Refactor llm module

1 files changed, 0 insertions, 159 deletions

diff --git a/llm/perplexity.go b/llm/perplexity.go
deleted file mode 100644
index df48ff6..0000000
--- a/llm/perplexity.go
+++ /dev/null
@@ -1,159 +0,0 @@
-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())
-	})
-
-	defer pb.Close()
-
-	n := 0
-
-	for d := range data.Texts("text") {
-		tokens := toInt64(e.tokenizer.Tokenize(d))
-
-		e.schedule(n, tokens, window, stride, e.batchSize)
-
-		pb.SetTotal(pb.Total() + e.estimateJobs(tokens, window, stride))
-
-		n++
-	}
-
-	close(e.jobs)
-
-	wg.Wait()
-
-	close(e.results)
-
-	return nil
-}
-
-func (e *Evaluator[R]) estimateJobs(tokens []int64, window, stride int) int {
-	if len(tokens) < window {
-		return 0
-	}
-
-	windows := ((len(tokens) - window) / stride) + 1
-	// jobs := (windows + batchSize - 1) / e.batchSize
-
-	return windows
-}
-
-func (e *Evaluator[R]) schedule(uid int, tokens []int64, contextSize, stride, batchSize int) {
-	b := newBatch(batchSize)
-
-	seen := 1 // first token as context
-	n := 0
-
-	// for i := 0; i+contextSize <= len(tokens); i += stride {
-	for i := 0; i < len(tokens); i += stride {
-		if b.Size() == batchSize {
-			e.jobs <- *b
-
-			b = newBatch(batchSize)
-		}
-
-		j := min(i+contextSize, len(tokens))
-
-		if j-i < contextSize {
-			break // don't add jobs with partial windows
-		}
-
-		// if j < seen {
-		// 	break // don't add jobs with no new tokens
-		// }
-
-		b.AddJob(Job{
-			Document: uid,
-			Position: n,
-			Tokens:   tokens[i:j],
-			Seen:     seen - i,
-		})
-
-		seen = i + contextSize
-
-		n++
-	}
-
-	if b.Size() > 0 {
-		e.jobs <- *b
-	}
-
-	e.scheduled.Add(int64(n))
-}
-
-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
-}