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talk-llama : sync llama.cpp

This commit is contained in:
Georgi Gerganov 2026-05-29 09:44:28 +03:00
parent cc65eb1816
commit 5828fba79f
11 changed files with 213 additions and 9 deletions
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6
examples/talk-llama/llama-arch.cpp
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@ -133,6 +133,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_LLAMA_EMBED, "llama-embed" },
{ LLM_ARCH_MAINCODER, "maincoder" },
{ LLM_ARCH_KIMI_LINEAR, "kimi-linear" },
{ LLM_ARCH_TALKIE, "talkie" },
{ LLM_ARCH_UNKNOWN, "(unknown)" },
};
@ -767,8 +768,9 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
{LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
// Nemotron 3 Super
{LLM_TENSOR_FFN_LATENT_DOWN, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
{LLM_TENSOR_FFN_LATENT_UP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
// latent projections feed ggml_mul_mat, the buft probe must use MUL_MAT to keep them on GPU
{LLM_TENSOR_FFN_LATENT_DOWN, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_FFN_LATENT_UP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
};
LLM_KV::LLM_KV(llm_arch arch, const char * suffix) : arch(arch), suffix(suffix) {}

1
examples/talk-llama/llama-arch.h
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@ -137,6 +137,7 @@ enum llm_arch {
LLM_ARCH_LLAMA_EMBED,
LLM_ARCH_MAINCODER,
LLM_ARCH_KIMI_LINEAR,
LLM_ARCH_TALKIE,
LLM_ARCH_UNKNOWN,
};

20
examples/talk-llama/llama-chat.cpp
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@ -62,6 +62,7 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
{ "rwkv-world", LLM_CHAT_TEMPLATE_RWKV_WORLD },
{ "granite", LLM_CHAT_TEMPLATE_GRANITE_3_X },
{ "granite-4.0", LLM_CHAT_TEMPLATE_GRANITE_4_0 },
{ "granite-4.1", LLM_CHAT_TEMPLATE_GRANITE_4_1 },
{ "gigachat", LLM_CHAT_TEMPLATE_GIGACHAT },
{ "megrez", LLM_CHAT_TEMPLATE_MEGREZ },
{ "yandex", LLM_CHAT_TEMPLATE_YANDEX },
@ -194,7 +195,10 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
return LLM_CHAT_TEMPLATE_RWKV_WORLD;
} else if (tmpl_contains("<|start_of_role|>")) {
if (tmpl_contains("<tool_call>") || tmpl_contains("<tools>")) {
return LLM_CHAT_TEMPLATE_GRANITE_4_0;
if (tmpl_contains("g4_default_system_message")) {
return LLM_CHAT_TEMPLATE_GRANITE_4_0;
}
return LLM_CHAT_TEMPLATE_GRANITE_4_1;
}
return LLM_CHAT_TEMPLATE_GRANITE_3_X;
} else if (tmpl_contains("message['role'] + additional_special_tokens[0] + message['content'] + additional_special_tokens[1]")) {
@ -651,6 +655,20 @@ int32_t llm_chat_apply_template(
if (add_ass) {
ss << "<|start_of_role|>assistant<|end_of_role|>";
}
} else if (tmpl == LLM_CHAT_TEMPLATE_GRANITE_4_1) {
// IBM Granite 4.1 template
for (const auto & message : chat) {
std::string role(message->role);
if (role == "assistant_tool_call") {
ss << "<|start_of_role|>assistant<|end_of_role|><|tool_call|>";
} else {
ss << "<|start_of_role|>" << role << "<|end_of_role|>";
}
ss << message->content << "<|end_of_text|>\n";
}
if (add_ass) {
ss << "<|start_of_role|>assistant<|end_of_role|>";
}
} else if (tmpl == LLM_CHAT_TEMPLATE_GIGACHAT) {
// GigaChat template
bool has_system = !chat.empty() && std::string(chat[0]->role) == "system";

1
examples/talk-llama/llama-chat.h
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@ -41,6 +41,7 @@ enum llm_chat_template {
LLM_CHAT_TEMPLATE_RWKV_WORLD,
LLM_CHAT_TEMPLATE_GRANITE_3_X,
LLM_CHAT_TEMPLATE_GRANITE_4_0,
LLM_CHAT_TEMPLATE_GRANITE_4_1,
LLM_CHAT_TEMPLATE_GIGACHAT,
LLM_CHAT_TEMPLATE_MEGREZ,
LLM_CHAT_TEMPLATE_YANDEX,

3
examples/talk-llama/llama-model.cpp
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@ -44,6 +44,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
return new llama_model_llama_embed(params);
case LLM_ARCH_MAINCODER:
return new llama_model_maincoder(params);
case LLM_ARCH_TALKIE:
return new llama_model_talkie(params);
case LLM_ARCH_DECI:
return new llama_model_deci(params);
case LLM_ARCH_BAICHUAN:
@ -2353,6 +2355,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
case LLM_ARCH_QWEN3NEXT:
case LLM_ARCH_MIMO2:
case LLM_ARCH_STEP35:
case LLM_ARCH_TALKIE:
return LLAMA_ROPE_TYPE_NEOX;
case LLM_ARCH_QWEN2VL:

2
examples/talk-llama/llama-model.h
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@ -488,7 +488,7 @@ struct llama_layer {
struct ggml_tensor * indexer_attn_k = nullptr;
struct ggml_tensor * indexer_attn_q_b = nullptr; // note: for lora a/b, not bias
// gemma4 layer output scale
// gemma4 layer output scale, reused for talkie embedding skip scale
struct ggml_tensor * out_scale = nullptr;
struct llama_layer_posnet posnet;

14
examples/talk-llama/llama-vocab.cpp
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@ -511,6 +511,14 @@ struct llm_tokenizer_bpe : llm_tokenizer {
};
byte_encode = false;
break;
case LLAMA_VOCAB_PRE_TYPE_MINICPM5:
regex_exprs = {
// original regex from tokenizer.json (openbmb/MiniCPM5-1B)
"\\p{N}{1,3}",
// "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}+| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
"(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}+| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
};
break;
default:
// default regex for BPE tokenization pre-processing
regex_exprs = {
@ -2039,6 +2047,9 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
} else if (tokenizer_pre == "default") {
pre_type = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
} else if (tokenizer_pre == "minicpm5") {
pre_type = LLAMA_VOCAB_PRE_TYPE_MINICPM5;
ignore_merges = true;
} else if (
tokenizer_pre == "llama3" ||
tokenizer_pre == "llama-v3" ||
@ -2196,7 +2207,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
} else if (
tokenizer_pre == "gpt-4o" ||
tokenizer_pre == "llama4" ||
tokenizer_pre == "kanana2") {
tokenizer_pre == "kanana2" ||
tokenizer_pre == "talkie") {
pre_type = LLAMA_VOCAB_PRE_TYPE_GPT4O;
clean_spaces = false;
} else if (

1
examples/talk-llama/llama-vocab.h
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@ -60,6 +60,7 @@ enum llama_vocab_pre_type {
LLAMA_VOCAB_PRE_TYPE_JAIS2 = 49,
LLAMA_VOCAB_PRE_TYPE_GEMMA4 = 50,
LLAMA_VOCAB_PRE_TYPE_SARVAM_MOE = 51,
LLAMA_VOCAB_PRE_TYPE_MINICPM5 = 52,
};
struct LLM_KV;

12
examples/talk-llama/models/mistral3.cpp
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@ -177,9 +177,9 @@ llama_model_mistral3::graph::graph(const llama_model & model, const llm_graph_pa
cb(cur, "ffn_norm", il);
cur = build_ffn(cur,
model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
model.layers[il].ffn_up, model.layers[il].ffn_up_b, model.layers[il].ffn_up_s,
model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, model.layers[il].ffn_gate_s,
model.layers[il].ffn_down, model.layers[il].ffn_down_b, model.layers[il].ffn_down_s,
NULL,
LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(cur, "ffn_out", il);
@ -200,7 +200,11 @@ llama_model_mistral3::graph::graph(const llama_model & model, const llm_graph_pa
LLM_FFN_SILU, true,
hparams.expert_weights_scale,
LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
il);
il,
nullptr, nullptr,
model.layers[il].ffn_up_exps_s,
model.layers[il].ffn_gate_exps_s,
model.layers[il].ffn_down_exps_s);
cb(cur, "ffn_moe_out", il);
}
cur = ggml_add(ctx0, cur, ffn_inp);

13
examples/talk-llama/models/models.h
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@ -186,6 +186,19 @@ struct llama_model_maincoder : public llama_model_base {
};
struct llama_model_talkie : public llama_model_base {
llama_model_talkie(const struct llama_model_params & params) : llama_model_base(params) {}
void load_arch_hparams(llama_model_loader & ml) override;
void load_arch_tensors(llama_model_loader & ml) override;
struct graph : public llm_graph_context {
graph(const llama_model & model, const llm_graph_params & params);
};
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
};
struct llama_model_deci : public llama_model_base {
llama_model_deci(const struct llama_model_params & params) : llama_model_base(params) {}
void load_arch_hparams(llama_model_loader & ml) override;

149
examples/talk-llama/models/talkie.cpp Normal file
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@ -0,0 +1,149 @@
#include "models.h"
void llama_model_talkie::load_arch_hparams(llama_model_loader & ml) {
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
switch (hparams.n_layer) {
case 40: type = LLM_TYPE_13B; break;
default: type = LLM_TYPE_UNKNOWN;
}
}
void llama_model_talkie::load_arch_tensors(llama_model_loader &) {
LLAMA_LOAD_LOCALS;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
for (int i = 0; i < n_layer; ++i) {
auto & layer = layers[i];
create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_gqa, n_embd_gqa, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
// no k gain
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {1, n_head}, 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);
layer.out_scale = create_tensor(tn(LLM_TENSOR_LAYER_OUT_SCALE, "weight", i), {1}, 0);
}
}
std::unique_ptr<llm_graph_context> llama_model_talkie::build_arch_graph(const llm_graph_params & params) const {
return std::make_unique<graph>(*this, params);
}
llama_model_talkie::graph::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
const int64_t n_embd_head = hparams.n_embd_head_k();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_v());
GGML_ASSERT(n_embd_head == n_rot);
ggml_tensor * cur;
ggml_tensor * inpL;
inpL = build_inp_embd(model.tok_embd);
inpL = build_norm(inpL, nullptr, nullptr, LLM_NORM_RMS, -1);
cb(inpL, "inp_norm", -1);
ggml_tensor * embd_skip = inpL;
// inp_pos - contains the positions
ggml_tensor * inp_pos = build_inp_pos();
auto * inp_attn = build_attn_inp_kv();
ggml_tensor * inp_out_ids = build_inp_out_ids();
const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
for (int il = 0; il < n_layer; ++il) {
ggml_tensor * inpSA = inpL;
ggml_tensor * inp_skip = embd_skip;
cur = build_norm(inpL, nullptr, nullptr, LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
// self-attention
{
auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
n_embd_head, n_head, n_head_kv, il);
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);
// reference applies qknorm after rope
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
cb(Qcur, "Qcur_norm", il);
Kcur = build_norm(Kcur, nullptr, nullptr, LLM_NORM_RMS, il);
cb(Kcur, "Kcur_norm", il);
cb(Vcur, "Vcur", il);
cur = build_attn(inp_attn,
model.layers[il].wo, nullptr, model.layers[il].wo_s,
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
cb(cur, "attn_out", il);
}
if (il == n_layer - 1 && inp_out_ids) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
inp_skip = ggml_get_rows(ctx0, inp_skip, inp_out_ids);
}
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
cur = build_norm(ffn_inp, nullptr, nullptr, LLM_NORM_RMS, il);
cb(cur, "ffn_norm", il);
cur = build_ffn(cur,
model.layers[il].ffn_up, nullptr, nullptr,
model.layers[il].ffn_gate, nullptr, nullptr,
model.layers[il].ffn_down, nullptr, model.layers[il].ffn_down_s,
nullptr,
LLM_FFN_SILU, LLM_FFN_PAR, il);
cb(cur, "ffn_out", il);
cur = ggml_add(ctx0, cur, ffn_inp);
ggml_tensor * skip = ggml_mul(ctx0, inp_skip, model.layers[il].out_scale);
cb(skip, "embd_skip", il);
cur = ggml_add(ctx0, cur, skip);
cur = build_cvec(cur, il);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
cur = build_norm(cur, nullptr, nullptr, LLM_NORM_RMS, -1);
cb(cur, "result_norm", -1);
res->t_embd = cur;
cur = build_lora_mm(model.output, cur);
cur = ggml_scale(ctx0, cur, hparams.f_logit_scale);
cb(cur, "result_output", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}