mirror of
https://github.com/ikawrakow/ik_llama.cpp.git
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ab50c6cdcb
* Mimo-2 support * Fix bug for head sizes not being the same It still does not solve the Mimo-2 quantized cache issue. * Fix quantized cache * Minor --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
233 lines
14 KiB
C++
233 lines
14 KiB
C++
#include "llama-arch.h"
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#include "llama-impl.h"
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#include <map>
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static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_LLAMA, "llama" },
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{ LLM_ARCH_LLAMA4, "llama4" },
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{ LLM_ARCH_DECI, "deci" },
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{ LLM_ARCH_FALCON, "falcon" },
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{ LLM_ARCH_GROK, "grok" },
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{ LLM_ARCH_GPT2, "gpt2" },
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{ LLM_ARCH_GPTJ, "gptj" },
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{ LLM_ARCH_GPTNEOX, "gptneox" },
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{ LLM_ARCH_MPT, "mpt" },
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{ LLM_ARCH_BAICHUAN, "baichuan" },
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{ LLM_ARCH_STARCODER, "starcoder" },
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{ LLM_ARCH_REFACT, "refact" },
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{ LLM_ARCH_BERT, "bert" },
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{ LLM_ARCH_NOMIC_BERT, "nomic-bert" },
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{ LLM_ARCH_JINA_BERT_V2, "jina-bert-v2" },
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{ LLM_ARCH_BLOOM, "bloom" },
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{ LLM_ARCH_STABLELM, "stablelm" },
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{ LLM_ARCH_QWEN, "qwen" },
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{ LLM_ARCH_QWEN2, "qwen2" },
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{ LLM_ARCH_QWEN2MOE, "qwen2moe" },
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{ LLM_ARCH_QWEN2VL, "qwen2vl" },
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{ LLM_ARCH_QWEN3, "qwen3" },
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{ LLM_ARCH_QWEN3MOE, "qwen3moe" },
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{ LLM_ARCH_QWEN3VL, "qwen3vl" },
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{ LLM_ARCH_QWEN3VLMOE, "qwen3vlmoe" },
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{ LLM_ARCH_PHI2, "phi2" },
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{ LLM_ARCH_PHI3, "phi3" },
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{ LLM_ARCH_PLAMO, "plamo" },
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{ LLM_ARCH_CODESHELL, "codeshell" },
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{ LLM_ARCH_ORION, "orion" },
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{ LLM_ARCH_INTERNLM2, "internlm2" },
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{ LLM_ARCH_MINICPM, "minicpm" },
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{ LLM_ARCH_GEMMA, "gemma" },
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{ LLM_ARCH_GEMMA2, "gemma2" },
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{ LLM_ARCH_GEMMA3, "gemma3" },
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{ LLM_ARCH_STARCODER2, "starcoder2" },
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{ LLM_ARCH_MAMBA, "mamba" },
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{ LLM_ARCH_XVERSE, "xverse" },
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{ LLM_ARCH_COMMAND_R, "command-r" },
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{ LLM_ARCH_DBRX, "dbrx" },
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{ LLM_ARCH_OLMO, "olmo" },
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{ LLM_ARCH_OPENELM, "openelm" },
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{ LLM_ARCH_ARCTIC, "arctic" },
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{ LLM_ARCH_DEEPSEEK2, "deepseek2" },
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{ LLM_ARCH_CHATGLM, "chatglm" },
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{ LLM_ARCH_GLM4, "glm4" },
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{ LLM_ARCH_GLM4_MOE, "glm4moe" },
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{ LLM_ARCH_BITNET, "bitnet" },
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{ LLM_ARCH_BITNET_25, "bitnet-25" },
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{ LLM_ARCH_BITNET_B158, "bitnet-b1.58" },
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{ LLM_ARCH_T5, "t5" },
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{ LLM_ARCH_T5ENCODER, "t5encoder" },
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{ LLM_ARCH_JAIS, "jais" },
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{ LLM_ARCH_GRANITE, "granite" },
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{ LLM_ARCH_GRANITE_MOE, "granitemoe" },
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{ LLM_ARCH_COHERE2, "cohere2" },
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{ LLM_ARCH_DOTS1, "dots1" },
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{ LLM_ARCH_ERNIE4_5, "ernie4_5" },
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{ LLM_ARCH_ERNIE4_5_MOE, "ernie4_5-moe" },
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{ LLM_ARCH_HUNYUAN_MOE, "hunyuan-moe" },
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{ LLM_ARCH_OPENAI_MOE, "gpt-oss" },
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{ LLM_ARCH_BAILINGMOE2, "bailingmoe2" },
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{ LLM_ARCH_MINIMAX_M2, "minimax-m2" },
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{ LLM_ARCH_SMOLLM3, "smollm3" },
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{ LLM_ARCH_MISTRAL3, "mistral3" },
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{ LLM_ARCH_MIMO2, "mimo2" },
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{ LLM_ARCH_UNKNOWN, "(unknown)" },
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};
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llm_arch llm_arch_from_string(const std::string & name) {
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for (const auto & kv : LLM_ARCH_NAMES) { // NOLINT
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if (kv.second == name) {
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return kv.first;
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}
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}
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return LLM_ARCH_UNKNOWN;
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}
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static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
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{ LLM_KV_GENERAL_TYPE, "general.type" },
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{ LLM_KV_GENERAL_ARCHITECTURE, "general.architecture" },
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{ LLM_KV_GENERAL_QUANTIZATION_VERSION, "general.quantization_version" },
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{ LLM_KV_GENERAL_ALIGNMENT, "general.alignment" },
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{ LLM_KV_GENERAL_NAME, "general.name" },
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{ LLM_KV_GENERAL_AUTHOR, "general.author" },
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{ LLM_KV_GENERAL_VERSION, "general.version" },
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{ LLM_KV_GENERAL_URL, "general.url" },
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{ LLM_KV_GENERAL_DESCRIPTION, "general.description" },
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{ LLM_KV_GENERAL_LICENSE, "general.license" },
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{ LLM_KV_GENERAL_SOURCE_URL, "general.source.url" },
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{ LLM_KV_GENERAL_SOURCE_HF_REPO, "general.source.huggingface.repository" },
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{ LLM_KV_VOCAB_SIZE, "%s.vocab_size" },
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{ LLM_KV_CONTEXT_LENGTH, "%s.context_length" },
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{ LLM_KV_EMBEDDING_LENGTH, "%s.embedding_length" },
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{ LLM_KV_BLOCK_COUNT, "%s.block_count" },
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{ LLM_KV_LEADING_DENSE_BLOCK_COUNT, "%s.leading_dense_block_count" },
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{ LLM_KV_FEED_FORWARD_LENGTH, "%s.feed_forward_length" },
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{ LLM_KV_EXPERT_FEED_FORWARD_LENGTH, "%s.expert_feed_forward_length" },
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{ LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, "%s.expert_shared_feed_forward_length" },
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{ LLM_KV_USE_PARALLEL_RESIDUAL, "%s.use_parallel_residual" },
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{ LLM_KV_TENSOR_DATA_LAYOUT, "%s.tensor_data_layout" },
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{ LLM_KV_EXPERT_COUNT, "%s.expert_count" },
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{ LLM_KV_EXPERT_USED_COUNT, "%s.expert_used_count" },
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{ LLM_KV_EXPERT_SHARED_COUNT, "%s.expert_shared_count" },
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{ LLM_KV_EXPERT_GROUP_COUNT, "%s.expert_group_count" },
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{ LLM_KV_EXPERT_GROUP_USED_COUNT, "%s.expert_group_used_count" },
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{ LLM_KV_EXPERT_WEIGHTS_SCALE, "%s.expert_weights_scale" },
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{ LLM_KV_EXPERT_WEIGHTS_NORM, "%s.expert_weights_norm" },
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{ LLM_KV_EXPERT_GATING_FUNC, "%s.expert_gating_func" },
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{ LLM_KV_NEXTN_PREDICT_LAYERS, "%s.nextn_predict_layers" },
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{ LLM_KV_NUM_DEEPSTACK_LAYERS, "%s.n_deepstack_layers" },
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{ LLM_KV_POOLING_TYPE, "%s.pooling_type" },
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{ LLM_KV_LOGIT_SCALE, "%s.logit_scale" },
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{ LLM_KV_DECODER_START_TOKEN_ID, "%s.decoder_start_token_id" },
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{ LLM_KV_ATTN_LOGIT_SOFTCAPPING, "%s.attn_logit_softcapping" },
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{ LLM_KV_ROUTER_LOGIT_SOFTCAPPING, "%s.router_logit_softcapping" },
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{ LLM_KV_FINAL_LOGIT_SOFTCAPPING, "%s.final_logit_softcapping" },
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{ LLM_KV_RESIDUAL_SCALE, "%s.residual_scale" },
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{ LLM_KV_EMBEDDING_SCALE, "%s.embedding_scale" },
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{ LLM_KV_TOKEN_SHIFT_COUNT, "%s.token_shift_count" },
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{ LLM_KV_INTERLEAVE_MOE_LAYER_STEP, "%s.interleave_moe_layer_step" },
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{ LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" },
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{ LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" },
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{ LLM_KV_ATTENTION_MAX_ALIBI_BIAS, "%s.attention.max_alibi_bias" },
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{ LLM_KV_ATTENTION_CLAMP_KQV, "%s.attention.clamp_kqv" },
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{ LLM_KV_ATTENTION_KEY_LENGTH, "%s.attention.key_length" },
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{ LLM_KV_ATTENTION_VALUE_LENGTH, "%s.attention.value_length" },
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{ LLM_KV_ATTENTION_LAYERNORM_EPS, "%s.attention.layer_norm_epsilon" },
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{ LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, "%s.attention.layer_norm_rms_epsilon" },
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{ LLM_KV_ATTENTION_CAUSAL, "%s.attention.causal" },
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{ LLM_KV_ATTENTION_Q_LORA_RANK, "%s.attention.q_lora_rank" },
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{ LLM_KV_ATTENTION_KV_LORA_RANK, "%s.attention.kv_lora_rank" },
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{ LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, "%s.attention.relative_buckets_count" },
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{ LLM_KV_ATTENTION_SLIDING_WINDOW, "%s.attention.sliding_window" },
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{ LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, "%s.attention.sliding_window_pattern" },
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{ LLM_KV_ATTENTION_SCALE, "%s.attention.scale" },
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{ LLM_KV_ATTENTION_OUTPUT_SCALE, "%s.attention.output_scale" },
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{ LLM_KV_ATTENTION_TEMPERATURE_LENGTH, "%s.attention.temperature_length" },
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{ LLM_KV_ATTENTION_TEMPERATURE_SCALE, "%s.attention.temperature_scale" },
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{ LLM_KV_ATTENTION_KEY_LENGTH_MLA, "%s.attention.key_length_mla" },
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{ LLM_KV_ATTENTION_VALUE_LENGTH_MLA, "%s.attention.value_length_mla" },
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{ LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" },
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{ LLM_KV_ROPE_DIMENSION_SECTIONS, "%s.rope.dimension_sections" },
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{ LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" },
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{ LLM_KV_ROPE_FREQ_BASE_SWA, "%s.rope.freq_base_swa" },
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{ LLM_KV_ROPE_SCALE_LINEAR, "%s.rope.scale_linear" },
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{ LLM_KV_ROPE_SCALING_TYPE, "%s.rope.scaling.type" },
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{ LLM_KV_ROPE_SCALING_FACTOR, "%s.rope.scaling.factor" },
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{ LLM_KV_ROPE_SCALING_ATTN_FACTOR, "%s.rope.scaling.attn_factor" },
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{ LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, "%s.rope.scaling.original_context_length" },
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{ LLM_KV_ROPE_SCALING_FINETUNED, "%s.rope.scaling.finetuned" },
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{ LLM_KV_ROPE_SCALING_YARN_LOG_MUL, "%s.rope.scaling.yarn_log_multiplier" },
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{ LLM_KV_ROPE_SCALING_YARN_EXT_FACTOR, "%s.rope.scaling.yarn_ext_factor" },
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{ LLM_KV_ROPE_SCALING_YARN_ATTN_FACTOR, "%s.rope.scaling.yarn_attn_factor" },
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{ LLM_KV_ROPE_SCALING_YARN_BETA_FAST, "%s.rope.scaling.yarn_beta_fast" },
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{ LLM_KV_ROPE_SCALING_YARN_BETA_SLOW, "%s.rope.scaling.yarn_beta_slow" },
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{ LLM_KV_SPLIT_NO, "split.no" },
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{ LLM_KV_SPLIT_COUNT, "split.count" },
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{ LLM_KV_SPLIT_TENSORS_COUNT, "split.tensors.count" },
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{ LLM_KV_SSM_CONV_KERNEL, "%s.ssm.conv_kernel" },
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{ LLM_KV_SSM_INNER_SIZE, "%s.ssm.inner_size" },
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{ LLM_KV_SSM_STATE_SIZE, "%s.ssm.state_size" },
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{ LLM_KV_SSM_TIME_STEP_RANK, "%s.ssm.time_step_rank" },
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{ LLM_KV_TOKENIZER_MODEL, "tokenizer.ggml.model" },
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{ LLM_KV_TOKENIZER_PRE, "tokenizer.ggml.pre" },
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{ LLM_KV_TOKENIZER_LIST, "tokenizer.ggml.tokens" },
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{ LLM_KV_TOKENIZER_TOKEN_TYPE, "tokenizer.ggml.token_type" },
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{ LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, "tokenizer.ggml.token_type_count" },
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{ LLM_KV_TOKENIZER_SCORES, "tokenizer.ggml.scores" },
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{ LLM_KV_TOKENIZER_MERGES, "tokenizer.ggml.merges" },
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{ LLM_KV_TOKENIZER_BOS_ID, "tokenizer.ggml.bos_token_id" },
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{ LLM_KV_TOKENIZER_EOS_ID, "tokenizer.ggml.eos_token_id" },
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{ LLM_KV_TOKENIZER_UNK_ID, "tokenizer.ggml.unknown_token_id" },
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{ LLM_KV_TOKENIZER_SEP_ID, "tokenizer.ggml.seperator_token_id" },
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{ LLM_KV_TOKENIZER_PAD_ID, "tokenizer.ggml.padding_token_id" },
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{ LLM_KV_TOKENIZER_CLS_ID, "tokenizer.ggml.cls_token_id" },
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{ LLM_KV_TOKENIZER_MASK_ID, "tokenizer.ggml.mask_token_id" },
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{ LLM_KV_TOKENIZER_ADD_BOS, "tokenizer.ggml.add_bos_token" },
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{ LLM_KV_TOKENIZER_ADD_EOS, "tokenizer.ggml.add_eos_token" },
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{ LLM_KV_TOKENIZER_ADD_SEP, "tokenizer.ggml.add_sep_token" },
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{ LLM_KV_TOKENIZER_ADD_PREFIX, "tokenizer.ggml.add_space_prefix" },
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{ LLM_KV_TOKENIZER_REMOVE_EXTRA_WS, "tokenizer.ggml.remove_extra_whitespaces" },
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{ LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP, "tokenizer.ggml.precompiled_charsmap" },
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{ LLM_KV_TOKENIZER_HF_JSON, "tokenizer.huggingface.json" },
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{ LLM_KV_TOKENIZER_RWKV, "tokenizer.rwkv.world" },
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{ LLM_KV_TOKENIZER_CHAT_TEMPLATE, "tokenizer.chat_template" },
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{ LLM_KV_TOKENIZER_CHAT_TEMPLATE_N, "tokenizer.chat_template.%s" },
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{ LLM_KV_TOKENIZER_FIM_PRE_ID, "tokenizer.ggml.fim_pre_token_id" },
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{ LLM_KV_TOKENIZER_FIM_SUF_ID, "tokenizer.ggml.fim_suf_token_id" },
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{ LLM_KV_TOKENIZER_FIM_MID_ID, "tokenizer.ggml.fim_mid_token_id" },
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{ LLM_KV_TOKENIZER_FIM_PAD_ID, "tokenizer.ggml.fim_pad_token_id" },
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{ LLM_KV_TOKENIZER_FIM_REP_ID, "tokenizer.ggml.fim_rep_token_id" },
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{ LLM_KV_TOKENIZER_FIM_SEP_ID, "tokenizer.ggml.fim_sep_token_id" },
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{ LLM_KV_TOKENIZER_PREFIX_ID, "tokenizer.ggml.prefix_token_id" },
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{ LLM_KV_TOKENIZER_SUFFIX_ID, "tokenizer.ggml.suffix_token_id" },
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{ LLM_KV_TOKENIZER_MIDDLE_ID, "tokenizer.ggml.middle_token_id" },
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{ LLM_KV_TOKENIZER_EOT_ID, "tokenizer.ggml.eot_token_id" },
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{ LLM_KV_TOKENIZER_EOM_ID, "tokenizer.ggml.eom_token_id" },
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{ LLM_KV_ADAPTER_TYPE, "adapter.type" },
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{ LLM_KV_ADAPTER_LORA_ALPHA, "adapter.lora.alpha" },
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};
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LLM_KV::LLM_KV(llm_arch arch, const char* suffix) : arch(arch), suffix(suffix) {}
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std::string LLM_KV::operator()(llm_kv kv) const {
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return suffix ? ::format(LLM_KV_NAMES.at(kv), LLM_ARCH_NAMES.at(arch), suffix)
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: ::format(LLM_KV_NAMES.at(kv), LLM_ARCH_NAMES.at(arch));
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}
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const char * llama_model_arch_name(llm_arch arch) {
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auto it = LLM_ARCH_NAMES.find(arch);
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if (it == LLM_ARCH_NAMES.end()) {
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return "unknown";
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}
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return it->second;
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}
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