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Merge commit '8387ffb28d3467b81bc73727588c3fde772f8ebe' into concedo_experimental

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# Conflicts:
#	docs/backend/VirtGPU.md
#	docs/backend/ZenDNN.md
#	ggml/src/ggml-cpu/amx/amx.cpp
#	ggml/src/ggml-cpu/amx/mmq.cpp
#	ggml/src/ggml-sycl/add-id.cpp
#	ggml/src/ggml-virtgpu/backend/backend-dispatched-backend.cpp
#	ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer-type.cpp
#	ggml/src/ggml-virtgpu/backend/backend-dispatched-buffer.cpp
#	ggml/src/ggml-virtgpu/backend/backend-dispatched.cpp
#	ggml/src/ggml-virtgpu/backend/backend-dispatched.gen.h
#	ggml/src/ggml-virtgpu/backend/backend-dispatched.h
#	ggml/src/ggml-virtgpu/backend/backend-virgl-apir.h
#	ggml/src/ggml-virtgpu/backend/backend.cpp
#	ggml/src/ggml-virtgpu/backend/shared/api_remoting.h
#	ggml/src/ggml-virtgpu/backend/shared/apir_backend.gen.h
#	ggml/src/ggml-virtgpu/backend/shared/apir_backend.h
#	ggml/src/ggml-virtgpu/backend/shared/apir_cs.h
#	ggml/src/ggml-virtgpu/backend/shared/apir_cs_ggml.h
#	ggml/src/ggml-virtgpu/backend/shared/apir_cs_rpc.h
#	ggml/src/ggml-virtgpu/ggml-backend-buffer-type.cpp
#	ggml/src/ggml-virtgpu/ggml-backend-device.cpp
#	ggml/src/ggml-virtgpu/ggml-backend-reg.cpp
#	ggml/src/ggml-virtgpu/ggml-backend.cpp
#	ggml/src/ggml-virtgpu/ggml-remoting.h
#	ggml/src/ggml-virtgpu/include/apir_hw.h
#	ggml/src/ggml-virtgpu/regenerate_remoting.py
#	ggml/src/ggml-virtgpu/virtgpu-forward-backend.cpp
#	ggml/src/ggml-virtgpu/virtgpu-forward-buffer-type.cpp
#	ggml/src/ggml-virtgpu/virtgpu-forward-buffer.cpp
#	ggml/src/ggml-virtgpu/virtgpu-forward-device.cpp
#	ggml/src/ggml-virtgpu/virtgpu-forward-impl.h
#	ggml/src/ggml-virtgpu/virtgpu-forward.gen.h
#	ggml/src/ggml-virtgpu/virtgpu.cpp
#	ggml/src/ggml-virtgpu/virtgpu.h
#	ggml/src/ggml-zendnn/CMakeLists.txt
#	ggml/src/ggml-zendnn/ggml-zendnn.cpp
#	src/CMakeLists.txt
#	tests/CMakeLists.txt
#	tests/test-tokenizer-0.sh
#	tools/cli/README.md
#	tools/completion/README.md
#	tools/imatrix/imatrix.cpp
#	tools/server/README.md
This commit is contained in:
Concedo
2026-02-28 12:45:16 +08:00
parent 5c40f07d4a 8387ffb28d
commit 4e358265a3
32 changed files with 521 additions and 80 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/llama-arch.cpp
+20
View File
@@ -26,6 +26,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_NEO_BERT, "neo-bert" },
{ LLM_ARCH_JINA_BERT_V2, "jina-bert-v2" },
{ LLM_ARCH_JINA_BERT_V3, "jina-bert-v3" },
{ LLM_ARCH_EUROBERT, "eurobert" },
{ LLM_ARCH_BLOOM, "bloom" },
{ LLM_ARCH_STABLELM, "stablelm" },
{ LLM_ARCH_QWEN, "qwen" },
@@ -348,6 +349,7 @@ static const std::map<llm_tensor, const char *> LLM_TENSOR_NAMES = {
{ LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" },
{ LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" },
{ LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
{ LLM_TENSOR_FFN_GATE_UP_EXPS, "blk.%d.ffn_gate_up_exps" },
{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
{ LLM_TENSOR_ATTN_POST_NORM, "blk.%d.post_attention_norm" },
@@ -819,6 +821,20 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
LLM_TENSOR_CLS,
LLM_TENSOR_CLS_OUT,
};
case LLM_ARCH_EUROBERT:
return {
LLM_TENSOR_TOKEN_EMBD,
LLM_TENSOR_OUTPUT_NORM,
LLM_TENSOR_ATTN_NORM,
LLM_TENSOR_ATTN_Q,
LLM_TENSOR_ATTN_K,
LLM_TENSOR_ATTN_V,
LLM_TENSOR_ATTN_OUT,
LLM_TENSOR_FFN_NORM,
LLM_TENSOR_FFN_GATE,
LLM_TENSOR_FFN_UP,
LLM_TENSOR_FFN_DOWN,
};
case LLM_ARCH_MODERN_BERT:
return {
LLM_TENSOR_TOKEN_EMBD,
@@ -989,6 +1005,7 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
LLM_TENSOR_FFN_GATE_EXPS,
LLM_TENSOR_FFN_DOWN_EXPS,
LLM_TENSOR_FFN_UP_EXPS,
LLM_TENSOR_FFN_GATE_UP_EXPS,
LLM_TENSOR_FFN_GATE_INP_SHEXP,
LLM_TENSOR_FFN_GATE_SHEXP,
LLM_TENSOR_FFN_DOWN_SHEXP,
@@ -1046,6 +1063,7 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
LLM_TENSOR_FFN_GATE_EXPS,
LLM_TENSOR_FFN_DOWN_EXPS,
LLM_TENSOR_FFN_UP_EXPS,
LLM_TENSOR_FFN_GATE_UP_EXPS,
LLM_TENSOR_FFN_GATE_INP_SHEXP,
LLM_TENSOR_FFN_GATE_SHEXP,
LLM_TENSOR_FFN_DOWN_SHEXP,
@@ -1586,6 +1604,7 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
LLM_TENSOR_FFN_GATE_EXPS,
LLM_TENSOR_FFN_DOWN_EXPS,
LLM_TENSOR_FFN_UP_EXPS,
LLM_TENSOR_FFN_GATE_UP_EXPS,
LLM_TENSOR_FFN_GATE_INP_SHEXP,
LLM_TENSOR_FFN_GATE_SHEXP,
LLM_TENSOR_FFN_DOWN_SHEXP,
@@ -2670,6 +2689,7 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
{LLM_TENSOR_FFN_DOWN_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
{LLM_TENSOR_FFN_GATE_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
{LLM_TENSOR_FFN_UP_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
{LLM_TENSOR_FFN_GATE_UP_EXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
{LLM_TENSOR_FFN_DOWN_CHEXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
{LLM_TENSOR_FFN_GATE_CHEXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
{LLM_TENSOR_FFN_UP_CHEXPS, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT_ID}},
src/llama-arch.h
+2
View File
@@ -30,6 +30,7 @@ enum llm_arch {
LLM_ARCH_NEO_BERT,
LLM_ARCH_JINA_BERT_V2,
LLM_ARCH_JINA_BERT_V3,
LLM_ARCH_EUROBERT,
LLM_ARCH_BLOOM,
LLM_ARCH_STABLELM,
LLM_ARCH_QWEN,
@@ -372,6 +373,7 @@ enum llm_tensor {
LLM_TENSOR_FFN_DOWN_EXPS, // merged experts
LLM_TENSOR_FFN_GATE_EXPS,
LLM_TENSOR_FFN_UP_EXPS,
LLM_TENSOR_FFN_GATE_UP_EXPS,
LLM_TENSOR_FFN_DOWN_SHEXP,
LLM_TENSOR_FFN_GATE_SHEXP,
LLM_TENSOR_FFN_UP_SHEXP,
src/llama-graph.cpp
+49 -21
View File
@@ -1165,7 +1165,8 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
float w_scale,
llama_expert_gating_func_type gating_op,
int il,
ggml_tensor * probs_in) const {
ggml_tensor * probs_in,
ggml_tensor * gate_up_exps) const {
return build_moe_ffn(
cur,
gate_inp, /* gate_inp_b */ nullptr,
@@ -1181,7 +1182,8 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
w_scale,
gating_op,
il,
probs_in
probs_in,
gate_up_exps
);
}
@@ -1204,7 +1206,9 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
float w_scale,
llama_expert_gating_func_type gating_op,
int il,
ggml_tensor * probs_in) const {
ggml_tensor * probs_in,
ggml_tensor * gate_up_exps,
ggml_tensor * gate_up_exps_b) const {
const int64_t n_embd = cur->ne[0];
const int64_t n_tokens = cur->ne[1];
const bool weight_before_ffn = arch == LLM_ARCH_LLAMA4; // for llama4, we apply the sigmoid-ed weights before the FFN
@@ -1343,26 +1347,48 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
cb(cur, "ffn_moe_weighted", il);
}
ggml_tensor * up = build_lora_mm_id(up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
cb(up, "ffn_moe_up", il);
if (up_exps_b) {
up = ggml_add_id(ctx0, up, up_exps_b, selected_experts);
cb(up, "ffn_moe_up_biased", il);
}
ggml_tensor * up = nullptr;
ggml_tensor * experts = nullptr;
if (gate_exps) {
cur = build_lora_mm_id(gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
if (gate_up_exps) {
// merged gate_up path: one mul_mat_id, then split into gate and up views
ggml_tensor * gate_up = build_lora_mm_id(gate_up_exps, cur, selected_experts); // [n_ff*2, n_expert_used, n_tokens]
cb(gate_up, "ffn_moe_gate_up", il);
if (gate_up_exps_b) {
gate_up = ggml_add_id(ctx0, gate_up, gate_up_exps_b, selected_experts);
cb(gate_up, "ffn_moe_gate_up_biased", il);
}
const int64_t n_ff = gate_up->ne[0] / 2;
cur = ggml_view_3d(ctx0, gate_up, n_ff, gate_up->ne[1], gate_up->ne[2], gate_up->nb[1], gate_up->nb[2], 0);
cb(cur, "ffn_moe_gate", il);
up = ggml_view_3d(ctx0, gate_up, n_ff, gate_up->ne[1], gate_up->ne[2], gate_up->nb[1], gate_up->nb[2], n_ff * gate_up->nb[0]);
cb(up, "ffn_moe_up", il);
} else {
cur = up;
// separate gate and up path
up = build_lora_mm_id(up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
cb(up, "ffn_moe_up", il);
if (up_exps_b) {
up = ggml_add_id(ctx0, up, up_exps_b, selected_experts);
cb(up, "ffn_moe_up_biased", il);
}
if (gate_exps) {
cur = build_lora_mm_id(gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
cb(cur, "ffn_moe_gate", il);
} else {
cur = up;
}
if (gate_exps_b) {
cur = ggml_add_id(ctx0, cur, gate_exps_b, selected_experts);
cb(cur, "ffn_moe_gate_biased", il);
}
}
if (gate_exps_b) {
cur = ggml_add_id(ctx0, cur, gate_exps_b, selected_experts);
cb(cur, "ffn_moe_gate_biased", il);
}
const bool has_gate = gate_exps || gate_up_exps;
switch (type_op) {
case LLM_FFN_SILU:
@@ -1385,7 +1411,9 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
break;
}
}
}
if (has_gate) {
cur = ggml_swiglu_split(ctx0, cur, up);
cb(cur, "ffn_moe_swiglu", il);
} else {
@@ -1393,7 +1421,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
cb(cur, "ffn_moe_silu", il);
} break;
case LLM_FFN_GELU:
if (gate_exps) {
if (has_gate) {
cur = ggml_geglu_split(ctx0, cur, up);
cb(cur, "ffn_moe_geglu", il);
} else {
@@ -1409,7 +1437,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
cb(cur, "ffn_moe_swiglu_oai", il);
} break;
case LLM_FFN_RELU:
if (gate_exps) {
if (has_gate) {
cur = ggml_reglu_split(ctx0, cur, up);
cb(cur, "ffn_moe_reglu", il);
} else {
@@ -1417,7 +1445,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
cb(cur, "ffn_moe_relu", il);
} break;
case LLM_FFN_RELU_SQR:
if (gate_exps) {
if (has_gate) {
// TODO: add support for gated squared relu
GGML_ABORT("fatal error: gated squared relu not implemented");
} else {
src/llama-graph.h
+5 -2
View File
@@ -814,7 +814,8 @@ struct llm_graph_context {
float w_scale,
llama_expert_gating_func_type gating_op,
int il,
ggml_tensor * probs_in = nullptr) const;
ggml_tensor * probs_in = nullptr,
ggml_tensor * gate_up_exps = nullptr) const;
ggml_tensor * build_moe_ffn(
ggml_tensor * cur,
@@ -835,7 +836,9 @@ struct llm_graph_context {
float w_scale,
llama_expert_gating_func_type gating_op,
int il,
ggml_tensor * probs_in = nullptr) const;
ggml_tensor * probs_in = nullptr,
ggml_tensor * gate_up_exps = nullptr,
ggml_tensor * gate_up_exps_b = nullptr) const;
//
// inputs
src/llama-kv-cache.cpp
+3
View File
@@ -978,6 +978,9 @@ bool llama_kv_cache::get_can_shift() const {
if (model.arch == LLM_ARCH_STEP35) {
return false;
}
if (hparams.n_pos_per_embd() > 1) {
return false;
}
return true;
}
src/llama-model.cpp
+52 -6
View File
@@ -53,6 +53,7 @@
#include "models/dream.cpp"
#include "models/ernie4-5-moe.cpp"
#include "models/ernie4-5.cpp"
#include "models/eurobert.cpp"
#include "models/exaone-moe.cpp"
#include "models/exaone.cpp"
#include "models/exaone4.cpp"
@@ -1092,6 +1093,16 @@ void llama_model::load_hparams(llama_model_loader & ml) {
type = LLM_TYPE_250M;
}
} break;
case LLM_ARCH_EUROBERT:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type);
if (hparams.n_layer == 12) {
type = LLM_TYPE_SMALL; // 0.2B
}
} break;
case LLM_ARCH_BLOOM:
{
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
@@ -3090,6 +3101,15 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
// TODO: move to a separate function
const auto tn = LLM_TN(arch);
// helper: try merged gate_up_exps first, fall back to separate gate and up
auto create_tensor_gate_up_exps = [&](llama_layer & layer, int bid, int64_t n_embd_, int64_t n_ff_, int64_t n_expert_, int flags) {
layer.ffn_gate_up_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_UP_EXPS, "weight", bid), {n_embd_, n_ff_ * 2, n_expert_}, TENSOR_NOT_REQUIRED);
if (layer.ffn_gate_up_exps == nullptr) {
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", bid), {n_embd_, n_ff_, n_expert_}, flags);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", bid), {n_embd_, n_ff_, n_expert_}, flags);
}
};
switch (arch) {
case LLM_ARCH_LLAMA:
case LLM_ARCH_REFACT:
@@ -3730,6 +3750,29 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
}
} break;
case LLM_ARCH_EUROBERT:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
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.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
}
} break;
case LLM_ARCH_JINA_BERT_V2:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); // word_embeddings
@@ -5348,9 +5391,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
}
// MoE branch
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0);
create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0);
// Shared expert branch
layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
@@ -7552,9 +7594,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
}
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, 0);
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0);
// Shared experts
layer.ffn_gate_inp_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), { n_embd }, 0);
@@ -7618,9 +7659,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
}
layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, 0);
layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
create_tensor_gate_up_exps(layer, i, n_embd, n_ff_exp, n_expert, 0);
// Shared experts
const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;
@@ -8342,6 +8382,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
case LLM_ARCH_NOMIC_BERT:
case LLM_ARCH_NOMIC_BERT_MOE:
case LLM_ARCH_NEO_BERT:
case LLM_ARCH_EUROBERT:
case LLM_ARCH_WAVTOKENIZER_DEC:
case LLM_ARCH_MODERN_BERT:
case LLM_ARCH_GEMMA_EMBEDDING:
@@ -8539,6 +8580,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
{
llm = std::make_unique<llm_build_neo_bert>(*this, params);
} break;
case LLM_ARCH_EUROBERT:
{
llm = std::make_unique<llm_build_eurobert>(*this, params);
} break;
case LLM_ARCH_BLOOM:
{
llm = std::make_unique<llm_build_bloom>(*this, params);
@@ -9165,6 +9210,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_MODERN_BERT:
case LLM_ARCH_NOMIC_BERT:
case LLM_ARCH_NOMIC_BERT_MOE:
case LLM_ARCH_EUROBERT:
case LLM_ARCH_STABLELM:
case LLM_ARCH_BITNET:
case LLM_ARCH_QWEN:
src/llama-model.h
+10 -8
View File
@@ -280,14 +280,16 @@ struct llama_layer {
struct ggml_tensor * ffn_up_enc = nullptr;
// ff MoE
struct ggml_tensor * ffn_gate_inp = nullptr;
struct ggml_tensor * ffn_gate_exps = nullptr;
struct ggml_tensor * ffn_down_exps = nullptr;
struct ggml_tensor * ffn_up_exps = nullptr;
struct ggml_tensor * ffn_gate_inp_b = nullptr;
struct ggml_tensor * ffn_gate_exps_b = nullptr;
struct ggml_tensor * ffn_down_exps_b = nullptr;
struct ggml_tensor * ffn_up_exps_b = nullptr;
struct ggml_tensor * ffn_gate_inp = nullptr;
struct ggml_tensor * ffn_gate_exps = nullptr;
struct ggml_tensor * ffn_down_exps = nullptr;
struct ggml_tensor * ffn_up_exps = nullptr;
struct ggml_tensor * ffn_gate_up_exps = nullptr;
struct ggml_tensor * ffn_gate_inp_b = nullptr;
struct ggml_tensor * ffn_gate_exps_b = nullptr;
struct ggml_tensor * ffn_down_exps_b = nullptr;
struct ggml_tensor * ffn_up_exps_b = nullptr;
struct ggml_tensor * ffn_gate_up_exps_b = nullptr;
// ff shared expert (shexp)
struct ggml_tensor * ffn_gate_inp_shexp = nullptr;
src/llama-vocab.cpp
+2 -1
View File
@@ -2126,7 +2126,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
tokenizer_pre == "falcon-h1" ||
tokenizer_pre == "pixtral" ||
tokenizer_pre == "midm-2.0" ||
tokenizer_pre == "lfm2") {
tokenizer_pre == "lfm2" ||
tokenizer_pre == "jina-v5-nano") {
pre_type = LLAMA_VOCAB_PRE_TYPE_LLAMA3;
ignore_merges = true;
add_bos = true;
src/models/deepseek2.cpp
+3 -1
View File
@@ -218,7 +218,9 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
LLM_FFN_SILU, hparams.expert_weights_norm,
hparams.expert_weights_scale, hparams.expert_weights_scale,
(llama_expert_gating_func_type) hparams.expert_gating_func,
il);
il,
nullptr,
model.layers[il].ffn_gate_up_exps);
cb(moe_out, "ffn_moe_out", il);
// FFN shared expert
src/models/eurobert.cpp
+97
View File
@@ -0,0 +1,97 @@
#include "models.h"
llm_build_eurobert::llm_build_eurobert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
const int64_t n_embd_head = hparams.n_embd_head_v;
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
ggml_tensor * cur;
ggml_tensor * inpL;
ggml_tensor * inp_pos = build_inp_pos();
inpL = build_inp_embd(model.tok_embd);
cb(inpL, "inp_embd", -1);
auto * inp_attn = build_attn_inp_no_cache();
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * cur = inpL;
cur = build_norm(inpL,
model.layers[il].attn_norm, NULL,
LLM_NORM_RMS, il);
{
ggml_tensor * Qcur;
ggml_tensor * Kcur;
ggml_tensor * Vcur;
Qcur = build_lora_mm(model.layers[il].wq, cur);
Kcur = build_lora_mm(model.layers[il].wk, cur);
Vcur = build_lora_mm(model.layers[il].wv, cur);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
Qcur = ggml_rope_ext(
ctx0, Qcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
Kcur = ggml_rope_ext(
ctx0, Kcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
cur = build_attn(inp_attn,
model.layers[il].wo, nullptr,
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
cb(cur, "kqv_out", il);
}
if (il == n_layer - 1 && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
}
cur = ggml_add(ctx0, cur, inpL);
ggml_tensor * ffn_inp = cur;
cb(ffn_inp, "ffn_inp", il);
cur = build_norm(ffn_inp,
model.layers[il].ffn_norm, NULL,
LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);
cur = build_ffn(cur,
model.layers[il].ffn_up, NULL, NULL,
model.layers[il].ffn_gate, NULL, NULL,
model.layers[il].ffn_down, NULL, NULL,
NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(cur, "ffn_out", il);
cur = ggml_add(ctx0, cur, ffn_inp);
inpL = cur;
}
cur = inpL;
cur = build_norm(cur,
model.output_norm, NULL,
LLM_NORM_RMS, -1);
cb(cur, "result_embd", -1);
res->t_embd = cur;
ggml_build_forward_expand(gf, cur);
}
src/models/models.h
+4
View File
@@ -424,6 +424,10 @@ struct llm_build_neo_bert : public llm_graph_context {
llm_build_neo_bert(const llama_model & model, const llm_graph_params & params);
};
struct llm_build_eurobert : public llm_graph_context {
llm_build_eurobert(const llama_model & model, const llm_graph_params & params);
};
template <bool iswa>
struct llm_build_olmo2 : public llm_graph_context {
llm_build_olmo2(const llama_model & model, const llm_graph_params & params);
src/models/qwen35moe.cpp
+2 -1
View File
@@ -380,7 +380,8 @@ ggml_tensor * llm_build_qwen35moe ::build_layer_ffn(ggml_tensor * cur, const int
model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps,
nullptr,
n_expert, n_expert_used, LLM_FFN_SILU,
true, false, 0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il);
true, false, 0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il,
nullptr, model.layers[il].ffn_gate_up_exps);
cb(moe_out, "ffn_moe_out", il);
// Add shared experts if present - following Qwen3Next reference implementation
src/models/qwen3next.cpp
+2 -1
View File
@@ -480,7 +480,8 @@ ggml_tensor * llm_build_qwen3next::build_layer_ffn(ggml_tensor * cur, const int
model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps,
nullptr,
n_expert, n_expert_used, LLM_FFN_SILU,
true, false, 0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il);
true, false, 0.0, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il,
nullptr, model.layers[il].ffn_gate_up_exps);
cb(moe_out, "ffn_moe_out", il);
// Add shared experts if present - following Qwen3Next reference implementation