Files
ollama/integration/basic_test.go
Jesse Gross c61023f554 ollamarunner: Fix off by one error with numPredict
When numPredict is set, the user will receive one less token
than the requested limit. In addition, the stats will incorrectly
show the number of tokens returned as the limit. In cases where
numPredict is not set, the number of tokens is reported correctly.

This occurs because numPredict is checked when setting up the next
batch but hitting the limit will terminate the current batch as well.
Instead, is is better to check the limit as we actually predict them.
2026-02-04 17:14:24 -08:00

191 lines
4.8 KiB
Go

//go:build integration
package integration
import (
"context"
"log/slog"
"os"
"runtime"
"testing"
"time"
"github.com/ollama/ollama/api"
)
func TestBlueSky(t *testing.T) {
ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
defer cancel()
// Set up the test data
req := api.ChatRequest{
Model: smol,
Messages: []api.Message{
{
Role: "user",
Content: blueSkyPrompt,
},
},
Stream: &stream,
Options: map[string]any{
"temperature": 0,
"seed": 123,
},
}
ChatTestHelper(ctx, t, req, blueSkyExpected)
}
func TestUnicode(t *testing.T) {
skipUnderMinVRAM(t, 6)
ctx, cancel := context.WithTimeout(context.Background(), 3*time.Minute)
defer cancel()
// Set up the test data
req := api.ChatRequest{
// DeepSeek has a Unicode tokenizer regex, making it a unicode torture test
Model: "deepseek-coder-v2:16b-lite-instruct-q2_K", // TODO is there an ollama-engine model we can switch to and keep the coverage?
Messages: []api.Message{
{
Role: "user",
Content: "天空为什么是蓝色的?", // Why is the sky blue?
},
},
Stream: &stream,
Options: map[string]any{
"temperature": 0,
"seed": 123,
// Workaround deepseek context shifting bug
"num_ctx": 8192,
"num_predict": 2048,
},
}
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
if err := PullIfMissing(ctx, client, req.Model); err != nil {
t.Fatal(err)
}
slog.Info("loading", "model", req.Model)
err := client.Generate(ctx, &api.GenerateRequest{Model: req.Model}, func(response api.GenerateResponse) error { return nil })
if err != nil {
t.Fatalf("failed to load model %s: %s", req.Model, err)
}
defer func() {
// best effort unload once we're done with the model
client.Generate(ctx, &api.GenerateRequest{Model: req.Model, KeepAlive: &api.Duration{Duration: 0}}, func(rsp api.GenerateResponse) error { return nil })
}()
skipIfNotGPULoaded(ctx, t, client, req.Model, 100)
DoChat(ctx, t, client, req, []string{
"散射", // scattering
"频率", // frequency
}, 120*time.Second, 120*time.Second)
}
func TestExtendedUnicodeOutput(t *testing.T) {
ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
defer cancel()
// Set up the test data
req := api.ChatRequest{
Model: "gemma2:2b",
Messages: []api.Message{
{
Role: "user",
Content: "Output some smily face emoji",
},
},
Stream: &stream,
Options: map[string]any{
"temperature": 0,
"seed": 123,
},
}
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
if err := PullIfMissing(ctx, client, req.Model); err != nil {
t.Fatal(err)
}
DoChat(ctx, t, client, req, []string{"😀", "😊", "😁", "😂", "😄", "😃"}, 120*time.Second, 120*time.Second)
}
func TestUnicodeModelDir(t *testing.T) {
// This is only useful for Windows with utf-16 characters, so skip this test for other platforms
if runtime.GOOS != "windows" {
t.Skip("Unicode test only applicable to windows")
}
// Only works for local testing
if os.Getenv("OLLAMA_TEST_EXISTING") != "" {
t.Skip("TestUnicodeModelDir only works for local testing, skipping")
}
modelDir, err := os.MkdirTemp("", "ollama_埃")
if err != nil {
t.Fatal(err)
}
defer os.RemoveAll(modelDir)
slog.Info("unicode", "OLLAMA_MODELS", modelDir)
t.Setenv("OLLAMA_MODELS", modelDir)
ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
defer cancel()
req := api.ChatRequest{
Model: smol,
Messages: []api.Message{
{
Role: "user",
Content: blueSkyPrompt,
},
},
Stream: &stream,
Options: map[string]any{
"temperature": 0,
"seed": 123,
},
}
ChatTestHelper(ctx, t, req, blueSkyExpected)
}
// TestNumPredict verifies that when num_predict is set, the model generates
// exactly that many tokens. It uses logprobs to count the actual tokens output.
func TestNumPredict(t *testing.T) {
ctx, cancel := context.WithTimeout(context.Background(), 2*time.Minute)
defer cancel()
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
if err := PullIfMissing(ctx, client, "qwen3:0.6b"); err != nil {
t.Fatalf("failed to pull model: %v", err)
}
req := api.GenerateRequest{
Model: "qwen3:0.6b",
Prompt: "Write a long story.",
Stream: &stream,
Logprobs: true,
Options: map[string]any{
"num_predict": 10,
"temperature": 0,
"seed": 123,
},
}
logprobCount := 0
var finalResponse api.GenerateResponse
err := client.Generate(ctx, &req, func(resp api.GenerateResponse) error {
logprobCount += len(resp.Logprobs)
if resp.Done {
finalResponse = resp
}
return nil
})
if err != nil {
t.Fatalf("generate failed: %v", err)
}
if logprobCount != 10 {
t.Errorf("expected 10 tokens (logprobs), got %d (EvalCount=%d, DoneReason=%s)",
logprobCount, finalResponse.EvalCount, finalResponse.DoneReason)
}
}