o Ci @sTddlZddlTdddZdddZddd Zddedfd d Zdedfd d ZdS)N)*cCsZ|dur tt|}t}t}t||D]\}}||||qt||||}|S)z builds the C++ vectors for the GPU indices and the resources. Handles the case where the resources are assigned to the list of GPUs N)rangelenGpuResourcesVector Int32Vectorzip push_backindex_cpu_to_gpu_multiple) resourcesindexcogpusvresvdeviresrj/var/www/html/fyndo/python/python_agents/rag_suite/venv/lib/python3.10/site-packages/faiss/gpu_wrappers.pyindex_cpu_to_gpu_multiple_pys   rcCst||d|d}|S)N)r r ngpu)index_cpu_to_gpus_list)r r r index_gpurrrindex_cpu_to_all_gpus$srcCsT|dur|dkrtt}n |dur|dkrt|}dd|D}t||||}|S)z Here we can pass list of GPU ids as a parameter or ngpu to use first n GPU's. gpus mut be a list or None. co is a GpuMultipleClonerOptions NrcSsg|]}tqSr)StandardGpuResources).0_rrr 2sz*index_cpu_to_gpus_list..)r get_num_gpusr)r r r rrrrrrr)s rcCs&|j\}} |jjr d} n|jjr|j}d} n tj|dd}d} t|} |jtj kr,t } n |jtj kr5t } nt d|j\} }|| ksDJ|jjrKd}n|jjrU|j}d}n tj|dd}d}t|}|jtj krkt }n |jtj krtt }nt d|durtj||ftj d}n|j||fksJ|jtj ksJt|}|durtj||ftjd}n |j||fksJt|}|jtjkrt}n|j|jkrtjkrnt dt}nt dt}||_||_| |_||_||_||_| |_| |_| |_| |_||_||_||_ ||_!||_"t#||||fS) aR Compute the k nearest neighbors of a vector on one GPU without constructing an index Parameters ---------- res : StandardGpuResources GPU resources to use during computation xq : array_like Query vectors, shape (nq, d) where d is appropriate for the index. `dtype` must be float32. xb : array_like Database vectors, shape (nb, d) where d is appropriate for the index. `dtype` must be float32. k : int Number of nearest neighbors. D : array_like, optional Output array for distances of the nearest neighbors, shape (nq, k) I : array_like, optional Output array for the nearest neighbors, shape (nq, k) metric : MetricType, optional Distance measure to use (either METRIC_L2 or METRIC_INNER_PRODUCT) device: int, optional Which CUDA device in the system to run the search on. -1 indicates that the current thread-local device state (via cudaGetDevice) should be used (can also be set via torch.cuda.set_device in PyTorch) Otherwise, an integer 0 <= device < numDevices indicates the GPU on which the computation should be run Returns ------- D : array_like Distances of the nearest neighbors, shape (nq, k) I : array_like Labels of the nearest neighbors, shape (nq, k) TFfloat32dtypezxq must be f32 or f16xb must be float32 or float16NzI must be i64 or i32)$shapeflags c_contiguous f_contiguousTnpascontiguousarrayswig_ptrr!rDistanceDataType_F32float16DistanceDataType_F16 TypeErroremptyint64IndicesDataType_I64int32IndicesDataType_I32GpuDistanceParamsmetrickdimsvectorsvectorsRowMajor vectorType numVectorsqueriesqueriesRowMajor queryType numQueries outDistances outIndicesoutIndicesTypedevicebfKnn)rxqxbr6DIr5rCnqd xq_row_majorxq_ptrxq_typenbd2 xb_row_majorxb_ptrxb_typeD_ptrI_ptrI_typeargsrrrknn_gpu9s| $        rWcCs|j\}}|jjr d}n|jjr|j}d}ntdt|} |jtj kr't } n|jtj kr0t } n tj |dd}d}|j\} } | |ksDJ|jjrKd} n|jjrU|j}d} n tj |dd}d} t|}|jtj krkt }n |jtj krtt }ntd|durtj|| ftj d}n|j|| fksJ|jtj ksJt|}t}||_d|_||_||_| |_||_| |_| |_||_| |_||_||_||_t|||S) ak Compute all pairwise distances between xq and xb on one GPU without constructing an index Parameters ---------- res : StandardGpuResources GPU resources to use during computation xq : array_like Query vectors, shape (nq, d) where d is appropriate for the index. `dtype` must be float32. xb : array_like Database vectors, shape (nb, d) where d is appropriate for the index. `dtype` must be float32. D : array_like, optional Output array for all pairwise distances, shape (nq, nb) metric : MetricType, optional Distance measure to use (either METRIC_L2 or METRIC_INNER_PRODUCT) device: int, optional Which CUDA device in the system to run the search on. -1 indicates that the current thread-local device state (via cudaGetDevice) should be used (can also be set via torch.cuda.set_device in PyTorch) Otherwise, an integer 0 <= device < numDevices indicates the GPU on which the computation should be run Returns ------- D : array_like All pairwise distances, shape (nq, nb) TFz5xq matrix should be row (C) or column-major (Fortran)rr r"Nr)r#r$r%r&r'r.r*r!r(rr+r,r-r)r/r4r5r6r7r8r9r:r;r<r=r>r?r@rCrD)rrErFrGr5rCrIrJrKrLrMrNrOrPrQrRrSrVrrrpairwise_distance_gpush        rX)NN)Nr)NNr) numpyr( faiss.loaderrrr METRIC_L2rWrXrrrrs    |