!6073 fix some error in doc

Merge pull request !6073 from chenhaozhe/fix-doc-error

model_zoo/official/nlp/bert/README.md

@@ -377,6 +377,12 @@ epoch: 0.0, current epoch percent: 0.000, step: 2, outpus are (Tensor(shape=[1],
 ...
 ```
 
+> **Attention** If you are running with a huge dataset, it's better to add an external environ variable to make sure the hccl won't timeout.
+> ```
+> export HCCL_CONNECT_TIMEOUT=600
+> ```
+> This will extend the timeout limits of hccl from the default 120 seconds to 600 seconds.
+
 ### Distributed Training
 #### Running on Ascend
 ```

model_zoo/official/nlp/bert/run_pretrain.py

@@ -178,8 +178,7 @@ def run_pretrain():
 
         if args_opt.accumulation_steps <= 1:
             net_with_grads = BertTrainOneStepWithLossScaleCell(net_with_loss, optimizer=optimizer,
-                                                               scale_update_cell=update_cell,
-                                                               enable_global_norm=cfg.enable_global_norm)
+                                                               scale_update_cell=update_cell)
         else:
             accumulation_steps = args_opt.accumulation_steps
             net_with_grads = BertTrainAccumulateStepsWithLossScaleCell(net_with_loss, optimizer=optimizer,

model_zoo/official/nlp/bert/src/bert_for_pre_training.py

@@ -350,13 +350,12 @@ class BertTrainOneStepWithLossScaleCell(nn.Cell):
         optimizer (Optimizer): Optimizer for updating the weights.
         scale_update_cell (Cell): Cell to do the loss scale. Default: None.
     """
-    def __init__(self, network, optimizer, scale_update_cell=None, enable_global_norm=False):
+    def __init__(self, network, optimizer, scale_update_cell=None):
         super(BertTrainOneStepWithLossScaleCell, self).__init__(auto_prefix=False)
         self.network = network
         self.network.set_grad()
         self.weights = optimizer.parameters
         self.optimizer = optimizer
-        self.enable_global_norm = enable_global_norm
         self.grad = C.GradOperation(get_by_list=True,
                                     sens_param=True)
         self.reducer_flag = False
@@ -423,10 +422,7 @@ class BertTrainOneStepWithLossScaleCell(nn.Cell):
         # apply grad reducer on grads
         grads = self.grad_reducer(grads)
         grads = self.hyper_map(F.partial(grad_scale, scaling_sens * self.degree), grads)
-        if self.enable_global_norm:
-            grads = ClipByGlobalNorm()(grads)
-        else:
-            grads = self.hyper_map(F.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE), grads)
+        grads = self.hyper_map(F.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE), grads)
         self.get_status(init)
         flag_sum = self.reduce_sum(init, (0,))
         if self.is_distributed:

model_zoo/utils/ascend_distributed_launcher/README.md

@@ -5,9 +5,9 @@ The number of D chips can be automatically allocated based on the device_num set
 
 
 ## how to use
-For example, if we want to run the distributed training of Bert model on D chip, we can in `/bert/` dir:
+For example, if we want to generate the launch command of the distributed training of Bert model on D chip, we can run the following command in `/bert/` dir:
 ```
-python model_zoo/utils/ascend_distributed_launcher/run_distributed.py --run_script_dir ./run_pretrain.py --hyper_parameter_config_dir model_zoo/utils/ascend_distributed_launcher/hyper_parameter_config.ini --data_dir /path/dataset/ --hccl_config_dir model_zoo/utils/hccl_tools/hccl_2p_56_x.x.x.x.json
+python ./scripts/ascend_distributed_launcher/get_distribute_pretrain_cmd.py --run_script_dir ./run_pretrain.py --hyper_parameter_config_dir ./scripts/ascend_distributed_launcher/hyper_parameter_config.ini --data_dir /path/dataset/ --hccl_config_dir model_zoo/utils/hccl_tools/hccl_2p_56_x.x.x.x.json
 ```
 
 output:
@@ -42,7 +42,8 @@ log file dir: ./LOG6/log.txt
 1. Note that `hccl_2p_56_x.x.x.x.json` can use [hccl_tools.py](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools) to generate.
 
 2. For hyper parameter, please note that you should customize the scripts `hyper_parameter_config.ini`. Please note that these two hyper parameters are not allowed to be configured here:
-    device_id
-    device_num
+    - device_id
+    - device_num
+    - data_dir
 
 3. For Other Model, please note that you should customize the option `run_script` and Corresponding `hyper_parameter_config.ini`.

model_zoo/utils/ascend_distributed_launcher/run_distributed.py → model_zoo/utils/ascend_distributed_launcher/get_distribute_pretrain_cmd.py

@@ -42,11 +42,21 @@ def parse_args():
                         help="Data path, it is better to use absolute path")
     parser.add_argument("--hccl_config_dir", type=str, default="",
                         help="Hccl config path, it is better to use absolute path")
+    parser.add_argument("--cmd_file", type=str, default="distributed_cmd.sh",
+                        help="Path of the generated cmd file.")
 
     args = parser.parse_args()
     return args
 
 
+def append_cmd(cmd, s):
+    cmd += s
+    cmd += "\n"
+    return cmd
+
+def append_cmd_env(cmd, key, value):
+    return append_cmd(cmd, "export" + str(key) + "=" + str(value))
+
 def distribute_pretrain():
     """
     distribute pretrain scripts. The number of D chips can be automatically allocated
@@ -92,6 +102,7 @@ def distribute_pretrain():
     print("avg_core_per_rank:", avg_core_per_rank)
 
     count = 0
+    cmd = ""
     for instance in this_server["device"]:
         device_id = instance["device_id"]
         rank_id = instance["rank_id"]
@@ -104,38 +115,44 @@ def distribute_pretrain():
         end = start + core_gap
         cmdopt = str(start) + "-" + str(end)
 
-        os.environ["DEVICE_ID"] = device_id
-        os.environ["RANK_ID"] = rank_id
-        os.environ["DEPLOY_MODE"] = "0"
-        os.environ["GE_USE_STATIC_MEMORY"] = "1"
+        cmd = append_cmd(cmd, "export DEVICE_ID=" + str(device_id))
+        cmd = append_cmd(cmd, "export RANK_ID=" + str(rank_id))
+        cmd = append_cmd(cmd, "export DEPLOY_MODE=0")
+        cmd = append_cmd(cmd, "export GE_USE_STATIC_MEMORY=1")
 
-        os.system("rm -rf LOG" + str(device_id))
-        os.system("mkdir ./LOG" + str(device_id))
-        os.system("cp *.py ./LOG" + str(device_id))
-        os.system("mkdir -p ./LOG" + str(device_id) + "/ms_log")
-        os.system("env > ./LOG" + str(device_id) + "/env.log")
+        cmd = append_cmd(cmd, "rm -rf LOG" + str(device_id))
+        cmd = append_cmd(cmd, "mkdir ./LOG" + str(device_id))
+        cmd = append_cmd(cmd, "cp *.py ./LOG" + str(device_id))
+        cmd = append_cmd(cmd, "mkdir -p ./LOG" + str(device_id) + "/ms_log")
+        cmd = append_cmd(cmd, "env > ./LOG" + str(device_id) + "/env.log")
 
         cur_dir = os.getcwd()
-        os.environ["GLOG_log_dir"] = cur_dir + "/LOG" + str(device_id) + "/ms_log"
-        os.environ["GLOG_logtostderr"] = "0"
+        cmd = append_cmd_env(cmd, "GLOG_LOG_DIR", cur_dir + "/LOG" + str(device_id) + "/ms_log")
+        cmd = append_cmd_env(cmd, "GLOG_logtostderr", "0")
 
         print("core_nums:", cmdopt)
         print("epoch_size:", str(cfg['epoch_size']))
         print("data_dir:", data_dir)
-        print("log_file_dir: ./LOG" + str(device_id) + "/log.txt")
+        print("log_file_dir: " + cur_dir + "/LOG" + str(device_id) + "/pretraining_log.txt")
+
+        cmd = append_cmd(cmd, "cd " + cur_dir + "/LOG" + str(device_id))
 
-        cmd = 'taskset -c ' + cmdopt + ' nohup python ' + run_script + " "
+        run_cmd = 'taskset -c ' + cmdopt + ' nohup python ' + run_script + " "
         opt = " ".join(["--" + key + "=" + str(cfg[key]) for key in cfg.keys()])
         if ('device_id' in opt) or ('device_num' in opt) or ('data_dir' in opt):
             raise ValueError("hyper_parameter_config.ini can not setting 'device_id',"
                              " 'device_num' or 'data_dir'! ")
-        cmd += opt
-        cmd += " --data_dir=" + data_dir
-        cmd += ' --device_id=' + str(device_id) + ' --device_num=' \
-               + str(rank_size) + ' >./LOG' + str(device_id) + '/log.txt 2>&1 &'
+        run_cmd += opt
+        run_cmd += " --data_dir=" + data_dir
+        run_cmd += ' --device_id=' + str(device_id) + ' --device_num=' \
+               + str(rank_size) + ' >./pretraining_log.txt 2>&1 &'
 
-        os.system(cmd)
+        cmd = append_cmd(cmd, run_cmd)
+        cmd = append_cmd(cmd, "cd -")
+        cmd += "\n"
 
+    with open(args.cmd_file, "w") as f:
+        f.write(cmd)
 
 if __name__ == "__main__":
     distribute_pretrain()

model_zoo/utils/ascend_distributed_launcher/hyper_parameter_config.ini

@@ -6,6 +6,7 @@ enable_lossscale=true
 do_shuffle=true
 enable_data_sink=true
 data_sink_steps=100
+accumulation_steps=1
 save_checkpoint_path=./checkpoint/
 save_checkpoint_steps=10000
-save_checkpoint_num=1
+save_checkpoint_num=1

model_zoo/utils/hccl_tools/README.md

@@ -5,7 +5,7 @@ mindspore distributed training launch helper utilty that will generate hccl conf
 # use
 
 ```
-python hccl_tools.py --device_num [0,8)
+python hccl_tools.py --device_num "[0,8)"
 ```
 
 output: