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- Removed outdated CI/CD workflows (tesla-k80-*.yml)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>
This commit is contained in:
Shang Chieh Tseng
2025-11-05 14:03:05 +08:00
parent fabe2c5cb7
commit ef14fb5b26
817 changed files with 241634 additions and 70888 deletions

View File

@@ -146,8 +146,6 @@ func (ftype FileType) ToTensorType() TensorType {
return TensorTypeQ4_0
case fileTypeQ4_1:
return TensorTypeQ4_1
case fileTypeMXFP4:
return TensorTypeMXFP4 // Formerly unused tensorTypeQ4_2
case FileTypeQ8_0:
return TensorTypeQ8_0
case fileTypeQ5_0:
@@ -176,6 +174,8 @@ func (ftype FileType) ToTensorType() TensorType {
return TensorTypeQ2_K
case FileTypeBF16:
return TensorTypeBF16
case fileTypeMXFP4:
return TensorTypeMXFP4
default:
slog.Warn("unsupported file type", "type", ftype)
return 0 // F32
@@ -187,45 +187,49 @@ func (ftype FileType) ToTensorType() TensorType {
type TensorType uint32
const (
TensorTypeF32 TensorType = 0
TensorTypeF16 = 1
TensorTypeQ4_0 = 2
TensorTypeQ4_1 = 3
// 4 = Q4_2 removed
// 5 = Q4_3 removed
TensorTypeQ5_0 = 6
TensorTypeQ5_1 = 7
TensorTypeQ8_0 = 8
TensorTypeQ8_1 = 9
TensorTypeQ2_K = 10
TensorTypeQ3_K = 11
TensorTypeQ4_K = 12
TensorTypeQ5_K = 13
TensorTypeQ6_K = 14
TensorTypeQ8_K = 15
tensorTypeIQ2_XXS = 16 // not supported by ollama
tensorTypeIQ2_XS = 17 // not supported by ollama
tensorTypeIQ3_XXS = 18 // not supported by ollama
tensorTypeIQ1_S = 19 // not supported by ollama
tensorTypeIQ4_NL = 20 // not supported by ollama
tensorTypeIQ3_S = 21 // not supported by ollama
tensorTypeIQ2_S = 22 // not supported by ollama
tensorTypeIQ4_XS = 23 // not supported by ollama
TensorTypeI8 = 24
TensorTypeI16 = 25
TensorTypeI32 = 26
TensorTypeI64 = 27
TensorTypeF64 = 28
tensorTypeIQ1_M = 29 // not supported by ollama
TensorTypeBF16 = 30
// 31-33 = Q4_0 variants removed
tensorTypeTQ1_0 = 34 // not supported by ollama
tensorTypeTQ2_0 = 35 // not supported by ollama
// 36-38 = IQ4_NL variants removed
TensorTypeMXFP4 = 39
TensorTypeF32 TensorType = iota
TensorTypeF16
TensorTypeQ4_0
TensorTypeQ4_1
tensorTypeQ4_2
tensorTypeQ4_3 // unused by GGML
TensorTypeQ5_0
TensorTypeQ5_1
TensorTypeQ8_0
TensorTypeQ8_1
TensorTypeQ2_K
TensorTypeQ3_K
TensorTypeQ4_K
TensorTypeQ5_K
TensorTypeQ6_K
TensorTypeQ8_K
tensorTypeIQ2_XXS // not supported by ollama
tensorTypeIQ2_XS // not supported by ollama
tensorTypeIQ3_XXS // not supported by ollama
tensorTypeIQ1_S // not supported by ollama
tensorTypeIQ4_NL // not supported by ollama
tensorTypeIQ3_S // not supported by ollama
tensorTypeIQ2_S // not supported by ollama
tensorTypeIQ4_XS // not supported by ollama
TensorTypeI8
TensorTypeI16
TensorTypeI32
TensorTypeI64
TensorTypeF64
tensorTypeIQ1_M // not supported by ollama
TensorTypeBF16
tensorTypeQ4_0_4_4 // unused by GGML
tensorTypeQ4_0_4_8 // unused by GGML
tensorTypeQ4_0_8_8 // unused by GGML
tensorTypeTQ1_0 // not supported by ollama
tensorTypeTQ2_0 // not supported by ollama
tensorTypeIQ4_NL_4_4 // unused by GGML
tensorTypeIQ4_NL_4_8 // unused by GGML
tensorTypeIQ4_NL_8_8 // unused by GGML
TensorTypeMXFP4
)
// ParseFileType parses the provided GGUF file type
// ParseTensorType parses the provided GGUF tensor type
// Only Ollama supported types are considered valid
func ParseTensorType(s string) (TensorType, error) {
switch s {
@@ -315,7 +319,7 @@ func (t TensorType) String() string {
return "F64"
case TensorTypeBF16:
return "BF16"
case TensorTypeMXFP4:
case 4, TensorTypeMXFP4:
return "MXFP4"
default:
return "unknown"