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Graph Slice node (resolves #483)#517

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ryanrhymes merged 3 commits intoowlbarn:masterfrom
mreppen:slice
Apr 16, 2020
Merged

Graph Slice node (resolves #483)#517
ryanrhymes merged 3 commits intoowlbarn:masterfrom
mreppen:slice

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@mreppen
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@mreppen mreppen commented Apr 14, 2020

A node producing a get_slice on its input.

Most code that is not boilerplate is for computing the output dimension. The calculation itself is pretty much the same as in _enumerate_slice_def (https://github.com/owlbarn/owl/blob/master/src/base/dense/owl_base_dense_ndarray_generic.ml#L99). The assert is a bit more aggressive (checks bounds) to avoid Assert_failure ("src/base/core/owl_base_slicing.ml", 90, 10). when running.

I am aware of special cases like that "later" axes do not need to be specified, and handle that. Are there others that I might have missed?

There is one potential extra feature: A bool flag to set whether trivial dimensions (those with length 1) should be remove, i.e., reshaped away. Is this too niche/specialized?

There is still a TODO for prettier (more detailed) printing.

Example:

# let nn = Neural.D.Graph.(input [| 5; 2 |] |> slice [[1; -1; 3]] |> get_network);;
val nn : Owl_neural.D.Graph.network =
  42878

[ Node input_0 ]:
    Input : in/out:[*,3,2]
    prev:[] next:[slice_1]

[ Node slice_1 ]:
    Slice : in:[*,3,2] out:[*,2,2]
    prev:[input_0] next:[]

# Neural.D.Graph.model nn (Arr.sequential [| 1; 5; 2 |]);;
- : Owl_algodiff_primal_ops.D.arr =

       C0 C1
R[0,0]  2  3
R[0,1]  8  9
# Arr.(sequential [|5;2|] |> get_slice [[1;-1;3]]);;
- : Arr.arr =
   C0 C1
R0  2  3
R1  8  9

@ryanrhymes ryanrhymes added enhancement R&D Core research and development labels Apr 14, 2020
@mreppen
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mreppen commented Apr 15, 2020

I added more detailed printing:

# ... slice [[1; -1; 3]; []; [0]] ...
...
[ Node slice_1 ]:
    Slice : in:[*,5,2,10] out:[*,2,2,1]
    Axes  : 0:[1; -1; 3] 1:[] 2:[0]
    prev:[input_0] next:[]

@jzstark
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jzstark commented Apr 15, 2020

Cool! Thanks for this PR! Looks good to me. I'm just wondering if we can put the slicing shape inference routine to the owl_utils_infer_shape.ml file.

@mreppen
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mreppen commented Apr 15, 2020

@jzstark I was not aware of that file, but that looks very appropriate. Latest commit moves it.

@ryanrhymes ryanrhymes merged commit c103674 into owlbarn:master Apr 16, 2020
mseri added a commit to mseri/opam-repository that referenced this pull request Oct 4, 2020
CHANGES:

* various documentation improvements (thanks @pveber, @UnixJunkie, @Fourchaux)
* Fix use of access operators (owlbarn/owl#543)
* Upgrade to ocamlformat 0.15.0 (thanks @gpetiot owlbarn/owl#535)
* keep_dims option (owlbarn/owl#531)
* stats: fix infinite loop in ecdf
* Use Fun.protect to ensure all file descriptors are being closed
* owl_ndarray_maths: improve user experience in case of errors
* owl_io: close file descriptors also in case of errors
* owl_dense_ndarray_generic: fix error on printing 0-ary arrays
* fixed bug in sub forward mode (owlbarn/owl#533)
* Add stack to Algodiff (owlbarn/owl#528)
* added log_sum_exp to Ndarray and Algodiff (owlbarn/owl#527)
* added single-precision and double-precision Bessel functions to Ndarray  (owlbarn/owl#526)
* Fixes owlbarn/owl#518 by introducing another `/` to resolve data directory (@jotterbach owlbarn/owl#519)
* Graph Slice node (resolves owlbarn/owl#483) (@mreppen owlbarn/owl#517)
* Graph subnetwork: Multiple outputs (@mreppen owlbarn/owl#515)
* Added kron and swap to Algodiff operations (owlbarn/owl#512)
* various other small fixes
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3 participants