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feat: get trainer from model_name in weight
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Signed-off-by: Sunyanan Choochotkaew <[email protected]>
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sunya-ch committed Aug 29, 2024
1 parent aa822ee commit 7b07762
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Showing 14 changed files with 99 additions and 20 deletions.
2 changes: 1 addition & 1 deletion data/model_weight/acpi_AbsPowerModel.json
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@@ -1 +1 @@
{"platform": {"All_Weights": {"Bias_Weight": 220.9079278650894, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 29.028228361462897}}}}}
{"model_name": "SGDRegressorTrainer_0", "platform": {"All_Weights": {"Bias_Weight": 220.9079278650894, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 29.028228361462897}}}}}
2 changes: 1 addition & 1 deletion data/model_weight/acpi_DynPowerModel.json
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@@ -1 +1 @@
{"platform": {"All_Weights": {"Bias_Weight": 49.56491877218095, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 28.501356366108837}}}}}
{"model_name": "SGDRegressorTrainer_0", "platform": {"All_Weights": {"Bias_Weight": 49.56491877218095, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 28.501356366108837}}}}}
2 changes: 1 addition & 1 deletion data/model_weight/intel_rapl_AbsPowerModel.json
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@@ -1 +1 @@
{"package": {"All_Weights": {"Bias_Weight": 69.91739430907396, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 22.16772409328642}}}}, "core": {"All_Weights": {"Bias_Weight": 0.0, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 0.0}}}}, "uncore": {"All_Weights": {"Bias_Weight": 0.0, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 0.0}}}}, "dram": {"All_Weights": {"Bias_Weight": 47.142633336743344, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 3.57348245077466}}}}}
{"model_name": "SGDRegressorTrainer_0", "package": {"All_Weights": {"Bias_Weight": 69.91739430907396, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 22.16772409328642}}}}, "core": {"All_Weights": {"Bias_Weight": 0.0, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 0.0}}}}, "uncore": {"All_Weights": {"Bias_Weight": 0.0, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 0.0}}}}, "dram": {"All_Weights": {"Bias_Weight": 47.142633336743344, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 3.57348245077466}}}}}
2 changes: 1 addition & 1 deletion data/model_weight/intel_rapl_DynPowerModel.json
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@@ -1 +1 @@
{"package": {"All_Weights": {"Bias_Weight": 38.856412561925055, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 22.258830113477515}}}}, "core": {"All_Weights": {"Bias_Weight": 0.0, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 0.0}}}}, "uncore": {"All_Weights": {"Bias_Weight": 0.0, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 0.0}}}}, "dram": {"All_Weights": {"Bias_Weight": 9.080889901856153, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 3.0358946796490924}}}}}
{"model_name": "SGDRegressorTrainer_0", "package": {"All_Weights": {"Bias_Weight": 38.856412561925055, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 22.258830113477515}}}}, "core": {"All_Weights": {"Bias_Weight": 0.0, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 0.0}}}}, "uncore": {"All_Weights": {"Bias_Weight": 0.0, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 0.0}}}}, "dram": {"All_Weights": {"Bias_Weight": 9.080889901856153, "Categorical_Variables": {}, "Numerical_Variables": {"bpf_cpu_time_ms": {"scale": 5911.969193263386, "mean": 0, "variance": 0, "weight": 3.0358946796490924}}}}}
3 changes: 2 additions & 1 deletion pkg/config/config.go
Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,7 @@ import (
"strconv"
"strings"

"github.com/sustainable-computing-io/kepler/pkg/model/types"
"golang.org/x/sys/unix"
"k8s.io/klog/v2"
)
Expand Down Expand Up @@ -129,7 +130,7 @@ var (
FixedTrainerNameKey = "TRAINER"
FixedNodeTypeKey = "NODE_TYPE"
ModelFiltersKey = "FILTERS"
DefaultTrainerName = "SGDRegressorTrainer"
DefaultTrainerName = types.LinearRegressionTrainer
////////////////////////////////////

// KubeConfig is used to start k8s client with the pod running outside the cluster
Expand Down
2 changes: 1 addition & 1 deletion pkg/model/estimator/local/regressor/exponential_test.go
Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,7 @@ import (

var (
exponentialCurveFits = []float64{1, 1, 1}
dummyExponentialWeightHandler = genHandlerFunc(exponentialCurveFits)
dummyExponentialWeightHandler = genHandlerFunc(exponentialCurveFits, types.ExponentialTrainer)
)

var _ = Describe("Test Exponential Predictor Unit", func() {
Expand Down
2 changes: 1 addition & 1 deletion pkg/model/estimator/local/regressor/logarithm_test.go
Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,7 @@ import (

var (
logarithmicCurveFits = []float64{1, 1, 1}
dummyLogarithmicWeightHandler = genHandlerFunc(logarithmicCurveFits)
dummyLogarithmicWeightHandler = genHandlerFunc(logarithmicCurveFits, types.LogarithmicTrainer)
)

var _ = Describe("Test Logarithmic Predictor Unit", func() {
Expand Down
2 changes: 1 addition & 1 deletion pkg/model/estimator/local/regressor/logistic_test.go
Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,7 @@ import (

var (
logisticCurveFits = []float64{1, 1, 1, 1}
dummyLogisticWeightHandler = genHandlerFunc(logisticCurveFits)
dummyLogisticWeightHandler = genHandlerFunc(logisticCurveFits, types.LogisticTrainer)
)

var _ = Describe("Test Logistic Predictor Unit", func() {
Expand Down
24 changes: 24 additions & 0 deletions pkg/model/estimator/local/regressor/model_weights.go
Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,9 @@ package regressor
import (
"errors"
"fmt"
"strings"

"github.com/sustainable-computing-io/kepler/pkg/model/types"

"github.com/sustainable-computing-io/kepler/pkg/config"
)
Expand Down Expand Up @@ -113,3 +116,24 @@ func (w ComponentModelWeights) String() string {
}
return fmt.Sprintf("%s (package: %v (core: %v, uncore: %v), dram: %v)", w.ModelName, w.Package, w.Core, w.Uncore, w.DRAM)
}

func (w ComponentModelWeights) Trainer() string {
if w.ModelName == "" {
return ""
}
modelNameSplits := strings.Split(w.ModelName, "_")
splitTrainer := strings.Join(modelNameSplits[0:len(modelNameSplits)-1], "_")
if isSupportedTrainer(splitTrainer) {
return splitTrainer
}
return ""
}

func isSupportedTrainer(trainer string) bool {
for _, supportedTrainer := range types.WeightSupportedTrainers {
if trainer == supportedTrainer {
return true
}
}
return false
}
21 changes: 16 additions & 5 deletions pkg/model/estimator/local/regressor/regressor.go
Original file line number Diff line number Diff line change
Expand Up @@ -186,7 +186,8 @@ func (r *Regressor) getWeightFromServer() (*ComponentModelWeights, error) {
return nil, fmt.Errorf("model unmarshal error: %v (%s)", err, string(body))
}
if weightResponse.ModelName != "" {
klog.V(3).Infof("Using weights trained by %s", weightResponse.ModelName)
r.TrainerName = weightResponse.Trainer()
klog.V(3).Infof("Using weights from model %s trained by %s for %s", weightResponse.ModelName, r.TrainerName, r.EnergySource)
}
r.updateCoreRatio(weightResponse.ModelMachineSpec)
return &weightResponse, nil
Expand All @@ -195,6 +196,7 @@ func (r *Regressor) getWeightFromServer() (*ComponentModelWeights, error) {
// loadWeightFromURLorLocal get weight from either local or URL
// if string start with '/', we take it as local file
func (r *Regressor) loadWeightFromURLorLocal() (*ComponentModelWeights, error) {
var modelName string // to be set by ModelWeightsURL
var body []byte
var err error

Expand All @@ -204,12 +206,21 @@ func (r *Regressor) loadWeightFromURLorLocal() (*ComponentModelWeights, error) {
if err != nil {
return nil, err
}
} else {
modelName = utils.GetModelNameFromURL(r.ModelWeightsURL)
}
var content ComponentModelWeights
err = json.Unmarshal(body, &content)
if err != nil {
return nil, fmt.Errorf("model unmarshal error: %v (%s)", err, string(body))
}
if content.ModelName == "" {
// Expect for the case loadWeightFromURL
// ModelWeightsFilepath should contain model_name field
content.ModelName = modelName
}
r.TrainerName = content.Trainer()
klog.V(3).Infof("Using weights from model %s trained by %s for %s", content.ModelName, r.TrainerName, r.EnergySource)
r.updateCoreRatio(content.ModelMachineSpec)
return &content, nil
}
Expand Down Expand Up @@ -250,13 +261,13 @@ func (r *Regressor) loadWeightFromURL() ([]byte, error) {
// Create Predictor based on trainer name
func (r *Regressor) createPredictor(weight ModelWeights) (predictor Predictor, err error) {
switch r.TrainerName {
case "SGDRegressorTrainer":
case types.LinearRegressionTrainer:
predictor, err = NewLinearPredictor(weight)
case "LogarithmicRegressionTrainer":
case types.LogarithmicTrainer:
predictor, err = NewLogarithmicPredictor(weight)
case "LogisticRegressionTrainer":
case types.LogisticTrainer:
predictor, err = NewLogisticPredictor(weight)
case "ExponentialRegressionTrainer":
case types.ExponentialTrainer:
predictor, err = NewExponentialPredictor(weight)
default:
predictor, err = NewLinearPredictor(weight)
Expand Down
29 changes: 22 additions & 7 deletions pkg/model/estimator/local/regressor/regressor_test.go
Original file line number Diff line number Diff line change
Expand Up @@ -65,16 +65,15 @@ var (
SampleDramNumbericalVars = map[string]NormalizedNumericalFeature{
"cache_miss": {Weight: 1.0, Scale: 2},
}
DummyWeightHandler = http.HandlerFunc(genHandlerFunc([]float64{}))
DummyModelName = "dummy"
DummyWeightHandler = http.HandlerFunc(genHandlerFunc([]float64{}, types.LinearRegressionTrainer))
ModelCores = config.GenerateSpec().Cores
ExpectedAbsPowerFromDummyWeightHandler = 2500
ExpectedIdlePowerFromDummyWeightHandler = 2000
)

func GenPlatformModelWeights(curveFitWeights []float64) ComponentModelWeights {
func GenPlatformModelWeights(curveFitWeights []float64, trainerName string) ComponentModelWeights {
return ComponentModelWeights{
ModelName: DummyModelName,
ModelName: trainerName + "_0",
ModelMachineSpec: &config.MachineSpec{
Cores: ModelCores,
},
Expand All @@ -100,7 +99,7 @@ func genWeights(numericalVars map[string]NormalizedNumericalFeature, curveFitWei
}
}

func genHandlerFunc(curvefit []float64) (handlerFunc func(w http.ResponseWriter, r *http.Request)) {
func genHandlerFunc(curvefit []float64, trainerName string) (handlerFunc func(w http.ResponseWriter, r *http.Request)) {
return func(w http.ResponseWriter, r *http.Request) {
reqBody, err := io.ReadAll(r.Body)
if err != nil {
Expand All @@ -114,7 +113,7 @@ func genHandlerFunc(curvefit []float64) (handlerFunc func(w http.ResponseWriter,
if req.EnergySource == types.ComponentEnergySource {
err = json.NewEncoder(w).Encode(GenComponentModelWeights(curvefit))
} else {
err = json.NewEncoder(w).Encode(GenPlatformModelWeights(curvefit))
err = json.NewEncoder(w).Encode(GenPlatformModelWeights(curvefit, trainerName))
}
if err != nil {
panic(err)
Expand Down Expand Up @@ -177,7 +176,7 @@ var _ = Describe("Test Regressor Weight Unit (default trainer)", func() {
})

It("Get Node Components Power By Default Regression Estimator with ModelServerEndpoint", func() {
compPowers := GetNodeComponentsPowerFromDummyServer(genHandlerFunc([]float64{}), "")
compPowers := GetNodeComponentsPowerFromDummyServer(genHandlerFunc([]float64{}, types.LinearRegressionTrainer), "")
// TODO: verify if the power makes sense
Expect(compPowers[0].Core).Should(BeEquivalentTo(3000))
})
Expand Down Expand Up @@ -317,4 +316,20 @@ var _ = Describe("Test Regressor Weight Unit (default trainer)", func() {
Entry("invalid model core", 16, 0, 1.0),
)
})

DescribeTable("Test ComponentModelWeights.Trainer", func(modelName, expectedTrainer string) {
w := ComponentModelWeights{
ModelName: modelName,
}
Expect(w.Trainer()).To(Equal(expectedTrainer))
},
Entry("empty model name", "", ""),
Entry("invalid model name", "some", ""),
Entry("invalid model name with _", "some_invalid", ""),
Entry("valid SGDRegressorTrainer", "SGDRegressorTrainer_0", "SGDRegressorTrainer"),
Entry("valid LogarithmicRegressionTrainer", "LogarithmicRegressionTrainer_0", "LogarithmicRegressionTrainer"),
Entry("valid LogisticRegressionTrainer", "LogisticRegressionTrainer_0", "LogisticRegressionTrainer"),
Entry("valid ExponentialRegressionTrainer", "ExponentialRegressionTrainer_0", "ExponentialRegressionTrainer"),
Entry("invalid GradientBoostingRegressorTrainer", "GradientBoostingRegressorTrainer_0", ""),
)
})
9 changes: 9 additions & 0 deletions pkg/model/model_test.go
Original file line number Diff line number Diff line change
Expand Up @@ -67,6 +67,15 @@ var _ = Describe("Test Model Unit", func() {
Entry("IdlePower with invalid coreRatio = -1", true, -1.0, 1.0),
Entry("IdlePower with invalid coreRatio > 1", true, 1.2, 1.0),
)

DescribeTable("Test GetModelNameFromURL()", func(url, expectedModelName string) {
Expect(utils.GetModelNameFromURL(url)).To(Equal(expectedModelName))
},
Entry("empty", "", ""),
Entry("some model with multiple subfolders", "http://some/path/to/some_model.json", "some_model"),
Entry("some model with direct path", "http://some_model.json", "some_model"),
Entry("some model without file extension", "http://some_model", "some_model"),
)
})

})
9 changes: 9 additions & 0 deletions pkg/model/types/types.go
Original file line number Diff line number Diff line change
Expand Up @@ -46,6 +46,15 @@ const (
ExponentialTrainer = "ExponentialRegressionTrainer"
)

var (
WeightSupportedTrainers = []string{
LinearRegressionTrainer,
LogarithmicTrainer,
LogisticTrainer,
ExponentialTrainer,
}
)

func getModelOutputTypeConverter() []string {
return []string{

Check failure on line 59 in pkg/model/types/types.go

View workflow job for this annotation

GitHub Actions / golang / escapes_detect

[]string{...} escapes to heap
"AbsPower", "DynPower",
Expand Down
10 changes: 10 additions & 0 deletions pkg/model/utils/utils.go
Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,7 @@ package utils

import (
"math"
"strings"

"github.com/sustainable-computing-io/kepler/pkg/sensors/components/source"
)
Expand Down Expand Up @@ -69,3 +70,12 @@ func GetCoreRatio(isIdlePower bool, inCoreRatio float64) float64 {
}
return coreRatio
}

func GetModelNameFromURL(url string) string {
urlSplits := strings.Split(url, "/")
if len(urlSplits) > 0 {
lastItem := urlSplits[len(urlSplits)-1]
return strings.Split(lastItem, ".")[0]
}
return ""
}

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