Files
ollama/integration/utils_test.go
Daniel Hiltgen 96b202d34b Add support for gemma4 (#15214)
* bench: add prompt calibration, context size flag, and NumCtx reporting

Add --num-ctx flag to set context size, and report NumCtx in model info
header. Calibrate tokens-per-word ratio during warmup using actual
tokenization metrics from the model, replacing the fixed 1.3 heuristic.
This produces more accurate prompt token counts for --prompt-tokens.

Also add fetchContextLength() to query running model context via /api/ps.

* integration: improve vision test robustness and add thinking tests

Add skipIfNoVisionOverride() to skip vision tests when OLLAMA_TEST_MODEL
is set to a non-vision model. Add Think:false to context exhaustion test
to prevent thinking models from using all context before the test can
measure it. Add third test image (ollama homepage) and replace OCR test
with ImageDescription test using it. Relax match strings for broader
model compatibility. Add TestThinkingEnabled and TestThinkingSuppressed
to verify thinking output and channel tag handling.

* gemma4: add Gemma 4 GGML model support

Add full Gemma 4 model family support (E2B, E4B, 26B MoE, 31B Dense)
for the GGML backend including text, vision, converter, parser, and
renderer.

Text model features:
- Sliding window + full attention with per-layer patterns
- KV sharing across layers with donor map
- Per-layer embeddings (PLE) with learned projections
- MoE routing with RMSNorm + learned scale
- Proportional RoPE with freq_factors for global attention
- Final logit softcapping

Vision model features:
- SigLIP vision encoder with 2D RoPE
- ClippableLinear with input/output clamping via packed v.clamp_data
- Adaptive average pooling with nMerge kernel
- Multi-modal projection with unweighted RMSNorm

Converter:
- Safetensors to GGUF with vision tensor renaming
- Fused MoE gate_up_proj splitting
- Vision patch embedding reshape (HF to Conv2D layout)
- Packed clamp data tensor for ClippableLinear bounds
- Proportional RoPE freq_factors generation

Also includes:
- BackendGet() on ml.Tensor for reading weight tensor data
- Q6_K CUDA get_rows kernel support
- MoE-aware ffn_down quantization layer counting
- Gemma4 parser with tool calling and thinking support
- Gemma4 renderer with structured tool format
- Architecture-based auto-detection of renderer/parser/stop tokens
- Integration test gemma4 model list additions

* gemma4: add audio support with USM conformer encoder

Add audio encoding for Gemma 4 using the USM conformer architecture:
- Converter: audio tensor mapping, SSCP/conformer/embedder name replacements,
  softplus repacker for per_dim_scale, F32 enforcement for conv weights
- GGML backend: Conv1DDW and PadExt tensor ops
- Audio encoder: SSCP Conv2D, 12 conformer blocks (FFW + block-local
  attention with relative position embeddings + LightConv1d + FFW),
  output projection, audio-to-text embedding projector
- Audio preprocessing: WAV decode, mel spectrogram, FFT (pure Go)
- Model wiring: WAV detection, audio token handling, unified PostTokenize

Correctly transcribes "why is the sky blue" from test audio.

* integration: add gemma4 audio tests including OpenAI API coverage

Test audio transcription and response via the Ollama native API, plus
two new tests exercising the OpenAI-compatible endpoints:
- /v1/audio/transcriptions (multipart form upload)
- /v1/chat/completions with input_audio content type

All tests use capability checks and skip models without audio support.

* gemma4: add OpenAI audio API support and capability detection

- Add CapabilityAudio and detect from audio.block_count in GGUF
- Add /v1/audio/transcriptions endpoint with TranscriptionMiddleware
- Add input_audio content type support in /v1/chat/completions
- Add TranscriptionRequest/Response types in openai package

* gemma4: add audio input support for run command

- /audio toggle in interactive mode for voice chat
- Platform-specific microphone recording (AVFoundation on macOS,
  PulseAudio/ALSA on Linux, WASAPI on Windows)
- Space to start/stop recording, automatic chunking for long audio

* gemma4: add transcribe command (ollama transcribe MODEL)

- Interactive mode with readline prompt and slash commands
- Non-interactive mode for piped audio or record-until-Ctrl+C
- Chunked streaming transcription for long recordings
- Word-wrapped output matching run command style

* gemma4: add parser, renderer, and integration test plumbing

* gemma4: fix renderer to emit BOS token

* gemma4: add OpenAI audio transcription API and input_audio support

* gemma4: update converter for new weight drop naming

* gemma4: add per_expert_scale to MoE router and fix moe_intermediate_size config

* gemma4: rewrite renderer to match HF Jinja2 template exactly

Fix 8 bugs found by building 55 reference tests verified against the
HF Jinja2 chat template (VERIFY_JINJA2=1 shells out to Python):

- Tool responses use separate <|turn>tool turns (not inline tags)
- Tool calls emitted before content in assistant messages
- Thinking content stripped from assistant history (strip_thinking)
- User, tool, and system content trimmed (template does | trim)
- Empty system message still emits system turn (check role, not content)
- Nested object properties rendered recursively with required field
- Array items specification rendered for array-type properties
- OBJECT/ARRAY type-specific rendering comma logic matches template

Also adds Required field to api.ToolProperty for nested object schemas,
replaces old gemma4_test.go with comprehensive gemma4_reference_test.go,
and commits the Jinja2 template as testdata for verification.

* gemma4: fix MoE fused gate_up split and multiline tool-call arg parsing

- Text MoE: split `ffn_gate_up_exps` into contiguous `[gate|up]` halves instead of stride-2 slices.
- Parser: escape control characters in `<|"|>...<|"|>` string literals when converting tool-call args to JSON.
- Fixes warnings like `invalid character '\n' in string literal` for multiline tool arguments.
- Add Gemma4 parser regressions for multiline tool-call args and `gemma4ArgsToJSON`.

* cmd: simplify audio input to dropped file attachments

* gemma4: use full SWA memory for better cache reuse

* gemma4: initialize clamps after backend load

* convert: align gemma4 audio tensor renames with llama.cpp

* Remove redundant comments in gemma4 vision model

* Format Gemma4 MoE block field alignment

* use 4096 kvcache.NewSWAMemCache

* convert: support new Gemma4 audio_tower tensor naming (#15221)

Co-authored-by: jmorganca <jmorganca@gmail.com>

* fix integration test defaults for audio

* review comments and lint fixes

* remove unused audio/video files

---------

Co-authored-by: jmorganca <jmorganca@gmail.com>
2026-04-02 11:33:33 -07:00

855 lines
22 KiB
Go

//go:build integration
package integration
import (
"bytes"
"context"
"errors"
"fmt"
"io"
"log/slog"
"math"
"math/rand"
"net"
"net/http"
"net/url"
"os"
"os/exec"
"path/filepath"
"runtime"
"slices"
"strconv"
"strings"
"sync"
"testing"
"time"
"github.com/ollama/ollama/api"
"github.com/ollama/ollama/format"
"github.com/ollama/ollama/types/model"
)
var (
smol = "llama3.2:1b"
stream = false
// testModel is set via OLLAMA_TEST_MODEL env var. When set, all tests
// that loop over model lists will test only this model, and smol is
// also overridden to use it.
testModel string
)
var (
started = time.Now()
// Note: add newer models at the top of the list to test them first
ollamaEngineChatModels = []string{
"gemma4",
"lfm2.5-thinking",
"ministral-3",
"qwen3-coder:30b",
"gpt-oss:20b",
"gemma3n:e2b",
"mistral-small3.2:latest",
"deepseek-r1:1.5b",
"llama3.2-vision:latest",
"qwen2.5-coder:latest",
"qwen2.5vl:3b",
"qwen3:0.6b", // dense
"qwen3:1.7b", // dense
"qwen3:30b", // MOE
"gemma3:1b",
"llama3.1:latest",
"llama3.2:latest",
"gemma2:latest",
"minicpm-v:latest", // arch=qwen2
"granite-code:latest", // arch=llama
}
llamaRunnerChatModels = []string{
"mistral:latest",
"falcon3:latest",
"granite3-moe:latest",
"command-r:latest",
"nemotron-mini:latest",
"phi3.5:latest",
"solar-pro:latest",
"internlm2:latest",
"codellama:latest", // arch=llama
"phi3:latest",
"falcon2:latest",
"gemma:latest",
"llama2:latest",
"nous-hermes:latest",
"orca-mini:latest",
"qwen:latest",
"stablelm2:latest", // Predictions are off, crashes on small VRAM GPUs
"falcon:latest",
}
// Some library models are quite large - ensure large VRAM and sufficient disk space
// before running scenarios based on this set
libraryChatModels = []string{
"alfred",
"athene-v2",
"aya-expanse",
"aya",
"bakllava",
"bespoke-minicheck",
"codebooga",
"codegeex4",
"codegemma",
"codellama",
"codeqwen",
"codestral",
"codeup",
"cogito",
"command-a",
"command-r-plus",
"command-r",
"command-r7b-arabic",
"command-r7b",
"dbrx",
"deepcoder",
"deepscaler",
"deepseek-coder-v2",
"deepseek-coder",
"deepseek-llm",
"deepseek-r1",
// "deepseek-v2.5", // requires 155 GB VRAM
"deepseek-v2",
// "deepseek-v3", // requires 482 GB VRAM
"devstral",
"dolphin-llama3",
"dolphin-mistral",
"dolphin-mixtral",
"dolphin-phi",
"dolphin3",
"dolphincoder",
"duckdb-nsql",
"everythinglm",
"exaone-deep",
"exaone3.5",
"falcon",
"falcon2",
"falcon3",
"firefunction-v2",
"gemma",
"gemma2",
"gemma3",
"gemma3n",
"gemma4",
"glm4",
"goliath",
"gpt-oss:20b",
"granite-code",
"granite3-dense",
"granite3-guardian",
"granite3-moe",
"granite3.1-dense",
"granite3.1-moe",
"granite3.2-vision",
"granite3.2",
"granite3.3",
"hermes3",
"internlm2",
"lfm2.5-thinking",
"llama-guard3",
"llama-pro",
"llama2-chinese",
"llama2-uncensored",
"llama2",
"llama3-chatqa",
"llama3-gradient",
"llama3-groq-tool-use",
"llama3.1",
"llama3.2-vision",
"llama3.2",
"llama3.3",
"llama3",
"llama4",
"llava-llama3",
"llava-phi3",
"llava",
"magicoder",
"magistral",
"marco-o1",
"mathstral",
"meditron",
"medllama2",
"megadolphin",
"minicpm-v",
"ministral-3",
"mistral-large",
"mistral-nemo",
"mistral-openorca",
"mistral-small",
"mistral-small3.1",
"mistral-small3.2",
"mistral",
"mistrallite",
"mixtral",
"moondream",
"nemotron-mini",
"nemotron",
"neural-chat",
"nexusraven",
"notus",
"nous-hermes",
"nous-hermes2-mixtral",
"nous-hermes2",
"nuextract",
"olmo2",
"open-orca-platypus2",
"openchat",
"opencoder",
"openhermes",
"openthinker",
"orca-mini",
"orca2",
// "phi", // unreliable
"phi3.5",
"phi3",
"phi4-mini-reasoning",
"phi4-mini",
"phi4-reasoning",
"phi4",
"phind-codellama",
"qwen",
"qwen2-math",
"qwen2.5-coder",
"qwen2.5",
"qwen2.5vl",
"qwen2",
"qwen3:0.6b", // dense
"qwen3:30b", // MOE
"qwq",
"r1-1776",
"reader-lm",
"reflection",
"sailor2",
"samantha-mistral",
"shieldgemma",
"smallthinker",
"smollm",
"smollm2",
"solar-pro",
"solar",
"sqlcoder",
"stable-beluga",
"stable-code",
"stablelm-zephyr",
"stablelm2",
"starcoder",
"starcoder2",
"starling-lm",
"tinydolphin",
"tinyllama",
"tulu3",
"vicuna",
"wizard-math",
"wizard-vicuna-uncensored",
"wizard-vicuna",
"wizardcoder",
"wizardlm-uncensored",
"wizardlm2",
"xwinlm",
"yarn-llama2",
"yarn-mistral",
"yi-coder",
"yi",
"zephyr",
}
libraryEmbedModels = []string{
"qwen3-embedding",
"embeddinggemma",
"nomic-embed-text",
"all-minilm",
"bge-large",
"bge-m3",
"granite-embedding",
"mxbai-embed-large",
"paraphrase-multilingual",
"snowflake-arctic-embed",
"snowflake-arctic-embed2",
}
libraryToolsModels = []string{
"gemma4",
"lfm2.5-thinking",
"qwen3-vl",
"gpt-oss:20b",
"gpt-oss:120b",
"qwen3",
"llama3.1",
"llama3.2",
"mistral",
"qwen2.5",
"qwen2",
"ministral-3",
"mistral-nemo",
"mistral-small",
"mixtral:8x22b",
"qwq",
"granite3.3",
}
blueSkyPrompt = "why is the sky blue? Be brief but factual in your reply"
blueSkyExpected = []string{"rayleigh", "scatter", "atmosphere", "nitrogen", "oxygen", "wavelength", "interact"}
rainbowPrompt = "how do rainbows form? Be brief but factual in your reply"
rainbowFollowups = []string{
"Explain the physics involved in them. Be brief in your reply",
"Explain the chemistry involved in them. Be brief in your reply",
"What are common myths related to them? Be brief in your reply",
"Can they form if there is no rain? Be brief in your reply",
"Can they form if there are no clouds? Be brief in your reply",
"Do they happen on other planets? Be brief in your reply",
}
rainbowExpected = []string{"water", "droplet", "mist", "glow", "refract", "reflect", "scatter", "particles", "wave", "color", "spectrum", "raindrop", "atmosphere", "frequency", "shower", "sky", "shimmer", "light", "storm", "sunny", "sunburst", "phenomenon", "mars", "venus", "jupiter", "rain", "sun", "rainbow", "optical", "gold", "cloud", "planet", "prism", "fog", "ice"}
)
func init() {
logger := slog.New(slog.NewTextHandler(os.Stdout, &slog.HandlerOptions{Level: slog.LevelDebug}))
slog.SetDefault(logger)
testModel = os.Getenv("OLLAMA_TEST_MODEL")
if testModel != "" {
slog.Info("test model override", "model", testModel)
smol = testModel
}
}
// testModels returns the override model as a single-element slice when
// OLLAMA_TEST_MODEL is set, otherwise returns the provided default list.
func testModels(defaults []string) []string {
if testModel != "" {
return []string{testModel}
}
return defaults
}
// requireCapability skips the test if the model does not advertise the
// given capability. It queries the server via Show and caches nothing —
// call it once per subtest. For local-only models where Show may not
// return capabilities (e.g. models created via ollama create), this is
// a best-effort check.
func requireCapability(ctx context.Context, t *testing.T, client *api.Client, modelName string, cap model.Capability) {
t.Helper()
resp, err := client.Show(ctx, &api.ShowRequest{Name: modelName})
if err != nil {
t.Fatalf("failed to show model %s: %v", modelName, err)
}
if len(resp.Capabilities) > 0 && !slices.Contains(resp.Capabilities, cap) {
t.Skipf("model %s does not have capability %q (has %v)", modelName, cap, resp.Capabilities)
}
}
// pullOrSkip pulls a model if it isn't already present locally. If the
// pull fails (e.g. model not in registry), the test is skipped instead
// of failed. PullIfMissing already checks Show first, so local-only
// models that exist will return immediately without hitting the registry.
func pullOrSkip(ctx context.Context, t *testing.T, client *api.Client, modelName string) {
t.Helper()
if err := PullIfMissing(ctx, client, modelName); err != nil {
t.Skipf("model %s not available: %v", modelName, err)
}
}
func FindPort() string {
port := 0
if a, err := net.ResolveTCPAddr("tcp", "localhost:0"); err == nil {
var l *net.TCPListener
if l, err = net.ListenTCP("tcp", a); err == nil {
port = l.Addr().(*net.TCPAddr).Port
l.Close()
}
}
if port == 0 {
port = rand.Intn(65535-49152) + 49152 // get a random port in the ephemeral range
}
return strconv.Itoa(port)
}
func GetTestEndpoint() (*api.Client, string) {
defaultPort := "11434"
ollamaHost := os.Getenv("OLLAMA_HOST")
scheme, hostport, ok := strings.Cut(ollamaHost, "://")
if !ok {
scheme, hostport = "http", ollamaHost
}
// trim trailing slashes
hostport = strings.TrimRight(hostport, "/")
host, port, err := net.SplitHostPort(hostport)
if err != nil {
host, port = "127.0.0.1", defaultPort
if ip := net.ParseIP(strings.Trim(hostport, "[]")); ip != nil {
host = ip.String()
} else if hostport != "" {
host = hostport
}
}
if os.Getenv("OLLAMA_TEST_EXISTING") == "" && runtime.GOOS != "windows" && port == defaultPort {
port = FindPort()
}
slog.Info("server connection", "host", host, "port", port)
return api.NewClient(
&url.URL{
Scheme: scheme,
Host: net.JoinHostPort(host, port),
},
http.DefaultClient), fmt.Sprintf("%s:%s", host, port)
}
// Server lifecycle management
var (
serverMutex sync.Mutex
serverReady bool
serverLog bytes.Buffer
serverDone chan int
serverCmd *exec.Cmd
)
func startServer(t *testing.T, ctx context.Context, ollamaHost string) error {
// Make sure the server has been built
CLIName, err := filepath.Abs("../ollama")
if err != nil {
return fmt.Errorf("failed to get absolute path: %w", err)
}
if runtime.GOOS == "windows" {
CLIName += ".exe"
}
_, err = os.Stat(CLIName)
if err != nil {
return fmt.Errorf("CLI missing, did you forget to 'go build .' first? %w", err)
}
serverMutex.Lock()
defer serverMutex.Unlock()
if serverReady {
return nil
}
serverDone = make(chan int)
serverLog.Reset()
if tmp := os.Getenv("OLLAMA_HOST"); tmp != ollamaHost {
slog.Info("setting env", "OLLAMA_HOST", ollamaHost)
t.Setenv("OLLAMA_HOST", ollamaHost)
}
serverCmd = exec.Command(CLIName, "serve")
serverCmd.Stderr = &serverLog
serverCmd.Stdout = &serverLog
go func() {
slog.Info("starting server", "url", ollamaHost)
if err := serverCmd.Run(); err != nil {
// "signal: killed" expected during normal shutdown
if !strings.Contains(err.Error(), "signal") {
slog.Info("failed to run server", "error", err)
}
}
var code int
if serverCmd.ProcessState != nil {
code = serverCmd.ProcessState.ExitCode()
}
slog.Info("server exited")
serverDone <- code
}()
serverReady = true
return nil
}
func PullIfMissing(ctx context.Context, client *api.Client, modelName string) error {
slog.Info("checking status of model", "model", modelName)
showReq := &api.ShowRequest{Name: modelName}
showCtx, cancel := context.WithDeadlineCause(
ctx,
time.Now().Add(20*time.Second),
fmt.Errorf("show for existing model %s took too long", modelName),
)
defer cancel()
_, err := client.Show(showCtx, showReq)
var statusError api.StatusError
switch {
case errors.As(err, &statusError) && statusError.StatusCode == http.StatusNotFound:
break
case err != nil:
return err
default:
slog.Info("model already present", "model", modelName)
return nil
}
slog.Info("model missing", "model", modelName)
stallDuration := 60 * time.Second // This includes checksum verification, which can take a while on larger models, and slower systems
stallTimer := time.NewTimer(stallDuration)
fn := func(resp api.ProgressResponse) error {
// fmt.Print(".")
if !stallTimer.Reset(stallDuration) {
return errors.New("stall was detected, aborting status reporting")
}
return nil
}
stream := true
pullReq := &api.PullRequest{Name: modelName, Stream: &stream}
var pullError error
done := make(chan int)
go func() {
pullError = client.Pull(ctx, pullReq, fn)
done <- 0
}()
select {
case <-stallTimer.C:
return errors.New("download stalled")
case <-done:
return pullError
}
}
var serverProcMutex sync.Mutex
// Returns an Client, the testEndpoint, and a cleanup function, fails the test on errors
// Starts the server if needed
func InitServerConnection(ctx context.Context, t *testing.T) (*api.Client, string, func()) {
client, testEndpoint := GetTestEndpoint()
cleanup := func() {}
if os.Getenv("OLLAMA_TEST_EXISTING") == "" && runtime.GOOS != "windows" {
var err error
err = startServer(t, ctx, testEndpoint)
if err != nil {
t.Fatal(err)
}
cleanup = func() {
serverMutex.Lock()
defer serverMutex.Unlock()
serverReady = false
slog.Info("shutting down server")
serverCmd.Process.Signal(os.Interrupt)
slog.Info("waiting for server to exit")
<-serverDone
slog.Info("terminate complete")
if t.Failed() {
slog.Warn("SERVER LOG FOLLOWS")
io.Copy(os.Stderr, &serverLog)
slog.Warn("END OF SERVER")
}
slog.Info("cleanup complete", "failed", t.Failed())
}
}
// Make sure server is online and healthy before returning
for {
select {
case <-ctx.Done():
t.Fatalf("context done before server ready: %v", ctx.Err())
break
default:
}
listCtx, cancel := context.WithDeadlineCause(
ctx,
time.Now().Add(10*time.Second),
fmt.Errorf("list models took too long"),
)
defer cancel()
models, err := client.ListRunning(listCtx)
if err != nil {
if runtime.GOOS == "windows" {
t.Fatalf("did you forget to start the server: %v", err)
}
time.Sleep(10 * time.Millisecond)
continue
}
if len(models.Models) > 0 {
names := make([]string, len(models.Models))
for i, m := range models.Models {
names[i] = m.Name
}
slog.Info("currently loaded", "models", names)
}
break
}
return client, testEndpoint, cleanup
}
func ChatTestHelper(ctx context.Context, t *testing.T, req api.ChatRequest, anyResp []string) {
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
pullOrSkip(ctx, t, client, req.Model)
DoChat(ctx, t, client, req, anyResp, 30*time.Second, 10*time.Second)
}
func DoGenerate(ctx context.Context, t *testing.T, client *api.Client, genReq api.GenerateRequest, anyResp []string, initialTimeout, streamTimeout time.Duration) []int {
stallTimer := time.NewTimer(initialTimeout)
var buf bytes.Buffer
var context []int
fn := func(response api.GenerateResponse) error {
// fmt.Print(".")
buf.Write([]byte(response.Response))
if !stallTimer.Reset(streamTimeout) {
return errors.New("stall was detected while streaming response, aborting")
}
if len(response.Context) > 0 {
context = response.Context
}
return nil
}
stream := true
genReq.Stream = &stream
done := make(chan int)
var genErr error
go func() {
genErr = client.Generate(ctx, &genReq, fn)
done <- 0
}()
var response string
verify := func() {
// Verify the response contains the expected data
response = buf.String()
atLeastOne := false
for _, resp := range anyResp {
if strings.Contains(strings.ToLower(response), resp) {
atLeastOne = true
break
}
}
if !atLeastOne {
t.Fatalf("%s: none of %v found in %s", genReq.Model, anyResp, response)
}
}
select {
case <-stallTimer.C:
if buf.Len() == 0 {
t.Errorf("generate never started. Timed out after :%s", initialTimeout.String())
} else {
t.Errorf("generate stalled. Response so far:%s", buf.String())
}
case <-done:
if genErr != nil && strings.Contains(genErr.Error(), "model requires more system memory") {
slog.Warn("model is too large for the target test system", "model", genReq.Model, "error", genErr)
return context
}
if genErr != nil {
t.Fatalf("%s failed with %s request prompt %s", genErr, genReq.Model, genReq.Prompt)
}
verify()
slog.Info("test pass", "model", genReq.Model, "prompt", genReq.Prompt, "contains", anyResp, "response", response)
case <-ctx.Done():
// On slow systems, we might timeout before some models finish rambling, so check what we have so far to see
// if it's considered a pass - the stallTimer will detect hangs, but we want to consider slow systems a pass
// if they are still generating valid responses
slog.Warn("outer test context done while waiting for generate")
verify()
}
return context
}
// Generate a set of requests
// By default each request uses llama3.2 as the model
func GenerateRequests() ([]api.GenerateRequest, [][]string) {
return []api.GenerateRequest{
{
Model: smol,
Prompt: "why is the ocean blue? Be brief but factual in your reply",
Stream: &stream,
KeepAlive: &api.Duration{Duration: 10 * time.Second},
}, {
Model: smol,
Prompt: "why is the color of dirt brown? Be brief but factual in your reply",
Stream: &stream,
KeepAlive: &api.Duration{Duration: 10 * time.Second},
}, {
Model: smol,
Prompt: rainbowPrompt,
Stream: &stream,
KeepAlive: &api.Duration{Duration: 10 * time.Second},
}, {
Model: smol,
Prompt: "what is the origin of independence day? Be brief but factual in your reply",
Stream: &stream,
KeepAlive: &api.Duration{Duration: 10 * time.Second},
}, {
Model: smol,
Prompt: "what is the composition of air? Be brief but factual in your reply",
Stream: &stream,
KeepAlive: &api.Duration{Duration: 10 * time.Second},
},
},
[][]string{
{"sunlight", "scatter", "interact", "color", "surface", "depth", "red", "orange", "yellow", "absorb", "wavelength", "water", "molecule"},
{"soil", "organic", "earth", "black", "tan", "chemical", "processes", "pigment", "particle", "iron oxide", "rust", "air", "water", "wet", "mixture", "mixing", "mineral", "element", "decomposed", "matter", "wavelength"},
rainbowExpected,
{"fourth", "july", "declaration", "independence"},
{"nitrogen", "oxygen", "carbon", "dioxide", "water", "vapor", "fluid", "particles", "gas"},
}
}
func DoChat(ctx context.Context, t *testing.T, client *api.Client, req api.ChatRequest, anyResp []string, initialTimeout, streamTimeout time.Duration) *api.Message {
stallTimer := time.NewTimer(initialTimeout)
var buf bytes.Buffer
role := "assistant"
fn := func(response api.ChatResponse) error {
// fmt.Print(".")
role = response.Message.Role
buf.Write([]byte(response.Message.Content))
if !stallTimer.Reset(streamTimeout) {
return errors.New("stall was detected while streaming response, aborting")
}
return nil
}
stream := true
req.Stream = &stream
done := make(chan int)
var genErr error
go func() {
genErr = client.Chat(ctx, &req, fn)
done <- 0
}()
var response string
verify := func() {
// Verify the response contains the expected data
response = buf.String()
atLeastOne := false
for _, resp := range anyResp {
if strings.Contains(strings.ToLower(response), resp) {
atLeastOne = true
break
}
}
if !atLeastOne {
t.Fatalf("%s: none of %v found in \"%s\" -- request was:%v", req.Model, anyResp, response, req.Messages)
}
}
select {
case <-stallTimer.C:
if buf.Len() == 0 {
t.Errorf("generate never started. Timed out after :%s", initialTimeout.String())
} else {
t.Errorf("generate stalled. Response so far:%s", buf.String())
}
case <-done:
if genErr != nil && strings.Contains(genErr.Error(), "model requires more system memory") {
slog.Warn("model is too large for the target test system", "model", req.Model, "error", genErr)
return nil
}
if genErr != nil {
t.Fatalf("%s failed with %s request prompt %v", genErr, req.Model, req.Messages)
}
verify()
slog.Info("test pass", "model", req.Model, "messages", req.Messages, "contains", anyResp, "response", response)
case <-ctx.Done():
// On slow systems, we might timeout before some models finish rambling, so check what we have so far to see
// if it's considered a pass - the stallTimer will detect hangs, but we want to consider slow systems a pass
// if they are still generating valid responses
slog.Warn("outer test context done while waiting for chat")
verify()
}
return &api.Message{Role: role, Content: buf.String()}
}
func ChatRequests() ([]api.ChatRequest, [][]string) {
genReqs, results := GenerateRequests()
reqs := make([]api.ChatRequest, len(genReqs))
// think := api.ThinkValue{Value: "low"}
for i := range reqs {
reqs[i].Model = genReqs[i].Model
reqs[i].Stream = genReqs[i].Stream
reqs[i].KeepAlive = genReqs[i].KeepAlive
// reqs[i].Think = &think
reqs[i].Messages = []api.Message{
{
Role: "user",
Content: genReqs[i].Prompt,
},
}
}
return reqs, results
}
func skipUnderMinVRAM(t *testing.T, gb uint64) {
// TODO use info API in the future
if s := os.Getenv("OLLAMA_MAX_VRAM"); s != "" {
maxVram, err := strconv.ParseUint(s, 10, 64)
if err != nil {
t.Fatal(err)
}
// Don't hammer on small VRAM cards...
if maxVram < gb*format.GibiByte {
t.Skip("skipping with small VRAM to avoid timeouts")
}
}
}
// Skip if the target model isn't X% GPU loaded to avoid excessive runtime
func skipIfNotGPULoaded(ctx context.Context, t *testing.T, client *api.Client, model string, minPercent int) {
gpuPercent := getGPUPercent(ctx, t, client, model)
if gpuPercent < minPercent {
t.Skip(fmt.Sprintf("test requires minimum %d%% GPU load, but model %s only has %d%%", minPercent, model, gpuPercent))
}
}
func getGPUPercent(ctx context.Context, t *testing.T, client *api.Client, model string) int {
models, err := client.ListRunning(ctx)
if err != nil {
t.Fatalf("failed to list running models: %s", err)
}
loaded := []string{}
for _, m := range models.Models {
loaded = append(loaded, m.Name)
if strings.Contains(model, ":") {
if m.Name != model {
continue
}
} else if strings.Contains(m.Name, ":") {
if !strings.HasPrefix(m.Name, model+":") {
continue
}
}
gpuPercent := 0
switch {
case m.SizeVRAM == 0:
gpuPercent = 0
case m.SizeVRAM == m.Size:
gpuPercent = 100
case m.SizeVRAM > m.Size || m.Size == 0:
t.Logf("unexpected size detected: %d", m.SizeVRAM)
default:
sizeCPU := m.Size - m.SizeVRAM
cpuPercent := math.Round(float64(sizeCPU) / float64(m.Size) * 110)
gpuPercent = int(100 - cpuPercent)
}
return gpuPercent
}
t.Fatalf("model %s not loaded - actually loaded: %v", model, loaded)
return 0
}
func getTimeouts(t *testing.T) (soft time.Duration, hard time.Duration) {
deadline, hasDeadline := t.Deadline()
if !hasDeadline {
return 8 * time.Minute, 10 * time.Minute
} else if deadline.Compare(time.Now().Add(2*time.Minute)) <= 0 {
t.Skip("too little time")
return time.Duration(0), time.Duration(0)
}
return -time.Since(deadline.Add(-2 * time.Minute)), -time.Since(deadline.Add(-20 * time.Second))
}