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package llm
import (
"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)
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())
})
defer pb.Close()
tb := NewTokenBuffer(e.tokenizer, window, stride)
tb.SetIncludeTail(false)
b := newBatch(e.batchSize)
doc := 0
pos := 0
for w := range tb.Stream(data.Texts()) {
n := w.Document
if n != doc {
pos = 0
doc = n
}
tokens := w.Tokens
seen := w.Seen
if seen == 0 {
seen = 1 // first token as context
}
b.AddJob(Job{
Document: doc,
Position: pos,
Tokens: tokens,
Seen: seen,
})
pos++
if b.Size() == e.batchSize {
e.jobs <- *b
b = newBatch(e.batchSize)
e.scheduled.Add(int64(e.batchSize))
pb.SetTotal(int(e.scheduled.Load()))
}
}
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) {
b := j.Size()
s := len(j.jobs[0].Tokens)
tokens := make([]int64, b*s)
for i, job := range j.jobs {
copy(tokens[i*s:], job.Tokens)
}
m := e.models[device]
logProbs := make([]float32, 0, b*(s-1))
if err := m.Score(tokens, b, &logProbs); err != nil {
panic(err) // TODO handle
}
for i, job := range j.jobs {
if job.Seen < 1 {
panic("empty context")
}
l := logProbs[i*(s-1)+job.Seen-1 : (i+1)*(s-1)]
t := toInt(job.Tokens[job.Seen:])
r := e.callback(job, l, t)
e.results <- r
}
}
|
