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Enable running objectives with 0 GPU. (#3878)

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* Enable running objectives with 0 GPU.

* Enable 0 GPU for objectives.
* Add doc for GPU objectives.
* Fix some objectives defaulted to running on all GPUs.
This commit is contained in:
Jiaming Yuan
2018-11-13 20:19:59 +13:00
committed by GitHub
parent 97984f4890
commit daf77ca7b7
6 changed files with 56 additions and 22 deletions
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2
src/objective/hinge.cu
View File

@@ -22,7 +22,7 @@ struct HingeObjParam : public dmlc::Parameter<HingeObjParam> {
int n_gpus;
int gpu_id;
DMLC_DECLARE_PARAMETER(HingeObjParam) {
DMLC_DECLARE_FIELD(n_gpus).set_default(1).set_lower_bound(-1)
DMLC_DECLARE_FIELD(n_gpus).set_default(1).set_lower_bound(GPUSet::kAll)
.describe("Number of GPUs to use for multi-gpu algorithms.");
DMLC_DECLARE_FIELD(gpu_id)
.set_lower_bound(0)

3
src/objective/multiclass_obj.cu
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@@ -31,7 +31,7 @@ struct SoftmaxMultiClassParam : public dmlc::Parameter<SoftmaxMultiClassParam> {
DMLC_DECLARE_PARAMETER(SoftmaxMultiClassParam) {
DMLC_DECLARE_FIELD(num_class).set_lower_bound(1)
.describe("Number of output class in the multi-class classification.");
DMLC_DECLARE_FIELD(n_gpus).set_default(1).set_lower_bound(-1)
DMLC_DECLARE_FIELD(n_gpus).set_default(1).set_lower_bound(GPUSet::kAll)
.describe("Number of GPUs to use for multi-gpu algorithms.");
DMLC_DECLARE_FIELD(gpu_id)
.set_lower_bound(0)
@@ -49,7 +49,6 @@ class SoftmaxMultiClassObj : public ObjFunction {
}
void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
param_.InitAllowUnknown(args);
CHECK(param_.n_gpus != 0) << "Must have at least one device"; // Default is -1
devices_ = GPUSet::All(param_.gpu_id, param_.n_gpus);
label_correct_.Resize(devices_.IsEmpty() ? 1 : devices_.Size());
}

12
src/objective/regression_obj.cu
View File

@@ -34,7 +34,7 @@ struct RegLossParam : public dmlc::Parameter<RegLossParam> {
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(n_gpus).set_default(1).set_lower_bound(-1)
DMLC_DECLARE_FIELD(n_gpus).set_default(1).set_lower_bound(GPUSet::kAll)
.describe("Number of GPUs to use for multi-gpu algorithms.");
DMLC_DECLARE_FIELD(gpu_id)
.set_lower_bound(0)
@@ -53,7 +53,6 @@ class RegLossObj : public ObjFunction {
void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
param_.InitAllowUnknown(args);
CHECK(param_.n_gpus != 0) << "Must have at least one device"; // Default is -1
devices_ = GPUSet::All(param_.gpu_id, param_.n_gpus);
label_correct_.Resize(devices_.IsEmpty() ? 1 : devices_.Size());
}
@@ -182,7 +181,7 @@ struct PoissonRegressionParam : public dmlc::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(n_gpus).set_default(-1).set_lower_bound(-1)
DMLC_DECLARE_FIELD(n_gpus).set_default(1).set_lower_bound(GPUSet::kAll)
.describe("Number of GPUs to use for multi-gpu algorithms.");
DMLC_DECLARE_FIELD(gpu_id)
.set_lower_bound(0)
@@ -197,7 +196,6 @@ class PoissonRegression : public ObjFunction {
// declare functions
void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
param_.InitAllowUnknown(args);
CHECK(param_.n_gpus != 0) << "Must have at least one device"; // Default is -1
devices_ = GPUSet::All(param_.gpu_id, param_.n_gpus);
label_correct_.Resize(devices_.IsEmpty() ? 1 : devices_.Size());
}
@@ -364,7 +362,7 @@ struct GammaRegressionParam : public dmlc::Parameter<GammaRegressionParam> {
int n_gpus;
int gpu_id;
DMLC_DECLARE_PARAMETER(GammaRegressionParam) {
DMLC_DECLARE_FIELD(n_gpus).set_default(-1).set_lower_bound(-1)
DMLC_DECLARE_FIELD(n_gpus).set_default(1).set_lower_bound(GPUSet::kAll)
.describe("Number of GPUs to use for multi-gpu algorithms.");
DMLC_DECLARE_FIELD(gpu_id)
.set_lower_bound(0)
@@ -379,7 +377,6 @@ class GammaRegression : public ObjFunction {
// declare functions
void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
param_.InitAllowUnknown(args);
CHECK(param_.n_gpus != 0) << "Must have at least one device"; // Default is -1
devices_ = GPUSet::All(param_.gpu_id, param_.n_gpus);
label_correct_.Resize(devices_.IsEmpty() ? 1 : devices_.Size());
}
@@ -461,7 +458,7 @@ struct TweedieRegressionParam : public dmlc::Parameter<TweedieRegressionParam> {
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(n_gpus).set_default(-1).set_lower_bound(-1)
DMLC_DECLARE_FIELD(n_gpus).set_default(1).set_lower_bound(GPUSet::kAll)
.describe("Number of GPUs to use for multi-gpu algorithms.");
DMLC_DECLARE_FIELD(gpu_id)
.set_lower_bound(0)
@@ -476,7 +473,6 @@ class TweedieRegression : public ObjFunction {
// declare functions
void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
param_.InitAllowUnknown(args);
CHECK(param_.n_gpus != 0) << "Must have at least one device"; // Default is -1
devices_ = GPUSet::All(param_.gpu_id, param_.n_gpus);
label_correct_.Resize(devices_.IsEmpty() ? 1 : devices_.Size());
}