mtmd: Add DeepSeekOCR Support (#17400)
* mtmd: llama.cpp DeepSeekOCR support init commit * loading sam tensors * mtmd: fix vision model processing * deepseek-ocr clip-vit model impl * mtmd: add DeepSeek-OCR LM support with standard attention * mtmd: successfully runs DeepSeek-OCR LM in llama-cli * mtmd: Fix RoPE type for DeepSeek-OCR LM. * loading LM testing Vision model loading * sam warmup working * sam erroneous return corrected * clip-vit: corrected cls_embd concat * clip-vit: model convert qkv_proj split * corrected combining of image encoders' results * fix: update callback for ffn_moe_weighted and add callback for attn_out in deepseek2 model * concat image_newline and image_seperator tokens * visual_model warmup (technically) works * window partitioning using standard ggml ops * sam implementation without using CPU only ops * clip: fixed warnings * Merge branch 'sf/deepseek-ocr' of github.com:sfallah/llama.cpp into sf/deepseek-ocr * mtmd: fix get_rel_pos * mtmd: fixed the wrong scaler for get_rel_pos * image encoding technically works but the output can't be checked singe image decoding fails * mtmd: minor changed * mtmd: add native resolution support * - image encoding debugged - issues fixed mainly related wrong config like n_patches etc. - configs need to be corrected in the converter * mtmd: correct token order * - dynamic resizing - changes are concerning PR https://github.com/sfallah/llama.cpp/pull/4 * mtmd: quick fix token order * mtmd: fix danling pointer * mtmd: SAM numerically works * mtmd: debug CLIP-L (vit_pre_ln) * mtmd: debug CLIP-L & first working DeepSeek-OCR model * mtmd : add --dsocr-mode CLI argument for DeepSeek-OCR resolution control & all native resolution modes work * mtmd: simplify SAM patch embedding * mtmd: adapt Pillow image resizing function * mtmd: simplify DeepSeek-OCR dynamic resolution preprocessing * mtmd: remove --dsocr-mode argument * mtmd: refactor code & remove unused helper functions * mtmd: fix tensor names for image newlines and view separator * clean up * reverting automatically removed spaces * reverting automatically removed spaces * mtmd: fixed bad ocr check in Deepseek2 (LM) * mtmd: support combined QKV projection in buid_vit * using common build_attn in sam * corrected code-branch when flash-attn disabled enabling usage of --flash-attn option * mtmd: minor fix * minor formatting and style * fixed flake8 lint issues * minor editorconfig-check fixes * minor editorconfig-check fixes * mtmd: simplify get_rel_pos * mtmd: make sam hparams configurable * mtmd: add detailed comments for resize_bicubic_pillow * mtmd: fixed wrong input setting * mtmd: convert model in FP16 * mtmd: minor fix * mtmd: remove tweak to llama-mtmd-cli & deepseek-ocr template * fix: test-1.jpg ORC issue with small (640) resolution setting min-resolution base (1024) max large (1280) for dynamic-resolution * minor: editconfig-check fix * merge with changes from https://github.com/ggml-org/llama.cpp/pull/17909 added new opt to tests.sh to disable flash-attn * minor: editconfig-check fix * testing deepseek-ocr quick and dirty test script comparing results of Qwen2.5-VL vs DeepSeek-OCR * quick and (potential) dirty merge with https://github.com/ggml-org/llama.cpp/pull/17909 * refactoring, one single builder function and static helpers * added deepseek-ocr test to tests.sh * minor formatting fixes * check with fixed expected resutls * minor formatting * editorconfig-check fix * merge with changes from https://github.com/ggml-org/llama.cpp/pull/18042 * minor - added GLM-4.6V to big tests - added missing deps for python test * convert: minor fix * mtmd: format code * convert: quick fix * convert: quick fix * minor python formatting * fixed merge build issue * merge resolved - fixed issues in convert - tested several deepseek models * minor fix * minor * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> * - removed clip_is_deepseekocr - removed redundant RESIZE_ALGO_BICUBIC_PILLOW resize-algo - simplified image-preprocessing - removed/simplified debug functions * - cleaning commented out code * fixing instabilities issues reintroducing resize_bicubic_pillow * - use f16 model for deepseek-ocr test - ignore llama-arch test for deepseek-ocr * rename fc_w --> mm_fc_w * add links to OCR discussion * cleaner loading code * add missing .weight to some tensors * add default jinja template (to be used by server) * move test model to ggml-org * rolling back upscale change * Update convert_hf_to_gguf.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> --------- Co-authored-by: bluebread <hotbread70127@gmail.com> Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> Co-authored-by: Xuan Son Nguyen <son@huggingface.co> Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>
This commit is contained in:
Saba Fallah
GitHub
+77
-1
@@ -1621,6 +1621,26 @@ void llama_model::load_hparams(llama_model_loader & ml) {
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default: type = LLM_TYPE_UNKNOWN;
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}
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} break;
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case LLM_ARCH_DEEPSEEK2OCR:
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{
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// similar to deepseek2, but without MLA
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false);
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ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
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ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
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ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
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ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
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ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
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if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
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hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX;
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}
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switch (hparams.n_layer) {
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case 12: type = LLM_TYPE_3B; break;
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default: type = LLM_TYPE_UNKNOWN;
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}
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} break;
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case LLM_ARCH_PLM:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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@@ -4951,6 +4971,60 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
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layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
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create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0);
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// Shared expert branch
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layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
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layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, 0);
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layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
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}
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}
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} break;
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case LLM_ARCH_DEEPSEEK2OCR:
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{
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// similar to deepseek2, but without MLA
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const int64_t n_ff_exp = hparams.n_ff_exp;
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const int64_t n_expert_shared = hparams.n_expert_shared;
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tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
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// output
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output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
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// try to load output.weight, if not found, use token_embd (tied embeddings)
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output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
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if (!output) {
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output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
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}
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for (int i = 0; i < n_layer; ++i) {
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auto & layer = layers[i];
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layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
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layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd}, 0);
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layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd}, 0);
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layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
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// norm
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layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
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layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
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if (i < (int) hparams.n_layer_dense_lead) {
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layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
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layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
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layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
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} else {
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layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
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layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
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if (n_expert == 0) {
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throw std::runtime_error("n_expert must be > 0");
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}
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if (n_expert_used == 0) {
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throw std::runtime_error("n_expert_used must be > 0");
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}
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// MoE branch
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layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
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create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0);
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// Shared expert branch
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layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
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layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, 0);
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@@ -7842,7 +7916,7 @@ void llama_model::print_info() const {
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LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale);
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}
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if (arch == LLM_ARCH_DEEPSEEK2 || arch == LLM_ARCH_GLM_DSA || arch == LLM_ARCH_MISTRAL4) {
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if (arch == LLM_ARCH_DEEPSEEK2 || arch == LLM_ARCH_DEEPSEEK2OCR || arch == LLM_ARCH_GLM_DSA || arch == LLM_ARCH_MISTRAL4) {
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LLAMA_LOG_INFO("%s: n_layer_dense_lead = %d\n", __func__, hparams.n_layer_dense_lead);
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LLAMA_LOG_INFO("%s: n_lora_q = %d\n", __func__, hparams.n_lora_q);
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LLAMA_LOG_INFO("%s: n_lora_kv = %d\n", __func__, hparams.n_lora_kv);
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@@ -8419,6 +8493,7 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
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llm = std::make_unique<llm_build_deepseek>(*this, params);
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} break;
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case LLM_ARCH_DEEPSEEK2:
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case LLM_ARCH_DEEPSEEK2OCR:
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case LLM_ARCH_GLM_DSA:
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case LLM_ARCH_MISTRAL4:
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{
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@@ -8819,6 +8894,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
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case LLM_ARCH_ARCTIC:
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case LLM_ARCH_DEEPSEEK:
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case LLM_ARCH_DEEPSEEK2:
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case LLM_ARCH_DEEPSEEK2OCR:
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case LLM_ARCH_PLM:
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case LLM_ARCH_CHATGLM:
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case LLM_ARCH_GRANITE:
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