1676 lines
87 KiB
C++
1676 lines
87 KiB
C++
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#include "llama-hparams.h"
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#include "llama-model-loader.h"
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#include "llama-model.h"
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#include <limits>
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#include <map>
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#define LLAMA_MAX_EXPERTS 512 // Qwen3 Next
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static const std::map<llama_rope_scaling_type, const char *> LLAMA_ROPE_SCALING_TYPES = {
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{ LLAMA_ROPE_SCALING_TYPE_NONE, "none" },
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{ LLAMA_ROPE_SCALING_TYPE_LINEAR, "linear" },
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{ LLAMA_ROPE_SCALING_TYPE_YARN, "yarn" },
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};
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static llama_rope_scaling_type llama_rope_scaling_type_from_string(const std::string & name) {
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for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) {
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if (kv.second == name) {
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return (llama_rope_scaling_type) kv.first;
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}
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}
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return LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
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}
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const char * llama_hparams::rope_scaling_type_name(llama_rope_scaling_type type) {
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return LLAMA_ROPE_SCALING_TYPES.at(type);
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}
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static inline const char * llm_expert_gating_func_name(llm_expert_gating_func_type type) {
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switch (type) {
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case LLM_EXPERT_GATING_FUNC_SOFTMAX: return "softmax";
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case LLM_EXPERT_GATING_FUNC_SIGMOID: return "sigmoid";
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case LLM_EXPERT_GATING_FUNC_TYPE_SOFTMAX_WEIGHT: return "weight";
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default: return "none";
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}
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}
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static bool load_dflash_target_layer_ids(
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llama_model_loader & ml,
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const std::string & key,
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llama_hparams & hparams,
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bool required) {
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const int kid = gguf_find_key(ml.meta, key.c_str());
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if (kid < 0 || gguf_get_kv_type(ml.meta, kid) != GGUF_TYPE_ARRAY) {
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if (required) {
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throw std::runtime_error(format("array key not found in model: %s", key.c_str()));
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}
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return false;
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}
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const enum gguf_type type = gguf_get_arr_type(ml.meta, kid);
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if (type != GGUF_TYPE_UINT32 && type != GGUF_TYPE_INT32) {
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throw std::runtime_error(format("dflash: %s must be a uint32/int32 array", key.c_str()));
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}
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uint32_t n = 0;
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ml.get_arr_n(key, n, true);
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if (n == 0) {
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throw std::runtime_error(format("dflash: %s must not be empty", key.c_str()));
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}
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if (n > 8) {
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throw std::runtime_error(format("dflash: %s has %u entries, max is 8", key.c_str(), n));
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}
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hparams.dflash_n_target_layers = n;
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for (uint32_t & id : hparams.dflash_target_layer_ids) {
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id = 0;
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}
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if (type == GGUF_TYPE_INT32) {
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std::array<int32_t, 8> layer_ids = {};
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ml.get_arr(key, layer_ids, true);
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for (uint32_t i = 0; i < hparams.dflash_n_target_layers; ++i) {
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if (layer_ids[i] < 0) {
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throw std::runtime_error(format("dflash: %s contains negative layer id %d", key.c_str(), layer_ids[i]));
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}
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hparams.dflash_target_layer_ids[i] = (uint32_t) layer_ids[i];
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}
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} else {
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std::array<uint32_t, 8> layer_ids = {};
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ml.get_arr(key, layer_ids, true);
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for (uint32_t i = 0; i < hparams.dflash_n_target_layers; ++i) {
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hparams.dflash_target_layer_ids[i] = layer_ids[i];
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}
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}
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for (uint32_t i = 0; i < hparams.dflash_n_target_layers; ++i) {
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const uint32_t id = hparams.dflash_target_layer_ids[i];
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for (uint32_t j = 0; j < i; ++j) {
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if (hparams.dflash_target_layer_ids[j] == id) {
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throw std::runtime_error(format(
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"dflash: %s contains duplicate layer id %u",
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key.c_str(),
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id));
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}
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}
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}
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return true;
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}
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static void validate_dflash_hparams(llama_hparams & hparams, llm_arch arch) {
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if (hparams.dflash_block_size <= 1) {
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throw std::runtime_error(format("%s: dflash block_size must be > 1", llama_model_arch_name(arch)));
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}
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if (hparams.dflash_n_target_layers == 0) {
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throw std::runtime_error(format("%s: dflash target_layer_ids are required", llama_model_arch_name(arch)));
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}
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// DFlash feature width is target-model specific. Keep the serialized metadata intact here
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// and validate it against the live target model during DFlash init.
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if (hparams.dflash_n_target_features == 0) {
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throw std::runtime_error(format(
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"%s: dflash n_target_features must be > 0",
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llama_model_arch_name(arch)));
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}
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if (hparams.dflash_n_target_features % hparams.dflash_n_target_layers != 0) {
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throw std::runtime_error(format(
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"%s: dflash n_target_features=%u must be divisible by n_target_layers=%u",
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llama_model_arch_name(arch),
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hparams.dflash_n_target_features,
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hparams.dflash_n_target_layers));
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}
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}
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void llm_load_hparams(
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llama_model_loader & ml,
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llama_model & model, bool ignore_vocab) {
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auto & hparams = model.hparams;
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const gguf_context * ctx = ml.meta;
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// get metadata as string
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for (int i = 0; i < gguf_get_n_kv(ctx); i++) {
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enum gguf_type type = gguf_get_kv_type(ctx, i);
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if (type == GGUF_TYPE_ARRAY) {
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continue;
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}
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const char * name = gguf_get_key(ctx, i);
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const std::string value = gguf_kv_to_str(ctx, i);
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model.gguf_kv.emplace(name, value);
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}
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ml.get_key(LLM_KV_BLOCK_COUNT, hparams.n_layer);
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// get general kv
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ml.get_key(LLM_KV_GENERAL_NAME, model.name, false);
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// get hparams kv
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ml.get_key(LLM_KV_VOCAB_SIZE, hparams.n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab, !ignore_vocab);
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// everything past this point is not vocab-related
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if (hparams.vocab_only) {
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return;
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}
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ml.get_key(LLM_KV_CONTEXT_LENGTH, hparams.n_ctx_train);
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ml.get_key(LLM_KV_EMBEDDING_LENGTH, hparams.n_embd);
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ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert, false);
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ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false);
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GGML_ASSERT(hparams.n_expert <= LLAMA_MAX_EXPERTS);
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GGML_ASSERT(hparams.n_expert_used <= hparams.n_expert);
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if (hparams.n_expert > 0) {
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GGML_ASSERT(hparams.n_expert_used > 0);
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} else {
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GGML_ASSERT(hparams.n_expert_used == 0);
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}
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// zero-out the per-layer hparams
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std::fill(hparams.n_head_arr.begin(), hparams.n_head_arr.end(), 0);
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std::fill(hparams.n_head_kv_arr.begin(), hparams.n_head_kv_arr.end(), 0);
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std::fill(hparams.n_ff_arr.begin(), hparams.n_ff_arr.end(), 0);
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std::fill(hparams.swa_layers.begin(), hparams.swa_layers.end(), 0);
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std::fill(hparams.recurrent_layer_arr.begin(), hparams.recurrent_layer_arr.end(), false);
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ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff_arr, hparams.n_layer, false);
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ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer, false);
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// n_head_kv is optional, default to n_head
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hparams.n_head_kv_arr = hparams.n_head_arr;
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ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT_KV, hparams.n_head_kv_arr, hparams.n_layer, false);
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bool rope_finetuned = false;
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ml.get_key(LLM_KV_ROPE_SCALING_FINETUNED, rope_finetuned, false);
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hparams.rope_finetuned = rope_finetuned;
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hparams.n_ctx_orig_yarn = hparams.n_ctx_train;
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ml.get_key(LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, hparams.n_ctx_orig_yarn, false);
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// rope_freq_base (optional)
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hparams.rope_freq_base_train = 10000.0f;
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ml.get_key(LLM_KV_ROPE_FREQ_BASE, hparams.rope_freq_base_train, false);
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std::string rope_scaling("linear");
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ml.get_key(LLM_KV_ROPE_SCALING_TYPE, rope_scaling, false);
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hparams.rope_scaling_type_train = llama_rope_scaling_type_from_string(rope_scaling);
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GGML_ASSERT(hparams.rope_scaling_type_train != LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED);
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// rope_freq_scale (inverse of the kv) is optional
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float ropescale = 0.0f;
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if (!ml.get_key(LLM_KV_ROPE_SCALING_FACTOR, ropescale, false)) {
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// try the old key name
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ml.get_key(LLM_KV_ROPE_SCALE_LINEAR, ropescale, false);
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}
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hparams.rope_freq_scale_train = ropescale == 0.0f ? 1.0f : 1.0f/ropescale;
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// by default assume that the sliding-window layers use the same scaling type as the non-sliding-window layers
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hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train;
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hparams.rope_freq_scale_train_swa = hparams.rope_freq_scale_train;
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ml.get_key(LLM_KV_ROPE_SCALING_ATTN_FACTOR, hparams.rope_attn_factor, false);
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// non-transformer models do not have attention heads
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if (hparams.n_head() > 0) {
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// gpt-neox n_rot = rotary_pct * (n_embd / n_head)
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// gpt-j n_rot = rotary_dim
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hparams.n_embd_head_k_full = hparams.n_embd / hparams.n_head();
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ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k_full, false);
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hparams.n_embd_head_v_full = hparams.n_embd / hparams.n_head();
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ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v_full, false);
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// sanity check for n_rot (optional)
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hparams.n_rot = hparams.n_embd_head_k_full;
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ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false);
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if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON || model.arch == LLM_ARCH_BITNET_25 || model.arch == LLM_ARCH_BITNET_B158 || model.arch == LLM_ARCH_DECI) {
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if (hparams.n_rot != hparams.n_embd_head_k_full) {
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throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd_head_k_full));
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}
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}
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} else {
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hparams.n_rot = 0;
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hparams.n_embd_head_k_full = 0;
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hparams.n_embd_head_v_full = 0;
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}
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{
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hparams.n_embd_head_k_swa = hparams.n_embd_head_k_full;
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hparams.n_embd_head_v_swa = hparams.n_embd_head_v_full;
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ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_SWA, hparams.n_embd_head_k_swa, false);
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ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_SWA, hparams.n_embd_head_v_swa, false);
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hparams.n_rot_swa = hparams.n_rot;
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ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT_SWA, hparams.n_rot_swa, false);
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}
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// arch-specific KVs
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switch (model.arch) {
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case LLM_ARCH_LLAMA:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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if (hparams.n_expert == 8) {
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switch (hparams.n_layer) {
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case 32: model.type = e_model::MODEL_8x7B; break;
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case 56: model.type = e_model::MODEL_8x22B; break;
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} else {
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switch (hparams.n_layer) {
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case 22: model.type = e_model::MODEL_1B; break;
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case 26: model.type = e_model::MODEL_3B; break;
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// granite uses a vocab with len 49152
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case 32: model.type = hparams.n_vocab == 49152 ? e_model::MODEL_3B : (hparams.n_vocab < 40000 ? e_model::MODEL_7B : e_model::MODEL_8B); break;
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case 36: model.type = e_model::MODEL_8B; break; // granite
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case 40: model.type = e_model::MODEL_13B; break;
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case 48: model.type = e_model::MODEL_34B; break;
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case 60: model.type = e_model::MODEL_30B; break;
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case 80: model.type = hparams.n_head() == hparams.n_head_kv() ? e_model::MODEL_65B : e_model::MODEL_70B; break;
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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}
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} break;
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case LLM_ARCH_LLAMA4:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
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ml.get_key(LLM_KV_INTERLEAVE_MOE_LAYER_STEP, hparams.n_moe_layer_step);
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hparams.n_swa_pattern = 4; // pattern: 3 chunked - 1 full
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hparams.n_attn_chunk = 8192; // should this be a gguf kv? currently it's the same for Scout and Maverick
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hparams.n_swa = 1; // TODO @ngxson : this is added to trigger the SWA branch (we store the chunked attn mask in the SWA tensor), will need to clean this up later
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switch (hparams.n_expert) {
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case 16: model.type = MODEL_17B_16E; break;
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case 128: model.type = MODEL_17B_128E; break;
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default: model.type = MODEL_UNKNOWN;
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}
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if (model.type == MODEL_17B_128E) {
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hparams.use_kq_norm = false;
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}
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} break;
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case LLM_ARCH_DECI:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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switch (hparams.n_layer) {
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case 32: model.type = e_model::MODEL_7B; break;
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case 80: model.type = e_model::MODEL_70B; break;
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case 162: model.type = e_model::MODEL_405B; break;
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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case LLM_ARCH_MINICPM:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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switch (hparams.n_layer) {
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case 40: model.type = e_model::MODEL_2B; break;
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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case LLM_ARCH_GROK:
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{
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// defaults for old GGUFs
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hparams.yarn_beta_fast = 8.0f;
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hparams.f_logit_scale = 0.5773502691896257f;
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hparams.f_embedding_scale = 78.38367176906169f;
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hparams.f_attn_out_scale = 0.08838834764831845f;
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hparams.f_attn_logit_softcapping = 30.0f;
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hparams.f_router_logit_softcapping = 30.0f;
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// no final_logit_softcapping in grok-1
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hparams.f_final_logit_softcapping = 0.0f;
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
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ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale, false);
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ml.get_key(LLM_KV_EMBEDDING_SCALE, hparams.f_embedding_scale, false);
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ml.get_key(LLM_KV_ATTENTION_OUTPUT_SCALE, hparams.f_attn_out_scale, false);
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ml.get_key(LLM_KV_ATTN_LOGIT_SOFTCAPPING, hparams.f_attn_logit_softcapping, false);
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ml.get_key(LLM_KV_ROUTER_LOGIT_SOFTCAPPING, hparams.f_router_logit_softcapping, false);
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ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING, hparams.f_final_logit_softcapping, false);
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ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_LENGTH, hparams.attn_temp_length, false);
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ml.get_key(LLM_KV_ROPE_SCALING_YARN_EXT_FACTOR, hparams.yarn_ext_factor, false);
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ml.get_key(LLM_KV_ROPE_SCALING_YARN_ATTN_FACTOR, hparams.yarn_attn_factor, false);
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ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_FAST, hparams.yarn_beta_fast, false);
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ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_SLOW, hparams.yarn_beta_slow, false);
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switch (hparams.n_layer) {
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case 64: model.type = e_model::MODEL_314B; break;
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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case LLM_ARCH_FALCON:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
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switch (hparams.n_layer) {
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case 32: model.type = e_model::MODEL_7B; break;
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case 60: model.type = e_model::MODEL_40B; break;
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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} break;
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case LLM_ARCH_BAICHUAN:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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switch (hparams.n_layer) {
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case 32: model.type = e_model::MODEL_7B; break;
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case 40: model.type = e_model::MODEL_13B; break;
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default: model.type = e_model::MODEL_UNKNOWN;
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}
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|
|
|
if (model.type == e_model::MODEL_13B) {
|
|
// TODO: become GGUF KV parameter
|
|
hparams.f_max_alibi_bias = 8.0f;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_STARCODER:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
switch (hparams.n_layer) {
|
|
case 24: model.type = e_model::MODEL_1B; break;
|
|
case 36: model.type = e_model::MODEL_3B; break;
|
|
case 42: model.type = e_model::MODEL_7B; break;
|
|
case 40: model.type = e_model::MODEL_15B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_REFACT:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 32: model.type = e_model::MODEL_1B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
|
|
// TODO: become GGUF KV parameter
|
|
hparams.f_max_alibi_bias = 8.0f;
|
|
} break;
|
|
case LLM_ARCH_BERT:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
|
|
ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
|
|
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 3:
|
|
model.type = e_model::MODEL_17M; break; // bge-micro
|
|
case 6:
|
|
model.type = e_model::MODEL_22M; break; // MiniLM-L6
|
|
case 12:
|
|
switch (hparams.n_embd) {
|
|
case 384: model.type = e_model::MODEL_33M; break; // MiniLM-L12, bge-small
|
|
case 768: model.type = e_model::MODEL_109M; break; // bge-base
|
|
} break;
|
|
case 24:
|
|
model.type = e_model::MODEL_335M; break; // bge-large
|
|
}
|
|
} break;
|
|
case LLM_ARCH_JINA_BERT_V2:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
|
|
ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
|
|
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type);
|
|
hparams.f_max_alibi_bias = 8.0f;
|
|
|
|
switch (hparams.n_layer) {
|
|
case 4: model.type = e_model::MODEL_33M; break; // jina-embeddings-small
|
|
case 12: model.type = e_model::MODEL_137M; break; // jina-embeddings-base
|
|
}
|
|
} break;
|
|
case LLM_ARCH_NOMIC_BERT:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
|
|
ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
|
|
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type);
|
|
|
|
if (hparams.n_layer == 12 && hparams.n_embd == 768) {
|
|
model.type = e_model::MODEL_137M;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_BLOOM:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 24: model.type = e_model::MODEL_1B; break;
|
|
case 30:
|
|
switch (hparams.n_embd) {
|
|
case 2560: model.type = e_model::MODEL_3B; break;
|
|
case 4096: model.type = e_model::MODEL_7B; break;
|
|
} break;
|
|
}
|
|
|
|
// TODO: become GGUF KV parameter
|
|
hparams.f_max_alibi_bias = 8.0f;
|
|
} break;
|
|
case LLM_ARCH_MPT:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv, false);
|
|
ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 32: model.type = e_model::MODEL_7B; break;
|
|
case 48: model.type = e_model::MODEL_30B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_STABLELM:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 24: model.type = e_model::MODEL_1B; break;
|
|
case 32: model.type = e_model::MODEL_3B; break;
|
|
case 40: model.type = e_model::MODEL_12B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_QWEN:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 32: model.type = e_model::MODEL_7B; break;
|
|
case 40: model.type = e_model::MODEL_13B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_QWEN2VL:
|
|
{
|
|
ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, true);
|
|
}
|
|
// fall through
|
|
case LLM_ARCH_QWEN2:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 24: model.type = hparams.n_embd == 1024 ? e_model::MODEL_0_5B : e_model::MODEL_1B; break;
|
|
case 32: model.type = e_model::MODEL_7B; break;
|
|
case 40: model.type = hparams.n_head() == 20 ? e_model::MODEL_4B : e_model::MODEL_13B; break;
|
|
case 80: model.type = e_model::MODEL_70B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_QWEN2MOE:
|
|
{
|
|
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
|
|
ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
|
|
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 24: model.type = e_model::MODEL_A2_7B; break;
|
|
case 28: model.type = e_model::MODEL_57B_A14B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
|
|
case LLM_ARCH_QWEN3:
|
|
{
|
|
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 28: model.type = hparams.n_embd == 1024 ? e_model::MODEL_0_6B : e_model::MODEL_1_7B; break;
|
|
case 36: model.type = hparams.n_embd == 2560 ? e_model::MODEL_4B : e_model::MODEL_8B; break;
|
|
case 40: model.type = e_model::MODEL_14B; break;
|
|
case 64: model.type = e_model::MODEL_32B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_QWEN3VL:
|
|
{
|
|
ml.get_key(LLM_KV_NUM_DEEPSTACK_LAYERS, hparams.n_deepstack_layers, false);
|
|
ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, true);
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 28: model.type = e_model::MODEL_1_7B; break;
|
|
case 36: model.type = hparams.n_embd == 2560 ? e_model::MODEL_4B : e_model::MODEL_8B; break;
|
|
case 64: model.type = e_model::MODEL_32B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
// since vision model stacks deepstack features along feature dim
|
|
// we also create a fake "n_embd" for text model to be the main embd + deepstack embds
|
|
hparams.n_embd *= hparams.n_deepstack_layers + 1;
|
|
} break;
|
|
case LLM_ARCH_QWEN3MOE:
|
|
{
|
|
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
|
|
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 48: model.type = e_model::MODEL_30B_A3B; break;
|
|
case 94: model.type = e_model::MODEL_235B_A22B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_MELLUM:
|
|
{
|
|
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
|
|
|
|
if (hparams.n_swa > 0) {
|
|
hparams.rope_freq_base_train_swa = hparams.rope_freq_base_train;
|
|
hparams.rope_freq_scale_train_swa = 1; //hparams.rope_freq_scale_train;
|
|
|
|
if (!ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer, false)) {
|
|
for (uint32_t i = 0; i < hparams.n_layer; ++i) {
|
|
hparams.swa_layers[i] = ((i + 1) % 4 != 0);
|
|
}
|
|
}
|
|
|
|
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
|
|
}
|
|
|
|
switch (hparams.n_layer) {
|
|
case 28: model.type = e_model::MODEL_12B_A2_5B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_QWEN3NEXT:
|
|
{
|
|
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
|
|
ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv);
|
|
ml.get_key(LLM_KV_SSM_INNER_SIZE, hparams.ssm_d_inner);
|
|
ml.get_key(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state);
|
|
ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
|
|
ml.get_key(LLM_KV_SSM_GROUP_COUNT, hparams.ssm_n_group);
|
|
|
|
// Upstream convention: every 4th layer is full attention, others are recurrent.
|
|
for (uint32_t i = 0; i < hparams.n_layer; ++i) {
|
|
hparams.recurrent_layer_arr[i] = ((i + 1) % 4 != 0);
|
|
}
|
|
|
|
switch (hparams.n_layer) {
|
|
case 48: model.type = e_model::MODEL_80B_A3B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_QWEN35MOE:
|
|
{
|
|
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
|
|
ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, true);
|
|
|
|
ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.nextn_predict_layers, false);
|
|
if (model.mtp) {
|
|
hparams.n_layer_kv_from_start = hparams.n_layer;
|
|
} else {
|
|
hparams.n_layer_kv_from_start = hparams.n_layer - hparams.nextn_predict_layers;
|
|
}
|
|
|
|
// Load linear attention (gated delta net) parameters
|
|
ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv);
|
|
ml.get_key(LLM_KV_SSM_INNER_SIZE, hparams.ssm_d_inner);
|
|
ml.get_key(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state);
|
|
ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
|
|
ml.get_key(LLM_KV_SSM_GROUP_COUNT, hparams.ssm_n_group);
|
|
|
|
// Mark recurrent layers (linear attention layers)
|
|
{
|
|
uint32_t full_attn_interval = 4;
|
|
ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
|
|
const uint32_t n_main_layers = hparams.n_layer - hparams.nextn_predict_layers;
|
|
for (uint32_t i = 0; i < hparams.n_layer; ++i) {
|
|
if (i < n_main_layers) {
|
|
hparams.recurrent_layer_arr[i] = ((i + 1) % full_attn_interval != 0);
|
|
} else {
|
|
hparams.recurrent_layer_arr[i] = false;
|
|
}
|
|
}
|
|
}
|
|
|
|
switch (hparams.n_layer) {
|
|
case 40:
|
|
case 41:
|
|
model.type = e_model::MODEL_35B_A3B; break;
|
|
case 48: model.type = e_model::MODEL_122B_A10B; break;
|
|
case 60: model.type = e_model::MODEL_397B_A17B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_QWEN35:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, true);
|
|
|
|
// NextN/MTP parameters
|
|
ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.nextn_predict_layers, false);
|
|
if (model.mtp) {
|
|
hparams.n_layer_kv_from_start = hparams.n_layer;
|
|
} else {
|
|
hparams.n_layer_kv_from_start = hparams.n_layer - hparams.nextn_predict_layers;
|
|
}
|
|
|
|
// Load linear attention (gated delta net) parameters
|
|
ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv);
|
|
ml.get_key(LLM_KV_SSM_INNER_SIZE, hparams.ssm_d_inner);
|
|
ml.get_key(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state);
|
|
ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
|
|
ml.get_key(LLM_KV_SSM_GROUP_COUNT, hparams.ssm_n_group);
|
|
|
|
// Mark recurrent layers (linear attention layers)
|
|
// MTP layers always use standard attention, not delta-net
|
|
{
|
|
uint32_t full_attn_interval = 4;
|
|
ml.get_key(LLM_KV_FULL_ATTENTION_INTERVAL, full_attn_interval, false);
|
|
const uint32_t n_main_layers = hparams.n_layer - hparams.nextn_predict_layers;
|
|
for (uint32_t i = 0; i < hparams.n_layer; ++i) {
|
|
if (i < n_main_layers) {
|
|
hparams.recurrent_layer_arr[i] = ((i + 1) % full_attn_interval != 0);
|
|
} else {
|
|
hparams.recurrent_layer_arr[i] = false;
|
|
}
|
|
}
|
|
}
|
|
|
|
switch (hparams.n_layer) {
|
|
case 24: // without MTP layer
|
|
case 25: // with MTP layer (24 main + 1 MTP)
|
|
model.type = hparams.n_embd == 1024 ? e_model::MODEL_0_8B : e_model::MODEL_2B; break;
|
|
case 32: // without MTP layer
|
|
case 33: // with MTP layer (32 main + 1 MTP)
|
|
model.type = hparams.n_embd == 2560 ? e_model::MODEL_4B : e_model::MODEL_9B; break;
|
|
case 64: // without MTP layer
|
|
case 65: // with MTP layer (64 main + 1 MTP)
|
|
model.type = e_model::MODEL_27B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_QWEN3VLMOE:
|
|
{
|
|
ml.get_key(LLM_KV_NUM_DEEPSTACK_LAYERS, hparams.n_deepstack_layers, false);
|
|
ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, true);
|
|
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 48: model.type = e_model::MODEL_30B_A3B; break;
|
|
case 94: model.type = e_model::MODEL_235B_A22B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
// since vision model stacks deepstack features along feature dim
|
|
// we also create a fake "n_embd" for text model to be the main embd + deepstack embds
|
|
hparams.n_embd *= hparams.n_deepstack_layers + 1;
|
|
} break;
|
|
case LLM_ARCH_PHI2:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 24: model.type = e_model::MODEL_1B; break;
|
|
case 32: model.type = e_model::MODEL_3B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_PHI3:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 24: model.type = e_model::MODEL_1B; break;
|
|
case 32: model.type = e_model::MODEL_3B; break;
|
|
case 40: model.type = e_model::MODEL_14B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
|
|
// for backward compatibility ; see: https://github.com/ggerganov/llama.cpp/pull/8931
|
|
if ((hparams.n_layer == 32 || hparams.n_layer == 40) && hparams.n_ctx_train == 4096) {
|
|
// default value for Phi-3-mini-4k-instruct and Phi-3-medium-4k-instruct
|
|
hparams.n_swa = 2047;
|
|
} else if (hparams.n_layer == 32 && hparams.n_head_kv(0) == 32 && hparams.n_ctx_train == 131072) {
|
|
// default value for Phi-3-mini-128k-instruct
|
|
hparams.n_swa = 262144;
|
|
} else if (hparams.n_layer == 40 && hparams.n_ctx_train == 131072) {
|
|
// default value for Phi-3-medium-128k-instruct
|
|
hparams.n_swa = 131072;
|
|
}
|
|
bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
|
|
if (!found_swa && hparams.n_swa == 0) {
|
|
throw std::runtime_error("invalid value for sliding_window");
|
|
}
|
|
} break;
|
|
case LLM_ARCH_PLAMO:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 40: model.type = e_model::MODEL_13B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GPT2:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
switch (hparams.n_layer) {
|
|
case 12: model.type = e_model::MODEL_SMALL; break;
|
|
case 24: model.type = e_model::MODEL_MEDIUM; break;
|
|
case 36: model.type = e_model::MODEL_LARGE; break;
|
|
case 48: model.type = e_model::MODEL_XL; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_CODESHELL:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
switch (hparams.n_layer) {
|
|
case 42: model.type = e_model::MODEL_7B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_ORION:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 40: model.type = e_model::MODEL_14B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_INTERNLM2:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 32: model.type = e_model::MODEL_7B; break;
|
|
case 48: model.type = e_model::MODEL_20B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GEMMA:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 18: model.type = e_model::MODEL_2B; break;
|
|
case 28: model.type = e_model::MODEL_7B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GEMMA2:
|
|
{
|
|
hparams.n_swa = 4096; // default value of gemma 2
|
|
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key(LLM_KV_ATTN_LOGIT_SOFTCAPPING, hparams.f_attn_logit_softcapping, false);
|
|
ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING, hparams.f_final_logit_softcapping, false);
|
|
hparams.attn_soft_cap = true;
|
|
|
|
switch (hparams.n_layer) {
|
|
case 26: model.type = e_model::MODEL_2B; break;
|
|
case 42: model.type = e_model::MODEL_9B; break;
|
|
case 46: model.type = e_model::MODEL_27B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GEMMA3:
|
|
{
|
|
hparams.n_swa_pattern = 6;
|
|
|
|
hparams.rope_freq_base_train_swa = 10000.0f;
|
|
hparams.rope_freq_scale_train_swa = 1.0f;
|
|
|
|
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 26: model.type = e_model::MODEL_2B; break;
|
|
case 34: model.type = e_model::MODEL_4B; break;
|
|
case 48: model.type = e_model::MODEL_12B; break;
|
|
case 62: model.type = e_model::MODEL_27B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
|
|
hparams.f_attention_scale = model.type == e_model::MODEL_27B
|
|
? 1.0f / std::sqrt(float(hparams.n_embd / hparams.n_head(0)))
|
|
: 1.0f / std::sqrt(float(hparams.n_embd_head_k_full));
|
|
} break;
|
|
case LLM_ARCH_GEMMA4:
|
|
{
|
|
//hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
|
|
ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer);
|
|
|
|
uint32_t n_kv_shared_layers = 0;
|
|
ml.get_key(LLM_KV_ATTENTION_SHARED_KV_LAYERS, n_kv_shared_layers, false);
|
|
|
|
hparams.n_layer_kv_from_start = hparams.n_layer - (int32_t)n_kv_shared_layers;
|
|
hparams.f_attention_scale = 1.0f; // Gemma4 uses self.scaling = 1.0 (no pre-attn scaling)
|
|
hparams.f_final_logit_softcapping = 30.0f;
|
|
|
|
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
|
|
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
|
|
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key(LLM_KV_EMBEDDING_LENGTH_PER_LAYER, hparams.n_embd_per_layer);
|
|
ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_SWA, hparams.n_embd_head_k_swa);
|
|
ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_SWA, hparams.n_embd_head_v_swa);
|
|
ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING, hparams.f_final_logit_softcapping, false);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 35: model.type = e_model::MODEL_2B; break;
|
|
case 42: model.type = e_model::MODEL_4B; break; // to confirm: E4B or E5B?
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GEMMA4_MTP:
|
|
case LLM_ARCH_GEMMA4_ASSISTANT:
|
|
{
|
|
if (model.arch == LLM_ARCH_GEMMA4_MTP) {
|
|
ml.get_key(LLM_KV_MTP_BACKBONE_EMBEDDING_LENGTH, hparams.mtp_backbone_n_embd);
|
|
ml.get_key(LLM_KV_MTP_CENTROID_COUNT, hparams.mtp_num_centroids, false);
|
|
ml.get_key(LLM_KV_MTP_CENTROID_TOP_K, hparams.mtp_centroid_top_k, false);
|
|
} else {
|
|
ml.get_key("gemma4-assistant.embedding_length_out", hparams.mtp_backbone_n_embd);
|
|
ml.get_key("gemma4-assistant.n_centroids", hparams.mtp_num_centroids, false);
|
|
ml.get_key("gemma4-assistant.centroid_top_k", hparams.mtp_centroid_top_k, false);
|
|
}
|
|
ml.get_key(LLM_KV_MTP_USE_ORDERED_EMBEDDINGS, hparams.mtp_use_ordered_embeddings, false);
|
|
|
|
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer);
|
|
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
|
|
|
|
hparams.n_layer_kv_from_start = hparams.n_layer;
|
|
hparams.f_attention_scale = 1.0f;
|
|
|
|
switch (hparams.mtp_backbone_n_embd) {
|
|
case 5376: model.type = e_model::MODEL_32B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_DFLASH_DRAFT:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key(LLM_KV_DFLASH_BLOCK_SIZE, hparams.dflash_block_size, false);
|
|
ml.get_key(LLM_KV_DFLASH_MASK_TOKEN_ID, hparams.dflash_mask_token_id, false);
|
|
ml.get_key(LLM_KV_DFLASH_N_TARGET_FEATURES, hparams.dflash_n_target_features, false);
|
|
ml.get_key(LLM_KV_DFLASH_BACKBONE_ROTARY_BASE, hparams.dflash_backbone_rotary_base, false);
|
|
load_dflash_target_layer_ids(ml, LLM_KV(model.arch)(LLM_KV_DFLASH_TARGET_LAYER_IDS), hparams, false);
|
|
ml.get_key(LLM_KV_ATTENTION_VALUE_SCALE, hparams.f_attn_v_scale, false);
|
|
// DFlash drafts may be trained with sliding-window attention (for long-context).
|
|
// Read the window + per-layer pattern so the SWA mask path activates; absent keys
|
|
// leave n_swa=0 / swa_layers all-zero (dense behavior, unchanged).
|
|
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
|
|
ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer, false);
|
|
validate_dflash_hparams(hparams, model.arch);
|
|
|
|
hparams.n_layer_kv_from_start = hparams.n_layer;
|
|
model.type = e_model::MODEL_UNKNOWN;
|
|
} break;
|
|
|
|
case LLM_ARCH_STARCODER2:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
switch (hparams.n_layer) {
|
|
case 30: model.type = e_model::MODEL_3B; break;
|
|
case 32: model.type = e_model::MODEL_7B; break;
|
|
case 40: model.type = e_model::MODEL_15B; break;
|
|
case 52: model.type = e_model::MODEL_20B; break; // granite
|
|
case 88: model.type = e_model::MODEL_34B; break; // granite
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_MAMBA:
|
|
{
|
|
ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv);
|
|
ml.get_key(LLM_KV_SSM_INNER_SIZE, hparams.ssm_d_inner);
|
|
ml.get_key(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state);
|
|
ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
|
|
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 24:
|
|
switch (hparams.n_embd) {
|
|
case 768: model.type = e_model::MODEL_SMALL; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
} break;
|
|
case 48:
|
|
switch (hparams.n_embd) {
|
|
case 1024: model.type = e_model::MODEL_MEDIUM; break;
|
|
case 1536: model.type = e_model::MODEL_LARGE; break;
|
|
case 2048: model.type = e_model::MODEL_XL; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
} break;
|
|
case 64:
|
|
switch (hparams.n_embd) {
|
|
case 2560: model.type = e_model::MODEL_3B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
} break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_XVERSE:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 32: model.type = e_model::MODEL_7B; break;
|
|
case 40: model.type = e_model::MODEL_13B; break;
|
|
case 80: model.type = e_model::MODEL_65B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_COMMAND_R:
|
|
{
|
|
ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
switch (hparams.n_layer) {
|
|
case 40: model.type = e_model::MODEL_35B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_DBRX:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 40: model.type = e_model::MODEL_16x12B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_OLMO:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv, false);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 22: model.type = e_model::MODEL_1B; break;
|
|
case 32: model.type = e_model::MODEL_7B; break;
|
|
case 80: model.type = e_model::MODEL_70B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_OPENELM:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 16: model.type = e_model::MODEL_270M; break;
|
|
case 20: model.type = e_model::MODEL_450M; break;
|
|
case 28: model.type = e_model::MODEL_1B; break;
|
|
case 36: model.type = e_model::MODEL_3B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GPTNEOX:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
ml.get_key(LLM_KV_USE_PARALLEL_RESIDUAL, hparams.use_par_res);
|
|
switch (hparams.n_layer) {
|
|
case 6:
|
|
switch (hparams.n_ff()) {
|
|
case 512: model.type = e_model::MODEL_14M; break;
|
|
case 2048: model.type = e_model::MODEL_70M; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
} break;
|
|
case 12:
|
|
switch (hparams.n_ff()) {
|
|
case 3072: model.type = e_model::MODEL_160M; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
} break;
|
|
case 16:
|
|
switch (hparams.n_ff()) {
|
|
case 8192: model.type = e_model::MODEL_1B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
} break;
|
|
case 24:
|
|
switch (hparams.n_ff()) {
|
|
case 4096: model.type = e_model::MODEL_410M; break;
|
|
case 8192: model.type = e_model::MODEL_1_4B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
} break;
|
|
case 32:
|
|
switch (hparams.n_ff()) {
|
|
case 10240: model.type = e_model::MODEL_2_8B; break;
|
|
case 16384: model.type = e_model::MODEL_6_9B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
} break;
|
|
case 36:
|
|
switch (hparams.n_ff()) {
|
|
case 20480: model.type = e_model::MODEL_12B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
} break;
|
|
case 44:
|
|
switch (hparams.n_ff()) {
|
|
case 24576: model.type = e_model::MODEL_20B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
} break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_ARCTIC:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
if (hparams.n_expert == 128) {
|
|
switch (hparams.n_layer) {
|
|
case 35: model.type = e_model::MODEL_10B_128x3_66B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} else {
|
|
model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_MISTRAL4:
|
|
case LLM_ARCH_DEEPSEEK2:
|
|
{
|
|
int expected_head_size_k = model.arch == LLM_ARCH_DEEPSEEK2 ? 576 : 320;
|
|
int expected_head_size_v = model.arch == LLM_ARCH_DEEPSEEK2 ? 512 : 256;
|
|
if (hparams.n_head_kv() == 1) {
|
|
int n_nead_kv = hparams.n_gqa();
|
|
if (n_nead_kv%4 != 0 || hparams.n_embd_head_k(0) != expected_head_size_k || hparams.n_embd_head_v(0) != expected_head_size_v ||
|
|
hparams.n_rot != 64) {
|
|
LLAMA_LOG_ERROR("==========================================================================\n");
|
|
LLAMA_LOG_ERROR("Detected incompatible DeepSeek model without a known way to fix it.\n");
|
|
LLAMA_LOG_ERROR("Consider making your own ik_llama.cpp compatible model or\n");
|
|
LLAMA_LOG_ERROR("ask the model provider to make one for you,\n\n");
|
|
LLAMA_LOG_ERROR("Sorry, uknown model => cannot fix it => bailing out\n");
|
|
LLAMA_LOG_ERROR("==========================================================================\n");
|
|
GGML_ABORT("Fatal error");
|
|
}
|
|
LLAMA_LOG_INFO("================= Adjusted mainline llama.cpp MLA tensors to ik_llama.cpp\n");
|
|
for (auto& item : hparams.n_head_kv_arr) item = n_nead_kv;
|
|
hparams.n_embd_head_k_full = 192;
|
|
hparams.n_embd_head_v_full = 128;
|
|
ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA, hparams.n_embd_head_k_full);
|
|
ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_full);
|
|
}
|
|
bool is_lite = (hparams.n_layer == 27 || hparams.n_layer == 26) || (hparams.n_layer == 48 && hparams.n_vocab == 128256);
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
|
|
if (!is_lite) {
|
|
ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
|
|
}
|
|
ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);
|
|
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
|
|
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
|
|
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
|
|
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
|
|
hparams.expert_gating_func = LLM_EXPERT_GATING_FUNC_TYPE_NONE;
|
|
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
|
|
if (hparams.expert_gating_func == LLM_EXPERT_GATING_FUNC_TYPE_NONE) {
|
|
// Older DeepSeek models from the 2.0/2.5 series may not have the experts gating function recorded in the GGUF.
|
|
// Such models use SOFTMAX as the experts gating function.
|
|
// The new (new as of this commit) GLM-4.7-Flash may also be missing the experts gating function.
|
|
// GLM-4.7-Flash uses SIGMOID as the experts gating function.
|
|
// Hence, we make the LLM_KV_EXPERT_GATING_FUNC entry optional, and set here if missing.
|
|
// We distinguish between GLM-4.7-Flash and DeepSeek-2/2.5 models by the number of layers.
|
|
// GLM-4.7-Flash has 47 layers (or 48, if an MTP layer is included in the GGUF).
|
|
hparams.expert_gating_func = hparams.n_layer == 47 || hparams.n_layer == 48 ?
|
|
LLM_EXPERT_GATING_FUNC_SIGMOID : LLM_EXPERT_GATING_FUNC_SOFTMAX;
|
|
LLAMA_LOG_INFO("================= Missing experts gating function -> set to %s\n",
|
|
llm_expert_gating_func_name(llm_expert_gating_func_type(hparams.expert_gating_func)));
|
|
}
|
|
ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, false);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 27: model.type = e_model::MODEL_16B; break;
|
|
case 36: model.type = e_model::MODEL_119B_A6B; break;
|
|
case 47: model.type = e_model::MODEL_30B_A3B; break; // GLM-4.7-Flash
|
|
case 60: model.type = e_model::MODEL_236B; break;
|
|
case 61: model.type = e_model::MODEL_671B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_CHATGLM:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 28: model.type = e_model::MODEL_6B; break;
|
|
case 40: model.type = e_model::MODEL_9B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GLM4:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 40: model.type = e_model::MODEL_9B; break;
|
|
case 61: model.type = e_model::MODEL_32B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GLM4_MOE:
|
|
{
|
|
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
// MoE parameters
|
|
ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert);
|
|
ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used);
|
|
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
|
|
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false);
|
|
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
|
|
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
|
|
|
|
// Expert gating function (GLM4_MOE uses sigmoid)
|
|
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
|
|
if (hparams.expert_gating_func == 0) {
|
|
hparams.expert_gating_func = LLM_EXPERT_GATING_FUNC_SIGMOID;
|
|
}
|
|
|
|
// NextN/MTP parameters
|
|
if (model.mtp) {
|
|
hparams.n_layer_kv_from_start = hparams.n_layer;
|
|
}
|
|
else {
|
|
hparams.n_layer_kv_from_start = hparams.n_layer - hparams.nextn_predict_layers;
|
|
}
|
|
ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.nextn_predict_layers, false);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 47: model.type = e_model::MODEL_106B_A12B; break; // GLM-4.5-Air (46 layers + 1 NextN layer)
|
|
case 93: model.type = e_model::MODEL_355B_A32B; break; // GLM-4.5 (92 layers + 1 NextN layer)
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_BITNET:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 26: model.type = e_model::MODEL_3B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_BITNET_B158:
|
|
case LLM_ARCH_BITNET_25:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 30: model.type = e_model::MODEL_2B; break; // bitnet2b_2501
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_T5:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, hparams.n_rel_attn_bkts);
|
|
|
|
uint32_t dec_start_token_id;
|
|
if (ml.get_key(LLM_KV_DECODER_START_TOKEN_ID, dec_start_token_id, false)) {
|
|
hparams.dec_start_token_id = dec_start_token_id;
|
|
}
|
|
|
|
switch (hparams.n_layer) {
|
|
case 6: model.type = e_model::MODEL_60M; break; // t5-small
|
|
case 8: model.type = e_model::MODEL_80M; break; // flan-t5-small
|
|
case 12:
|
|
switch (hparams.n_ff()) {
|
|
case 3072: model.type = e_model::MODEL_220M; break; // t5-base
|
|
case 2048: model.type = e_model::MODEL_250M; break; // flan-t5-base
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
} break;
|
|
case 24:
|
|
switch (hparams.n_ff()) {
|
|
case 4096: model.type = e_model::MODEL_770M; break; // t5-large
|
|
case 2816: model.type = e_model::MODEL_780M; break; // flan-t5-large
|
|
case 16384: model.type = e_model::MODEL_3B; break; // t5-3b
|
|
case 5120: model.type = e_model::MODEL_3B; break; // flan-t5-xl
|
|
case 65536: model.type = e_model::MODEL_11B; break; // t5-11b
|
|
case 10240: model.type = e_model::MODEL_11B; break; // flan-t5-xxl
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
} break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_T5ENCODER:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, hparams.n_rel_attn_bkts);
|
|
model.type = e_model::MODEL_UNKNOWN;
|
|
} break;
|
|
case LLM_ARCH_JAIS:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 24: model.type = e_model::MODEL_1_3B; break;
|
|
case 40: model.type = e_model::MODEL_13B; break;
|
|
/* TODO: add variants */
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GRANITE:
|
|
case LLM_ARCH_GRANITE_MOE:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
|
|
ml.get_key(LLM_KV_RESIDUAL_SCALE, hparams.f_residual_scale);
|
|
ml.get_key(LLM_KV_EMBEDDING_SCALE, hparams.f_embedding_scale);
|
|
ml.get_key(LLM_KV_ATTENTION_SCALE, hparams.f_attention_scale);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 32: model.type = e_model::MODEL_3B; break;
|
|
case 40: model.type = e_model::MODEL_3B; break;
|
|
// Add additional layer/vocab/etc checks here for other model sizes
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_COHERE2:
|
|
{
|
|
hparams.n_swa_pattern = 4;
|
|
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
|
|
ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
|
|
switch (hparams.n_layer) {
|
|
case 32: model.type = e_model::MODEL_8B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_COHERE2_MOE:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
|
|
ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer);
|
|
ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
|
|
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
|
|
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
|
|
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
|
|
if (hparams.expert_gating_func == LLM_EXPERT_GATING_FUNC_TYPE_NONE) {
|
|
hparams.expert_gating_func = LLM_EXPERT_GATING_FUNC_SIGMOID;
|
|
}
|
|
switch (hparams.n_layer) {
|
|
case 49: model.type = e_model::MODEL_30B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_BAILINGMOE2:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
|
|
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
|
|
ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp);
|
|
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
|
|
ml.get_key(LLM_KV_EXPERT_GROUP_COUNT, hparams.n_expert_groups);
|
|
ml.get_key(LLM_KV_EXPERT_GROUP_USED_COUNT, hparams.n_group_used);
|
|
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
|
|
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
|
|
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func);
|
|
ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.nextn_predict_layers, false);
|
|
|
|
// TODO: when MTP is implemented, this should probably be updated if needed
|
|
hparams.n_layer_kv_from_start = hparams.n_layer - hparams.nextn_predict_layers;
|
|
|
|
switch (hparams.n_layer) {
|
|
case 20: model.type = MODEL_16B_A1B; break;
|
|
case 21: model.type = MODEL_16B_A1B; break;
|
|
case 32: model.type = MODEL_100B_A6B; break;
|
|
case 33: model.type = MODEL_100B_A6B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_DOTS1:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
|
|
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
|
|
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
|
|
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
|
|
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
|
|
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
|
|
switch (hparams.n_layer) {
|
|
case 62: model.type = e_model::MODEL_142B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_ERNIE4_5:
|
|
case LLM_ARCH_ERNIE4_5_MOE:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
if (model.arch == LLM_ARCH_ERNIE4_5_MOE) {
|
|
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
|
|
ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
|
|
ml.get_key(LLM_KV_INTERLEAVE_MOE_LAYER_STEP, hparams.n_moe_layer_step);
|
|
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
|
|
}
|
|
|
|
switch (hparams.n_layer) {
|
|
case 18: model.type = e_model::MODEL_0_3B; break;
|
|
case 28: model.type = e_model::MODEL_21B_A3B; break;
|
|
case 54: model.type = e_model::MODEL_300B_A47B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_HUNYUAN_MOE:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
|
|
ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 32: model.type = e_model::MODEL_80B_A13B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_OPENAI_MOE:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
|
|
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
|
|
|
|
//TODO OAI_MOE: SWA
|
|
//hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
|
|
//hparams.set_swa_pattern(2);
|
|
|
|
// TODO: switch (hparams.n_layer)
|
|
|
|
} break;
|
|
case LLM_ARCH_MINIMAX_M2:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
|
|
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 62: model.type = e_model::MODEL_230B_A10B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_MINIMAX_M3:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false);
|
|
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
|
|
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
|
|
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
|
|
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
|
|
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
|
|
|
|
model.type = e_model::MODEL_UNKNOWN;
|
|
} break;
|
|
case LLM_ARCH_SMOLLM3:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
hparams.n_no_rope_layer_step = 4;
|
|
|
|
switch (hparams.n_layer) {
|
|
case 36: model.type = e_model::MODEL_3B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_MISTRAL3:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE, hparams.f_attn_temp_scale, false);
|
|
|
|
ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_FAST, hparams.yarn_beta_fast, false);
|
|
ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_SLOW, hparams.yarn_beta_slow, false);
|
|
ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, false);
|
|
|
|
if (hparams.f_attn_temp_scale != 0.0f) {
|
|
hparams.n_attn_temp_floor_scale = hparams.n_ctx_orig_yarn;
|
|
if (hparams.n_attn_temp_floor_scale == 0) {
|
|
throw std::runtime_error("invalid n_ctx_orig_yarn for attention temperature scaling");
|
|
}
|
|
}
|
|
|
|
// TODO: this seems to be correct with the case of mscale == mscale_all_dims == 1.0f
|
|
// but may need further verification with other values
|
|
if (hparams.rope_yarn_log_mul != 0.0f) {
|
|
float factor = 1.0f / hparams.rope_freq_scale_train;
|
|
float mscale = 1.0f;
|
|
float mscale_all_dims = hparams.rope_yarn_log_mul;
|
|
static auto get_mscale = [](float scale, float mscale) {
|
|
return scale <= 1.0f ? 1.0f : (0.1f * mscale * logf(scale) + 1.0f);
|
|
};
|
|
hparams.yarn_attn_factor = get_mscale(factor, mscale) / get_mscale(factor, mscale_all_dims);
|
|
}
|
|
|
|
switch (hparams.n_layer) {
|
|
case 26: model.type = e_model::MODEL_3B; break;
|
|
case 34: model.type = e_model::MODEL_8B; break;
|
|
case 40: model.type = e_model::MODEL_14B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_MIMO2:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
|
|
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
|
|
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa);
|
|
ml.get_key(LLM_KV_ATTENTION_VALUE_SCALE, hparams.f_attn_v_scale, false);
|
|
//TODO
|
|
//hparams.swa_type = LLAMA_SWA_TYPE_STANDARD; // which is the same as OpenAI
|
|
ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer);
|
|
|
|
switch (hparams.n_layer) {
|
|
case 48: model.type = e_model::MODEL_310B_A15B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
|
|
} break;
|
|
case LLM_ARCH_SEED_OSS:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
switch (hparams.n_layer) {
|
|
case 64: model.type = e_model::MODEL_36B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_STEP35:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
//hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
|
|
// MoE + SWA parameters
|
|
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
|
|
ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
|
|
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
|
|
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
|
|
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
|
|
// Step35 uses sigmoid gating by default (if not set in GGUF)
|
|
if (hparams.expert_gating_func == LLM_EXPERT_GATING_FUNC_TYPE_NONE) {
|
|
hparams.expert_gating_func = LLM_EXPERT_GATING_FUNC_SIGMOID;
|
|
}
|
|
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
|
|
bool have_rfb_train_swa = ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
|
|
ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer);
|
|
if (!ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_COUNT_PER_LAYER, hparams.rope_dim_per_layer, hparams.n_layer, false)) {
|
|
for (int i = 0; i < hparams.n_layer; ++i) {
|
|
hparams.rope_dim_per_layer[i] = hparams.swa_layers[i] ? hparams.n_rot : hparams.n_rot/2;
|
|
}
|
|
}
|
|
// The following two parameters: one of the two versions must be present in the GGUF
|
|
if (!ml.get_key_or_arr(LLM_KV_SWIGLU_LIMITS, hparams.swiglu_limits, hparams.n_layer, false)) {
|
|
ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_EXP, hparams.swiglu_limits, hparams.n_layer, true);
|
|
}
|
|
if (!ml.get_key_or_arr(LLM_KV_SWIGLU_LIMITS_SHARED, hparams.swiglu_limits_shared, hparams.n_layer, false)) {
|
|
ml.get_key_or_arr(LLM_KV_SWIGLU_CLAMP_SHEXP, hparams.swiglu_limits_shared, hparams.n_layer, true);
|
|
}
|
|
// Optional: Step35-only gating for applying rope scaling (HF: yarn_only_types).
|
|
// Default is 3 (apply on all layers) if the key is absent.
|
|
//ml.get_key(format("%s.rope.scaling.apply_mask", ml.get_arch_name().c_str()),
|
|
// hparams.rope_scaling_apply_mask, false);
|
|
//hparams.has_rope_freq_base_per_layer = ml.get_key_or_arr(
|
|
// format("%s.rope.freq_base_per_layer", ml.get_arch_name().c_str()),
|
|
// hparams.rope_freq_base_per_layer, hparams.n_layer, false);
|
|
ml.get_key(format("%s.rope.scaling.apply_mask", ml.get_arch_name().c_str()),
|
|
hparams.rope_scaling_apply_mask, false);
|
|
hparams.has_rope_freq_base_per_layer = ml.get_key_or_arr(LLM_KV_ROPE_FREQ_BASE_PER_LAYER,
|
|
hparams.rope_freq_base_per_layer, hparams.n_layer, false);
|
|
GGML_ASSERT(hparams.has_rope_freq_base_per_layer || have_rfb_train_swa);
|
|
} break;
|
|
case LLM_ARCH_LAGUNA:
|
|
{
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
|
|
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
|
|
ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
|
|
hparams.expert_gating_func = LLM_EXPERT_GATING_FUNC_TYPE_NONE;
|
|
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
|
|
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
|
|
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
|
|
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false);
|
|
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared, false);
|
|
// Older Laguna GGUFs encode one shared expert through the shared FFN length.
|
|
if (hparams.n_expert_shared == 0 && hparams.n_ff_shexp > 0) {
|
|
hparams.n_expert_shared = 1;
|
|
}
|
|
if (hparams.expert_gating_func == LLM_EXPERT_GATING_FUNC_TYPE_NONE) {
|
|
hparams.expert_gating_func = LLM_EXPERT_GATING_FUNC_SIGMOID;
|
|
}
|
|
|
|
ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
|
|
ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
|
|
hparams.rope_freq_scale_train_swa = 1.0f;
|
|
if (!ml.get_key_or_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.swa_layers, hparams.n_layer, false)) {
|
|
// Laguna XS.2 alternates full-attention and SWA layers via per-layer head counts.
|
|
const uint32_t n_head_full = hparams.n_head(0);
|
|
for (uint32_t i = 0; i < hparams.n_layer; ++i) {
|
|
hparams.swa_layers[i] = hparams.n_head(i) != n_head_full;
|
|
}
|
|
}
|
|
|
|
const bool found_rope_dim = ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false);
|
|
const bool found_rope_dim_swa = ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT_SWA, hparams.n_rot_swa, false);
|
|
|
|
// Laguna GGUFs store the number of scalar Q/K dimensions that ggml_rope_ext
|
|
// rotates. Correct files carry those values explicitly. Some early public
|
|
// XS.2 GGUFs omitted both keys, so fall back to the HF XS.2 layout only for
|
|
// missing metadata: full-attention layers rotate half the head, SWA layers
|
|
// rotate the full head. Explicit but wrong halved metadata still needs repair.
|
|
if (hparams.n_swa > 0) {
|
|
if (!found_rope_dim) {
|
|
hparams.n_rot = hparams.n_embd_head_k_full / 2;
|
|
}
|
|
if (!found_rope_dim_swa) {
|
|
hparams.n_rot_swa = hparams.n_embd_head_k_swa;
|
|
}
|
|
}
|
|
|
|
ml.get_key(LLM_KV_ROPE_SCALING_YARN_EXT_FACTOR, hparams.yarn_ext_factor, false);
|
|
ml.get_key(LLM_KV_ROPE_SCALING_YARN_ATTN_FACTOR, hparams.yarn_attn_factor, false);
|
|
ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_FAST, hparams.yarn_beta_fast, false);
|
|
ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_SLOW, hparams.yarn_beta_slow, false);
|
|
|
|
if (!ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_COUNT_PER_LAYER, hparams.rope_dim_per_layer, hparams.n_layer, false)) {
|
|
for (uint32_t i = 0; i < hparams.n_layer; ++i) {
|
|
hparams.rope_dim_per_layer[i] = hparams.swa_layers[i] ? hparams.n_rot_swa : hparams.n_rot;
|
|
}
|
|
}
|
|
|
|
switch (hparams.n_layer) {
|
|
case 40: model.type = e_model::MODEL_33B_A3B; break;
|
|
default: model.type = e_model::MODEL_UNKNOWN;
|
|
}
|
|
} break;
|
|
case LLM_ARCH_GLM_DSA:
|
|
{
|
|
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
|
|
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
|
// GLM-DSA lightning-indexer k_norm is a (non-RMS) LayerNorm built via LLM_NORM,
|
|
// which uses hparams.f_norm_eps in ggml_norm(). The GGUF only carries the RMS eps,
|
|
// so f_norm_eps stays 0 and CPU ggml_norm aborts (GGML_ASSERT(eps > 0)). On CUDA
|
|
// the kernel does not assert (eps=0 is numerically tolerable), which is why the
|
|
// CPU attention path was never exercised. Mirror the RMS eps so the indexer
|
|
// LayerNorm gets a valid epsilon on all backends. (HF hardcodes 1e-6 for this
|
|
// LayerNorm, but 1e-6 vs 1e-5 is within 4-chunk PPL noise here, so keep the mirror.)
|
|
if (hparams.f_norm_eps <= 0.0f) hparams.f_norm_eps = hparams.f_norm_rms_eps;
|
|
ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
|
|
|
|
// MoE parameters
|
|
ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert);
|
|
ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used);
|
|
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
|
|
ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead, false);
|
|
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
|
|
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
|
|
|
|
// deepseek MLA parameters
|
|
ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
|
|
ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);
|
|
//ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA, hparams.n_embd_head_k_mla_impl, false);
|
|
//ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla_impl, false);
|
|
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
|
|
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
|
|
|
|
// DSA parameters
|
|
ml.get_key(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, hparams.indexer_n_head);
|
|
ml.get_key(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, hparams.indexer_head_size);
|
|
ml.get_key(LLM_KV_ATTENTION_INDEXER_TOP_K, hparams.indexer_top_k);
|
|
|
|
// GLM-5.2 IndexShare: per-layer full/shared indexer map. "full" layers compute their own
|
|
// top-k; "shared" layers reuse the previous full layer's selection (transformers
|
|
// modeling_glm_moe_dsa.py: shared layer indexer=None, topk_indices=prev_topk_indices).
|
|
// Derived from GLM-5.2's config indexer_types rule (full iff il<=1 or il%4==2), verified
|
|
// to reproduce the config's full set {0,1,2,6,10,...} exactly. Existing GGUFs carry no
|
|
// per-layer metadata, so the derivation is the source of truth; a future metadata key can
|
|
// override this for GLM-DSA variants with a different pattern.
|
|
for (uint32_t il = 0; il < hparams.n_layer; ++il) {
|
|
hparams.indexer_is_full[il] = (il <= 1) || (il % 4 == 2);
|
|
}
|
|
|
|
// Expert gating function (GLM-4.5 uses sigmoid)
|
|
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
|
|
if (hparams.expert_gating_func == LLM_EXPERT_GATING_FUNC_TYPE_NONE) {
|
|
hparams.expert_gating_func = LLM_EXPERT_GATING_FUNC_SIGMOID;
|
|
}
|
|
|
|
// NextN/MTP parameters
|
|
ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.nextn_predict_layers, false);
|
|
|
|
if (model.mtp) {
|
|
hparams.n_layer_kv_from_start = hparams.n_layer;
|
|
}
|
|
else {
|
|
hparams.n_layer_kv_from_start = hparams.n_layer - hparams.nextn_predict_layers;
|
|
}
|
|
|
|
switch (hparams.n_layer) {
|
|
case 79: model.type = MODEL_744B_A40B; break;
|
|
default: model.type = MODEL_UNKNOWN;
|
|
}
|
|
if (hparams.n_head_kv() == 1) {
|
|
int n_nead_kv = hparams.n_gqa();
|
|
if (n_nead_kv%4 != 0 || hparams.n_embd_head_k_full != 576 || hparams.n_embd_head_v_full != 512 ||
|
|
hparams.n_rot != 64) {
|
|
LLAMA_LOG_ERROR("==========================================================================\n");
|
|
LLAMA_LOG_ERROR("Detected incompatible DeepSeek model without a known way to fix it.\n");
|
|
LLAMA_LOG_ERROR("Sorry, uknown model => cannot fix it => bailing out\n");
|
|
LLAMA_LOG_ERROR("==========================================================================\n");
|
|
GGML_ABORT("Fatal error");
|
|
}
|
|
LLAMA_LOG_INFO("================= Adjusted mainline llama.cpp MLA tensors to ik_llama.cpp\n");
|
|
for (auto& item : hparams.n_head_kv_arr) item = n_nead_kv;
|
|
hparams.n_embd_head_k_full = 192;
|
|
hparams.n_embd_head_v_full = 128;
|
|
ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA, hparams.n_embd_head_k_full);
|
|
ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_full);
|
|
}
|
|
} break;
|
|
default: (void)0;
|
|
}
|
|
|
|
model.ftype = ml.ftype;
|
|
|
|
if (hparams.f_max_alibi_bias > 0.0f) {
|
|
hparams.use_alibi = true;
|
|
}
|
|
|
|
hparams.rope_type = llama_rope_type(&model);
|
|
}
|