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|
package gpt2
import (
"errors"
"math"
"os"
"slices"
"sort"
"strconv"
ort "github.com/yalue/onnxruntime_go"
)
type Model struct {
name string
deviceID string
config Config
options Options
session *ort.DynamicAdvancedSession
allocator *Allocator
}
func NewModel(name string, cfg Config, opts Options) *Model {
deviceID := CUDADeviceID()
return &Model{
name: name,
deviceID: deviceID,
config: cfg,
options: opts,
}
}
func CUDADeviceID() string {
id, ok := os.LookupEnv("ONNXRUNTIME_CUDA_DEVICE_ID")
if !ok {
return defaultDeviceID
}
return id
}
func SharedLibraryPath() string {
p, ok := os.LookupEnv("ONNXRUNTIME_SHARED_LIBRARY_PATH")
if !ok {
// TODO embed runtime binaries
}
return p
}
func IntraOpNumThreads() int {
s, ok := os.LookupEnv("ONNXRUNTIME_INTRA_OP_NUM_THREADS")
if !ok {
return 0
}
n, err := strconv.Atoi(s)
if err != nil {
return 0
}
return n
}
func (m *Model) Init() error {
m.allocator = NewAllocator(m.config, m.options, 1)
var options *ort.SessionOptions
if o, err := ort.NewSessionOptions(); err != nil {
return err
} else {
options = o
defer options.Destroy()
}
if m.deviceID != "" {
if err := WithCUDAProvider(options, m.deviceID); err != nil {
return err
}
}
if n := IntraOpNumThreads(); n > 0 {
if err := options.SetIntraOpNumThreads(n); err != nil {
return err
}
}
if s, err := ort.NewDynamicAdvancedSession(m.name, m.allocator.InputNames(), m.allocator.OutputNames(), options); err != nil {
return err
} else {
m.session = s
}
return nil
}
func (m *Model) Destroy() error {
m.allocator.Destroy()
return m.session.Destroy()
}
func (m *Model) Generate(prompt []int64, steps int64, logits *[][]float32) ([]int64, error) {
if !m.options.WithLogits {
panic("generate requires logits output")
}
if steps > 0 && !m.options.WithCache {
panic("generate with steps > 0 requires cache")
}
if len(prompt) == 0 {
return nil, errors.New("empty prompt")
}
if int64(len(prompt))+steps > int64(m.config.NumPositions) {
return nil, errors.New("sequence length exceeds context limit")
}
if err := m.allocator.Init(prompt); err != nil {
return nil, err
}
if err := m.forward(m.allocator); err != nil {
return nil, err
}
r := make([]int64, steps)
for step := range steps {
l := m.logits(m.allocator.Value("logits"))
if logits != nil {
*logits = append(*logits, l...)
}
next := m.sample(l[len(l)-1])
r[step] = next
if err := m.allocator.Step(next); err != nil {
return nil, err
}
if err := m.forward(m.allocator); err != nil {
return nil, err
}
}
if logits != nil {
for _, l := range m.logits(m.allocator.Value("logits")) {
*logits = append(*logits, l)
}
}
return r, nil
}
func (m *Model) Score(tokens []int64, batchSize int, logProbs *[]float32) error {
if !m.options.WithLogProbs {
panic("score requires token_logprobs output")
}
m.allocator.SetBatchSize(batchSize)
if err := m.allocator.Init(tokens); err != nil {
return err
}
if err := m.forward(m.allocator); err != nil {
return err
}
if logProbs != nil {
d := m.allocator.Value("token_logprobs").(*ort.Tensor[float32]).GetData()
*logProbs = append(*logProbs, d...)
}
return nil
}
func (m *Model) logits(output ort.Value) [][]float32 {
d := output.(*ort.Tensor[float32]).GetData()
n := len(d) / m.config.VocabSize
l := make([][]float32, n)
for i := range n {
s := i * m.config.VocabSize
l[i] = d[s : s+m.config.VocabSize : s+m.config.VocabSize]
}
return l
}
func (m *Model) sample(logits []float32) int64 {
idx, _ := topK(softmax(logits), 5)
return int64(idx[0])
}
func (m *Model) forward(alloc *Allocator) error {
var binding *ort.IoBinding
if b, err := m.session.CreateIoBinding(); err != nil {
return err
} else {
binding = b
defer binding.Destroy()
}
inputNames, inputs := alloc.Inputs()
outputNames, outputs := alloc.Outputs()
for i, name := range inputNames {
if err := binding.BindInput(name, inputs[i]); err != nil {
return err
}
}
for i, name := range outputNames {
if err := binding.BindOutput(name, outputs[i]); err != nil {
return err
}
}
return m.session.RunWithBinding(binding)
}
func softmax(logits []float32) []float32 {
m := slices.Max(logits)
s := float32(0.0)
r := make([]float32, len(logits))
for i, v := range logits {
e := float32(math.Exp(float64(v - m)))
r[i] = e
s += e
}
for i := range r {
r[i] /= s
}
return r
}
func topK(p []float32, k int) ([]int, []float32) {
n := len(p)
if k > n {
k = n
}
idx := make([]int, n)
for i := range idx {
idx[i] = i
}
sort.Slice(idx, func(i, j int) bool {
return p[idx[i]] > p[idx[j]]
})
topIdx := idx[:k]
topP := make([]float32, k)
for i := 0; i < k; i++ {
topP[i] = p[topIdx[i]]
}
return topIdx, topP
}
|
