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@z-a-f z-a-f commented Jun 9, 2020

Stack from ghstack:

Differential Revision: D22087071

@z-a-f z-a-f requested a review from apaszke as a code owner June 9, 2020 07:46
z-a-f pushed a commit that referenced this pull request Jun 9, 2020
ghstack-source-id: 9148871
Pull Request resolved: #39714
@facebook-github-bot facebook-github-bot added the oncall: jit Add this issue/PR to JIT oncall triage queue label Jun 9, 2020
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💊 CI failures summary and remediations

As of commit 582205a (more details on the Dr. CI page):


  • 2/2 failures possibly* introduced in this PR
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Extra GitHub checks: 1 failed


ci.pytorch.org: 1 failed


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This comment has been revised 184 times.

z-a-f pushed a commit that referenced this pull request Jun 9, 2020
ghstack-source-id: c454ab9
Pull Request resolved: #39714
z-a-f pushed a commit that referenced this pull request Jun 11, 2020
ghstack-source-id: 8a3d039
Pull Request resolved: #39714
z-a-f pushed a commit that referenced this pull request Jun 11, 2020
ghstack-source-id: 931913f
Pull Request resolved: #39714
@z-a-f z-a-f added the oncall: quantization Quantization support in PyTorch label Jun 11, 2020
@z-a-f z-a-f requested review from jamesr66a and jerryzh168 June 11, 2020 08:25
z-a-f pushed a commit that referenced this pull request Jun 12, 2020
ghstack-source-id: 2c7d3eb
Pull Request resolved: #39714
@z-a-f z-a-f requested a review from raghuramank100 June 12, 2020 22:36
z-a-f pushed a commit that referenced this pull request Jun 20, 2020
ghstack-source-id: 939b91c
Pull Request resolved: #39714
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looks reasonable, ideally we could review this for real when the serialization story is agreed upon

kSpatialDim,
"D convolution.");
const int output_channels = weight.size(0);
const int output_channels_idx = transpose ? 1 : 0;
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optional: if you want these to be more readable, you could define an enum ConvType which can be ConvType::CONV | ConvType::CONV_TRANSPOSE and pass that around instead

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I love this solution -- I was originally thinking of casting boolean to int (as in int(transpose)), but thought that would be confusing when used as a channel index

{(uint32_t)dilation_[1], (uint32_t)dilation_[0]},
{(uint32_t)padding_[0], (uint32_t)padding_[1],
(uint32_t)padding_[0], (uint32_t)padding_[1]},
/*adjustment=*/{0, 0},
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what does "adjustment" mean here / is it ok to replace with output padding?

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Yea, the terminology sucks -- technically, this is adjustment to the output size, which is the same as the output_padding. The only difference is that adjustment refers to the size in general, while padding refers to the padding to every side separately.

Basically, I am using asjustment here because the qnnpack uses this as terminology, while pytorch uses "padding" -- should I change it?

@z-a-f z-a-f requested review from jamesr66a, jerryzh168 and vkuzo July 30, 2020 01:41
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lg if CI passes

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codecov bot commented Sep 3, 2020

Codecov Report

❗ No coverage uploaded for pull request base (gh/z-a-f/35/base@70e8e7c). Click here to learn what that means.
The diff coverage is n/a.

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@@                 Coverage Diff                 @@
##             gh/z-a-f/35/base   #39714   +/-   ##
===================================================
  Coverage                    ?   69.34%           
===================================================
  Files                       ?      381           
  Lines                       ?    47170           
  Branches                    ?        0           
===================================================
  Hits                        ?    32712           
  Misses                      ?    14458           
  Partials                    ?        0           

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@z-a-f merged this pull request in e05fa2f.

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