!617 modify parallel checkpoint

Merge pull request !617 from changzherui/mod_parall_ckpt

tutorials/source_en/advanced_use/checkpoint_for_hybrid_parallel.md

@@ -282,8 +282,8 @@ The following uses a specific model parameter as an example. The parameter name
 3. Modify values of model parameters.
 
    ```
-   new_param.set_parameter_data(tensor_slice) 
-   new_param_moments.set_parameter_data(tensor_slice_moments) 
+   new_param.set_parameter_data(tensor_slice, True)
+   new_param_moments.set_parameter_data(tensor_slice_moments, True)
    ```
 
    - `set_parameter_data`: sets the value of a model parameter. The API parameter type is Tensor or number.
@@ -389,7 +389,7 @@ User process:
            # merge the parallel parameters of the model
            allgather_net = get_allgather_cell()
            param_data = allgather_net(param_data)
-           layerwise_param.set_parameter_data(param_data)
+           layerwise_param.set_parameter_data(param_data, True)
 
        # convert param_dict to list type data
        param_list = []
@@ -553,7 +553,7 @@ User process:
            rank = get_rank()
            tensor_slice = Tensor(slice_list[rank])
            # modify model parameter data values
-           new_param.set_parameter_data(tensor_slice)
+           new_param.set_parameter_data(tensor_slice, True)
 
            # load the modified parameter data into the network
            weight = np.ones([4, 8]).astype(np.float32)

tutorials/source_en/advanced_use/customized_debugging_information.md

@@ -151,13 +151,15 @@ metrics = {
 net = ResNet()
 loss = CrossEntropyLoss()
 opt = Momentum()
-model = Model(net, loss_fn=loss, optimizer=opt, metrics=metrics)
+model = Model(net, loss_fn=loss, optimizer=opt, metrics=metrics, callbacks=TimeMonitor())
 ds_eval = create_dataset()
 output = model.eval(ds_eval)
 ```
 
 The `model.eval` method returns a dictionary that contains the metrics and results transferred to the metrics.
 
+The callback function can also be used in the eval process, and the user can call the related API or customize the callback method to achieve the desired function.
+
 You can also define your own metrics class by inheriting the `Metric` base class and rewriting the `clear`, `update`, and `eval` methods.
 
 The `accuracy` operator is used as an example to describe the internal implementation principle.

tutorials/source_zh_cn/advanced_use/checkpoint_for_hybrid_parallel.md

@@ -286,8 +286,8 @@ param_dict = load_checkpoint("./CKP-Integrated_1-4_32.ckpt")
 3. 修改模型参数数据值。 
 
     ```
-    new_param.set_parameter_data(tensor_slice) 
-    new_param_moments.set_parameter_data(tensor_slice_moments) 
+    new_param.set_parameter_data(tensor_slice, True)
+    new_param_moments.set_parameter_data(tensor_slice_moments, True)
     ```
 
     - `set_parameter_data`：设置模型参数的值，接口参数类型为Tensor 或number。
@@ -392,7 +392,7 @@ load_param_into_net(opt, param_dict)
             # merge the parallel parameters of the model
             allgather_net = get_allgather_cell()
             param_data = allgather_net(param_data)
-            layerwise_param.set_parameter_data(param_data)
+            layerwise_param.set_parameter_data(param_data, True)
     
         # convert param_dict to list type data
         param_list = []
@@ -556,7 +556,7 @@ load_param_into_net(opt, param_dict)
             rank = get_rank()
             tensor_slice = Tensor(slice_list[rank])
             # modify model parameter data values
-            new_param.set_parameter_data(tensor_slice)
+            new_param.set_parameter_data(tensor_slice, True)
         
             # load the modified parameter data into the network
             weight = np.ones([4, 8]).astype(np.float32)

tutorials/source_zh_cn/advanced_use/customized_debugging_information.md

@@ -133,6 +133,7 @@ epoch: 20 step: 32 loss: 2.298344373703003
 同时可以对字典内的值进行修改和添加，上述用例中，在`begin`中定义一个`init_time`对象传递给`cb_params`字典。
 在每次`step_end`会做出判断，当训练时间大于设置的时间阈值时，会向`run_context`传递终止训练的信号，提前终止训练，并打印当前的`epoch`、`step`、`loss`的值。
 
+
 ## MindSpore metrics功能介绍
 
 当训练结束后，可以使用metrics评估训练结果的好坏。
@@ -152,13 +153,15 @@ metrics = {
 net = ResNet()
 loss = CrossEntropyLoss()
 opt = Momentum()
-model = Model(net, loss_fn=loss, optimizer=opt, metrics=metrics)
+model = Model(net, loss_fn=loss, optimizer=opt, metrics=metrics, callbacks=TimeMonitor())
 ds_eval = create_dataset()
 output = model.eval(ds_eval)
 ```
 
 `model.eval`方法会返回一个字典，里面是传入metrics的指标和结果。
 
+在eval过程中也可以使用callback功能，用户可以调用相关API或自定义callback方法实现想要的功能。
+
 用户也可以定义自己的`metrics`类，通过继承`Metric`基类，并重写`clear`、`update`、`eval`三个方法即可实现。
 
 以`accuracy`算子举例说明其内部实现原理：