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Add TernaryFunctors and the betainc SpecialFunction.
TernaryFunctors and their executors allow operations on 3-tuples of inputs. API fully implemented for Arrays and Tensors based on binary functors. Ported the cephes betainc function (regularized incomplete beta integral) to Eigen, with support for CPU and GPU, floats, doubles, and half types. Added unit tests in array.cpp and cxx11_tensor_cuda.cu Collapsed revision * Merged helper methods for betainc across floats and doubles. * Added TensorGlobalFunctions with betainc(). Removed betainc() from TensorBase. * Clean up CwiseTernaryOp checks, change igamma_helper to cephes_helper. * betainc: merge incbcf and incbd into incbeta_cfe. and more cleanup. * Update TernaryOp and SpecialFunctions (betainc) based on review comments.
This commit is contained in:
Eugene Brevdo
@@ -1019,6 +1019,153 @@ void test_cuda_erfc(const Scalar stddev)
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cudaFree(d_out);
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}
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template <typename Scalar>
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void test_cuda_betainc()
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{
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Tensor<Scalar, 1> in_x(125);
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Tensor<Scalar, 1> in_a(125);
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Tensor<Scalar, 1> in_b(125);
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Tensor<Scalar, 1> out(125);
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Tensor<Scalar, 1> expected_out(125);
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out.setZero();
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Scalar nan = std::numeric_limits<Scalar>::quiet_NaN();
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Array<Scalar, 1, Dynamic> x(125);
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Array<Scalar, 1, Dynamic> a(125);
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Array<Scalar, 1, Dynamic> b(125);
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Array<Scalar, 1, Dynamic> v(125);
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a << 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
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0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
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0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
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0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
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0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
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0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
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0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
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0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
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0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999,
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0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999,
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0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 31.62177660168379,
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31.62177660168379, 31.62177660168379, 31.62177660168379,
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31.62177660168379, 31.62177660168379, 31.62177660168379,
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31.62177660168379, 31.62177660168379, 31.62177660168379,
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31.62177660168379, 31.62177660168379, 31.62177660168379,
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31.62177660168379, 31.62177660168379, 31.62177660168379,
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31.62177660168379, 31.62177660168379, 31.62177660168379,
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31.62177660168379, 31.62177660168379, 31.62177660168379,
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31.62177660168379, 31.62177660168379, 31.62177660168379, 999.999, 999.999,
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999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999,
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999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999,
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999.999, 999.999, 999.999, 999.999, 999.999, 999.999, 999.999;
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b << 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379, 0.03062277660168379,
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0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.999,
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0.999, 0.999, 0.999, 0.999, 31.62177660168379, 31.62177660168379,
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31.62177660168379, 31.62177660168379, 31.62177660168379, 999.999, 999.999,
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999.999, 999.999, 999.999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379,
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0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
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0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999, 31.62177660168379,
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31.62177660168379, 31.62177660168379, 31.62177660168379,
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31.62177660168379, 999.999, 999.999, 999.999, 999.999, 999.999, 0.0, 0.0,
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0.0, 0.0, 0.0, 0.03062277660168379, 0.03062277660168379,
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0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.999,
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0.999, 0.999, 0.999, 0.999, 31.62177660168379, 31.62177660168379,
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31.62177660168379, 31.62177660168379, 31.62177660168379, 999.999, 999.999,
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999.999, 999.999, 999.999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03062277660168379,
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0.03062277660168379, 0.03062277660168379, 0.03062277660168379,
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0.03062277660168379, 0.999, 0.999, 0.999, 0.999, 0.999, 31.62177660168379,
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31.62177660168379, 31.62177660168379, 31.62177660168379,
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31.62177660168379, 999.999, 999.999, 999.999, 999.999, 999.999, 0.0, 0.0,
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0.0, 0.0, 0.0, 0.03062277660168379, 0.03062277660168379,
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0.03062277660168379, 0.03062277660168379, 0.03062277660168379, 0.999,
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0.999, 0.999, 0.999, 0.999, 31.62177660168379, 31.62177660168379,
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31.62177660168379, 31.62177660168379, 31.62177660168379, 999.999, 999.999,
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999.999, 999.999, 999.999;
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x << -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8,
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1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5,
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0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2,
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0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1,
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0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1,
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-0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8,
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1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5,
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0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2,
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0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1, -0.1, 0.2, 0.5, 0.8, 1.1;
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v << nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
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nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,
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nan, nan, 0.47972119876364683, 0.5, 0.5202788012363533, nan, nan,
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0.9518683957740043, 0.9789663010413743, 0.9931729188073435, nan, nan,
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0.999995949033062, 0.9999999999993698, 0.9999999999999999, nan, nan,
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0.9999999999999999, 0.9999999999999999, 0.9999999999999999, nan, nan, nan,
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nan, nan, nan, nan, 0.006827081192655869, 0.0210336989586256,
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0.04813160422599567, nan, nan, 0.20014344256217678, 0.5000000000000001,
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0.7998565574378232, nan, nan, 0.9991401428435834, 0.999999999698403,
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0.9999999999999999, nan, nan, 0.9999999999999999, 0.9999999999999999,
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0.9999999999999999, nan, nan, nan, nan, nan, nan, nan,
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1.0646600232370887e-25, 6.301722877826246e-13, 4.050966937974938e-06, nan,
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nan, 7.864342668429763e-23, 3.015969667594166e-10, 0.0008598571564165444,
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nan, nan, 6.031987710123844e-08, 0.5000000000000007, 0.9999999396801229,
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nan, nan, 0.9999999999999999, 0.9999999999999999, 0.9999999999999999, nan,
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nan, nan, nan, nan, nan, nan, 0.0, 7.029920380986636e-306,
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2.2450728208591345e-101, nan, nan, 0.0, 9.275871147869727e-302,
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1.2232913026152827e-97, nan, nan, 0.0, 3.0891393081932924e-252,
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2.9303043666183996e-60, nan, nan, 2.248913486879199e-196,
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0.5000000000004947, 0.9999999999999999, nan;
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for (int i = 0; i < 125; ++i) {
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in_x(i) = x(i);
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in_a(i) = a(i);
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in_b(i) = b(i);
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expected_out(i) = v(i);
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}
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std::size_t bytes = in_x.size() * sizeof(Scalar);
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Scalar* d_in_x;
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Scalar* d_in_a;
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Scalar* d_in_b;
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Scalar* d_out;
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cudaMalloc((void**)(&d_in_x), bytes);
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cudaMalloc((void**)(&d_in_a), bytes);
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cudaMalloc((void**)(&d_in_b), bytes);
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cudaMalloc((void**)(&d_out), bytes);
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cudaMemcpy(d_in_x, in_x.data(), bytes, cudaMemcpyHostToDevice);
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cudaMemcpy(d_in_a, in_a.data(), bytes, cudaMemcpyHostToDevice);
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cudaMemcpy(d_in_b, in_b.data(), bytes, cudaMemcpyHostToDevice);
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Eigen::CudaStreamDevice stream;
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Eigen::GpuDevice gpu_device(&stream);
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Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_x(d_in_x, 125);
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Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_a(d_in_a, 125);
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Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_in_b(d_in_b, 125);
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Eigen::TensorMap<Eigen::Tensor<Scalar, 1> > gpu_out(d_out, 125);
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gpu_out.device(gpu_device) = betainc(gpu_in_a, gpu_in_b, gpu_in_x);
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assert(cudaMemcpyAsync(out.data(), d_out, bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess);
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assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess);
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for (int i = 1; i < 125; ++i) {
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if ((std::isnan)(expected_out(i))) {
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VERIFY((std::isnan)(out(i)));
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} else {
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VERIFY_IS_APPROX(out(i), expected_out(i));
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}
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}
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cudaFree(d_in_x);
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cudaFree(d_in_a);
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cudaFree(d_in_b);
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cudaFree(d_out);
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}
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void test_cxx11_tensor_cuda()
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{
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CALL_SUBTEST_1(test_cuda_elementwise_small());
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@@ -1086,5 +1233,8 @@ void test_cxx11_tensor_cuda()
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CALL_SUBTEST_5(test_cuda_igamma<double>());
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CALL_SUBTEST_5(test_cuda_igammac<double>());
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CALL_SUBTEST_6(test_cuda_betainc<float>());
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CALL_SUBTEST_6(test_cuda_betainc<double>());
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#endif
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}
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