From 291fc0f00b558c2242b97bd1d9172fa5cd8165a1 Mon Sep 17 00:00:00 2001 From: Kaipeng Deng Date: Wed, 9 Jun 2021 14:06:55 +0800 Subject: [PATCH] add random state generate in DataLoader worker (#33310) * add random state generate in DataLoader worker. test=develop --- python/paddle/fluid/dataloader/worker.py | 92 +++++++++++++++++++ .../test_multiprocess_dataloader_dataset.py | 14 +++ 2 files changed, 106 insertions(+) diff --git a/python/paddle/fluid/dataloader/worker.py b/python/paddle/fluid/dataloader/worker.py index 26bd1f06e12..409f55efebc 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 977882543a8..4c69d003d80 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() -- GitLab