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feat: ExaTrkX edge building KDTree on CPU + fixes + refactor + tests (#…
…2360) * Replaces brute force edge building with `Acts::KDTree` method * Abstracts `std::vector` to `torch::Tensor` conversions * Fixes bug in edge duplicate removal * Add more unit tests and enable in CI
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36 changes: 0 additions & 36 deletions
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Plugins/ExaTrkX/include/Acts/Plugins/ExaTrkX/buildEdges.hpp
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Plugins/ExaTrkX/include/Acts/Plugins/ExaTrkX/detail/TensorVectorConversion.hpp
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| Original file line number | Diff line number | Diff line change |
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| // This file is part of the Acts project. | ||
| // | ||
| // Copyright (C) 2023 CERN for the benefit of the Acts project | ||
| // | ||
| // This Source Code Form is subject to the terms of the Mozilla Public | ||
| // License, v. 2.0. If a copy of the MPL was not distributed with this | ||
| // file, You can obtain one at http://mozilla.org/MPL/2.0/. | ||
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| #pragma once | ||
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| #include "Acts/Utilities/Concepts.hpp" | ||
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| #include <cstdint> | ||
| #include <vector> | ||
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| #include <torch/torch.h> | ||
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| namespace Acts::detail { | ||
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| /// So far this is only needed for integers | ||
| template <typename T> | ||
| struct TorchTypeMap {}; | ||
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| template <> | ||
| struct TorchTypeMap<int64_t> { | ||
| constexpr static torch::Dtype type = torch::kInt64; | ||
| }; | ||
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| template <> | ||
| struct TorchTypeMap<int32_t> { | ||
| constexpr static torch::Dtype type = torch::kInt32; | ||
| }; | ||
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| template <> | ||
| struct TorchTypeMap<int16_t> { | ||
| constexpr static torch::Dtype type = torch::kInt16; | ||
| }; | ||
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| template <> | ||
| struct TorchTypeMap<int8_t> { | ||
| constexpr static torch::Dtype type = torch::kInt8; | ||
| }; | ||
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| template <> | ||
| struct TorchTypeMap<float> { | ||
| constexpr static torch::Dtype type = torch::kFloat32; | ||
| }; | ||
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| template <> | ||
| struct TorchTypeMap<double> { | ||
| constexpr static torch::Dtype type = torch::kFloat64; | ||
| }; | ||
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| /// Converts vector to 2D tensor | ||
| /// Make sure your vector has a even number of elements! | ||
| /// @Note Input must be mutable, due to torch API. | ||
| /// @Note Tensor does not take ownership! `.clone()` afterwards to get | ||
| /// ownership of the data | ||
| template <typename T> | ||
| at::Tensor vectorToTensor2D(std::vector<T> &vec, std::size_t cols) { | ||
| assert(vec.size() % cols == 0); | ||
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| auto opts = | ||
| at::TensorOptions().dtype(TorchTypeMap<T>::type).device(torch::kCPU); | ||
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| return torch::from_blob( | ||
| vec.data(), | ||
| {static_cast<long>(vec.size() / cols), static_cast<long>(cols)}, opts); | ||
| } | ||
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| /// Converts 2D tensor to vector | ||
| /// @Note Automatically converts tensor to target type! | ||
| template <typename T> | ||
| std::vector<T> tensor2DToVector(const at::Tensor &tensor) { | ||
| assert(tensor.sizes().size() == 2); | ||
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| // clone to make sure we own the data | ||
| // bring to CPU | ||
| // convert to requested type | ||
| // ensure the tensor is contiguous (e.g. not the case if indexed with step) | ||
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| at::Tensor transformedTensor = | ||
| tensor.to(torch::kCPU).to(TorchTypeMap<T>::type).contiguous(); | ||
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| std::vector<T> edgeIndex( | ||
| transformedTensor.template data_ptr<T>(), | ||
| transformedTensor.template data_ptr<T>() + transformedTensor.numel()); | ||
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| return edgeIndex; | ||
| } | ||
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| } // namespace Acts::detail |
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Plugins/ExaTrkX/include/Acts/Plugins/ExaTrkX/detail/buildEdges.hpp
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,47 @@ | ||
| // This file is part of the Acts project. | ||
| // | ||
| // Copyright (C) 2022 CERN for the benefit of the Acts project | ||
| // | ||
| // This Source Code Form is subject to the terms of the Mozilla Public | ||
| // License, v. 2.0. If a copy of the MPL was not distributed with this | ||
| // file, You can obtain one at http://mozilla.org/MPL/2.0/. | ||
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| #pragma once | ||
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| #include <cstdint> | ||
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| namespace at { | ||
| class Tensor; | ||
| } | ||
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| namespace Acts { | ||
| namespace detail { | ||
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| /// Post process edges | ||
| at::Tensor postprocessEdgeTensor(at::Tensor edges, bool removeSelfLoops = true, | ||
| bool removeDuplicates = true, | ||
| bool flipDirections = false); | ||
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| /// Edge building using FRNN and CUDA. | ||
| /// Raises an exception if not built with CUDA | ||
| at::Tensor buildEdgesFRNN(at::Tensor& embedFeatures, float rVal, int kVal, | ||
| bool flipDirections = false); | ||
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| /// Edge building using the Acts KD-Tree implementation | ||
| /// Note that this implementation has no maximum number of neighbours | ||
| /// in the NN search. kVal is only a hint for reserving memory | ||
| at::Tensor buildEdgesKDTree(at::Tensor& embedFeatures, float rVal, int kVal, | ||
| bool flipDirections = false); | ||
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| /// Dispatches either to FRNN or KD-Tree based edge building | ||
| /// | ||
| /// @param embedFeatures Tensor of shape (n_nodes, embedding_dim) | ||
| /// @param rVal radius for NN search | ||
| /// @param kVal max number of neighbours in NN search | ||
| /// @param flipDirections if we want to randomly flip directions of the | ||
| /// edges after the edge building | ||
| at::Tensor buildEdges(at::Tensor& embedFeatures, float rVal, int kVal, | ||
| bool flipDirections = false); | ||
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| } // namespace detail | ||
| } // namespace Acts |
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