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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with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was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>
92 lines
3.9 KiB
C
Vendored
92 lines
3.9 KiB
C
Vendored
#ifndef MTMD_HELPER_H
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#define MTMD_HELPER_H
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#include "ggml.h"
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#include "llama.h"
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#include "mtmd.h"
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#include <stddef.h>
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#include <stdint.h>
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#include <stdbool.h>
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#ifdef __cplusplus
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extern "C" {
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#endif
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//
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// libmtmd helper functions
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//
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// Please note that these helpers are not guaranteed to be stable.
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// BREAKING CHANGES are expected.
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//
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// helper function to construct a mtmd_bitmap from a file
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// it calls mtmd_helper_bitmap_init_from_buf() internally
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// returns nullptr on failure
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// this function is thread-safe
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MTMD_API mtmd_bitmap * mtmd_helper_bitmap_init_from_file(mtmd_context * ctx, const char * fname);
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// helper function to construct a mtmd_bitmap from a buffer containing a file
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// supported formats:
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// image: formats supported by stb_image: jpg, png, bmp, gif, etc.
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// audio: formats supported by miniaudio: wav, mp3, flac
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// note: audio files will be auto-detected based on magic bytes
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// returns nullptr on failure
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// this function is thread-safe
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MTMD_API mtmd_bitmap * mtmd_helper_bitmap_init_from_buf(mtmd_context * ctx, const unsigned char * buf, size_t len);
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// helper to count the total number of tokens from a list of chunks, useful to keep track of KV cache
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MTMD_API size_t mtmd_helper_get_n_tokens(const mtmd_input_chunks * chunks);
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// helper to count the total position of tokens from a list of chunks, useful to keep track of n_past
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// normally, n_pos is equal to n_tokens, but for M-RoPE it is different
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MTMD_API llama_pos mtmd_helper_get_n_pos(const mtmd_input_chunks * chunks);
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// helper function that automatically:
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// 1. run llama_decode() on text chunks
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// 2. run mtmd_encode() on image chunks, then mtmd_get_output_embd() and then llama_decode()
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// if any of the mtmd_encode() or llama_decode() calls return non-zero, stop and forward the error
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// otherwise, returns 0 on success
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// this function is NOT thread-safe
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MTMD_API int32_t mtmd_helper_eval_chunks(mtmd_context * ctx,
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struct llama_context * lctx,
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const mtmd_input_chunks * chunks,
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llama_pos n_past,
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llama_seq_id seq_id,
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int32_t n_batch,
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bool logits_last,
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llama_pos * new_n_past);
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// works like mtmd_helper_eval_chunks(), but only for a single chunk
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// this function is NOT thread-safe
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MTMD_API int32_t mtmd_helper_eval_chunk_single(mtmd_context * ctx,
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struct llama_context * lctx,
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const mtmd_input_chunk * chunk,
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llama_pos n_past,
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llama_seq_id seq_id,
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int32_t n_batch,
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bool logits_last,
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llama_pos * new_n_past);
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// helper function to decode an image whose embeddings have already been calculated
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// this helper will handle batching and pre/post decoding setup (for ex. gemma 3 requires non-causal attention)
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// ret 0 on success, -1 on chunk not being a valid image chunk, 1 on decode failure
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MTMD_API int32_t mtmd_helper_decode_image_chunk(mtmd_context * ctx,
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struct llama_context * lctx,
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const mtmd_input_chunk * chunk,
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float * encoded_embd,
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llama_pos n_past,
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llama_seq_id seq_id,
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int32_t n_batch,
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llama_pos * new_n_past);
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#ifdef __cplusplus
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} // extern "C"
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#endif
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//
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// C++ wrappers
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//
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#endif
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