MegEngine/dnn/test/naive/rng.cpp

#include "test/naive/rng.h"
#include "megdnn.h"
#include "test/common/tensor.h"
#include "test/naive/fixture.h"

namespace megdnn {

namespace test {

template <typename ctype>
void assert_uniform_correct(const ctype* src, size_t size) {
    for (size_t i = 0; i < size; ++i) {
        ASSERT_GT(src[i], ctype(0));
        ASSERT_LE(src[i], ctype(1));
    }
    auto stat = get_mean_var(src, size, ctype(0.5));
    ASSERT_LE(std::abs(stat.first - 0.5), 1e-3);
    ASSERT_LE(std::abs(stat.second - 1.0 / 12), 1e-3);
}

namespace {
template <typename dtype>
void run_uniform(Handle* handle) {
    auto opr = handle->create_operator<UniformRNG>();
    opr->param().dtype = DTypeTrait<dtype>::enumv;
    Tensor<typename DTypeTrait<dtype>::ctype> t(handle, {TensorShape{200000}, dtype()});
    opr->exec(t.tensornd(), {});
    assert_uniform_correct(t.ptr(), t.layout().total_nr_elems());
}

template <typename dtype>
void run_gaussian(Handle* handle) {
    using ctype = typename DTypeTrait<dtype>::ctype;
    auto opr = handle->create_operator<GaussianRNG>();
    opr->param().mean = 0.8;
    opr->param().std = 2.3;
    opr->param().dtype = DTypeTrait<dtype>::enumv;
    Tensor<ctype> t(handle, {TensorShape{200001}, dtype()});
    opr->exec(t.tensornd(), {});

    auto ptr = t.ptr();
    auto size = t.layout().total_nr_elems();
    for (size_t i = 0; i < size; ++i) {
        ASSERT_LE(std::abs(ptr[i] - 0.8), ctype(15));
    }
    auto stat = get_mean_var(ptr, size, ctype(0.8));

    ASSERT_LE(std::abs(stat.first - 0.8), 5e-3);
    ASSERT_LE(std::abs(stat.second - 2.3 * 2.3), 5e-2);
}

template <typename dtype>
void run_gamma(Handle* handle) {
    using ctype = typename DTypeTrait<dtype>::ctype;
    auto opr = handle->create_operator<GammaRNG>();

    TensorLayout ly{TensorShape{2000000 * 5}, dtype()};

    Tensor<ctype> out(handle, ly);
    Tensor<ctype> shape(handle, ly);
    Tensor<ctype> scale(handle, ly);

    auto shape_ptr = shape.ptr();
    auto scale_ptr = scale.ptr();
    for (int i = 0; i < 5; ++i) {
        for (int j = 0; j < 2000000; ++j) {
            shape_ptr[i * 2000000 + j] = 2 * 0.3 * i + 0.5;
            scale_ptr[i * 2000000 + j] = i * 0.2 + 0.1;
        }
    }
    opr->exec(shape.tensornd(), scale.tensornd(), out.tensornd(), {});

    auto ptr = out.ptr();
    for (int i = 0; i < 5; ++i) {
        float a = 2 * 0.3 * i + 0.5, b = i * 0.2 + 0.1;
        float mean = a * b;
        float std = a * (b * b);
        auto stat = get_mean_var(ptr + i * 2000000, 2000000, ctype(mean));
        ASSERT_LE(std::abs(stat.first - mean), 0.01);
        ASSERT_LE(std::abs(stat.second - std), 0.01);
    }
}

template <typename dtype>
void run_poisson(Handle* handle) {
    using ctype = typename DTypeTrait<dtype>::ctype;
    auto opr = handle->create_operator<PoissonRNG>();

    TensorLayout ly{TensorShape{200000 * 5}, dtype()};

    Tensor<ctype> out(handle, ly);
    Tensor<ctype> lam(handle, ly);

    auto lam_ptr = lam.ptr();
    for (int i = 0; i < 5; ++i) {
        for (int j = 0; j < 200000; ++j) {
            lam_ptr[i * 200000 + j] = ctype(i + 1);
        }
    }
    opr->exec(lam.tensornd(), out.tensornd(), {});

    auto ptr = out.ptr();
    for (int i = 0; i < 5; ++i) {
        auto stat = get_mean_var(ptr + i * 200000, 200000, ctype(i + 1));
        ASSERT_LE(std::abs(stat.first - ctype(i + 1)), 0.01);
        ASSERT_LE(std::abs(stat.second - ctype(i + 1)), 0.01);
    }
}

template <typename dtype>
void run_beta(Handle* handle) {
    using ctype = typename DTypeTrait<dtype>::ctype;
    auto opr = handle->create_operator<BetaRNG>();

    TensorLayout ly{TensorShape{200000 * 5}, dtype()};

    Tensor<ctype> out(handle, ly);
    Tensor<ctype> alpha(handle, ly);
    Tensor<ctype> beta(handle, ly);

    auto alpha_ptr = alpha.ptr();
    auto beta_ptr = beta.ptr();
    for (int i = 0; i < 5; ++i) {
        for (int j = 0; j < 200000; ++j) {
            alpha_ptr[i * 200000 + j] = 0.3 * i + 0.1;
            beta_ptr[i * 200000 + j] = 2 * i * 0.3 + 0.1;
        }
    }
    opr->exec(alpha.tensornd(), beta.tensornd(), out.tensornd(), {});

    auto ptr = out.ptr();
    for (int i = 0; i < 5; ++i) {
        float a = 0.3 * i + 0.1, b = 2 * i * 0.3 + 0.1;
        float mean = a / (a + b);
        float std = a * b / ((a + b) * (a + b) * (a + b + 1));
        auto stat = get_mean_var(ptr + i * 200000, 200000, ctype(mean));
        ASSERT_LE(std::abs(stat.first - mean), 0.01);
        ASSERT_LE(std::abs(stat.second - std), 0.01);
    }
}

template <typename dtype>
void run_permutation(Handle* handle) {
    using ctype = typename DTypeTrait<dtype>::ctype;
    size_t sample_num =
            std::min(200000, static_cast<int>(DTypeTrait<dtype>::max()) - 10);

    auto opr = handle->create_operator<PermutationRNG>();
    opr->param().dtype = DTypeTrait<dtype>::enumv;
    TensorLayout ly{TensorShape{sample_num}, dtype()};
    Tensor<ctype> t(handle, ly);
    opr->exec(t.tensornd(), {});

    auto ptr = t.ptr();
    auto size = t.layout().total_nr_elems();

    std::vector<ctype> res(size);
    int not_same = 0;
    for (size_t i = 0; i < size; ++i) {
        if ((ptr[i] - ctype(i)) >= 1)
            not_same++;
        res[i] = ptr[i];
    }
    ASSERT_GT(not_same, 5000);
    std::sort(res.begin(), res.end());
    for (size_t i = 0; i < size; ++i) {
        ASSERT_LE(std::abs(res[i] - ctype(i)), 1e-8);
    }
}

template <typename T>
void run_shuffle(Handle* handle, bool bwd_flag) {
    using ctype = typename DTypeTrait<T>::ctype;
    auto run = [&](TensorShape shape) {
        auto opr = handle->create_operator<ShuffleRNGForward>();
        TensorLayout srclay{shape, T()};
        TensorLayout dstlay{shape, T()};
        TensorLayout indexlay{TensorShape{shape[0]}, dtype::Int32()};
        Tensor<dt_byte> workspace(
                handle,
                {TensorShape{opr->get_workspace_in_bytes(srclay, dstlay, indexlay)},
                 dtype::Byte()});
        Tensor<ctype> src(handle, srclay);
        Tensor<ctype> dst(handle, dstlay);
        Tensor<DTypeTrait<dt_int32>::ctype> index(handle, indexlay);
        auto sptr = src.ptr();
        size_t size = src.layout().total_nr_elems();
        for (size_t j = 0; j < size; ++j) {
            sptr[j] = j;
        }
        opr->exec(
                src.tensornd(), dst.tensornd(), index.tensornd(),
                {workspace.ptr(), workspace.layout().total_nr_elems()});

        auto dptr = dst.ptr();
        auto iptr = index.ptr();
        size_t len = index.layout().total_nr_elems();
        size_t step = size / len;
        for (size_t i = 0; i < len; ++i) {
            for (size_t j = 0; j < step; ++j) {
                ASSERT_EQ(dptr[i * step + j], sptr[iptr[i] * step + j]);
            }
        }
        if (bwd_flag) {
            for (size_t j = 0; j < size; ++j) {
                sptr[j] = 0;
            }
            auto oprbwd = handle->create_operator<ShuffleRNGBackward>();
            oprbwd->exec(
                    dst.tensornd(), index.tensornd(), src.tensornd(),
                    {workspace.ptr(), workspace.layout().total_nr_elems()});
            for (size_t i = 0; i < len; ++i) {
                for (size_t j = 0; j < step; ++j) {
                    ASSERT_EQ(dptr[i * step + j], sptr[iptr[i] * step + j]);
                }
            }
        }
    };

    run({10});
    run({6, 3});
}

template <typename T>
void run_dropout(Handle* handle) {
    using ctype = typename DTypeTrait<T>::ctype;
    auto run = [&](TensorShape shape, float drop_prob) {
        auto fwd = handle->create_operator<DropoutForward>();
        auto bwd = handle->create_operator<DropoutBackward>();
        fwd->param().drop_prob = drop_prob;
        bwd->param().drop_prob = drop_prob;
        double scale = 1.0 / (1.0 - drop_prob);

        TensorLayout inp_lay{shape, T()};
        TensorLayout oup_lay{shape, T()};
        TensorLayout mask_lay{{fwd->get_mask_size_in_bytes(inp_lay)}, dtype::Byte()};
        TensorLayout doup_lay{shape, T()};
        TensorLayout dinp_lay{shape, T()};
        TensorLayout fwd_ws_lay{
                {fwd->get_workspace_in_bytes(inp_lay, oup_lay, mask_lay)},
                dtype::Byte()};
        TensorLayout bwd_ws_lay{
                {bwd->get_workspace_in_bytes(doup_lay, mask_lay, dinp_lay)},
                dtype::Byte()};

        Tensor<ctype> inp(handle, inp_lay);
        Tensor<ctype> oup(handle, oup_lay);
        Tensor<DTypeTrait<dt_byte>::ctype> mask(handle, mask_lay);
        Tensor<ctype> doup(handle, doup_lay);
        Tensor<ctype> dinp(handle, dinp_lay);
        Tensor<DTypeTrait<dt_byte>::ctype> fwd_ws(handle, fwd_ws_lay);
        Tensor<DTypeTrait<dt_byte>::ctype> bwd_ws(handle, bwd_ws_lay);

        for (size_t i = 0; i < inp.layout().total_nr_elems(); ++i) {
            inp.ptr()[i] = 1;
            doup.ptr()[i] = 1;
        }

        fwd->exec(
                inp.tensornd(), oup.tensornd(), mask.tensornd(),
                {fwd_ws.ptr(), fwd_ws.layout().total_nr_elems()});
        size_t droped_cnt = 0;
        for (size_t i = 0; i < inp.layout().total_nr_elems(); ++i) {
            ASSERT_TRUE(oup.ptr()[i] == 0 || oup.ptr()[i] == static_cast<ctype>(scale));
            if (oup.ptr()[i] == 0) {
                droped_cnt++;
            }
        }
        float real_drop = droped_cnt * 1.0 / inp.layout().total_nr_elems();
        ASSERT_LT(abs(drop_prob - real_drop), 1e-2);

        bwd->exec(
                doup.tensornd(), mask.tensornd(), dinp.tensornd(),
                {bwd_ws.ptr(), bwd_ws.layout().total_nr_elems()});
        for (size_t i = 0; i < inp.layout().total_nr_elems(); ++i) {
            ASSERT_TRUE(oup.ptr()[i] == dinp.ptr()[i]);
        }
    };

    run({32, 32, 32, 32}, 0.2);
    run({100000}, 0.3);
}

}  // namespace

TEST_F(NAIVE, UNIFORM_RNG_F32) {
    run_uniform<dtype::Float32>(handle());
}

TEST_F(NAIVE, UNIFORM_RNG_F16) {
    DNN_INC_FLOAT16(run_uniform<dtype::Float16>(handle()));
}

TEST_F(NAIVE, GAUSSIAN_RNG_F32) {
    run_gaussian<dtype::Float32>(handle());
}

TEST_F(NAIVE, GAUSSIAN_RNG_F16) {
    DNN_INC_FLOAT16(run_gaussian<dtype::Float16>(handle()));
}

TEST_F(NAIVE, GAMMA_RNG_F32) {
    run_gamma<dtype::Float32>(handle());
}

TEST_F(NAIVE, GAMMA_RNG_F16) {
    DNN_INC_FLOAT16(run_gamma<dtype::Float16>(handle()));
}

TEST_F(NAIVE, POISSON_RNG_F32) {
    run_poisson<dtype::Float32>(handle());
}

TEST_F(NAIVE, POISSON_RNG_F16) {
    DNN_INC_FLOAT16(run_poisson<dtype::Float16>(handle()));
}

TEST_F(NAIVE, BETA_RNG_F32) {
    run_beta<dtype::Float32>(handle());
}

TEST_F(NAIVE, BETA_RNG_F16) {
    DNN_INC_FLOAT16(run_beta<dtype::Float16>(handle()));
}

TEST_F(NAIVE, PERMUTATION_RNG_F32) {
    run_permutation<dtype::Float32>(handle());
}

TEST_F(NAIVE, PERMUTATION_RNG_INT32) {
    run_permutation<dtype::Int32>(handle());
}

TEST_F(NAIVE, PERMUTATION_RNG_INT16) {
    run_permutation<dtype::Int16>(handle());
}

TEST_F(NAIVE, SHUFFLE_RNG_FWD_F32) {
    run_shuffle<dtype::Float32>(handle(), false);
}

TEST_F(NAIVE, SHUFFLE_RNG_FWD_INT32) {
    run_shuffle<dtype::Int32>(handle(), false);
}

TEST_F(NAIVE, SHUFFLE_RNG_FWD_F16) {
    run_shuffle<dtype::Float16>(handle(), false);
}

TEST_F(NAIVE, SHUFFLE_RNG_BWD_F32) {
    run_shuffle<dtype::Float32>(handle(), true);
}

TEST_F(NAIVE, SHUFFLE_RNG_BWD_INT32) {
    run_shuffle<dtype::Int32>(handle(), true);
}

TEST_F(NAIVE, SHUFFLE_RNG_BWD_F16) {
    run_shuffle<dtype::Float16>(handle(), true);
}

TEST_F(NAIVE, DROPOUT_F32) {
    run_dropout<dtype::Float32>(handle());
}

TEST_F(NAIVE, DROPOUT_F16) {
    run_dropout<dtype::Float16>(handle());
}

}  // namespace test
}  // namespace megdnn

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