[GitHub] reminisce opened a new pull request #8558: slice operator supporting arbitrary values of step

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

reminisce opened a new pull request #8558: slice operator supporting arbitrary values of step
URL: https://github.com/apache/incubator-mxnet/pull/8558
 
 
   ## Description ##
   - Re-implemented slice operator using Kernel::Launch
   - Added support for arbitrary values of `step` along with `begin` and `end`, i.e. `slice(data, begin, end, step)`, where `step` is an optional parameter.
   
   ## Checklist ##
   ### Essentials ###
   - [x] Passed code style checking (`make lint`)
   - [x] Changes are complete (i.e. I finished coding on this PR)
   - [x] All changes have test coverage
   - [x] For user-facing API changes, API doc string has been updated.
   - [x] To my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change
   
   ## Comments ##
   - The kernel implementation is based upon the parallelization approach of slice operator in mshadow with additional support for arbitrary value of `step`.
   - This operator will be used in https://github.com/apache/incubator-mxnet/pull/8246 to support slicing `NDArray` with `step!=1`. Currently, it uses `gather_nd` op to realize that functionality, which has heavy overhead of making index `NDArray` from slices.
   - Mini-benchmark `slice_v1` (**mshadow version**) and `slice` (**Kernel::Launch version**).
   
   **Hardware**
   p2.xlarge (4 omp threads)
   
   **Commit**
   [ba9de66](https://github.com/reminisce/mxnet/commit/ba9de669a752f232dcb21b0c11f9a4f91533af23)
   
   **GPU build**
   mx.nd.slice_v1: 10000 repeats costs 0.561281 seconds
   mx.nd.slice:      10000 repeats costs 0.562561 seconds
   
   **CPU-only build**
   mx.nd.slice_v1: 10000 repeats costs 1.049587 seconds
   mx.nd.slice:      10000 repeats costs 1.130866 seconds
   
   **Benchmark script**
   ```python
    import mxnet as mx
    import numpy as np
    import time
    from mxnet.test_utils import same
    
    #ctx = mx.gpu(0)
    ctx = mx.cpu(0)
    
    repeat = 10000
    shape = (16, 16, 16, 16)
    a = mx.nd.arange(np.prod(shape), ctx=ctx).reshape(shape=shape)
    begin = (None, 1, 2)
    end = (shape[0], shape[1], shape[2])
    
    # warm up
    for i in range(100):
        b = mx.nd.slice_v1(a, begin=begin, end=end)
        c = mx.nd.slice(a, begin=begin, end=end)
    
    mx.nd.waitall()
    start = time.time()
    for i in range(repeat):
        c = mx.nd.slice_v1(a, begin=begin, end=end)
    mx.nd.waitall()
    elapsed = time.time() - start
    print("mx.nd.slice_v1: %d repeats costs %f seconds" % (repeat, elapsed))
    
    start = time.time()
    for i in range(repeat):
        b = mx.nd.slice(a, begin=begin, end=end)
    mx.nd.waitall()
    elapsed = time.time() - start
    print("mx.nd.slice: %d repeats costs %f seconds" % (repeat, elapsed))
    
    assert same(c.asnumpy(), b.asnumpy())
   ```
   @piiswrong @eric-haibin-lin @anirudh2290 @rahul003 @cjolivier01 
   

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