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https://gitlab.com/libeigen/eigen.git
synced 2025-08-08 09:49:03 +08:00
Fix compile-time error caused by chip static asserts
This commit is contained in:
Antonio Sanchez
Antonio Sánchez
parent
2c6b61c006
commit
2692fb2b71
@ -24,7 +24,7 @@ using Eigen::internal::TiledEvaluation;
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// Default assignment that does no use block evaluation or vectorization.
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// We assume that default coefficient evaluation is well tested and correct.
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template <typename Dst, typename Expr>
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static void DefaultAssign(Dst& dst, Expr expr) {
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void DefaultAssign(Dst& dst, Expr expr) {
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using Assign = Eigen::TensorAssignOp<Dst, const Expr>;
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using Executor = Eigen::internal::TensorExecutor<const Assign, DefaultDevice,
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/*Vectorizable=*/false,
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@ -35,7 +35,7 @@ static void DefaultAssign(Dst& dst, Expr expr) {
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// Assignment with specified device and tiling strategy.
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template <bool Vectorizable, TiledEvaluation Tiling, typename Device, typename Dst, typename Expr>
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static void DeviceAssign(Device& d, Dst& dst, Expr expr) {
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void DeviceAssign(Device& d, Dst& dst, Expr expr) {
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using Assign = Eigen::TensorAssignOp<Dst, const Expr>;
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using Executor = Eigen::internal::TensorExecutor<const Assign, Device, Vectorizable, Tiling>;
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@ -52,7 +52,7 @@ static array<Index, NumDims> RandomDims(int min_dim = 1, int max_dim = 20) {
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}
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template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
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static void test_execute_unary_expr(Device d) {
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void test_execute_unary_expr(Device d) {
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static constexpr int Options = 0 | Layout;
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// Pick a large enough tensor size to bypass small tensor block evaluation
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@ -77,7 +77,7 @@ static void test_execute_unary_expr(Device d) {
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}
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template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
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static void test_execute_binary_expr(Device d) {
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void test_execute_binary_expr(Device d) {
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static constexpr int Options = 0 | Layout;
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// Pick a large enough tensor size to bypass small tensor block evaluation
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@ -105,7 +105,7 @@ static void test_execute_binary_expr(Device d) {
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}
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template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
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static void test_execute_broadcasting(Device d) {
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void test_execute_broadcasting(Device d) {
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static constexpr int Options = 0 | Layout;
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auto dims = RandomDims<NumDims>(1, 10);
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@ -134,92 +134,101 @@ static void test_execute_broadcasting(Device d) {
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}
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template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
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static void test_execute_chipping_rvalue(Device d) {
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auto dims = RandomDims<NumDims>(1, 10);
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Tensor<T, NumDims, Layout, Index> src(dims);
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src.setRandom();
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struct test_execute_chipping_rvalue_runner {
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template <int ChipDim>
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static void run_dim(Device& d, const array<Index, NumDims>& dims, const Tensor<T, NumDims, Layout, Index>& src) {
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const auto offset = internal::random<Index>(0, dims[(ChipDim)] - 1);
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const auto expr = src.template chip<ChipDim>(offset);
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#define TEST_CHIPPING(CHIP_DIM) \
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if (NumDims > (CHIP_DIM)) { \
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const auto offset = internal::random<Index>(0, dims[(CHIP_DIM)] - 1); \
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const auto expr = src.template chip<(CHIP_DIM)>(offset); \
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\
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Tensor<T, NumDims - 1, Layout, Index> golden; \
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golden = expr; \
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\
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Tensor<T, NumDims - 1, Layout, Index> dst(golden.dimensions()); \
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\
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using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>; \
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using Executor = internal::TensorExecutor<const Assign, Device, Vectorizable, Tiling>; \
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\
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Executor::run(Assign(dst, expr), d); \
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\
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for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) { \
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VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i)); \
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} \
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Tensor<T, NumDims - 1, Layout, Index> golden;
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golden = expr;
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Tensor<T, NumDims - 1, Layout, Index> dst(golden.dimensions());
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using Assign = TensorAssignOp<decltype(dst), const decltype(expr)>;
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using Executor = internal::TensorExecutor<const Assign, Device, Vectorizable, Tiling>;
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Executor::run(Assign(dst, expr), d);
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for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
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VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i));
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}
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// Recursively reduce chip dimension.
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run_dim<ChipDim - 1>(d, dims, src);
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}
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TEST_CHIPPING(0)
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TEST_CHIPPING(1)
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TEST_CHIPPING(2)
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TEST_CHIPPING(3)
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TEST_CHIPPING(4)
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TEST_CHIPPING(5)
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template <>
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void run_dim<-1>(Device&, const array<Index, NumDims>&, const Tensor<T, NumDims, Layout, Index>&) {}
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#undef TEST_CHIPPING
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static void run(Device d) {
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auto dims = RandomDims<NumDims>(1, 10);
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Tensor<T, NumDims, Layout, Index> src(dims);
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src.setRandom();
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run_dim<NumDims - 1>(d, dims, src);
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}
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};
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template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
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void test_execute_chipping_rvalue(Device d) {
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test_execute_chipping_rvalue_runner<T, NumDims, Device, Vectorizable, Tiling, Layout>::run(d);
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}
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template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
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static void test_execute_chipping_lvalue(Device d) {
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auto dims = RandomDims<NumDims>(1, 10);
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struct test_execute_chipping_lvalue_runner {
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template <int ChipDim>
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static void run_dim(Device& d, const array<Index, NumDims>& dims) {
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/* Generate random data that we'll assign to the chipped tensor dim. */
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array<Index, NumDims - 1> src_dims;
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for (int i = 0; i < NumDims - 1; ++i) {
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int dim = i < (ChipDim) ? i : i + 1;
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src_dims[i] = dims[dim];
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}
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#define TEST_CHIPPING(CHIP_DIM) \
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if (NumDims > (CHIP_DIM)) { \
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/* Generate random data that we'll assign to the chipped tensor dim. */ \
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array<Index, NumDims - 1> src_dims; \
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for (int i = 0; i < NumDims - 1; ++i) { \
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int dim = i < (CHIP_DIM) ? i : i + 1; \
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src_dims[i] = dims[dim]; \
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} \
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\
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Tensor<T, NumDims - 1, Layout, Index> src(src_dims); \
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src.setRandom(); \
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\
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const auto offset = internal::random<Index>(0, dims[(CHIP_DIM)] - 1); \
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\
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Tensor<T, NumDims, Layout, Index> random(dims); \
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random.setZero(); \
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\
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Tensor<T, NumDims, Layout, Index> golden(dims); \
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golden = random; \
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golden.template chip<(CHIP_DIM)>(offset) = src; \
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\
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Tensor<T, NumDims, Layout, Index> dst(dims); \
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dst = random; \
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auto expr = dst.template chip<(CHIP_DIM)>(offset); \
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\
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using Assign = TensorAssignOp<decltype(expr), const decltype(src)>; \
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using Executor = internal::TensorExecutor<const Assign, Device, Vectorizable, Tiling>; \
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\
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Executor::run(Assign(expr, src), d); \
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\
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for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) { \
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VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i)); \
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} \
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Tensor<T, NumDims - 1, Layout, Index> src(src_dims);
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src.setRandom();
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const auto offset = internal::random<Index>(0, dims[(ChipDim)] - 1);
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Tensor<T, NumDims, Layout, Index> random(dims);
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random.setZero();
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Tensor<T, NumDims, Layout, Index> golden(dims);
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golden = random;
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golden.template chip<(ChipDim)>(offset) = src;
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Tensor<T, NumDims, Layout, Index> dst(dims);
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dst = random;
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auto expr = dst.template chip<(ChipDim)>(offset);
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using Assign = TensorAssignOp<decltype(expr), const decltype(src)>;
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using Executor = internal::TensorExecutor<const Assign, Device, Vectorizable, Tiling>;
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Executor::run(Assign(expr, src), d);
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for (Index i = 0; i < dst.dimensions().TotalSize(); ++i) {
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VERIFY_IS_EQUAL(dst.coeff(i), golden.coeff(i));
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}
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run_dim<ChipDim - 1>(d, dims);
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}
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TEST_CHIPPING(0)
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TEST_CHIPPING(1)
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TEST_CHIPPING(2)
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TEST_CHIPPING(3)
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TEST_CHIPPING(4)
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TEST_CHIPPING(5)
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template <>
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void run_dim<-1>(Device&, const array<Index, NumDims>&) {}
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#undef TEST_CHIPPING
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static void run(Device d) {
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auto dims = RandomDims<NumDims>(1, 10);
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run_dim<NumDims - 1>(d, dims);
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}
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};
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template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
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void test_execute_chipping_lvalue(Device d) {
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test_execute_chipping_lvalue_runner<T, NumDims, Device, Vectorizable, Tiling, Layout>::run(d);
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}
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template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
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static void test_execute_shuffle_rvalue(Device d) {
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void test_execute_shuffle_rvalue(Device d) {
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static constexpr int Options = 0 | Layout;
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auto dims = RandomDims<NumDims>(1, 10);
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@ -255,7 +264,7 @@ static void test_execute_shuffle_rvalue(Device d) {
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}
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template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
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static void test_execute_shuffle_lvalue(Device d) {
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void test_execute_shuffle_lvalue(Device d) {
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static constexpr int Options = 0 | Layout;
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auto dims = RandomDims<NumDims>(5, 10);
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@ -289,7 +298,7 @@ static void test_execute_shuffle_lvalue(Device d) {
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}
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template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
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static void test_execute_reshape(Device d) {
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void test_execute_reshape(Device d) {
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static_assert(NumDims >= 2, "NumDims must be greater or equal than 2");
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static constexpr int ReshapedDims = NumDims - 1;
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@ -326,7 +335,7 @@ static void test_execute_reshape(Device d) {
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}
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template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
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static void test_execute_slice_rvalue(Device d) {
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void test_execute_slice_rvalue(Device d) {
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static_assert(NumDims >= 2, "NumDims must be greater or equal than 2");
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static constexpr int Options = 0 | Layout;
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@ -362,7 +371,7 @@ static void test_execute_slice_rvalue(Device d) {
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}
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template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
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static void test_execute_slice_lvalue(Device d) {
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void test_execute_slice_lvalue(Device d) {
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static_assert(NumDims >= 2, "NumDims must be greater or equal than 2");
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static constexpr int Options = 0 | Layout;
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@ -402,7 +411,7 @@ static void test_execute_slice_lvalue(Device d) {
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}
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template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
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static void test_execute_broadcasting_of_forced_eval(Device d) {
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void test_execute_broadcasting_of_forced_eval(Device d) {
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static constexpr int Options = 0 | Layout;
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auto dims = RandomDims<NumDims>(1, 10);
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@ -442,7 +451,7 @@ struct DummyGenerator {
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};
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template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
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static void test_execute_generator_op(Device d) {
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void test_execute_generator_op(Device d) {
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static constexpr int Options = 0 | Layout;
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auto dims = RandomDims<NumDims>(20, 30);
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@ -470,7 +479,7 @@ static void test_execute_generator_op(Device d) {
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}
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template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
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static void test_execute_reverse_rvalue(Device d) {
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void test_execute_reverse_rvalue(Device d) {
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static constexpr int Options = 0 | Layout;
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auto dims = RandomDims<NumDims>(1, numext::pow(1000000.0, 1.0 / NumDims));
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@ -502,7 +511,7 @@ static void test_execute_reverse_rvalue(Device d) {
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}
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template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
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static void test_async_execute_unary_expr(Device d) {
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void test_async_execute_unary_expr(Device d) {
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static constexpr int Options = 0 | Layout;
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// Pick a large enough tensor size to bypass small tensor block evaluation
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@ -532,7 +541,7 @@ static void test_async_execute_unary_expr(Device d) {
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}
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template <typename T, int NumDims, typename Device, bool Vectorizable, TiledEvaluation Tiling, int Layout>
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static void test_async_execute_binary_expr(Device d) {
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void test_async_execute_binary_expr(Device d) {
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static constexpr int Options = 0 | Layout;
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// Pick a large enough tensor size to bypass small tensor block evaluation
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