[GitHub] eric-haibin-lin opened a new pull request #9846: Fix non-determinism in sparse embedding

[GitBox <gi...@apache.org>]

eric-haibin-lin opened a new pull request #9846: Fix non-determinism in sparse embedding
URL: https://github.com/apache/incubator-mxnet/pull/9846
 
 
   ## Description ##
   The original GPU sparse embedding operator uses atomic add which results non-deterministic gradient due to limited precision of fp32 and non-deterministic execution order. This PR replaces  atomic add with sort to guarantee determinism. 
   
   Tested with `example/rnn/word_lm/train.py`. Using `SparseEmbedding` and `Embedding` results the same loss with fixed seed. 
   
   The fixes makes the backward pass ~50% slower compared to the atomic_add implementation, measured by the script at the end. Further optimization can be done using `cub::Unique` instead of `cub::InclusiveSum` to generate lookup table. 
   
   @ZiyueHuang @sxjscience 
   
   ## Checklist ##
   ### Essentials ###
   - [ ] Passed code style checking (`make lint`)
   - [ ] Changes are complete (i.e. I finished coding on this PR)
   - [ ] All changes have test coverage:
   - Unit tests are added for small changes to verify correctness (e.g. adding a new operator)
   - Nightly tests are added for complicated/long-running ones (e.g. changing distributed kvstore)
   - Build tests will be added for build configuration changes (e.g. adding a new build option with NCCL)
   - [ ] Code is well-documented: 
   - For user-facing API changes, API doc string has been updated. 
   - For new C++ functions in header files, their functionalities and arguments are documented. 
   - For new examples, README.md is added to explain the what the example does, the source of the dataset, expected performance on test set and reference to the original paper if applicable
   - [ ] To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change
   
   ### Changes ###
   - [ ] Feature1, tests, (and when applicable, API doc)
   - [ ] Feature2, tests, (and when applicable, API doc)
   
   ## Comments ##
   - If this change is a backward incompatible change, why must this change be made.
   - Interesting edge cases to note here
   
   
   ```
   # the benchmark script also requires other files under example/rnn/word_lm
   
   import numpy as np
   import mxnet as mx, math
   import argparse, math
   import logging
   from data import Corpus, CorpusIter
   from model import *
   from module import *
   from mxnet.model import BatchEndParam
   
   parser = argparse.ArgumentParser(description='PennTreeBank LSTM Language Model')
   parser.add_argument('--data', type=str, default='./data/ptb.',
                       help='location of the data corpus')
   parser.add_argument('--batch_size', type=int, default=128,
                       help='batch size')
   parser.add_argument('--bptt', type=int, default=35,
                       help='sequence length')
   parser.add_argument('--dim', type=int, default=1024*1024,
                       help='dim')
   args = parser.parse_args()
   
   if __name__ == '__main__':
       # args
       head = '%(asctime)-15s %(message)s'
       logging.basicConfig(level=logging.DEBUG, format=head)
       args = parser.parse_args()
       logging.info(args)
       ctx = mx.gpu()
       batch_size = args.batch_size
       bptt = args.bptt
   
       # data
       ctx = mx.gpu()
       corpus = Corpus(args.data)
       ntokens = len(corpus.dictionary)
       train_data = CorpusIter(corpus.train, batch_size, bptt)
       data = []
       for i in range(1):
           data.append(train_data.next().data[0].reshape((-1,)).astype('int64'))
       word = mx.sym.var('data')
       weight = mx.sym.var('embed_weight', stype='row_sparse')
       embed = mx.sym.contrib.SparseEmbedding(data=word, weight=weight, input_dim=args.dim, output_dim=512, name='embed')
       grad_req = {'data': 'null', 'embed_weight': 'write'}
       exe_test = embed.simple_bind(mx.gpu(), grad_req=grad_req, data=(data[0].shape[0],))
       arg_map = dict(zip(embed.list_arguments(), exe_test.arg_arrays))
       grad_map = dict(zip(embed.list_arguments(), exe_test.grad_arrays))
       # init data
       arg_map["data"][:] = data[0].astype('float32')
       print(data[0])
       grad = mx.nd.ones(exe_test.outputs[0].shape).copyto(mx.gpu(0))
       # weight
       weight = arg_map["embed_weight"]
       weight[:] = 1
       exe_test.forward()
       # warm up
       for i in range(10):
           exe_test.backward([grad])
       import time
       mx.nd.waitall()
       a = time.time()
       for i in range(10000):
           exe_test.backward([grad])
       mx.nd.waitall()
       b = time.time()
       print(b - a)
   ```

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