Customize the printing of namedtuple return by zasdfgbnm · Pull Request #17136 · pytorch/pytorch

zasdfgbnm · 2019-02-14T23:57:57Z

print("good", torch.randn(5,5,5).max(1))
print("terrible", torch.randn(5,5,10).max(1))
print("not as good", torch.randn(5,5,500).max(1))
print ("old behaviour = gold standard")
print(tuple(torch.randn(5,5,5).max(1)))
print(tuple(torch.randn(5,5,10).max(1)))
print(tuple(torch.randn(5,5,500).max(1)))

now gives

>>> import torch
>>> print("good", torch.randn(5,5,5).max(1))
good torch.return_types.max(
values=tensor([[ 1.2821,  1.8063,  1.8075,  1.3082, -0.1267],
        [ 0.3437,  0.7353,  1.2619,  0.7557,  1.6662],
        [ 0.8583,  1.8906,  1.0246,  1.7598,  1.1184],
        [ 1.7821,  0.0230,  0.9452,  1.0318,  1.0823],
        [ 0.4116, -0.0379, -0.1843,  1.4129,  1.8796]]),
indices=tensor([[4, 4, 3, 2, 1],
        [1, 2, 4, 1, 1],
        [2, 4, 0, 2, 1],
        [0, 2, 0, 3, 1],
        [0, 4, 4, 4, 4]]))
>>> print("terrible", torch.randn(5,5,10).max(1))
terrible torch.return_types.max(
values=tensor([[ 2.1272,  1.3664,  2.2067,  1.3974, -0.0883,  1.2505,  1.0074,  1.1217,
          0.3849,  0.6936],
        [ 0.6288, -0.4560,  1.2748,  1.5482,  1.2777,  1.6874,  0.7151,  0.6041,
          1.3572,  1.6232],
        [ 1.6703,  1.0075,  1.6480,  2.2839,  1.3390,  0.4938,  1.6449,  1.7628,
          0.8141,  2.5714],
        [ 0.7079,  1.8677,  3.2478,  1.5591,  2.4870,  0.8635, -0.1450,  1.6923,
          1.4924,  1.6298],
        [ 2.4056,  0.8002,  0.9317,  0.7455,  0.7866,  2.1191,  0.3492,  1.2095,
          1.8637,  1.7470]]),
indices=tensor([[1, 1, 0, 0, 0, 0, 3, 4, 4, 4],
        [4, 2, 2, 1, 2, 2, 3, 1, 1, 3],
        [0, 3, 3, 0, 2, 1, 4, 1, 0, 1],
        [4, 1, 3, 0, 3, 2, 0, 1, 4, 3],
        [1, 0, 3, 2, 1, 0, 0, 1, 0, 1]]))
>>> print("not as good", torch.randn(5,5,500).max(1))
not as good torch.return_types.max(
values=tensor([[ 0.3877,  0.7873,  1.8701,  ...,  0.5971,  1.6103, -0.3435],
        [ 1.1300,  2.2418,  1.4239,  ...,  1.3943,  0.3872,  1.6475],
        [ 2.0656,  1.3136,  0.9896,  ...,  2.3918,  0.8226,  1.0517],
        [ 1.1054,  0.9945,  1.0561,  ...,  2.1039,  1.1524,  3.0304],
        [ 1.5041,  2.2809,  1.0883,  ...,  0.8504,  2.4774,  1.1041]]),
indices=tensor([[4, 3, 1,  ..., 1, 4, 0],
        [4, 4, 4,  ..., 3, 0, 3],
        [3, 0, 1,  ..., 2, 2, 4],
        [0, 1, 1,  ..., 4, 2, 2],
        [1, 0, 4,  ..., 2, 0, 2]]))
>>> print ("old behaviour = gold standard")
old behaviour = gold standard
>>> print(tuple(torch.randn(5,5,5).max(1)))
(tensor([[ 1.1908,  1.1807,  1.3151,  1.7184,  0.3556],
        [ 0.3798,  0.9213,  0.3001,  1.3087,  2.2419],
        [ 1.4233,  1.4814,  1.9900,  1.7744,  1.3059],
        [ 1.0026, -0.0330,  1.3061,  1.8730,  2.0685],
        [ 1.3041,  1.6458,  1.3449,  1.8948,  3.6206]]), tensor([[0, 4, 3, 4, 0],
        [1, 1, 4, 0, 4],
        [4, 1, 0, 3, 3],
        [1, 2, 1, 4, 0],
        [3, 3, 0, 3, 3]]))
>>> print(tuple(torch.randn(5,5,10).max(1)))
(tensor([[-0.1232,  0.8275,  0.6732,  1.1223,  0.8247,  1.2851,  1.6009,  1.9979,
          1.9109,  0.7313],
        [ 0.2260,  0.5922,  1.6928,  0.6024,  2.1158,  3.0619,  0.5653,  0.7426,
          0.8316,  0.6346],
        [ 0.4319,  0.2231,  0.5255,  1.7620,  1.1657,  0.8875,  0.5782,  0.6506,
          0.5032,  1.7097],
        [ 0.4137,  1.7265,  1.4260,  2.0301,  1.2244,  0.7128,  2.6345,  0.7230,
          1.3553,  1.6508],
        [ 1.0684,  1.7195,  1.4068,  0.7076, -0.0242,  0.8474,  0.8754,  1.7108,
          0.2188,  1.1584]]), tensor([[0, 1, 3, 4, 2, 3, 4, 2, 1, 0],
        [1, 4, 0, 0, 3, 2, 0, 0, 3, 3],
        [2, 3, 1, 1, 4, 0, 1, 4, 4, 4],
        [0, 4, 1, 3, 2, 0, 2, 0, 3, 1],
        [1, 0, 0, 0, 0, 3, 3, 3, 2, 0]]))
>>> print(tuple(torch.randn(5,5,500).max(1)))
(tensor([[0.9395, 1.5572, 1.8797,  ..., 2.0494, 0.8202, 0.9623],
        [1.7937, 0.7225, 1.8836,  ..., 0.7927, 1.4976, 1.1813],
        [0.8558, 1.6943, 1.4192,  ..., 0.8327, 1.9661, 0.4197],
        [1.2993, 1.4995, 0.9357,  ..., 0.7810, 1.3030, 2.6216],
        [1.4206, 1.8315, 1.0338,  ..., 1.4312, 1.3198, 1.5233]]), tensor([[0, 4, 3,  ..., 3, 0, 2],
        [0, 1, 0,  ..., 0, 4, 3],
        [3, 4, 3,  ..., 3, 0, 0],
        [3, 2, 3,  ..., 1, 2, 1],
        [1, 2, 4,  ..., 3, 1, 3]]))

zasdfgbnm · 2019-02-14T23:58:23Z

cc: @t-vi @ssnl and maybe @ezyang

zasdfgbnm · 2019-02-15T00:01:39Z

torch/csrc/utils/structseq.cpp

+PyObject *returned_structseq_repr(PyStructSequence *obj) {
+    PyTypeObject *typ = Py_TYPE(obj);
+    std::stringstream ss;
+    ss << typ->tp_name << "(\n";


The default behavior does not have \n here, I added this just because I think it looks better.

zasdfgbnm · 2019-02-15T00:02:09Z

torch/csrc/utils/structseq.cpp

+
+        ss << cname << '=' << crepr;
+        if (i < num_elements - 1)
+            ss << ",\n";


The default behavior has a space instead of a \n here, I changed this just because I think it looks better.

t-vi · 2019-02-15T07:15:37Z

Thank you for doing this! Personally, I like your insertion of newlines, too.

…dtuple-printing

zasdfgbnm · 2019-02-16T22:41:16Z

Note: This PR also provide code to convert structseq to tuple in C++ side, which would be useful to modify tuple checks to support structseq as tuple(#16813).

zasdfgbnm · 2019-02-23T19:16:27Z

Hi Adam @apaszke , Let's discuss your comment #16186 (comment) at here. What do you think is a good printing? I suggest:

tuple(
[0] values =
tensor([[ 1.2821,  1.8063,  1.8075,  1.3082, -0.1267],
        [ 0.3437,  0.7353,  1.2619,  0.7557,  1.6662],
        [ 0.8583,  1.8906,  1.0246,  1.7598,  1.1184],
        [ 1.7821,  0.0230,  0.9452,  1.0318,  1.0823],
        [ 0.4116, -0.0379, -0.1843,  1.4129,  1.8796]]),
[1] indices =
tensor([[4, 4, 3, 2, 1],
        [1, 2, 4, 1, 1],
        [2, 4, 0, 2, 1],
        [0, 2, 0, 3, 1],
        [0, 4, 4, 4, 4]])
)

I mean, the name of the returned type i.e. torch.return_types.max is good to use. We just need to change the way it prints, not the name of the type.

VitalyFedyunin

Everything looks good, except few python ref counters leaks.

torch/csrc/utils/structseq.cpp

…dtuple-printing

zasdfgbnm · 2019-02-26T17:42:23Z

@pytorchbot rebase this please

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torch/csrc/utils/structseq.cpp

…dtuple-printing

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VitalyFedyunin · 2019-02-28T17:01:53Z

LGTM, I will land this as soon as all checks completed.

VitalyFedyunin · 2019-02-28T20:57:07Z

@zasdfgbnm could you please update quotes to brackets in all includes. This is to follow same style as everywhere is the code.

) Summary: Currently the following code gives an error on python 2 because `ret` is a structseq which is not a tuple ```python ret = a.max(dim=0) ret1 = torch.max(a, dim=0, out=ret) ``` This PR modify tuple check in python arg parser to allow structseq to be input of operators where tuple is expected, which would make the above code work. Depend on: #17136 Partially fixes: #16813 Pull Request resolved: #17208 Differential Revision: D14280198 Pulled By: VitalyFedyunin fbshipit-source-id: beffebfd3951c4f5c7c8fe99a5847616a89491f3

Do not prune for structseq repr

a51315d

empty line in the end

a985898

zasdfgbnm commented Feb 15, 2019

View reviewed changes

zasdfgbnm added 6 commits February 14, 2019 19:20

python2 compatibility

9e2a26f

python2 compatibility

e4f6ff8

test repr

d4568f6

copy paste more from cpython2

92e895a

Fix linter & add copyright

0d70f14

sort by alpha

a66c92e

zasdfgbnm added 4 commits February 16, 2019 14:53

Merge branch 'master' of https://github.com/pytorch/pytorch into name…

5c883e0

…dtuple-printing

cleanup py2-py3 logic

b939afb

more cleanup py2-py3 logic

00ab07d

fix namespace

a808b39

zasdfgbnm mentioned this pull request Feb 16, 2019

Allow structseq to be input of operators where tuple is expected #17208

Closed

fix python2

f8deedc

fix typo

e326d37

zasdfgbnm changed the title ~~Do not prune for structseq repr~~ Customize the printing of namedtuple return Feb 23, 2019

zasdfgbnm mentioned this pull request Feb 23, 2019

Add namedtuple return for min, median, mode, kthvalue, add test for namedtuple return API #16186

Closed

VitalyFedyunin suggested changes Feb 25, 2019

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zasdfgbnm added 2 commits February 25, 2019 13:16

Merge branch 'master' of https://github.com/pytorch/pytorch into name…

19bd1ba

…dtuple-printing

fix ref count leakage

9a182ca

Merge remote-tracking branch 'origin/master' into HEAD

1b9c66b

facebook-github-bot reviewed Feb 27, 2019

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VitalyFedyunin reviewed Feb 28, 2019

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zasdfgbnm added 2 commits February 28, 2019 11:38

Merge branch 'master' of https://github.com/pytorch/pytorch into name…

73e84c3

…dtuple-printing

{}

e35ae34

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VitalyFedyunin self-assigned this Feb 28, 2019

facebook-github-bot closed this in 2e5a8ce Feb 28, 2019

pytorchbot added the merged label Feb 28, 2019

zasdfgbnm deleted the namedtuple-printing branch February 28, 2019 21:37

ezyang added the open source label Jun 24, 2019

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zasdfgbnm commented Feb 14, 2019

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