Files
ollama37/model/sentencepiece_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- Removed outdated CI/CD workflows (tesla-k80-*.yml)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

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This file contains ambiguous Unicode characters
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package model
import (
"log/slog"
"os"
"path/filepath"
"slices"
"testing"
"google.golang.org/protobuf/proto"
"github.com/ollama/ollama/convert/sentencepiece"
)
func loadSentencePieceVocab(t *testing.T) SentencePiece {
t.Helper()
bts, err := os.ReadFile(filepath.Join("testdata", "gemma2", "tokenizer.model"))
if err != nil {
t.Fatal(err)
}
var spm sentencepiece.ModelProto
if err := proto.Unmarshal(bts, &spm); err != nil {
t.Fatal(err)
}
var v Vocabulary
for _, piece := range spm.GetPieces() {
v.Values = append(v.Values, piece.GetPiece())
v.Scores = append(v.Scores, piece.GetScore())
switch t := piece.GetType(); t {
case sentencepiece.ModelProto_SentencePiece_UNKNOWN,
sentencepiece.ModelProto_SentencePiece_CONTROL,
sentencepiece.ModelProto_SentencePiece_UNUSED,
sentencepiece.ModelProto_SentencePiece_BYTE:
v.Types = append(v.Types, int32(t))
default:
tt := int32(sentencepiece.ModelProto_SentencePiece_NORMAL)
// todo parse the special tokens file
// - this will roundtrip correctly but the <start_of_turn> and
// <end_of_turn> tokens aren't processed
v.Types = append(v.Types, tt)
}
}
return NewSentencePiece(&v)
}
func TestSentencePieceEncode(t *testing.T) {
logger := slog.New(slog.NewTextHandler(os.Stdout, &slog.HandlerOptions{Level: slog.LevelDebug}))
slog.SetDefault(logger)
tokenizer := loadSentencePieceVocab(t)
t.Run("basic roundtrip", func(t *testing.T) {
t.Parallel()
cases := []string{
"hello",
"hello ",
"hello ",
" hello",
" hello ",
" hello ",
"hello world",
"请考试我的软件12345",
"你好",
"Hello 你好 world!",
"Special characters: !@#$%^&*()_+-=[]{}|;':\",./<>?",
"Multilingual: 你好 こんにちは Привет Hola مرحبا",
"Numbers and symbols: 123456789 +- */",
"Special tokens: <bos> text <eos>",
"Code snippets: func main() { fmt.Println(\"Hello World\") }",
"Long text: " + "Lorem ipsum dolor sit amet, consectetur adipiscing elit. " +
"Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. " +
"Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris.",
}
for _, want := range cases {
ids, err := tokenizer.Encode(want, true)
if err != nil {
t.Fatal(err)
}
if got, err := tokenizer.Decode(ids); err != nil {
t.Fatal(err)
} else if got != want {
t.Errorf("got %q, want %q [%#v]", got, want, ids)
}
}
})
t.Run("special tokens", func(t *testing.T) {
type candidate struct {
token string
ids []int32
}
cases := []candidate{
{"<bos>", []int32{2}},
{"<eos>", []int32{1}},
}
for _, want := range cases {
ids, err := tokenizer.Encode(want.token, true)
if err != nil {
t.Fatal(err)
}
if !slices.Equal(ids, want.ids) {
t.Errorf("got %#v, want %#v", ids, want.ids)
}
}
})
}
func TestSentencePieceDecodeByteTokens(t *testing.T) {
vocab := &Vocabulary{
Values: []string{
"normal",
"<0xEA>",
"<0x41>",
"<0xC3>",
"<0xA3>",
},
Types: []int32{
TOKEN_TYPE_NORMAL,
TOKEN_TYPE_BYTE,
TOKEN_TYPE_BYTE,
TOKEN_TYPE_BYTE,
TOKEN_TYPE_BYTE,
},
Scores: []float32{0, 0, 0, 0, 0},
}
spm := NewSentencePiece(vocab)
tests := []struct {
name string
ids []int32
expected string
}{
{
name: "single byte token",
ids: []int32{1},
expected: "\xea",
},
{
name: "ASCII byte token",
ids: []int32{2},
expected: "A",
},
{
name: "multiple byte tokens forming UTF-8 character",
ids: []int32{3, 4},
expected: "ã",
},
}
for _, tt := range tests {
t.Run(tt.name, func(t *testing.T) {
result, err := spm.Decode(tt.ids)
if err != nil {
t.Errorf("failed to decode token IDs %v: %v", tt.ids, err)
}
if result != tt.expected {
t.Errorf("got %q, want %q", result, tt.expected)
}
})
}
}