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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with comprehensive build instructions - Documented toolchain constraints and CPU architecture trade-offs - Removed outdated CI/CD workflows (tesla-k80-*.yml) - Cleaned up temporary development artifacts ## Rationale This fork maintains Tesla K80 GPU support (compute 3.7) which was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>
107 lines
4.3 KiB
C
Vendored
107 lines
4.3 KiB
C
Vendored
#pragma once
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#include "ggml.h"
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#include <stddef.h>
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#include <stdint.h>
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// !!! Internal header, to be used by mtmd only !!!
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struct clip_ctx;
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struct clip_image_size {
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int width;
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int height;
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};
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struct clip_image_f32;
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struct clip_image_u8_batch;
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struct clip_image_f32_batch;
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enum clip_modality {
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CLIP_MODALITY_VISION,
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CLIP_MODALITY_AUDIO,
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};
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struct clip_context_params {
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bool use_gpu;
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enum ggml_log_level verbosity;
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};
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struct clip_init_result {
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struct clip_ctx * ctx_v; // vision context
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struct clip_ctx * ctx_a; // audio context
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};
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struct clip_init_result clip_init(const char * fname, struct clip_context_params ctx_params);
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void clip_free(struct clip_ctx * ctx);
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size_t clip_embd_nbytes(const struct clip_ctx * ctx);
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size_t clip_embd_nbytes_by_img(const struct clip_ctx * ctx, int img_w, int img_h);
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int32_t clip_get_image_size (const struct clip_ctx * ctx);
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int32_t clip_get_patch_size (const struct clip_ctx * ctx);
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int32_t clip_get_hidden_size(const struct clip_ctx * ctx);
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// TODO: should be enum, not string
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const char * clip_patch_merge_type(const struct clip_ctx * ctx);
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int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * img);
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// for M-RoPE, this will be the number of token positions in X and Y directions
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// for other models, X will be the total number of tokens and Y will be 1
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int clip_n_output_tokens_x(const struct clip_ctx * ctx, struct clip_image_f32 * img);
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int clip_n_output_tokens_y(const struct clip_ctx * ctx, struct clip_image_f32 * img);
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// this should be equal to the embedding dimension of the text model
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int clip_n_mmproj_embd(const struct clip_ctx * ctx);
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struct clip_image_size * clip_image_size_init(void);
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struct clip_image_u8 * clip_image_u8_init (void);
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struct clip_image_f32 * clip_image_f32_init(void);
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struct clip_image_f32_batch * clip_image_f32_batch_init(void); // only used by libllava
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// nx, ny are the output image dimensions
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unsigned char * clip_image_u8_get_data(struct clip_image_u8 * img, uint32_t * nx, uint32_t * ny);
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void clip_image_size_free (struct clip_image_size * img_size);
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void clip_image_u8_free (struct clip_image_u8 * img);
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void clip_image_f32_free(struct clip_image_f32 * img);
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void clip_image_u8_batch_free (struct clip_image_u8_batch * batch);
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void clip_image_f32_batch_free(struct clip_image_f32_batch * batch);
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// use for accessing underlay data of clip_image_f32_batch
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size_t clip_image_f32_batch_n_images(const struct clip_image_f32_batch * batch); // equivalent to batch->size()
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size_t clip_image_f32_batch_nx(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->nx
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size_t clip_image_f32_batch_ny(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->ny
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struct clip_image_f32 * clip_image_f32_get_img(const struct clip_image_f32_batch * batch, int idx); // equivalent to batch[idx]->data
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/**
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* Build image from pixels decoded by other libraries instead of stb_image.h for better performance.
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* The memory layout is RGBRGBRGB..., input buffer length must be 3*nx*ny bytes
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*/
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void clip_build_img_from_pixels(const unsigned char * rgb_pixels, int nx, int ny, struct clip_image_u8 * img);
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/** preprocess img and store the result in res_imgs, pad_to_square may be overridden to false depending on model configuration */
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bool clip_image_preprocess(struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32_batch * res_imgs );
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struct ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx);
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bool clip_image_encode (struct clip_ctx * ctx, int n_threads, struct clip_image_f32 * img, float * vec);
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bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, const struct clip_image_f32_batch * imgs, float * vec);
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int clip_is_minicpmv(const struct clip_ctx * ctx);
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bool clip_is_glm(const struct clip_ctx * ctx);
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bool clip_is_qwen2vl(const struct clip_ctx * ctx);
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bool clip_is_llava(const struct clip_ctx * ctx);
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bool clip_is_gemma3(const struct clip_ctx * ctx);
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bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec);
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// use by audio input
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void clip_image_f32_batch_add_mel(struct clip_image_f32_batch * batch, int n_mel, int n_frames, float * mel);
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bool clip_has_vision_encoder(const struct clip_ctx * ctx);
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bool clip_has_audio_encoder(const struct clip_ctx * ctx);
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bool clip_has_whisper_encoder(const struct clip_ctx * ctx);
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