186 lines
6.3 KiB
Plaintext
186 lines
6.3 KiB
Plaintext
/*******************************************************************************
|
||
* Copyright (c) 2015-2018 Skymind, Inc.
|
||
*
|
||
* This program and the accompanying materials are made available under the
|
||
* terms of the Apache License, Version 2.0 which is available at
|
||
* https://www.apache.org/licenses/LICENSE-2.0.
|
||
*
|
||
* Unless required by applicable law or agreed to in writing, software
|
||
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
|
||
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
|
||
* License for the specific language governing permissions and limitations
|
||
* under the License.
|
||
*
|
||
* SPDX-License-Identifier: Apache-2.0
|
||
******************************************************************************/
|
||
|
||
//
|
||
// Created by Yurii Shyrma on 11.12.2017
|
||
//
|
||
|
||
#include<cmath>
|
||
#include <DataTypeUtils.h>
|
||
#include<ops/declarable/helpers/betaInc.h>
|
||
#include <PointersManager.h>
|
||
|
||
namespace nd4j {
|
||
namespace ops {
|
||
namespace helpers {
|
||
|
||
|
||
///////////////////////////////////////////////////////////////////
|
||
// modified Lentz’s algorithm for continued fractions,
|
||
// reference: Lentz, W.J. 1976, “Generating Bessel Functions in Mie Scattering Calculations Using Continued Fractions,”
|
||
template <typename T>
|
||
__device__ T continuedFractionCuda(const T a, const T b, const T x) {
|
||
|
||
extern __shared__ unsigned char shmem[];
|
||
T* coeffs = reinterpret_cast<T*>(shmem);
|
||
|
||
const T min = DataTypeUtils::min<T>() / DataTypeUtils::eps<T>();
|
||
const T aPlusb = a + b;
|
||
T val, delta, aPlus2i;
|
||
|
||
// first iteration
|
||
T c = 1;
|
||
T d = static_cast<T>(1) - aPlusb * x / (a + static_cast<T>(1));
|
||
if(math::nd4j_abs<T>(d) < min)
|
||
d = min;
|
||
d = static_cast<T>(1) / d;
|
||
T f = d;
|
||
|
||
for(uint i = 1; i <= maxIter; i += 2) {
|
||
|
||
aPlus2i = a + static_cast<T>(2*i);
|
||
|
||
/***** even part *****/
|
||
// d
|
||
d = static_cast<T>(1) + coeffs[i - 1] * d;
|
||
if(math::nd4j_abs<T>(d) < min)
|
||
d = min;
|
||
d = static_cast<T>(1) / d;
|
||
// c
|
||
c = static_cast<T>(1) + coeffs[i - 1] / c;
|
||
if(math::nd4j_abs<T>(c) < min)
|
||
c = min;
|
||
// f
|
||
f *= c * d;
|
||
|
||
|
||
/***** odd part *****/
|
||
// d
|
||
d = static_cast<T>(1) + coeffs[i] * d;
|
||
if(math::nd4j_abs<T>(d) < min)
|
||
d = min;
|
||
d = static_cast<T>(1) / d;
|
||
// c
|
||
c = static_cast<T>(1) + coeffs[i] / c;
|
||
if(math::nd4j_abs<T>(c) < min)
|
||
c = min;
|
||
// f
|
||
delta = c * d;
|
||
f *= delta;
|
||
|
||
// condition to stop loop
|
||
if(math::nd4j_abs<T>(delta - static_cast<T>(1)) <= DataTypeUtils::eps<T>())
|
||
return f;
|
||
}
|
||
|
||
return 1.f / 0.f; // no convergence, more iterations is required
|
||
}
|
||
|
||
///////////////////////////////////////////////////////////////////
|
||
// evaluates incomplete beta function for positive a and b, and x between 0 and 1.
|
||
template <typename T>
|
||
__device__ T betaIncCoreCuda(T a, T b, T x) {
|
||
|
||
const T gammaPart = lgamma(a) + lgamma(b) - lgamma(a + b);
|
||
const T front = math::nd4j_exp<T,T>(math::nd4j_log<T, T>(x) * a + math::nd4j_log<T, T>(1 - x) * b - gammaPart) / a;
|
||
|
||
if (x <= (a + static_cast<T>(1)) / (a + b + static_cast<T>(2)))
|
||
return front * continuedFractionCuda(a, b, x);
|
||
else // symmetry relation
|
||
return static_cast<T>(1) - front * continuedFractionCuda(b, a, static_cast<T>(1) - x);
|
||
}
|
||
|
||
///////////////////////////////////////////////////////////////////
|
||
template<typename T>
|
||
__global__ void betaIncForArrayCuda(const void* va, const Nd4jLong* aShapeInfo,
|
||
const void* vb, const Nd4jLong* bShapeInfo,
|
||
const void* vx, const Nd4jLong* xShapeInfo,
|
||
void* vz, const Nd4jLong* zShapeInfo) {
|
||
|
||
extern __shared__ unsigned char shmem[];
|
||
T* sharedMem = reinterpret_cast<T*>(shmem);
|
||
|
||
const Nd4jLong j = blockIdx.x; // one block per each element
|
||
|
||
Nd4jLong len = shape::length(xShapeInfo);
|
||
|
||
const T a = *(reinterpret_cast<const T*>(va) + shape::getIndexOffset(j, aShapeInfo, len));
|
||
const T b = *(reinterpret_cast<const T*>(vb) + shape::getIndexOffset(j, bShapeInfo, len));
|
||
const T x = *(reinterpret_cast<const T*>(vx) + shape::getIndexOffset(j, xShapeInfo, len));
|
||
T& z = *(reinterpret_cast<T*>(vz) + shape::getIndexOffset(j, zShapeInfo, len));
|
||
|
||
// t^{n-1} * (1 - t)^{n-1} is symmetric function with respect to x = 0.5
|
||
if(a == b && x == static_cast<T>(0.5)) {
|
||
z = static_cast<T>(0.5);
|
||
return;
|
||
}
|
||
|
||
if (x == static_cast<T>(0) || x == static_cast<T>(1)) {
|
||
z = x;
|
||
return;
|
||
}
|
||
|
||
if(threadIdx.x % 2 == 0) { /***** even part *****/
|
||
const int m = threadIdx.x + 1;
|
||
sharedMem[threadIdx.x] = m * (b - m) * x / ((a + 2 * m - static_cast<T>(1)) * (a + 2 * m));
|
||
}
|
||
else { /***** odd part *****/
|
||
const int m = threadIdx.x;
|
||
sharedMem[threadIdx.x] = -(a + m) * (a + b + m) * x / ((a + 2 * m + static_cast<T>(1)) * (a + 2 * m));
|
||
}
|
||
|
||
__syncthreads();
|
||
|
||
if(threadIdx.x == 0)
|
||
z = betaIncCoreCuda(a, b, x);
|
||
}
|
||
|
||
///////////////////////////////////////////////////////////////////
|
||
template<typename T>
|
||
static void betaIncForArrayCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream,
|
||
const void* va, const Nd4jLong* aShapeInfo,
|
||
const void* vb, const Nd4jLong* bShapeInfo,
|
||
const void* vx, const Nd4jLong* xShapeInfo,
|
||
void* vz, const Nd4jLong* zShapeInfo) {
|
||
|
||
betaIncForArrayCuda<T><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(va, aShapeInfo, vb, bShapeInfo, vx, xShapeInfo, vz, zShapeInfo);
|
||
}
|
||
|
||
///////////////////////////////////////////////////////////////////
|
||
// overload betaInc for arrays, shapes of a, b and x must be the same !!!
|
||
void betaInc(nd4j::LaunchContext* context, const NDArray& a, const NDArray& b, const NDArray& x, NDArray& output) {
|
||
|
||
const int threadsPerBlock = maxIter;
|
||
const int blocksPerGrid = output.lengthOf();
|
||
const int sharedMem = output.sizeOfT() * threadsPerBlock + 128;
|
||
|
||
const auto xType = x.dataType();
|
||
|
||
PointersManager manager(context, "betaInc");
|
||
|
||
NDArray::prepareSpecialUse({&output}, {&a, &b, &x});
|
||
BUILD_SINGLE_SELECTOR(xType, betaIncForArrayCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), a.getSpecialBuffer(), a.getSpecialShapeInfo(), b.getSpecialBuffer(), b.getSpecialShapeInfo(), x.getSpecialBuffer(), x.getSpecialShapeInfo(), output.specialBuffer(), output.specialShapeInfo()), FLOAT_TYPES);
|
||
NDArray::registerSpecialUse({&output}, {&a, &b, &x});
|
||
|
||
manager.synchronize();
|
||
}
|
||
|
||
|
||
}
|
||
}
|
||
}
|
||
|