diff --git a/python/paddle/fluid/dataloader/worker.py b/python/paddle/fluid/dataloader/worker.py
index 26bd1f06e12e84a9da24fad76091de4e71d3add4..409f55efebc8a67647f82d06368d788b8fa3b7af 100644
--- a/python/paddle/fluid/dataloader/worker.py
+++ b/python/paddle/fluid/dataloader/worker.py
@@ -168,6 +168,89 @@ class _WorkerException(object):
         raise self.exc_type(msg)
 
 
+# The function `_generate_states` is adapted from `numpy.random.SeedSequence`
+# from https://github.com/numpy/numpy/blob/main/numpy/random/bit_generator.pyx
+# Here is the copyright:
+
+# SeedSequence is derived from Melissa E. O'Neill's C++11 `std::seed_seq`
+# implementation, as it has a lot of nice properties that we want.
+# https://gist.github.com/imneme/540829265469e673d045
+# http://www.pcg-random.org/posts/developing-a-seed_seq-alternative.html
+
+# The MIT License (MIT)
+
+# Copyright (c) 2015 Melissa E. O'Neill
+# Copyright (c) 2019 NumPy Developers
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in
+# all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+INIT_A = 0x43b0d7e5
+MULT_A = 0x931e8875
+INIT_B = 0x8b51f9dd
+MULT_B = 0x58f38ded
+MIX_MULT_L = 0xca01f9dd
+MIX_MULT_R = 0x4973f715
+XSHIFT = np.dtype(np.uint32).itemsize * 8 // 2
+MASK32 = 0xFFFFFFFF
+
+
+def _generate_states(base_seed=0, worker_id=0):
+    # init hash constant
+    hash_const_A = INIT_A
+    hash_const_B = INIT_B
+
+    def hash(value):
+        nonlocal hash_const_A
+        value = (value ^ hash_const_A) & MASK32
+        hash_const_A = (hash_const_A * MULT_A) & MASK32
+        value = (value * hash_const_A) & MASK32
+        value = (value ^ (value >> XSHIFT)) & MASK32
+        return value
+
+    def mix(x, y):
+        result_x = (MIX_MULT_L * x) & MASK32
+        result_y = (MIX_MULT_R * y) & MASK32
+        result = (result_x - result_y) & MASK32
+        result = (result ^ (result >> XSHIFT)) & MASK32
+        return result
+
+    # init entropys with based_seed and worker_id and calculate pool
+    entropys = [worker_id, base_seed & MASK32, base_seed >> 32, 0]
+    pool = [hash(entropy) for entropy in entropys]
+
+    # mix all bits together
+    for i in range(len(pool)):
+        for j in range(len(pool)):
+            if i != j:
+                pool[j] = mix(pool[j], hash(pool[i]))
+
+    states = []
+    for p in pool:
+        state = (p ^ hash_const_B) & MASK32
+        hash_const_B = (hash_const_B * MULT_B) & MASK32
+        state = (state * hash_const_B) & MASK32
+        state = (state ^ (state >> XSHIFT)) & MASK32
+        states.append(state)
+
+    return states
+
+
 def _worker_loop(dataset, dataset_kind, indices_queue, out_queue, done_event,
                  auto_collate_batch, collate_fn, init_fn, worker_id,
                  num_workers, use_shared_memory):
@@ -181,6 +264,15 @@ def _worker_loop(dataset, dataset_kind, indices_queue, out_queue, done_event,
         # set signal handler
         core._set_process_signal_handler()
 
+        # set different numpy seed for each worker
+        try:
+            import numpy as np
+            import time
+        except ImportError:
+            pass
+        else:
+            np.random.seed(_generate_states(int(time.time()), worker_id))
+
         global _worker_info
         _worker_info = WorkerInfo(
             id=worker_id, num_workers=num_workers, dataset=dataset)
diff --git a/python/paddle/fluid/tests/unittests/test_multiprocess_dataloader_dataset.py b/python/paddle/fluid/tests/unittests/test_multiprocess_dataloader_dataset.py
index 977882543a8886c38c6ed98290462664b397d013..4c69d003d80f8b7153f9409cbcf789dfe80d2d44 100755
--- a/python/paddle/fluid/tests/unittests/test_multiprocess_dataloader_dataset.py
+++ b/python/paddle/fluid/tests/unittests/test_multiprocess_dataloader_dataset.py
@@ -330,5 +330,19 @@ class TestComplextDataset(unittest.TestCase):
             self.run_main(num_workers)
 
 
+class TestDataLoaderGenerateStates(unittest.TestCase):
+    def setUp(self):
+        self.inputs = [(0, 1), (0, 2), (1, 3)]
+        self.outputs = [[1835504127, 1731038949, 1320224556, 2330041505],
+                        [2834126987, 2358157858, 1860244682, 1437227251],
+                        [457190280, 2660306227, 859341110, 354512857]]
+
+    def test_main(self):
+        from paddle.fluid.dataloader.worker import _generate_states
+        for inp, outp in zip(self.inputs, self.outputs):
+            out = _generate_states(*inp)
+            assert out == outp
+
+
 if __name__ == '__main__':
     unittest.main()