Kwa Jie Hao
98d2d2884e
* feat: (vocab) fix stray text appended in llama_decode_text Remove accidental concatenation of the full `text` string when formatting UNK_BYTE hex escapes. Only the closing "]" should be appended. * feat(mtmd): add Yasa2 vision encoder support Add a Yasa2 (ConvNeXtV2-based) vision encoder for reka-edge: - Register PROJECTOR_TYPE_YASA2 and tensor name definitions - Add yasa2_block/yasa2_stage model structs - Implement graph builder with ConvNeXt stages, GRN, adaptive pooling - Wire into clip.cpp switch statements and mtmd.cpp init_vision - Use mtmd_image_preprocessor_fixed_size for image preprocessing * feat(chat): add reka-edge template handler (tools, thinking) - Add chat-reka.cpp/h implementing PEG-based parser for reka-edge format - Add Reka-Edge.jinja chat template - Detect reka-edge template in try_specialized_template() - Add LLAMA_EXAMPLE_MTMD to chat-template-file arg * feat: add reka vlm to gguf conversion script Converts Reka Yasa2 hf checkpoints to GGUF format: - Text decoder: Llama-arch with tiktoken/BPE vocab - Mmproj (--mmproj): ConvNeXt vision backbone + language_projection - Generates 2D sincos positional embeddings for vision encoder * test: add Reka Edge chat template and parser tests - test-chat-template: oracle tests comparing Jinja engine output vs common_chat_templates_apply for text, tools, thinking, images, video - test-chat: PEG parser tests for Reka Edge format, round-trip tests for image/video content parts, common path integration tests * scripts: add Reka Edge mixed quantization helper Q4_0 base quantization with Q8_0 override for the last 8 transformer blocks (layers 24-31) via --tensor-type regex. * fix: adapt chat-reka and tests to upstream API - Use autoparser::generation_params (not templates_params) - Add p.prefix(generation_prompt) to PEG parser - Simplify reasoning parser to match LFM2 pattern - Remove image/video oracle tests (unsupported by oaicompat parser; no other multimodal models test this path) * fix: avoid duplicate tensor loading in yasa2 vision encoder TN_YASA_PATCH_W and TN_PATCH_EMBD both resolve to "v.patch_embd.weight", causing the same tensor to be loaded twice into ctx_data and overflowing the memory pool. Reuse the tensors already loaded by the common section. * chore: update image pre-processing settings The reka-edge model depends on the following settings in an older fork of llama.cpp: 1. Fixed square resize 2. BICUBIC 3. add_padding=false In current llama.cpp, this means setting: - image_resize_algo = RESIZE_ALGO_BICUBIC - image_resize_pad = false * chore: remove reka gguf conversion script * chore: remove reka quantization script * chore: remove unnecessary changes from PR scope This commit removes a couple of unnecessary changes for the PR scope: 1. BPE decoder bug fix - this affects reka edge because there's a bug in our tokenization that doesn't represent <think> tokens as special tokens. However this isn't meant to be a thinking model so when run with --reasoning off the edge case does not affect us 2. --chat-template-file support from llama-mtmd-cli - the focus is on llama-server and the reka edge gguf contains the necessary metadata to detect the chat template 3. reka edge oracle test cases - no other model has similar test cases, so I removed it for standardization * chore: remove unnecessary ggml_cast This commit removes unnecessary ggml_cast after updating the reka vlm -> gguf conversion script on hugging face. * chore: remove redundant code * chore: remove unnecessary ggml_cont calls This commit removes all ggml_cont calls except the four that precede ggml_reshape_3d/ggml_reshape_4d. Those are necessary because ggml_reshape recomputes strides assuming contiguous layout and asserts ggml_is_contiguous. Other operations (ggml_mean, ggml_add, ggml_mul etc.) use stride-based indexing and handle non-contiguous inputs correctly and so we are ok to remove ggml_cont for those. * chore: remove unnecessary ggml_repeat calls This commit removes unnecessary ggml_repeat calls because the underlying ops already broadcast automatically. Every ggml_repeat in yasa2.cpp was expanding a smaller tensor to match a larger one's shape before passing both to an elementwise op (ggml_add, ggml_sub, ggml_mul, or ggml_div). This is unnecessary because all four of these ops already support broadcasting internally. * chore: restore ggml_cont needed for cpu operations * refactor: locate reka chat template handler in chat.cpp * chore: remove unnecessary warmup tokens * chore: add code comments on image_resize_pad * chore: remove custom reka parsing code * chore: revert common/chat.cpp * Uncomment debug logging for PEG input parsing --------- Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
174 lines
5.8 KiB
C
174 lines
5.8 KiB
C
#pragma once
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#include "../clip-graph.h"
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/*
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* IMPORTANT: The mtmd module does NOT accept pull requests that are fully or predominantly AI-generated.
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* We encourage human contributors to ensure the quality and reliability of the codebase.
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*/
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struct clip_graph_siglip : clip_graph {
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clip_graph_siglip(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
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ggml_cgraph * build() override;
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};
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struct clip_graph_gemma4v : clip_graph {
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clip_graph_gemma4v(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
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ggml_cgraph * build() override;
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ggml_tensor * build_mm(ggml_tensor * w, ggml_tensor * x) const override;
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};
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struct clip_graph_pixtral : clip_graph {
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clip_graph_pixtral(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
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ggml_cgraph * build() override;
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};
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struct clip_graph_qwen2vl : clip_graph {
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clip_graph_qwen2vl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
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ggml_cgraph * build() override;
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};
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struct clip_graph_qwen3vl : clip_graph {
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clip_graph_qwen3vl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
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ggml_cgraph * build() override;
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};
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struct clip_graph_step3vl : clip_graph {
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clip_graph_step3vl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
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ggml_cgraph * build() override;
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};
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struct clip_graph_youtuvl : clip_graph {
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clip_graph_youtuvl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
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ggml_cgraph * build() override;
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};
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struct clip_graph_yasa2 : clip_graph {
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clip_graph_yasa2(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
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ggml_cgraph * build() override;
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ggml_tensor * layer_norm_channels(ggml_tensor * inp, ggml_tensor * w, ggml_tensor * b, float eps = 1e-6f);
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ggml_tensor * convnext_grn(ggml_tensor * inp, ggml_tensor * w, ggml_tensor * b);
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};
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struct clip_graph_minicpmv : clip_graph {
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clip_graph_minicpmv(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
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ggml_cgraph * build() override;
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};
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struct clip_graph_internvl : clip_graph {
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clip_graph_internvl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
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ggml_cgraph * build() override;
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};
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struct clip_graph_nemotron_v2_vl : clip_graph {
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clip_graph_nemotron_v2_vl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
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ggml_cgraph * build() override;
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};
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struct clip_graph_llama4 : clip_graph {
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clip_graph_llama4(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
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ggml_cgraph * build() override;
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};
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struct clip_graph_kimivl : clip_graph {
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clip_graph_kimivl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
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ggml_cgraph * build() override;
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};
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struct clip_graph_paddleocr : clip_graph {
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clip_graph_paddleocr(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
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ggml_cgraph * build() override;
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};
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struct clip_graph_dotsocr : clip_graph {
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clip_graph_dotsocr(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
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ggml_cgraph * build() override;
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};
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struct clip_graph_cogvlm : clip_graph {
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clip_graph_cogvlm(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
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ggml_cgraph * build() override;
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};
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struct clip_graph_llava : clip_graph {
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clip_graph_llava(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
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ggml_cgraph * build() override;
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};
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struct clip_graph_whisper_enc : clip_graph {
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clip_graph_whisper_enc(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
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ggml_cgraph * build() override;
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};
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struct clip_graph_deepseekocr : clip_graph {
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clip_graph_deepseekocr(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
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ggml_cgraph * build() override;
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};
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struct clip_graph_conformer : clip_graph {
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clip_graph_conformer(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
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ggml_cgraph * build() override;
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};
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struct clip_graph_gemma4a : clip_graph {
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clip_graph_gemma4a(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
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ggml_cgraph * build() override;
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ggml_tensor * build_mm(ggml_tensor * w, ggml_tensor * x) const override;
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};
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struct clip_graph_glm4v : clip_graph {
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clip_graph_glm4v(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
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ggml_cgraph * build() override;
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};
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struct clip_graph_hunyuanocr : clip_graph {
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clip_graph_hunyuanocr(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
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ggml_cgraph * build() override;
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};
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struct clip_graph_mobilenetv5 : clip_graph {
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clip_graph_mobilenetv5(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
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ggml_cgraph * build() override;
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ggml_tensor * rms_norm_2d(
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ggml_tensor * inp,
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ggml_tensor * weight,
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float eps = 1e-6f);
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ggml_tensor* pad_same_2d(
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ggml_tensor* inp,
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int kernel_h,
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int kernel_w,
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int stride_h,
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int stride_w,
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int dilation_h = 1,
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int dilation_w = 1);
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ggml_tensor * build_edge_residual(
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ggml_tensor * inp,
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const mobilenetv5_block & block,
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int stride);
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ggml_tensor * build_inverted_residual(
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ggml_tensor * inp,
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const mobilenetv5_block & block,
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int stride);
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ggml_tensor * build_mobilenet_attn(
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ggml_tensor * inp,
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const mobilenetv5_block & block);
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};
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struct clip_graph_qwen3a : clip_graph {
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clip_graph_qwen3a(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
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ggml_cgraph * build() override;
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};
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struct clip_graph_kimik25 : clip_graph {
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clip_graph_kimik25(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
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ggml_cgraph * build() override;
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ggml_tensor * resize_position_embeddings_3d(uint32_t interpolation_mode);
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};
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