Files
llmgw/internal/agent/train/runner_test.go
T
Ilker ManapandClaude Opus 5.5 79b3e9f887 LoRA adapters trained on the nodes: llmgw ctl train
A job names the catalog model the adapter is for, the safetensors weights to
train on, the node and the MiB of a card it needs, and question/answer pairs.
The control plane sends it when the node's agent is there and nothing else
trains on it; the agent trains with its installed vLLM's torch and a pinned
overlay of peft and accelerate (no second environment), as the engines'
user, on the card with that much free — beside an engine, if it leaves that
much — converts the adapter to GGUF with the installed llama.cpp's own
conversion code when the model is served by llama.cpp, and sends what it
made back in pieces, kept in the store to download. Progress, loss and peak
card memory are reported as it goes; a node that goes fails its job.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
2026-10-08 20:25:57 +02:00

165 lines
5.0 KiB
Go

package train
import (
"archive/tar"
"bytes"
"compress/gzip"
"context"
"io"
"os"
"path/filepath"
"strings"
"sync"
"testing"
)
// fakePython stands in for an engine's interpreter: as the trainer it
// prints progress and writes an adapter; as the converter it writes a GGUF;
// told to fail, it fails.
const fakePython = `#!/bin/sh
case "$1" in
*trainer.py)
out=$(sed -n 's/.*"out": *"\([^"]*\)".*/\1/p' "$2")
grep -q FAIL "$(dirname "$2")/data.jsonl" && { echo "CUDA out of memory" >&2; exit 1; }
echo '{"state":"preparing","message":"fetching base"}'
echo '{"state":"running","step":5,"steps":10,"loss":0.9,"peak_mib":1900}'
mkdir -p "$out"; echo '{"r":16}' > "$out/adapter_config.json"; echo weights > "$out/adapter_model.safetensors"
echo '{"state":"trained","step":10,"steps":10,"peak_mib":2100,"base_dir":"/base"}'
;;
*convert_lora_to_gguf.py)
while [ $# -gt 0 ]; do [ "$1" = "--outfile" ] && echo GGUF > "$2"; shift; done
;;
esac
`
func runner(t *testing.T, free int) *Runner {
t.Helper()
dir := t.TempDir()
py := filepath.Join(dir, "python3")
_ = os.WriteFile(py, []byte(fakePython), 0o755)
uv := filepath.Join(dir, "uv")
_ = os.WriteFile(uv, []byte("#!/bin/sh\nwhile [ $# -gt 0 ]; do [ \"$1\" = \"--target\" ] && mkdir -p \"$2\"; shift; done\n"), 0o755)
return New(Env{WorkDir: filepath.Join(dir, "work"), Python: py, UV: uv, ConvDir: dir,
GPUs: func(context.Context) ([]GPU, error) { return []GPU{{0, 500}, {1, free}}, nil }})
}
type sink struct {
mu sync.Mutex
statuses []Status
parts map[string][]byte
lasts int
}
func (s *sink) status(st Status) { s.mu.Lock(); s.statuses = append(s.statuses, st); s.mu.Unlock() }
func (s *sink) artifact(kind string, _ int, b []byte, last bool) error {
s.mu.Lock()
defer s.mu.Unlock()
if s.parts == nil {
s.parts = map[string][]byte{}
}
s.parts[kind] = append(s.parts[kind], b...)
if last {
s.lasts++
}
return nil
}
func TestAJobTrainsOnTheFreestCardAndSendsWhatItMade(t *testing.T) {
r := runner(t, 3000)
s := &sink{}
err := r.Run(context.Background(), Job{ID: "j1", BaseRepo: "Qwen/x", Data: []byte(`{"q":"a","a":"b"}`), GPUMemoryMiB: 2500, GGUF: true},
s.status, s.artifact)
if err != nil {
t.Fatal(err)
}
var running bool
for _, st := range s.statuses {
if st.State == "running" && st.Step == 5 && st.Steps == 10 && st.PeakMiB == 1900 {
running = true
}
}
if !running {
t.Errorf("statuses %+v", s.statuses)
}
if string(s.parts["gguf"]) != "GGUF\n" || s.lasts != 2 {
t.Errorf("gguf %q, lasts %d", s.parts["gguf"], s.lasts)
}
files := map[string]string{}
gz, err := gzip.NewReader(bytes.NewReader(s.parts["peft"]))
if err != nil {
t.Fatal(err)
}
tr := tar.NewReader(gz)
for {
h, err := tr.Next()
if err != nil {
break
}
b, _ := io.ReadAll(tr)
files[h.Name] = string(b)
}
if files["adapter_model.safetensors"] != "weights\n" || files["adapter_config.json"] == "" {
t.Errorf("peft archive %v", files)
}
// Nothing of the job is left on the node.
if entries, _ := os.ReadDir(filepath.Join(r.env.WorkDir, "jobs")); len(entries) != 0 {
t.Errorf("left behind: %v", entries)
}
}
func TestAJobWaitsForNoCardItDoesNotFit(t *testing.T) {
r := runner(t, 1000)
err := r.Run(context.Background(), Job{ID: "j1", Data: []byte(`{"q":"a","a":"b"}`), GPUMemoryMiB: 2500}, (&sink{}).status, (&sink{}).artifact)
if err == nil || !strings.Contains(err.Error(), "no card here has 2500 MiB free") {
t.Errorf("got %v", err)
}
}
func TestATrainerThatFailsSaysWhy(t *testing.T) {
r := runner(t, 3000)
err := r.Run(context.Background(), Job{ID: "j1", Data: []byte(`{"q":"FAIL","a":"b"}`)}, (&sink{}).status, (&sink{}).artifact)
if err == nil || !strings.Contains(err.Error(), "CUDA out of memory") {
t.Errorf("got %v", err)
}
}
func TestOneJobAtATime(t *testing.T) {
r := runner(t, 3000)
r.job = "other"
if err := r.Run(context.Background(), Job{ID: "j2"}, (&sink{}).status, (&sink{}).artifact); err != ErrBusy {
t.Errorf("got %v", err)
}
}
func TestCardsAreReadFromNvidiaSMI(t *testing.T) {
got := parseGPUs("0, 2310\n1, 80000\nbad line\n")
if len(got) != 2 || got[1].FreeMiB != 80000 || got[0].Index != 0 {
t.Errorf("parsed %+v", got)
}
}
func TestOnlyTheConversionCodeIsKeptOfLlamaCpp(t *testing.T) {
var buf bytes.Buffer
gz := gzip.NewWriter(&buf)
tw := tar.NewWriter(gz)
for _, name := range []string{"llama.cpp-b1/convert_lora_to_gguf.py", "llama.cpp-b1/conversion/qwen.py",
"llama.cpp-b1/gguf-py/gguf/__init__.py", "llama.cpp-b1/src/llama.cpp", "llama.cpp-b1/../evil"} {
_ = tw.WriteHeader(&tar.Header{Name: name, Mode: 0o644, Size: 1, Typeflag: tar.TypeReg})
_, _ = tw.Write([]byte("x"))
}
_ = tw.Close()
_ = gz.Close()
dir := filepath.Join(t.TempDir(), "conv")
if err := untarConversion(&buf, dir); err != nil {
t.Fatal(err)
}
for _, want := range []string{"convert_lora_to_gguf.py", "conversion/qwen.py", "gguf-py/gguf/__init__.py"} {
if _, err := os.Stat(filepath.Join(dir, want)); err != nil {
t.Errorf("%s not kept", want)
}
}
if _, err := os.Stat(filepath.Join(dir, "src")); err == nil {
t.Error("the rest of llama.cpp was kept")
}
}