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AVX gradients (#2878)

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* AVX gradients

* Add google test for AVX

* Create fallback implementation, remove fma instruction

* Improved accuracy of AVX exp function
This commit is contained in:
Rory Mitchell
2017-11-27 08:56:01 +13:00
committed by GitHub
parent 3dcf966bc3
commit 24f527a1c0
7 changed files with 465 additions and 47 deletions
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88
src/objective/regression_obj.cc
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@@ -11,6 +11,7 @@
#include <algorithm>
#include <utility>
#include "../common/math.h"
#include "../common/avx_helpers.h"
namespace xgboost {
namespace obj {
@@ -20,22 +21,28 @@ DMLC_REGISTRY_FILE_TAG(regression_obj);
// common regressions
// linear regression
struct LinearSquareLoss {
static bst_float PredTransform(bst_float x) { return x; }
template <typename T>
static T PredTransform(T x) { return x; }
static bool CheckLabel(bst_float x) { return true; }
static bst_float FirstOrderGradient(bst_float predt, bst_float label) { return predt - label; }
static bst_float SecondOrderGradient(bst_float predt, bst_float label) { return 1.0f; }
template <typename T>
static T FirstOrderGradient(T predt, T label) { return predt - label; }
template <typename T>
static T SecondOrderGradient(T predt, T label) { return T(1.0f); }
static bst_float ProbToMargin(bst_float base_score) { return base_score; }
static const char* LabelErrorMsg() { return ""; }
static const char* DefaultEvalMetric() { return "rmse"; }
};
// logistic loss for probability regression task
struct LogisticRegression {
static bst_float PredTransform(bst_float x) { return common::Sigmoid(x); }
template <typename T>
static T PredTransform(T x) { return common::Sigmoid(x); }
static bool CheckLabel(bst_float x) { return x >= 0.0f && x <= 1.0f; }
static bst_float FirstOrderGradient(bst_float predt, bst_float label) { return predt - label; }
static bst_float SecondOrderGradient(bst_float predt, bst_float label) {
const float eps = 1e-16f;
return std::max(predt * (1.0f - predt), eps);
template <typename T>
static T FirstOrderGradient(T predt, T label) { return predt - label; }
template <typename T>
static T SecondOrderGradient(T predt, T label) {
const T eps = T(1e-16f);
return std::max(predt * (T(1.0f) - predt), eps);
}
static bst_float ProbToMargin(bst_float base_score) {
CHECK(base_score > 0.0f && base_score < 1.0f)
@@ -53,28 +60,28 @@ struct LogisticClassification : public LogisticRegression {
};
// logistic loss, but predict un-transformed margin
struct LogisticRaw : public LogisticRegression {
static bst_float PredTransform(bst_float x) { return x; }
static bst_float FirstOrderGradient(bst_float predt, bst_float label) {
template <typename T>
static T PredTransform(T x) { return x; }
template <typename T>
static T FirstOrderGradient(T predt, T label) {
predt = common::Sigmoid(predt);
return predt - label;
}
static bst_float SecondOrderGradient(bst_float predt, bst_float label) {
const float eps = 1e-16f;
template <typename T>
static T SecondOrderGradient(T predt, T label) {
const T eps = T(1e-16f);
predt = common::Sigmoid(predt);
return std::max(predt * (1.0f - predt), eps);
return std::max(predt * (T(1.0f) - predt), eps);
}
static const char* DefaultEvalMetric() { return "auc"; }
};
struct RegLossParam : public dmlc::Parameter<RegLossParam> {
float scale_pos_weight;
int nthread;
// declare parameters
DMLC_DECLARE_PARAMETER(RegLossParam) {
DMLC_DECLARE_FIELD(scale_pos_weight).set_default(1.0f).set_lower_bound(0.0f)
.describe("Scale the weight of positive examples by this factor");
DMLC_DECLARE_FIELD(nthread).set_default(0).describe(
"Number of threads to use.");
}
};
@@ -98,23 +105,36 @@ class RegLossObj : public ObjFunction {
this->LazyCheckLabels(info.labels);
out_gpair->resize(preds.size());
const omp_ulong n = static_cast<omp_ulong>(preds.size());
auto gpair_ptr = out_gpair->data();
avx::Float8 scale(param_.scale_pos_weight);
// start calculating gradient
const omp_ulong ndata = static_cast<omp_ulong>(preds.size());
int nthread = param_.nthread == 0 ? omp_get_num_procs() : param_.nthread;
#pragma omp parallel for schedule(static) num_threads(nthread)
for (omp_ulong i = 0; i < ndata; ++i) {
const omp_ulong remainder = n % 8;
int nthread = omp_get_max_threads();
// Use a maximum of 8 threads
#pragma omp parallel for schedule(static) num_threads(std::min(8, nthread))
for (int i = 0; i < n - remainder; i += 8) {
avx::Float8 y(&info.labels[i]);
avx::Float8 p = Loss::PredTransform(avx::Float8(&preds[i]));
avx::Float8 w = info.weights.empty() ? avx::Float8(1.0f)
: avx::Float8(&info.weights[i]);
// Adjust weight
w += y * (scale * w - w);
avx::Float8 grad = Loss::FirstOrderGradient(p, y);
avx::Float8 hess = Loss::SecondOrderGradient(p, y);
avx::StoreGpair(gpair_ptr + i, grad * w, hess * w);
}
for (omp_ulong i = n - remainder; i < n; ++i) {
auto y = info.labels[i];
bst_float p = Loss::PredTransform(preds[i]);
bst_float w = info.GetWeight(i);
// Branchless version of the below function
// The branch is particularly slow as the cpu cannot predict the label
// with any accuracy resulting in frequent pipeline stalls
// if (info.labels[i] == 1.0f) w *= param_.scale_pos_weight;
w += y * ((param_.scale_pos_weight * w) - w);
(*out_gpair)[i] = bst_gpair(Loss::FirstOrderGradient(p, y) * w,
Loss::SecondOrderGradient(p, y) * w);
}
// Reset omp max threads
omp_set_num_threads(nthread);
}
const char *DefaultEvalMetric() const override {
return Loss::DefaultEvalMetric();
@@ -165,13 +185,10 @@ XGBOOST_REGISTER_OBJECTIVE(LogisticRaw, "binary:logitraw")
// declare parameter
struct PoissonRegressionParam : public dmlc::Parameter<PoissonRegressionParam> {
float max_delta_step;
int nthread;
DMLC_DECLARE_PARAMETER(PoissonRegressionParam) {
DMLC_DECLARE_FIELD(max_delta_step).set_lower_bound(0.0f).set_default(0.7f)
.describe("Maximum delta step we allow each weight estimation to be." \
" This parameter is required for possion regression.");
DMLC_DECLARE_FIELD(nthread).set_default(0).describe(
"Number of threads to use.");
}
};
@@ -194,8 +211,7 @@ class PoissonRegression : public ObjFunction {
bool label_correct = true;
// start calculating gradient
const omp_ulong ndata = static_cast<omp_ulong>(preds.size()); // NOLINT(*)
int nthread = param_.nthread == 0 ? omp_get_num_procs() : param_.nthread;
#pragma omp parallel for schedule(static) num_threads(nthread)
#pragma omp parallel for schedule(static)
for (omp_ulong i = 0; i < ndata; ++i) { // NOLINT(*)
bst_float p = preds[i];
bst_float w = info.GetWeight(i);
@@ -212,8 +228,7 @@ class PoissonRegression : public ObjFunction {
void PredTransform(std::vector<bst_float> *io_preds) override {
std::vector<bst_float> &preds = *io_preds;
const long ndata = static_cast<long>(preds.size()); // NOLINT(*)
int nthread = param_.nthread == 0 ? omp_get_num_procs() : param_.nthread;
#pragma omp parallel for schedule(static) num_threads(nthread)
#pragma omp parallel for schedule(static)
for (long j = 0; j < ndata; ++j) { // NOLINT(*)
preds[j] = std::exp(preds[j]);
}
@@ -297,12 +312,9 @@ XGBOOST_REGISTER_OBJECTIVE(GammaRegression, "reg:gamma")
// declare parameter
struct TweedieRegressionParam : public dmlc::Parameter<TweedieRegressionParam> {
float tweedie_variance_power;
int nthread;
DMLC_DECLARE_PARAMETER(TweedieRegressionParam) {
DMLC_DECLARE_FIELD(tweedie_variance_power).set_range(1.0f, 2.0f).set_default(1.5f)
.describe("Tweedie variance power. Must be between in range [1, 2).");
DMLC_DECLARE_FIELD(nthread).set_default(0).describe(
"Number of threads to use.");
}
};
@@ -325,8 +337,7 @@ class TweedieRegression : public ObjFunction {
bool label_correct = true;
// start calculating gradient
const omp_ulong ndata = static_cast<omp_ulong>(preds.size()); // NOLINT(*)
int nthread = param_.nthread == 0 ? omp_get_num_procs() : param_.nthread;
#pragma omp parallel for schedule(static) num_threads(nthread)
#pragma omp parallel for schedule(static)
for (omp_ulong i = 0; i < ndata; ++i) { // NOLINT(*)
bst_float p = preds[i];
bst_float w = info.GetWeight(i);
@@ -346,8 +357,7 @@ class TweedieRegression : public ObjFunction {
void PredTransform(std::vector<bst_float> *io_preds) override {
std::vector<bst_float> &preds = *io_preds;
const long ndata = static_cast<long>(preds.size()); // NOLINT(*)
int nthread = param_.nthread == 0 ? omp_get_num_procs() : param_.nthread;
#pragma omp parallel for schedule(static) num_threads(nthread)
#pragma omp parallel for schedule(static)
for (long j = 0; j < ndata; ++j) { // NOLINT(*)
preds[j] = std::exp(preds[j]);
}