365 lines
15 KiB
Plaintext
365 lines
15 KiB
Plaintext
/*******************************************************************************
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* Copyright (c) 2019 Konduit K.K.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author sgazeos@gmail.com
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//
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#include <ops/declarable/helpers/random.h>
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//#include <NativeOps.h>
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#include <vector>
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#include <memory>
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#include <graph/Context.h>
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#include <helpers/RandomLauncher.h>
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#include <helpers/ShapeUtils.h>
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#include <array/NDArrayFactory.h>
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#include <exceptions/cuda_exception.h>
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#include <helpers/ConstantTadHelper.h>
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#include <helpers/PointersManager.h>
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namespace sd {
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namespace ops {
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namespace helpers {
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/*
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* fillGammaKernel - fill up output with gamma distributed values
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*
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* uList - uniformly distributed values set
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* uLength - length of uList
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* alpha - alpha param
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* beta - beta param
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* output - distributed output.
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* */
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template <typename T>
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static __global__ void fillGammaKernel(T* uList, Nd4jLong uLength, T* alpha, Nd4jLong* alphaShape,
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T* beta, Nd4jLong* betaShape, T* output, Nd4jLong* outputShape) {
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// fill up
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__shared__ Nd4jLong aLength;
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if (threadIdx.x == 0) {
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aLength = shape::length(alphaShape);
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}
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__syncthreads();
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for (auto k = blockIdx.x; k < (int)uLength; k += gridDim.x) {
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auto pos = k * aLength;
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auto u = uList[k]; // this is a vector
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for (auto e = threadIdx.x; e < (int)aLength; e += blockDim.x) {
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auto aIndex = shape::getIndexOffset(e, alphaShape);
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auto bIndex = betaShape?shape::getIndexOffset(e, betaShape):-1LL;
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auto betaV = T(beta != nullptr ? beta[bIndex] * u : u);
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auto zIndex = shape::getIndexOffset(e + pos, outputShape);
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output[zIndex] = math::nd4j_igamma<T, T, T>(alpha[aIndex], betaV);
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}
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}
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}
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template <typename T>
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static void fillRandomGamma_(LaunchContext* context, graph::RandomGenerator& rng, NDArray* alpha, NDArray* beta, NDArray* output) {
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// To fill up output need to broadcast alpha and beta to the same shape and in
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Nd4jLong* broadcasted = nullptr;
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if (beta != nullptr)
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ShapeUtils::evalBroadcastShapeInfo(*alpha, *beta, true, broadcasted, context->getWorkspace());
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else
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broadcasted = alpha->shapeInfo();
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auto step = shape::length(broadcasted);
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auto shift = output->lengthOf() / step;
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auto copyAlpha = alpha;
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auto copyBeta = beta;
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if (beta != nullptr) {
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NDArray alphaBroadcasted(broadcasted, alpha->dataType(), true, context);
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NDArray betaBroadcasted(broadcasted, beta->dataType(), true, context);
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copyAlpha = new NDArray(alphaBroadcasted.applyTrueBroadcast(BroadcastOpsTuple::Assign(), *alpha));
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copyBeta = new NDArray(betaBroadcasted.applyTrueBroadcast(BroadcastOpsTuple::Assign(), *beta));
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copyAlpha->tickWriteDevice(); copyBeta->tickWriteDevice();
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}
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auto stream = context->getCudaStream();
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NDArray uniform = NDArrayFactory::create<T>('c', {shift}, context);
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uniform.syncToDevice();
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// fill up uniform with given length
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RandomLauncher::fillUniform(context, rng, &uniform, 0., 1.);
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fillGammaKernel<T><<<128, 128, 256, *stream>>>(uniform.dataBuffer()->specialAsT<T>(), shift,
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copyAlpha->dataBuffer()->specialAsT<T>(), copyAlpha->specialShapeInfo(),
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beta?copyBeta->dataBuffer()->specialAsT<T>():(T*)nullptr,
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beta?copyBeta->specialShapeInfo():(Nd4jLong*)nullptr,
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output->dataBuffer()->specialAsT<T>(), output->specialShapeInfo());
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if (beta != nullptr) {
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delete copyAlpha;
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delete copyBeta;
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//delete broadcasted;
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}
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}
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void fillRandomGamma(LaunchContext* context, graph::RandomGenerator& rng, NDArray* alpha, NDArray* beta, NDArray* output) {
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if (beta)
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NDArray::prepareSpecialUse({output}, {alpha, beta});
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else
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NDArray::prepareSpecialUse({output}, {alpha});
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BUILD_SINGLE_SELECTOR(output->dataType(), fillRandomGamma_, (context, rng, alpha, beta, output), FLOAT_NATIVE);
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if (beta)
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NDArray::registerSpecialUse({output}, {alpha, beta});
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else
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NDArray::prepareSpecialUse({output}, {alpha});
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}
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BUILD_SINGLE_TEMPLATE(template void fillRandomGamma_, (LaunchContext* context, graph::RandomGenerator& rng, NDArray* alpha, NDArray* beta, NDArray* output), FLOAT_NATIVE);
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/*
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* algorithm Poisson generator based upon the inversion by sequential search
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*
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init:
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Let x ← 0, p ← e−λ, s ← p.
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using uniformly random sequence U (u in U) distributed at [0, 1].
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while u > s do:
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x ← x + 1.
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p ← p * λ / x.
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s ← s + p.
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return x.
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* */
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template <typename T>
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static __global__ void fillPoissonKernel(T* uList, Nd4jLong uLength, T* lambda, Nd4jLong* lambdaShape, T* output,
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Nd4jLong* outputShape) {
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__shared__ Nd4jLong step;
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if (threadIdx.x == 0) {
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step = shape::length(lambdaShape);
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}
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__syncthreads();
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for (auto k = blockIdx.x; k < (int)uLength; k += gridDim.x) {
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auto pos = k * step;
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auto u = uList[k];
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for (auto e = threadIdx.x; e < step; e += blockDim.x) {
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auto p = math::nd4j_exp<T,T>(-lambda[e]);
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auto s = p;
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auto x = T(0.f);
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auto lIndex = shape::getIndexOffset(e, lambdaShape);
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auto zIndex = shape::getIndexOffset(e + pos, outputShape);
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while (u > s) {
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x += T(1.);
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p *= lambda[lIndex] / x;
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s += p;
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}
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output[zIndex] = x;
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}
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}
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}
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template <typename T>
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static void fillRandomPoisson_(LaunchContext* context, graph::RandomGenerator& rng, NDArray* lambda, NDArray* output) {
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auto shift = output->lengthOf() / lambda->lengthOf();
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NDArray uniform('c', {shift}, output->dataType());
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auto stream = context->getCudaStream();
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// fill up uniform with given length
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RandomLauncher::fillUniform(context, rng, &uniform, 0., 1.);
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fillPoissonKernel<T><<<128, 256, 128, *stream>>>(uniform.dataBuffer()->specialAsT<T>(), uniform.lengthOf(),
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lambda->dataBuffer()->specialAsT<T>(), lambda->specialShapeInfo(),
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output->dataBuffer()->specialAsT<T>(), output->specialShapeInfo());
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}
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void fillRandomPoisson(LaunchContext* context, graph::RandomGenerator& rng, NDArray* lambda, NDArray* output) {
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NDArray::prepareSpecialUse({output}, {lambda});
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BUILD_SINGLE_SELECTOR(output->dataType(), fillRandomPoisson_, (context, rng, lambda, output), FLOAT_NATIVE);
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NDArray::registerSpecialUse({output}, {lambda});
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}
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BUILD_SINGLE_TEMPLATE(template void fillRandomPoisson_, (LaunchContext* context, graph::RandomGenerator& rng, NDArray* lambda, NDArray* output), FLOAT_NATIVE);
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template <typename T>
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static __global__ void fillUniformKernel(graph::RandomGenerator* devRng, T from, T to, T* output, Nd4jLong* outputShape) {
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auto start = blockIdx.x * blockDim.x + threadIdx.x;
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auto step = blockDim.x * gridDim.x;
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__shared__ Nd4jLong outputLen;
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if (0 == threadIdx.x) {
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outputLen = shape::length(outputShape);
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}
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__syncthreads();
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for (auto i = start; i < outputLen; i += step) {
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auto zIndex = shape::getIndexOffset(i, outputShape);
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output[zIndex] = devRng->relativeT<T>(i, from, to);
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}
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}
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template <typename T>
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static void fillRandomUniform_(LaunchContext* context, graph::RandomGenerator& rng, NDArray* min, NDArray* max, NDArray* output) {
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T minVal = T(0);
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T maxVal = DataTypeUtils::infOrMax<T>();
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if (min)
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minVal = min->t<T>(0);
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if (max)
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maxVal = max->t<T>(0);
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if (output->isR())
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RandomLauncher::fillUniform(context, rng, output, minVal, maxVal);
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else {
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auto stream = context->getCudaStream();
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graph::RandomGenerator *devRng;
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auto err = cudaMalloc(&devRng, sizeof(graph::RandomGenerator));
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if (err != 0) {
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cuda_exception::build("fillRandomUniform_: Cannot allocate device memory for random generator due error", err);
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}
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err = cudaMemcpy(devRng, &rng, sizeof(graph::RandomGenerator), cudaMemcpyHostToDevice);
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if (err != 0) {
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cuda_exception::build("fillRandomUniform_: Cannot copy random generator to device", err);
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}
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auto outputBuf = output->dataBuffer()->specialAsT<T>();
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auto outputShape = output->specialShapeInfo();
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fillUniformKernel<T><<<128, 128, 128, *stream>>>(devRng, minVal, maxVal, outputBuf, outputShape);
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err = cudaStreamSynchronize(*stream);
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if (err != 0) {
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cuda_exception::build("fillRandomUniform_: Cannot successfully finish kernel call", err);
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}
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err = cudaFree(devRng);
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if (err != 0) {
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cuda_exception::build("fillRandomUniform_: Cannot deallocate device memory for random generator", err);
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}
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}
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}
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void fillRandomUniform(LaunchContext* context, graph::RandomGenerator& rng, NDArray* min, NDArray* max, NDArray* output) {
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BUILD_SINGLE_SELECTOR(output->dataType(), fillRandomUniform_, (context, rng, min, max, output), NUMERIC_TYPES);
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}
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BUILD_SINGLE_TEMPLATE(template void fillRandomUniform_, (LaunchContext* context,
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graph::RandomGenerator& rng, NDArray* min, NDArray* max, NDArray* output), NUMERIC_TYPES);
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///////////////////////////////////////////////////////////////////
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// used https://en.wikipedia.org/wiki/Categorical_distribution
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// methods: gumbel trick + softmax + argmax
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template<typename X, typename Z>
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__global__ static void fillMultiNomialCuda_(graph::RandomGenerator* devRng, const void* vx, const Nd4jLong* xShapeInfo,
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void* vz, const Nd4jLong* zShapeInfo, const Nd4jLong batchValue,
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const Nd4jLong numOfSamples, const Nd4jLong numOfClassX,
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const Nd4jLong dimA, const X minVal, const X maxVal) {
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const X* x = reinterpret_cast<const X*>(vx);
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Z* z = reinterpret_cast<Z*>(vz);
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__shared__ Nd4jLong xDimAstride, zDimAstride, xDimCstride, zDimCstride, dimC;
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if (0 == threadIdx.x) {
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dimC = (0 == dimA) ? 1 : 0;
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zDimAstride = shape::stride(zShapeInfo)[dimA];
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xDimAstride = shape::stride(xShapeInfo)[dimA];
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zDimCstride = shape::stride(zShapeInfo)[dimC];
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xDimCstride = shape::stride(xShapeInfo)[dimC];
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}
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__syncthreads();
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const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
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for (Nd4jLong index = tid; index < batchValue*numOfSamples; index += gridDim.x * blockDim.x) {
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Nd4jLong nBatchIndex = index / numOfSamples;
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Nd4jLong nSampleIndexInBatch = index - (nBatchIndex * numOfSamples);
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const X* xTad = x + (nBatchIndex * xDimCstride);
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Z* zTad = z + (nBatchIndex * zDimCstride);
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Z& arg = zTad[nSampleIndexInBatch * zDimAstride];
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X Max = -minVal;
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Nd4jLong nSamplesPerBatch = nBatchIndex * numOfClassX * numOfSamples;
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Nd4jLong nClassPerSamples = nSampleIndexInBatch * numOfClassX;
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for (Nd4jLong nClass = 0; nClass < numOfClassX; nClass++) {
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Nd4jLong nIndex = nSamplesPerBatch + nClassPerSamples + nClass;
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X tValue = (xTad[nClass * xDimAstride] - sd::math::nd4j_log<X, X>(-sd::math::nd4j_log<X, X>(devRng->relativeT<X>(nIndex, minVal, maxVal))));
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if (tValue > Max) {
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Max = tValue;
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arg = nClass;
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}
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}
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}
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}
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//////////////////////////////////////////////////////////////////////////
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template<typename X, typename Z>
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__host__ static void fillMultiNomialCudaLauncher(
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const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t* stream,
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graph::RandomGenerator* devRng, const void* vx, const Nd4jLong* xShapeInfo,
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void* vz, const Nd4jLong* zShapeInfo,
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const Nd4jLong batchValue, const Nd4jLong numOfSamples,
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const Nd4jLong numOfClassX, const Nd4jLong dimA){
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const X minVal = DataTypeUtils::min<X>();
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const X maxVal = 1.0;
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fillMultiNomialCuda_<X, Z> <<< blocksPerGrid, threadsPerBlock, 256, * stream >>> (
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devRng, vx, xShapeInfo, vz, zShapeInfo, batchValue,
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numOfSamples, numOfClassX, dimA, minVal, maxVal);
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}
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///////////////////////////////////////////////////////////////////
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void fillRandomMultiNomial(LaunchContext* context, graph::RandomGenerator& rng, NDArray& input, NDArray& output, const Nd4jLong numOfSamples, const int dimC) {
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Nd4jLong dimA = (0 == dimC) ? 1 : 0;
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const Nd4jLong batchValue = output.sizeAt(dimC);
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const Nd4jLong numOfClassX = input.sizeAt(dimA);
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const int threadsPerBlock = MAX_NUM_THREADS / 2;
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const int blocksPerGrid = (batchValue * numOfSamples + threadsPerBlock - 1) / threadsPerBlock;
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PointersManager manager(context, "fillMultinomial");
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graph::RandomGenerator *devRng;
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auto err = cudaMalloc(&devRng, sizeof(graph::RandomGenerator));
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if (err != 0) {
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cuda_exception::build("fillRandomMultiNomial: Cannot allocate device memory for random generator due error", err);
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}
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err = cudaStreamSynchronize(*context->getCudaStream());
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if (err != 0) {
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cuda_exception::build("fillRandomMultiNomial: Cannot synchronize stream for random generator due error", err);
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}
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err = cudaMemcpyAsync(devRng, &rng, sizeof(graph::RandomGenerator), cudaMemcpyHostToDevice, *context->getCudaStream());
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if (err != 0) {
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cuda_exception::build("fillRandomMultiNomial: Cannot copy random generator to device", err);
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}
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NDArray::prepareSpecialUse({ &output }, { &input });
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BUILD_DOUBLE_SELECTOR(input.dataType(), output.dataType(), fillMultiNomialCudaLauncher,
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(blocksPerGrid, threadsPerBlock, context->getCudaStream(), devRng, input.getSpecialBuffer(),
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input.getSpecialShapeInfo(), output.specialBuffer(),
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output.specialShapeInfo(), batchValue, numOfSamples,
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numOfClassX, dimA), FLOAT_TYPES, INDEXING_TYPES);
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NDArray::registerSpecialUse({ &output }, { &input });
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manager.synchronize();
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err = cudaFree(devRng);
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if (err != 0) {
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cuda_exception::build("fillRandomMultiNomial: Cannot deallocate device memory for random generator", err);
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}
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rng.rewindH(output.lengthOf() * numOfClassX);
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}
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}
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}
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} |