Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

[GPU-Plugin] Unify gpu_gpair/bst_gpair. Refactor. (#2477)

Browse Source
This commit is contained in:
Rory Mitchell 2017-07-01 17:31:13 +12:00 committed by GitHub
parent d535340459
commit 5f1b0bb386
14 changed files with 230 additions and 426 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

59
include/xgboost/base.h
View File

@ -19,7 +19,8 @@
/*!
* \brief Whether always log console message with time.
* It will display like, with timestamp appended to head of the message.
* "[21:47:50] 6513x126 matrix with 143286 entries loaded from ../data/agaricus.txt.train"
* "[21:47:50] 6513x126 matrix with 143286 entries loaded from
* ../data/agaricus.txt.train"
*/
#ifndef XGBOOST_LOG_WITH_TIME
#define XGBOOST_LOG_WITH_TIME 1
@ -36,7 +37,7 @@
* \brief Whether to customize global PRNG.
*/
#ifndef XGBOOST_CUSTOMIZE_GLOBAL_PRNG
#define XGBOOST_CUSTOMIZE_GLOBAL_PRNG XGBOOST_STRICT_R_MODE
#define XGBOOST_CUSTOMIZE_GLOBAL_PRNG XGBOOST_STRICT_R_MODE
#endif
/*!
@ -48,16 +49,27 @@
#define XGBOOST_ALIGNAS(X)
#endif
#if defined(__GNUC__) && __GNUC__ == 4 && __GNUC_MINOR__ >= 8 && !defined(__CUDACC__)
#if defined(__GNUC__) && __GNUC__ == 4 && __GNUC_MINOR__ >= 8 && \
!defined(__CUDACC__)
#include <parallel/algorithm>
#define XGBOOST_PARALLEL_SORT(X, Y, Z) __gnu_parallel::sort((X), (Y), (Z))
#define XGBOOST_PARALLEL_STABLE_SORT(X, Y, Z) __gnu_parallel::stable_sort((X), (Y), (Z))
#define XGBOOST_PARALLEL_STABLE_SORT(X, Y, Z) \
__gnu_parallel::stable_sort((X), (Y), (Z))
#else
#define XGBOOST_PARALLEL_SORT(X, Y, Z) std::sort((X), (Y), (Z))
#define XGBOOST_PARALLEL_STABLE_SORT(X, Y, Z) std::stable_sort((X), (Y), (Z))
#endif
/*! \brief namespace of xgboo st*/
/*!
* \brief Tag function as usable by device
*/
#ifdef __NVCC__
#define XGBOOST_DEVICE __host__ __device__
#else
#define XGBOOST_DEVICE
#endif
/*! \brief namespace of xgboost*/
namespace xgboost {
/*!
* \brief unsigned integer type used in boost,
@ -76,8 +88,41 @@ struct bst_gpair {
bst_float grad;
/*! \brief second order gradient statistics */
bst_float hess;
bst_gpair() {}
bst_gpair(bst_float grad, bst_float hess) : grad(grad), hess(hess) {}
XGBOOST_DEVICE bst_gpair() : grad(0), hess(0) {}
XGBOOST_DEVICE bst_gpair(bst_float grad, bst_float hess)
: grad(grad), hess(hess) {}
XGBOOST_DEVICE bst_gpair &operator+=(const bst_gpair &rhs) {
grad += rhs.grad;
hess += rhs.hess;
return *this;
}
XGBOOST_DEVICE bst_gpair operator+(const bst_gpair &rhs) const {
bst_gpair g;
g.grad = grad + rhs.grad;
g.hess = hess + rhs.hess;
return g;
}
XGBOOST_DEVICE bst_gpair &operator-=(const bst_gpair &rhs) {
grad -= rhs.grad;
hess -= rhs.hess;
return *this;
}
XGBOOST_DEVICE bst_gpair operator-(const bst_gpair &rhs) const {
bst_gpair g;
g.grad = grad - rhs.grad;
g.hess = hess - rhs.hess;
return g;
}
XGBOOST_DEVICE bst_gpair(int value) {
*this = bst_gpair(static_cast<float>(value), static_cast<float>(value));
}
};
/*! \brief small eps gap for minimum split decision. */

44
plugin/updater_gpu/src/common.cuh
View File

@ -15,33 +15,29 @@
namespace xgboost {
namespace tree {
// When we split on a value which has no left neighbour, define its left
// neighbour as having left_fvalue = current_fvalue - FVALUE_EPS
// This produces a split value slightly lower than the current instance
#define FVALUE_EPS 0.0001
__device__ inline float device_calc_loss_chg(const GPUTrainingParam& param,
const gpu_gpair& scan,
const gpu_gpair& missing,
const gpu_gpair& parent_sum,
const bst_gpair& scan,
const bst_gpair& missing,
const bst_gpair& parent_sum,
const float& parent_gain,
bool missing_left) {
gpu_gpair left = scan;
bst_gpair left = scan;
if (missing_left) {
left += missing;
}
gpu_gpair right = parent_sum - left;
bst_gpair right = parent_sum - left;
float left_gain = CalcGain(param, left.grad(), left.hess());
float right_gain = CalcGain(param, right.grad(), right.hess());
float left_gain = CalcGain(param, left.grad, left.hess);
float right_gain = CalcGain(param, right.grad, right.hess);
return left_gain + right_gain - parent_gain;
}
__device__ float inline loss_chg_missing(const gpu_gpair& scan,
const gpu_gpair& missing,
const gpu_gpair& parent_sum,
__device__ float inline loss_chg_missing(const bst_gpair& scan,
const bst_gpair& missing,
const bst_gpair& parent_sum,
const float& parent_gain,
const GPUTrainingParam& param,
bool& missing_left_out) { // NOLINT
@ -134,39 +130,39 @@ inline void dense2sparse_tree(RegTree* p_tree,
tree[nid].set_split(n.split.findex, n.split.fvalue, n.split.missing_left);
tree.stat(nid).loss_chg = n.split.loss_chg;
tree.stat(nid).base_weight = n.weight;
tree.stat(nid).sum_hess = n.sum_gradients.hess();
tree.stat(nid).sum_hess = n.sum_gradients.hess;
tree[tree[nid].cleft()].set_leaf(0);
tree[tree[nid].cright()].set_leaf(0);
nid++;
} else if (flag == LEAF) {
tree[nid].set_leaf(n.weight * param.learning_rate);
tree.stat(nid).sum_hess = n.sum_gradients.hess();
tree.stat(nid).sum_hess = n.sum_gradients.hess;
nid++;
}
}
}
// Set gradient pair to 0 with p = 1 - subsample
inline void subsample_gpair(dh::dvec<gpu_gpair>* p_gpair, float subsample,
inline void subsample_gpair(dh::dvec<bst_gpair>* p_gpair, float subsample,
int offset) {
if (subsample == 1.0) {
return;
}
dh::dvec<gpu_gpair>& gpair = *p_gpair;
dh::dvec<bst_gpair>& gpair = *p_gpair;
auto d_gpair = gpair.data();
dh::BernoulliRng rng(subsample, common::GlobalRandom()());
dh::launch_n(gpair.device_idx(), gpair.size(), [=] __device__(int i) {
if (!rng(i + offset)) {
d_gpair[i] = gpu_gpair();
d_gpair[i] = bst_gpair();
}
});
}
// Set gradient pair to 0 with p = 1 - subsample
inline void subsample_gpair(dh::dvec<gpu_gpair>* p_gpair, float subsample) {
inline void subsample_gpair(dh::dvec<bst_gpair>* p_gpair, float subsample) {
int offset = 0;
subsample_gpair(p_gpair, subsample, offset);
}
@ -182,11 +178,11 @@ inline std::vector<int> col_sample(std::vector<int> features, float colsample) {
}
struct GpairCallbackOp {
// Running prefix
gpu_gpair running_total;
bst_gpair running_total;
// Constructor
__device__ GpairCallbackOp() : running_total(gpu_gpair()) {}
__device__ gpu_gpair operator()(gpu_gpair block_aggregate) {
gpu_gpair old_prefix = running_total;
__device__ GpairCallbackOp() : running_total(bst_gpair()) {}
__device__ bst_gpair operator()(bst_gpair block_aggregate) {
bst_gpair old_prefix = running_total;
running_total += block_aggregate;
return old_prefix;
}

24
plugin/updater_gpu/src/exact/argmax_by_key.cuh
View File

@ -17,8 +17,8 @@
#include "../../../../src/tree/param.h"
#include "../common.cuh"
#include "loss_functions.cuh"
#include "node.cuh"
#include "../types.cuh"
namespace xgboost {
namespace tree {
@ -66,10 +66,10 @@ DEV_INLINE void atomicArgMax(Split* address, Split val) {
template <typename node_id_t>
DEV_INLINE void argMaxWithAtomics(
int id, Split* nodeSplits, const gpu_gpair* gradScans,
const gpu_gpair* gradSums, const float* vals, const int* colIds,
int id, Split* nodeSplits, const bst_gpair* gradScans,
const bst_gpair* gradSums, const float* vals, const int* colIds,
const node_id_t* nodeAssigns, const Node<node_id_t>* nodes, int nUniqKeys,
node_id_t nodeStart, int len, const TrainParam& param) {
node_id_t nodeStart, int len, const GPUTrainingParam& param) {
int nodeId = nodeAssigns[id];
///@todo: this is really a bad check! but will be fixed when we move
/// to key-based reduction
@ -78,14 +78,14 @@ DEV_INLINE void argMaxWithAtomics(
(vals[id] == vals[id - 1]))) {
if (nodeId != UNUSED_NODE) {
int sumId = abs2uniqKey(id, nodeAssigns, colIds, nodeStart, nUniqKeys);
gpu_gpair colSum = gradSums[sumId];
bst_gpair colSum = gradSums[sumId];
int uid = nodeId - nodeStart;
Node<node_id_t> n = nodes[nodeId];
gpu_gpair parentSum = n.gradSum;
bst_gpair parentSum = n.gradSum;
float parentGain = n.score;
bool tmp;
Split s;
gpu_gpair missing = parentSum - colSum;
bst_gpair missing = parentSum - colSum;
s.score = loss_chg_missing(gradScans[id], missing, parentSum, parentGain,
param, tmp);
s.index = id;
@ -96,7 +96,7 @@ DEV_INLINE void argMaxWithAtomics(
template <typename node_id_t>
__global__ void atomicArgMaxByKeyGmem(
Split* nodeSplits, const gpu_gpair* gradScans, const gpu_gpair* gradSums,
Split* nodeSplits, const bst_gpair* gradScans, const bst_gpair* gradSums,
const float* vals, const int* colIds, const node_id_t* nodeAssigns,
const Node<node_id_t>* nodes, int nUniqKeys, node_id_t nodeStart, int len,
const TrainParam param) {
@ -104,13 +104,13 @@ __global__ void atomicArgMaxByKeyGmem(
const int stride = blockDim.x * gridDim.x;
for (; id < len; id += stride) {
argMaxWithAtomics(id, nodeSplits, gradScans, gradSums, vals, colIds,
nodeAssigns, nodes, nUniqKeys, nodeStart, len, param);
nodeAssigns, nodes, nUniqKeys, nodeStart, len, GPUTrainingParam(param));
}
}
template <typename node_id_t>
__global__ void atomicArgMaxByKeySmem(
Split* nodeSplits, const gpu_gpair* gradScans, const gpu_gpair* gradSums,
Split* nodeSplits, const bst_gpair* gradScans, const bst_gpair* gradSums,
const float* vals, const int* colIds, const node_id_t* nodeAssigns,
const Node<node_id_t>* nodes, int nUniqKeys, node_id_t nodeStart, int len,
const TrainParam param) {
@ -153,8 +153,8 @@ __global__ void atomicArgMaxByKeySmem(
* @param algo which algorithm to use for argmax_by_key
*/
template <typename node_id_t, int BLKDIM = 256, int ITEMS_PER_THREAD = 4>
void argMaxByKey(Split* nodeSplits, const gpu_gpair* gradScans,
const gpu_gpair* gradSums, const float* vals,
void argMaxByKey(Split* nodeSplits, const bst_gpair* gradScans,
const bst_gpair* gradSums, const float* vals,
const int* colIds, const node_id_t* nodeAssigns,
const Node<node_id_t>* nodes, int nUniqKeys,
node_id_t nodeStart, int len, const TrainParam param,

51
plugin/updater_gpu/src/exact/fused_scan_reduce_by_key.cuh
View File

@ -16,7 +16,6 @@
#pragma once
#include "../common.cuh"
#include "gradients.cuh"
namespace xgboost {
namespace tree {
@ -24,11 +23,11 @@ namespace exact {
/**
* @struct Pair fused_scan_reduce_by_key.cuh
* @brief Pair used for key basd scan operations on gpu_gpair
* @brief Pair used for key basd scan operations on bst_gpair
*/
struct Pair {
int key;
gpu_gpair value;
bst_gpair value;
};
/** define a key that's not used at all in the entire boosting process */
@ -61,15 +60,27 @@ struct AddByKey {
}
};
/**
* @brief Gradient value getter function
* @param id the index into the vals or instIds array to which to fetch
* @param vals the gradient value buffer
* @param instIds instance index buffer
* @return the expected gradient value
*/
HOST_DEV_INLINE bst_gpair get(int id, const bst_gpair* vals, const int* instIds) {
id = instIds[id];
return vals[id];
}
template <typename node_id_t, int BLKDIM_L1L3>
__global__ void cubScanByKeyL1(gpu_gpair* scans, const gpu_gpair* vals,
const int* instIds, gpu_gpair* mScans,
__global__ void cubScanByKeyL1(bst_gpair* scans, const bst_gpair* vals,
const int* instIds, bst_gpair* mScans,
int* mKeys, const node_id_t* keys, int nUniqKeys,
const int* colIds, node_id_t nodeStart,
const int size) {
Pair rootPair = {NONE_KEY, gpu_gpair(0.f, 0.f)};
Pair rootPair = {NONE_KEY, bst_gpair(0.f, 0.f)};
int myKey;
gpu_gpair myValue;
bst_gpair myValue;
typedef cub::BlockScan<Pair, BLKDIM_L1L3> BlockScan;
__shared__ typename BlockScan::TempStorage temp_storage;
Pair threadData;
@ -98,14 +109,14 @@ __global__ void cubScanByKeyL1(gpu_gpair* scans, const gpu_gpair* vals,
}
if (threadIdx.x == BLKDIM_L1L3 - 1) {
threadData.value =
(myKey == previousKey) ? threadData.value : gpu_gpair(0.0f, 0.0f);
(myKey == previousKey) ? threadData.value : bst_gpair(0.0f, 0.0f);
mKeys[blockIdx.x] = myKey;
mScans[blockIdx.x] = threadData.value + myValue;
}
}
template <int BLKSIZE>
__global__ void cubScanByKeyL2(gpu_gpair* mScans, int* mKeys, int mLength) {
__global__ void cubScanByKeyL2(bst_gpair* mScans, int* mKeys, int mLength) {
typedef cub::BlockScan<Pair, BLKSIZE, cub::BLOCK_SCAN_WARP_SCANS> BlockScan;
Pair threadData;
__shared__ typename BlockScan::TempStorage temp_storage;
@ -119,9 +130,9 @@ __global__ void cubScanByKeyL2(gpu_gpair* mScans, int* mKeys, int mLength) {
}
template <typename node_id_t, int BLKDIM_L1L3>
__global__ void cubScanByKeyL3(gpu_gpair* sums, gpu_gpair* scans,
const gpu_gpair* vals, const int* instIds,
const gpu_gpair* mScans, const int* mKeys,
__global__ void cubScanByKeyL3(bst_gpair* sums, bst_gpair* scans,
const bst_gpair* vals, const int* instIds,
const bst_gpair* mScans, const int* mKeys,
const node_id_t* keys, int nUniqKeys,
const int* colIds, node_id_t nodeStart,
const int size) {
@ -130,19 +141,19 @@ __global__ void cubScanByKeyL3(gpu_gpair* sums, gpu_gpair* scans,
// to avoid the following warning from nvcc:
// __shared__ memory variable with non-empty constructor or destructor
// (potential race between threads)
__shared__ char gradBuff[sizeof(gpu_gpair)];
__shared__ char gradBuff[sizeof(bst_gpair)];
__shared__ int s_mKeys;
gpu_gpair* s_mScans = (gpu_gpair*)gradBuff;
bst_gpair* s_mScans = (bst_gpair*)gradBuff;
if (tid >= size) return;
// cache block-wide partial scan info
if (relId == 0) {
s_mKeys = (blockIdx.x > 0) ? mKeys[blockIdx.x - 1] : NONE_KEY;
s_mScans[0] = (blockIdx.x > 0) ? mScans[blockIdx.x - 1] : gpu_gpair();
s_mScans[0] = (blockIdx.x > 0) ? mScans[blockIdx.x - 1] : bst_gpair();
}
int myKey = abs2uniqKey(tid, keys, colIds, nodeStart, nUniqKeys);
int previousKey = tid == 0 ? NONE_KEY : abs2uniqKey(tid - 1, keys, colIds,
nodeStart, nUniqKeys);
gpu_gpair myValue = scans[tid];
bst_gpair myValue = scans[tid];
__syncthreads();
if (blockIdx.x > 0 && s_mKeys == previousKey) {
myValue += s_mScans[0];
@ -152,7 +163,7 @@ __global__ void cubScanByKeyL3(gpu_gpair* sums, gpu_gpair* scans,
}
if ((previousKey != myKey) && (previousKey >= 0)) {
sums[previousKey] = myValue;
myValue = gpu_gpair(0.0f, 0.0f);
myValue = bst_gpair(0.0f, 0.0f);
}
scans[tid] = myValue;
}
@ -178,12 +189,12 @@ __global__ void cubScanByKeyL3(gpu_gpair* sums, gpu_gpair* scans,
* @param nodeStart index of the leftmost node in the current level
*/
template <typename node_id_t, int BLKDIM_L1L3 = 256, int BLKDIM_L2 = 512>
void reduceScanByKey(gpu_gpair* sums, gpu_gpair* scans, const gpu_gpair* vals,
void reduceScanByKey(bst_gpair* sums, bst_gpair* scans, const bst_gpair* vals,
const int* instIds, const node_id_t* keys, int size,
int nUniqKeys, int nCols, gpu_gpair* tmpScans,
int nUniqKeys, int nCols, bst_gpair* tmpScans,
int* tmpKeys, const int* colIds, node_id_t nodeStart) {
int nBlks = dh::div_round_up(size, BLKDIM_L1L3);
cudaMemset(sums, 0, nUniqKeys * nCols * sizeof(gpu_gpair));
cudaMemset(sums, 0, nUniqKeys * nCols * sizeof(bst_gpair));
cubScanByKeyL1<node_id_t, BLKDIM_L1L3><<<nBlks, BLKDIM_L1L3>>>(
scans, vals, instIds, tmpScans, tmpKeys, keys, nUniqKeys, colIds,
nodeStart, size);

30
plugin/updater_gpu/src/exact/gpu_builder.cuh
View File

@ -19,13 +19,11 @@
#include <vector>
#include "../../../../src/tree/param.h"
#include "../common.cuh"
#include "argmax_by_key.cuh"
#include "cub/cub.cuh"
#include "fused_scan_reduce_by_key.cuh"
#include "gradients.cuh"
#include "loss_functions.cuh"
#include <vector>
#include "node.cuh"
#include "split2node.cuh"
#include "argmax_by_key.cuh"
#include "fused_scan_reduce_by_key.cuh"
#include "xgboost/tree_updater.h"
namespace xgboost {
@ -33,13 +31,13 @@ namespace tree {
namespace exact {
template <typename node_id_t>
__global__ void initRootNode(Node<node_id_t>* nodes, const gpu_gpair* sums,
__global__ void initRootNode(Node<node_id_t>* nodes, const bst_gpair* sums,
const TrainParam param) {
// gradients already evaluated inside transferGrads
Node<node_id_t> n;
n.gradSum = sums[0];
n.score = CalcGain(param, n.gradSum.g, n.gradSum.h);
n.weight = CalcWeight(param, n.gradSum.g, n.gradSum.h);
n.score = CalcGain(param, n.gradSum.grad , n.gradSum.hess);
n.weight = CalcWeight(param, n.gradSum.grad , n.gradSum.hess);
n.id = 0;
nodes[0] = n;
}
@ -198,13 +196,13 @@ class GPUBuilder {
dh::dvec<int> instIds_cached;
/** column offsets for these feature values */
dh::dvec<int> colOffsets;
dh::dvec<gpu_gpair> gradsInst;
dh::dvec<bst_gpair> gradsInst;
dh::dvec2<node_id_t> nodeAssigns;
dh::dvec2<int> nodeLocations;
dh::dvec<Node<node_id_t>> nodes;
dh::dvec<node_id_t> nodeAssignsPerInst;
dh::dvec<gpu_gpair> gradSums;
dh::dvec<gpu_gpair> gradScans;
dh::dvec<bst_gpair> gradSums;
dh::dvec<bst_gpair> gradScans;
dh::dvec<Split> nodeSplits;
int nVals;
int nRows;
@ -212,7 +210,7 @@ class GPUBuilder {
int maxNodes;
int maxLeaves;
dh::CubMemory tmp_mem;
dh::dvec<gpu_gpair> tmpScanGradBuff;
dh::dvec<bst_gpair> tmpScanGradBuff;
dh::dvec<int> tmpScanKeyBuff;
dh::dvec<int> colIds;
dh::bulk_allocator<dh::memory_type::DEVICE> ba;
@ -310,10 +308,10 @@ class GPUBuilder {
void transferGrads(const std::vector<bst_gpair>& gpair) {
// HACK
dh::safe_cuda(cudaMemcpy(gradsInst.data(), &(gpair[0]),
sizeof(gpu_gpair) * nRows,
sizeof(bst_gpair) * nRows,
cudaMemcpyHostToDevice));
// evaluate the full-grad reduction for the root node
sumReduction<gpu_gpair>(tmp_mem, gradsInst, gradSums, nRows);
sumReduction<bst_gpair>(tmp_mem, gradsInst, gradSums, nRows);
}
void initNodeData(int level, node_id_t nodeStart, int nNodes) {
@ -371,13 +369,13 @@ class GPUBuilder {
const Node<node_id_t>& n = hNodes[i];
if ((i != 0) && hNodes[i].isLeaf()) {
tree[nodeId].set_leaf(n.weight * param.learning_rate);
tree.stat(nodeId).sum_hess = n.gradSum.h;
tree.stat(nodeId).sum_hess = n.gradSum.hess;
++nodeId;
} else if (!hNodes[i].isUnused()) {
tree.AddChilds(nodeId);
tree[nodeId].set_split(n.colIdx, n.threshold, n.dir == LeftDir);
tree.stat(nodeId).loss_chg = n.score;
tree.stat(nodeId).sum_hess = n.gradSum.h;
tree.stat(nodeId).sum_hess = n.gradSum.hess;
tree.stat(nodeId).base_weight = n.weight;
tree[tree[nodeId].cleft()].set_leaf(0);
tree[tree[nodeId].cright()].set_leaf(0);

91
plugin/updater_gpu/src/exact/gradients.cuh
View File

@ -1,91 +0,0 @@
/*
* Copyright (c) 2017, NVIDIA CORPORATION, Xgboost contributors. All rights
* reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://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.
*/
#pragma once
#include "../common.cuh"
namespace xgboost {
namespace tree {
namespace exact {
/**
* @struct gpu_gpair gradients.cuh
* @brief The first/second order gradients for iteratively building the tree
*/
struct gpu_gpair {
/** the 'g_i' as it appears in the xgboost paper */
float g;
/** the 'h_i' as it appears in the xgboost paper */
float h;
HOST_DEV_INLINE gpu_gpair() : g(0.f), h(0.f) {}
HOST_DEV_INLINE gpu_gpair(const float& _g, const float& _h) : g(_g), h(_h) {}
HOST_DEV_INLINE gpu_gpair(const gpu_gpair& a) : g(a.g), h(a.h) {}
/**
* @brief Checks whether the hessian is more than the defined weight
* @param minWeight minimum weight to be compared against
* @return true if the hessian is greater than the minWeight
* @note this is useful in deciding whether to further split to child node
*/
HOST_DEV_INLINE bool isSplittable(float minWeight) const {
return (h > minWeight);
}
HOST_DEV_INLINE gpu_gpair& operator+=(const gpu_gpair& a) {
g += a.g;
h += a.h;
return *this;
}
HOST_DEV_INLINE gpu_gpair& operator-=(const gpu_gpair& a) {
g -= a.g;
h -= a.h;
return *this;
}
HOST_DEV_INLINE friend gpu_gpair operator+(const gpu_gpair& a,
const gpu_gpair& b) {
return gpu_gpair(a.g + b.g, a.h + b.h);
}
HOST_DEV_INLINE friend gpu_gpair operator-(const gpu_gpair& a,
const gpu_gpair& b) {
return gpu_gpair(a.g - b.g, a.h - b.h);
}
HOST_DEV_INLINE gpu_gpair(int value) {
*this = gpu_gpair((float)value, (float)value);
}
};
/**
* @brief Gradient value getter function
* @param id the index into the vals or instIds array to which to fetch
* @param vals the gradient value buffer
* @param instIds instance index buffer
* @return the expected gradient value
*/
HOST_DEV_INLINE gpu_gpair get(int id, const gpu_gpair* vals,
const int* instIds) {
id = instIds[id];
return vals[id];
}
} // namespace exact
} // namespace tree
} // namespace xgboost

60
plugin/updater_gpu/src/exact/loss_functions.cuh
View File

@ -1,60 +0,0 @@
/*
* Copyright (c) 2017, NVIDIA CORPORATION, Xgboost contributors. All rights
* reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://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.
*/
#pragma once
#include "../common.cuh"
#include "gradients.cuh"
namespace xgboost {
namespace tree {
namespace exact {
HOST_DEV_INLINE float device_calc_loss_chg(
const TrainParam &param, const gpu_gpair &scan, const gpu_gpair &missing,
const gpu_gpair &parent_sum, const float &parent_gain, bool missing_left) {
gpu_gpair left = scan;
if (missing_left) {
left += missing;
}
gpu_gpair right = parent_sum - left;
float left_gain = CalcGain(param, left.g, left.h);
float right_gain = CalcGain(param, right.g, right.h);
return left_gain + right_gain - parent_gain;
}
HOST_DEV_INLINE float loss_chg_missing(const gpu_gpair &scan,
const gpu_gpair &missing,
const gpu_gpair &parent_sum,
const float &parent_gain,
const TrainParam &param,
bool &missing_left_out) {
float missing_left_loss =
device_calc_loss_chg(param, scan, missing, parent_sum, parent_gain, true);
float missing_right_loss = device_calc_loss_chg(
param, scan, missing, parent_sum, parent_gain, false);
if (missing_left_loss >= missing_right_loss) {
missing_left_out = true;
return missing_left_loss;
} else {
missing_left_out = false;
return missing_right_loss;
}
}
} // namespace exact
} // namespace tree
} // namespace xgboost

3
plugin/updater_gpu/src/exact/node.cuh
View File

@ -17,7 +17,6 @@
#pragma once
#include "../common.cuh"
#include "gradients.cuh"
namespace xgboost {
namespace tree {
@ -67,7 +66,7 @@ template <typename node_id_t>
class Node {
public:
/** sum of gradients across all training samples part of this node */
gpu_gpair gradSum;
bst_gpair gradSum;
/** the optimal score for this node */
float score;
/** weightage for this node */

24
plugin/updater_gpu/src/exact/split2node.cuh
View File

@ -16,8 +16,6 @@
#pragma once
#include "../../../../src/tree/param.h"
#include "gradients.cuh"
#include "loss_functions.cuh"
#include "node.cuh"
namespace xgboost {
@ -37,11 +35,11 @@ namespace exact {
*/
template <typename node_id_t>
DEV_INLINE void updateOneChildNode(Node<node_id_t>* nodes, int nid,
const gpu_gpair& grad,
const bst_gpair& grad,
const TrainParam& param) {
nodes[nid].gradSum = grad;
nodes[nid].score = CalcGain(param, grad.g, grad.h);
nodes[nid].weight = CalcWeight(param, grad.g, grad.h);
nodes[nid].score = CalcGain(param, grad.grad, grad.hess);
nodes[nid].weight = CalcWeight(param, grad.grad, grad.hess);
nodes[nid].id = nid;
}
@ -56,7 +54,7 @@ DEV_INLINE void updateOneChildNode(Node<node_id_t>* nodes, int nid,
*/
template <typename node_id_t>
DEV_INLINE void updateChildNodes(Node<node_id_t>* nodes, int pid,
const gpu_gpair& gradL, const gpu_gpair& gradR,
const bst_gpair& gradL, const bst_gpair& gradR,
const TrainParam& param) {
int childId = (pid * 2) + 1;
updateOneChildNode(nodes, childId, gradL, param);
@ -66,15 +64,15 @@ DEV_INLINE void updateChildNodes(Node<node_id_t>* nodes, int pid,
template <typename node_id_t>
DEV_INLINE void updateNodeAndChildren(Node<node_id_t>* nodes, const Split& s,
const Node<node_id_t>& n, int absNodeId,
int colId, const gpu_gpair& gradScan,
const gpu_gpair& colSum, float thresh,
int colId, const bst_gpair& gradScan,
const bst_gpair& colSum, float thresh,
const TrainParam& param) {
bool missingLeft = true;
// get the default direction for the current node
gpu_gpair missing = n.gradSum - colSum;
bst_gpair missing = n.gradSum - colSum;
loss_chg_missing(gradScan, missing, n.gradSum, n.score, param, missingLeft);
// get the score/weight/id/gradSum for left and right child nodes
gpu_gpair lGradSum, rGradSum;
bst_gpair lGradSum, rGradSum;
if (missingLeft) {
lGradSum = gradScan + n.gradSum - colSum;
} else {
@ -90,8 +88,8 @@ DEV_INLINE void updateNodeAndChildren(Node<node_id_t>* nodes, const Split& s,
template <typename node_id_t, int BLKDIM = 256>
__global__ void split2nodeKernel(
Node<node_id_t>* nodes, const Split* nodeSplits, const gpu_gpair* gradScans,
const gpu_gpair* gradSums, const float* vals, const int* colIds,
Node<node_id_t>* nodes, const Split* nodeSplits, const bst_gpair* gradScans,
const bst_gpair* gradSums, const float* vals, const int* colIds,
const int* colOffsets, const node_id_t* nodeAssigns, int nUniqKeys,
node_id_t nodeStart, int nCols, const TrainParam param) {
int uid = (blockIdx.x * blockDim.x) + threadIdx.x;
@ -132,7 +130,7 @@ __global__ void split2nodeKernel(
*/
template <typename node_id_t, int BLKDIM = 256>
void split2node(Node<node_id_t>* nodes, const Split* nodeSplits,
const gpu_gpair* gradScans, const gpu_gpair* gradSums,
const bst_gpair* gradScans, const bst_gpair* gradSums,
const float* vals, const int* colIds, const int* colOffsets,
const node_id_t* nodeAssigns, int nUniqKeys,
node_id_t nodeStart, int nCols, const TrainParam param) {

11
plugin/updater_gpu/src/functions.cuh
View File

@ -1,11 +0,0 @@
/*!
* Copyright 2016 Rory mitchell
*/
#pragma once
#include "../../../src/common/random.h"
#include "../../../src/tree/param.h"
#include "types.cuh"
namespace xgboost {
namespace tree {} // namespace tree
} // namespace xgboost

106
plugin/updater_gpu/src/gpu_hist_builder.cu
View File

@ -40,10 +40,10 @@ void DeviceHist::Init(int n_bins_in) {
void DeviceHist::Reset(int device_idx) {
cudaSetDevice(device_idx);
data.fill(gpu_gpair());
data.fill(bst_gpair());
}
gpu_gpair* DeviceHist::GetLevelPtr(int depth) {
bst_gpair* DeviceHist::GetLevelPtr(int depth) {
return data.data() + n_nodes(depth - 1) * n_bins;
}
@ -53,20 +53,20 @@ HistBuilder DeviceHist::GetBuilder() {
return HistBuilder(data.data(), n_bins);
}
HistBuilder::HistBuilder(gpu_gpair* ptr, int n_bins)
HistBuilder::HistBuilder(bst_gpair* ptr, int n_bins)
: d_hist(ptr), n_bins(n_bins) {}
__device__ void HistBuilder::Add(gpu_gpair gpair, int gidx, int nidx) const {
__device__ void HistBuilder::Add(bst_gpair gpair, int gidx, int nidx) const {
int hist_idx = nidx * n_bins + gidx;
atomicAdd(&(d_hist[hist_idx]._grad), gpair._grad); // OPTMARK: This and below
atomicAdd(&(d_hist[hist_idx].grad), gpair.grad); // OPTMARK: This and below
// line lead to about 3X
// slowdown due to memory
// dependency and access
// pattern issues.
atomicAdd(&(d_hist[hist_idx]._hess), gpair._hess);
atomicAdd(&(d_hist[hist_idx].hess), gpair.hess);
}
__device__ gpu_gpair HistBuilder::Get(int gidx, int nidx) const {
__device__ bst_gpair HistBuilder::Get(int gidx, int nidx) const {
return d_hist[nidx * n_bins + gidx];
}
@ -362,7 +362,7 @@ void GPUHistBuilder::BuildHist(int depth) {
if (!is_smallest && depth > 0) return;
int gidx = d_gidx[local_idx];
gpu_gpair gpair = d_gpair[ridx - row_begin];
bst_gpair gpair = d_gpair[ridx - row_begin];
hist_builder.Add(gpair, gidx, nidx); // OPTMARK: This is slow, could use
// shared memory or cache results
@ -382,14 +382,14 @@ void GPUHistBuilder::BuildHist(int depth) {
// TODO(JCM): use out of place with pre-allocated buffer, but then have to
// copy
// back on device
// fprintf(stderr,"sizeof(gpu_gpair)/sizeof(float)=%d\n",sizeof(gpu_gpair)/sizeof(float));
// fprintf(stderr,"sizeof(bst_gpair)/sizeof(float)=%d\n",sizeof(bst_gpair)/sizeof(float));
for (int d_idx = 0; d_idx < n_devices; d_idx++) {
int device_idx = dList[d_idx];
dh::safe_cuda(cudaSetDevice(device_idx));
dh::safe_nccl(ncclAllReduce(
reinterpret_cast<const void*>(hist_vec[d_idx].GetLevelPtr(depth)),
reinterpret_cast<void*>(hist_vec[d_idx].GetLevelPtr(depth)),
hist_vec[d_idx].LevelSize(depth) * sizeof(gpu_gpair) / sizeof(float),
hist_vec[d_idx].LevelSize(depth) * sizeof(bst_gpair) / sizeof(float),
ncclFloat, ncclSum, comms[d_idx], *(streams[d_idx])));
}
@ -423,9 +423,9 @@ void GPUHistBuilder::BuildHist(int depth) {
}
int gidx = idx % hist_builder.n_bins;
gpu_gpair parent = hist_builder.Get(gidx, parent_nidx(nidx));
bst_gpair parent = hist_builder.Get(gidx, parent_nidx(nidx));
int other_nidx = left_smallest ? nidx - 1 : nidx + 1;
gpu_gpair other = hist_builder.Get(gidx, other_nidx);
bst_gpair other = hist_builder.Get(gidx, other_nidx);
hist_builder.Add(parent - other, gidx,
nidx); // OPTMARK: This is slow, could use shared
// memory or cache results intead of writing to
@ -438,16 +438,16 @@ void GPUHistBuilder::BuildHist(int depth) {
template <int BLOCK_THREADS>
__global__ void find_split_kernel(
const gpu_gpair* d_level_hist, int* d_feature_segments, int depth,
const bst_gpair* d_level_hist, int* d_feature_segments, int depth,
int n_features, int n_bins, Node* d_nodes, Node* d_nodes_temp,
Node* d_nodes_child_temp, int nodes_offset_device, float* d_fidx_min_map,
float* d_gidx_fvalue_map, GPUTrainingParam gpu_param,
bool* d_left_child_smallest_temp, bool colsample, int* d_feature_flags) {
typedef cub::KeyValuePair<int, float> ArgMaxT;
typedef cub::BlockScan<gpu_gpair, BLOCK_THREADS, cub::BLOCK_SCAN_WARP_SCANS>
typedef cub::BlockScan<bst_gpair, BLOCK_THREADS, cub::BLOCK_SCAN_WARP_SCANS>
BlockScanT;
typedef cub::BlockReduce<ArgMaxT, BLOCK_THREADS> MaxReduceT;
typedef cub::BlockReduce<gpu_gpair, BLOCK_THREADS> SumReduceT;
typedef cub::BlockReduce<bst_gpair, BLOCK_THREADS> SumReduceT;
union TempStorage {
typename BlockScanT::TempStorage scan;
@ -456,12 +456,12 @@ __global__ void find_split_kernel(
};
struct UninitializedSplit : cub::Uninitialized<Split> {};
struct UninitializedGpair : cub::Uninitialized<gpu_gpair> {};
struct UninitializedGpair : cub::Uninitialized<bst_gpair> {};
__shared__ UninitializedSplit uninitialized_split;
Split& split = uninitialized_split.Alias();
__shared__ UninitializedGpair uninitialized_sum;
gpu_gpair& shared_sum = uninitialized_sum.Alias();
bst_gpair& shared_sum = uninitialized_sum.Alias();
__shared__ ArgMaxT block_max;
__shared__ TempStorage temp_storage;
@ -484,12 +484,12 @@ __global__ void find_split_kernel(
int gidx = (begin - (level_node_idx * n_bins)) + threadIdx.x;
bool thread_active = threadIdx.x < end - begin;
gpu_gpair feature_sum = gpu_gpair();
bst_gpair feature_sum = bst_gpair();
for (int reduce_begin = begin; reduce_begin < end;
reduce_begin += BLOCK_THREADS) {
// Scan histogram
gpu_gpair bin = thread_active ? d_level_hist[reduce_begin + threadIdx.x]
: gpu_gpair();
bst_gpair bin = thread_active ? d_level_hist[reduce_begin + threadIdx.x]
: bst_gpair();
feature_sum +=
SumReduceT(temp_storage.sum_reduce).Reduce(bin, cub::Sum());
@ -503,17 +503,17 @@ __global__ void find_split_kernel(
GpairCallbackOp prefix_op = GpairCallbackOp();
for (int scan_begin = begin; scan_begin < end;
scan_begin += BLOCK_THREADS) {
gpu_gpair bin =
thread_active ? d_level_hist[scan_begin + threadIdx.x] : gpu_gpair();
bst_gpair bin =
thread_active ? d_level_hist[scan_begin + threadIdx.x] : bst_gpair();
BlockScanT(temp_storage.scan)
.ExclusiveScan(bin, bin, cub::Sum(), prefix_op);
// Calculate gain
gpu_gpair parent_sum = d_nodes[node_idx].sum_gradients;
bst_gpair parent_sum = d_nodes[node_idx].sum_gradients;
float parent_gain = d_nodes[node_idx].root_gain;
gpu_gpair missing = parent_sum - shared_sum;
bst_gpair missing = parent_sum - shared_sum;
bool missing_left;
float gain = thread_active
@ -543,8 +543,8 @@ __global__ void find_split_kernel(
fvalue = d_gidx_fvalue_map[gidx - 1];
}
gpu_gpair left = missing_left ? bin + missing : bin;
gpu_gpair right = parent_sum - left;
bst_gpair left = missing_left ? bin + missing : bin;
bst_gpair right = parent_sum - left;
split.Update(gain, missing_left, fvalue, fidx, left, right, gpu_param);
}
@ -581,16 +581,16 @@ __global__ void find_split_kernel(
*Nodeleft = Node(
split.left_sum,
CalcGain(gpu_param, split.left_sum.grad(), split.left_sum.hess()),
CalcWeight(gpu_param, split.left_sum.grad(), split.left_sum.hess()));
CalcGain(gpu_param, split.left_sum.grad, split.left_sum.hess),
CalcWeight(gpu_param, split.left_sum.grad, split.left_sum.hess));
*Noderight = Node(
split.right_sum,
CalcGain(gpu_param, split.right_sum.grad(), split.right_sum.hess()),
CalcWeight(gpu_param, split.right_sum.grad(), split.right_sum.hess()));
CalcGain(gpu_param, split.right_sum.grad, split.right_sum.hess),
CalcWeight(gpu_param, split.right_sum.grad, split.right_sum.hess));
// Record smallest node
if (split.left_sum.hess() <= split.right_sum.hess()) {
if (split.left_sum.hess <= split.right_sum.hess) {
*left_child_smallest = true;
} else {
*left_child_smallest = false;
@ -654,11 +654,11 @@ void GPUHistBuilder::LaunchFindSplit(int depth) {
int nodes_offset_device = d_idx * num_nodes_device;
find_split_kernel<BLOCK_THREADS><<<GRID_SIZE, BLOCK_THREADS>>>(
(const gpu_gpair*)(hist_vec[d_idx].GetLevelPtr(depth)),
(const bst_gpair*)(hist_vec[d_idx].GetLevelPtr(depth)),
feature_segments[d_idx].data(), depth, (info->num_col),
(hmat_.row_ptr.back()), nodes[d_idx].data(), nodes_temp[d_idx].data(),
nodes_child_temp[d_idx].data(), nodes_offset_device,
fidx_min_map[d_idx].data(), gidx_fvalue_map[d_idx].data(), gpu_param,
fidx_min_map[d_idx].data(), gidx_fvalue_map[d_idx].data(), GPUTrainingParam(param),
left_child_smallest_temp[d_idx].data(), colsample,
feature_flags[d_idx].data());
}
@ -751,11 +751,11 @@ void GPUHistBuilder::LaunchFindSplit(int depth) {
int nodes_offset_device = d_idx * num_nodes_device;
find_split_kernel<BLOCK_THREADS><<<GRID_SIZE, BLOCK_THREADS>>>(
(const gpu_gpair*)(hist_vec[d_idx].GetLevelPtr(depth)),
(const bst_gpair*)(hist_vec[d_idx].GetLevelPtr(depth)),
feature_segments[d_idx].data(), depth, (info->num_col),
(hmat_.row_ptr.back()), nodes[d_idx].data(), NULL, NULL,
nodes_offset_device, fidx_min_map[d_idx].data(),
gidx_fvalue_map[d_idx].data(), gpu_param,
gidx_fvalue_map[d_idx].data(), GPUTrainingParam(param),
left_child_smallest[d_idx].data(), colsample,
feature_flags[d_idx].data());
@ -805,11 +805,11 @@ void GPUHistBuilder::LaunchFindSplit(int depth) {
int nodes_offset_device = 0;
find_split_kernel<BLOCK_THREADS><<<GRID_SIZE, BLOCK_THREADS>>>(
(const gpu_gpair*)(hist_vec[d_idx].GetLevelPtr(depth)),
(const bst_gpair*)(hist_vec[d_idx].GetLevelPtr(depth)),
feature_segments[d_idx].data(), depth, (info->num_col),
(hmat_.row_ptr.back()), nodes[d_idx].data(), NULL, NULL,
nodes_offset_device, fidx_min_map[d_idx].data(),
gidx_fvalue_map[d_idx].data(), gpu_param,
gidx_fvalue_map[d_idx].data(), GPUTrainingParam(param),
left_child_smallest[d_idx].data(), colsample,
feature_flags[d_idx].data());
}
@ -827,21 +827,21 @@ void GPUHistBuilder::InitFirstNode(const std::vector<bst_gpair>& gpair) {
// and C:/Program Files (x86)/Microsoft Visual Studio
// 14.0/VC/bin/../../VC/INCLUDE\future(1888): error : no instance of function
// template "std::_Invoke_stored" matches the argument list
std::vector<gpu_gpair> future_results(n_devices);
std::vector<bst_gpair> future_results(n_devices);
for (int d_idx = 0; d_idx < n_devices; d_idx++) {
int device_idx = dList[d_idx];
auto begin = device_gpair[d_idx].tbegin();
auto end = device_gpair[d_idx].tend();
gpu_gpair init = gpu_gpair();
auto binary_op = thrust::plus<gpu_gpair>();
bst_gpair init = bst_gpair();
auto binary_op = thrust::plus<bst_gpair>();
dh::safe_cuda(cudaSetDevice(device_idx));
future_results[d_idx] = thrust::reduce(begin, end, init, binary_op);
}
// sum over devices on host (with blocking get())
gpu_gpair sum = gpu_gpair();
bst_gpair sum = bst_gpair();
for (int d_idx = 0; d_idx < n_devices; d_idx++) {
int device_idx = dList[d_idx];
sum += future_results[d_idx];
@ -849,7 +849,7 @@ void GPUHistBuilder::InitFirstNode(const std::vector<bst_gpair>& gpair) {
#else
// asynch reduce per device
std::vector<std::future<gpu_gpair>> future_results(n_devices);
std::vector<std::future<bst_gpair>> future_results(n_devices);
for (int d_idx = 0; d_idx < n_devices; d_idx++) {
// std::async captures the algorithm parameters by value
// use std::launch::async to ensure the creation of a new thread
@ -858,14 +858,14 @@ void GPUHistBuilder::InitFirstNode(const std::vector<bst_gpair>& gpair) {
dh::safe_cuda(cudaSetDevice(device_idx));
auto begin = device_gpair[d_idx].tbegin();
auto end = device_gpair[d_idx].tend();
gpu_gpair init = gpu_gpair();
auto binary_op = thrust::plus<gpu_gpair>();
bst_gpair init = bst_gpair();
auto binary_op = thrust::plus<bst_gpair>();
return thrust::reduce(begin, end, init, binary_op);
});
}
// sum over devices on host (with blocking get())
gpu_gpair sum = gpu_gpair();
bst_gpair sum = bst_gpair();
for (int d_idx = 0; d_idx < n_devices; d_idx++) {
int device_idx = dList[d_idx];
sum += future_results[d_idx].get();
@ -879,15 +879,15 @@ void GPUHistBuilder::InitFirstNode(const std::vector<bst_gpair>& gpair) {
int device_idx = dList[d_idx];
auto d_nodes = nodes[d_idx].data();
auto gpu_param_alias = gpu_param;
auto gpu_param = GPUTrainingParam(param);
dh::launch_n(device_idx, 1, [=] __device__(int idx) {
gpu_gpair sum_gradients = sum;
bst_gpair sum_gradients = sum;
d_nodes[idx] = Node(
sum_gradients,
CalcGain(gpu_param_alias, sum_gradients.grad(), sum_gradients.hess()),
CalcWeight(gpu_param_alias, sum_gradients.grad(),
sum_gradients.hess()));
CalcGain(gpu_param, sum_gradients.grad, sum_gradients.hess),
CalcWeight(gpu_param, sum_gradients.grad,
sum_gradients.hess));
});
}
// synch all devices to host before moving on (No, can avoid because BuildHist
@ -916,7 +916,7 @@ void GPUHistBuilder::UpdatePositionDense(int depth) {
size_t end = device_row_segments[d_idx + 1];
dh::launch_n(device_idx, end - begin, [=] __device__(int local_idx) {
NodeIdT pos = d_position[local_idx];
int pos = d_position[local_idx];
if (!is_active(pos, depth)) {
return;
}
@ -961,7 +961,7 @@ void GPUHistBuilder::UpdatePositionSparse(int depth) {
// Update missing direction
dh::launch_n(device_idx, row_end - row_begin,
[=] __device__(int local_idx) {
NodeIdT pos = d_position[local_idx];
int pos = d_position[local_idx];
if (!is_active(pos, depth)) {
d_position_tmp[local_idx] = pos;
return;
@ -985,7 +985,7 @@ void GPUHistBuilder::UpdatePositionSparse(int depth) {
dh::launch_n(
device_idx, element_end - element_begin, [=] __device__(int local_idx) {
int ridx = d_ridx[local_idx];
NodeIdT pos = d_position[ridx - row_begin];
int pos = d_position[ridx - row_begin];
if (!is_active(pos, depth)) {
return;
}

17
plugin/updater_gpu/src/gpu_hist_builder.cuh
View File

@ -31,16 +31,16 @@ struct DeviceGMat {
};
struct HistBuilder {
gpu_gpair *d_hist;
bst_gpair *d_hist;
int n_bins;
__host__ __device__ HistBuilder(gpu_gpair *ptr, int n_bins);
__device__ void Add(gpu_gpair gpair, int gidx, int nidx) const;
__device__ gpu_gpair Get(int gidx, int nidx) const;
__host__ __device__ HistBuilder(bst_gpair *ptr, int n_bins);
__device__ void Add(bst_gpair gpair, int gidx, int nidx) const;
__device__ bst_gpair Get(int gidx, int nidx) const;
};
struct DeviceHist {
int n_bins;
dh::dvec<gpu_gpair> data;
dh::dvec<bst_gpair> data;
void Init(int max_depth);
@ -48,7 +48,7 @@ struct DeviceHist {
HistBuilder GetBuilder();
gpu_gpair *GetLevelPtr(int depth);
bst_gpair *GetLevelPtr(int depth);
int LevelSize(int depth);
};
@ -61,8 +61,6 @@ class GPUHistBuilder {
void UpdateParam(const TrainParam &param) {
this->param = param;
this->gpu_param = GPUTrainingParam(param.min_child_weight, param.reg_lambda,
param.reg_alpha, param.max_delta_step);
}
void InitData(const std::vector<bst_gpair> &gpair, DMatrix &fmat, // NOLINT
@ -85,7 +83,6 @@ class GPUHistBuilder {
std::vector<bst_float> *p_out_preds);
TrainParam param;
GPUTrainingParam gpu_param;
common::HistCutMatrix hmat_;
common::GHistIndexMatrix gmat_;
MetaInfo *info;
@ -124,7 +121,7 @@ class GPUHistBuilder {
std::vector<dh::dvec<int>> position;
std::vector<dh::dvec<int>> position_tmp;
std::vector<DeviceGMat> device_matrix;
std::vector<dh::dvec<gpu_gpair>> device_gpair;
std::vector<dh::dvec<bst_gpair>> device_gpair;
std::vector<dh::dvec<int>> gidx_feature_map;
std::vector<dh::dvec<float>> gidx_fvalue_map;

119
plugin/updater_gpu/src/types.cuh
View File

@ -11,85 +11,6 @@
namespace xgboost {
namespace tree {
typedef int16_t NodeIdT;
// gpair type defined with device accessible functions
struct gpu_gpair {
float _grad;
float _hess;
__host__ __device__ __forceinline__ float grad() const { return _grad; }
__host__ __device__ __forceinline__ float hess() const { return _hess; }
__host__ __device__ gpu_gpair() : _grad(0), _hess(0) {}
__host__ __device__ gpu_gpair(float g, float h) : _grad(g), _hess(h) {}
__host__ __device__ gpu_gpair(bst_gpair gpair)
: _grad(gpair.grad), _hess(gpair.hess) {}
__host__ __device__ bool operator==(const gpu_gpair &rhs) const {
return (_grad == rhs._grad) && (_hess == rhs._hess);
}
__host__ __device__ bool operator!=(const gpu_gpair &rhs) const {
return !(*this == rhs);
}
__host__ __device__ gpu_gpair &operator+=(const gpu_gpair &rhs) {
_grad += rhs._grad;
_hess += rhs._hess;
return *this;
}
__host__ __device__ gpu_gpair operator+(const gpu_gpair &rhs) const {
gpu_gpair g;
g._grad = _grad + rhs._grad;
g._hess = _hess + rhs._hess;
return g;
}
__host__ __device__ gpu_gpair &operator-=(const gpu_gpair &rhs) {
_grad -= rhs._grad;
_hess -= rhs._hess;
return *this;
}
__host__ __device__ gpu_gpair operator-(const gpu_gpair &rhs) const {
gpu_gpair g;
g._grad = _grad - rhs._grad;
g._hess = _hess - rhs._hess;
return g;
}
friend std::ostream &operator<<(std::ostream &os, const gpu_gpair &g) {
os << g.grad() << "/" << g.hess();
return os;
}
__host__ __device__ void print() const {
printf("%1.4f/%1.4f\n", grad(), hess());
}
__host__ __device__ bool approximate_compare(const gpu_gpair &b,
float g_eps = 0.1,
float h_eps = 0.1) const {
float gdiff = abs(this->grad() - b.grad());
float hdiff = abs(this->hess() - b.hess());
return (gdiff <= g_eps) && (hdiff <= h_eps);
}
};
typedef thrust::device_vector<bst_uint>::iterator uint_iter;
typedef thrust::device_vector<gpu_gpair>::iterator gpair_iter;
typedef thrust::device_vector<float>::iterator float_iter;
typedef thrust::device_vector<NodeIdT>::iterator node_id_iter;
typedef thrust::permutation_iterator<gpair_iter, uint_iter> gpair_perm_iter;
typedef thrust::tuple<gpair_perm_iter, float_iter, node_id_iter> ItemTuple;
typedef thrust::zip_iterator<ItemTuple> ItemIter;
struct GPUTrainingParam {
// minimum amount of hessian(weight) allowed in a child
float min_child_weight;
@ -104,6 +25,12 @@ struct GPUTrainingParam {
__host__ __device__ GPUTrainingParam() {}
__host__ __device__ GPUTrainingParam(const TrainParam &param)
: min_child_weight(param.min_child_weight),
reg_lambda(param.reg_lambda),
reg_alpha(param.reg_alpha),
max_delta_step(param.max_delta_step) {}
__host__ __device__ GPUTrainingParam(float min_child_weight_in,
float reg_lambda_in, float reg_alpha_in,
float max_delta_step_in)
@ -118,19 +45,19 @@ struct Split {
bool missing_left;
float fvalue;
int findex;
gpu_gpair left_sum;
gpu_gpair right_sum;
bst_gpair left_sum;
bst_gpair right_sum;
__host__ __device__ Split()
: loss_chg(-FLT_MAX), missing_left(true), fvalue(0), findex(-1) {}
__device__ void Update(float loss_chg_in, bool missing_left_in,
float fvalue_in, int findex_in, gpu_gpair left_sum_in,
gpu_gpair right_sum_in,
float fvalue_in, int findex_in, bst_gpair left_sum_in,
bst_gpair right_sum_in,
const GPUTrainingParam &param) {
if (loss_chg_in > loss_chg &&
left_sum_in.hess() >= param.min_child_weight &&
right_sum_in.hess() >= param.min_child_weight) {
left_sum_in.hess>= param.min_child_weight &&
right_sum_in.hess>= param.min_child_weight) {
loss_chg = loss_chg_in;
missing_left = missing_left_in;
fvalue = fvalue_in;
@ -152,16 +79,16 @@ struct Split {
}
}
__host__ __device__ void Print() {
printf("Loss: %1.4f\n", loss_chg);
printf("Missing left: %d\n", missing_left);
printf("fvalue: %1.4f\n", fvalue);
printf("Left sum: ");
left_sum.print();
//__host__ __device__ void Print() {
// printf("Loss: %1.4f\n", loss_chg);
// printf("Missing left: %d\n", missing_left);
// printf("fvalue: %1.4f\n", fvalue);
// printf("Left sum: ");
// left_sum.print();
printf("Right sum: ");
right_sum.print();
}
// printf("Right sum: ");
// right_sum.print();
//}
};
struct split_reduce_op {
@ -173,7 +100,7 @@ struct split_reduce_op {
};
struct Node {
gpu_gpair sum_gradients;
bst_gpair sum_gradients;
float root_gain;
float weight;
@ -181,7 +108,7 @@ struct Node {
__host__ __device__ Node() : weight(0), root_gain(0) {}
__host__ __device__ Node(gpu_gpair sum_gradients_in, float root_gain_in,
__host__ __device__ Node(bst_gpair sum_gradients_in, float root_gain_in,
float weight_in) {
sum_gradients = sum_gradients_in;
root_gain = root_gain_in;

17
src/tree/param.h
View File

@ -14,11 +14,6 @@
#include <limits>
#include <vector>
#ifdef __NVCC__
#define XGB_DEVICE __host__ __device__
#else
#define XGB_DEVICE
#endif
namespace xgboost {
namespace tree {
@ -234,7 +229,7 @@ struct TrainParam : public dmlc::Parameter<TrainParam> {
// functions for L1 cost
template <typename T1, typename T2>
XGB_DEVICE inline static T1 ThresholdL1(T1 w, T2 lambda) {
XGBOOST_DEVICE inline static T1 ThresholdL1(T1 w, T2 lambda) {
if (w > +lambda)
return w - lambda;
if (w < -lambda)
@ -243,18 +238,18 @@ XGB_DEVICE inline static T1 ThresholdL1(T1 w, T2 lambda) {
}
template <typename T>
XGB_DEVICE inline static T Sqr(T a) { return a * a; }
XGBOOST_DEVICE inline static T Sqr(T a) { return a * a; }
// calculate the cost of loss function
template <typename TrainingParams, typename T>
XGB_DEVICE inline T CalcGainGivenWeight(const TrainingParams &p, T sum_grad,
XGBOOST_DEVICE inline T CalcGainGivenWeight(const TrainingParams &p, T sum_grad,
T sum_hess, T w) {
return -(2.0 * sum_grad * w + (sum_hess + p.reg_lambda) * Sqr(w));
}
// calculate the cost of loss function
template <typename TrainingParams, typename T>
XGB_DEVICE inline T CalcGain(const TrainingParams &p, T sum_grad, T sum_hess) {
XGBOOST_DEVICE inline T CalcGain(const TrainingParams &p, T sum_grad, T sum_hess) {
if (sum_hess < p.min_child_weight)
return 0.0;
if (p.max_delta_step == 0.0f) {
@ -276,7 +271,7 @@ XGB_DEVICE inline T CalcGain(const TrainingParams &p, T sum_grad, T sum_hess) {
}
// calculate cost of loss function with four statistics
template <typename TrainingParams, typename T>
XGB_DEVICE inline T CalcGain(const TrainingParams &p, T sum_grad, T sum_hess,
XGBOOST_DEVICE inline T CalcGain(const TrainingParams &p, T sum_grad, T sum_hess,
T test_grad, T test_hess) {
T w = CalcWeight(sum_grad, sum_hess);
T ret = test_grad * w + 0.5 * (test_hess + p.reg_lambda) * Sqr(w);
@ -288,7 +283,7 @@ XGB_DEVICE inline T CalcGain(const TrainingParams &p, T sum_grad, T sum_hess,
}
// calculate weight given the statistics
template <typename TrainingParams, typename T>
XGB_DEVICE inline T CalcWeight(const TrainingParams &p, T sum_grad,
XGBOOST_DEVICE inline T CalcWeight(const TrainingParams &p, T sum_grad,
T sum_hess) {
if (sum_hess < p.min_child_weight)
return 0.0;