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dcfb7a105c
* add build to .dockerignore * test: only build one arch * add build to .gitignore * fix ccache path * filter amdgpu targets * only filter if autodetecting * Don't clobber gpu list for default runner This ensures the GPU specific environment variables are set properly * explicitly set CXX compiler for HIP * Update build_windows.ps1 This isn't complete, but is close. Dependencies are missing, and it only builds the "default" preset. * build: add ollama subdir * add .git to .dockerignore * docs: update development.md * update build_darwin.sh * remove unused scripts * llm: add cwd and build/lib/ollama to library paths * default DYLD_LIBRARY_PATH to LD_LIBRARY_PATH in runner on macOS * add additional cmake output vars for msvc * interim edits to make server detection logic work with dll directories like lib/ollama/cuda_v12 * remove unncessary filepath.Dir, cleanup * add hardware-specific directory to path * use absolute server path * build: linux arm * cmake install targets * remove unused files * ml: visit each library path once * build: skip cpu variants on arm * build: install cpu targets * build: fix workflow * shorter names * fix rocblas install * docs: clean up development.md * consistent build dir removal in development.md * silence -Wimplicit-function-declaration build warnings in ggml-cpu * update readme * update development readme * llm: update library lookup logic now that there is one runner (#8587) * tweak development.md * update docs * add windows cuda/rocm tests --------- Co-authored-by: jmorganca <jmorganca@gmail.com> Co-authored-by: Daniel Hiltgen <daniel@ollama.com>
49 lines
1.8 KiB
C
Vendored
49 lines
1.8 KiB
C
Vendored
#ifndef LLAVA_H
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#define LLAVA_H
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#include "ggml.h"
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#ifdef LLAMA_SHARED
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# if defined(_WIN32) && !defined(__MINGW32__)
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# ifdef LLAMA_BUILD
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# define LLAVA_API __declspec(dllexport)
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# else
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# define LLAVA_API __declspec(dllimport)
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# endif
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# else
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# define LLAVA_API __attribute__ ((visibility ("default")))
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# endif
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#else
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# define LLAVA_API
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#endif
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#ifdef __cplusplus
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extern "C" {
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#endif
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struct clip_ctx;
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struct llava_image_embed {
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float * embed;
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int n_image_pos;
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};
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/** sanity check for clip <-> llava embed size match */
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LLAVA_API bool llava_validate_embed_size(const struct llama_context * ctx_llama, const struct clip_ctx * ctx_clip);
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LLAVA_API bool llava_image_embed_make_with_clip_img(struct clip_ctx * ctx_clip, int n_threads, const struct clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out);
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/** build an image embed from image file bytes */
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LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length);
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/** build an image embed from a path to an image filename */
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LLAVA_API struct llava_image_embed * llava_image_embed_make_with_filename(struct clip_ctx * ctx_clip, int n_threads, const char * image_path);
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/** free an embedding made with llava_image_embed_make_* */
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LLAVA_API void llava_image_embed_free(struct llava_image_embed * embed);
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/** write the image represented by embed into the llama context with batch size n_batch, starting at context pos n_past. on completion, n_past points to the next position in the context after the image embed. */
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LLAVA_API bool llava_eval_image_embed(struct llama_context * ctx_llama, const struct llava_image_embed * embed, int n_batch, int * n_past);
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#ifdef __cplusplus
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}
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#endif
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#endif
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