Public/koboldcpp
1
0
Fork 0
mirror of https://github.com/LostRuins/koboldcpp.git synced 2026-09-19 01:05:09 +02:00
Code Issues Packages Projects Releases Wiki Activity

Merge branch 'upstream' into concedo_experimental

Browse Source 
# Conflicts:
#	docs/ops.md
#	docs/ops/WebGPU.csv
#	embd_res/templates/stepfun-ai-Step-3.5-Flash.jinja
#	ggml/src/ggml-webgpu/ggml-webgpu.cpp
#	ggml/src/ggml-webgpu/wgsl-shaders/unary.wgsl
#	src/CMakeLists.txt
#	tests/test-backend-ops.cpp
#	tests/test-chat.cpp
#	tools/mtmd/CMakeLists.txt
This commit is contained in:
Concedo
2026-02-20 15:16:26 +08:00
parent 07c45ced56 77d6ae4ac8
commit e626de2430
26 changed files with 618 additions and 29 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/llama-model.cpp
+66
View File
@@ -75,6 +75,7 @@
#include "models/hunyuan-moe.cpp"
#include "models/internlm2.cpp"
#include "models/jais.cpp"
#include "models/jais2.cpp"
#include "models/jamba.cpp"
#include "models/kimi-linear.cpp"
#include "models/lfm2.cpp"
@@ -100,6 +101,7 @@
#include "models/openai-moe-iswa.cpp"
#include "models/openelm.cpp"
#include "models/orion.cpp"
#include "models/paddleocr.cpp"
#include "models/pangu-embedded.cpp"
#include "models/phi2.cpp"
#include "models/phi3.cpp"
@@ -2048,6 +2050,16 @@ void llama_model::load_hparams(llama_model_loader & ml) {
default: type = LLM_TYPE_UNKNOWN;
}
} break;
case LLM_ARCH_JAIS2:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
switch (hparams.n_layer) {
case 32: type = LLM_TYPE_8B; break;
case 68: type = LLM_TYPE_70B; break;
default: type = LLM_TYPE_UNKNOWN;
}
} break;
case LLM_ARCH_NEMOTRON:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
@@ -2345,7 +2357,11 @@ void llama_model::load_hparams(llama_model_loader & ml) {
} break;
case LLM_ARCH_ERNIE4_5:
case LLM_ARCH_ERNIE4_5_MOE:
case LLM_ARCH_PADDLEOCR:
{
// paddleocr need mrope_section
ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
if (arch == LLM_ARCH_ERNIE4_5_MOE) {
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
@@ -5533,6 +5549,45 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0);
}
} break;
case LLM_ARCH_JAIS2:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
// output
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
output_norm_b = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, 0);
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
if (!output) {
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
}
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
layer.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, 0);
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
// attention biases - all have shape n_embd (output dimension of projections)
layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0);
layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd}, 0);
layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd}, 0);
layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
layer.ffn_norm_b = create_tensor(tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, 0);
// Jais-2 uses simple MLP (no gate) with biases
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0);
}
} break;
case LLM_ARCH_CHATGLM:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@@ -6740,6 +6795,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
} break;
case LLM_ARCH_ERNIE4_5:
case LLM_ARCH_ERNIE4_5_MOE:
case LLM_ARCH_PADDLEOCR:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@@ -8720,6 +8776,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
{
llm = std::make_unique<llm_build_jais>(*this, params);
} break;
case LLM_ARCH_JAIS2:
{
llm = std::make_unique<llm_build_jais2>(*this, params);
} break;
case LLM_ARCH_NEMOTRON:
{
llm = std::make_unique<llm_build_nemotron>(*this, params);
@@ -8815,6 +8875,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
{
llm = std::make_unique<llm_build_ernie4_5_moe>(*this, params);
} break;
case LLM_ARCH_PADDLEOCR:
{
llm = std::make_unique<llm_build_paddleocr>(*this, params);
} break;
case LLM_ARCH_HUNYUAN_MOE:
{
llm = std::make_unique<llm_build_hunyuan_moe>(*this, params);
@@ -9132,6 +9196,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_BAILINGMOE2:
case LLM_ARCH_DOTS1:
case LLM_ARCH_HUNYUAN_MOE:
case LLM_ARCH_JAIS2:
case LLM_ARCH_OPENAI_MOE:
case LLM_ARCH_HUNYUAN_DENSE:
case LLM_ARCH_LFM2:
@@ -9150,6 +9215,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
return LLAMA_ROPE_TYPE_NEOX;
case LLM_ARCH_QWEN2VL:
case LLM_ARCH_PADDLEOCR:
return LLAMA_ROPE_TYPE_MROPE;
case LLM_ARCH_QWEN3VL:
case LLM_ARCH_QWEN3VLMOE: