vbatts/llama.cpp
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Merge branch 'master' into xsn/vision_2

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This commit is contained in:
Xuan Son Nguyen 2025-01-22 13:28:31 +01:00
commit 32daa38333
65 changed files with 7551 additions and 952 deletions
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2
src/CMakeLists.txt
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@ -30,7 +30,7 @@ add_library(llama
unicode-data.cpp
)
target_include_directories(llama PUBLIC . ../include)
target_include_directories(llama PUBLIC . ../include ../common)
target_compile_features (llama PUBLIC cxx_std_17) # don't bump
target_link_libraries(llama PUBLIC ggml)

6
src/llama-arch.cpp
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@ -183,6 +183,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
{ LLM_KV_TOKENIZER_HF_JSON, "tokenizer.huggingface.json" },
{ LLM_KV_TOKENIZER_RWKV, "tokenizer.rwkv.world" },
{ LLM_KV_TOKENIZER_CHAT_TEMPLATE, "tokenizer.chat_template" },
{ LLM_KV_TOKENIZER_CHAT_TEMPLATE_N, "tokenizer.chat_template.%s" },
{ LLM_KV_TOKENIZER_FIM_PRE_ID, "tokenizer.ggml.fim_pre_token_id" },
{ LLM_KV_TOKENIZER_FIM_SUF_ID, "tokenizer.ggml.fim_suf_token_id" },
{ LLM_KV_TOKENIZER_FIM_MID_ID, "tokenizer.ggml.fim_mid_token_id" },
@ -1532,10 +1533,11 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
{LLM_TENSOR_CONVNEXT_GAMMA, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL}},
};
LLM_KV::LLM_KV(llm_arch arch) : arch(arch) {}
LLM_KV::LLM_KV(llm_arch arch, const char * suffix) : arch(arch), suffix(suffix) {}
std::string LLM_KV::operator()(llm_kv kv) const {
return ::format(LLM_KV_NAMES.at(kv), LLM_ARCH_NAMES.at(arch));
return suffix ? ::format(LLM_KV_NAMES.at(kv), LLM_ARCH_NAMES.at(arch), suffix)
: ::format(LLM_KV_NAMES.at(kv), LLM_ARCH_NAMES.at(arch));
}
std::string LLM_TN_IMPL::str() const {

4
src/llama-arch.h
View file

@ -181,6 +181,7 @@ enum llm_kv {
LLM_KV_TOKENIZER_HF_JSON,
LLM_KV_TOKENIZER_RWKV,
LLM_KV_TOKENIZER_CHAT_TEMPLATE,
LLM_KV_TOKENIZER_CHAT_TEMPLATE_N,
LLM_KV_TOKENIZER_FIM_PRE_ID,
LLM_KV_TOKENIZER_FIM_SUF_ID,
LLM_KV_TOKENIZER_FIM_MID_ID,
@ -387,9 +388,10 @@ enum llm_tensor_layer {
};
struct LLM_KV {
LLM_KV(llm_arch arch);
LLM_KV(llm_arch arch, const char * suffix = nullptr);
llm_arch arch;
const char * suffix;
std::string operator()(llm_kv kv) const;
};

2
src/llama-chat.cpp
View file

@ -152,7 +152,7 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
return LLM_CHAT_TEMPLATE_MINICPM;
} else if (tmpl_contains("'Assistant: ' + message['content'] + eos_token")) {
return LLM_CHAT_TEMPLATE_DEEPSEEK_2;
} else if (tmpl_contains(LU8("'<｜Assistant｜>' + message['content'] + '<｜end▁of▁sentence｜>'"))) {
} else if (tmpl_contains(LU8("<｜Assistant｜>")) && tmpl_contains(LU8("<｜User｜>")) && tmpl_contains(LU8("<｜end▁of▁sentence｜>"))) {
return LLM_CHAT_TEMPLATE_DEEPSEEK_3;
} else if (tmpl_contains("[|system|]") && tmpl_contains("[|assistant|]") && tmpl_contains("[|endofturn|]")) {
// ref: https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct/discussions/8#66bae61b1893d14ee8ed85bb

1
src/llama-mmap.cpp
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@ -7,6 +7,7 @@
#include <cstring>
#include <climits>
#include <stdexcept>
#include <cerrno>
#ifdef __has_include
#if __has_include(<unistd.h>)

50
src/llama-model.cpp
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@ -2253,6 +2253,50 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
}
} break;
case LLM_ARCH_PHIMOE:
{
const int64_t n_embd_head = n_embd / n_head;
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 }, 0);
output_b = create_tensor(tn(LLM_TENSOR_OUTPUT, "bias"), { n_vocab }, 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.attn_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "bias", i), { n_embd }, 0);
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED);
if (layer.wqkv == nullptr) {
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, 0);
layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, 0);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, 0);
}
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, 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);
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, n_expert}, 0);
layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert}, 0);
layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0);
layer.rope_long = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight", i), { n_embd_head/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_embd_head/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));
}
} break;
case LLM_ARCH_PLAMO:
{
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@ -4071,8 +4115,10 @@ uint64_t llama_model_size(const struct llama_model * model) {
return model->size();
}
const char * llama_model_chat_template(const struct llama_model * model) {
const auto & it = model->gguf_kv.find(LLM_KV(model->arch)(LLM_KV_TOKENIZER_CHAT_TEMPLATE));
const char * llama_model_chat_template(const struct llama_model * model, const char * name) {
const auto key = name ? LLM_KV(model->arch, name)(LLM_KV_TOKENIZER_CHAT_TEMPLATE_N)
: LLM_KV(model->arch)(LLM_KV_TOKENIZER_CHAT_TEMPLATE);
const auto & it = model->gguf_kv.find(key);
if (it == model->gguf_kv.end()) {
return nullptr;
}

3
src/llama-vocab.cpp
View file

@ -1523,7 +1523,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
pre_type = LLAMA_VOCAB_PRE_TYPE_COMMAND_R;
clean_spaces = false;
} else if (
tokenizer_pre == "qwen2") {
tokenizer_pre == "qwen2" ||
tokenizer_pre == "deepseek-r1-qwen") {
pre_type = LLAMA_VOCAB_PRE_TYPE_QWEN2;
clean_spaces = false;
} else if (

5
src/unicode.cpp
View file

@ -7,18 +7,17 @@
#include <algorithm>
#include <cassert>
#include <codecvt>
#include <cstddef>
#include <cstdint>
#include <locale>
#include <map>
#include <regex>
#include <stdexcept>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
#include <locale>
#include <codecvt>
size_t unicode_len_utf8(char src) {
const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };