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Merge branch 'upstream' into concedo_experimental

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# Conflicts:
#	.devops/openvino.Dockerfile
#	.github/workflows/build-self-hosted.yml
#	.github/workflows/build.yml
#	common/chat.cpp
#	docs/backend/OPENVINO.md
#	examples/speculative-simple/speculative-simple.cpp
#	ggml/src/ggml-hexagon/ggml-hexagon.cpp
#	ggml/src/ggml-hexagon/htp/CMakeLists.txt
#	ggml/src/ggml-hexagon/htp/htp-ctx.h
#	ggml/src/ggml-hexagon/htp/htp-ops.h
#	ggml/src/ggml-hexagon/htp/main.c
#	ggml/src/ggml-hexagon/libggml-htp.inf
#	ggml/src/ggml-openvino/ggml-decoder.cpp
#	ggml/src/ggml-openvino/ggml-openvino-extra.cpp
#	ggml/src/ggml-openvino/ggml-openvino.cpp
#	ggml/src/ggml-openvino/ggml-quants.cpp
#	ggml/src/ggml-openvino/openvino/op/rope.cpp
#	ggml/src/ggml-openvino/openvino/op_table.cpp
#	ggml/src/ggml-openvino/openvino/op_table.h
#	ggml/src/ggml-openvino/openvino/translate_session.cpp
#	ggml/src/ggml-openvino/openvino/utils.cpp
#	ggml/src/ggml-openvino/openvino/utils.h
#	ggml/src/ggml-openvino/utils.cpp
#	ggml/src/ggml-openvino/utils.h
#	ggml/src/ggml-sycl/common.hpp
#	ggml/src/ggml-sycl/convert.cpp
#	ggml/src/ggml-sycl/convert.hpp
#	ggml/src/ggml-sycl/gemm.hpp
#	ggml/src/ggml-sycl/ggml-sycl.cpp
#	ggml/src/ggml-sycl/set_rows.cpp
#	ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp
#	ggml/src/ggml-webgpu/ggml-webgpu.cpp
#	scripts/sync_vendor.py
#	tests/CMakeLists.txt
#	tests/test-chat.cpp
#	tools/cli/cli.cpp
#	tools/mtmd/CMakeLists.txt
#	tools/server/CMakeLists.txt
This commit is contained in:
Concedo
2026-04-23 00:55:05 +08:00
parent becf70d49b 8bccdbbff9
commit 0755f27372
42 changed files with 1531 additions and 3199 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/llama-model.cpp
+22
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@@ -852,6 +852,13 @@ 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);
@@ -930,6 +937,7 @@ 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) {
@@ -2707,9 +2715,18 @@ 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;
@@ -7106,6 +7123,7 @@ 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);
@@ -9126,6 +9144,7 @@ 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);
@@ -9475,6 +9494,9 @@ 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");