Files
ollama/llm/server_test.go
Jesse Gross 734b57da0e ggml: Remove allocation status reporting
For each memory allocation we report the size of the (attempted)
allocation and whether it succeeded or failed. The latter status
reporting proved to be not that useful in practice as systems
such as Windows can automatically overflow from VRAM into RAM,
resultings in successful allocations even when there isn't
enough memory where we wanted.

As a result, this information is only used for debug logging,
which isn't worthwhile enough for the amount of code. It
also isn't fully accurate, as multiple allocations may result
in partial failures.
2025-09-30 15:04:43 -07:00

242 lines
7.3 KiB
Go

package llm
import (
"context"
"errors"
"fmt"
"strings"
"testing"
"github.com/ollama/ollama/api"
"github.com/ollama/ollama/discover"
"github.com/ollama/ollama/format"
"github.com/ollama/ollama/ml"
"golang.org/x/sync/semaphore"
)
func TestLLMServerFitGPU(t *testing.T) {
type gpu struct {
library string
free int
}
tests := []struct {
name string
gpus []gpu
layers []int
numGPU int
requireFull bool
expected ml.GPULayersList
expectedErr error
}{
{
name: "No GPU",
layers: []int{50 * format.MebiByte, 50 * format.MebiByte, 50 * format.MebiByte},
numGPU: -1,
expected: ml.GPULayersList{},
},
{
name: "Full single GPU",
gpus: []gpu{{free: 256 * format.MebiByte}},
layers: []int{50 * format.MebiByte, 50 * format.MebiByte, 50 * format.MebiByte},
numGPU: -1,
expected: ml.GPULayersList{{ID: "gpu0", Layers: []int{0, 1, 2}}},
},
{
name: "Partial single GPU",
gpus: []gpu{{free: 256 * format.MebiByte}},
layers: []int{100 * format.MebiByte, 100 * format.MebiByte, 100 * format.MebiByte, 100 * format.MebiByte},
numGPU: -1,
expected: ml.GPULayersList{{ID: "gpu0", Layers: []int{1, 2}}},
},
{
name: "Single GPU with numGPU 1",
gpus: []gpu{{free: 256 * format.MebiByte}},
layers: []int{50 * format.MebiByte, 50 * format.MebiByte, 50 * format.MebiByte},
numGPU: 1,
expected: ml.GPULayersList{{ID: "gpu0", Layers: []int{1}}},
},
{
name: "Single GPU with numGPU 0",
gpus: []gpu{{free: 256 * format.MebiByte}},
layers: []int{50 * format.MebiByte, 50 * format.MebiByte, 50 * format.MebiByte},
numGPU: 0,
expected: ml.GPULayersList{},
},
{
name: "Single GPU with numGPU 999",
gpus: []gpu{{free: 256 * format.MebiByte}},
layers: []int{100 * format.MebiByte, 100 * format.MebiByte, 100 * format.MebiByte, 100 * format.MebiByte},
numGPU: 999,
expected: ml.GPULayersList{{ID: "gpu0", Layers: []int{0, 1, 2, 3}}},
},
{
name: "Multi GPU fits on one",
gpus: []gpu{{free: 128 * format.MebiByte}, {free: 256 * format.MebiByte}},
layers: []int{50 * format.MebiByte, 50 * format.MebiByte, 50 * format.MebiByte},
numGPU: -1,
expected: ml.GPULayersList{{ID: "gpu1", Layers: []int{0, 1, 2}}},
},
{
name: "Multi GPU split",
gpus: []gpu{{free: 128 * format.MebiByte}, {free: 256 * format.MebiByte}},
layers: []int{256 * format.MebiByte, 50 * format.MebiByte, 50 * format.MebiByte},
numGPU: -1,
expected: ml.GPULayersList{{ID: "gpu1", Layers: []int{0}}, {ID: "gpu0", Layers: []int{1, 2}}},
},
{
name: "Multi GPU partial",
gpus: []gpu{{free: 128 * format.MebiByte}, {free: 256 * format.MebiByte}},
layers: []int{256 * format.MebiByte, 256 * format.MebiByte, 50 * format.MebiByte},
numGPU: -1,
expected: ml.GPULayersList{{ID: "gpu1", Layers: []int{1}}},
},
{
name: "Multi GPU numGPU 1",
gpus: []gpu{{free: 128 * format.MebiByte}, {free: 256 * format.MebiByte}},
layers: []int{50 * format.MebiByte, 50 * format.MebiByte, 50 * format.MebiByte},
numGPU: 1,
expected: ml.GPULayersList{{ID: "gpu1", Layers: []int{1}}},
},
{
name: "Multi GPU numGPU 2",
gpus: []gpu{{free: 128 * format.MebiByte}, {free: 256 * format.MebiByte}},
layers: []int{256 * format.MebiByte, 50 * format.MebiByte, 50 * format.MebiByte},
numGPU: 2,
expected: ml.GPULayersList{{ID: "gpu1", Layers: []int{0}}, {ID: "gpu0", Layers: []int{1}}},
},
{
name: "Multi GPU numGPU 999",
gpus: []gpu{{free: 128 * format.MebiByte}, {free: 256 * format.MebiByte}},
layers: []int{256 * format.MebiByte, 256 * format.MebiByte, 50 * format.MebiByte},
numGPU: 999,
expected: ml.GPULayersList{{ID: "gpu1", Layers: []int{0, 1}}, {ID: "gpu0", Layers: []int{2}}},
},
{
name: "Multi GPU different libraries",
gpus: []gpu{{library: "cuda", free: 128 * format.MebiByte}, {library: "rocm", free: 256 * format.MebiByte}},
layers: []int{128 * format.MebiByte, 128 * format.MebiByte, 50 * format.MebiByte},
numGPU: -1,
expected: ml.GPULayersList{{ID: "gpu1", Layers: []int{0, 1}}},
},
{
name: "requireFull",
gpus: []gpu{{free: 256 * format.MebiByte}},
layers: []int{100 * format.MebiByte, 100 * format.MebiByte, 100 * format.MebiByte, 100 * format.MebiByte},
numGPU: -1,
requireFull: true,
expectedErr: ErrLoadRequiredFull,
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
var systemInfo discover.SystemInfo
systemInfo.System.TotalMemory = format.GibiByte
systemInfo.System.FreeMemory = 512 * format.MebiByte
systemInfo.System.FreeSwap = 256 * format.MebiByte
gpus := make(discover.GpuInfoList, len(tt.gpus))
for i := range tt.gpus {
gpus[i].ID = fmt.Sprintf("gpu%d", i)
gpus[i].Library = tt.gpus[i].library
gpus[i].FreeMemory = uint64(tt.gpus[i].free)
}
s := &ollamaServer{
llmServer: llmServer{
totalLayers: uint64(len(tt.layers)),
options: api.Options{
Runner: api.Runner{
NumGPU: tt.numGPU,
},
},
},
}
s.mem = &ml.BackendMemory{CPU: ml.DeviceMemory{
Weights: make([]uint64, s.totalLayers),
Cache: make([]uint64, s.totalLayers),
}, GPUs: make([]ml.DeviceMemory, len(gpus))}
for i := range tt.layers {
s.mem.CPU.Weights[i] = uint64(tt.layers[i])
}
for i := range s.mem.GPUs {
s.mem.GPUs[i].ID = fmt.Sprintf("gpu%d", i)
s.mem.GPUs[i].Weights = make([]uint64, s.totalLayers)
s.mem.GPUs[i].Cache = make([]uint64, s.totalLayers)
}
gpuLayers, err := s.createLayout(systemInfo, gpus, s.mem, tt.requireFull, 0)
if err != tt.expectedErr {
t.Fatalf("fitGPU returned error: %v", err)
}
if gpuLayers.Hash() != tt.expected.Hash() {
t.Errorf("fitGPU assigned %v, want %v", gpuLayers, tt.expected)
}
})
}
}
func TestLLMServerCompletionFormat(t *testing.T) {
// This test was written to fix an already deployed issue. It is a bit
// of a mess, and but it's good enough, until we can refactoring the
// Completion method to be more testable.
ctx, cancel := context.WithCancel(t.Context())
s := &llmServer{
sem: semaphore.NewWeighted(1), // required to prevent nil panic
}
checkInvalid := func(format string) {
t.Helper()
err := s.Completion(ctx, CompletionRequest{
Options: new(api.Options),
Format: []byte(format),
}, nil)
want := fmt.Sprintf("invalid format: %q; expected \"json\" or a valid JSON Schema", format)
if err == nil || !strings.Contains(err.Error(), want) {
t.Fatalf("err = %v; want %q", err, want)
}
}
checkInvalid("X") // invalid format
checkInvalid(`"X"`) // invalid JSON Schema
cancel() // prevent further processing if request makes it past the format check
checkValid := func(err error) {
t.Helper()
if !errors.Is(err, context.Canceled) {
t.Fatalf("Completion: err = %v; expected context.Canceled", err)
}
}
valids := []string{
// "missing"
``,
`""`,
`null`,
// JSON
`"json"`,
`{"type":"object"}`,
}
for _, valid := range valids {
err := s.Completion(ctx, CompletionRequest{
Options: new(api.Options),
Format: []byte(valid),
}, nil)
checkValid(err)
}
err := s.Completion(ctx, CompletionRequest{
Options: new(api.Options),
Format: nil, // missing format
}, nil)
checkValid(err)
}