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#include "../clip-graph.h"
/*
* IMPORTANT: The mtmd module does NOT accept pull requests that are fully or predominantly AI-generated.
* We encourage human contributors to ensure the quality and reliability of the codebase.
*/
struct clip_graph_siglip : clip_graph {
clip_graph_siglip(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
};
struct clip_graph_gemma4v : clip_graph {
clip_graph_gemma4v(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
ggml_tensor * build_mm(ggml_tensor * w, ggml_tensor * x) const override;
bool support_batch() const override { return true; }
};
struct clip_graph_gemma4uv : clip_graph {
clip_graph_gemma4uv(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
};
struct clip_graph_pixtral : clip_graph {
clip_graph_pixtral(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
};
struct clip_graph_qwen2vl : clip_graph {
clip_graph_qwen2vl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
ggml_tensor * build_inp_with_temporal_merge();
};
struct clip_graph_qwen3vl : clip_graph_qwen2vl {
clip_graph_qwen3vl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph_qwen2vl(ctx, img) {}
ggml_cgraph * build() override;
};
struct clip_graph_mimovl : clip_graph {
clip_graph_mimovl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
// Force F32 mat-mul accumulation to avoid F16 overflow in the FFN down-proj
// when the mmproj is stored in F16 (the source weights are BF16; downcasting
// to F16 reduces dynamic range below the SwiGLU output magnitude on the last few layers).
ggml_tensor * build_mm(ggml_tensor * w, ggml_tensor * x) const override;
};
struct clip_graph_step3vl : clip_graph {
clip_graph_step3vl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
};
struct clip_graph_youtuvl : clip_graph {
clip_graph_youtuvl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
};
struct clip_graph_yasa2 : clip_graph {
clip_graph_yasa2(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
ggml_tensor * layer_norm_channels(ggml_tensor * inp, ggml_tensor * w, ggml_tensor * b, float eps = 1e-6f);
ggml_tensor * convnext_grn(ggml_tensor * inp, ggml_tensor * w, ggml_tensor * b);
};
struct clip_graph_minicpmv : clip_graph {
clip_graph_minicpmv(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
};
struct clip_graph_minicpmv4_6 : clip_graph {
clip_graph_minicpmv4_6(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
};
struct clip_graph_internvl : clip_graph {
clip_graph_internvl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
bool support_batch() const override { return true; }
};
struct clip_graph_nemotron_v2_vl : clip_graph {
clip_graph_nemotron_v2_vl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
};
struct clip_graph_llama4 : clip_graph {
clip_graph_llama4(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
};
struct clip_graph_kimivl : clip_graph {
clip_graph_kimivl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
};
struct clip_graph_paddleocr : clip_graph {
clip_graph_paddleocr(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
};
struct clip_graph_dotsocr : clip_graph {
clip_graph_dotsocr(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
};
struct clip_graph_cogvlm : clip_graph {
clip_graph_cogvlm(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
};
struct clip_graph_llava : clip_graph {
clip_graph_llava(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
};
struct clip_graph_whisper_enc : clip_graph {
clip_graph_whisper_enc(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
};
struct clip_graph_deepseekocr : clip_graph {
clip_graph_deepseekocr(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
ggml_tensor * build_sam(ggml_tensor * inp); // build the SAM model
// bool support_batch() const override { return true; } // TODO: support batch for DeepSeek-OCR v1
};
struct clip_graph_deepseekocr2 : clip_graph_deepseekocr {
clip_graph_deepseekocr2(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph_deepseekocr(ctx, img) {}
ggml_cgraph * build() override; // reuses build_sam() from base
};
struct clip_graph_conformer : clip_graph {
clip_graph_conformer(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
};
struct clip_graph_granite_speech : clip_graph {
clip_graph_granite_speech(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
};
struct clip_graph_gemma4a : clip_graph {
clip_graph_gemma4a(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
ggml_tensor * build_mm(ggml_tensor * w, ggml_tensor * x) const override;
};
struct clip_graph_gemma4ua : clip_graph {
clip_graph_gemma4ua(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
};
struct clip_graph_glm4v : clip_graph {
clip_graph_glm4v(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
};
struct clip_graph_hunyuanvl : clip_graph {
clip_graph_hunyuanvl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
};
struct clip_graph_mobilenetv5 : clip_graph {
clip_graph_mobilenetv5(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
ggml_tensor * rms_norm_2d(
ggml_tensor * inp,
ggml_tensor * weight,
float eps = 1e-6f);
ggml_tensor* pad_same_2d(
ggml_tensor* inp,
int kernel_h,
int kernel_w,
int stride_h,
int stride_w,
int dilation_h = 1,
int dilation_w = 1);
ggml_tensor * build_edge_residual(
ggml_tensor * inp,
const mobilenetv5_block & block,
int stride);
ggml_tensor * build_inverted_residual(
ggml_tensor * inp,
const mobilenetv5_block & block,
int stride);
ggml_tensor * build_mobilenet_attn(
ggml_tensor * inp,
const mobilenetv5_block & block);
};
struct clip_graph_qwen3a : clip_graph {
clip_graph_qwen3a(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
};
struct clip_graph_kimik25 : clip_graph {
clip_graph_kimik25(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
ggml_tensor * resize_position_embeddings_3d(uint32_t interpolation_mode);
};
struct clip_graph_exaone4_5 : clip_graph {
clip_graph_exaone4_5(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
ggml_cgraph * build() override;
};
struct clip_graph_granite4_vision : clip_graph {
clip_graph_granite4_vision(clip_ctx * ctx, const clip_image_f32 & img)
: clip_graph(ctx, img),
add_newline(img.add_newline) {}
ggml_cgraph * build() override;
private:
// The graph is per-tile since only batch-size 1 is supported in clip. As
// such, this value is set at construct time based on the tile that will be
// encoded, then used during build to determine how to handle newlines.
const bool add_newline;
ggml_tensor * gather(ggml_tensor * src, const std::string & name, int idx_len);
ggml_tensor * interp_down(ggml_tensor * src, int side, int new_side);
ggml_tensor * build_block(const qf_block & blk, ggml_tensor * h, int bid,
int spatial_offset, int image_side, int window_side,
int query_side, float qformer_eps);
ggml_tensor * build_newline_row(ggml_context * ctx0);
ggml_tensor * append_rowwise_newlines(ggml_context * ctx0, ggml_tensor * tile_output);
};
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