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hugot_test.go
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hugot_test.go
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package hugot
import (
"context"
_ "embed"
"encoding/json"
"fmt"
"math"
"os"
"strings"
"testing"
"github.com/stretchr/testify/assert"
"github.com/knights-analytics/hugot/pipelines"
util "github.com/knights-analytics/hugot/utils"
ort "github.com/yalue/onnxruntime_go"
)
//go:embed testData/tokenExpected.json
var tokenExpectedByte []byte
//go:embed testData/vectors.json
var resultsByte []byte
// use the system library for the tests
const onnxRuntimeSharedLibrary = "/usr/lib64/onnxruntime.so"
// test download validation
func TestDownloadValidation(t *testing.T) {
err := validateDownloadHfModel("KnightsAnalytics/distilbert-base-uncased-finetuned-sst-2-english", "main", "")
assert.NoError(t, err)
// a model without tokenizer.json or .onnx model should error
err = validateDownloadHfModel("ByteDance/SDXL-Lightning", "main", "")
assert.Error(t, err)
// a model with the required files in a subfolder should not error
err = validateDownloadHfModel("distilbert/distilbert-base-uncased-finetuned-sst-2-english", "main", "")
assert.NoError(t, err)
}
// FEATURE EXTRACTION
func TestFeatureExtractionPipelineValidation(t *testing.T) {
session, err := NewSession(WithOnnxLibraryPath(onnxRuntimeSharedLibrary))
check(t, err)
defer func(session *Session) {
err := session.Destroy()
check(t, err)
}(session)
modelPath := "./models/sentence-transformers_all-MiniLM-L6-v2"
config := FeatureExtractionConfig{
ModelPath: modelPath,
Name: "testPipeline",
}
pipeline, err := NewPipeline(session, config)
check(t, err)
pipeline.InputsMeta[0].Dimensions = ort.NewShape(-1, -1, -1)
err = pipeline.Validate()
assert.Error(t, err)
pipeline.InputsMeta[0].Dimensions = ort.NewShape(1, 1, 1, 1)
err = pipeline.Validate()
assert.Error(t, err)
}
func TestFeatureExtractionPipeline(t *testing.T) {
session, err := NewSession(WithOnnxLibraryPath(onnxRuntimeSharedLibrary))
check(t, err)
defer func(session *Session) {
err := session.Destroy()
check(t, err)
}(session)
modelPath := "./models/sentence-transformers_all-MiniLM-L6-v2"
config := FeatureExtractionConfig{
ModelPath: modelPath,
Name: "testPipeline",
}
pipeline, err := NewPipeline(session, config)
check(t, err)
var expectedResults map[string][][]float32
err = json.Unmarshal(resultsByte, &expectedResults)
check(t, err)
var testResults [][]float32
// test 'robert smith'
testResults = expectedResults["test1output"]
batchResult, err := pipeline.RunPipeline([]string{"robert smith"})
if err != nil {
t.Fatalf(err.Error())
}
for i := range batchResult.Embeddings {
e := floatsEqual(batchResult.Embeddings[i], testResults[i])
if e != nil {
t.Logf("Test 1: The neural network didn't produce the correct result on loop %d: %s\n", i, e)
t.FailNow()
}
}
// test ['robert smith junior', 'francis ford coppola']
testResults = expectedResults["test2output"]
batchResult, err = pipeline.RunPipeline([]string{"robert smith junior", "francis ford coppola"})
if err != nil {
t.FailNow()
}
for i := range batchResult.Embeddings {
e := floatsEqual(batchResult.Embeddings[i], testResults[i])
if e != nil {
t.Logf("Test 1: The neural network didn't produce the correct result on loop %d: %s\n", i, e)
t.FailNow()
}
}
// determinism test to make sure embeddings of a string are not influenced by other strings in the batch
testPairs := map[string][][]string{}
testPairs["identity"] = [][]string{{"sinopharm", "yo"}, {"sinopharm", "yo"}}
testPairs["contextOverlap"] = [][]string{{"sinopharm", "yo"}, {"sinopharm", "yo mama yo"}}
testPairs["contextDisjoint"] = [][]string{{"sinopharm", "yo"}, {"sinopharm", "another test"}}
for k, sentencePair := range testPairs {
// these vectors should be the same
firstBatchResult, err2 := pipeline.RunPipeline(sentencePair[0])
check(t, err2)
firstEmbedding := firstBatchResult.Embeddings[0]
secondBatchResult, err3 := pipeline.RunPipeline(sentencePair[1])
check(t, err3)
secondEmbedding := secondBatchResult.Embeddings[0]
e := floatsEqual(firstEmbedding, secondEmbedding)
if e != nil {
t.Logf("Equality failed for determinism test %s test with pairs %s and %s", k, strings.Join(sentencePair[0], ","), strings.Join(sentencePair[1], ","))
t.Log("First vector", firstEmbedding)
t.Log("second vector", secondEmbedding)
t.Fail()
}
}
zero := uint64(0)
assert.Greater(t, pipeline.PipelineTimings.NumCalls, zero, "PipelineTimings.NumCalls should be greater than 0")
assert.Greater(t, pipeline.PipelineTimings.TotalNS, zero, "PipelineTimings.TotalNS should be greater than 0")
assert.Greater(t, pipeline.TokenizerTimings.NumCalls, zero, "TokenizerTimings.NumCalls should be greater than 0")
assert.Greater(t, pipeline.TokenizerTimings.TotalNS, zero, "TokenizerTimings.TotalNS should be greater than 0")
// test normalization
testResults = expectedResults["normalizedOutput"]
config = FeatureExtractionConfig{
ModelPath: modelPath,
Name: "testPipelineNormalise",
Options: []FeatureExtractionOption{
pipelines.WithNormalization(),
},
}
pipeline, err = NewPipeline(session, config)
check(t, err)
normalizationStrings := []string{"Onnxruntime is a great inference backend"}
normalizedEmbedding, err := pipeline.RunPipeline(normalizationStrings)
check(t, err)
for i, embedding := range normalizedEmbedding.Embeddings {
e := floatsEqual(embedding, testResults[i])
if e != nil {
t.Fatalf("Normalization test failed: %s", normalizationStrings[i])
}
}
// test getting sentence embeddings
configSentence := FeatureExtractionConfig{
ModelPath: modelPath,
Name: "testPipelineSentence",
Options: []FeatureExtractionOption{pipelines.WithOutputName("sentence_embedding")},
}
pipelineSentence, err := NewPipeline(session, configSentence)
check(t, err)
outputSentence, err := pipelineSentence.RunPipeline([]string{"Onnxruntime is a great inference backend"})
if err != nil {
t.FailNow()
}
fmt.Println(outputSentence.Embeddings[0])
configSentence = FeatureExtractionConfig{
ModelPath: modelPath,
Name: "testPipelineToken",
}
pipelineToken, err := NewPipeline(session, configSentence)
check(t, err)
_, err = pipelineToken.RunPipeline([]string{"Onnxruntime is a great inference backend"})
if err != nil {
t.FailNow()
}
}
// Text classification
func TestTextClassificationPipeline(t *testing.T) {
session, err := NewSession(
WithOnnxLibraryPath(onnxRuntimeSharedLibrary),
WithTelemetry(),
WithCpuMemArena(true),
WithMemPattern(true),
WithIntraOpNumThreads(1),
WithInterOpNumThreads(1),
)
check(t, err)
defer func(session *Session) {
errDestroy := session.Destroy()
check(t, errDestroy)
}(session)
modelPath := "./models/KnightsAnalytics_distilbert-base-uncased-finetuned-sst-2-english"
modelPathMulti := "./models/SamLowe_roberta-base-go_emotions-onnx"
config := TextClassificationConfig{
ModelPath: modelPath,
Name: "testPipelineSimple",
Options: []TextClassificationOption{
pipelines.WithSoftmax(),
},
}
sentimentPipeline, err := NewPipeline(session, config)
check(t, err)
configMulti := TextClassificationConfig{
ModelPath: modelPathMulti,
Name: "testPipelineSimpleMulti",
OnnxFilename: "model.onnx",
Options: []TextClassificationOption{
pipelines.WithMultiLabel(),
pipelines.WithSigmoid(),
},
}
sentimentPipelineMulti, err := NewPipeline(session, configMulti)
check(t, err)
tests := []struct {
pipeline *pipelines.TextClassificationPipeline
name string
strings []string
expected pipelines.TextClassificationOutput
}{
{
pipeline: sentimentPipeline,
name: "Basic tests",
strings: []string{"This movie is disgustingly good!", "The director tried too much"},
expected: pipelines.TextClassificationOutput{
ClassificationOutputs: [][]pipelines.ClassificationOutput{
{
{
Label: "POSITIVE",
Score: 0.9998536109924316,
},
},
{
{
Label: "NEGATIVE",
Score: 0.9975218176841736,
},
},
},
},
},
{
pipeline: sentimentPipelineMulti,
name: "Multiclass pipeline test",
strings: []string{"ONNX is seriously fast for small batches. Impressive"},
expected: pipelines.TextClassificationOutput{
ClassificationOutputs: [][]pipelines.ClassificationOutput{
{
{
Label: "admiration",
Score: 0.9217681,
},
{
Label: "amusement",
Score: 0.001201711,
},
{
Label: "anger",
Score: 0.001109502,
},
{
Label: "annoyance",
Score: 0.0034009134,
},
{
Label: "approval",
Score: 0.05643816,
},
{
Label: "caring",
Score: 0.0011591336,
},
{
Label: "confusion",
Score: 0.0018672282,
},
{
Label: "curiosity",
Score: 0.0026787464,
},
{
Label: "desire",
Score: 0.00085846696,
},
{
Label: "disappointment",
Score: 0.0027759627,
},
{
Label: "disapproval",
Score: 0.004615115,
},
{
Label: "disgust",
Score: 0.00075303164,
},
{
Label: "embarrassment",
Score: 0.0003314704,
},
{
Label: "excitement",
Score: 0.005340109,
},
{
Label: "fear",
Score: 0.00042834174,
},
{
Label: "gratitude",
Score: 0.013405683,
},
{
Label: "grief",
Score: 0.00029952865,
},
{
Label: "joy",
Score: 0.0026875956,
},
{
Label: "love",
Score: 0.00092915917,
},
{
Label: "nervousness",
Score: 0.00012843,
},
{
Label: "optimism",
Score: 0.006792505,
},
{
Label: "pride",
Score: 0.0033409835,
},
{
Label: "realization",
Score: 0.007224476,
},
{
Label: "relief",
Score: 0.00071489986,
},
{
Label: "remorse",
Score: 0.00026071363,
},
{
Label: "sadness",
Score: 0.0009562365,
},
{
Label: "surprise",
Score: 0.0037120024,
},
{
Label: "neutral",
Score: 0.04079749,
},
},
},
},
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
batchResult, err := tt.pipeline.RunPipeline(tt.strings)
check(t, err)
for i, expected := range tt.expected.ClassificationOutputs {
checkClassificationOutput(t, expected, batchResult.ClassificationOutputs[i])
}
})
}
// check get stats
session.GetStats()
}
func TestTextClassificationPipelineValidation(t *testing.T) {
session, err := NewSession(WithOnnxLibraryPath(onnxRuntimeSharedLibrary))
check(t, err)
defer func(session *Session) {
err := session.Destroy()
check(t, err)
}(session)
modelPath := "./models/KnightsAnalytics_distilbert-base-uncased-finetuned-sst-2-english"
config := TextClassificationConfig{
ModelPath: modelPath,
Name: "testPipelineSimple",
Options: []TextClassificationOption{
pipelines.WithSingleLabel(),
},
}
sentimentPipeline, err := NewPipeline(session, config)
check(t, err)
t.Run("id-label-map", func(t *testing.T) {
labelMapInitial := sentimentPipeline.IDLabelMap
defer func() {
sentimentPipeline.IDLabelMap = labelMapInitial
}()
sentimentPipeline.IDLabelMap = map[int]string{}
err = sentimentPipeline.Validate()
assert.Error(t, err)
})
t.Run("output-shape", func(t *testing.T) {
dimensionInitial := sentimentPipeline.OutputsMeta[0].Dimensions
defer func() {
sentimentPipeline.OutputsMeta[0].Dimensions = dimensionInitial
}()
sentimentPipeline.OutputsMeta[0].Dimensions = ort.NewShape(-1, -1, -1)
err = sentimentPipeline.Validate()
assert.Error(t, err)
})
}
func TestZeroShotClassificationPipeline(t *testing.T) {
session, err := NewSession()
check(t, err)
defer func(session *Session) {
err := session.Destroy()
check(t, err)
}(session)
modelPath := "./models/protectai_deberta-v3-base-zeroshot-v1-onnx"
config := ZeroShotClassificationConfig{
ModelPath: modelPath,
Name: "testPipeline",
Options: []pipelines.PipelineOption[*pipelines.ZeroShotClassificationPipeline]{
pipelines.WithHypothesisTemplate("This example is {}."),
pipelines.WithLabels([]string{"fun", "dangerous"}),
},
}
classificationPipeline, err := NewPipeline(session, config)
check(t, err)
tests := []struct {
pipeline *pipelines.ZeroShotClassificationPipeline
name string
sequences []string
labels []string
multilabel bool
expected pipelines.ZeroShotOutput
}{
{
pipeline: classificationPipeline,
name: "single sequence, single label, no multilabel",
sequences: []string{"I am going to the park"},
labels: []string{"fun"},
multilabel: false,
expected: pipelines.ZeroShotOutput{
ClassificationOutputs: []pipelines.ZeroShotClassificationOutput{
{
Sequence: "I am going to the park",
SortedValues: []struct {
Key string
Value float64
}{
{
Key: "fun",
Value: 0.0009069009101949632,
},
},
},
},
},
},
{
pipeline: classificationPipeline,
name: "multiple sequences, multiple labels, no multilabel",
sequences: []string{"I am going to the park", "I will watch Interstellar tonight"},
labels: []string{"fun", "movie"},
multilabel: false,
expected: pipelines.ZeroShotOutput{
ClassificationOutputs: []pipelines.ZeroShotClassificationOutput{
{
Sequence: "I am going to the park",
SortedValues: []struct {
Key string
Value float64
}{
{
Key: "fun",
Value: 0.7746766209602356,
},
{
Key: "movie",
Value: 0.2253233790397644,
},
},
},
{
Sequence: "I will watch Interstellar tonight",
SortedValues: []struct {
Key string
Value float64
}{
{
Key: "movie",
Value: 0.9984978437423706,
},
{
Key: "fun",
Value: 0.001502170693129301,
},
},
},
},
},
},
{
pipeline: classificationPipeline,
name: "multiple sequences, multiple labels, multilabel",
sequences: []string{"I am going to the park", "I will watch Interstellar tonight"},
labels: []string{"fun", "movie"},
multilabel: true,
expected: pipelines.ZeroShotOutput{
ClassificationOutputs: []pipelines.ZeroShotClassificationOutput{
{
Sequence: "I am going to the park",
SortedValues: []struct {
Key string
Value float64
}{
{
Key: "fun",
Value: 0.0009069009101949632,
},
{
Key: "movie",
Value: 0.00009480675362283364,
},
},
},
{
Sequence: "I will watch Interstellar tonight",
SortedValues: []struct {
Key string
Value float64
}{
{
Key: "movie",
Value: 0.9985591769218445,
},
{
Key: "fun",
Value: 0.0006653196760453284,
},
},
},
},
},
},
{
pipeline: classificationPipeline,
name: "multiple sequences, single label, multilabel",
sequences: []string{"I am going to the park", "I will watch Interstellar tonight"},
labels: []string{"fun"},
multilabel: true,
expected: pipelines.ZeroShotOutput{
ClassificationOutputs: []pipelines.ZeroShotClassificationOutput{
{
Sequence: "I am going to the park",
SortedValues: []struct {
Key string
Value float64
}{
{
Key: "fun",
Value: 0.0009069009101949632,
},
},
},
{
Sequence: "I will watch Interstellar tonight",
SortedValues: []struct {
Key string
Value float64
}{
{
Key: "fun",
Value: 0.0006653196760453284,
},
},
},
},
},
},
{
pipeline: classificationPipeline,
name: "single sequence, multiple labels, multilabel=false",
sequences: []string{"Please don't bother me, I'm in a rush"},
labels: []string{"busy", "relaxed", "stressed"},
multilabel: false,
expected: pipelines.ZeroShotOutput{
ClassificationOutputs: []pipelines.ZeroShotClassificationOutput{
{
Sequence: "Please don't bother me, I'm in a rush",
SortedValues: []struct {
Key string
Value float64
}{
{
Key: "stressed",
Value: 0.8865461349487305,
},
{
Key: "busy",
Value: 0.10629364103078842,
},
{
Key: "relaxed",
Value: 0.007160270120948553,
},
},
},
},
},
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
classificationPipeline.Labels = tt.labels
classificationPipeline.Multilabel = tt.multilabel
batchResult, _ := tt.pipeline.RunPipeline(tt.sequences)
assert.Equal(t, len(batchResult.GetOutput()), len(tt.expected.ClassificationOutputs))
for ind, expected := range tt.expected.ClassificationOutputs {
expectedResult := expected.SortedValues
testResult := batchResult.ClassificationOutputs[ind].SortedValues
assert.Equal(t, len(expectedResult), len(testResult))
assert.Equal(t, tt.expected.ClassificationOutputs[ind].Sequence, batchResult.ClassificationOutputs[ind].Sequence)
for i := range testResult {
assert.True(t, almostEqual(testResult[i].Value, expectedResult[i].Value))
}
}
})
}
}
func TestZeroShotClassificationPipelineValidation(t *testing.T) {
session, err := NewSession(WithOnnxLibraryPath(onnxRuntimeSharedLibrary))
check(t, err)
defer func(session *Session) {
err := session.Destroy()
check(t, err)
}(session)
modelPath := "./models/protectai_deberta-v3-base-zeroshot-v1-onnx"
config := TextClassificationConfig{
ModelPath: modelPath,
Name: "testPipelineSimple",
}
sentimentPipeline, err := NewPipeline(session, config)
check(t, err)
t.Run("id-label-map", func(t *testing.T) {
labelMapInitial := sentimentPipeline.IDLabelMap
defer func() {
sentimentPipeline.IDLabelMap = labelMapInitial
}()
sentimentPipeline.IDLabelMap = map[int]string{}
err = sentimentPipeline.Validate()
assert.Error(t, err)
})
t.Run("output-shape", func(t *testing.T) {
dimensionInitial := sentimentPipeline.OutputsMeta[0].Dimensions
defer func() {
sentimentPipeline.OutputsMeta[0].Dimensions = dimensionInitial
}()
sentimentPipeline.OutputsMeta[0].Dimensions = ort.NewShape(-1, -1, -1)
err = sentimentPipeline.Validate()
assert.Error(t, err)
})
}
// Token classification
func TestTokenClassificationPipeline(t *testing.T) {
session, err := NewSession(WithOnnxLibraryPath(onnxRuntimeSharedLibrary))
check(t, err)
defer func(session *Session) {
err := session.Destroy()
check(t, err)
}(session)
modelPath := "./models/KnightsAnalytics_distilbert-NER"
configSimple := TokenClassificationConfig{
ModelPath: modelPath,
Name: "testPipelineSimple",
Options: []TokenClassificationOption{
pipelines.WithSimpleAggregation(),
pipelines.WithIgnoreLabels([]string{"O"}),
},
}
pipelineSimple, err2 := NewPipeline(session, configSimple)
check(t, err2)
configNone := TokenClassificationConfig{
ModelPath: modelPath,
Name: "testPipelineNone",
Options: []TokenClassificationOption{
pipelines.WithoutAggregation(),
},
}
pipelineNone, err3 := NewPipeline(session, configNone)
check(t, err3)
var expectedResults map[int]pipelines.TokenClassificationOutput
err4 := json.Unmarshal(tokenExpectedByte, &expectedResults)
check(t, err4)
tests := []struct {
pipeline *pipelines.TokenClassificationPipeline
name string
strings []string
expected pipelines.TokenClassificationOutput
}{
{
pipeline: pipelineSimple,
name: "Simple aggregation",
strings: []string{"My name is Wolfgang and I live in Berlin."},
expected: expectedResults[0],
},
{
pipeline: pipelineNone,
name: "No aggregation",
strings: []string{"My name is Wolfgang and I live in Berlin."},
expected: expectedResults[1],
},
{
pipeline: pipelineSimple,
name: "Parsing of batch with different token length",
strings: []string{"Microsoft incorporated.", "Yesterday I went to Berlin and met with Jack Brown."},
expected: expectedResults[2],
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
batchResult, err := tt.pipeline.RunPipeline(tt.strings)
check(t, err)
printTokenEntities(batchResult)
for i, predictedEntities := range batchResult.Entities {
assert.Equal(t, len(tt.expected.Entities[i]), len(predictedEntities))
for j, entity := range predictedEntities {
expectedEntity := tt.expected.Entities[i][j]
assert.Equal(t, expectedEntity.Entity, entity.Entity)
assert.Equal(t, expectedEntity.Word, entity.Word)
}
}
})
}
}
func TestTokenClassificationPipelineValidation(t *testing.T) {
session, err := NewSession(WithOnnxLibraryPath(onnxRuntimeSharedLibrary))
check(t, err)
defer func(session *Session) {
err := session.Destroy()
check(t, err)
}(session)
modelPath := "./models/KnightsAnalytics_distilbert-NER"
configSimple := TokenClassificationConfig{
ModelPath: modelPath,
Name: "testPipelineSimple",
Options: []TokenClassificationOption{
pipelines.WithSimpleAggregation(),
pipelines.WithIgnoreLabels([]string{"O"}),
},
}
pipelineSimple, err2 := NewPipeline(session, configSimple)
check(t, err2)
t.Run("id-label-map", func(t *testing.T) {
labelMapInitial := pipelineSimple.IDLabelMap
defer func() {
pipelineSimple.IDLabelMap = labelMapInitial
}()
pipelineSimple.IDLabelMap = map[int]string{}
err = pipelineSimple.Validate()
assert.Error(t, err)
})
t.Run("output-shape", func(t *testing.T) {
dimensionInitial := pipelineSimple.OutputsMeta[0].Dimensions
defer func() {
pipelineSimple.OutputsMeta[0].Dimensions = dimensionInitial
}()
pipelineSimple.OutputsMeta[0].Dimensions = ort.NewShape(-1, -1, -1)
err = pipelineSimple.Validate()
assert.Error(t, err)
})
}
func TestNoSameNamePipeline(t *testing.T) {
session, err := NewSession(WithOnnxLibraryPath(onnxRuntimeSharedLibrary))
check(t, err)
defer func(session *Session) {
err := session.Destroy()
check(t, err)
}(session)
modelPath := "./models/KnightsAnalytics_distilbert-NER"
configSimple := TokenClassificationConfig{
ModelPath: modelPath,
Name: "testPipelineSimple",
Options: []TokenClassificationOption{
pipelines.WithSimpleAggregation(),
pipelines.WithIgnoreLabels([]string{"O"}),
},
}
_, err2 := NewPipeline(session, configSimple)
if err2 != nil {
t.FailNow()
}
_, err3 := NewPipeline(session, configSimple)
assert.Error(t, err3)
}
// README: test the readme examples
func TestReadmeExample(t *testing.T) {
check := func(err error) {
if err != nil {
panic(err.Error())
}
}
// start a new session. This looks for the onnxruntime.so library in its default path, e.g. /usr/lib/onnxruntime.so
session, err := NewSession()
// if your onnxruntime.so is somewhere else, you can explicitly set it by using WithOnnxLibraryPath
// session, err := hugot.NewSession(WithOnnxLibraryPath("/path/to/onnxruntime.so"))
check(err)
// A successfully created hugot session needs to be destroyed when you're done
defer func(session *Session) {
err := session.Destroy()
check(err)
}(session)
// Let's download an onnx sentiment test classification model in the current directory
// note: if you compile your library with build flag NODOWNLOAD, this will exclude the downloader.
// Useful in case you just want the core engine (because you already have the models) and want to
// drop the dependency on huggingfaceModelDownloader.
modelPath, err := session.DownloadModel("KnightsAnalytics/distilbert-base-uncased-finetuned-sst-2-english", "./", NewDownloadOptions())
check(err)
defer func(modelPath string) {
err := util.FileSystem.Delete(context.Background(), modelPath)
if err != nil {
t.FailNow()
}
}(modelPath)
// we now create the configuration for the text classification pipeline we want to create.
// Options to the pipeline can be set here using the Options field
config := TextClassificationConfig{
ModelPath: modelPath,
Name: "testPipeline",
}
// then we create out pipeline.
// Note: the pipeline will also be added to the session object so all pipelines can be destroyed at once
sentimentPipeline, err := NewPipeline(session, config)
check(err)
// we can now use the pipeline for prediction on a batch of strings
batch := []string{"This movie is disgustingly good !", "The director tried too much"}
batchResult, err := sentimentPipeline.RunPipeline(batch)
check(err)
// and do whatever we want with it :)
s, err := json.Marshal(batchResult)
check(err)
fmt.Println(string(s))
// {"ClassificationOutputs":[[{"Label":"POSITIVE","Score":0.9998536}],[{"Label":"NEGATIVE","Score":0.99752176}]]}
}
func TestCuda(t *testing.T) {
if os.Getenv("CI") != "" {
t.SkipNow()
}
opts := []WithOption{
WithOnnxLibraryPath("/usr/lib64/onnxruntime-gpu/libonnxruntime.so"),
WithCuda(map[string]string{
"device_id": "0",
}),
}
session, err := NewSession(opts...)
check(t, err)
defer func(session *Session) {
errDestroy := session.Destroy()
if errDestroy != nil {
panic(errDestroy)
}
}(session)
modelPath := "./models/sentence-transformers_all-MiniLM-L6-v2"
config := FeatureExtractionConfig{
ModelPath: modelPath,
Name: "benchmarkEmbedding",
}
pipelineEmbedder, err2 := NewPipeline(session, config)
check(t, err2)
res, err := pipelineEmbedder.Run([]string{"Test with cuda", "Test with cuda 1"})
check(t, err)
fmt.Println(res.GetOutput())
}
// Benchmarks
func runBenchmarkEmbedding(strings *[]string, cuda bool) {
var opts []WithOption
switch cuda {
case true:
opts = []WithOption{
WithOnnxLibraryPath("/usr/lib64/onnxruntime-gpu/libonnxruntime.so"),
WithCuda(map[string]string{
"device_id": "0",
}),
}
default:
opts = []WithOption{WithOnnxLibraryPath("/usr/lib64/onnxruntime.so")}
}
session, err := NewSession(opts...)
if err != nil {
panic(err)
}
defer func(session *Session) {
errDestroy := session.Destroy()
if errDestroy != nil {
panic(errDestroy)
}
}(session)
modelPath := "./models/KnightsAnalytics_all-MiniLM-L6-v2"
config := FeatureExtractionConfig{
ModelPath: modelPath,
Name: "benchmarkEmbedding",
}
pipelineEmbedder, err2 := NewPipeline(session, config)
if err2 != nil {
panic(err2)
}
res, err := pipelineEmbedder.Run(*strings)
if err != nil {
panic(err)
}
fmt.Println(len(res.GetOutput()))
}
func BenchmarkCudaEmbedding(b *testing.B) {
if os.Getenv("CI") != "" {
b.SkipNow()
}
p := make([]string, 30000)
for i := 0; i < 30000; i++ {
p[i] = "The goal of this library is to provide an easy, scalable, and hassle-free way to run huggingface transformer pipelines in golang applications."
}
for i := 0; i < b.N; i++ {
runBenchmarkEmbedding(&p, true)
}
}
func BenchmarkCPUEmbedding(b *testing.B) {
if os.Getenv("CI") != "" {