数据集没有__transfer_dataset__属性
         if not hasattr(dataset, '__transfer_dataset__'):
+            # 如果数据集有__loop_size__属性
             if hasattr(dataset, '__loop_size__'):
                 # PS mode does not support loop sink and need get the real sink size.
+                # 如果不是worker角色或者不是ps模式，则设置sink_size为dataset的循环大小
                 if not (_is_role_worker() and _is_ps_mode()):
                     self.sink_size = dataset.__loop_size__
+            # 如果sink_size为1，sink_count为1，dataset的大小不为1，并且设备目标为Ascend，则创建数据信息队列
             create_data_info_queue = (sink_size == 1 and self.sink_count == 1 and dataset.get_dataset_size() != 1
                                       and context.get_context("device_target") == "Ascend")
+            # 执行数据图，并将sink_size和create_data_info_queue作为参数传入
             dataset.__transfer_dataset__ = _exec_datagraph(dataset, self.sink_size,
                                                            create_data_info_queue=create_data_info_queue)
 
+            # 如果dataset没有__no_send__属性，则发送数据
             if not hasattr(dataset, '__no_send__'):
                 _send_data(dataset, epoch_num)
         else:
@@ -384,33 +415,48 @@ class _DatasetIter:
                 _cell_graph_executor.set_queue_name(dataset.__transfer_dataset__.queue_name)
             _send_data_no_flag(dataset, epoch_num)
 
+        # 获取dataset的stop_send方法
         self.stop_send = dataset.__transfer_dataset__.stop_send
+        # 获取dataset的release方法
         self.release = dataset.__transfer_dataset__.release
+        # 获取dataset的continue_send方法
         self.continue_send = dataset.__transfer_dataset__.continue_send
+        # 获取dataset的get_data_info方法
         self.get_data_info = dataset.__transfer_dataset__.get_data_info
+        # 获取dataset的dynamic_min_max_shapes属性
         self.dynamic_min_max_shapes = dataset.dynamic_min_max_shapes
+        # 获取dataset的数据类型和数据形状
         self.dataset_types, self.dataset_shapes = _get_types_and_shapes(dataset)
+        # 如果dataset的__transfer_dataset__属性中有_reset方法，则获取该_reset方法
         if hasattr(dataset.__transfer_dataset__, "_reset"):
             self._reset = dataset.__transfer_dataset__._reset  # pylint: disable=W0212
 
     def __iter__(self):
+        # 初始化索引为0
         self.index = 0
+        # 返回self
         return self
-
+    # 迭代器的下一项
     def __next__(self):
+        # 如果索引大于等于sink_count，抛出StopIteration异常
         if self.index >= self.sink_count:
             raise StopIteration()
+        # 索引加1
         self.index += 1
+        # 返回op()的返回值
         return self.op()
 
     def types_shapes(self):
         """
-        Return the types and shapes of the dataset. The type and shape of each data in the dataset
+        返回数据集的类型和形状。数据集中每个数据的类型和形状应该是一致的。
-        should be consistent.
         """
         return self.dataset_types, self.dataset_shapes
-
     def get_sink_count(self, dataset):
+        """
+        获取数据集的sink次数
+        :param dataset: 数据集对象
+        :return: sink次数
+        """
         sink_count = 1
         if hasattr(dataset, '__loop_size__'):
             loop_size = dataset.__loop_size__
@@ -421,7 +467,10 @@ class _DatasetIter:
         return sink_count
 
     def get_sink_size(self):
-        """get sink_size to device"""
+        """
+        获取设备的sink大小
+        :return: sink大小
+        """
         sink_size = 1
         if hasattr(self.dataset, '__loop_size__'):
             sink_size = self.dataset.__loop_size__

src/mindspore2022/setup.py

@@ -75,6 +75,7 @@ def _write_device_target(file):
 
 def build_dependencies():
     """generate python file"""
+    # 生成version.py文件
     version_file = os.path.join(pkg_dir, 'mindspore', 'version.py')
     with open(version_file, 'w') as f:
         _write_version(f)
@@ -83,6 +84,7 @@ def build_dependencies():
     with open(version_file, 'w') as f:
         _write_version(f)
 
+    # 生成default_config.py文件
     config_file = os.path.join(pkg_dir, 'mindspore', 'default_config.py')
     with open(config_file, 'w') as f:
         _write_config(f)
@@ -91,6 +93,7 @@ def build_dependencies():
     with open(config_file, 'w') as f:
         _write_config(f)
 
+    # 向default_config.py文件中追加device_target
     target = os.path.join(pkg_dir, 'mindspore', 'default_config.py')
     with open(target, 'a') as f:
         _write_device_target(f)
@@ -99,6 +102,7 @@ def build_dependencies():
     with open(target, 'a') as f:
         _write_device_target(f)
 
+    # 向default_config.py文件中追加package_name
     package_info = os.path.join(pkg_dir, 'mindspore', 'default_config.py')
     with open(package_info, 'a') as f:
         _write_package_name(f)
@@ -107,6 +111,7 @@ def build_dependencies():
     with open(package_info, 'a') as f:
         _write_package_name(f)
 
+    # 生成.commit_id文件
     commit_file = os.path.join(pkg_dir, 'mindspore', '.commit_id')
     with open(commit_file, 'w') as f:
         _write_commit_file(f)
@@ -156,16 +161,24 @@ def update_permissions(path):
     Args:
         path (str): Target directory path.
     """
+    # 判断操作系统是否为Windows
     if platform.system() == "Windows":
         return
 
+    # 遍历目标目录下的所有文件和文件夹
     for dirpath, dirnames, filenames in os.walk(path):
+        # 遍历文件夹
         for dirname in dirnames:
+            # 获取文件夹的完整路径
             dir_fullpath = os.path.join(dirpath, dirname)
+            # 更新文件夹的权限
             os.chmod(dir_fullpath, stat.S_IREAD | stat.S_IWRITE |
                      stat.S_IEXEC | stat.S_IRGRP | stat.S_IXGRP)
+        # 遍历文件
         for filename in filenames:
+            # 获取文件的完整路径
             file_fullpath = os.path.join(dirpath, filename)
+            # 更新文件的权限
             os.chmod(file_fullpath, stat.S_IREAD)
 
 
@@ -174,7 +187,9 @@ class EggInfo(egg_info):
 
     def run(self):
         super().run()
+        # 获取egg-info目录的路径
         egg_info_dir = os.path.join(pkg_dir, 'mindspore.egg-info')
+        # 更新egg-info目录的权限
         update_permissions(egg_info_dir)
 
 
@@ -183,41 +198,64 @@ class BuildPy(build_py):
 
     def run(self):
         super().run()
+        # 获取build目录下的lib/mindspore目录的路径
         mindspore_dir = os.path.join(pkg_dir, 'build', 'lib', 'mindspore')
+        # 更新lib/mindspore目录的权限
         update_permissions(mindspore_dir)
+        # 获取build目录下的lib/mindspore/_akg目录的路径
         mindspore_dir = os.path.join(pkg_dir, 'build', 'lib', 'mindspore', '_akg')
+        # 更新lib/mindspore/_akg目录的权限
         update_permissions(mindspore_dir)
 
 
+# 设置包的名称
 setup(
     name=package_name,
+    # 设置包的版本
     version=version,
+    # 设置包的作者
     author='The MindSpore Authors',
+    # 设置包的作者邮箱
     author_email='contact@mindspore.cn',
+    # 设置包的网址
     url='https://www.mindspore.cn',
+    # 设置包的下载网址
     download_url='https://github.com/mindspore-ai/mindspore/tags',
+    # 设置包的源代码网址
     project_urls={
         'Sources': 'https://github.com/mindspore-ai/mindspore',
+        # 设置包的问题追踪网址
         'Issue Tracker': 'https://github.com/mindspore-ai/mindspore/issues',
     },
+    # 设置包的描述
     description='MindSpore is a new open source deep learning training/inference '
     'framework that could be used for mobile, edge and cloud scenarios.',
+    # 读取readme文件作为包的详细描述
     long_description=readme,
+    # 设置详细描述的格式
     long_description_content_type="text/markdown",
+    # 查找包中的所有模块
     packages=find_packages(),
+    # 设置包的数据
     package_data=package_data,
+    # 包含包中的所有数据
     include_package_data=True,
+    # 设置自定义的命令类
     cmdclass={
         'egg_info': EggInfo,
         'build_py': BuildPy,
     },
+    # 设置包的入口点
     entry_points={
         'console_scripts': [
             'cache_admin=mindspore.dataset.engine.cache_admin:main',
         ],
     },
+    # 设置包的Python版本要求
     python_requires='>=3.7',
+    # 设置包的依赖
     install_requires=required_package,
+    # 设置包的分类器
     classifiers=[
         'Development Status :: 4 - Beta',
         'Environment :: Console',
@@ -234,6 +272,8 @@ setup(
         'Topic :: Software Development :: Libraries',
         'Topic :: Software Development :: Libraries :: Python Modules',
     ],
+    # 设置包的许可证
     license='Apache 2.0',
+    # 设置包的关键词
     keywords='mindspore machine learning',
 )