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Merge pull request #24476 from fengyuentau:attention_layer

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dnn: add attention layer #24476

Resolves #24609

Merge with: https://github.com/opencv/opencv_extra/pull/1128.

Attention operator spec from onnxruntime: https://github.com/microsoft/onnxruntime/blob/v1.16.1/docs/ContribOperators.md#com.microsoft.Attention.

TODO:
- [x] benchmark (before this PR vs. with this PR vs. ORT).
- [x] Layer fusion: Take care Slice with end=INT64_MAX.
- [x] Layer fusion: match more potential attention (VIT) patterns.
    - [x] Single-head attention is supported.
- [x] Test AttentionSubgraph fusion.
- [x] Add acc tests for VIT_B_32 and VitTrack
- [x] Add perf tests for VIT_B_32 and VitTrack

## Benchmarks

Platform: Macbook Air M1.

### Attention Subgraph

Input scale: [1, 197, 768].

|                        | mean (ms) | median (ms) | min (ms) |
| ---------------------- | --------- | ----------- | -------- |
| w/ Attention (this PR) | 3.75      | 3.68        | 3.22     |
| w/o Attention          | 9.06      | 9.01        | 8.24     |
| ORT (python)           | 4.32      | 2.63        | 2.50     |

### ViTs

All data in millisecond (ms).

| ViTs     | With Attention | Without Attention | ORT    |
| -------- | -------------- | ----------------- | ------ |
| vit_b_16 | 302.77         | 365.35            | 109.70 |
| vit_b_32 | 89.92          | 116.22            | 30.36  |
| vit_l_16 | 1593.32        | 1730.74           | 419.92 |
| vit_l_32 | 468.11         | 577.41            | 134.12 |
| VitTrack | 3.80           | 3.87              | 2.25   |

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Yuantao Feng
2023-12-21 00:35:07 +08:00
committed by GitHub
parent e64c5dc4c6
commit 0521a3a384
13 changed files with 891 additions and 66 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/dnn/perf/perf_layer.cpp
+57
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@@ -739,6 +739,62 @@ PERF_TEST_P_(Layer_InstanceNorm, InstanceNorm)
test_layer({N, C, H, W});
}
struct Layer_Attention : public TestBaseWithParam<tuple<Backend, Target>> {
void test_layer(const std::vector<int> x_shape, const std::vector<int> qkv_hidden_sizes, const int num_heads) {
int backendId = get<0>(GetParam());
int targetId = get<1>(GetParam());
auto qk_hidden_size = qkv_hidden_sizes[0];
auto v_hidden_size = qkv_hidden_sizes[2];
auto input_hidden_size = x_shape[2];
auto hidden_size = qk_hidden_size + qk_hidden_size + v_hidden_size;
Mat x(x_shape, CV_32F);
Mat weight(std::vector<int>{input_hidden_size, hidden_size}, CV_32F);
Mat bias(std::vector<int>{hidden_size}, CV_32F);
randu(x, 0.f, 1.f);
randu(weight, 0.f, 1.f);
randu(bias, 0.f, 1.f);
LayerParams lp;
lp.type = "Attention";
lp.name = "testLayer";
lp.set("num_heads", num_heads);
lp.set("qkv_hidden_sizes", DictValue::arrayInt(qkv_hidden_sizes.data(), qkv_hidden_sizes.size()));
Net net;
int id = net.addLayerToPrev(lp.name, lp.type, lp);
net.connect(0, 0, id, 0);
net.connect(0, 1, id, 1);
net.connect(0, 2, id, 2);
{
std::vector<std::string> input_names{"x", "weight", "bias"};
net.setInputsNames(input_names);
net.setInput(x, input_names[0]);
net.setInput(weight, input_names[1]);
net.setInput(bias, input_names[2]);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
Mat out = net.forward();
}
TEST_CYCLE()
{
Mat out = net.forward();
}
SANITY_CHECK_NOTHING();
}
};
PERF_TEST_P_(Layer_Attention, VisionTransformer) {
test_layer({1, 197, 768}, {768, 768, 768}, 12);
}
INSTANTIATE_TEST_CASE_P(/**/, Layer_Slice, dnnBackendsAndTargets(false, false));
INSTANTIATE_TEST_CASE_P(/**/, Layer_NaryEltwise, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
#ifdef HAVE_CUDA
@@ -750,6 +806,7 @@ INSTANTIATE_TEST_CASE_P(/**/, Layer_LayerNorm, testing::Values(std::make_tuple(D
INSTANTIATE_TEST_CASE_P(/**/, Layer_LayerNormExpanded, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
INSTANTIATE_TEST_CASE_P(/**/, Layer_GatherElements, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
INSTANTIATE_TEST_CASE_P(/**/, Layer_InstanceNorm, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
INSTANTIATE_TEST_CASE_P(/**/, Layer_Attention, testing::Values(std::make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU)));
typedef TestBaseWithParam<tuple<Vec4i, int, bool, tuple<Backend, Target> > > Layer_FullyConnected;