mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2026-05-11 19:44:06 +00:00
994118a183
* git-friendly migration * add build_graph * nits * exclude old code from build * wip * add llm_arch_model_i * prepare downstream functions * nits * nits * wip * wip * add back create_tensor_qkv * fix files missing include * enforce one llm_build per arch * cmake: use glob * missing model params * nits * wip * wip (2) * wip (3) * test-llama-archs is happy * improve switch case * move more stuff into llm_arch_model_i * fix downstream code * nits * nits (2) * fix order * llama_model_base * LLAMA_LOAD_LOCALS * small fix * fix build errors * auto * rm migration script and ifdef
2518 lines
111 KiB
C++
2518 lines
111 KiB
C++
#include "llama-model.h"
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#include "llama-arch.h"
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#include "llama-ext.h"
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#include "llama-hparams.h"
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#include "llama-impl.h"
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#include "llama-mmap.h"
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#include "llama-cparams.h"
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#include "llama-model-loader.h"
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#include "llama-kv-cache.h"
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#include "llama-kv-cache-iswa.h"
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#include "llama-memory-hybrid.h"
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#include "llama-memory-hybrid-iswa.h"
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#include "llama-memory-recurrent.h"
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#include "models/models.h"
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#include "ggml.h"
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#include "ggml-cpp.h"
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#include <algorithm>
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#include <cassert>
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#include <cfloat>
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#include <cstdint>
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#include <cstring>
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#include <cmath>
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#include <functional>
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#include <map>
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#include <numeric>
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#include <regex>
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#include <sstream>
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#include <stdexcept>
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#include <string>
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#include <vector>
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static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params & params) {
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switch (arch) {
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case LLM_ARCH_LLAMA:
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return new llama_model_llama(params);
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case LLM_ARCH_LLAMA4:
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return new llama_model_llama4(params);
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case LLM_ARCH_LLAMA_EMBED:
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return new llama_model_llama_embed(params);
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case LLM_ARCH_MAINCODER:
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return new llama_model_maincoder(params);
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case LLM_ARCH_DECI:
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return new llama_model_deci(params);
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case LLM_ARCH_BAICHUAN:
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return new llama_model_baichuan(params);
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case LLM_ARCH_FALCON:
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return new llama_model_falcon(params);
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case LLM_ARCH_GROK:
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return new llama_model_grok(params);
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case LLM_ARCH_STARCODER:
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return new llama_model_starcoder(params);
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case LLM_ARCH_REFACT:
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return new llama_model_refact(params);
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case LLM_ARCH_BERT:
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return new llama_model_bert(params);
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case LLM_ARCH_JINA_BERT_V2:
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return new llama_model_jina_bert_v2(params);
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case LLM_ARCH_JINA_BERT_V3:
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return new llama_model_jina_bert_v3(params);
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case LLM_ARCH_NOMIC_BERT:
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return new llama_model_nomic_bert(params);
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case LLM_ARCH_NOMIC_BERT_MOE:
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return new llama_model_nomic_bert_moe(params);
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case LLM_ARCH_MODERN_BERT:
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return new llama_model_modern_bert(params);
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case LLM_ARCH_NEO_BERT:
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return new llama_model_neo_bert(params);
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case LLM_ARCH_EUROBERT:
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return new llama_model_eurobert(params);
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case LLM_ARCH_BLOOM:
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return new llama_model_bloom(params);
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case LLM_ARCH_MPT:
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return new llama_model_mpt(params);
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case LLM_ARCH_STABLELM:
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return new llama_model_stablelm(params);
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case LLM_ARCH_QWEN:
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return new llama_model_qwen(params);
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case LLM_ARCH_QWEN2:
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return new llama_model_qwen2(params);
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case LLM_ARCH_DREAM:
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return new llama_model_dream(params);
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case LLM_ARCH_LLADA:
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return new llama_model_llada(params);
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case LLM_ARCH_LLADA_MOE:
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return new llama_model_llada_moe(params);
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case LLM_ARCH_RND1:
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return new llama_model_rnd1(params);
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case LLM_ARCH_QWEN2VL:
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return new llama_model_qwen2vl(params);
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case LLM_ARCH_QWEN2MOE:
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return new llama_model_qwen2moe(params);
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case LLM_ARCH_QWEN3:
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return new llama_model_qwen3(params);
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case LLM_ARCH_QWEN3MOE:
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return new llama_model_qwen3moe(params);
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case LLM_ARCH_QWEN3VL:
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return new llama_model_qwen3vl(params);
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case LLM_ARCH_QWEN3VLMOE:
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return new llama_model_qwen3vlmoe(params);
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case LLM_ARCH_PHI2:
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return new llama_model_phi2(params);
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case LLM_ARCH_PHI3:
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return new llama_model_phi3(params);
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case LLM_ARCH_PHIMOE:
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return new llama_model_phimoe(params);
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case LLM_ARCH_PLAMO:
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return new llama_model_plamo(params);
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case LLM_ARCH_PLAMO2:
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return new llama_model_plamo2(params);
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case LLM_ARCH_PLAMO3:
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return new llama_model_plamo3(params);
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case LLM_ARCH_GPT2:
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return new llama_model_gpt2(params);
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case LLM_ARCH_CODESHELL:
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return new llama_model_codeshell(params);
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case LLM_ARCH_ORION:
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return new llama_model_orion(params);
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case LLM_ARCH_INTERNLM2:
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return new llama_model_internlm2(params);
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case LLM_ARCH_MINICPM3:
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return new llama_model_minicpm3(params);
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case LLM_ARCH_GEMMA:
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return new llama_model_gemma(params);
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case LLM_ARCH_GEMMA2:
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return new llama_model_gemma2(params);
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case LLM_ARCH_GEMMA3:
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return new llama_model_gemma3(params);
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case LLM_ARCH_GEMMA3N:
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return new llama_model_gemma3n(params);
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case LLM_ARCH_GEMMA4:
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return new llama_model_gemma4(params);
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case LLM_ARCH_GEMMA_EMBEDDING:
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return new llama_model_gemma_embedding(params);
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case LLM_ARCH_STARCODER2:
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return new llama_model_starcoder2(params);
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case LLM_ARCH_MAMBA:
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return new llama_model_mamba(params);
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case LLM_ARCH_MAMBA2:
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return new llama_model_mamba2(params);
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case LLM_ARCH_JAMBA:
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return new llama_model_jamba(params);
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case LLM_ARCH_XVERSE:
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return new llama_model_xverse(params);
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case LLM_ARCH_COMMAND_R:
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return new llama_model_command_r(params);
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case LLM_ARCH_COHERE2:
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return new llama_model_cohere2(params);
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case LLM_ARCH_DBRX:
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return new llama_model_dbrx(params);
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case LLM_ARCH_OLMO:
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return new llama_model_olmo(params);
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case LLM_ARCH_OLMO2:
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return new llama_model_olmo2(params);
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case LLM_ARCH_OLMOE:
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return new llama_model_olmoe(params);
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case LLM_ARCH_OPENELM:
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return new llama_model_openelm(params);
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case LLM_ARCH_GPTNEOX:
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return new llama_model_gptneox(params);
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case LLM_ARCH_ARCTIC:
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return new llama_model_arctic(params);
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case LLM_ARCH_DEEPSEEK:
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return new llama_model_deepseek(params);
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case LLM_ARCH_DEEPSEEK2:
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return new llama_model_deepseek2(params);
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case LLM_ARCH_DEEPSEEK2OCR:
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return new llama_model_deepseek2ocr(params);
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case LLM_ARCH_GLM_DSA:
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return new llama_model_glm_dsa(params);
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case LLM_ARCH_MISTRAL4:
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return new llama_model_mistral4(params);
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case LLM_ARCH_CHATGLM:
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return new llama_model_chatglm(params);
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case LLM_ARCH_GLM4:
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return new llama_model_glm4(params);
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case LLM_ARCH_GLM4_MOE:
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return new llama_model_glm4_moe(params);
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case LLM_ARCH_BITNET:
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return new llama_model_bitnet(params);
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case LLM_ARCH_T5:
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return new llama_model_t5(params);
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case LLM_ARCH_T5ENCODER:
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return new llama_model_t5encoder(params);
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case LLM_ARCH_JAIS:
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return new llama_model_jais(params);
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case LLM_ARCH_JAIS2:
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return new llama_model_jais2(params);
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case LLM_ARCH_NEMOTRON:
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return new llama_model_nemotron(params);
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case LLM_ARCH_NEMOTRON_H:
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return new llama_model_nemotron_h(params);
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case LLM_ARCH_NEMOTRON_H_MOE:
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return new llama_model_nemotron_h_moe(params);
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case LLM_ARCH_EXAONE:
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return new llama_model_exaone(params);
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case LLM_ARCH_EXAONE4:
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return new llama_model_exaone4(params);
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case LLM_ARCH_EXAONE_MOE:
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return new llama_model_exaone_moe(params);
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case LLM_ARCH_RWKV6:
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return new llama_model_rwkv6(params);
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case LLM_ARCH_RWKV6QWEN2:
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return new llama_model_rwkv6qwen2(params);
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case LLM_ARCH_RWKV7:
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return new llama_model_rwkv7(params);
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case LLM_ARCH_ARWKV7:
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return new llama_model_arwkv7(params);
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case LLM_ARCH_GRANITE:
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return new llama_model_granite(params);
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case LLM_ARCH_GRANITE_MOE:
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return new llama_model_granite_moe(params);
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case LLM_ARCH_MINICPM:
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return new llama_model_minicpm(params);
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case LLM_ARCH_GRANITE_HYBRID:
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return new llama_model_granite_hybrid(params);
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case LLM_ARCH_CHAMELEON:
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return new llama_model_chameleon(params);
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case LLM_ARCH_WAVTOKENIZER_DEC:
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return new llama_model_wavtokenizer_dec(params);
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case LLM_ARCH_PLM:
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return new llama_model_plm(params);
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case LLM_ARCH_BAILINGMOE:
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return new llama_model_bailingmoe(params);
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case LLM_ARCH_BAILINGMOE2:
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return new llama_model_bailingmoe2(params);
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case LLM_ARCH_SEED_OSS:
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return new llama_model_seed_oss(params);
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case LLM_ARCH_DOTS1:
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return new llama_model_dots1(params);
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case LLM_ARCH_ARCEE:
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return new llama_model_arcee(params);
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case LLM_ARCH_AFMOE:
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return new llama_model_afmoe(params);
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case LLM_ARCH_ERNIE4_5:
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return new llama_model_ernie4_5(params);
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case LLM_ARCH_ERNIE4_5_MOE:
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return new llama_model_ernie4_5_moe(params);
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case LLM_ARCH_PADDLEOCR:
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return new llama_model_paddleocr(params);
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case LLM_ARCH_HUNYUAN_MOE:
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return new llama_model_hunyuan_moe(params);
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case LLM_ARCH_HUNYUAN_VL:
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return new llama_model_hunyuan_vl(params);
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case LLM_ARCH_HUNYUAN_DENSE:
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return new llama_model_hunyuan_dense(params);
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case LLM_ARCH_SMOLLM3:
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return new llama_model_smollm3(params);
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case LLM_ARCH_OPENAI_MOE:
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return new llama_model_openai_moe(params);
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case LLM_ARCH_FALCON_H1:
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return new llama_model_falcon_h1(params);
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case LLM_ARCH_LFM2:
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return new llama_model_lfm2(params);
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case LLM_ARCH_LFM2MOE:
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return new llama_model_lfm2moe(params);
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case LLM_ARCH_SMALLTHINKER:
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return new llama_model_smallthinker(params);
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case LLM_ARCH_GROVEMOE:
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return new llama_model_grovemoe(params);
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case LLM_ARCH_APERTUS:
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return new llama_model_apertus(params);
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case LLM_ARCH_MINIMAX_M2:
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return new llama_model_minimax_m2(params);
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case LLM_ARCH_COGVLM:
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return new llama_model_cogvlm(params);
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case LLM_ARCH_PANGU_EMBED:
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return new llama_model_pangu_embed(params);
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case LLM_ARCH_QWEN3NEXT:
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return new llama_model_qwen3next(params);
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case LLM_ARCH_QWEN35:
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return new llama_model_qwen35(params);
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case LLM_ARCH_QWEN35MOE:
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return new llama_model_qwen35moe(params);
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case LLM_ARCH_MISTRAL3:
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return new llama_model_mistral3(params);
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case LLM_ARCH_MIMO2:
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return new llama_model_mimo2(params);
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case LLM_ARCH_KIMI_LINEAR:
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return new llama_model_kimi_linear(params);
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case LLM_ARCH_STEP35:
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return new llama_model_step35(params);
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default:
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GGML_ABORT("unimplemented model class");
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}
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}
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llama_model * llama_model_create(llm_arch arch, const llama_model_params & params) {
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llama_model * model = llama_model_mapping(arch, params);
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if (model != nullptr) {
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model->arch = arch;
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auto & devices = model->devices;
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if (!devices.empty() && devices[0].is_meta && !llm_arch_supports_sm_tensor(arch)) {
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throw std::runtime_error(std::string("LLAMA_SPLIT_MODE_TENSOR not implemented for architecture '") + llm_arch_name(arch) + "'");
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}
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}
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return model;
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}
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llama_model * llama_model_create(llama_model_loader & ml, const llama_model_params & params) {
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llm_arch arch = ml.get_arch();
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if (arch == LLM_ARCH_UNKNOWN) {
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throw std::runtime_error("unknown model architecture: '" + ml.get_arch_name() + "'");
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}
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return llama_model_create(arch, params);
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}
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struct ggml_backend_meta_split_state llama_meta_device_get_split_state(const struct ggml_tensor * tensor, void * userdata) {
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const llama_meta_device_get_split_state_userdata * ud = (const llama_meta_device_get_split_state_userdata *) userdata;
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const llama_hparams & hparams = ud->model->hparams;
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const std::string tensor_name = tensor->name;
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const std::regex pattern_q_weight ("blk\\.\\d*\\.attn_q.weight");
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const std::regex pattern_kv_weight ("blk\\.\\d*\\.attn_(k|v).weight");
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const std::regex pattern_qkv_weight ("blk\\.\\d*\\.attn_qkv.weight");
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const std::regex pattern_q_bias ("blk\\.\\d*\\.attn_q\\.bias");
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const std::regex pattern_kv_bias ("blk\\.\\d*\\.attn_(k|v)\\.bias");
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const std::regex pattern_qkv_bias ("blk\\.\\d*\\.attn_qkv.bias");
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const std::regex pattern_qk_norm ("blk\\.\\d*\\.attn_(q|k)_norm\\.weight");
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const std::regex pattern_kv_cache ("cache_(k|v)_l\\d*");
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const std::regex pattern_attn_sinks ("blk\\.\\d*\\.attn_sinks.weight");
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const std::regex pattern_attn_out_weight ("blk\\.\\d*\\.attn_output.weight");
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const std::regex pattern_attn_out_bias ("blk\\.\\d*\\.attn_output.bias");
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const std::regex pattern_attn_gate_weight("blk\\.\\d*\\.attn_gate.weight");
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const std::regex pattern_ssm_dt ("blk\\.\\d*\\.ssm_dt.bias");
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const std::regex pattern_ssm_a ("blk\\.\\d*\\.ssm_a");
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const std::regex pattern_ssm_alpha ("blk\\.\\d*\\.ssm_alpha.weight");
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const std::regex pattern_ssm_beta ("blk\\.\\d*\\.ssm_beta.weight");
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const std::regex pattern_ssm_beta_alpha ("blk\\.\\d*\\.ssm_ba.weight");
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const std::regex pattern_r_cache ("cache_r_l\\d*");
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const std::regex pattern_s_cache ("cache_s_l\\d*");
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const std::regex pattern_ssm_conv1d ("blk\\.\\d*\\.ssm_conv1d.weight");
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const std::regex pattern_ssm_out_weight ("blk\\.\\d*\\.ssm_out.weight");
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const std::regex pattern_ffn_up_gate_weight("blk\\.\\d*\\.ffn_(up|gate)(_exps)?.weight");
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const std::regex pattern_ffn_up_gate_bias ("blk\\.\\d*\\.ffn_(up|gate)(_exps)?.bias");
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const std::regex pattern_ffn_gate_up_weight("blk\\.\\d*\\.ffn_gate_up(_exps)?.weight");
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const std::regex pattern_ffn_down_weight ("blk\\.\\d*\\.ffn_down(_exps)?.weight");
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const std::regex pattern_ffn_down_bias ("blk\\.\\d*\\.ffn_down.bias");
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const std::regex pattern_ffn_down_exps_bias("blk\\.\\d*\\.ffn_down_exps.bias");
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const std::regex pattern_output_weight("output\\.weight");
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const std::regex pattern_output_bias ("output\\.bias");
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struct tensor_config {
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ggml_backend_meta_split_axis axis;
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const ggml_tensor * tensor_axis_0;
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uint32_t il;
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size_t rotation; // when assigning tensor slices, rotate how the rounding is done for more even allocation
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};
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auto get_tensor_config_impl = [&](
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const ggml_backend_meta_split_axis axis, const std::string & suffix = "", const std::string & suffix_fallback = "") -> tensor_config {
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// the layers in a tensor can be inhomogeneous, if the pattern is cleanly divided by the number of GPUs there can be aliasing effects,
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// count only the same type of previous layers to avoid this
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auto get_il_eff = [&](const size_t il){
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size_t ret = 0;
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const bool il_is_recurrent = hparams.is_recurrent(il);
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const bool il_is_swa = hparams.is_swa(il);
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for (size_t il_prev = 0; il_prev < il; il_prev++) {
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ret += hparams.is_recurrent(il_prev) == il_is_recurrent && hparams.is_swa(il_prev) == il_is_swa;
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}
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return ret;
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};
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uint32_t il;
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std::string prefix;
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size_t rotation;
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if (tensor_name.substr(0, 4) == "blk.") {
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const size_t length_prefix = tensor_name.find('.', 4);
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GGML_ASSERT(length_prefix != std::string::npos);
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prefix = tensor_name.substr(0, length_prefix + 1);
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il = std::stoull(tensor_name.substr(4, length_prefix));
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rotation = get_il_eff(il) % ud->n_devices;
|
|
} else if (tensor_name.substr(0, 6) == "cache_") {
|
|
const size_t layer_index_start = tensor_name.find("_l", 6);
|
|
GGML_ASSERT(layer_index_start != std::string::npos);
|
|
il = std::stoull(tensor_name.substr(layer_index_start + 2));
|
|
prefix = "blk." + std::to_string(il) + ".";
|
|
rotation = get_il_eff(il) % ud->n_devices;
|
|
} else {
|
|
il = 0;
|
|
rotation = hparams.n_layer % ud->n_devices;
|
|
}
|
|
const ggml_tensor * tensor_axis_0 = suffix.empty() ? tensor : ud->model->get_tensor((prefix + suffix).c_str());
|
|
if (tensor_axis_0 == nullptr) {
|
|
GGML_ASSERT(!suffix_fallback.empty());
|
|
tensor_axis_0 = ud->model->get_tensor((prefix + suffix_fallback).c_str());
|
|
}
|
|
GGML_ASSERT(tensor_axis_0 != nullptr);
|
|
return {axis, tensor_axis_0, il, rotation};
|
|
};
|
|
|
|
auto get_tensor_config = [&]() -> tensor_config {
|
|
// standard attention
|
|
if (std::regex_match(tensor_name, pattern_q_weight) || std::regex_match(tensor_name, pattern_kv_weight)) {
|
|
return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_1, "attn_output.weight");
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_q_bias) || std::regex_match(tensor_name, pattern_kv_bias)) {
|
|
return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_0, "attn_output.weight");
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_qkv_weight)) {
|
|
return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_1);
|
|
}
|
|
if ( std::regex_match(tensor_name, pattern_qkv_bias)) {
|
|
return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_0);
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_qk_norm)) {
|
|
return get_tensor_config_impl(tensor->ne[1] == 1 ? GGML_BACKEND_SPLIT_AXIS_MIRRORED : GGML_BACKEND_SPLIT_AXIS_1, "attn_output.weight");
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_kv_cache) || std::regex_match(tensor_name, pattern_attn_sinks)) {
|
|
return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_0, "attn_output.weight");
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_attn_out_weight)) {
|
|
return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_0);
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_attn_out_bias)) {
|
|
return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_MIRRORED);
|
|
}
|
|
|
|
if (std::regex_match(tensor_name, pattern_attn_gate_weight)) {
|
|
return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_1);
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_ssm_dt) || std::regex_match(tensor_name, pattern_ssm_a)) {
|
|
return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_0, "ssm_out.weight");
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_ssm_alpha) || std::regex_match(tensor_name, pattern_ssm_beta) ||
|
|
std::regex_match(tensor_name, pattern_ssm_beta_alpha)) {
|
|
return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_1, "ssm_out.weight");
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_r_cache) || std::regex_match(tensor_name, pattern_s_cache)) {
|
|
return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_0, "ssm_out.weight");
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_ssm_conv1d)) {
|
|
return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_1, "ssm_out.weight");
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_ssm_out_weight)) {
|
|
return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_0);
|
|
}
|
|
|
|
// FFN
|
|
if (std::regex_match(tensor_name, pattern_ffn_up_gate_weight)) {
|
|
return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_1, "ffn_down.weight", "ffn_down_exps.weight");
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_ffn_up_gate_bias)) {
|
|
return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_0, "ffn_down.weight", "ffn_down_exps.weight");
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_ffn_gate_up_weight)) {
|
|
return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_1, "ffn_down.weight", "ffn_down_exps.weight");
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_ffn_down_weight)) {
|
|
return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_0, "ffn_down.weight", "ffn_down_exps.weight");
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_ffn_down_bias)) {
|
|
return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_MIRRORED);
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_ffn_down_exps_bias)) {
|
|
return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_PARTIAL);
|
|
}
|
|
|
|
// output
|
|
if (std::regex_match(tensor_name, pattern_output_weight)) {
|
|
return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_1);
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_output_bias)) {
|
|
const ggml_tensor * output_weight = ud->model->get_tensor("output.weight");
|
|
GGML_ASSERT(output_weight != nullptr);
|
|
return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_0);
|
|
}
|
|
|
|
// everything else
|
|
return get_tensor_config_impl(GGML_BACKEND_SPLIT_AXIS_MIRRORED);
|
|
};
|
|
|
|
auto get_split_segments = [&](int axis, uint32_t il) -> std::vector<int64_t> {
|
|
if (ud->model->arch == LLM_ARCH_QWEN3NEXT || ud->model->arch == LLM_ARCH_QWEN35 || ud->model->arch == LLM_ARCH_QWEN35MOE) {
|
|
const int64_t head_k_dim = hparams.ssm_d_state;
|
|
const int64_t head_v_dim = hparams.ssm_d_state;
|
|
const int64_t n_k_heads = hparams.ssm_n_group;
|
|
const int64_t n_v_heads = hparams.ssm_dt_rank;
|
|
const int64_t key_dim = head_k_dim * n_k_heads;
|
|
const int64_t value_dim = head_v_dim * n_v_heads;
|
|
|
|
// both Qwen 3 Next and Qwen 3.5 support n_v_heads > n_k_heads but the broadcasting pattern is different:
|
|
// - Qwen 3 Next: [k0_v0, k0_v1, k1_v2, k1_v3] (this is the default split pattern)
|
|
// - Qwen 3.5: [k0_v0, k1_v1, k0_v2, k1_v3] (needs segmenting of V on the scale of K to get the correct pattern)
|
|
if (ud->model->arch == LLM_ARCH_QWEN3NEXT) {
|
|
if (std::regex_match(tensor_name, pattern_qkv_weight) || std::regex_match(tensor_name, pattern_ssm_conv1d)) {
|
|
GGML_ASSERT(tensor->ne[axis] == 2*key_dim + value_dim);
|
|
return {key_dim, key_dim, value_dim};
|
|
}
|
|
} else {
|
|
const int64_t head_ratio = n_v_heads / n_k_heads;
|
|
if (std::regex_match(tensor_name, pattern_qkv_weight) || std::regex_match(tensor_name, pattern_ssm_conv1d)) {
|
|
GGML_ASSERT(tensor->ne[axis] == 2*key_dim + value_dim);
|
|
return std::vector<int64_t>(2 + head_ratio, key_dim);
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_attn_gate_weight) || std::regex_match(tensor_name, pattern_ssm_out_weight)) {
|
|
return std::vector<int64_t>(head_ratio, key_dim);
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_ssm_dt) || std::regex_match(tensor_name, pattern_ssm_a) ||
|
|
std::regex_match(tensor_name, pattern_ssm_alpha) || std::regex_match(tensor_name, pattern_ssm_beta)) {
|
|
return std::vector<int64_t>(head_ratio, n_k_heads);
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_r_cache)) {
|
|
return std::vector<int64_t>(2 + head_ratio, key_dim * (hparams.ssm_d_conv - 1));
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_s_cache)) {
|
|
return std::vector<int64_t>(head_ratio, n_k_heads * head_v_dim * head_v_dim);
|
|
}
|
|
}
|
|
|
|
// the FFN is the same for Qwen 3 Next and Qwen 3.5:
|
|
if (std::regex_match(tensor_name, pattern_ffn_gate_up_weight)) {
|
|
const int64_t n_ff_exp = hparams.n_ff_exp;
|
|
GGML_ASSERT(tensor->ne[axis] == 2*n_ff_exp);
|
|
return {n_ff_exp, n_ff_exp};
|
|
}
|
|
return {tensor->ne[axis]};
|
|
}
|
|
|
|
if (std::regex_match(tensor_name, pattern_qkv_weight) || std::regex_match(tensor_name, pattern_qkv_bias)) {
|
|
const int64_t n_embd = hparams.n_embd;
|
|
const int64_t n_embd_gqa = hparams.n_embd_v_gqa(il);
|
|
GGML_ASSERT(hparams.n_embd_k_gqa() == n_embd_gqa);
|
|
GGML_ASSERT(tensor->ne[axis] == n_embd + 2*n_embd_gqa);
|
|
return {n_embd, n_embd_gqa, n_embd_gqa};
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_ffn_gate_up_weight)) {
|
|
const int64_t n_ff_exp = hparams.n_ff_exp;
|
|
GGML_ASSERT(tensor->ne[axis] == 2*n_ff_exp);
|
|
return {n_ff_exp, n_ff_exp};
|
|
}
|
|
return {tensor->ne[axis]};
|
|
};
|
|
|
|
auto get_split_granularity = [&](int64_t blck_size, uint32_t il, const std::vector<int64_t> & segments) -> std::vector<int64_t> {
|
|
if (hparams.is_recurrent(il)) {
|
|
// linear attention
|
|
const int64_t head_dim = hparams.ssm_d_state;
|
|
const int64_t granularity_qkv = std::lcm(blck_size, head_dim);
|
|
if (std::regex_match(tensor_name, pattern_qkv_weight) || std::regex_match(tensor_name, pattern_attn_gate_weight) ||
|
|
std::regex_match(tensor_name, pattern_ssm_conv1d) || std::regex_match(tensor_name, pattern_ssm_out_weight)) {
|
|
return std::vector<int64_t>(segments.size(), granularity_qkv);
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_ssm_dt) || std::regex_match(tensor_name, pattern_ssm_a) ||
|
|
std::regex_match(tensor_name, pattern_ssm_alpha) || std::regex_match(tensor_name, pattern_ssm_beta)) {
|
|
return std::vector<int64_t>(segments.size(), granularity_qkv / head_dim);
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_ssm_beta_alpha)) {
|
|
return std::vector<int64_t>(segments.size(), 2 * (granularity_qkv / head_dim));
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_r_cache)) {
|
|
return std::vector<int64_t>(segments.size(), granularity_qkv * (hparams.ssm_d_conv - 1));
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_s_cache)) {
|
|
return std::vector<int64_t>(segments.size(), granularity_qkv * head_dim);
|
|
}
|
|
} else {
|
|
// regular attention
|
|
const uint32_t n_gqa = hparams.n_gqa(il);
|
|
const uint32_t n_embd_q = n_gqa * hparams.n_embd_head_k(il);
|
|
if (std::regex_match(tensor_name, pattern_attn_sinks)) {
|
|
GGML_ASSERT(segments.size() == 1);
|
|
return {std::lcm(n_embd_q, blck_size)/n_embd_q * n_gqa};
|
|
}
|
|
|
|
const int64_t granularity_q = std::lcm(n_embd_q, blck_size);
|
|
if (std::regex_match(tensor_name, pattern_q_weight) || std::regex_match(tensor_name, pattern_q_bias)) {
|
|
GGML_ASSERT(segments.size() == 1);
|
|
// some models have Q gate tensors, for those cases the granularity needs to be doubled:
|
|
if (ud->model->arch == LLM_ARCH_QWEN3NEXT || ud->model->arch == LLM_ARCH_QWEN35 || ud->model->arch == LLM_ARCH_QWEN35MOE) {
|
|
return {std::lcm(2*n_embd_q, blck_size)};
|
|
}
|
|
return {granularity_q};
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_attn_out_weight)) {
|
|
GGML_ASSERT(segments.size() == 1);
|
|
return {granularity_q};
|
|
}
|
|
|
|
const int64_t granularity_kv = granularity_q / n_gqa;
|
|
if (std::regex_match(tensor_name, pattern_kv_weight) ||
|
|
std::regex_match(tensor_name, pattern_kv_bias) ||
|
|
std::regex_match(tensor_name, pattern_kv_cache)) {
|
|
GGML_ASSERT(segments.size() == 1);
|
|
return {granularity_kv};
|
|
}
|
|
if (std::regex_match(tensor_name, pattern_qkv_weight) || std::regex_match(tensor_name, pattern_qkv_bias)) {
|
|
GGML_ASSERT(segments.size() == 3);
|
|
return {granularity_q, granularity_kv, granularity_kv};
|
|
}
|
|
}
|
|
|
|
// FFN
|
|
if (std::regex_match(tensor_name, pattern_ffn_up_gate_weight) || std::regex_match(tensor_name, pattern_ffn_up_gate_bias) ||
|
|
std::regex_match(tensor_name, pattern_ffn_gate_up_weight) || std::regex_match(tensor_name, pattern_ffn_down_weight)) {
|
|
GGML_ASSERT(segments.size() <= 2);
|
|
return std::vector<int64_t>(segments.size(), blck_size);
|
|
}
|
|
|
|
// everything else
|
|
GGML_ASSERT(segments.size() == 1);
|
|
return {1};
|
|
};
|
|
|
|
ggml_backend_meta_split_state split_state;
|
|
memset(&split_state, 0, sizeof(split_state));
|
|
tensor_config tc = get_tensor_config();
|
|
split_state.axis = tc.axis;
|
|
if (split_state.axis >= 0 && split_state.axis < GGML_MAX_DIMS) {
|
|
const int64_t ne_full = tensor->ne[split_state.axis];
|
|
const int64_t blck_size = ggml_blck_size(tc.tensor_axis_0->type);
|
|
const float * tensor_split = ud->model->tensor_split();
|
|
std::vector<float> tensor_split_scan;
|
|
tensor_split_scan.reserve(ud->n_devices);
|
|
for (size_t j = 0; j < ud->n_devices; j++) {
|
|
tensor_split_scan.push_back(tensor_split == nullptr ? 0.0f : tensor_split[(j + tc.rotation) % ud->n_devices]);
|
|
if (j > 0) {
|
|
tensor_split_scan[j] += tensor_split_scan[j - 1];
|
|
}
|
|
}
|
|
const std::vector<int64_t> segments = get_split_segments(split_state.axis, tc.il);
|
|
const std::vector<int64_t> granularity = get_split_granularity(blck_size, tc.il, segments);
|
|
for (size_t is = 0; is < segments.size(); is++) {
|
|
const int64_t ne_s = segments[is];
|
|
const int64_t g_s = granularity[is];
|
|
GGML_ASSERT(ne_full % g_s == 0);
|
|
int64_t low = 0;
|
|
size_t j = 0;
|
|
for (; j < ud->n_devices - 1; j++) {
|
|
int64_t high = tensor_split_scan.back() == 0.0f ?
|
|
ne_s * (j+1)/ud->n_devices : ne_s * tensor_split_scan[j]/tensor_split_scan.back();
|
|
if (high % g_s != 0) {
|
|
high -= high % g_s;
|
|
}
|
|
split_state.ne[is*ud->n_devices + (j + tc.rotation) % ud->n_devices] = high - low;
|
|
low = high;
|
|
}
|
|
split_state.ne[is*ud->n_devices + (j + tc.rotation) % ud->n_devices] = ne_s - low;
|
|
}
|
|
split_state.n_segments = segments.size();
|
|
} else {
|
|
memset(split_state.ne, 0, sizeof(split_state.ne));
|
|
split_state.n_segments = 1;
|
|
}
|
|
return split_state;
|
|
GGML_UNUSED(userdata);
|
|
}
|
|
|
|
const char * llm_type_name(llm_type type) {
|
|
switch (type) {
|
|
case LLM_TYPE_14M: return "14M";
|
|
case LLM_TYPE_17M: return "17M";
|
|
case LLM_TYPE_22M: return "22M";
|
|
case LLM_TYPE_33M: return "33M";
|
|
case LLM_TYPE_47M: return "47M";
|
|
case LLM_TYPE_60M: return "60M";
|
|
case LLM_TYPE_70M: return "70M";
|
|
case LLM_TYPE_80M: return "80M";
|
|
case LLM_TYPE_109M: return "109M";
|
|
case LLM_TYPE_137M: return "137M";
|
|
case LLM_TYPE_140M: return "140M";
|
|
case LLM_TYPE_149M: return "149M";
|
|
case LLM_TYPE_160M: return "160M";
|
|
case LLM_TYPE_190M: return "190M";
|
|
case LLM_TYPE_220M: return "220M";
|
|
case LLM_TYPE_250M: return "250M";
|
|
case LLM_TYPE_256M: return "256M";
|
|
case LLM_TYPE_270M: return "270M";
|
|
case LLM_TYPE_335M: return "335M";
|
|
case LLM_TYPE_350M: return "350M";
|
|
case LLM_TYPE_360M: return "360M";
|
|
case LLM_TYPE_395M: return "395M";
|
|
case LLM_TYPE_410M: return "410M";
|
|
case LLM_TYPE_450M: return "450M";
|
|
case LLM_TYPE_475M: return "475M";
|
|
case LLM_TYPE_558M: return "558M";
|
|
case LLM_TYPE_700M: return "700M";
|
|
case LLM_TYPE_770M: return "770M";
|
|
case LLM_TYPE_780M: return "780M";
|
|
case LLM_TYPE_950M: return "950M";
|
|
case LLM_TYPE_0_3B: return "0.3B";
|
|
case LLM_TYPE_0_5B: return "0.5B";
|
|
case LLM_TYPE_0_6B: return "0.6B";
|
|
case LLM_TYPE_0_8B: return "0.8B";
|
|
case LLM_TYPE_1B: return "1B";
|
|
case LLM_TYPE_1_2B: return "1.2B";
|
|
case LLM_TYPE_1_3B: return "1.3B";
|
|
case LLM_TYPE_1_4B: return "1.4B";
|
|
case LLM_TYPE_1_5B: return "1.5B";
|
|
case LLM_TYPE_1_6B: return "1.6B";
|
|
case LLM_TYPE_1_7B: return "1.7B";
|
|
case LLM_TYPE_1_8B: return "1.8B";
|
|
case LLM_TYPE_2B: return "2B";
|
|
case LLM_TYPE_2_6B: return "2.6B";
|
|
case LLM_TYPE_2_8B: return "2.8B";
|
|
case LLM_TYPE_2_9B: return "2.9B";
|
|
case LLM_TYPE_3B: return "3B";
|
|
case LLM_TYPE_4B: return "4B";
|
|
case LLM_TYPE_6B: return "6B";
|
|
case LLM_TYPE_6_9B: return "6.9B";
|
|
case LLM_TYPE_7B: return "7B";
|
|
case LLM_TYPE_8B: return "8B";
|
|
case LLM_TYPE_9B: return "9B";
|
|
case LLM_TYPE_11B: return "11B";
|
|
case LLM_TYPE_12B: return "12B";
|
|
case LLM_TYPE_13B: return "13B";
|
|
case LLM_TYPE_14B: return "14B";
|
|
case LLM_TYPE_15B: return "15B";
|
|
case LLM_TYPE_16B: return "16B";
|
|
case LLM_TYPE_20B: return "20B";
|
|
case LLM_TYPE_26B: return "26B";
|
|
case LLM_TYPE_27B: return "27B";
|
|
case LLM_TYPE_30B: return "30B";
|
|
case LLM_TYPE_31B: return "31B";
|
|
case LLM_TYPE_32B: return "32B";
|
|
case LLM_TYPE_34B: return "34B";
|
|
case LLM_TYPE_35B: return "35B";
|
|
case LLM_TYPE_36B: return "36B";
|
|
case LLM_TYPE_40B: return "40B";
|
|
case LLM_TYPE_65B: return "65B";
|
|
case LLM_TYPE_70B: return "70B";
|
|
case LLM_TYPE_120B: return "120B";
|
|
case LLM_TYPE_142B: return "142B";
|
|
case LLM_TYPE_236B: return "236B";
|
|
case LLM_TYPE_290B: return "290B";
|
|
case LLM_TYPE_314B: return "314B";
|
|
case LLM_TYPE_405B: return "405B";
|
|
case LLM_TYPE_671B: return "671B";
|
|
case LLM_TYPE_SMALL: return "0.1B";
|
|
case LLM_TYPE_MEDIUM: return "0.4B";
|
|
case LLM_TYPE_LARGE: return "0.8B";
|
|
case LLM_TYPE_XL: return "1.5B";
|
|
case LLM_TYPE_A1_7B: return "A1.7B";
|
|
case LLM_TYPE_A2_7B: return "A2.7B";
|
|
case LLM_TYPE_8x7B: return "8x7B";
|
|
case LLM_TYPE_8x22B: return "8x22B";
|
|
case LLM_TYPE_16x12B: return "16x12B";
|
|
case LLM_TYPE_16x3_8B: return "16x3.8B";
|
|
case LLM_TYPE_10B_128x3_66B: return "10B+128x3.66B";
|
|
case LLM_TYPE_57B_A14B: return "57B.A14B";
|
|
case LLM_TYPE_17B_16E: return "17Bx16E (Scout)";
|
|
case LLM_TYPE_17B_128E: return "17Bx128E (Maverick)";
|
|
case LLM_TYPE_A13B: return "A13B";
|
|
case LLM_TYPE_7B_A1B: return "7B.A1B";
|
|
case LLM_TYPE_8B_A1B: return "8B.A1B";
|
|
case LLM_TYPE_16B_A1B: return "16B.A1B";
|
|
case LLM_TYPE_21B_A3B: return "21B.A3B";
|
|
case LLM_TYPE_24B_A2B: return "24B.A2B";
|
|
case LLM_TYPE_26B_A4B: return "26B.A4B";
|
|
case LLM_TYPE_30B_A3B: return "30B.A3B";
|
|
case LLM_TYPE_31B_A3_5B: return "31B.A3.5B";
|
|
case LLM_TYPE_35B_A3B: return "35B.A3B";
|
|
case LLM_TYPE_48B_A3B: return "48B.A3B";
|
|
case LLM_TYPE_80B_A3B: return "80B.A3B";
|
|
case LLM_TYPE_100B_A6B: return "100B.A6B";
|
|
case LLM_TYPE_102B_A12B: return "102B.A12B";
|
|
case LLM_TYPE_106B_A12B: return "106B.A12B";
|
|
case LLM_TYPE_120B_A12B: return "120B.A12B";
|
|
case LLM_TYPE_122B_A10B: return "122B.A10B";
|
|
case LLM_TYPE_196B_A11B: return "196B.A11B";
|
|
case LLM_TYPE_230B_A10B: return "230B.A10B";
|
|
case LLM_TYPE_235B_A22B: return "235B.A22B";
|
|
case LLM_TYPE_300B_A47B: return "300B.A47B";
|
|
case LLM_TYPE_310B_A15B: return "310B.A15B";
|
|
case LLM_TYPE_355B_A32B: return "355B.A32B";
|
|
case LLM_TYPE_397B_A17B: return "397B.A17B";
|
|
case LLM_TYPE_744B_A40B: return "744B.A40B";
|
|
case LLM_TYPE_E2B: return "E2B";
|
|
case LLM_TYPE_E4B: return "E4B";
|
|
default: return "?B";
|
|
}
|
|
}
|
|
|
|
static const char * llama_expert_gating_func_name(llama_expert_gating_func_type type) {
|
|
switch (type) {
|
|
case LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX: return "softmax";
|
|
case LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID: return "sigmoid";
|
|
default: return "unknown";
|
|
}
|
|
}
|
|
|
|
static const std::map<llama_rope_scaling_type, const char *> LLAMA_ROPE_SCALING_TYPES = {
|
|
{ LLAMA_ROPE_SCALING_TYPE_NONE, "none" },
|
|
{ LLAMA_ROPE_SCALING_TYPE_LINEAR, "linear" },
|
|
{ LLAMA_ROPE_SCALING_TYPE_YARN, "yarn" },
|
|
{ LLAMA_ROPE_SCALING_TYPE_LONGROPE, "longrope" },
|
|
};
|
|
|
|
std::string llama_rope_scaling_type_name(llama_rope_scaling_type rope_scaling_type) {
|
|
return LLAMA_ROPE_SCALING_TYPES.at(rope_scaling_type);
|
|
}
|
|
|
|
static llama_rope_scaling_type llama_rope_scaling_type_from_string(const std::string & name) {
|
|
for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) {
|
|
if (kv.second == name) {
|
|
return (llama_rope_scaling_type) kv.first;
|
|
}
|
|
}
|
|
|
|
return LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
|
|
}
|
|
|
|
// CPU: ACCEL -> GPU host -> CPU extra -> CPU
|
|
static buft_list_t make_cpu_buft_list(const std::vector<llama_device> & devices, bool use_extra_bufts, bool no_host) {
|
|
buft_list_t buft_list;
|
|
|
|
// add ACCEL buffer types
|
|
for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
|
|
ggml_backend_dev_t dev = ggml_backend_dev_get(i);
|
|
if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_ACCEL) {
|
|
auto * buft = ggml_backend_dev_buffer_type(dev);
|
|
// skip
|
|
if (buft != ggml_backend_cpu_buffer_type()) {
|
|
buft_list.emplace_back(dev, buft);
|
|
}
|
|
}
|
|
}
|
|
|
|
// add a host buffer type
|
|
// storing the tensors in a host buffer is useful when the processing of large batches
|
|
// is offloaded to a GPU device, since it reduces the time spent on data transfers
|
|
// generally, this will be done using the first device in the list
|
|
// a better approach would be to handle this on a weight-by-weight basis using the offload_op
|
|
// function of the device to determine if it would benefit from being stored in a host buffer
|
|
if (!no_host) {
|
|
for (const auto & dev : devices) {
|
|
ggml_backend_buffer_type_t buft = ggml_backend_dev_host_buffer_type(dev.dev);
|
|
if (buft) {
|
|
buft_list.emplace_back(dev.dev, buft);
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
// add extra buffer types
|
|
if (use_extra_bufts) {
|
|
auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
|
|
if (cpu_dev == nullptr) {
|
|
throw std::runtime_error(format("%s: no CPU backend found", __func__));
|
|
}
|
|
|
|
auto * cpu_reg = ggml_backend_dev_backend_reg(cpu_dev);
|
|
auto ggml_backend_dev_get_extra_bufts_fn = (ggml_backend_dev_get_extra_bufts_t)
|
|
ggml_backend_reg_get_proc_address(cpu_reg, "ggml_backend_dev_get_extra_bufts");
|
|
if (ggml_backend_dev_get_extra_bufts_fn) {
|
|
ggml_backend_buffer_type_t * extra_bufts = ggml_backend_dev_get_extra_bufts_fn(cpu_dev);
|
|
while (extra_bufts && *extra_bufts) {
|
|
buft_list.emplace_back(cpu_dev, *extra_bufts);
|
|
++extra_bufts;
|
|
}
|
|
}
|
|
}
|
|
|
|
// add the CPU buffer type
|
|
for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
|
|
ggml_backend_dev_t dev = ggml_backend_dev_get(i);
|
|
if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU) {
|
|
buft_list.emplace_back(dev, ggml_backend_dev_buffer_type(dev));
|
|
}
|
|
}
|
|
|
|
return buft_list;
|
|
}
|
|
|
|
// GPU: split if LLAMA_SPLIT_MODE_ROW -> GPU
|
|
static buft_list_t make_gpu_buft_list(ggml_backend_dev_t dev, llama_split_mode split_mode, const float * tensor_split) {
|
|
buft_list_t buft_list;
|
|
|
|
// add the device split buffer type if requested and available
|
|
if (split_mode == LLAMA_SPLIT_MODE_ROW) {
|
|
ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev);
|
|
auto ggml_backend_split_buffer_type_fn = (ggml_backend_split_buffer_type_t)
|
|
ggml_backend_reg_get_proc_address(reg, "ggml_backend_split_buffer_type");
|
|
if (ggml_backend_split_buffer_type_fn) {
|
|
size_t dev_index = [&]() {
|
|
auto * reg = ggml_backend_dev_backend_reg(dev);
|
|
for (size_t i = 0; i < ggml_backend_reg_dev_count(reg); ++i) {
|
|
if (ggml_backend_reg_dev_get(reg, i) == dev) {
|
|
return i;
|
|
}
|
|
}
|
|
throw std::runtime_error(format("device %s not found in its backend reg", ggml_backend_dev_name(dev)));
|
|
}();
|
|
auto * buft = ggml_backend_split_buffer_type_fn(dev_index, tensor_split);
|
|
if (buft != nullptr) {
|
|
buft_list.emplace_back(dev, buft);
|
|
}
|
|
}
|
|
}
|
|
|
|
// add the device default buffer type
|
|
buft_list.emplace_back(dev, ggml_backend_dev_buffer_type(dev));
|
|
|
|
// add the device extra buffer type (if any)
|
|
ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev);
|
|
if (reg) {
|
|
auto ggml_backend_dev_get_extra_bufts_fn = (ggml_backend_dev_get_extra_bufts_t)
|
|
ggml_backend_reg_get_proc_address(reg, "ggml_backend_dev_get_extra_bufts");
|
|
|
|
if (ggml_backend_dev_get_extra_bufts_fn) {
|
|
ggml_backend_buffer_type_t * extra_bufts = ggml_backend_dev_get_extra_bufts_fn(dev);
|
|
while (extra_bufts && *extra_bufts) {
|
|
buft_list.emplace_back(dev, *extra_bufts);
|
|
++extra_bufts;
|
|
}
|
|
}
|
|
}
|
|
|
|
return buft_list;
|
|
}
|
|
|
|
struct llama_model::impl {
|
|
impl() = default;
|
|
~impl() = default;
|
|
|
|
uint64_t n_elements = 0;
|
|
|
|
size_t n_bytes = 0;
|
|
|
|
std::string desc_str;
|
|
|
|
// model memory mapped files
|
|
llama_mmaps mappings;
|
|
|
|
// objects representing data potentially being locked in memory
|
|
llama_mlocks mlock_bufs;
|
|
llama_mlocks mlock_mmaps;
|
|
|
|
// contexts where the model tensors metadata is stored as well as the corresponding buffers:
|
|
std::vector<std::pair<ggml_context_ptr, std::vector<ggml_backend_buffer_ptr>>> ctxs_bufs;
|
|
|
|
buft_list_t cpu_buft_list;
|
|
std::map<ggml_backend_dev_t, buft_list_t> gpu_buft_list;
|
|
|
|
struct layer_dev {
|
|
ggml_backend_dev_t dev;
|
|
buft_list_t * buft_list;
|
|
};
|
|
|
|
layer_dev dev_input = {};
|
|
layer_dev dev_output = {};
|
|
std::vector<layer_dev> dev_layer;
|
|
|
|
bool has_tensor_overrides;
|
|
};
|
|
|
|
llama_model::llama_model(const llama_model_params & params) : params(params), pimpl(std::make_unique<impl>()) {
|
|
pimpl->has_tensor_overrides = params.tensor_buft_overrides && params.tensor_buft_overrides[0].pattern;
|
|
}
|
|
|
|
llama_model::~llama_model() {
|
|
for (auto * lora : loras) {
|
|
delete lora;
|
|
}
|
|
}
|
|
|
|
void llama_model_base::load_stats(llama_model_loader & ml) {
|
|
pimpl->n_elements = ml.n_elements;
|
|
pimpl->n_bytes = ml.n_bytes;
|
|
}
|
|
|
|
void llama_model_base::load_hparams(llama_model_loader & ml) {
|
|
const gguf_context * ctx = ml.metadata;
|
|
|
|
// get metadata as string
|
|
for (int i = 0; i < gguf_get_n_kv(ctx); i++) {
|
|
gguf_type type = gguf_get_kv_type(ctx, i);
|
|
if (type == GGUF_TYPE_ARRAY) {
|
|
continue;
|
|
}
|
|
const char * name = gguf_get_key(ctx, i);
|
|
const std::string value = gguf_kv_to_str(ctx, i);
|
|
gguf_kv.emplace(name, value);
|
|
}
|
|
|
|
// get general kv
|
|
ml.get_key(LLM_KV_GENERAL_NAME, name, false);
|
|
|
|
// everything past this point is not vocab-related
|
|
// for CLIP models, we only need to load tensors, no hparams
|
|
if (hparams.vocab_only || ml.get_arch() == LLM_ARCH_CLIP) {
|
|
return;
|
|
}
|
|
|
|
ml.get_key(LLM_KV_CONTEXT_LENGTH, hparams.n_ctx_train);
|
|
ml.get_key(LLM_KV_EMBEDDING_LENGTH, hparams.n_embd);
|
|
ml.get_key(LLM_KV_EMBEDDING_LENGTH_OUT, hparams.n_embd_out_impl, false);
|
|
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn, false);
|
|
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
|
|
ml.get_key(LLM_KV_BLOCK_COUNT, hparams.n_layer);
|
|
ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert, false);
|
|
ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false);
|
|
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);
|
|
|
|
ml.get_key(LLM_KV_POSNET_EMBEDDING_LENGTH, hparams.posnet.n_embd);
|
|
ml.get_key(LLM_KV_POSNET_BLOCK_COUNT, hparams.posnet.n_layer);
|
|
|
|
ml.get_key(LLM_KV_CONVNEXT_EMBEDDING_LENGTH, hparams.convnext.n_embd);
|
|
ml.get_key(LLM_KV_CONVNEXT_BLOCK_COUNT, hparams.convnext.n_layer);
|
|
}
|
|
|
|
GGML_ASSERT(hparams.n_expert <= LLAMA_MAX_EXPERTS);
|
|
GGML_ASSERT(hparams.n_expert_used <= hparams.n_expert);
|
|
if (hparams.n_expert > 0) {
|
|
GGML_ASSERT(hparams.n_expert_used > 0);
|
|
GGML_ASSERT(hparams.n_expert_groups < hparams.n_expert);
|
|
if (hparams.n_expert_groups > 1) {
|
|
GGML_ASSERT(hparams.n_expert % hparams.n_expert_groups == 0);
|
|
GGML_ASSERT(hparams.n_group_used > 0);
|
|
GGML_ASSERT(hparams.n_group_used < hparams.n_expert_groups);
|
|
}
|
|
} else {
|
|
GGML_ASSERT(hparams.n_expert_used == 0);
|
|
GGML_ASSERT(hparams.n_expert_groups == 0);
|
|
}
|
|
|
|
std::fill(hparams.n_head_arr.begin(), hparams.n_head_arr.end(), 0);
|
|
std::fill(hparams.n_head_kv_arr.begin(), hparams.n_head_kv_arr.end(), 0);
|
|
std::fill(hparams.n_ff_arr.begin(), hparams.n_ff_arr.end(), 0);
|
|
std::fill(
|
|
hparams.recurrent_layer_arr.begin(),
|
|
hparams.recurrent_layer_arr.end(),
|
|
llm_arch_is_recurrent(ml.get_arch()));
|
|
|
|
std::fill(hparams.rope_sections.begin(), hparams.rope_sections.end(), 0);
|
|
std::fill(hparams.swa_layers.begin(), hparams.swa_layers.end(), 0);
|
|
|
|
std::fill(hparams.xielu_alpha_n.begin(), hparams.xielu_alpha_n.end(), 0.0f);
|
|
std::fill(hparams.xielu_alpha_p.begin(), hparams.xielu_alpha_p.end(), 0.0f);
|
|
std::fill(hparams.xielu_beta.begin(), hparams.xielu_beta.end(), 0.0f);
|
|
std::fill(hparams.xielu_eps.begin(), hparams.xielu_eps.end(), 0.0f);
|
|
std::fill(hparams.swiglu_clamp_exp.begin(), hparams.swiglu_clamp_exp.end(), 0.0f);
|
|
std::fill(hparams.swiglu_clamp_shexp.begin(), hparams.swiglu_clamp_shexp.end(), 0.0f);
|
|
|
|
ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff_arr, hparams.n_layer, false);
|
|
ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer, false);
|
|
|
|
// n_head_kv is optional, default to n_head
|
|
hparams.n_head_kv_arr = hparams.n_head_arr;
|
|
|
|
ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT_KV, hparams.n_head_kv_arr, hparams.n_layer, false);
|
|
|
|
bool rope_finetuned = false;
|
|
ml.get_key(LLM_KV_ROPE_SCALING_FINETUNED, rope_finetuned, false);
|
|
hparams.rope_finetuned = rope_finetuned;
|
|
|
|
hparams.n_ctx_orig_yarn = hparams.n_ctx_train;
|
|
ml.get_key(LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, hparams.n_ctx_orig_yarn, false);
|
|
|
|
// rope_freq_base (optional)
|
|
hparams.rope_freq_base_train = 10000.0f;
|
|
ml.get_key(LLM_KV_ROPE_FREQ_BASE, hparams.rope_freq_base_train, false);
|
|
|
|
std::string rope_scaling("linear");
|
|
ml.get_key(LLM_KV_ROPE_SCALING_TYPE, rope_scaling, false);
|
|
hparams.rope_scaling_type_train = llama_rope_scaling_type_from_string(rope_scaling);
|
|
GGML_ASSERT(hparams.rope_scaling_type_train != LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED);
|
|
|
|
// TODO: Handle SWA metadata similarly when models start implementing it
|
|
// rope_freq_scale (inverse of the kv) is optional
|
|
float ropescale = 0.0f;
|
|
if (!ml.get_key(LLM_KV_ROPE_SCALING_FACTOR, ropescale, false)) {
|
|
// try the old key name
|
|
ml.get_key(LLM_KV_ROPE_SCALE_LINEAR, ropescale, false);
|
|
}
|
|
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) {
|
|
// gpt-neox n_rot = rotary_pct * (n_embd / n_head)
|
|
// gpt-j n_rot = rotary_dim
|
|
|
|
hparams.n_embd_head_k_full = hparams.n_embd / hparams.n_head();
|
|
ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k_full, false);
|
|
|
|
hparams.n_embd_head_v_full = hparams.n_embd / hparams.n_head();
|
|
ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v_full, false);
|
|
|
|
// sanity check for n_rot (optional)
|
|
hparams.n_rot_full = hparams.n_embd_head_k_full;
|
|
|
|
ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot_full, false);
|
|
|
|
if (arch == LLM_ARCH_LLAMA || arch == LLM_ARCH_DECI || arch == LLM_ARCH_FALCON || arch == LLM_ARCH_LLAMA_EMBED) {
|
|
if (hparams.n_rot_full != hparams.n_embd_head_k_full) {
|
|
throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot_full, hparams.n_embd_head_k_full));
|
|
}
|
|
}
|
|
} else {
|
|
hparams.n_rot_full = 0;
|
|
hparams.n_embd_head_k_full = 0;
|
|
hparams.n_embd_head_v_full = 0;
|
|
}
|
|
|
|
// head size and n_rot for SWA layers
|
|
{
|
|
hparams.n_embd_head_k_swa = hparams.n_embd_head_k_full;
|
|
hparams.n_embd_head_v_swa = hparams.n_embd_head_v_full;
|
|
ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_SWA, hparams.n_embd_head_k_swa, false);
|
|
ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_SWA, hparams.n_embd_head_v_swa, false);
|
|
|
|
hparams.n_rot_swa = hparams.n_rot_full;
|
|
ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT_SWA, hparams.n_rot_swa, false);
|
|
}
|
|
|
|
// for differentiating model types
|
|
uint32_t n_vocab = 0;
|
|
ml.get_key(LLM_KV_VOCAB_SIZE, n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, n_vocab, false);
|
|
|
|
// for classifier models
|
|
ml.get_arr(LLM_KV_CLASSIFIER_OUTPUT_LABELS, classifier_labels, false);
|
|
if (!classifier_labels.empty()) {
|
|
hparams.n_cls_out = classifier_labels.size();
|
|
}
|
|
|
|
// per-arch hparams
|
|
load_arch_hparams(ml);
|
|
|
|
pimpl->n_bytes = ml.n_bytes;
|
|
|
|
pimpl->desc_str = arch_name() + " " + type_name() + " " + ml.ftype_name();
|
|
|
|
if (hparams.f_max_alibi_bias > 0.0f) {
|
|
hparams.use_alibi = true;
|
|
}
|
|
|
|
hparams.rope_type = llama_model_rope_type(this);
|
|
}
|
|
|
|
void llama_model_base::load_vocab(llama_model_loader & ml) {
|
|
const auto kv = LLM_KV(arch);
|
|
|
|
vocab.load(ml, kv);
|
|
}
|
|
|
|
bool llama_model_base::load_tensors(llama_model_loader & ml) {
|
|
const auto & split_mode = params.split_mode;
|
|
const auto & use_mlock = params.use_mlock;
|
|
const auto & tensor_split = params.tensor_split;
|
|
|
|
const int n_layer = hparams.n_layer;
|
|
const int n_gpu_layers = this->n_gpu_layers();
|
|
|
|
const bool use_mmap_buffer = true;
|
|
|
|
this->ml = &ml; // to be used by create_tensor() and load_arch_tensors()
|
|
|
|
LLAMA_LOG_INFO("%s: loading model tensors, this can take a while... (mmap = %s, direct_io = %s)\n",
|
|
__func__, ml.use_mmap ? "true" : "false", ml.use_direct_io ? "true" : "false");
|
|
|
|
// build a list of buffer types for the CPU and GPU devices
|
|
pimpl->cpu_buft_list = make_cpu_buft_list(devices, params.use_extra_bufts, params.no_host);
|
|
for (const auto & dev : devices) {
|
|
buft_list_t buft_list = make_gpu_buft_list(dev.dev, split_mode, tensor_split);
|
|
// add CPU buffer types as a fallback
|
|
buft_list.insert(buft_list.end(), pimpl->cpu_buft_list.begin(), pimpl->cpu_buft_list.end());
|
|
pimpl->gpu_buft_list.emplace(dev.dev, std::move(buft_list));
|
|
}
|
|
|
|
ggml_backend_dev_t cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
|
|
if (cpu_dev == nullptr) {
|
|
throw std::runtime_error(format("%s: no CPU backend found", __func__));
|
|
}
|
|
|
|
// calculate the split points
|
|
bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + n_devices(), [](float x) { return x == 0.0f; });
|
|
std::vector<float> splits(n_devices());
|
|
if (all_zero) {
|
|
// default split, by free memory
|
|
for (size_t i = 0; i < n_devices(); ++i) {
|
|
ggml_backend_dev_t dev = devices[i].dev;
|
|
size_t total;
|
|
size_t free;
|
|
ggml_backend_dev_memory(dev, &free, &total);
|
|
|
|
// devices can return 0 bytes for free and total memory if they do not
|
|
// have any to report. in this case, we will use the host memory as a fallback
|
|
// fixes: https://github.com/ggml-org/llama.cpp/issues/18577
|
|
if (free == 0 && total == 0) {
|
|
ggml_backend_dev_memory(cpu_dev, &free, &total);
|
|
}
|
|
splits[i] = free;
|
|
}
|
|
} else {
|
|
std::copy(tensor_split, tensor_split + n_devices(), splits.begin());
|
|
}
|
|
|
|
// sum and normalize the splits to get the split points
|
|
float split_sum = 0.0f;
|
|
for (size_t i = 0; i < n_devices(); ++i) {
|
|
split_sum += splits[i];
|
|
splits[i] = split_sum;
|
|
}
|
|
for (size_t i = 0; i < n_devices(); ++i) {
|
|
splits[i] /= split_sum;
|
|
}
|
|
|
|
const int i_gpu_start = std::max(int(hparams.n_layer) + 1 - n_gpu_layers, 0);
|
|
const int act_gpu_layers = devices.empty() ? 0 : std::min(n_gpu_layers, int(n_layer) + 1);
|
|
auto get_layer_buft_list = [&](int il) -> llama_model::impl::layer_dev {
|
|
const bool is_swa = il < int(hparams.n_layer) && hparams.is_swa(il);
|
|
if (il < i_gpu_start || (il - i_gpu_start) >= act_gpu_layers) {
|
|
LLAMA_LOG_DEBUG("load_tensors: layer %3d assigned to device %s, is_swa = %d\n", il, ggml_backend_dev_name(cpu_dev), is_swa);
|
|
return {cpu_dev, &pimpl->cpu_buft_list};
|
|
}
|
|
const int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + n_devices(), float(il - i_gpu_start)/act_gpu_layers) - splits.begin();
|
|
auto * dev = devices.at(layer_gpu).dev;
|
|
LLAMA_LOG_DEBUG("load_tensors: layer %3d assigned to device %s, is_swa = %d\n", il, ggml_backend_dev_name(dev), is_swa);
|
|
return {dev, &pimpl->gpu_buft_list.at(dev)};
|
|
};
|
|
|
|
// assign the input layer
|
|
// there is very little benefit to offloading the input layer, so always keep it on the CPU
|
|
pimpl->dev_input = { cpu_dev, &pimpl->cpu_buft_list };
|
|
|
|
// assign the repeating layers to the devices according to the splits
|
|
pimpl->dev_layer.resize(n_layer);
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
pimpl->dev_layer[il] = get_layer_buft_list(il);
|
|
}
|
|
|
|
// assign the output layer
|
|
pimpl->dev_output = get_layer_buft_list(n_layer);
|
|
|
|
const auto TENSOR_NOT_REQUIRED = llama_model_loader::TENSOR_NOT_REQUIRED;
|
|
|
|
// create tensors for the weights
|
|
{
|
|
// TODO: move to a separate function
|
|
const auto tn = LLM_TN(arch);
|
|
|
|
const int64_t n_expert = hparams.n_expert;
|
|
const int64_t n_expert_used = hparams.n_expert_used;
|
|
|
|
if (n_expert > 0 && n_expert_used == 0) {
|
|
throw std::runtime_error("model has expert layers but no expert layers are used");
|
|
}
|
|
|
|
layers.resize(n_layer);
|
|
|
|
// call the per-model loading function
|
|
load_arch_tensors(ml);
|
|
|
|
// generic pass: load optional per-tensor/per-expert ".scale" tensors (e.g. NVFP4 scale2)
|
|
// this avoids having to add scale loading to every architecture
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = layers[i];
|
|
|
|
// attention weight scales (per-tensor, shape {1})
|
|
if (!layer.wq_s && layer.wq) {
|
|
layer.wq_s = create_tensor(tn(LLM_TENSOR_ATTN_Q, "scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.wk_s && layer.wk) {
|
|
layer.wk_s = create_tensor(tn(LLM_TENSOR_ATTN_K, "scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.wv_s && layer.wv) {
|
|
layer.wv_s = create_tensor(tn(LLM_TENSOR_ATTN_V, "scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.wo_s && layer.wo) {
|
|
layer.wo_s = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.wqkv_s && layer.wqkv) {
|
|
layer.wqkv_s = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.wqkv_gate_s && layer.wqkv_gate) {
|
|
layer.wqkv_gate_s = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
|
|
// dense FFN weight scales (per-tensor, shape {1})
|
|
if (!layer.ffn_gate_s && layer.ffn_gate) {
|
|
layer.ffn_gate_s = create_tensor(tn(LLM_TENSOR_FFN_GATE, "scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.ffn_down_s && layer.ffn_down) {
|
|
layer.ffn_down_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.ffn_up_s && layer.ffn_up) {
|
|
layer.ffn_up_s = create_tensor(tn(LLM_TENSOR_FFN_UP, "scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.ffn_gate_shexp_s && layer.ffn_gate_shexp) {
|
|
layer.ffn_gate_shexp_s = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.ffn_down_shexp_s && layer.ffn_down_shexp) {
|
|
layer.ffn_down_shexp_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.ffn_up_shexp_s && layer.ffn_up_shexp) {
|
|
layer.ffn_up_shexp_s = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
|
|
// MoE expert weight scales (per-expert, shape {n_expert})
|
|
if (!layer.ffn_gate_exps_s && layer.ffn_gate_exps) {
|
|
layer.ffn_gate_exps_s = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "scale", i), {n_expert}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.ffn_down_exps_s && layer.ffn_down_exps) {
|
|
layer.ffn_down_exps_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "scale", i), {n_expert}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.ffn_up_exps_s && layer.ffn_up_exps) {
|
|
layer.ffn_up_exps_s = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "scale", i), {n_expert}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
|
|
// recurrent / linear-attention weight scales (per-tensor, shape {1})
|
|
if (!layer.ssm_in_s && layer.ssm_in) {
|
|
layer.ssm_in_s = create_tensor(tn(LLM_TENSOR_SSM_IN, "scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.ssm_out_s && layer.ssm_out) {
|
|
layer.ssm_out_s = create_tensor(tn(LLM_TENSOR_SSM_OUT, "scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.ssm_alpha_s && layer.ssm_alpha) {
|
|
layer.ssm_alpha_s = create_tensor(tn(LLM_TENSOR_SSM_ALPHA, "scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.ssm_beta_s && layer.ssm_beta) {
|
|
layer.ssm_beta_s = create_tensor(tn(LLM_TENSOR_SSM_BETA, "scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
|
|
// input scales
|
|
if (!layer.wq_in_s && layer.wq) {
|
|
layer.wq_in_s = create_tensor(tn(LLM_TENSOR_ATTN_Q, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.wk_in_s && layer.wk) {
|
|
layer.wk_in_s = create_tensor(tn(LLM_TENSOR_ATTN_K, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.wv_in_s && layer.wv) {
|
|
layer.wv_in_s = create_tensor(tn(LLM_TENSOR_ATTN_V, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.wo_in_s && layer.wo) {
|
|
layer.wo_in_s = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.wqkv_in_s && layer.wqkv) {
|
|
layer.wqkv_in_s = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.wqkv_gate_in_s && layer.wqkv_gate) {
|
|
layer.wqkv_gate_in_s = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.ffn_gate_in_s && layer.ffn_gate) {
|
|
layer.ffn_gate_in_s = create_tensor(tn(LLM_TENSOR_FFN_GATE, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.ffn_down_in_s && layer.ffn_down) {
|
|
layer.ffn_down_in_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.ffn_up_in_s && layer.ffn_up) {
|
|
layer.ffn_up_in_s = create_tensor(tn(LLM_TENSOR_FFN_UP, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.ffn_gate_exps_in_s && layer.ffn_gate_exps) {
|
|
layer.ffn_gate_exps_in_s = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "input_scale", i), {n_expert}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.ffn_down_exps_in_s && layer.ffn_down_exps) {
|
|
layer.ffn_down_exps_in_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "input_scale", i), {n_expert}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.ffn_up_exps_in_s && layer.ffn_up_exps) {
|
|
layer.ffn_up_exps_in_s = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "input_scale", i), {n_expert}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.ffn_gate_shexp_in_s && layer.ffn_gate_shexp) {
|
|
layer.ffn_gate_shexp_in_s = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.ffn_down_shexp_in_s && layer.ffn_down_shexp) {
|
|
layer.ffn_down_shexp_in_s = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.ffn_up_shexp_in_s && layer.ffn_up_shexp) {
|
|
layer.ffn_up_shexp_in_s = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.ssm_in_in_s && layer.ssm_in) {
|
|
layer.ssm_in_in_s = create_tensor(tn(LLM_TENSOR_SSM_IN, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.ssm_out_in_s && layer.ssm_out) {
|
|
layer.ssm_out_in_s = create_tensor(tn(LLM_TENSOR_SSM_OUT, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.ssm_alpha_in_s && layer.ssm_alpha) {
|
|
layer.ssm_alpha_in_s = create_tensor(tn(LLM_TENSOR_SSM_ALPHA, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
if (!layer.ssm_beta_in_s && layer.ssm_beta) {
|
|
layer.ssm_beta_in_s = create_tensor(tn(LLM_TENSOR_SSM_BETA, "input_scale", i), {1}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
}
|
|
}
|
|
|
|
ml.done_getting_tensors();
|
|
|
|
// populate tensors_by_name
|
|
for (auto & [_, ctx_ptr] : ml.ctx_map) {
|
|
for (auto * cur = ggml_get_first_tensor(ctx_ptr.get()); cur != NULL; cur = ggml_get_next_tensor(ctx_ptr.get(), cur)) {
|
|
tensors_by_name.emplace_back(ggml_get_name(cur), cur);
|
|
}
|
|
}
|
|
|
|
ml.init_mappings(true, use_mlock ? &pimpl->mlock_mmaps : nullptr);
|
|
pimpl->mappings.reserve(ml.mappings.size());
|
|
|
|
// create the backend buffers
|
|
std::vector<std::pair<ggml_context *, llama_buf_map>> ctx_buf_maps;
|
|
ctx_buf_maps.reserve(ml.ctx_map.size());
|
|
|
|
// Ensure we have enough capacity for the maximum backend buffer we will potentially create
|
|
const size_t n_max_backend_buffer = ml.ctx_map.size() * ml.files.size();
|
|
pimpl->ctxs_bufs.reserve(n_max_backend_buffer);
|
|
|
|
for (auto & [buft, ctx_ptr] : ml.ctx_map) {
|
|
ggml_context * ctx = ctx_ptr.get();
|
|
|
|
// skip contexts without tensors
|
|
if (ggml_get_first_tensor(ctx) == nullptr) {
|
|
continue;
|
|
}
|
|
|
|
llama_buf_map buf_map;
|
|
buf_map.reserve(n_max_backend_buffer);
|
|
|
|
// check if it is possible to use buffer_from_host_ptr with this buffer type
|
|
ggml_backend_dev_t dev = ggml_backend_buft_get_device(buft);
|
|
if (!dev) {
|
|
// FIXME: workaround for CPU backend buft having a NULL device
|
|
dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
|
|
if (!dev) {
|
|
throw std::runtime_error(format("%s: no CPU backend found", __func__));
|
|
}
|
|
}
|
|
ggml_backend_dev_props props;
|
|
ggml_backend_dev_get_props(dev, &props);
|
|
bool buffer_from_host_ptr_supported = props.caps.buffer_from_host_ptr;
|
|
bool is_default_buft = buft == ggml_backend_dev_buffer_type(dev);
|
|
|
|
std::vector<ggml_backend_buffer_ptr> bufs;
|
|
if (ml.use_mmap && use_mmap_buffer && buffer_from_host_ptr_supported && is_default_buft) {
|
|
GGML_ASSERT(!ml.no_alloc);
|
|
for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
|
|
// only the mmap region containing the tensors in the model is mapped to the backend buffer
|
|
// this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer,
|
|
// then we could just use metal for all layers
|
|
// this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size
|
|
void * addr = nullptr;
|
|
size_t first, last; // NOLINT
|
|
ml.get_mapping_range(&first, &last, &addr, idx, ctx);
|
|
if (first >= last) {
|
|
continue;
|
|
}
|
|
const size_t max_size = ggml_get_max_tensor_size(ctx);
|
|
ggml_backend_buffer_t buf = ggml_backend_dev_buffer_from_host_ptr(dev, (char *) addr + first, last - first, max_size);
|
|
if (buf == nullptr) {
|
|
throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft)));
|
|
}
|
|
bufs.emplace_back(buf);
|
|
buf_map.emplace(idx, buf);
|
|
}
|
|
} else {
|
|
ggml_backend_buffer_t buf;
|
|
if (ml.no_alloc) {
|
|
buf = ggml_backend_buft_alloc_buffer(buft, /*size =*/ 0); // dummy buffer
|
|
for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
|
|
t->buffer = buf; // set dummy buffer for weights so that the backend scheduler won't try to allocate them
|
|
}
|
|
} else {
|
|
buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); // real buffer
|
|
}
|
|
if (buf == nullptr) {
|
|
throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft)));
|
|
}
|
|
if (use_mlock && ggml_backend_buffer_is_host(buf)) {
|
|
pimpl->mlock_bufs.emplace_back(new llama_mlock);
|
|
auto & mlock_buf = pimpl->mlock_bufs.back();
|
|
mlock_buf->init (ggml_backend_buffer_get_base(buf));
|
|
mlock_buf->grow_to(ggml_backend_buffer_get_size(buf));
|
|
}
|
|
bufs.emplace_back(buf);
|
|
for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
|
|
buf_map.emplace(idx, buf);
|
|
}
|
|
}
|
|
|
|
for (auto & buf : bufs) {
|
|
// indicate that this buffer contains weights
|
|
// this is used by ggml_backend_sched to improve op scheduling: ops that use a weight are preferably scheduled to the backend that contains the weight
|
|
ggml_backend_buffer_set_usage(buf.get(), GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
|
|
}
|
|
|
|
pimpl->ctxs_bufs.emplace_back(std::move(ctx_ptr), std::move(bufs));
|
|
|
|
ctx_buf_maps.emplace_back(ctx, buf_map);
|
|
}
|
|
|
|
if (llama_supports_gpu_offload()) {
|
|
const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
|
|
|
|
int n_repeating = n_gpu;
|
|
if (n_repeating > 0) {
|
|
LLAMA_LOG_INFO("%s: offloading output layer to GPU\n", __func__);
|
|
n_repeating--;
|
|
}
|
|
LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_repeating);
|
|
|
|
const int max_backend_supported_layers = hparams.n_layer + 1;
|
|
const int max_offloadable_layers = hparams.n_layer + 1;
|
|
|
|
LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
|
|
}
|
|
|
|
// print memory requirements per buffer type
|
|
for (auto & [_, bufs] : pimpl->ctxs_bufs) {
|
|
for (auto & buf: bufs) {
|
|
LLAMA_LOG_INFO("%s: %12s model buffer size = %8.2f MiB\n",
|
|
__func__, ggml_backend_buffer_name(buf.get()), ggml_backend_buffer_get_size(buf.get()) / 1024.0 / 1024.0);
|
|
}
|
|
}
|
|
|
|
if (ml.no_alloc) {
|
|
return true;
|
|
}
|
|
|
|
// load tensor data
|
|
for (auto & [ctx, buf_map] : ctx_buf_maps) {
|
|
if (!ml.load_all_data(ctx, buf_map, use_mlock ? &pimpl->mlock_mmaps : NULL, params.progress_callback, params.progress_callback_user_data)) {
|
|
return false;
|
|
}
|
|
}
|
|
|
|
if (use_mmap_buffer) {
|
|
for (auto & mapping : ml.mappings) {
|
|
pimpl->mappings.emplace_back(std::move(mapping));
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
ggml_tensor * llama_model_base::create_tensor(llama_model_loader & ml, const LLM_TN_IMPL & tn, const std::initializer_list<int64_t> & ne, int flags) {
|
|
const buft_list_t * buft_list_layer = tn.bid == -1 ? nullptr : pimpl->dev_layer.at(tn.bid).buft_list;
|
|
return ml.create_tensor(
|
|
hparams, &pimpl->cpu_buft_list, pimpl->dev_input.buft_list, pimpl->dev_output.buft_list, buft_list_layer,
|
|
tn, ne, flags);
|
|
}
|
|
|
|
std::string llama_model::arch_name() const {
|
|
return llm_arch_name(arch);
|
|
}
|
|
|
|
std::string llama_model::type_name() const {
|
|
return llm_type_name(type);
|
|
}
|
|
|
|
std::string llama_model::desc() const {
|
|
return pimpl->desc_str;
|
|
}
|
|
|
|
size_t llama_model::size() const {
|
|
return pimpl->n_bytes;
|
|
}
|
|
|
|
size_t llama_model::n_tensors() const {
|
|
return tensors_by_name.size();
|
|
}
|
|
|
|
size_t llama_model::n_devices() const {
|
|
return devices.size();
|
|
}
|
|
|
|
const float * llama_model::tensor_split() const {
|
|
return params.tensor_split;
|
|
}
|
|
|
|
uint32_t llama_model::n_gpu_layers() const {
|
|
return params.n_gpu_layers >= 0 ? params.n_gpu_layers : hparams.n_layer + 1;
|
|
}
|
|
|
|
llama_split_mode llama_model::split_mode() const {
|
|
return params.split_mode;
|
|
}
|
|
|
|
std::map<ggml_backend_buffer_type_t, size_t> llama_model::memory_breakdown() const {
|
|
std::map<ggml_backend_buffer_type_t, size_t> ret;
|
|
for (const auto & [ctx, bufs] : pimpl->ctxs_bufs) {
|
|
if (hparams.no_alloc) {
|
|
GGML_ASSERT(bufs.size() == 1);
|
|
ggml_backend_buffer_t buf = bufs[0].get();
|
|
GGML_ASSERT(ggml_backend_buffer_get_base(buf) == nullptr);
|
|
ggml_backend_buffer_type_t buft = ggml_backend_buffer_get_type(buf);
|
|
ret[buft] += ggml_backend_alloc_ctx_tensors_from_buft_size(ctx.get(), buft);
|
|
} else {
|
|
for (const auto & buf : bufs) {
|
|
// GGML_ASSERT(ggml_backend_buffer_get_base(buf.get()) != nullptr); // multi_buffer does not have a defined base
|
|
ret[ggml_backend_buffer_get_type(buf.get())] += ggml_backend_buffer_get_size(buf.get());
|
|
}
|
|
}
|
|
}
|
|
return ret;
|
|
}
|
|
|
|
uint64_t llama_model::n_elements() const {
|
|
return pimpl->n_elements;
|
|
}
|
|
|
|
void llama_model::print_info() const {
|
|
const std::string rope_scaling_type = llama_rope_scaling_type_name(hparams.rope_scaling_type_train);
|
|
|
|
auto print_f = [](const std::function<uint32_t(uint32_t)> & f, uint32_t n) {
|
|
bool is_var = false;
|
|
|
|
std::vector<uint32_t> v;
|
|
for (uint32_t i = 0; i < n; ++i) {
|
|
v.push_back(f(i));
|
|
if (v[i] != v[0]) {
|
|
is_var = true;
|
|
}
|
|
}
|
|
|
|
std::stringstream ss;
|
|
|
|
if (is_var) {
|
|
ss << "[";
|
|
for (uint32_t i = 0; i < n; ++i) {
|
|
ss << v[i];
|
|
if (i < n - 1) {
|
|
ss << ", ";
|
|
}
|
|
}
|
|
ss << "]";
|
|
} else {
|
|
ss << v[0];
|
|
}
|
|
|
|
return ss.str();
|
|
};
|
|
|
|
// hparams
|
|
LLAMA_LOG_INFO("%s: arch = %s\n", __func__, arch_name().c_str());
|
|
LLAMA_LOG_INFO("%s: vocab_only = %d\n", __func__, hparams.vocab_only);
|
|
LLAMA_LOG_INFO("%s: no_alloc = %d\n", __func__, hparams.no_alloc);
|
|
|
|
if (!hparams.vocab_only) {
|
|
LLAMA_LOG_INFO("%s: n_ctx_train = %u\n", __func__, hparams.n_ctx_train);
|
|
LLAMA_LOG_INFO("%s: n_embd = %u\n", __func__, hparams.n_embd);
|
|
LLAMA_LOG_INFO("%s: n_embd_inp = %u\n", __func__, hparams.n_embd_inp());
|
|
LLAMA_LOG_INFO("%s: n_layer = %u\n", __func__, hparams.n_layer);
|
|
LLAMA_LOG_INFO("%s: n_head = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_head(il); }, hparams.n_layer).c_str());
|
|
LLAMA_LOG_INFO("%s: n_head_kv = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_head_kv(il); }, hparams.n_layer).c_str());
|
|
LLAMA_LOG_INFO("%s: n_rot = %u\n", __func__, hparams.n_rot_full);
|
|
LLAMA_LOG_INFO("%s: n_swa = %u\n", __func__, hparams.n_swa);
|
|
LLAMA_LOG_INFO("%s: is_swa_any = %u\n", __func__, hparams.is_swa_any());
|
|
LLAMA_LOG_INFO("%s: n_embd_head_k = %u\n", __func__, hparams.n_embd_head_k_full);
|
|
LLAMA_LOG_INFO("%s: n_embd_head_v = %u\n", __func__, hparams.n_embd_head_v_full);
|
|
LLAMA_LOG_INFO("%s: n_gqa = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_gqa(il); }, hparams.n_layer).c_str());
|
|
LLAMA_LOG_INFO("%s: n_embd_k_gqa = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_embd_k_gqa(il); }, hparams.n_layer).c_str());
|
|
LLAMA_LOG_INFO("%s: n_embd_v_gqa = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_embd_v_gqa(il); }, hparams.n_layer).c_str());
|
|
LLAMA_LOG_INFO("%s: f_norm_eps = %.1e\n", __func__, hparams.f_norm_eps);
|
|
LLAMA_LOG_INFO("%s: f_norm_rms_eps = %.1e\n", __func__, hparams.f_norm_rms_eps);
|
|
LLAMA_LOG_INFO("%s: f_clamp_kqv = %.1e\n", __func__, hparams.f_clamp_kqv);
|
|
LLAMA_LOG_INFO("%s: f_max_alibi_bias = %.1e\n", __func__, hparams.f_max_alibi_bias);
|
|
LLAMA_LOG_INFO("%s: f_logit_scale = %.1e\n", __func__, hparams.f_logit_scale);
|
|
LLAMA_LOG_INFO("%s: f_attn_scale = %.1e\n", __func__, hparams.f_attention_scale);
|
|
LLAMA_LOG_INFO("%s: n_ff = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_ff(il); }, hparams.n_layer).c_str());
|
|
LLAMA_LOG_INFO("%s: n_expert = %u\n", __func__, hparams.n_expert);
|
|
LLAMA_LOG_INFO("%s: n_expert_used = %u\n", __func__, hparams.n_expert_used);
|
|
LLAMA_LOG_INFO("%s: n_expert_groups = %d\n", __func__, hparams.n_expert_groups);
|
|
LLAMA_LOG_INFO("%s: n_group_used = %d\n", __func__, hparams.n_group_used);
|
|
LLAMA_LOG_INFO("%s: causal attn = %d\n", __func__, hparams.causal_attn);
|
|
LLAMA_LOG_INFO("%s: pooling type = %d\n", __func__, hparams.pooling_type);
|
|
LLAMA_LOG_INFO("%s: rope type = %d\n", __func__, hparams.rope_type);
|
|
LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type.c_str());
|
|
LLAMA_LOG_INFO("%s: freq_base_train = %.1f\n", __func__, hparams.rope_freq_base_train);
|
|
LLAMA_LOG_INFO("%s: freq_scale_train = %g\n", __func__, hparams.rope_freq_scale_train);
|
|
if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
|
|
LLAMA_LOG_INFO("%s: freq_base_swa = %.1f\n", __func__, hparams.rope_freq_base_train_swa);
|
|
LLAMA_LOG_INFO("%s: freq_scale_swa = %g\n", __func__, hparams.rope_freq_scale_train_swa);
|
|
LLAMA_LOG_INFO("%s: n_embd_head_k_swa = %u\n", __func__, hparams.n_embd_head_k_swa);
|
|
LLAMA_LOG_INFO("%s: n_embd_head_v_swa = %u\n", __func__, hparams.n_embd_head_v_swa);
|
|
LLAMA_LOG_INFO("%s: n_rot_swa = %u\n", __func__, hparams.n_rot_swa);
|
|
}
|
|
LLAMA_LOG_INFO("%s: n_ctx_orig_yarn = %u\n", __func__, hparams.n_ctx_orig_yarn);
|
|
LLAMA_LOG_INFO("%s: rope_yarn_log_mul = %.4f\n", __func__, hparams.rope_yarn_log_mul);
|
|
LLAMA_LOG_INFO("%s: rope_finetuned = %s\n", __func__, hparams.rope_finetuned ? "yes" : "unknown");
|
|
// MRoPE (Multi-axis Rotary Position Embedding) sections
|
|
if (const auto & s = hparams.rope_sections; s[0] || s[1] || s[2] || s[3]) {
|
|
LLAMA_LOG_INFO("%s: mrope sections = [%d, %d, %d, %d]\n", __func__, s[0], s[1], s[2], s[3]);
|
|
}
|
|
if (!classifier_labels.empty()) {
|
|
LLAMA_LOG_INFO("%s: n_cls_out = %u\n", __func__, hparams.n_cls_out);
|
|
|
|
size_t i = 0;
|
|
for (const auto & label : classifier_labels) {
|
|
LLAMA_LOG_INFO("%s: cls_label[%2zu] = %s\n", __func__, i++, label.c_str());
|
|
}
|
|
}
|
|
|
|
if (arch == LLM_ARCH_MAMBA ||
|
|
arch == LLM_ARCH_MAMBA2 ||
|
|
arch == LLM_ARCH_JAMBA ||
|
|
arch == LLM_ARCH_FALCON_H1 ||
|
|
arch == LLM_ARCH_PLAMO2 ||
|
|
arch == LLM_ARCH_GRANITE_HYBRID ||
|
|
arch == LLM_ARCH_QWEN3NEXT ||
|
|
arch == LLM_ARCH_QWEN35 ||
|
|
arch == LLM_ARCH_QWEN35MOE ||
|
|
arch == LLM_ARCH_NEMOTRON_H ||
|
|
arch == LLM_ARCH_NEMOTRON_H_MOE) {
|
|
LLAMA_LOG_INFO("%s: ssm_d_conv = %u\n", __func__, hparams.ssm_d_conv);
|
|
LLAMA_LOG_INFO("%s: ssm_d_inner = %u\n", __func__, hparams.ssm_d_inner);
|
|
LLAMA_LOG_INFO("%s: ssm_d_state = %u\n", __func__, hparams.ssm_d_state);
|
|
LLAMA_LOG_INFO("%s: ssm_dt_rank = %u\n", __func__, hparams.ssm_dt_rank);
|
|
LLAMA_LOG_INFO("%s: ssm_n_group = %u\n", __func__, hparams.ssm_n_group);
|
|
LLAMA_LOG_INFO("%s: ssm_dt_b_c_rms = %d\n", __func__, hparams.ssm_dt_b_c_rms);
|
|
}
|
|
|
|
LLAMA_LOG_INFO("%s: model type = %s\n", __func__, type_name().c_str());
|
|
if (pimpl->n_elements >= 1e12) {
|
|
LLAMA_LOG_INFO("%s: model params = %.2f T\n", __func__, pimpl->n_elements*1e-12);
|
|
} else if (pimpl->n_elements >= 1e9) {
|
|
LLAMA_LOG_INFO("%s: model params = %.2f B\n", __func__, pimpl->n_elements*1e-9);
|
|
} else if (pimpl->n_elements >= 1e6) {
|
|
LLAMA_LOG_INFO("%s: model params = %.2f M\n", __func__, pimpl->n_elements*1e-6);
|
|
} else {
|
|
LLAMA_LOG_INFO("%s: model params = %.2f K\n", __func__, pimpl->n_elements*1e-3);
|
|
}
|
|
|
|
// general kv
|
|
LLAMA_LOG_INFO("%s: general.name = %s\n", __func__, name.c_str());
|
|
|
|
if (arch == LLM_ARCH_DEEPSEEK) {
|
|
LLAMA_LOG_INFO("%s: n_layer_dense_lead = %d\n", __func__, hparams.n_layer_dense_lead);
|
|
LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
|
|
LLAMA_LOG_INFO("%s: n_expert_shared = %d\n", __func__, hparams.n_expert_shared);
|
|
LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale);
|
|
}
|
|
|
|
if (arch == LLM_ARCH_DEEPSEEK2 || arch == LLM_ARCH_DEEPSEEK2OCR || arch == LLM_ARCH_GLM_DSA || arch == LLM_ARCH_MISTRAL4) {
|
|
LLAMA_LOG_INFO("%s: n_layer_dense_lead = %d\n", __func__, hparams.n_layer_dense_lead);
|
|
LLAMA_LOG_INFO("%s: n_lora_q = %d\n", __func__, hparams.n_lora_q);
|
|
LLAMA_LOG_INFO("%s: n_lora_kv = %d\n", __func__, hparams.n_lora_kv);
|
|
LLAMA_LOG_INFO("%s: n_embd_head_k_mla = %d\n", __func__, hparams.n_embd_head_k_mla());
|
|
LLAMA_LOG_INFO("%s: n_embd_head_v_mla = %d\n", __func__, hparams.n_embd_head_v_mla());
|
|
LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
|
|
LLAMA_LOG_INFO("%s: n_expert_shared = %d\n", __func__, hparams.n_expert_shared);
|
|
LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale);
|
|
LLAMA_LOG_INFO("%s: expert_weights_norm = %d\n", __func__, hparams.expert_weights_norm);
|
|
LLAMA_LOG_INFO("%s: expert_gating_func = %s\n", __func__, llama_expert_gating_func_name((llama_expert_gating_func_type) hparams.expert_gating_func));
|
|
}
|
|
|
|
if (arch == LLM_ARCH_QWEN2MOE) {
|
|
LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
|
|
LLAMA_LOG_INFO("%s: n_ff_shexp = %d\n", __func__, hparams.n_ff_shexp);
|
|
}
|
|
|
|
if (arch == LLM_ARCH_QWEN3MOE || arch == LLM_ARCH_OPENAI_MOE || arch == LLM_ARCH_QWEN3VLMOE || arch == LLM_ARCH_RND1) {
|
|
LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
|
|
}
|
|
|
|
if (arch == LLM_ARCH_MINICPM ||
|
|
arch == LLM_ARCH_GRANITE ||
|
|
arch == LLM_ARCH_GRANITE_MOE ||
|
|
arch == LLM_ARCH_GRANITE_HYBRID ||
|
|
arch == LLM_ARCH_NEMOTRON_H_MOE) {
|
|
LLAMA_LOG_INFO("%s: f_embedding_scale = %f\n", __func__, hparams.f_embedding_scale);
|
|
LLAMA_LOG_INFO("%s: f_residual_scale = %f\n", __func__, hparams.f_residual_scale);
|
|
LLAMA_LOG_INFO("%s: f_attention_scale = %f\n", __func__, hparams.f_attention_scale);
|
|
LLAMA_LOG_INFO("%s: n_ff_shexp = %d\n", __func__, hparams.n_ff_shexp);
|
|
}
|
|
|
|
if (arch == LLM_ARCH_BAILINGMOE) {
|
|
LLAMA_LOG_INFO("%s: n_layer_dense_lead = %d\n", __func__, hparams.n_layer_dense_lead);
|
|
LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
|
|
LLAMA_LOG_INFO("%s: n_expert_shared = %d\n", __func__, hparams.n_expert_shared);
|
|
LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale);
|
|
LLAMA_LOG_INFO("%s: expert_weights_norm = %d\n", __func__, hparams.expert_weights_norm);
|
|
}
|
|
|
|
if (arch == LLM_ARCH_BAILINGMOE2) {
|
|
LLAMA_LOG_INFO("%s: n_layer_dense_lead = %d\n", __func__, hparams.n_layer_dense_lead);
|
|
LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
|
|
LLAMA_LOG_INFO("%s: n_ff_shexp = %d\n", __func__, hparams.n_ff_shexp);
|
|
LLAMA_LOG_INFO("%s: n_expert_shared = %d\n", __func__, hparams.n_expert_shared);
|
|
LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale);
|
|
LLAMA_LOG_INFO("%s: expert_weights_norm = %d\n", __func__, hparams.expert_weights_norm);
|
|
LLAMA_LOG_INFO("%s: expert_gating_func = %s\n", __func__, llama_expert_gating_func_name((llama_expert_gating_func_type) hparams.expert_gating_func));
|
|
LLAMA_LOG_INFO("%s: nextn_predict_layers = %d\n", __func__, hparams.nextn_predict_layers);
|
|
}
|
|
|
|
if (arch == LLM_ARCH_SMALLTHINKER || arch == LLM_ARCH_LFM2MOE) {
|
|
LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
|
|
LLAMA_LOG_INFO("%s: expert_gating_func = %s\n", __func__, llama_expert_gating_func_name((llama_expert_gating_func_type) hparams.expert_gating_func));
|
|
}
|
|
|
|
if (arch == LLM_ARCH_GROVEMOE) {
|
|
LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
|
|
LLAMA_LOG_INFO("%s: n_ff_chexp = %d\n", __func__, hparams.n_ff_chexp);
|
|
LLAMA_LOG_INFO("%s: n_group_experts = %d\n", __func__, hparams.n_group_experts);
|
|
LLAMA_LOG_INFO("%s: expert_group_scale = %.2f\n", __func__, hparams.expert_group_scale);
|
|
}
|
|
}
|
|
|
|
vocab.print_info();
|
|
}
|
|
|
|
ggml_backend_dev_t llama_model::dev_layer(int il) const {
|
|
return pimpl->dev_layer.at(il).dev;
|
|
}
|
|
|
|
ggml_backend_dev_t llama_model::dev_output() const {
|
|
return pimpl->dev_output.dev;
|
|
}
|
|
|
|
template<typename F>
|
|
static bool buft_supported(ggml_backend_buffer_type_t buft, ggml_backend_dev_t dev, F & fn) {
|
|
ggml_init_params params = {
|
|
/*.mem_size =*/ ggml_tensor_overhead()*8,
|
|
/*.mem_buffer =*/ NULL,
|
|
/*.no_alloc =*/ true,
|
|
};
|
|
|
|
ggml_context_ptr ctx { ggml_init(params) };
|
|
if (!ctx) {
|
|
throw std::runtime_error(format("failed to create ggml context"));
|
|
}
|
|
|
|
ggml_backend_buffer_ptr buf { ggml_backend_buft_alloc_buffer(buft, 0) };
|
|
ggml_tensor * op_tensor = fn(ctx.get());
|
|
for (int i = 0; i < GGML_MAX_SRC; i++) {
|
|
if (op_tensor->src[i] != nullptr) {
|
|
assert(op_tensor->src[i]->buffer == nullptr);
|
|
op_tensor->src[i]->buffer = buf.get();
|
|
}
|
|
}
|
|
|
|
bool op_supported = ggml_backend_dev_supports_op(dev, op_tensor);
|
|
|
|
return op_supported;
|
|
}
|
|
|
|
template<typename F>
|
|
static ggml_backend_buffer_type_t select_buft(const buft_list_t & buft_list, const F & fn) {
|
|
for (const auto & cur : buft_list) {
|
|
ggml_backend_dev_t cur_dev = cur.first;
|
|
ggml_backend_buffer_type_t cur_buft = cur.second;
|
|
if (buft_supported(cur_buft, cur_dev, fn)) {
|
|
return cur_buft;
|
|
}
|
|
}
|
|
|
|
throw std::runtime_error(format("no suitable buffer type found"));
|
|
}
|
|
|
|
ggml_backend_buffer_type_t llama_model::select_buft(int il) const {
|
|
return ::select_buft(
|
|
*pimpl->dev_layer.at(il).buft_list,
|
|
[&](ggml_context * ctx) {
|
|
ggml_tensor * cur = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hparams.n_embd);
|
|
ggml_tensor * layer_dir = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hparams.n_embd);
|
|
return ggml_add(ctx, cur, layer_dir);
|
|
});
|
|
}
|
|
|
|
bool llama_model::has_tensor_overrides() const {
|
|
return pimpl->has_tensor_overrides;
|
|
}
|
|
|
|
const ggml_tensor * llama_model::get_tensor(const char * name) const {
|
|
auto it = std::find_if(tensors_by_name.begin(), tensors_by_name.end(),
|
|
[name](const std::pair<std::string, ggml_tensor *> & it) {
|
|
return it.first == name;
|
|
});
|
|
if (it == tensors_by_name.end()) {
|
|
return nullptr;
|
|
}
|
|
|
|
return it->second;
|
|
}
|
|
|
|
float llama_model::get_rope_freq_base (const llama_cparams & cparams, int il) const {
|
|
return hparams.is_swa(il) ? hparams.rope_freq_base_train_swa : cparams.rope_freq_base;
|
|
}
|
|
|
|
float llama_model::get_rope_freq_scale(const llama_cparams & cparams, int il) const {
|
|
return hparams.is_swa(il) ? hparams.rope_freq_scale_train_swa : cparams.rope_freq_scale;
|
|
}
|
|
|
|
ggml_tensor * llama_model::get_rope_factors(const llama_cparams & cparams, int il) const {
|
|
const uint32_t n_ctx_seq = cparams.n_ctx_seq;
|
|
|
|
// choose long/short freq factors based on the context size
|
|
if (layers[il].rope_freqs != nullptr) {
|
|
return layers[il].rope_freqs;
|
|
}
|
|
|
|
if (n_ctx_seq > hparams.n_ctx_orig_yarn) {
|
|
return layers[il].rope_long;
|
|
}
|
|
|
|
return layers[il].rope_short;
|
|
}
|
|
|
|
llama_memory_i * llama_model::create_memory(const llama_memory_params & params, const llama_cparams & cparams) const {
|
|
llama_memory_i * res;
|
|
|
|
switch (arch) {
|
|
// Models that need specific instantiation should be handled in the
|
|
// switch statement
|
|
case LLM_ARCH_BERT:
|
|
case LLM_ARCH_JINA_BERT_V2:
|
|
case LLM_ARCH_JINA_BERT_V3:
|
|
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:
|
|
case LLM_ARCH_DREAM:
|
|
case LLM_ARCH_LLADA:
|
|
case LLM_ARCH_LLADA_MOE:
|
|
case LLM_ARCH_RND1:
|
|
{
|
|
res = nullptr;
|
|
} break;
|
|
// Models that need standard caching should rely on recurrent/hybrid
|
|
// checks
|
|
default:
|
|
{
|
|
if (llm_arch_is_recurrent(arch)) {
|
|
res = new llama_memory_recurrent(
|
|
*this,
|
|
GGML_TYPE_F32,
|
|
GGML_TYPE_F32,
|
|
cparams.offload_kqv,
|
|
std::max((uint32_t) 1, cparams.n_seq_max),
|
|
cparams.n_seq_max,
|
|
nullptr);
|
|
} else if (llm_arch_is_hybrid(arch)) {
|
|
// The main difference between hybrid architectures is the
|
|
// layer filters, so pick the right one here
|
|
llama_memory_hybrid::layer_filter_cb filter_attn = nullptr;
|
|
llama_memory_hybrid::layer_filter_cb filter_recr = nullptr;
|
|
if (arch == LLM_ARCH_FALCON_H1) {
|
|
filter_attn = [&](int32_t) { return true; };
|
|
filter_recr = [&](int32_t) { return true; };
|
|
} else if (arch == LLM_ARCH_NEMOTRON_H || arch == LLM_ARCH_NEMOTRON_H_MOE) {
|
|
filter_attn = [&](int32_t il) {
|
|
return !hparams.is_recurrent(il) && hparams.n_ff(il) == 0;
|
|
};
|
|
filter_recr = [&](int32_t il) {
|
|
return hparams.is_recurrent(il) && hparams.n_ff(il) == 0;
|
|
};
|
|
}
|
|
|
|
if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
|
|
// Use hybrid-iswa for hybrid models with SWA
|
|
res = new llama_memory_hybrid_iswa(
|
|
/* model */ *this,
|
|
/* attn_type_k */ params.type_k,
|
|
/* attn_type_v */ params.type_v,
|
|
/* attn_v_trans */ !cparams.flash_attn,
|
|
/* attn_swa_full */ params.swa_full,
|
|
/* attn_kv_size */ cparams.n_ctx_seq,
|
|
/* attn_n_ubatch */ cparams.n_ubatch,
|
|
/* attn_n_pad */ 1,
|
|
/* recurrent_type_r */ GGML_TYPE_F32,
|
|
/* recurrent_type_s */ GGML_TYPE_F32,
|
|
/* recurrent_rs_size */ std::max((uint32_t) 1, cparams.n_seq_max),
|
|
/* n_seq_max */ cparams.n_seq_max,
|
|
/* offload */ cparams.offload_kqv,
|
|
/* unified */ cparams.kv_unified,
|
|
/* filter_attn */ std::move(filter_attn),
|
|
/* filter_recr */ std::move(filter_recr));
|
|
} else {
|
|
res = new llama_memory_hybrid(
|
|
/* model */ *this,
|
|
/* attn_type_k */ params.type_k,
|
|
/* attn_type_v */ params.type_v,
|
|
/* attn_v_trans */ !cparams.flash_attn,
|
|
/* attn_kv_size */ cparams.n_ctx_seq,
|
|
/* attn_n_pad */ 1,
|
|
/* attn_n_swa */ hparams.n_swa,
|
|
/* attn_swa_type */ hparams.swa_type,
|
|
/* recurrent_type_k */ GGML_TYPE_F32,
|
|
/* recurrent_type_v */ GGML_TYPE_F32,
|
|
/* recurrent_kv_size */ std::max((uint32_t) 1, cparams.n_seq_max),
|
|
/* n_seq_max */ cparams.n_seq_max,
|
|
/* offload */ cparams.offload_kqv,
|
|
/* unified */ cparams.kv_unified,
|
|
/* filter_attn */ std::move(filter_attn),
|
|
/* filter_recr */ std::move(filter_recr));
|
|
}
|
|
} else {
|
|
llama_memory_i::layer_reuse_cb reuse = nullptr;
|
|
|
|
if (arch == LLM_ARCH_GEMMA3N || arch == LLM_ARCH_GEMMA4) {
|
|
reuse = [&](int32_t il) {
|
|
if (il >= (int32_t) hparams.n_layer_kv_from_start) {
|
|
return (int32_t) hparams.n_layer_kv_from_start - (hparams.is_swa(il) ? 2 : 1);
|
|
}
|
|
|
|
return -1;
|
|
};
|
|
}
|
|
|
|
if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
|
|
GGML_ASSERT(hparams.is_swa_any());
|
|
|
|
res = new llama_kv_cache_iswa(
|
|
*this,
|
|
params.type_k,
|
|
params.type_v,
|
|
!cparams.flash_attn,
|
|
cparams.offload_kqv,
|
|
params.swa_full,
|
|
cparams.kv_unified,
|
|
cparams.n_ctx_seq,
|
|
cparams.n_seq_max,
|
|
cparams.n_ubatch,
|
|
1,
|
|
nullptr,
|
|
reuse);
|
|
} else {
|
|
GGML_ASSERT(!hparams.is_swa_any());
|
|
|
|
res = new llama_kv_cache(
|
|
*this,
|
|
params.type_k,
|
|
params.type_v,
|
|
!cparams.flash_attn,
|
|
cparams.offload_kqv,
|
|
cparams.kv_unified,
|
|
cparams.n_ctx_seq,
|
|
cparams.n_seq_max,
|
|
1,
|
|
hparams.n_swa,
|
|
hparams.swa_type,
|
|
nullptr,
|
|
nullptr);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
return res;
|
|
}
|
|
|
|
ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
|
|
std::unique_ptr<llm_graph_context> llm = build_arch_graph(params);
|
|
|
|
// add on pooling layer
|
|
llm->build_pooling(cls, cls_b, cls_out, cls_out_b, cls_norm);
|
|
|
|
// add backend sampling layers (if any)
|
|
llm->build_sampling();
|
|
|
|
// if the gguf model was converted with --sentence-transformers-dense-modules
|
|
// there will be two additional dense projection layers
|
|
// dense linear projections are applied after pooling
|
|
// TODO: move reranking logic here and generalize
|
|
llm->build_dense_out(dense_2_out_layers, dense_2_out_layers_b, dense_3_out_layers);
|
|
|
|
llm->res->set_outputs();
|
|
|
|
return llm->res->get_gf();
|
|
}
|
|
|
|
|
|
//
|
|
// interface implementation
|
|
//
|
|
|
|
llama_model_params llama_model_default_params() {
|
|
llama_model_params result = {
|
|
/*.devices =*/ nullptr,
|
|
/*.tensor_buft_overrides =*/ nullptr,
|
|
/*.n_gpu_layers =*/ -1,
|
|
/*.split_mode =*/ LLAMA_SPLIT_MODE_LAYER,
|
|
/*.main_gpu =*/ 0,
|
|
/*.tensor_split =*/ nullptr,
|
|
/*.progress_callback =*/ nullptr,
|
|
/*.progress_callback_user_data =*/ nullptr,
|
|
/*.kv_overrides =*/ nullptr,
|
|
/*.vocab_only =*/ false,
|
|
/*.use_mmap =*/ true,
|
|
/*.use_direct_io =*/ false,
|
|
/*.use_mlock =*/ false,
|
|
/*.check_tensors =*/ false,
|
|
/*.use_extra_bufts =*/ true,
|
|
/*.no_host =*/ false,
|
|
/*.no_alloc =*/ false,
|
|
};
|
|
|
|
return result;
|
|
}
|
|
|
|
const llama_vocab * llama_model_get_vocab(const llama_model * model) {
|
|
return &model->vocab;
|
|
}
|
|
|
|
void llama_free_model(llama_model * model) {
|
|
llama_model_free(model);
|
|
}
|
|
|
|
void llama_model_free(llama_model * model) {
|
|
delete model;
|
|
}
|
|
|
|
int32_t llama_model_n_ctx_train(const llama_model * model) {
|
|
return model->hparams.n_ctx_train;
|
|
}
|
|
|
|
int32_t llama_model_n_embd(const llama_model * model) {
|
|
return model->hparams.n_embd;
|
|
}
|
|
|
|
int32_t llama_model_n_embd_inp(const llama_model * model) {
|
|
return model->hparams.n_embd_inp();
|
|
}
|
|
|
|
int32_t llama_model_n_embd_out(const llama_model * model) {
|
|
return model->hparams.n_embd_out();
|
|
}
|
|
|
|
int32_t llama_model_n_layer(const llama_model * model) {
|
|
return model->hparams.n_layer;
|
|
}
|
|
|
|
int32_t llama_model_n_head(const llama_model * model) {
|
|
return model->hparams.n_head();
|
|
}
|
|
|
|
int32_t llama_model_n_head_kv(const llama_model * model) {
|
|
return model->hparams.n_head_kv();
|
|
}
|
|
|
|
int32_t llama_model_n_swa(const llama_model * model) {
|
|
return model->hparams.n_swa;
|
|
}
|
|
|
|
uint32_t llama_model_n_cls_out(const struct llama_model * model) {
|
|
return model->hparams.n_cls_out;
|
|
}
|
|
|
|
const char * llama_model_cls_label(const struct llama_model * model, uint32_t i) {
|
|
if (i < model->classifier_labels.size()) {
|
|
return model->classifier_labels[i].c_str();
|
|
}
|
|
|
|
return nullptr;
|
|
}
|
|
|
|
// deprecated
|
|
int32_t llama_n_ctx_train(const llama_model * model) {
|
|
return llama_model_n_ctx_train(model);
|
|
}
|
|
|
|
// deprecated
|
|
int32_t llama_n_embd(const llama_model * model) {
|
|
return llama_model_n_embd(model);
|
|
}
|
|
|
|
// deprecated
|
|
int32_t llama_n_layer(const llama_model * model) {
|
|
return llama_model_n_layer(model);
|
|
}
|
|
|
|
// deprecated
|
|
int32_t llama_n_head(const llama_model * model) {
|
|
return llama_model_n_head(model);
|
|
}
|
|
|
|
llama_rope_type llama_model_rope_type(const llama_model * model) {
|
|
switch (model->arch) {
|
|
// these models do not use RoPE
|
|
case LLM_ARCH_CLIP:
|
|
case LLM_ARCH_GPT2:
|
|
case LLM_ARCH_GPTJ:
|
|
case LLM_ARCH_MPT:
|
|
case LLM_ARCH_REFACT:
|
|
case LLM_ARCH_BLOOM:
|
|
case LLM_ARCH_MAMBA:
|
|
case LLM_ARCH_MAMBA2:
|
|
case LLM_ARCH_JAMBA:
|
|
case LLM_ARCH_JINA_BERT_V2:
|
|
case LLM_ARCH_T5:
|
|
case LLM_ARCH_T5ENCODER:
|
|
case LLM_ARCH_JAIS:
|
|
case LLM_ARCH_RWKV6:
|
|
case LLM_ARCH_RWKV6QWEN2:
|
|
case LLM_ARCH_RWKV7:
|
|
case LLM_ARCH_ARWKV7:
|
|
case LLM_ARCH_WAVTOKENIZER_DEC:
|
|
case LLM_ARCH_NEMOTRON_H:
|
|
case LLM_ARCH_NEMOTRON_H_MOE:
|
|
case LLM_ARCH_KIMI_LINEAR:
|
|
return LLAMA_ROPE_TYPE_NONE;
|
|
|
|
// use what we call a normal RoPE, operating on pairs of consecutive head values
|
|
case LLM_ARCH_LLAMA:
|
|
case LLM_ARCH_LLADA:
|
|
case LLM_ARCH_LLAMA4:
|
|
case LLM_ARCH_DECI:
|
|
case LLM_ARCH_BAICHUAN:
|
|
case LLM_ARCH_STARCODER:
|
|
case LLM_ARCH_INTERNLM2:
|
|
case LLM_ARCH_MINICPM:
|
|
case LLM_ARCH_XVERSE:
|
|
case LLM_ARCH_COMMAND_R:
|
|
case LLM_ARCH_COHERE2:
|
|
case LLM_ARCH_OLMO:
|
|
case LLM_ARCH_ARCTIC:
|
|
case LLM_ARCH_DEEPSEEK:
|
|
case LLM_ARCH_DEEPSEEK2:
|
|
case LLM_ARCH_DEEPSEEK2OCR:
|
|
case LLM_ARCH_PLM:
|
|
case LLM_ARCH_CHATGLM:
|
|
case LLM_ARCH_GRANITE:
|
|
case LLM_ARCH_GRANITE_MOE:
|
|
case LLM_ARCH_GRANITE_HYBRID:
|
|
case LLM_ARCH_CHAMELEON:
|
|
case LLM_ARCH_BAILINGMOE:
|
|
case LLM_ARCH_NEO_BERT:
|
|
case LLM_ARCH_SMOLLM3:
|
|
case LLM_ARCH_ARCEE:
|
|
case LLM_ARCH_ERNIE4_5:
|
|
case LLM_ARCH_ERNIE4_5_MOE:
|
|
case LLM_ARCH_MISTRAL3:
|
|
case LLM_ARCH_MISTRAL4:
|
|
case LLM_ARCH_LLAMA_EMBED:
|
|
case LLM_ARCH_MAINCODER:
|
|
case LLM_ARCH_GLM_DSA:
|
|
return LLAMA_ROPE_TYPE_NORM;
|
|
|
|
// the pairs of head values are offset by n_rot/2
|
|
case LLM_ARCH_FALCON:
|
|
case LLM_ARCH_FALCON_H1:
|
|
case LLM_ARCH_GROK:
|
|
case LLM_ARCH_DBRX:
|
|
case LLM_ARCH_BERT:
|
|
case LLM_ARCH_JINA_BERT_V3:
|
|
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:
|
|
case LLM_ARCH_QWEN2:
|
|
case LLM_ARCH_DREAM:
|
|
case LLM_ARCH_QWEN2MOE:
|
|
case LLM_ARCH_QWEN3:
|
|
case LLM_ARCH_QWEN3MOE:
|
|
case LLM_ARCH_LLADA_MOE:
|
|
case LLM_ARCH_RND1:
|
|
case LLM_ARCH_OLMO2:
|
|
case LLM_ARCH_OLMOE:
|
|
case LLM_ARCH_PHI2:
|
|
case LLM_ARCH_PHI3:
|
|
case LLM_ARCH_PHIMOE:
|
|
case LLM_ARCH_PLAMO:
|
|
case LLM_ARCH_PLAMO2:
|
|
case LLM_ARCH_PLAMO3:
|
|
case LLM_ARCH_GEMMA:
|
|
case LLM_ARCH_GEMMA2:
|
|
case LLM_ARCH_GEMMA3:
|
|
case LLM_ARCH_GEMMA3N:
|
|
case LLM_ARCH_GEMMA4:
|
|
case LLM_ARCH_GEMMA_EMBEDDING:
|
|
case LLM_ARCH_STARCODER2:
|
|
case LLM_ARCH_OPENELM:
|
|
case LLM_ARCH_GPTNEOX:
|
|
case LLM_ARCH_CODESHELL:
|
|
case LLM_ARCH_ORION:
|
|
case LLM_ARCH_NEMOTRON:
|
|
case LLM_ARCH_EXAONE:
|
|
case LLM_ARCH_EXAONE4:
|
|
case LLM_ARCH_EXAONE_MOE:
|
|
case LLM_ARCH_MINICPM3:
|
|
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:
|
|
case LLM_ARCH_LFM2MOE:
|
|
case LLM_ARCH_SMALLTHINKER:
|
|
case LLM_ARCH_SEED_OSS:
|
|
case LLM_ARCH_GROVEMOE:
|
|
case LLM_ARCH_APERTUS:
|
|
case LLM_ARCH_MINIMAX_M2:
|
|
case LLM_ARCH_COGVLM:
|
|
case LLM_ARCH_PANGU_EMBED:
|
|
case LLM_ARCH_AFMOE:
|
|
case LLM_ARCH_QWEN3NEXT:
|
|
case LLM_ARCH_MIMO2:
|
|
case LLM_ARCH_STEP35:
|
|
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:
|
|
case LLM_ARCH_QWEN35:
|
|
case LLM_ARCH_QWEN35MOE:
|
|
return LLAMA_ROPE_TYPE_IMROPE;
|
|
|
|
case LLM_ARCH_GLM4:
|
|
return model->hparams.use_mrope() ? LLAMA_ROPE_TYPE_MROPE : LLAMA_ROPE_TYPE_NORM;
|
|
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");
|
|
}
|
|
|
|
return LLAMA_ROPE_TYPE_NONE;
|
|
}
|
|
|
|
float llama_model_rope_freq_scale_train(const llama_model * model) {
|
|
return model->hparams.rope_freq_scale_train;
|
|
}
|
|
|
|
int32_t llama_model_meta_val_str(const llama_model * model, const char * key, char * buf, size_t buf_size) {
|
|
const auto & it = model->gguf_kv.find(key);
|
|
if (it == model->gguf_kv.end()) {
|
|
if (buf_size > 0) {
|
|
buf[0] = '\0';
|
|
}
|
|
return -1;
|
|
}
|
|
return snprintf(buf, buf_size, "%s", it->second.c_str());
|
|
}
|
|
|
|
int32_t llama_model_meta_count(const llama_model * model) {
|
|
return (int)model->gguf_kv.size();
|
|
}
|
|
|
|
const char * llama_model_meta_key_str(llama_model_meta_key key) {
|
|
switch (key) {
|
|
case LLAMA_MODEL_META_KEY_SAMPLING_SEQUENCE: return "general.sampling.sequence";
|
|
case LLAMA_MODEL_META_KEY_SAMPLING_TOP_K: return "general.sampling.top_k";
|
|
case LLAMA_MODEL_META_KEY_SAMPLING_TOP_P: return "general.sampling.top_p";
|
|
case LLAMA_MODEL_META_KEY_SAMPLING_MIN_P: return "general.sampling.min_p";
|
|
case LLAMA_MODEL_META_KEY_SAMPLING_XTC_PROBABILITY: return "general.sampling.xtc_probability";
|
|
case LLAMA_MODEL_META_KEY_SAMPLING_XTC_THRESHOLD: return "general.sampling.xtc_threshold";
|
|
case LLAMA_MODEL_META_KEY_SAMPLING_TEMP: return "general.sampling.temp";
|
|
case LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_LAST_N: return "general.sampling.penalty_last_n";
|
|
case LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_REPEAT: return "general.sampling.penalty_repeat";
|
|
case LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT: return "general.sampling.mirostat";
|
|
case LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_TAU: return "general.sampling.mirostat_tau";
|
|
case LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_ETA: return "general.sampling.mirostat_eta";
|
|
default: return nullptr;
|
|
}
|
|
}
|
|
|
|
int32_t llama_model_meta_key_by_index(const llama_model * model, int i, char * buf, size_t buf_size) {
|
|
if (i < 0 || i >= (int)model->gguf_kv.size()) {
|
|
if (buf_size > 0) {
|
|
buf[0] = '\0';
|
|
}
|
|
return -1;
|
|
}
|
|
auto it = model->gguf_kv.begin();
|
|
std::advance(it, i);
|
|
return snprintf(buf, buf_size, "%s", it->first.c_str());
|
|
}
|
|
|
|
int32_t llama_model_meta_val_str_by_index(const llama_model * model, int32_t i, char * buf, size_t buf_size) {
|
|
if (i < 0 || i >= (int)model->gguf_kv.size()) {
|
|
if (buf_size > 0) {
|
|
buf[0] = '\0';
|
|
}
|
|
return -1;
|
|
}
|
|
auto it = model->gguf_kv.begin();
|
|
std::advance(it, i);
|
|
return snprintf(buf, buf_size, "%s", it->second.c_str());
|
|
}
|
|
|
|
int32_t llama_model_desc(const llama_model * model, char * buf, size_t buf_size) {
|
|
return snprintf(buf, buf_size, "%s", model->desc().c_str());
|
|
}
|
|
|
|
uint64_t llama_model_size(const llama_model * model) {
|
|
return model->size();
|
|
}
|
|
|
|
const char * llama_model_chat_template(const llama_model * model, const char * name) {
|
|
const auto key = name ? LLM_KV(model->arch, name)(LLM_KV_TOKENIZER_CHAT_TEMPLATE)
|
|
: LLM_KV(model->arch)(LLM_KV_TOKENIZER_CHAT_TEMPLATE);
|
|
const auto & it = model->gguf_kv.find(key);
|
|
if (it == model->gguf_kv.end()) {
|
|
// one-off fix for very popular models (so we are not flooded with issues)
|
|
// do not extend this list unless absolutely necessary
|
|
// Mistral-Small-2503 does not have built-in chat template
|
|
llama_vocab_pre_type pre_type = model->vocab.get_pre_type();
|
|
if (!name && pre_type == LLAMA_VOCAB_PRE_TYPE_TEKKEN && model->layers.size() == 40) {
|
|
return "mistral-v7-tekken";
|
|
}
|
|
|
|
return nullptr;
|
|
}
|
|
|
|
return it->second.c_str();
|
|
}
|
|
|
|
uint64_t llama_model_n_params(const llama_model * model) {
|
|
return model->n_elements();
|
|
}
|
|
|
|
bool llama_model_has_encoder(const llama_model * model) {
|
|
switch (model->arch) {
|
|
case LLM_ARCH_T5: return true;
|
|
case LLM_ARCH_T5ENCODER: return true;
|
|
default: return false;
|
|
}
|
|
}
|
|
|
|
bool llama_model_has_decoder(const llama_model * model) {
|
|
switch (model->arch) {
|
|
case LLM_ARCH_T5ENCODER: return false;
|
|
default: return true;
|
|
}
|
|
}
|
|
|
|
llama_token llama_model_decoder_start_token(const llama_model * model) {
|
|
return model->hparams.dec_start_token_id;
|
|
}
|
|
|
|
bool llama_model_is_recurrent(const llama_model * model) {
|
|
return llm_arch_is_recurrent(model->arch);
|
|
}
|
|
|
|
bool llama_model_is_hybrid(const llama_model * model) {
|
|
return llm_arch_is_hybrid(model->arch);
|
|
}
|
|
|
|
bool llama_model_is_diffusion(const llama_model * model) {
|
|
return llm_arch_is_diffusion(model->arch);
|
|
}
|
|
|
|
const std::vector<std::pair<std::string, ggml_tensor *>> & llama_internal_get_tensor_map(const llama_model * model) {
|
|
return model->tensors_by_name;
|
|
}
|
|
|
|
int32_t llama_model_n_expert(const struct llama_model * model) {
|
|
return model->hparams.n_expert;
|
|
}
|
|
|
|
int32_t llama_model_n_devices(const struct llama_model * model) {
|
|
return (int32_t)model->devices.size();
|
|
}
|
|
|
|
ggml_backend_dev_t llama_model_get_device(const struct llama_model * model, int i) {
|
|
if (i < 0 || i >= (int)model->devices.size()) {
|
|
return nullptr;
|
|
}
|
|
return model->devices[i].dev;
|
|
}
|
|
|
|
//
|
|
// llama_model_base
|
|
//
|
|
|
|
llama_model_base::llama_model_base(const struct llama_model_params & params) : llama_model(params), model(this), tn(model->arch),
|
|
TENSOR_DUPLICATED (llama_model_loader::TENSOR_DUPLICATED),
|
|
TENSOR_NOT_REQUIRED (llama_model_loader::TENSOR_NOT_REQUIRED),
|
|
TENSOR_SKIP (llama_model_loader::TENSOR_SKIP),
|
|
TENSOR_SKIP_IF_VIRTUAL(llama_model_loader::TENSOR_SKIP_IF_VIRTUAL) {}
|
|
|
|
ggml_tensor * llama_model_base::create_tensor(const LLM_TN_IMPL & tn, const std::initializer_list<int64_t> & ne, int flags) {
|
|
GGML_ASSERT(ml != nullptr);
|
|
return create_tensor(*ml, tn, ne, flags);
|
|
}
|
|
|
|
void llama_model_base::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);
|
|
}
|
|
}
|
|
|
|
void llama_model_base::create_tensor_qkv(llama_layer & layer, int bid,
|
|
int64_t n_embd_, int64_t n_embd_q_, int64_t n_embd_k_, int64_t n_embd_v_,
|
|
int flags) {
|
|
const int64_t n_embd_qkv = n_embd_q_ + n_embd_k_ + n_embd_v_;
|
|
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", bid), {n_embd_, n_embd_qkv}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL);
|
|
if (layer.wqkv) {
|
|
layer.wqkv_b = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "bias", bid), {n_embd_qkv}, TENSOR_NOT_REQUIRED | TENSOR_SKIP_IF_VIRTUAL);
|
|
} else {
|
|
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", bid), {n_embd_, n_embd_q_}, flags);
|
|
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", bid), {n_embd_, n_embd_k_}, flags);
|
|
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", bid), {n_embd_, n_embd_v_}, flags);
|
|
layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", bid), {n_embd_q_}, TENSOR_NOT_REQUIRED);
|
|
layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", bid), {n_embd_k_}, TENSOR_NOT_REQUIRED);
|
|
layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", bid), {n_embd_v_}, TENSOR_NOT_REQUIRED);
|
|
}
|
|
}
|