Files
ollama37/llama/llama.cpp/tools/mtmd/clip.h
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- Removed outdated CI/CD workflows (tesla-k80-*.yml)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

107 lines
4.3 KiB
C
Vendored

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