/**
 * Copyright (c) Facebook, Inc. and its affiliates.
 *
 * This source code is licensed under the MIT license found in the
 * LICENSE file in the root directory of this source tree.
 */

#include <faiss/IndexFlat.h>
#include <faiss/gpu/GpuIndexFlat.h>
#include <faiss/gpu/perf/IndexWrapper.h>
#include <faiss/gpu/test/TestUtils.h>
#include <faiss/gpu/utils/DeviceUtils.h>
#include <faiss/gpu/utils/Timer.h>
#include <faiss/utils/random.h>
#include <gflags/gflags.h>
#include <faiss/gpu/utils/DeviceTensor.cuh>
#include <faiss/gpu/utils/HostTensor.cuh>
#include <map>
#include <memory>
#include <vector>

#include <cuda_profiler_api.h>

DEFINE_bool(l2, true, "L2 or inner product");
DEFINE_int32(k, 3, "final number of closest results returned");
DEFINE_int32(num, 128, "# of vecs");
DEFINE_int32(dim, 128, "# of dimensions");
DEFINE_int32(num_queries, 3, "number of query vectors");
DEFINE_bool(diff, true, "show exact distance + index output discrepancies");
DEFINE_bool(use_float16, false, "use encodings in float16");
DEFINE_bool(use_float16_math, false, "perform math in float16");
DEFINE_bool(transposed, false, "store vectors transposed");
DEFINE_int64(seed, -1, "specify random seed");
DEFINE_int32(num_gpus, 1, "number of gpus to use");
DEFINE_int64(pinned_mem, 0, "pinned memory allocation to use");
DEFINE_bool(cpu, true, "run the CPU code for timing and comparison");
DEFINE_bool(use_unified_mem, false, "use Pascal unified memory for the index");

using namespace faiss::gpu;

int main(int argc, char** argv) {
    gflags::ParseCommandLineFlags(&argc, &argv, true);

    cudaProfilerStop();

    auto seed = FLAGS_seed != -1L ? FLAGS_seed : time(nullptr);
    printf("using seed %ld\n", seed);

    auto numQueries = FLAGS_num_queries;

    auto index = std::unique_ptr<faiss::IndexFlat>(new faiss::IndexFlat(
            FLAGS_dim,
            FLAGS_l2 ? faiss::METRIC_L2 : faiss::METRIC_INNER_PRODUCT));

    HostTensor<float, 2, true> vecs({FLAGS_num, FLAGS_dim});
    faiss::float_rand(vecs.data(), vecs.numElements(), seed);

    index->add(FLAGS_num, vecs.data());

    printf("Database: dim %d num vecs %d\n", FLAGS_dim, FLAGS_num);
    printf("%s lookup: %d queries, total k %d\n",
           FLAGS_l2 ? "L2" : "IP",
           numQueries,
           FLAGS_k);
    printf("float16 encoding %s\n", FLAGS_use_float16 ? "enabled" : "disabled");
    printf("transposed storage %s\n",
           FLAGS_transposed ? "enabled" : "disabled");

    // Convert to GPU index
    printf("Copying index to %d GPU(s)...\n", FLAGS_num_gpus);

    auto initFn = [&index](faiss::gpu::GpuResourcesProvider* res, int dev)
            -> std::unique_ptr<faiss::gpu::GpuIndexFlat> {
        ((faiss::gpu::StandardGpuResources*)res)
                ->setPinnedMemory(FLAGS_pinned_mem);

        GpuIndexFlatConfig config;
        config.device = dev;
        config.useFloat16 = FLAGS_use_float16;
        config.storeTransposed = FLAGS_transposed;
        config.memorySpace = FLAGS_use_unified_mem ? MemorySpace::Unified
                                                   : MemorySpace::Device;

        auto p = std::unique_ptr<faiss::gpu::GpuIndexFlat>(
                new faiss::gpu::GpuIndexFlat(res, index.get(), config));
        return p;
    };

    IndexWrapper<faiss::gpu::GpuIndexFlat> gpuIndex(FLAGS_num_gpus, initFn);
    printf("copy done\n");

    // Build query vectors
    HostTensor<float, 2, true> cpuQuery({numQueries, FLAGS_dim});
    faiss::float_rand(cpuQuery.data(), cpuQuery.numElements(), seed);

    // Time faiss CPU
    HostTensor<float, 2, true> cpuDistances({numQueries, FLAGS_k});
    HostTensor<faiss::Index::idx_t, 2, true> cpuIndices({numQueries, FLAGS_k});

    if (FLAGS_cpu) {
        float cpuTime = 0.0f;

        CpuTimer timer;
        index->search(
                numQueries,
                cpuQuery.data(),
                FLAGS_k,
                cpuDistances.data(),
                cpuIndices.data());

        cpuTime = timer.elapsedMilliseconds();
        printf("CPU time %.3f ms\n", cpuTime);
    }

    HostTensor<float, 2, true> gpuDistances({numQueries, FLAGS_k});
    HostTensor<faiss::Index::idx_t, 2, true> gpuIndices({numQueries, FLAGS_k});

    CUDA_VERIFY(cudaProfilerStart());
    faiss::gpu::synchronizeAllDevices();

    float gpuTime = 0.0f;

    // Time GPU
    {
        CpuTimer timer;

        gpuIndex.getIndex()->search(
                cpuQuery.getSize(0),
                cpuQuery.data(),
                FLAGS_k,
                gpuDistances.data(),
                gpuIndices.data());

        // There is a device -> host copy above, so no need to time
        // additional synchronization with the GPU
        gpuTime = timer.elapsedMilliseconds();
    }

    CUDA_VERIFY(cudaProfilerStop());
    printf("GPU time %.3f ms\n", gpuTime);

    if (FLAGS_cpu) {
        compareLists(
                cpuDistances.data(),
                cpuIndices.data(),
                gpuDistances.data(),
                gpuIndices.data(),
                numQueries,
                FLAGS_k,
                "",
                true,
                FLAGS_diff,
                false);
    }

    CUDA_VERIFY(cudaDeviceSynchronize());

    return 0;
}
