mirror of
https://github.com/dogkeeper886/ollama37.git
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ef14fb5b26
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with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was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>
86 lines
2.9 KiB
Go
86 lines
2.9 KiB
Go
package ggml
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// #cgo CPPFLAGS: -I${SRCDIR}/ggml/src
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// #include <stdlib.h>
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// #include <stdint.h>
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// #include "ggml.h"
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// #include "ggml-cpu.h"
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// #include "ggml-backend.h"
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// #include "ggml-quants.h"
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import "C"
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import (
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"unsafe"
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fsggml "github.com/ollama/ollama/fs/ggml"
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)
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// ConvertToF32 converts (dequantizes) the raw data to F32 so we can then quantize it
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func ConvertToF32(data []byte, dtype uint32, nelements uint64) []float32 {
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f32s := make([]float32, nelements)
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elems := C.int64_t(nelements)
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switch dtype {
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case C.GGML_TYPE_F16:
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C.ggml_fp16_to_fp32_row((*C.uint16_t)(unsafe.Pointer(&data[0])), (*C.float)(&f32s[0]), elems)
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case C.GGML_TYPE_Q4_0:
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C.dequantize_row_q4_0((*C.block_q4_0)(unsafe.Pointer(&data[0])), (*C.float)(&f32s[0]), elems)
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case C.GGML_TYPE_Q4_1:
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C.dequantize_row_q4_1((*C.block_q4_1)(unsafe.Pointer(&data[0])), (*C.float)(&f32s[0]), elems)
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case C.GGML_TYPE_Q5_0:
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C.dequantize_row_q5_0((*C.block_q5_0)(unsafe.Pointer(&data[0])), (*C.float)(&f32s[0]), elems)
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case C.GGML_TYPE_Q5_1:
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C.dequantize_row_q5_1((*C.block_q5_1)(unsafe.Pointer(&data[0])), (*C.float)(&f32s[0]), elems)
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case C.GGML_TYPE_Q8_0:
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C.dequantize_row_q8_0((*C.block_q8_0)(unsafe.Pointer(&data[0])), (*C.float)(&f32s[0]), elems)
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case C.GGML_TYPE_Q2_K:
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C.dequantize_row_q2_K((*C.block_q2_K)(unsafe.Pointer(&data[0])), (*C.float)(&f32s[0]), elems)
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case C.GGML_TYPE_Q3_K:
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C.dequantize_row_q3_K((*C.block_q3_K)(unsafe.Pointer(&data[0])), (*C.float)(&f32s[0]), elems)
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case C.GGML_TYPE_Q4_K:
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C.dequantize_row_q4_K((*C.block_q4_K)(unsafe.Pointer(&data[0])), (*C.float)(&f32s[0]), elems)
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case C.GGML_TYPE_Q5_K:
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C.dequantize_row_q5_K((*C.block_q5_K)(unsafe.Pointer(&data[0])), (*C.float)(&f32s[0]), elems)
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case C.GGML_TYPE_Q6_K:
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C.dequantize_row_q6_K((*C.block_q6_K)(unsafe.Pointer(&data[0])), (*C.float)(&f32s[0]), elems)
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case C.GGML_TYPE_BF16:
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C.ggml_bf16_to_fp32_row((*C.ggml_bf16_t)(unsafe.Pointer(&data[0])), (*C.float)(&f32s[0]), elems)
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case C.GGML_TYPE_MXFP4:
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C.dequantize_row_mxfp4((*C.block_mxfp4)(unsafe.Pointer(&data[0])), (*C.float)(&f32s[0]), elems)
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default:
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panic("unsupported quantization format")
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}
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return f32s
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}
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func Quantize(newType fsggml.TensorType, f32s []float32, shape []uint64) []byte {
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buf := make([]byte, len(f32s)*4) // upper bound on size
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nPerRow := C.int64_t(shape[0])
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nrows := C.int64_t(1)
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if len(shape) > 1 {
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nrows = C.int64_t(shape[1])
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}
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shape2 := C.int64_t(1)
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if len(shape) > 2 {
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shape2 = C.int64_t(shape[2])
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}
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nelements_matrix := nPerRow * nrows
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newSize := C.size_t(0)
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for i03 := C.int64_t(0); i03 < shape2; i03++ {
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f32s_03 := i03 * nelements_matrix
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buf_03 := C.int64_t(C.ggml_row_size(uint32(newType), nPerRow)) * i03 * nrows
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newSize += C.ggml_quantize_chunk(
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uint32(newType),
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(*C.float)(&f32s[f32s_03]),
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unsafe.Pointer((uintptr)(unsafe.Pointer(&buf[0]))+uintptr(buf_03)),
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0,
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nrows,
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nPerRow,
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nil)
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}
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return buf[:newSize]
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}
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func QuantizationVersion() uint32 {
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return uint32(C.GGML_QNT_VERSION)
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}
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