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mtmd, llama : Update HunyuanVL vision-language model support (#22037)

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* mtmd, llama : add HunyuanVL vision-language model support

- add LLM_ARCH_HUNYUAN_VL with M-RoPE (XD-RoPE) support
- add PROJECTOR_TYPE_HUNYUANVL with PatchMerger vision encoder
- add HunyuanVL-specific M-RoPE position encoding for image tokens
- add GGUF conversion for HunyuanVL vision and text models
- add smoke test in tools/mtmd/tests.sh

* fix: fix HunyuanVL XD-RoPE h/w section order

* fix: Remove redundant code

* convert : fix HunyuanOCR / HunyuanVL conversion
 - Tested locally: both HunyuanOCR and HunyuanVL-4B convert to GGUF
 - successfully and produce correct inference output on Metal (F16 / Q8_0).

* clip : fix -Werror=misleading-indentation in bilinear resize

* fix CI: convert_hf_to_gguf type check error
 - convert_hf_to_gguf.py: give HunyuanVLTextModel.__init__ an explicit `dir_model: Path` parameter so ty can infer the type for load_hparams instead of reporting `Unknown | None`.

---------

Co-authored-by: wendadawen <wendadawen@tencent.com>
This commit is contained in:
manayang
2026-04-22 17:58:43 +08:00
committed by GitHub
parent 750579ff14
commit 7bfe60fdf9
13 changed files with 336 additions and 27 deletions
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src/llama-model.cpp
+22
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@@ -737,6 +737,13 @@ void llama_model::load_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_EXPERT_GROUP_COUNT, hparams.n_expert_groups, false);
ml.get_key(LLM_KV_EXPERT_GROUP_USED_COUNT, hparams.n_group_used, false);
if (arch == LLM_ARCH_HUNYUAN_VL || arch == LLM_ARCH_HUNYUAN_DENSE) {
if (hparams.n_expert <= 1) {
hparams.n_expert = 0;
hparams.n_expert_used = 0;
}
}
if (arch == LLM_ARCH_WAVTOKENIZER_DEC) {
ml.get_key(LLM_KV_FEATURES_LENGTH, hparams.n_embd);
ml.get_key(LLM_KV_EMBEDDING_LENGTH, hparams.n_embd_out_impl);
@@ -815,6 +822,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
hparams.rope_freq_scale_train = ropescale == 0.0f ? 1.0f : 1.0f/ropescale;
ml.get_key(LLM_KV_ROPE_SCALING_ATTN_FACTOR, hparams.rope_attn_factor, false);
ml.get_key(LLM_KV_ROPE_SCALING_ALPHA, hparams.rope_scaling_alpha, false);
// non-transformer models do not have attention heads
if (hparams.n_head() > 0) {
@@ -2592,9 +2600,18 @@ void llama_model::load_hparams(llama_model_loader & ml) {
default: type = LLM_TYPE_UNKNOWN;
}
} break;
case LLM_ARCH_HUNYUAN_VL:
case LLM_ARCH_HUNYUAN_DENSE:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
// XDRoPE / NTK-aware scaling: base = rope_theta * alpha^(dim / (dim - 2))
if (hparams.rope_scaling_alpha > 0.0f) {
const int dim = hparams.n_embd_head_k();
hparams.rope_freq_base_train = hparams.rope_freq_base_train
* powf(hparams.rope_scaling_alpha, (float)dim / (float)(dim - 2));
}
switch (hparams.n_embd) {
case 1024: type = LLM_TYPE_0_5B; break;
@@ -6947,6 +6964,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0);
}
} break;
case LLM_ARCH_HUNYUAN_VL:
case LLM_ARCH_HUNYUAN_DENSE:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@@ -8967,6 +8985,7 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
{
llm = std::make_unique<llm_build_hunyuan_moe>(*this, params);
} break;
case LLM_ARCH_HUNYUAN_VL:
case LLM_ARCH_HUNYUAN_DENSE:
{
llm = std::make_unique<llm_build_hunyuan_dense>(*this, params);
@@ -9316,6 +9335,9 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_GLM4_MOE:
return model->hparams.use_mrope() ? LLAMA_ROPE_TYPE_MROPE : LLAMA_ROPE_TYPE_NEOX;
case LLM_ARCH_HUNYUAN_VL:
return model->hparams.use_mrope() ? LLAMA_ROPE_TYPE_MROPE : LLAMA_ROPE_TYPE_NEOX;
// all model arches should be listed explicitly here
case LLM_ARCH_UNKNOWN:
GGML_ABORT("unknown architecture");