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Add a generic Eigen backend based on clang vector extensions
The goal of this MR is to implement a generic SIMD backend (packet ops) for Eigen that uses clang vector extensions instead of platform-dependent intrinsics. Ideally, this should make it possible to build Eigen and achieve reasonable speed on any platform that has a recent clang compiler, without having to write any inline assembly or intrinsics. Caveats: * The current implementation is a proof of concept and supports vectorization for float, double, int32_t, and int64_t using fixed-size 512-bit vectors (a somewhat arbitrary choice). I have not done much to tune this for speed yet. * For now, there is no way to enable this other than setting -DEIGEN_VECTORIZE_GENERIC on the command line. * This only compiles with newer versions of clang. I have tested that it compiles and all tests pass with clang 19.1.7. https://clang.llvm.org/docs/LanguageExtensions.html#vectors-and-extended-vectors Closes #2998 and #2997 See merge request libeigen/eigen!2051 Co-authored-by: Rasmus Munk Larsen <rmlarsen@google.com> Co-authored-by: Antonio Sánchez <cantonios@google.com>
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@@ -1224,7 +1224,7 @@ EIGEN_DEVICE_FUNC inline typename unpacket_traits<Packet>::type pfirst(const Pac
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template <typename Packet>
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EIGEN_DEVICE_FUNC inline std::conditional_t<(unpacket_traits<Packet>::size % 8) == 0,
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typename unpacket_traits<Packet>::half, Packet>
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predux_half_dowto4(const Packet& a) {
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predux_half(const Packet& a) {
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return a;
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}
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@@ -1342,7 +1342,7 @@ struct pmadd_impl {
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return psub(c, pmul(a, b));
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}
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static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet pnmsub(const Packet& a, const Packet& b, const Packet& c) {
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return pnegate(pmadd(a, b, c));
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return pnegate(padd(pmul(a, b), c));
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}
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};
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@@ -1476,8 +1476,8 @@ struct PacketBlock {
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Packet packet[N];
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};
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template <typename Packet>
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EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet, 1>& /*kernel*/) {
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template <typename Packet, int size = 1>
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EIGEN_DEVICE_FUNC inline void ptranspose(PacketBlock<Packet, size>& /*kernel*/) {
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// Nothing to do in the scalar case, i.e. a 1x1 matrix.
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}
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