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add BroadcastIndexesRange - #8864
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/8864
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764977bCompare| // TODO: add optimization for particular input tensors not being | ||
| // broadcasted? | ||
| for (auto ii = output_dim_ - 1; ii >= 0; --ii) { | ||
| // You might wonder what happens if output_shape_[ii] == 0. In that case, |
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shouldn't we check for this before starting to iterate at all?
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This comment is meant to be explaining why every caller does check for that already -- in that case begin() == end() and any loop that uses this thing won't be entered. I'll see if I can make that a bit clearer.
| result[idx] = 0; | ||
| } | ||
| const auto t_sizes = t.sizes(); | ||
| const auto t_strides = t.strides(); |
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does this take dim order into account?
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I don't recall how dim_order affects strides and sizes. if the tests pass, either it works or we have no tests for dim_order support (which would mean it didn't work before this diff).
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at the minimal you should add checks for the dim order assumptions that are being made here. That is is assumes whatever the default dim order is, nothing fancy. If in future when the tests with dim order are added, at least this will be caught more gracefully rather than having to go down the debug rabbit hole
| // output_dim. This is straightforwardly implementable with an | ||
| // adjusted stride array that contains 0s where the padded input | ||
| // shape would contain 1s. | ||
| std::array<ShapeType, kNumInputs> effective_input_broadcast_strides_ = { |
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I love this kNumInputs generalization. This is great!
| // [1, W] -> [H, W] | ||
| // [H, 1] -> [H, W] | ||
| // [H, W] -> [H, W] | ||
| // Cover all these at the same time to also exercise multiple input tensors. |
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you are also covering [1, 1] -> [H, W] and [W] -> [H, W] here. would be good to mention as well.
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good catch, I'll rename it to OneAndTwoDExhaustive
| } | ||
| // Here we assume that the previous tests established that padding | ||
| // with leading 1s is working, and test: |
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You are testing 5 out of 8 possibilities here. You might as well add the remaining 3:
[1, 1, 1] -> [C, H, W]
[1, 1, W] -> [C, H, W] (this one in particular would be good to have, i.e. multiple leading ones)
[1, H, W] -> [C, H, W]
| EXPECT_EQ(expected, actual); | ||
| } | ||
| // 4-D should generalize, but we will go ahead and test: |
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@swolchok has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
| // [1, C, 1, W] -> [N, C, H, W] | ||
| TEST(BroadcastIndexesRangeTest, FourDBroadcasting) { | ||
| TensorFactory<ScalarType::Int> tf; | ||
| Tensor out = tf.zeros({2, 3, 4, 5}); |
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What about out = {2, 3, 1, 5} and a = {1, 3, 1, 5} and b = {2, 1, 1, 5}
Mainly highlighting that it there is size 1 dim in the output, is it taken care of? From cursory looks I presume the answer is yes, but not sure
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it there is size 1 dim in the output, is it taken care of? From cursory looks I presume the answer is yes, but not sure
why would H == 1 be special? I'll add an exhaustive test for that for 1- and 2-D just in case in a follow-up.
See class comment. In brief, this adds an iterable range to make broadcasting ops convenient and efficient to implement.
ghstack-comment-id: 2691804929 ghstack-source-id: ae9a6ce ghstack-comment-id: 2691808818 Pull Request resolved: pytorch/executorch#8864
See class comment. In brief, this adds an iterable range to make broadcasting ops convenient and efficient to implement.