Files
ollama37/llm/memory_test.go
Shang Chieh Tseng ef14fb5b26 Sync with upstream ollama/ollama and restore Tesla K80 (compute 3.7) support
This commit represents a complete rework after pulling the latest changes from
official ollama/ollama repository and re-applying Tesla K80 compatibility patches.

## Key Changes

### CUDA Compute Capability 3.7 Support (Tesla K80)
- Added sm_37 (compute 3.7) to CMAKE_CUDA_ARCHITECTURES in CMakeLists.txt
- Updated CMakePresets.json to include compute 3.7 in "CUDA 11" preset
- Using 37-virtual (PTX with JIT compilation) for maximum compatibility

### Legacy Toolchain Compatibility
- **NVIDIA Driver**: 470.256.02 (last version supporting Kepler/K80)
- **CUDA Version**: 11.4.4 (last CUDA 11.x supporting compute 3.7)
- **GCC Version**: 10.5.0 (required by CUDA 11.4 host_config.h)

### CPU Architecture Trade-offs
Due to GCC 10.5 limitation, sacrificed newer CPU optimizations:
- Alderlake CPU variant enabled WITHOUT AVX_VNNI (requires GCC 11+)
- Still supports: SSE4.2, AVX, F16C, AVX2, BMI2, FMA
- Performance impact: ~3-7% on newer CPUs (acceptable for K80 compatibility)

### Build System Updates
- Modified ml/backend/ggml/ggml/src/ggml-cuda/CMakeLists.txt for compute 3.7
- Added -Wno-deprecated-gpu-targets flag to suppress warnings
- Updated ml/backend/ggml/ggml/src/CMakeLists.txt for Alderlake without AVX_VNNI

### Upstream Sync
Merged latest llama.cpp changes including:
- Enhanced KV cache management with ISWA and hybrid memory support
- Improved multi-modal support (mtmd framework)
- New model architectures (Gemma3, Llama4, Qwen3, etc.)
- GPU backend improvements for CUDA, Metal, and ROCm
- Updated quantization support and GGUF format handling

### Documentation
- Updated CLAUDE.md with comprehensive build instructions
- Documented toolchain constraints and CPU architecture trade-offs
- Removed outdated CI/CD workflows (tesla-k80-*.yml)
- Cleaned up temporary development artifacts

## Rationale

This fork maintains Tesla K80 GPU support (compute 3.7) which was dropped in
official Ollama due to legacy driver/CUDA requirements. The toolchain constraint
creates a deadlock:
- K80 → Driver 470 → CUDA 11.4 → GCC 10 → No AVX_VNNI

We accept the loss of cutting-edge CPU optimizations to enable running modern
LLMs on legacy but still capable Tesla K80 hardware (12GB VRAM per GPU).

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-05 14:03:05 +08:00

131 lines
4.4 KiB
Go

package llm
import (
"bytes"
"fmt"
"os"
"testing"
"github.com/stretchr/testify/assert"
"github.com/stretchr/testify/require"
"github.com/ollama/ollama/api"
"github.com/ollama/ollama/format"
"github.com/ollama/ollama/fs/ggml"
"github.com/ollama/ollama/ml"
)
func TestEstimateGPULayers(t *testing.T) {
t.Setenv("OLLAMA_DEBUG", "1")
t.Setenv("OLLAMA_KV_CACHE_TYPE", "") // Ensure default f16
t.Setenv("OLLAMA_CONTEXT_LENGTH", "2048")
modelName := "dummy"
f, err := os.CreateTemp(t.TempDir(), modelName)
require.NoError(t, err)
defer f.Close()
inputLayerCount := 5
tensors := []*ggml.Tensor{
{Name: "blk.0.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
{Name: "blk.1.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
{Name: "blk.2.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
{Name: "blk.3.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
{Name: "blk.4.attn.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
{Name: "output.weight", Kind: uint32(0), Offset: uint64(0), Shape: []uint64{1, 1, 1, 1}, WriterTo: bytes.NewReader(make([]byte, 32))},
}
assert.Len(t, tensors, inputLayerCount+1)
err = ggml.WriteGGUF(f, ggml.KV{
"general.architecture": "llama",
"llama.context_length": uint32(32),
"llama.embedding_length": uint32(4096),
"llama.block_count": uint32(inputLayerCount),
"llama.attention.head_count": uint32(32),
"llama.attention.head_count_kv": uint32(32),
"tokenizer.ggml.tokens": []string{" "},
"tokenizer.ggml.scores": []float32{0},
"tokenizer.ggml.token_type": []int32{0},
}, tensors)
require.NoError(t, err)
ggml, err := LoadModel(f.Name(), 0)
if err != nil {
t.Fatal(err)
}
// Simple CPU scenario
gpus := []ml.DeviceInfo{}
projectors := []string{}
opts := api.DefaultOptions()
t.Run("cpu", func(t *testing.T) {
estimate := estimateGPULayers(gpus, ggml, projectors, opts, 1)
assert.Equal(t, 0, estimate.Layers)
assert.Equal(t, uint64(0), estimate.Graph)
})
// derived from the dummy ggml file above
graphPartialOffload := uint64(202377216)
graphFullOffload := uint64(171968512)
layerSize := uint64(33554436)
projectorSize := uint64(0)
memoryLayerOutput := uint64(4)
// Dual CUDA scenario with asymmetry
gpuMinimumMemory := uint64(457 * format.MebiByte)
gpus = []ml.DeviceInfo{
{
DeviceID: ml.DeviceID{
Library: "CUDA",
},
},
{
DeviceID: ml.DeviceID{
Library: "CUDA",
},
},
}
// Nested array: GPU0 layer space, GPU1 layer space, expected gpu0, expected gpu1
for i, s := range []struct {
layer0, layer1 uint64
expect0, expect1 int
}{
{1, 1, 1, 1},
{2, 1, 2, 1},
{2, 2, 2, 2},
{1, 2, 1, 2},
{3, 3, 3, 3},
{4, 4, 3, 3},
{6, 6, 3, 3},
{0, 3, 0, 3},
} {
t.Run(fmt.Sprintf("%v", s), func(t *testing.T) {
gpus[0].FreeMemory = 0
gpus[1].FreeMemory = 0
gpus[0].FreeMemory += projectorSize
if s.layer0 > 0 {
gpus[0].FreeMemory += memoryLayerOutput
} else {
gpus[1].FreeMemory += memoryLayerOutput
}
gpus[0].FreeMemory += gpuMinimumMemory + layerSize + s.layer0*layerSize + 1
gpus[1].FreeMemory += gpuMinimumMemory + layerSize + s.layer1*layerSize + 1
gpus[0].FreeMemory += max(graphFullOffload, graphPartialOffload)
gpus[1].FreeMemory += max(graphFullOffload, graphPartialOffload)
estimate := estimateGPULayers(gpus, ggml, projectors, opts, 1)
assert.Equal(t, s.expect0+s.expect1, estimate.Layers, "scenario %d: %v", i, s)
assert.Equal(t, []int{s.expect0, s.expect1}, estimate.TensorSplit, "scenario %d: %v", i, s)
var layerSums uint64
for _, b := range estimate.GPUSizes {
layerSums += b
}
if estimate.Layers < inputLayerCount+1 {
assert.Less(t, estimate.VRAMSize, estimate.TotalSize, "scenario %d: %v %+v", i, s, estimate)
assert.Equal(t, estimate.VRAMSize, layerSums, "scenario %d: %v %+v", i, s, estimate)
} else {
assert.Equal(t, estimate.VRAMSize, estimate.TotalSize, "scenario %d: %v %+v", i, s, estimate)
assert.Equal(t, estimate.TotalSize, layerSums, "scenario %d: %v %+v", i, s, estimate)
}
})
}
}