fix random seed bug for pksampler in DDP

ppcls/data/dataloader/pk_sampler.py

@@ -18,7 +18,9 @@ from __future__ import division
 from collections import defaultdict
 
 import numpy as np
+import paddle.distributed as dist
 from paddle.io import DistributedBatchSampler
+
 from ppcls.utils import logger
 
 
@@ -94,6 +96,11 @@ class PKSampler(DistributedBatchSampler):
                     format(diff))
 
     def __iter__(self):
+        # shuffing label_list manually in distributed environment
+        if self.nranks > 1:
+            cur_rank = dist.get_rank()
+            np.random.RandomState(42 + cur_rank).shuffle(self.label_list)
+
         label_per_batch = self.batch_size // self.sample_per_id
         for _ in range(len(self)):
             batch_index = []

ppcls/engine/engine.py

@@ -126,16 +126,18 @@ class Engine(object):
                 self.config["DataLoader"], "Train", self.device, self.use_dali)
             if self.config["DataLoader"].get('UnLabelTrain', None) is not None:
                 self.unlabel_train_dataloader = build_dataloader(
-                        self.config["DataLoader"], "UnLabelTrain", self.device,
-                        self.use_dali)
+                    self.config["DataLoader"], "UnLabelTrain", self.device,
+                    self.use_dali)
             else:
                 self.unlabel_train_dataloader = None
 
-            self.iter_per_epoch = len(self.train_dataloader) - 1 if platform.system(
+            self.iter_per_epoch = len(
+                self.train_dataloader) - 1 if platform.system(
                 ) == "Windows" else len(self.train_dataloader)
             if self.config["Global"].get("iter_per_epoch", None):
                 # set max iteration per epoch mannualy, when training by iteration(s), such as XBM, FixMatch.
-                self.iter_per_epoch = self.config["Global"].get("iter_per_epoch")
+                self.iter_per_epoch = self.config["Global"].get(
+                    "iter_per_epoch")
             self.iter_per_epoch = self.iter_per_epoch // self.update_freq * self.update_freq
 
         if self.mode == "eval" or (self.mode == "train" and
@@ -329,6 +331,20 @@ class Engine(object):
             )) > 0:
                 self.train_loss_func = paddle.DataParallel(
                     self.train_loss_func)
+
+            # set different seed in different GPU manually in distributed environment
+            if seed is None:
+                logger.warning(
+                    "The random seed cannot be None in a distributed environment. Global.seed has been set to 42 by default"
+                )
+                self.config["Global"]["seed"] = seed = 42
+            logger.info(
+                f"Set random seed to ({seed} + $PADDLE_TRAINER_ID) for different trainer"
+            )
+            paddle.seed(seed + dist.get_rank())
+            np.random.seed(seed + dist.get_rank())
+            random.seed(seed + dist.get_rank())
+
         # build postprocess for infer
         if self.mode == 'infer':
             self.preprocess_func = create_operators(self.config["Infer"][