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2017-02-23 06:26:44 +08:00
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< div class = "fragment" > < div class = "line" > < a name = "l00001" > < / a > < span class = "lineno" > 1< / span >   < span class = "comment" > /**< / span > < / div >
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< div class = "line" > < a name = "l00002" > < / a > < span class = "lineno" > 2< / span >   < span class = "comment" > * Copyright (c) Facebook, Inc. and its affiliates.< / span > < / div >
< div class = "line" > < a name = "l00003" > < / a > < span class = "lineno" > 3< / span >   < span class = "comment" > *< / span > < / div >
< div class = "line" > < a name = "l00004" > < / a > < span class = "lineno" > 4< / span >   < span class = "comment" > * This source code is licensed under the MIT license found in the< / span > < / div >
< div class = "line" > < a name = "l00005" > < / a > < span class = "lineno" > 5< / span >   < span class = "comment" > * LICENSE file in the root directory of this source tree.< / span > < / div >
< div class = "line" > < a name = "l00006" > < / a > < span class = "lineno" > 6< / span >   < span class = "comment" > */< / span > < / div >
< div class = "line" > < a name = "l00007" > < / a > < span class = "lineno" > 7< / span >   < / div >
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< div class = "line" > < a name = "l00009" > < / a > < span class = "lineno" > 9< / span >   < span class = "preprocessor" > #pragma once< / span > < / div >
< div class = "line" > < a name = "l00010" > < / a > < span class = "lineno" > 10< / span >   < span class = "preprocessor" > < / span > < / div >
< div class = "line" > < a name = "l00011" > < / a > < span class = "lineno" > 11< / span >   < span class = "preprocessor" > #include " ../../FaissAssert.h" < / span > < / div >
< div class = "line" > < a name = "l00012" > < / a > < span class = "lineno" > 12< / span >   < span class = "preprocessor" > #include " Tensor.cuh" < / span > < / div >
< div class = "line" > < a name = "l00013" > < / a > < span class = "lineno" > 13< / span >   < span class = "preprocessor" > #include " DeviceUtils.h" < / span > < / div >
< div class = "line" > < a name = "l00014" > < / a > < span class = "lineno" > 14< / span >   < span class = "preprocessor" > #include " StaticUtils.h" < / span > < / div >
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< div class = "line" > < a name = "l00015" > < / a > < span class = "lineno" > 15< / span >   < span class = "preprocessor" > #include < cuda.h> < / span > < / div >
< div class = "line" > < a name = "l00016" > < / a > < span class = "lineno" > 16< / span >   < / div >
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< div class = "line" > < a name = "l00017" > < / a > < span class = "lineno" > 17< / span >   < span class = "keyword" > namespace < / span > faiss { < span class = "keyword" > namespace < / span > gpu {< / div >
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< div class = "line" > < a name = "l00018" > < / a > < span class = "lineno" > 18< / span >   < / div >
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< div class = "line" > < a name = "l00019" > < / a > < span class = "lineno" > 19< / span >   < span class = "keyword" > template< / span > < < span class = "keyword" > typename< / span > T, < span class = "keyword" > typename< / span > IndexT> < / div >
< div class = "line" > < a name = "l00020" > < / a > < span class = "lineno" > < a class = "line" href = "structfaiss_1_1gpu_1_1TensorInfo.html" > 20< / a > < / span >   < span class = "keyword" > struct < / span > < a class = "code" href = "structfaiss_1_1gpu_1_1TensorInfo.html" > TensorInfo< / a > {< / div >
< div class = "line" > < a name = "l00021" > < / a > < span class = "lineno" > 21< / span >   < span class = "keyword" > static< / span > constexpr < span class = "keywordtype" > int< / span > kMaxDims = 8;< / div >
< div class = "line" > < a name = "l00022" > < / a > < span class = "lineno" > 22< / span >   < / div >
< div class = "line" > < a name = "l00023" > < / a > < span class = "lineno" > 23< / span >   T* data;< / div >
< div class = "line" > < a name = "l00024" > < / a > < span class = "lineno" > 24< / span >   IndexT sizes[kMaxDims];< / div >
< div class = "line" > < a name = "l00025" > < / a > < span class = "lineno" > 25< / span >   IndexT strides[kMaxDims];< / div >
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< div class = "line" > < a name = "l00028" > < / a > < span class = "lineno" > 28< / span >   < / div >
< div class = "line" > < a name = "l00029" > < / a > < span class = "lineno" > 29< / span >   < span class = "keyword" > template< / span > < < span class = "keyword" > typename< / span > T, < span class = "keyword" > typename< / span > IndexT, < span class = "keywordtype" > int< / span > Dim> < / div >
< div class = "line" > < a name = "l00030" > < / a > < span class = "lineno" > < a class = "line" href = "structfaiss_1_1gpu_1_1TensorInfoOffset.html" > 30< / a > < / span >   < span class = "keyword" > struct < / span > < a class = "code" href = "structfaiss_1_1gpu_1_1TensorInfoOffset.html" > TensorInfoOffset< / a > {< / div >
< div class = "line" > < a name = "l00031" > < / a > < span class = "lineno" > 31< / span >   __device__ < span class = "keyword" > inline< / span > < span class = "keyword" > static< / span > < span class = "keywordtype" > unsigned< / span > < span class = "keywordtype" > int< / span > < span class = "keyword" > get< / span > (< span class = "keyword" > const< / span > < a class = "code" href = "structfaiss_1_1gpu_1_1TensorInfo.html" > TensorInfo< T, IndexT> < / a > & info,< / div >
< div class = "line" > < a name = "l00032" > < / a > < span class = "lineno" > 32< / span >   IndexT linearId) {< / div >
< div class = "line" > < a name = "l00033" > < / a > < span class = "lineno" > 33< / span >   IndexT offset = 0;< / div >
< div class = "line" > < a name = "l00034" > < / a > < span class = "lineno" > 34< / span >   < / div >
< div class = "line" > < a name = "l00035" > < / a > < span class = "lineno" > 35< / span >   < span class = "preprocessor" > #pragma unroll< / span > < / div >
< div class = "line" > < a name = "l00036" > < / a > < span class = "lineno" > 36< / span >   < span class = "preprocessor" > < / span > < span class = "keywordflow" > for< / span > (< span class = "keywordtype" > int< / span > i = Dim - 1; i > = 0; --i) {< / div >
< div class = "line" > < a name = "l00037" > < / a > < span class = "lineno" > 37< / span >   IndexT curDimIndex = linearId % info.sizes[i];< / div >
< div class = "line" > < a name = "l00038" > < / a > < span class = "lineno" > 38< / span >   IndexT curDimOffset = curDimIndex * info.strides[i];< / div >
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< div class = "line" > < a name = "l00040" > < / a > < span class = "lineno" > 40< / span >   offset += curDimOffset;< / div >
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< div class = "line" > < a name = "l00041" > < / a > < span class = "lineno" > 41< / span >   < / div >
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< div class = "line" > < a name = "l00042" > < / a > < span class = "lineno" > 42< / span >   < span class = "keywordflow" > if< / span > (i > 0) {< / div >
< div class = "line" > < a name = "l00043" > < / a > < span class = "lineno" > 43< / span >   linearId /= info.sizes[i];< / div >
< div class = "line" > < a name = "l00044" > < / a > < span class = "lineno" > 44< / span >   }< / div >
< div class = "line" > < a name = "l00045" > < / a > < span class = "lineno" > 45< / span >   }< / div >
< div class = "line" > < a name = "l00046" > < / a > < span class = "lineno" > 46< / span >   < / div >
< div class = "line" > < a name = "l00047" > < / a > < span class = "lineno" > 47< / span >   < span class = "keywordflow" > return< / span > offset;< / div >
< div class = "line" > < a name = "l00048" > < / a > < span class = "lineno" > 48< / span >   }< / div >
< div class = "line" > < a name = "l00049" > < / a > < span class = "lineno" > 49< / span >   };< / div >
< div class = "line" > < a name = "l00050" > < / a > < span class = "lineno" > 50< / span >   < / div >
< div class = "line" > < a name = "l00051" > < / a > < span class = "lineno" > 51< / span >   < span class = "keyword" > template< / span > < < span class = "keyword" > typename< / span > T, < span class = "keyword" > typename< / span > IndexT> < / div >
< div class = "line" > < a name = "l00052" > < / a > < span class = "lineno" > < a class = "line" href = "structfaiss_1_1gpu_1_1TensorInfoOffset_3_01T_00_01IndexT_00-1_01_4.html" > 52< / a > < / span >   < span class = "keyword" > struct < / span > < a class = "code" href = "structfaiss_1_1gpu_1_1TensorInfoOffset.html" > TensorInfoOffset< / a > < T, IndexT, -1> {< / div >
< div class = "line" > < a name = "l00053" > < / a > < span class = "lineno" > 53< / span >   __device__ < span class = "keyword" > inline< / span > < span class = "keyword" > static< / span > < span class = "keywordtype" > unsigned< / span > < span class = "keywordtype" > int< / span > < span class = "keyword" > get< / span > (< span class = "keyword" > const< / span > < a class = "code" href = "structfaiss_1_1gpu_1_1TensorInfo.html" > TensorInfo< T, IndexT> < / a > & info,< / div >
< div class = "line" > < a name = "l00054" > < / a > < span class = "lineno" > 54< / span >   IndexT linearId) {< / div >
< div class = "line" > < a name = "l00055" > < / a > < span class = "lineno" > 55< / span >   < span class = "keywordflow" > return< / span > linearId;< / div >
< div class = "line" > < a name = "l00056" > < / a > < span class = "lineno" > 56< / span >   }< / div >
< div class = "line" > < a name = "l00057" > < / a > < span class = "lineno" > 57< / span >   };< / div >
< div class = "line" > < a name = "l00058" > < / a > < span class = "lineno" > 58< / span >   < / div >
< div class = "line" > < a name = "l00059" > < / a > < span class = "lineno" > 59< / span >   < span class = "keyword" > template< / span > < < span class = "keyword" > typename< / span > T, < span class = "keyword" > typename< / span > IndexT, < span class = "keywordtype" > int< / span > Dim> < / div >
< div class = "line" > < a name = "l00060" > < / a > < span class = "lineno" > 60< / span >   < a class = "code" href = "structfaiss_1_1gpu_1_1TensorInfo.html" > TensorInfo< T, IndexT> < / a > getTensorInfo(< span class = "keyword" > const< / span > < a class = "code" href = "classfaiss_1_1gpu_1_1Tensor.html" > Tensor< T, Dim, true> < / a > & t) {< / div >
< div class = "line" > < a name = "l00061" > < / a > < span class = "lineno" > 61< / span >   < a class = "code" href = "structfaiss_1_1gpu_1_1TensorInfo.html" > TensorInfo< T, IndexT> < / a > info;< / div >
< div class = "line" > < a name = "l00062" > < / a > < span class = "lineno" > 62< / span >   < / div >
< div class = "line" > < a name = "l00063" > < / a > < span class = "lineno" > 63< / span >   < span class = "keywordflow" > for< / span > (< span class = "keywordtype" > int< / span > i = 0; i < Dim; ++i) {< / div >
< div class = "line" > < a name = "l00064" > < / a > < span class = "lineno" > 64< / span >   info.sizes[i] = (IndexT) t.< a class = "code" href = "classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c" > getSize< / a > (i);< / div >
< div class = "line" > < a name = "l00065" > < / a > < span class = "lineno" > 65< / span >   info.strides[i] = (IndexT) t.< a class = "code" href = "classfaiss_1_1gpu_1_1Tensor.html#a0b8bba630f7a1fa217f90b20d298420a" > getStride< / a > (i);< / div >
< div class = "line" > < a name = "l00066" > < / a > < span class = "lineno" > 66< / span >   }< / div >
< div class = "line" > < a name = "l00067" > < / a > < span class = "lineno" > 67< / span >   < / div >
< div class = "line" > < a name = "l00068" > < / a > < span class = "lineno" > 68< / span >   info.data = t.< a class = "code" href = "classfaiss_1_1gpu_1_1Tensor.html#a50411ce4d0fa32ef715e3321b6e33212" > data< / a > ();< / div >
< div class = "line" > < a name = "l00069" > < / a > < span class = "lineno" > 69< / span >   info.dims = Dim;< / div >
< div class = "line" > < a name = "l00070" > < / a > < span class = "lineno" > 70< / span >   < / div >
< div class = "line" > < a name = "l00071" > < / a > < span class = "lineno" > 71< / span >   < span class = "keywordflow" > return< / span > info;< / div >
< div class = "line" > < a name = "l00072" > < / a > < span class = "lineno" > 72< / span >   }< / div >
< div class = "line" > < a name = "l00073" > < / a > < span class = "lineno" > 73< / span >   < / div >
< div class = "line" > < a name = "l00074" > < / a > < span class = "lineno" > 74< / span >   < span class = "keyword" > template< / span > < < span class = "keyword" > typename< / span > T, < span class = "keyword" > typename< / span > IndexT, < span class = "keywordtype" > int< / span > DimInput, < span class = "keywordtype" > int< / span > DimOutput> < / div >
< div class = "line" > < a name = "l00075" > < / a > < span class = "lineno" > 75< / span >   __global__ < span class = "keywordtype" > void< / span > transposeAny(TensorInfo< T, IndexT> input,< / div >
< div class = "line" > < a name = "l00076" > < / a > < span class = "lineno" > 76< / span >   TensorInfo< T, IndexT> output,< / div >
< div class = "line" > < a name = "l00077" > < / a > < span class = "lineno" > 77< / span >   IndexT totalSize) {< / div >
< div class = "line" > < a name = "l00078" > < / a > < span class = "lineno" > 78< / span >   < span class = "keywordflow" > for< / span > (IndexT i = blockIdx.x * blockDim.x + threadIdx.x;< / div >
< div class = "line" > < a name = "l00079" > < / a > < span class = "lineno" > 79< / span >   i < totalSize;< / div >
< div class = "line" > < a name = "l00080" > < / a > < span class = "lineno" > 80< / span >   i += gridDim.x + blockDim.x) {< / div >
< div class = "line" > < a name = "l00081" > < / a > < span class = "lineno" > 81< / span >   < span class = "keyword" > auto< / span > inputOffset = TensorInfoOffset< T, IndexT, DimInput> ::get(input, i);< / div >
< div class = "line" > < a name = "l00082" > < / a > < span class = "lineno" > 82< / span >   < span class = "keyword" > auto< / span > outputOffset = TensorInfoOffset< T, IndexT, DimOutput> ::get(output, i);< / div >
< div class = "line" > < a name = "l00083" > < / a > < span class = "lineno" > 83< / span >   < / div >
< div class = "line" > < a name = "l00084" > < / a > < span class = "lineno" > 84< / span >   < span class = "preprocessor" > #if __CUDA_ARCH__ > = 350< / span > < / div >
< div class = "line" > < a name = "l00085" > < / a > < span class = "lineno" > 85< / span >   < span class = "preprocessor" > < / span > output.data[outputOffset] = __ldg(& input.data[inputOffset]);< / div >
< div class = "line" > < a name = "l00086" > < / a > < span class = "lineno" > 86< / span >   < span class = "preprocessor" > #else< / span > < / div >
< div class = "line" > < a name = "l00087" > < / a > < span class = "lineno" > 87< / span >   < span class = "preprocessor" > < / span > output.data[outputOffset] = input.data[inputOffset];< / div >
< div class = "line" > < a name = "l00088" > < / a > < span class = "lineno" > 88< / span >   < span class = "preprocessor" > #endif< / span > < / div >
< div class = "line" > < a name = "l00089" > < / a > < span class = "lineno" > 89< / span >   < span class = "preprocessor" > < / span > }< / div >
< div class = "line" > < a name = "l00090" > < / a > < span class = "lineno" > 90< / span >   }< / div >
< div class = "line" > < a name = "l00091" > < / a > < span class = "lineno" > 91< / span >   < span class = "comment" > < / span > < / div >
< div class = "line" > < a name = "l00092" > < / a > < span class = "lineno" > 92< / span >   < span class = "comment" > /// Performs an out-of-place transposition between any two dimensions.< / span > < / div >
< div class = "line" > < a name = "l00093" > < / a > < span class = "lineno" > 93< / span >   < span class = "comment" > /// Best performance is if the transposed dimensions are not< / span > < / div >
< div class = "line" > < a name = "l00094" > < / a > < span class = "lineno" > 94< / span >   < span class = "comment" > /// innermost, since the reads and writes will be coalesced.< / span > < / div >
< div class = "line" > < a name = "l00095" > < / a > < span class = "lineno" > 95< / span >   < span class = "comment" > /// Could include a shared memory transposition if the dimensions< / span > < / div >
< div class = "line" > < a name = "l00096" > < / a > < span class = "lineno" > 96< / span >   < span class = "comment" > /// being transposed are innermost, but would require support for< / span > < / div >
< div class = "line" > < a name = "l00097" > < / a > < span class = "lineno" > 97< / span >   < span class = "comment" > /// arbitrary rectangular matrices.< / span > < / div >
< div class = "line" > < a name = "l00098" > < / a > < span class = "lineno" > 98< / span >   < span class = "comment" > /// This linearized implementation seems to perform well enough,< / span > < / div >
< div class = "line" > < a name = "l00099" > < / a > < span class = "lineno" > 99< / span >   < span class = "comment" > /// especially for cases that we care about (outer dimension< / span > < / div >
< div class = "line" > < a name = "l00100" > < / a > < span class = "lineno" > 100< / span >   < span class = "comment" > /// transpositions).< / span > < / div >
< div class = "line" > < a name = "l00101" > < / a > < span class = "lineno" > 101< / span >   < span class = "comment" > < / span > < span class = "keyword" > template< / span > < < span class = "keyword" > typename< / span > T, < span class = "keywordtype" > int< / span > Dim> < / div >
< div class = "line" > < a name = "l00102" > < / a > < span class = "lineno" > 102< / span >   < span class = "keywordtype" > void< / span > runTransposeAny(Tensor< T, Dim, true> & in,< / div >
< div class = "line" > < a name = "l00103" > < / a > < span class = "lineno" > 103< / span >   < span class = "keywordtype" > int< / span > dim1, < span class = "keywordtype" > int< / span > dim2,< / div >
< div class = "line" > < a name = "l00104" > < / a > < span class = "lineno" > 104< / span >   Tensor< T, Dim, true> & out,< / div >
< div class = "line" > < a name = "l00105" > < / a > < span class = "lineno" > 105< / span >   cudaStream_t stream) {< / div >
< div class = "line" > < a name = "l00106" > < / a > < span class = "lineno" > 106< / span >   static_assert(Dim < = TensorInfo< T, unsigned int> ::kMaxDims,< / div >
< div class = "line" > < a name = "l00107" > < / a > < span class = "lineno" > 107< / span >   < span class = "stringliteral" > " too many dimensions" < / span > );< / div >
< div class = "line" > < a name = "l00108" > < / a > < span class = "lineno" > 108< / span >   < / div >
< div class = "line" > < a name = "l00109" > < / a > < span class = "lineno" > 109< / span >   FAISS_ASSERT(dim1 != dim2);< / div >
< div class = "line" > < a name = "l00110" > < / a > < span class = "lineno" > 110< / span >   FAISS_ASSERT(dim1 < Dim & & dim2 < Dim);< / div >
< div class = "line" > < a name = "l00111" > < / a > < span class = "lineno" > 111< / span >   < / div >
< div class = "line" > < a name = "l00112" > < / a > < span class = "lineno" > 112< / span >   < span class = "keywordtype" > int< / span > outSize[Dim];< / div >
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< div class = "line" > < a name = "l00113" > < / a > < span class = "lineno" > 113< / span >   < / div >
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< div class = "line" > < a name = "l00114" > < / a > < span class = "lineno" > 114< / span >   < span class = "keywordflow" > for< / span > (< span class = "keywordtype" > int< / span > i = 0; i < Dim; ++i) {< / div >
< div class = "line" > < a name = "l00115" > < / a > < span class = "lineno" > 115< / span >   outSize[i] = in.getSize(i);< / div >
< div class = "line" > < a name = "l00116" > < / a > < span class = "lineno" > 116< / span >   }< / div >
< div class = "line" > < a name = "l00117" > < / a > < span class = "lineno" > 117< / span >   < / div >
< div class = "line" > < a name = "l00118" > < / a > < span class = "lineno" > 118< / span >   std::swap(outSize[dim1], outSize[dim2]);< / div >
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< div class = "line" > < a name = "l00119" > < / a > < span class = "lineno" > 119< / span >   < / div >
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< div class = "line" > < a name = "l00120" > < / a > < span class = "lineno" > 120< / span >   < span class = "keywordflow" > for< / span > (< span class = "keywordtype" > int< / span > i = 0; i < Dim; ++i) {< / div >
< div class = "line" > < a name = "l00121" > < / a > < span class = "lineno" > 121< / span >   FAISS_ASSERT(out.getSize(i) == outSize[i]);< / div >
< div class = "line" > < a name = "l00122" > < / a > < span class = "lineno" > 122< / span >   }< / div >
< div class = "line" > < a name = "l00123" > < / a > < span class = "lineno" > 123< / span >   < / div >
< div class = "line" > < a name = "l00124" > < / a > < span class = "lineno" > 124< / span >   < span class = "keywordtype" > size_t< / span > totalSize = in.numElements();< / div >
< div class = "line" > < a name = "l00125" > < / a > < span class = "lineno" > 125< / span >   < span class = "keywordtype" > size_t< / span > block = std::min((< span class = "keywordtype" > size_t< / span > ) getMaxThreadsCurrentDevice(), totalSize);< / div >
< div class = "line" > < a name = "l00126" > < / a > < span class = "lineno" > 126< / span >   < / div >
< div class = "line" > < a name = "l00127" > < / a > < span class = "lineno" > 127< / span >   < span class = "keywordflow" > if< / span > (totalSize < = (< span class = "keywordtype" > size_t< / span > ) std::numeric_limits< int> ::max()) {< / div >
< div class = "line" > < a name = "l00128" > < / a > < span class = "lineno" > 128< / span >   < span class = "comment" > // div/mod seems faster with unsigned types< / span > < / div >
< div class = "line" > < a name = "l00129" > < / a > < span class = "lineno" > 129< / span >   < span class = "keyword" > auto< / span > inInfo = getTensorInfo< T, unsigned int, Dim> (in);< / div >
< div class = "line" > < a name = "l00130" > < / a > < span class = "lineno" > 130< / span >   < span class = "keyword" > auto< / span > outInfo = getTensorInfo< T, unsigned int, Dim> (out);< / div >
< div class = "line" > < a name = "l00131" > < / a > < span class = "lineno" > 131< / span >   < / div >
< div class = "line" > < a name = "l00132" > < / a > < span class = "lineno" > 132< / span >   std::swap(inInfo.sizes[dim1], inInfo.sizes[dim2]);< / div >
< div class = "line" > < a name = "l00133" > < / a > < span class = "lineno" > 133< / span >   std::swap(inInfo.strides[dim1], inInfo.strides[dim2]);< / div >
< div class = "line" > < a name = "l00134" > < / a > < span class = "lineno" > 134< / span >   < / div >
< div class = "line" > < a name = "l00135" > < / a > < span class = "lineno" > 135< / span >   < span class = "keyword" > auto< / span > grid = std::min(utils::divUp(totalSize, block), (< span class = "keywordtype" > size_t< / span > ) 4096);< / div >
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< div class = "line" > < a name = "l00136" > < / a > < span class = "lineno" > 136< / span >   < / div >
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< div class = "line" > < a name = "l00137" > < / a > < span class = "lineno" > 137< / span >   transposeAny< T, < span class = "keywordtype" > unsigned< / span > int, Dim, -1> < / div >
< div class = "line" > < a name = "l00138" > < / a > < span class = "lineno" > 138< / span >   < < < grid, block, 0, stream> > > (inInfo, outInfo, totalSize);< / div >
< div class = "line" > < a name = "l00139" > < / a > < span class = "lineno" > 139< / span >   } < span class = "keywordflow" > else< / span > {< / div >
< div class = "line" > < a name = "l00140" > < / a > < span class = "lineno" > 140< / span >   < span class = "keyword" > auto< / span > inInfo = getTensorInfo< T, unsigned long, Dim> (in);< / div >
< div class = "line" > < a name = "l00141" > < / a > < span class = "lineno" > 141< / span >   < span class = "keyword" > auto< / span > outInfo = getTensorInfo< T, unsigned long, Dim> (out);< / div >
< div class = "line" > < a name = "l00142" > < / a > < span class = "lineno" > 142< / span >   < / div >
< div class = "line" > < a name = "l00143" > < / a > < span class = "lineno" > 143< / span >   std::swap(inInfo.sizes[dim1], inInfo.sizes[dim2]);< / div >
< div class = "line" > < a name = "l00144" > < / a > < span class = "lineno" > 144< / span >   std::swap(inInfo.strides[dim1], inInfo.strides[dim2]);< / div >
< div class = "line" > < a name = "l00145" > < / a > < span class = "lineno" > 145< / span >   < / div >
< div class = "line" > < a name = "l00146" > < / a > < span class = "lineno" > 146< / span >   < span class = "keyword" > auto< / span > grid = std::min(utils::divUp(totalSize, block), (< span class = "keywordtype" > size_t< / span > ) 4096);< / div >
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< div class = "line" > < a name = "l00147" > < / a > < span class = "lineno" > 147< / span >   < / div >
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< div class = "line" > < a name = "l00148" > < / a > < span class = "lineno" > 148< / span >   transposeAny< T, < span class = "keywordtype" > unsigned< / span > long, Dim, -1> < / div >
< div class = "line" > < a name = "l00149" > < / a > < span class = "lineno" > 149< / span >   < < < grid, block, 0, stream> > > (inInfo, outInfo, totalSize);< / div >
< div class = "line" > < a name = "l00150" > < / a > < span class = "lineno" > 150< / span >   }< / div >
< div class = "line" > < a name = "l00151" > < / a > < span class = "lineno" > 151< / span >   CUDA_TEST_ERROR();< / div >
< div class = "line" > < a name = "l00152" > < / a > < span class = "lineno" > 152< / span >   }< / div >
< div class = "line" > < a name = "l00153" > < / a > < span class = "lineno" > 153< / span >   < / div >
< div class = "line" > < a name = "l00154" > < / a > < span class = "lineno" > 154< / span >   } } < span class = "comment" > // namespace< / span > < / div >
< div class = "ttc" id = "structfaiss_1_1gpu_1_1TensorInfo_html" > < div class = "ttname" > < a href = "structfaiss_1_1gpu_1_1TensorInfo.html" > faiss::gpu::TensorInfo< / a > < / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "Transpose_8cuh_source.html#l00020" > Transpose.cuh:20< / a > < / div > < / div >
< div class = "ttc" id = "classfaiss_1_1gpu_1_1Tensor_html_a6699c311648457f257afa340c61f417c" > < div class = "ttname" > < a href = "classfaiss_1_1gpu_1_1Tensor.html#a6699c311648457f257afa340c61f417c" > faiss::gpu::Tensor::getSize< / a > < / div > < div class = "ttdeci" > __host__ __device__ IndexT getSize(int i) const < / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "Tensor_8cuh_source.html#l00222" > Tensor.cuh:222< / a > < / div > < / div >
< div class = "ttc" id = "classfaiss_1_1gpu_1_1Tensor_html_a50411ce4d0fa32ef715e3321b6e33212" > < div class = "ttname" > < a href = "classfaiss_1_1gpu_1_1Tensor.html#a50411ce4d0fa32ef715e3321b6e33212" > faiss::gpu::Tensor::data< / a > < / div > < div class = "ttdeci" > __host__ __device__ DataPtrType data()< / div > < div class = "ttdoc" > Returns a raw pointer to the start of our data. < / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "Tensor_8cuh_source.html#l00174" > Tensor.cuh:174< / a > < / div > < / div >
< div class = "ttc" id = "classfaiss_1_1gpu_1_1Tensor_html" > < div class = "ttname" > < a href = "classfaiss_1_1gpu_1_1Tensor.html" > faiss::gpu::Tensor< / a > < / div > < div class = "ttdoc" > Our tensor type. < / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "Tensor_8cuh_source.html#l00028" > Tensor.cuh:28< / a > < / div > < / div >
< div class = "ttc" id = "classfaiss_1_1gpu_1_1Tensor_html_a0b8bba630f7a1fa217f90b20d298420a" > < div class = "ttname" > < a href = "classfaiss_1_1gpu_1_1Tensor.html#a0b8bba630f7a1fa217f90b20d298420a" > faiss::gpu::Tensor::getStride< / a > < / div > < div class = "ttdeci" > __host__ __device__ IndexT getStride(int i) const < / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "Tensor_8cuh_source.html#l00228" > Tensor.cuh:228< / a > < / div > < / div >
< div class = "ttc" id = "structfaiss_1_1gpu_1_1TensorInfoOffset_html" > < div class = "ttname" > < a href = "structfaiss_1_1gpu_1_1TensorInfoOffset.html" > faiss::gpu::TensorInfoOffset< / a > < / div > < div class = "ttdef" > < b > Definition:< / b > < a href = "Transpose_8cuh_source.html#l00030" > Transpose.cuh:30< / a > < / div > < / div >
2017-02-23 06:26:44 +08:00
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