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ollama/integration/llm_image_test.go

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Guard integration tests with a tag This should help CI avoid running the integration test logic in a container where it's not currently possible.
2023-12-23 08:33:27 +08:00
//go:build integration
Revamp go based integration tests This uplevels the integration tests to run the server which can allow testing an existing server, or a remote server.
2024-03-23 21:24:18 +08:00
package integration
Add automated test for multimodal A simple test case that verifies llava:7b can read text in an image
2023-12-14 06:29:09 +08:00
import (
"context"
"encoding/base64"
"testing"
"time"
change `github.com/jmorganca/ollama` to `github.com/ollama/ollama` (#3347)
2024-03-27 04:04:17 +08:00
"github.com/ollama/ollama/api"
Add automated test for multimodal A simple test case that verifies llava:7b can read text in an image
2023-12-14 06:29:09 +08:00
)
Integration test improvements (#9654) Add some new test coverage for various model architectures, and switch from orca-mini to the small llama model.
2025-04-17 05:25:55 +08:00
func TestVisionModels(t *testing.T) {
skipUnderMinVRAM(t, 6)
type testCase struct {
model string
}
testCases := []testCase{
{
integration: add qwen2.5-vl (#10815) Replace the older llava model with qwen2.5 for vision tests Skip split-batch test on small VRAM systems to avoid excessive test time
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model: "qwen2.5vl",
Integration test improvements (#9654) Add some new test coverage for various model architectures, and switch from orca-mini to the small llama model.
2025-04-17 05:25:55 +08:00
},
{
model: "llama3.2-vision",
Revamp go based integration tests This uplevels the integration tests to run the server which can allow testing an existing server, or a remote server.
2024-03-23 21:24:18 +08:00
},
Integration test improvements (#9654) Add some new test coverage for various model architectures, and switch from orca-mini to the small llama model.
2025-04-17 05:25:55 +08:00
{
model: "gemma3",
Add automated test for multimodal A simple test case that verifies llava:7b can read text in an image
2023-12-14 06:29:09 +08:00
},
}
Revamp go based integration tests This uplevels the integration tests to run the server which can allow testing an existing server, or a remote server.
2024-03-23 21:24:18 +08:00
Integration test improvements (#9654) Add some new test coverage for various model architectures, and switch from orca-mini to the small llama model.
2025-04-17 05:25:55 +08:00
for _, v := range testCases {
t.Run(v.model, func(t *testing.T) {
image, err := base64.StdEncoding.DecodeString(imageEncoding)
perf: build graph for next batch async to keep GPU busy (#11863) * perf: build graph for next batch in parallel to keep GPU busy This refactors the main run loop of the ollama runner to perform the main GPU intensive tasks (Compute+Floats) in a go routine so we can prepare the next batch in parallel to reduce the amount of time the GPU stalls waiting for the next batch of work. * tests: tune integration tests for ollama engine This tunes the integration tests to focus more on models supported by the new engine.
2025-08-30 05:20:28 +08:00
if err != nil {
t.Fatal(err)
}
Integration test improvements (#9654) Add some new test coverage for various model architectures, and switch from orca-mini to the small llama model.
2025-04-17 05:25:55 +08:00
req := api.GenerateRequest{
Model: v.model,
Prompt: "what does the text in this image say?",
Stream: &stream,
Options: map[string]any{
"seed": 42,
"temperature": 0.0,
},
Images: []api.ImageData{
image,
},
}
ctx, cancel := context.WithTimeout(context.Background(), 5*time.Minute)
defer cancel()
client, _, cleanup := InitServerConnection(ctx, t)
Add automated test for multimodal A simple test case that verifies llava:7b can read text in an image
2023-12-14 06:29:09 +08:00
Integration test improvements (#9654) Add some new test coverage for various model architectures, and switch from orca-mini to the small llama model.
2025-04-17 05:25:55 +08:00
// Note: sometimes it returns "the ollamas" sometimes "the ollams"
resp := "the ollam"
defer cleanup()
perf: build graph for next batch async to keep GPU busy (#11863) * perf: build graph for next batch in parallel to keep GPU busy This refactors the main run loop of the ollama runner to perform the main GPU intensive tasks (Compute+Floats) in a go routine so we can prepare the next batch in parallel to reduce the amount of time the GPU stalls waiting for the next batch of work. * tests: tune integration tests for ollama engine This tunes the integration tests to focus more on models supported by the new engine.
2025-08-30 05:20:28 +08:00
if err := PullIfMissing(ctx, client, req.Model); err != nil {
t.Fatal(err)
}
Integration test improvements (#9654) Add some new test coverage for various model architectures, and switch from orca-mini to the small llama model.
2025-04-17 05:25:55 +08:00
// llava models on CPU can be quite slow to start
DoGenerate(ctx, t, client, req, []string{resp}, 240*time.Second, 30*time.Second)
})
Add basic mllama integration tests (#7455)
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}
}
ml: Allow models to constrain inputs to a single batch Models may require that a set of inputs all be processed as part of the same batch. For example, if an image has multiple patches with fully connected attention between them, we should not split the batch in the middle of an image. Fixes #9697
2025-03-13 07:56:11 +08:00
func TestIntegrationSplitBatch(t *testing.T) {
integration: add qwen2.5-vl (#10815) Replace the older llava model with qwen2.5 for vision tests Skip split-batch test on small VRAM systems to avoid excessive test time
2025-05-23 00:12:32 +08:00
skipUnderMinVRAM(t, 6)
ml: Allow models to constrain inputs to a single batch Models may require that a set of inputs all be processed as part of the same batch. For example, if an image has multiple patches with fully connected attention between them, we should not split the batch in the middle of an image. Fixes #9697
2025-03-13 07:56:11 +08:00
image, err := base64.StdEncoding.DecodeString(imageEncoding)
perf: build graph for next batch async to keep GPU busy (#11863) * perf: build graph for next batch in parallel to keep GPU busy This refactors the main run loop of the ollama runner to perform the main GPU intensive tasks (Compute+Floats) in a go routine so we can prepare the next batch in parallel to reduce the amount of time the GPU stalls waiting for the next batch of work. * tests: tune integration tests for ollama engine This tunes the integration tests to focus more on models supported by the new engine.
2025-08-30 05:20:28 +08:00
if err != nil {
t.Fatal(err)
}
ml: Allow models to constrain inputs to a single batch Models may require that a set of inputs all be processed as part of the same batch. For example, if an image has multiple patches with fully connected attention between them, we should not split the batch in the middle of an image. Fixes #9697
2025-03-13 07:56:11 +08:00
req := api.GenerateRequest{
Model: "gemma3:4b",
// Fill up a chunk of the batch so the image will partially spill over into the next one
System: "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed aliquet, justo in malesuada lobortis, odio ligula volutpat quam, quis faucibus ipsum magna quis sapien. Aliquam in venenatis diam, eu viverra magna. Phasellus imperdiet hendrerit volutpat. Vivamus sem ex, facilisis placerat felis non, dictum elementum est. Phasellus aliquam imperdiet lacus, eget placerat ligula sodales vel. Pellentesque nec auctor mi. Curabitur arcu nisi, faucibus eget nunc id, viverra interdum mi. Curabitur ornare ipsum ex, ac euismod ex aliquam in. Vestibulum id magna at purus accumsan fermentum. Proin scelerisque posuere nunc quis interdum. Maecenas sed mollis nisl. Etiam vitae ipsum interdum, placerat est quis, tincidunt velit. Nullam tempor nibh non lorem volutpat efficitur. Cras laoreet diam imperdiet ipsum auctor bibendum. Suspendisse ultrices urna sed metus sagittis suscipit. Quisque ullamcorper aliquam nibh ut mollis. Aenean dapibus mauris pharetra, venenatis elit ac, hendrerit odio. Cras vestibulum erat tempor, lobortis justo eu, lobortis ipsum. Nam laoreet dapibus sem. Proin vel diam ultrices, elementum ante et, ornare lectus. Proin eu accumsan nisl. Praesent ac ex vitae ipsum vulputate tristique facilisis sit amet lacus. Nullam faucibus magna a pellentesque pretium. Nunc lacinia ullamcorper sollicitudin. Donec vitae accumsan turpis, sed porttitor est. Donec porttitor mi vitae augue faucibus, vel mollis diam tincidunt.",
Prompt: "what does the text in this image say?",
Stream: &stream,
chore(all): replace instances of interface with any (#10067) Both interface{} and any (which is just an alias for interface{} introduced in Go 1.18) represent the empty interface that all types satisfy.
2025-04-03 00:44:27 +08:00
Options: map[string]any{
ml: Allow models to constrain inputs to a single batch Models may require that a set of inputs all be processed as part of the same batch. For example, if an image has multiple patches with fully connected attention between them, we should not split the batch in the middle of an image. Fixes #9697
2025-03-13 07:56:11 +08:00
"seed": 42,
"temperature": 0.0,
},
Images: []api.ImageData{
image,
},
}
// Note: sometimes it returns "the ollamas" sometimes "the ollams"
resp := "the ollam"
ctx, cancel := context.WithTimeout(context.Background(), 3*time.Minute)
defer cancel()
client, _, cleanup := InitServerConnection(ctx, t)
defer cleanup()
perf: build graph for next batch async to keep GPU busy (#11863) * perf: build graph for next batch in parallel to keep GPU busy This refactors the main run loop of the ollama runner to perform the main GPU intensive tasks (Compute+Floats) in a go routine so we can prepare the next batch in parallel to reduce the amount of time the GPU stalls waiting for the next batch of work. * tests: tune integration tests for ollama engine This tunes the integration tests to focus more on models supported by the new engine.
2025-08-30 05:20:28 +08:00
if err := PullIfMissing(ctx, client, req.Model); err != nil {
t.Fatal(err)
}
ml: Allow models to constrain inputs to a single batch Models may require that a set of inputs all be processed as part of the same batch. For example, if an image has multiple patches with fully connected attention between them, we should not split the batch in the middle of an image. Fixes #9697
2025-03-13 07:56:11 +08:00
// llava models on CPU can be quite slow to start,
DoGenerate(ctx, t, client, req, []string{resp}, 120*time.Second, 30*time.Second)
}
Add automated test for multimodal A simple test case that verifies llava:7b can read text in an image
2023-12-14 06:29:09 +08:00
const imageEncoding = `iVBORw0KGgoAAAANSUhEUgAAANIAAAB4CAYAAACHHqzKAAAAAXNSR0IArs4c6QAAAIRlWElmTU0AKgAAAAgABQESAAMAAAABAAEAAAEaAAUAAAABAAAASgEb
AAUAAAABAAAAUgEoAAMAAAABAAIAAIdpAAQAAAABAAAAWgAAAAAAAABIAAAAAQAAAEgAAAABAAOgAQADAAAAAQABAACgAgAEAAAAAQAAANKgAwAEAAAAAQAA
AHgAAAAAXdsepgAAAAlwSFlzAAALEwAACxMBAJqcGAAAAVlpVFh0WE1MOmNvbS5hZG9iZS54bXAAAAAAADx4OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6
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bS90aWZmLzEuMC8iPgogICAgICAgICA8dGlmZjpPcmllbnRhdGlvbj4xPC90aWZmOk9yaWVudGF0aW9uPgogICAgICA8L3JkZjpEZXNjcmlwdGlvbj4KICAg
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