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synced 2024-12-21 07:19:46 +08:00
Fixed a number of compilation warnings generated by the cuda tests
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@ -25,6 +25,16 @@ template <typename T, size_t n> class array {
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE const T& operator[] (size_t index) const { return values[index]; }
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE T& front() { return values[0]; }
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE const T& front() const { return values[0]; }
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE T& back() { return values[n-1]; }
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE const T& back() const { return values[n-1]; }
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EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE
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static std::size_t size() { return n; }
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@ -123,13 +133,33 @@ template <typename T> class array<T, 0> {
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE T& operator[] (size_t) {
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eigen_assert(false && "Can't index a zero size array");
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return *static_cast<T*>(NULL);
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return dummy;
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}
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE const T& operator[] (size_t) const {
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eigen_assert(false && "Can't index a zero size array");
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return *static_cast<const T*>(NULL);
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return dummy;
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}
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE T& front() {
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eigen_assert(false && "Can't index a zero size array");
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return dummy;
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}
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE const T& front() const {
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eigen_assert(false && "Can't index a zero size array");
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return dummy;
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}
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE T& back() {
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eigen_assert(false && "Can't index a zero size array");
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return dummy;
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}
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EIGEN_DEVICE_FUNC
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EIGEN_STRONG_INLINE const T& back() const {
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eigen_assert(false && "Can't index a zero size array");
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return dummy;
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}
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static EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE std::size_t size() { return 0; }
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@ -142,6 +172,9 @@ template <typename T> class array<T, 0> {
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eigen_assert(l.size() == 0);
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}
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#endif
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private:
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T dummy;
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};
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namespace internal {
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@ -21,7 +21,7 @@ namespace Eigen {
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*/
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namespace internal {
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template <typename Index, typename InputDims, size_t NumKernelDims, int Layout>
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template <typename Index, typename InputDims, int NumKernelDims, int Layout>
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class IndexMapper {
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public:
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IndexMapper(const InputDims& input_dims, const array<Index, NumKernelDims>& kernel_dims,
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@ -123,7 +123,7 @@ class IndexMapper {
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}
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inputIndex += p * m_inputStrides[NumKernelDims];
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} else {
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int limit = 0;
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std::ptrdiff_t limit = 0;
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if (NumKernelDims < NumDims) {
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limit = NumDims - NumKernelDims - 1;
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}
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@ -147,7 +147,7 @@ class IndexMapper {
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}
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outputIndex += p * m_outputStrides[NumKernelDims];
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} else {
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int limit = 0;
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std::ptrdiff_t limit = 0;
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if (NumKernelDims < NumDims) {
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limit = NumDims - NumKernelDims - 1;
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}
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@ -206,7 +206,7 @@ class IndexMapper {
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}
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private:
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static const size_t NumDims = internal::array_size<InputDims>::value;
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static const int NumDims = internal::array_size<InputDims>::value;
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array<Index, NumDims> m_inputStrides;
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array<Index, NumDims> m_outputStrides;
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array<Index, NumDims> m_cudaInputStrides;
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@ -463,7 +463,7 @@ struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType>, Device>
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m_outputStrides[i] = m_outputStrides[i - 1] * m_dimensions[i - 1];
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}
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} else {
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m_outputStrides[NumOutputDims - 1] = 1;
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m_outputStrides.back() = 1;
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for (int i = NumOutputDims - 2; i >= 0; --i) {
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m_outputStrides[i] = m_outputStrides[i + 1] * m_dimensions[i + 1];
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}
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@ -479,7 +479,7 @@ struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType>, Device>
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input_strides[i] = input_strides[i-1] * input_dims[i-1];
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}
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} else {
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input_strides[NumInputDims - 1] = 1;
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input_strides.back() = 1;
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for (int i = NumInputDims - 2; i >= 0; --i) {
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input_strides[i] = input_strides[i + 1] * input_dims[i + 1];
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}
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