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package llm
import (
"fmt"
"sync"
"time"
"go.jknobloc.com/x/dataset"
"go.jknobloc.com/x/tui"
)
func (e *Evaluator[R]) Run(title string, data dataset.Reader, window, stride int) error {
devices := make([]int, len(e.models))
for i := range len(devices) {
devices[i] = i
}
devicePool := newPool(devices...)
var wg sync.WaitGroup
for range e.numWorkers {
wg.Add(1)
go func() {
defer wg.Done()
for b := range e.jobs {
device := devicePool.Acquire()
func() {
defer devicePool.Release(device)
defer func() {
if r := recover(); r != nil {
fmt.Println("HOUSTON") // TODO handle
}
}()
e.execute(&b, device)
}()
e.completed.Add(int64(b.Size()))
}
}()
}
pb := tui.NewProgressBar(title, 20, 0, time.Now())
pb.Start(1*time.Second, func() int {
return int(e.completed.Load())
})
n := 0
m := 0
defer pb.Close()
tb := NewTokenBuffer(e.tokenizer, window, stride)
tb.SetIncludeTail(false)
b := newBatch(e.batchSize)
for d := range data.Texts("text") {
for w, s := range tb.Push(n, d) {
if s == 0 {
s = 1 // first token as context
}
b.AddJob(Job{
Document: n,
Position: m,
Tokens: w,
Seen: s,
})
m++
if b.Size() == e.batchSize {
e.jobs <- *b
b = newBatch(e.batchSize)
e.scheduled.Add(int64(e.batchSize))
pb.SetTotal(int(e.scheduled.Load()))
}
}
n++
m = 0
}
if s := b.Size(); s > 0 {
e.jobs <- *b
e.scheduled.Add(int64(s))
pb.SetTotal(int(e.scheduled.Load()))
}
close(e.jobs)
wg.Wait()
close(e.results)
return nil
}
func (e *Evaluator[R]) execute(j *batch, device int) {
if j.Size() != 1 {
panic("unimplemented")
}
job := j.jobs[0]
if job.Seen < 1 {
panic("empty context")
}
m := e.models[device]
logits := make([][]float32, 0, len(job.Tokens))
if _, err := m.Generate(job.Tokens, 0, &logits); err != nil {
panic(err) // TODO handle
}
l := logits[job.Seen-1 : len(logits)-1]
t := toInt(job.Tokens[job.Seen:])
r := e.callback(job, l, t)
e.results <- r
return
}
|
