mirror of
https://github.com/ikawrakow/ik_llama.cpp.git
synced 2026-02-07 15:00:11 +00:00
764eefd1bc
* WIP: enable and clean up warnings in src * All warnings handled --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2457 lines
132 KiB
C++
2457 lines
132 KiB
C++
#include "llama-model-loader.h"
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#include "llama-impl.h"
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#include "llama-mmap.h"
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#include "llama-model.h"
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#include "ggml.h"
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#include <set>
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#include <map>
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#include <array>
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#include <future>
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#include <regex>
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#define LLAMA_API_INTERNAL
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struct create_tensors_helper : public create_tensors_helper_interface {
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create_tensors_helper(llama_model_loader & ml, llama_model & model);
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~create_tensors_helper() = default;
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//virtual std::map<ggml_backend_buffer_type_t, int> & buft_layer_count_map() override {
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// return buft_layer_count;
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//}
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virtual std::map<ggml_backend_buffer_type_t, ggml_context *> & get_ctx_map() override {
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return ctx_map;
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}
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virtual size_t get_ctx_size() const override { return ctx_size; }
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bool create_tensors() override;
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bool create_llama_tensors(const LLM_TN & tn);
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bool create_deci_tensors(const LLM_TN & tn);
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bool create_llama4_tensors(const LLM_TN & tn);
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bool create_grok_tensors(const LLM_TN & tn);
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bool create_dbrx_tensors(const LLM_TN & tn);
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bool create_baichuan_tensors(const LLM_TN & tn, bool with_ffn_norm = true);
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bool create_falcon_tensors(const LLM_TN & tn);
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bool create_starcoder_tensors(const LLM_TN & tn);
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bool create_bert_tensors(const LLM_TN & tn);
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bool create_jina_bert2_tensors(const LLM_TN & tn);
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bool create_bloom_tensors(const LLM_TN & tn);
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bool create_mpt_tensors(const LLM_TN & tn);
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bool create_stablelm_tensors(const LLM_TN & tn);
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bool create_qwen_tensors(const LLM_TN & tn);
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bool create_qwen2_tensors(const LLM_TN & tn);
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bool create_qwen2_moe_tensors(const LLM_TN & tn);
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bool create_qwen3_tensors(const LLM_TN & tn);
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bool create_qwen3_moe_tensors(const LLM_TN & tn);
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bool create_phi2_tensors(const LLM_TN & tn);
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bool create_phi3_tensors(const LLM_TN & tn);
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bool create_gpt2_tensors(const LLM_TN & tn);
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bool create_codeshell_tensors(const LLM_TN & tn);
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bool create_orion_tensors(const LLM_TN & tn);
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bool create_internlm_tensors(const LLM_TN & tn);
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bool create_gemma_tensors(const LLM_TN & tn, int version);
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bool create_starcoder2_tensors(const LLM_TN & tn);
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bool create_mamba_tensors(const LLM_TN & tn);
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bool create_xverse_tensors(const LLM_TN & tn);
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bool create_command_r_tensors(const LLM_TN & tn);
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bool create_olmo_tensors(const LLM_TN & tn);
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bool create_openelm_tensors(const LLM_TN & tn);
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bool create_gptneox_tensors(const LLM_TN & tn);
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bool create_arctix_tensors(const LLM_TN & tn);
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bool create_deepseek2_tensors(const LLM_TN & tn);
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bool create_glm4_tensors(const LLM_TN & tn);
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bool create_glm4_moe_tensors(const LLM_TN & tn);
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bool create_bitnet_tensors(const LLM_TN & tn);
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bool create_bitnet2_tensors(const LLM_TN & tn);
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bool create_t5_tensors(const LLM_TN & tn);
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bool create_tsencoder_tensors(const LLM_TN & tn);
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bool create_jais_tensors(const LLM_TN & tn);
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bool create_chatglm_tensors(const LLM_TN & tn);
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bool create_cohere2_tensors(const LLM_TN & tn);
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bool create_dots1_tensors(const LLM_TN & tn);
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bool create_ernie45_tensors(const LLM_TN & tn);
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bool create_hunyuan_tensors(const LLM_TN & tn);
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bool create_openai_moe_tensors(const LLM_TN & tn);
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llama_model_loader & ml;
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llama_model & model;
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ggml_tensor * create_tensor(ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, int flags = 0);
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void create_default_embd_output(const LLM_TN & tn, int n_embd, int n_vocab, bool norm_bias);
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void create_embd_output(const LLM_TN & tn, int n_embd, int n_vocab, bool has_norm = true);
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void create_std_attn(int i, const LLM_TN & tn, llama_layer & layer, int n_embd, int n_embd_gqa, ggml_context * ctx_split);
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void create_std_ffn(int i, const LLM_TN & tn, llama_layer & layer, int n_ff, int n_embd, ggml_context * ctx_split);
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inline ggml_context * ctx_for_layer(int i) const;
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inline ggml_context * ctx_for_layer_split(int i) const;
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std::map<ggml_backend_buffer_type_t, int> buft_layer_count;
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std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
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size_t ctx_size;
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ggml_context * ctx_input;
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ggml_context * ctx_output;
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ggml_context * ctx_output_split;
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};
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create_tensors_helper::create_tensors_helper(llama_model_loader & _ml, llama_model & _model) : ml(_ml), model(_model) {
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const int n_layer = model.hparams.n_layer;
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buft_layer_count[model.buft_input.buft]++;
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buft_layer_count[model.buft_input.buft_matrix]++;
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buft_layer_count[model.buft_output.buft]++;
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buft_layer_count[model.buft_output.buft_matrix]++;
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for (int i = 0; i < n_layer; ++i) {
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buft_layer_count[model.buft_layer[i].buft]++;
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buft_layer_count[model.buft_layer[i].buft_matrix]++;
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}
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ctx_size = ggml_tensor_overhead()*(ml.n_tensors + 1); // +1 for models where tok_embd is duplicated as output
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ctx_size += ggml_tensor_overhead()*n_layer*3; // for moe merged tensors
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for (auto & it : buft_layer_count) {
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struct ggml_init_params params = {
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/*.mem_size =*/ ctx_size,
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/*.mem_buffer =*/ NULL,
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/*.no_alloc =*/ true,
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};
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ggml_context * ctx = ggml_init(params);
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if (!ctx) {
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throw std::runtime_error(format("failed to create context"));
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}
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ctx_map[it.first] = ctx;
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model.ctxs.push_back(ctx);
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}
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}
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ggml_tensor * create_tensors_helper::create_tensor(ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, int flags) {
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if (ml.tensor_buft_overrides) {
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for (const auto * overrides = ml.tensor_buft_overrides; overrides->pattern != nullptr; ++overrides) {
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std::regex pattern(overrides->pattern);
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if (std::regex_search(name, pattern)) {
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LLAMA_LOG_INFO("Tensor %s buffer type overriden to %s\n", name.c_str(), ggml_backend_buft_name(overrides->buft));
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if (auto it = ctx_map.find(overrides->buft); it != ctx_map.end()) ctx = it->second;
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else {
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ggml_init_params params = {
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/*.mem_size =*/ ctx_size,
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/*.mem_buffer =*/ NULL,
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/*.no_alloc =*/ true,
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};
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ggml_context * ctx = ggml_init(params);
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if (!ctx) {
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throw std::runtime_error(format("failed to create ggml context"));
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}
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ctx_map[overrides->buft] = ctx;
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model.ctxs.emplace_back(ctx);
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}
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break;
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}
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}
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}
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return ml.create_tensor(ctx, name, ne, flags);
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}
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ggml_context * create_tensors_helper::ctx_for_layer(int i) const {
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return ctx_map.at(model.buft_layer[i].buft);
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}
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ggml_context * create_tensors_helper::ctx_for_layer_split(int i) const {
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return ctx_map.at(model.buft_layer[i].buft_matrix);
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}
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#define LOADING_PRELUDE \
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[[maybe_unused]] const auto & hparams = model.hparams; \
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[[maybe_unused]] const int64_t n_layer = hparams.n_layer; \
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[[maybe_unused]] const int64_t n_head = hparams.n_head(); \
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[[maybe_unused]] const int64_t n_head_kv = hparams.n_head_kv(); \
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[[maybe_unused]] const int64_t n_embd = hparams.n_embd; \
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[[maybe_unused]] const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); \
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[[maybe_unused]] const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(); \
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[[maybe_unused]] const int64_t n_embd_head_k = hparams.n_embd_head_k; \
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[[maybe_unused]] const int64_t n_embd_head_v = hparams.n_embd_head_v; \
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[[maybe_unused]] const int64_t n_ff = hparams.n_ff(); \
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[[maybe_unused]] const int64_t n_embd_gqa = n_embd_v_gqa; \
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[[maybe_unused]] const int64_t n_vocab = hparams.n_vocab; \
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[[maybe_unused]] const int64_t n_vocab_type = hparams.n_vocab_type; \
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[[maybe_unused]] const int64_t n_rot = hparams.n_rot; \
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[[maybe_unused]] const int64_t n_expert = hparams.n_expert; \
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[[maybe_unused]] const int64_t n_expert_used = hparams.n_expert_used; \
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[[maybe_unused]] const int64_t n_ctx_train = hparams.n_ctx_train; \
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if (n_expert > 0 && hparams.n_expert_used == 0) { \
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throw std::runtime_error("model has expert layers but no expert layers are used"); \
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} \
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ctx_input = ctx_map.at(model.buft_input.buft); \
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ctx_output = ctx_map.at(model.buft_output.buft); \
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ctx_output_split = ctx_map.at(model.buft_output.buft_matrix); \
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model.layers.resize(n_layer);\
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bool use_mmap_buffer = true;
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void create_tensors_helper::create_embd_output(const LLM_TN & tn, int n_embd, int n_vocab, bool has_norm) {
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model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
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if (has_norm) {
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model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
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}
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model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
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// if output is NULL, init from the input tok embed
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if (model.output == NULL) {
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model.output = create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
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}
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}
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void create_tensors_helper::create_std_attn(int i, const LLM_TN & tn, llama_layer & layer, int n_embd, int n_embd_gqa, ggml_context * ctx_split) {
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layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
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layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
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layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
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layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
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}
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void create_tensors_helper::create_std_ffn(int i, const LLM_TN & tn, llama_layer & layer, int n_ff, int n_embd, ggml_context * ctx_split) {
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layer.ffn_gate = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
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layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
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layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
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}
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bool create_tensors_helper::create_llama_tensors(const LLM_TN & tn) {
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LOADING_PRELUDE
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create_embd_output(tn, n_embd, n_vocab);
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for (int i = 0; i < n_layer; ++i) {
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ggml_context * ctx_layer = ctx_for_layer(i);
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ggml_context * ctx_split = ctx_for_layer_split(i);
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auto & layer = model.layers[i];
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layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
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layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head});
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layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
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layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
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layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
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// optional bias tensors
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layer.bq = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
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layer.bk = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
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layer.bv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
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layer.bo = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
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layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
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layer.rope_freqs = create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FREQS, "weight"), {n_embd/n_head/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
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if (n_expert == 0) {
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create_std_ffn(i, tn, layer, n_ff, n_embd, ctx_split);
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// optional MLP bias
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layer.ffn_gate_b = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
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layer.ffn_down_b = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
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layer.ffn_up_b = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
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} else {
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layer.ffn_gate_inp = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
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layer.ffn_gate_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
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if (layer.ffn_gate_exps) {
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layer.ffn_down_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert});
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layer.ffn_up_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert});
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} else {
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// merge split expert into a single tensor for compatibility with older models
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// requires disabling mmap
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use_mmap_buffer = false;
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ggml_type type_gate = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, 0).c_str())->type;
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ggml_type type_down = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, 0).c_str())->type;
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ggml_type type_up = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, 0).c_str())->type;
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layer.ffn_gate_exps = ggml_new_tensor_3d(ctx_split, type_gate, n_embd, n_ff, n_expert);
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layer.ffn_down_exps = ggml_new_tensor_3d(ctx_split, type_down, n_ff, n_embd, n_expert);
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layer.ffn_up_exps = ggml_new_tensor_3d(ctx_split, type_up, n_embd, n_ff, n_expert);
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ggml_set_name(layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i).c_str());
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ggml_set_name(layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i).c_str());
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ggml_set_name(layer.ffn_up_exps, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i).c_str());
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for (uint32_t x = 0; x < n_expert; ++x) {
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// the individual experts are loaded into a view of the merged tensor
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ml.create_tensor_as_view(ctx_split, layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), { n_embd, n_ff }, layer.ffn_gate_exps->nb[2]*x);
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ml.create_tensor_as_view(ctx_split, layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd }, layer.ffn_down_exps->nb[2]*x);
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ml.create_tensor_as_view(ctx_split, layer.ffn_up_exps, tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, x), { n_embd, n_ff }, layer.ffn_up_exps->nb[2]*x);
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}
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}
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}
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}
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return use_mmap_buffer;
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}
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bool create_tensors_helper::create_deci_tensors(const LLM_TN & tn) {
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LOADING_PRELUDE
|
|
|
|
create_embd_output(tn, n_embd, n_vocab);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(i);
|
|
const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(i);
|
|
const int64_t n_embd_gqa = hparams.n_embd_v_gqa(i);
|
|
const int64_t n_ff = hparams.n_ff(i);
|
|
const int64_t n_head = hparams.n_head(i);
|
|
const int64_t n_head_kv = hparams.n_head_kv(i);
|
|
|
|
if (n_head_kv == 0 && n_head > 0) {
|
|
// linear attention for DeciLMCausalModel
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.wo = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
}
|
|
else if (n_head_kv > 0) {
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.wq = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head});
|
|
layer.wk = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
|
|
layer.wv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
|
|
layer.wo = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
|
|
}
|
|
|
|
// optional bias tensors
|
|
|
|
|
|
layer.bq = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.bk = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.bv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.bo = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
if (n_ff > 0) {
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
}
|
|
|
|
if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {
|
|
layer.rope_long = create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight"), { n_rot/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
|
|
layer.rope_short = create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight"), { n_rot/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
|
|
}
|
|
else {
|
|
layer.rope_freqs = create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FREQS, "weight"), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
|
|
}
|
|
|
|
if (n_ff > 0) {
|
|
create_std_ffn(i, tn, layer, n_ff, n_embd, ctx_split);
|
|
}
|
|
|
|
// optional MLP bias
|
|
layer.ffn_gate_b = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.ffn_down_b = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.ffn_up_b = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_llama4_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
create_embd_output(tn, n_embd, n_vocab);
|
|
|
|
GGML_ASSERT(hparams.n_moe_layer_step > 0 && "Llama 4 requires n_moe_layer_step > 0");
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
bool is_moe_layer = (i + 1) % hparams.n_moe_layer_step == 0;
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
|
|
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
|
|
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.rope_freqs = create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2},
|
|
llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
|
|
|
|
if (is_moe_layer) {
|
|
int n_ff_exp = hparams.n_ff_exp;
|
|
|
|
layer.ffn_gate_inp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
|
|
layer.ffn_gate_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
|
|
layer.ffn_down_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
|
|
layer.ffn_up_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, 0);
|
|
|
|
// Shared expert
|
|
const int64_t n_ff_shexp = n_ff_exp;
|
|
layer.ffn_gate_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp}, 0);
|
|
layer.ffn_down_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd }, 0);
|
|
layer.ffn_up_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp}, 0);
|
|
} else {
|
|
create_std_ffn(i, tn, layer, n_ff, n_embd, ctx_split);
|
|
}
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_grok_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
if (n_expert == 0) {
|
|
throw std::runtime_error("Grok model cannot have zero experts");
|
|
}
|
|
|
|
create_embd_output(tn, n_embd, n_vocab);
|
|
|
|
const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff/* / n_expert_used*/; // grok-1 n_ff_exp == n_ff
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
layer.attn_out_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.ffn_gate = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
layer.ffn_gate_inp = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
|
|
layer.ffn_gate_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff_exp, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
if (layer.ffn_gate_exps) {
|
|
layer.ffn_down_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert});
|
|
layer.ffn_up_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert });
|
|
} else {
|
|
// merge split expert into a single tensor for compatibility with older models
|
|
// requires disabling mmap
|
|
use_mmap_buffer = false;
|
|
|
|
ggml_type type_gate = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, 0).c_str())->type;
|
|
ggml_type type_down = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, 0).c_str())->type;
|
|
ggml_type type_up = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, 0).c_str())->type;
|
|
|
|
layer.ffn_gate_exps = ggml_new_tensor_3d(ctx_split, type_gate, n_embd, n_ff, n_expert);
|
|
layer.ffn_down_exps = ggml_new_tensor_3d(ctx_split, type_down, n_ff, n_embd, n_expert);
|
|
layer.ffn_up_exps = ggml_new_tensor_3d(ctx_split, type_up, n_embd, n_ff, n_expert);
|
|
|
|
ggml_set_name(layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i).c_str());
|
|
ggml_set_name(layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i).c_str());
|
|
ggml_set_name(layer.ffn_up_exps, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i).c_str());
|
|
|
|
for (uint32_t x = 0; x < n_expert; ++x) {
|
|
// the individual experts are loaded into a view of the merged tensor
|
|
ml.create_tensor_as_view(ctx_split, layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), { n_embd, n_ff }, layer.ffn_gate_exps->nb[2]*x);
|
|
ml.create_tensor_as_view(ctx_split, layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd }, layer.ffn_down_exps->nb[2]*x);
|
|
ml.create_tensor_as_view(ctx_split, layer.ffn_up_exps, tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, x), { n_embd, n_ff }, layer.ffn_up_exps->nb[2]*x);
|
|
}
|
|
}
|
|
|
|
layer.ffn_post_norm = create_tensor(ctx_layer,tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), { n_embd }, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
if (!layer.ffn_post_norm) {
|
|
layer.ffn_post_norm = create_tensor(ctx_layer,tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), { n_embd }, 0);
|
|
}
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_dbrx_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
if (n_expert == 0) {
|
|
throw std::runtime_error("DBRX model cannot have zero experts");
|
|
}
|
|
|
|
create_default_embd_output(tn, n_embd, n_vocab, false);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.wqkv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
layer.attn_out_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
|
|
|
|
layer.ffn_gate_inp = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
|
|
layer.ffn_gate_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert});
|
|
layer.ffn_down_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert});
|
|
layer.ffn_up_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert});
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_baichuan_tensors(const LLM_TN & tn, bool with_ffn_norm) {
|
|
LOADING_PRELUDE
|
|
create_default_embd_output(tn, n_embd, n_vocab, false);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
if (with_ffn_norm) {
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
}
|
|
|
|
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
|
|
create_std_ffn(i, tn, layer, n_ff, n_embd, ctx_split);
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_falcon_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output_norm_b = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
|
|
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
if (!model.output) {
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.attn_norm_2 = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.attn_norm_2_b = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
layer.wqkv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
|
|
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_starcoder_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
model.pos_embd = create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output_norm_b = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
if (!model.output) {
|
|
// needs to be on GPU
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
|
|
}
|
|
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.wqkv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
|
|
layer.bqkv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa});
|
|
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
layer.bo = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
layer.ffn_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
|
|
layer.ffn_down_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
|
|
|
|
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_up_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_bert_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
model.type_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type});
|
|
|
|
if (model.arch == LLM_ARCH_BERT) {
|
|
model.pos_embd = create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train});
|
|
}
|
|
|
|
model.tok_norm = create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd});
|
|
model.tok_norm_b = create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd});
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
if (model.arch == LLM_ARCH_BERT) {
|
|
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.bq = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
|
|
|
|
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.bk = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
|
|
|
|
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.bv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
|
|
} else {
|
|
layer.wqkv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
|
|
}
|
|
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
layer.attn_out_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
|
|
layer.attn_out_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd});
|
|
|
|
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
|
|
|
|
if (model.arch == LLM_ARCH_BERT) {
|
|
layer.bo = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
|
|
layer.ffn_up_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
|
|
layer.ffn_down_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
|
|
} else {
|
|
layer.ffn_gate = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
|
|
}
|
|
|
|
layer.layer_out_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
|
|
layer.layer_out_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd});
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_jina_bert2_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // word_embeddings
|
|
model.type_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}); // token_type_embeddings
|
|
|
|
model.tok_norm = create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); // LayerNorm
|
|
model.tok_norm_b = create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}); //LayerNorm bias
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i]; // JinaBertLayer
|
|
|
|
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.bq = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
|
|
|
|
layer.attn_q_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.attn_q_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.bk = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
|
|
|
|
layer.attn_k_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.attn_k_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.bv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
|
|
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); //output_dens
|
|
layer.bo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); //output_dens
|
|
|
|
layer.attn_out_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); //output_norm
|
|
layer.attn_out_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd});
|
|
|
|
layer.attn_norm_2 = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.attn_norm_2_b = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_gate = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
|
|
|
|
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
|
|
layer.ffn_down_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
|
|
|
|
layer.layer_out_norm = create_tensor(ctx_split, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
|
|
layer.layer_out_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd});
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_bloom_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
model.tok_norm = create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd});
|
|
model.tok_norm_b = create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output_norm_b = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.wqkv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
|
|
layer.bqkv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa});
|
|
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
layer.bo = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
layer.ffn_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
|
|
layer.ffn_down_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
|
|
|
|
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_up_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_mpt_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
model.pos_embd = create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
// output
|
|
{
|
|
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output_norm_b = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
if (!model.output) {
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
layer.wqkv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
|
|
layer.bqkv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
layer.bo = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
layer.ffn_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
|
|
layer.ffn_down_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_up_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
layer.attn_q_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.attn_q_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
layer.attn_k_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.attn_k_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
// AWQ ScaleActivation layer
|
|
layer.ffn_act = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_stablelm_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
|
|
create_default_embd_output(tn, n_embd, n_vocab, true);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
// optional bias tensors, present in Stable LM 2 1.6B
|
|
layer.bq = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.bk = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.bv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
// optional q and k layernorms, present in StableLM 2 12B
|
|
layer.attn_q_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.attn_k_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
// optional FFN norm, not present in StableLM 2 12B which uses parallel residual
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.ffn_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
create_std_ffn(i, tn, layer, n_ff, n_embd, ctx_split);
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_qwen_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
create_embd_output(tn, n_embd, n_vocab);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.wqkv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd*3});
|
|
layer.bqkv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd*3});
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
|
|
create_std_ffn(i, tn, layer, n_ff/2, n_embd, ctx_split);
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_qwen2_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
model.output_b = create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "bias"), {n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
// if output is NULL, init from the input tok embed
|
|
if (model.output == NULL) {
|
|
model.output = create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
// optional bias tensors
|
|
layer.bq = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
|
|
layer.bk = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
|
|
layer.bv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
|
|
create_std_ffn(i, tn, layer, n_ff, n_embd, ctx_split);
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_qwen2_moe_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
|
|
create_embd_output(tn, n_embd, n_vocab);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
// optional bias tensors
|
|
layer.bq = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
|
|
layer.bk = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
|
|
layer.bv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.ffn_gate_inp = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
|
|
|
|
GGML_ASSERT(n_expert > 0);
|
|
GGML_ASSERT(n_expert_used > 0);
|
|
|
|
// MoE branch
|
|
const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
|
|
|
|
layer.ffn_gate_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert});
|
|
layer.ffn_down_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert});
|
|
layer.ffn_up_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert});
|
|
|
|
// Shared expert branch
|
|
const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;
|
|
|
|
layer.ffn_gate_inp_shexp = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), {n_embd});
|
|
layer.ffn_gate_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp});
|
|
layer.ffn_down_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd});
|
|
layer.ffn_up_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp});
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_qwen3_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
// if output is NULL, init from the input tok embed
|
|
if (model.output == NULL) {
|
|
model.output = create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head});
|
|
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
|
|
|
|
layer.attn_k_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k});
|
|
layer.attn_q_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k});
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
create_std_ffn(i, tn, layer, n_ff, n_embd, ctx_split);
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_qwen3_moe_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
create_embd_output(tn, n_embd, n_vocab);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head});
|
|
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
|
|
|
|
layer.attn_k_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k});
|
|
layer.attn_q_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k});
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.ffn_gate_inp = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
|
|
|
|
GGML_ASSERT(n_expert > 0);
|
|
GGML_ASSERT(n_expert_used > 0);
|
|
|
|
// MoE branch
|
|
const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
|
|
|
|
layer.ffn_gate_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert});
|
|
layer.ffn_down_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert});
|
|
layer.ffn_up_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert});
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_phi2_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output_norm_b = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
|
|
model.output_b = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT, "bias"), {n_vocab});
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.wqkv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.bqkv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
if (layer.wqkv == nullptr) {
|
|
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.bq = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
|
|
|
|
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.bk = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
|
|
|
|
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.bv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
|
|
}
|
|
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
layer.bo = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
|
|
|
|
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
|
|
layer.ffn_down_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
|
|
|
|
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_up_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_phi3_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
const int64_t n_embd_head = n_embd / n_head;
|
|
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd });
|
|
|
|
layer.wqkv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd });
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd });
|
|
|
|
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd });
|
|
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, 2 * n_ff });
|
|
|
|
layer.rope_long = create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight"), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
|
|
layer.rope_short = create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight"), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_gpt2_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
model.pos_embd = create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output_norm_b = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.wqkv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
|
|
layer.bqkv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa});
|
|
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
layer.bo = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
layer.ffn_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
|
|
layer.ffn_down_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
|
|
|
|
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_up_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_codeshell_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
create_default_embd_output(tn, n_embd, n_vocab, true);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.wqkv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
|
|
layer.bqkv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa});
|
|
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
layer.bo = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
layer.ffn_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
|
|
layer.ffn_down_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
|
|
|
|
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_up_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
void create_tensors_helper::create_default_embd_output(const LLM_TN & tn, int n_embd, int n_vocab, bool norm_bias) {
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
if (norm_bias) {
|
|
model.output_norm_b = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
|
|
}
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
|
|
}
|
|
|
|
bool create_tensors_helper::create_orion_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
create_default_embd_output(tn, n_embd, n_vocab, true);
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
layer.ffn_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
|
|
|
|
create_std_ffn(i, tn, layer, n_ff, n_embd, ctx_split);
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_internlm_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
create_default_embd_output(tn, n_embd, n_vocab, false);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
// layer.wqkv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
|
|
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
create_std_ffn(i, tn, layer, n_ff, n_embd, ctx_split);
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_gemma_tensors(const LLM_TN & tn, int version) {
|
|
LOADING_PRELUDE
|
|
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output = create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head});
|
|
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
|
|
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
|
|
if (version > 1) {
|
|
layer.attn_post_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd});
|
|
}
|
|
if (version > 2) {
|
|
layer.attn_k_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k});
|
|
layer.attn_q_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k});
|
|
}
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
create_std_ffn(i, tn, layer, n_ff, n_embd, ctx_split);
|
|
if (version > 1) {
|
|
layer.ffn_post_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd});
|
|
}
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_starcoder2_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output_norm_b = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
|
|
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
// if output is NULL, init from the input tok embed
|
|
if (model.output == NULL) {
|
|
model.output = create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
|
|
}
|
|
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
// optional bias tensors
|
|
layer.bq = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
|
|
layer.bk = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
|
|
layer.bv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
|
|
layer.bo = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
layer.ffn_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
|
|
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
|
|
// optional bias tensors
|
|
layer.ffn_down_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
|
|
layer.ffn_up_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP , "bias", i), { n_ff});
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_mamba_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
|
|
const int64_t d_conv = hparams.ssm_d_conv;
|
|
const int64_t d_inner = hparams.ssm_d_inner;
|
|
const int64_t d_state = hparams.ssm_d_state;
|
|
const int64_t dt_rank = hparams.ssm_dt_rank;
|
|
|
|
// only an expansion factor of 2 is supported for now
|
|
GGML_ASSERT(2 * n_embd == d_inner);
|
|
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
// if output is NULL, init from the input tok embed, duplicated to allow offloading
|
|
if (model.output == NULL) {
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
// norm
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.ssm_in = create_tensor(ctx_split, tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2*d_inner});
|
|
|
|
layer.ssm_conv1d = create_tensor(ctx_split, tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner});
|
|
layer.ssm_conv1d_b = create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner});
|
|
|
|
layer.ssm_x = create_tensor(ctx_split, tn(LLM_TENSOR_SSM_X, "weight", i), {d_inner, dt_rank + 2*d_state});
|
|
|
|
layer.ssm_dt = create_tensor(ctx_split, tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_rank, d_inner});
|
|
layer.ssm_dt_b = create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner});
|
|
|
|
// no "weight" suffix for these
|
|
layer.ssm_a = create_tensor(ctx_split, tn(LLM_TENSOR_SSM_A, i), {d_state, d_inner});
|
|
layer.ssm_d = create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_D, i), {d_inner});
|
|
|
|
// out_proj
|
|
layer.ssm_out = create_tensor(ctx_split, tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd});
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_xverse_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
|
|
create_embd_output(tn, n_embd, n_vocab);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
create_std_ffn(i, tn, layer, n_ff, n_embd, ctx_split);
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_command_r_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
// init output from the input tok embed
|
|
model.output = create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
if (n_layer >= 64){
|
|
layer.attn_q_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head});
|
|
layer.attn_k_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv});
|
|
}
|
|
|
|
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
create_std_ffn(i, tn, layer, n_ff, n_embd, ctx_split);
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_olmo_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
|
|
create_embd_output(tn, n_embd, n_vocab, false);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
create_std_attn(i, tn, layer, n_embd, n_embd_gqa, ctx_split);
|
|
create_std_ffn (i, tn, layer, n_ff, n_embd, ctx_split);
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_openelm_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
// init output from the input tok embed
|
|
model.output = create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
const int64_t n_head = hparams.n_head(i);
|
|
const int64_t n_head_qkv = 2*hparams.n_head_kv(i) + n_head;
|
|
const int64_t n_ff = hparams.n_ff(i);
|
|
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.wqkv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_head_qkv*n_embd_head_k});
|
|
layer.attn_q_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k});
|
|
layer.attn_k_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k});
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head*n_embd_head_k, n_embd});
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
create_std_ffn(i, tn, layer, n_ff, n_embd, ctx_split);
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_gptneox_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output_norm_b = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.wqkv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
|
|
layer.bqkv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa});
|
|
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
layer.bo = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
layer.ffn_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
|
|
layer.ffn_down_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
|
|
|
|
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_up_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_arctix_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
|
|
create_embd_output(tn, n_embd, n_vocab);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
create_std_attn(i, tn, layer, n_embd, n_embd_gqa, ctx_split);
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
|
|
create_std_ffn (i, tn, layer, n_embd, n_embd, ctx_split);
|
|
|
|
layer.ffn_gate_inp = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
|
|
layer.ffn_norm_exps = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM_EXPS, "weight", i), {n_embd});
|
|
layer.ffn_gate_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, false);
|
|
layer.ffn_down_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert});
|
|
layer.ffn_up_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert});
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_deepseek2_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
|
|
const bool is_lite = (hparams.n_layer == 27);
|
|
|
|
const int64_t n_embd_head_qk_rope = hparams.n_rot;
|
|
const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
|
|
|
|
const int64_t q_lora_rank = hparams.n_lora_q;
|
|
const int64_t kv_lora_rank = hparams.n_lora_kv;
|
|
|
|
const int64_t n_ff_exp = hparams.n_ff_exp;
|
|
const int64_t n_expert_shared = hparams.n_expert_shared;
|
|
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
if (!is_lite) {
|
|
layer.attn_q_a_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank});
|
|
}
|
|
|
|
layer.attn_kv_a_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank});
|
|
|
|
if (!is_lite) {
|
|
layer.wq_a = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank});
|
|
layer.wq_b = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k});
|
|
} else {
|
|
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa});
|
|
}
|
|
|
|
layer.wkv_a_mqa = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i),{n_embd, kv_lora_rank + (n_embd_head_qk_rope)});
|
|
layer.wkv_b = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_B, "weight", i),
|
|
{kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
if (!layer.wkv_b) {
|
|
// Incompatible mainline model. Let's see if we can still load it
|
|
layer.wk_b = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_qk_nope, kv_lora_rank, n_head}, 0);
|
|
layer.wv_b = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v, n_head}, 0);
|
|
|
|
} else {
|
|
layer.wk_b = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_qk_nope, n_head * kv_lora_rank}, 1);
|
|
layer.wv_b = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_head * n_embd_head_v}, 1);
|
|
}
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_head * ( n_embd_head_v), n_embd});
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
|
|
if (i < (int) hparams.n_layer_dense_lead) {
|
|
layer.ffn_gate = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
|
|
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
} else {
|
|
layer.ffn_gate_inp = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
|
|
layer.ffn_exp_probs_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 1);
|
|
|
|
GGML_ASSERT(n_expert > 0);
|
|
GGML_ASSERT(n_expert_used > 0);
|
|
|
|
// MoE branch
|
|
layer.ffn_gate_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert});
|
|
layer.ffn_down_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert});
|
|
layer.ffn_up_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert});
|
|
|
|
// Shared expert branch
|
|
layer.ffn_gate_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared});
|
|
layer.ffn_down_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd});
|
|
layer.ffn_up_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared});
|
|
}
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_glm4_moe_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
|
|
const int64_t n_expert_shared = hparams.n_expert_shared;
|
|
|
|
GGML_ASSERT(hparams.n_expert > 0 && "n_expert must be > 0 for GLM4_MOE MoE layers");
|
|
GGML_ASSERT(hparams.n_expert_used > 0 && "n_expert_used must be > 0 for GLM4_MOE MoE layers");
|
|
|
|
create_embd_output(tn, n_embd, n_vocab);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
int flags = 0;
|
|
if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
|
|
// skip all tensors in the NextN layers
|
|
flags |= llama_model_loader::TENSOR_SKIP;
|
|
}
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
|
|
|
|
// GLM-style attention with bias terms
|
|
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head }, flags);
|
|
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_k_gqa }, flags);
|
|
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_v_gqa }, flags);
|
|
layer.bq = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), { n_embd_head_k * n_head }, flags);
|
|
layer.bk = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), { n_embd_k_gqa }, flags);
|
|
layer.bv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), { n_embd_v_gqa }, flags);
|
|
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, flags);
|
|
|
|
// K/Q norm tensors (optional for GLM-4.5 355B variant)
|
|
layer.attn_q_norm = create_tensor(ctx_layer,
|
|
tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), { n_embd_head_k }, llama_model_loader::TENSOR_NOT_REQUIRED | flags);
|
|
layer.attn_k_norm = create_tensor(ctx_layer,
|
|
tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), { n_embd_head_k }, llama_model_loader::TENSOR_NOT_REQUIRED | flags);
|
|
|
|
layer.attn_post_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), { n_embd }, flags);
|
|
|
|
// Check if this layer uses MoE or dense FFN based on n_layer_dense_lead
|
|
// GLM 4.5 uses hybrid architecture: layer 0 is dense, layers 1+ are MoE
|
|
const bool use_moe = (static_cast<uint32_t>(i) >= hparams.n_layer_dense_lead);
|
|
|
|
if (use_moe) {
|
|
// MoE layers
|
|
layer.ffn_gate_inp = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, flags);
|
|
// gate bias
|
|
layer.ffn_exp_probs_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), { n_expert }, flags);
|
|
|
|
// MoE branch
|
|
const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
|
|
|
|
layer.ffn_gate_exps = create_tensor(ctx_split,
|
|
tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, flags);
|
|
layer.ffn_down_exps = create_tensor(ctx_split,
|
|
tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, flags);
|
|
layer.ffn_up_exps = create_tensor(ctx_split,
|
|
tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, flags);
|
|
|
|
// Shared expert
|
|
if (n_expert_shared > 0) {
|
|
const int64_t n_ff_shexp = n_ff_exp * n_expert_shared;
|
|
layer.ffn_gate_shexp = create_tensor(ctx_split,
|
|
tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
|
|
layer.ffn_down_shexp = create_tensor(ctx_split,
|
|
tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_shexp, n_embd }, flags);
|
|
layer.ffn_up_shexp = create_tensor(ctx_split,
|
|
tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp }, flags);
|
|
}
|
|
} else {
|
|
// Dense layers (first k layers) - GLM uses separate gate/up projections
|
|
layer.ffn_gate = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), { n_embd, n_ff }, flags);
|
|
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, flags);
|
|
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, n_ff }, flags);
|
|
}
|
|
// --- NextN / MTP tensors (preserved but unused), on the final layer ---
|
|
if (hparams.nextn_predict_layers > 0 && static_cast<uint32_t>(i) >= n_layer - hparams.nextn_predict_layers) {
|
|
const int final_layer = n_layer - 1;
|
|
// EH_PROJ: [2*embd, embd]
|
|
layer.nextn.eh_proj = create_tensor(ctx_for_layer(final_layer),
|
|
tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", final_layer),
|
|
{ 2*n_embd, n_embd },
|
|
flags);
|
|
// EMBED_TOKENS: [embd, vocab]
|
|
layer.nextn.embed_tokens = create_tensor(ctx_for_layer(final_layer),
|
|
tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", final_layer),
|
|
{ n_embd, n_vocab },
|
|
flags | llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
// ENORM, HNORM: [embd]
|
|
layer.nextn.enorm = create_tensor(ctx_for_layer(final_layer),
|
|
tn(LLM_TENSOR_NEXTN_ENORM, "weight", final_layer),
|
|
{ n_embd },
|
|
flags);
|
|
layer.nextn.hnorm = create_tensor(ctx_for_layer(final_layer),
|
|
tn(LLM_TENSOR_NEXTN_HNORM, "weight", final_layer),
|
|
{ n_embd },
|
|
flags);
|
|
// SHARED_HEAD_HEAD: [embd, vocab]
|
|
layer.nextn.shared_head_head = create_tensor(ctx_for_layer(final_layer),
|
|
tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", final_layer),
|
|
{ n_embd, n_vocab },
|
|
flags | llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
// SHARED_HEAD_NORM: [embd]
|
|
layer.nextn.shared_head_norm = create_tensor(ctx_for_layer(final_layer),
|
|
tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", final_layer),
|
|
{ n_embd },
|
|
flags | llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
}
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_bitnet_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output = create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_sub_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_SUB_NORM, "weight", i), {n_embd});
|
|
|
|
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
|
|
layer.wq_scale = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wk_scale = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
|
|
layer.wv_scale = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
layer.wo_scale = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
layer.ffn_sub_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_SUB_NORM, "weight", i), {n_ff});
|
|
|
|
layer.ffn_gate = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_gate_scale = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
|
|
layer.ffn_down_scale = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_up_scale = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "scale", i), {1}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_bitnet2_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
// if output is NULL, init from the input tok embed
|
|
if (model.output == NULL) {
|
|
model.output = create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.attn_sub_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_SUB_NORM, "weight", i), {n_embd});
|
|
layer.ffn_sub_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_SUB_NORM, "weight", i), {n_ff});
|
|
|
|
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head});
|
|
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
|
|
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
|
|
|
|
// optional bias tensors
|
|
layer.bq = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.bk = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.bv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.bo = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.rope_freqs = create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FREQS, "weight"), {n_rot/2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
|
|
|
|
if (n_expert == 0) {
|
|
layer.ffn_gate = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
|
|
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
|
|
// optional MLP bias
|
|
layer.ffn_gate_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.ffn_down_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.ffn_up_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
} else {
|
|
layer.ffn_gate_inp = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
|
|
|
|
layer.ffn_gate_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
if (layer.ffn_gate_exps) {
|
|
layer.ffn_down_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert});
|
|
layer.ffn_up_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert});
|
|
} else {
|
|
// merge split expert into a single tensor for compatibility with older models
|
|
// requires disabling mmap
|
|
use_mmap_buffer = false;
|
|
|
|
ggml_type type_gate = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, 0).c_str())->type;
|
|
ggml_type type_down = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, 0).c_str())->type;
|
|
ggml_type type_up = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, 0).c_str())->type;
|
|
|
|
layer.ffn_gate_exps = ggml_new_tensor_3d(ctx_split, type_gate, n_embd, n_ff, n_expert);
|
|
layer.ffn_down_exps = ggml_new_tensor_3d(ctx_split, type_down, n_ff, n_embd, n_expert);
|
|
layer.ffn_up_exps = ggml_new_tensor_3d(ctx_split, type_up, n_embd, n_ff, n_expert);
|
|
|
|
ggml_set_name(layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i).c_str());
|
|
ggml_set_name(layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i).c_str());
|
|
ggml_set_name(layer.ffn_up_exps, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i).c_str());
|
|
|
|
for (uint32_t x = 0; x < n_expert; ++x) {
|
|
// the individual experts are loaded into a view of the merged tensor
|
|
ml.create_tensor_as_view(ctx_split, layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), { n_embd, n_ff }, layer.ffn_gate_exps->nb[2]*x);
|
|
ml.create_tensor_as_view(ctx_split, layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd }, layer.ffn_down_exps->nb[2]*x);
|
|
ml.create_tensor_as_view(ctx_split, layer.ffn_up_exps, tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, x), { n_embd, n_ff }, layer.ffn_up_exps->nb[2]*x);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_t5_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts;
|
|
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm_enc = create_tensor(ctx_output, tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_DEC_OUTPUT_NORM, "weight"), {n_embd});
|
|
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
// if output is NULL, init from the input tok embed
|
|
if (model.output == NULL) {
|
|
model.output = create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm_enc = create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_rel_b_enc = create_tensor(ctx_input, tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
layer.wq_enc = create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa});
|
|
layer.wk_enc = create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
|
|
layer.wv_enc = create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
|
|
layer.wo_enc = create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd});
|
|
|
|
layer.ffn_norm_enc = create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd});
|
|
layer.ffn_gate_enc = create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.ffn_down_enc = create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), { n_ff, n_embd});
|
|
layer.ffn_up_enc = create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_rel_b = create_tensor(ctx_input, tn(LLM_TENSOR_DEC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa});
|
|
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
|
|
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd});
|
|
|
|
layer.attn_norm_cross = create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_CROSS_ATTN_NORM, "weight", i), {n_embd});
|
|
// this tensor seems to be unused in HF transformers implementation
|
|
layer.attn_rel_b_cross = create_tensor(ctx_input, tn(LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
layer.wq_cross = create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa});
|
|
layer.wk_cross = create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
|
|
layer.wv_cross = create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
|
|
layer.wo_cross = create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd});
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_FFN_NORM, "weight", i), {n_embd});
|
|
layer.ffn_gate = create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_FFN_GATE, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_DEC_FFN_DOWN, "weight", i), { n_ff, n_embd});
|
|
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_DEC_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_tsencoder_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts;
|
|
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm_enc = create_tensor(ctx_output, tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
// if output is NULL, init from the input tok embed
|
|
if (model.output == NULL) {
|
|
model.output = create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
|
|
}
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm_enc = create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_rel_b_enc = create_tensor(ctx_input, tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
layer.wq_enc = create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa});
|
|
layer.wk_enc = create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
|
|
layer.wv_enc = create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
|
|
layer.wo_enc = create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd});
|
|
|
|
layer.ffn_norm_enc = create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd});
|
|
layer.ffn_gate_enc = create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.ffn_down_enc = create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), { n_ff, n_embd});
|
|
layer.ffn_up_enc = create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_jais_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// Output
|
|
{
|
|
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output_norm_b = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
layer.attn_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.wqkv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
|
|
layer.bqkv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa});
|
|
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
layer.bo = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
layer.ffn_norm_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
|
|
|
|
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
|
|
layer.ffn_down_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
|
|
|
|
layer.ffn_gate = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_gate_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff});
|
|
|
|
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
|
layer.ffn_up_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_chatglm_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
|
|
|
// output
|
|
{
|
|
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.wqkv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + (hparams.n_embd_head_k << 2)});
|
|
layer.bqkv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + (hparams.n_embd_head_k << 2)});
|
|
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
|
|
|
|
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff * 2});
|
|
|
|
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_cohere2_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
|
|
|
|
// output
|
|
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
|
|
// init output from the input tok embed
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab },
|
|
llama_model_loader::TENSOR_DUPLICATED);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = model.layers[i];
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
|
|
|
|
create_std_attn(i, tn, layer, n_embd, n_embd_gqa, ctx_split);
|
|
create_std_ffn (i, tn, layer, n_ff, n_embd, ctx_split);
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_glm4_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
// output
|
|
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
// if output is NULL, init from the input tok embed
|
|
if (model.output == NULL) {
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
|
|
}
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.wqkv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.bqkv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
if (layer.wqkv == nullptr) {
|
|
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
|
|
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
|
|
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
|
|
layer.bq = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.bk = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.bv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
}
|
|
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
|
|
|
|
layer.attn_post_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff * 2}, 0);
|
|
|
|
layer.ffn_post_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_dots1_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
|
|
const int64_t n_ff_exp = hparams.n_ff_exp;
|
|
const int64_t n_expert_shared = hparams.n_expert_shared;
|
|
model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto & layer = model.layers[i];
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
|
|
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
|
|
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
|
|
|
|
layer.attn_k_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
|
|
layer.attn_q_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
|
if (i < (int) hparams.n_layer_dense_lead) {
|
|
layer.ffn_gate = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
|
layer.ffn_down = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
|
layer.ffn_up = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
|
} else {
|
|
layer.ffn_gate_inp = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
|
|
layer.ffn_exp_probs_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
if (n_expert == 0) {
|
|
throw std::runtime_error("n_expert must be > 0");
|
|
}
|
|
if (n_expert_used == 0) {
|
|
throw std::runtime_error("n_expert_used must be > 0");
|
|
}
|
|
// MoE branch
|
|
layer.ffn_gate_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0);
|
|
layer.ffn_down_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
|
|
layer.ffn_up_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0);
|
|
// Shared expert branch
|
|
layer.ffn_gate_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
|
|
layer.ffn_down_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, 0);
|
|
layer.ffn_up_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, 0);
|
|
}
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_ernie45_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
|
|
create_embd_output(tn, n_embd, n_vocab);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
auto& layer = model.layers[i];
|
|
ggml_context* ctx_layer = ctx_for_layer(i);
|
|
ggml_context* ctx_split = ctx_for_layer_split(i);
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
|
|
|
|
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head }, 0);
|
|
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_gqa }, 0);
|
|
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_gqa }, 0);
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
|
|
|
|
// optional bias tensors
|
|
layer.bq = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), { n_embd }, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.bk = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), { n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.bv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), { n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.bo = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), { n_embd }, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
|
|
layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
|
|
|
|
if (model.arch == LLM_ARCH_ERNIE4_5_MOE && static_cast<uint32_t>(i) >= hparams.n_layer_dense_lead) { // MoE layers
|
|
int n_ff_exp = hparams.n_ff_exp;
|
|
|
|
layer.ffn_gate_inp = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert }, 0);
|
|
layer.ffn_exp_probs_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), { n_expert }, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.ffn_gate_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, llama_model_loader::TENSOR_NOT_REQUIRED);
|
|
layer.ffn_down_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff_exp, n_embd, n_expert }, 0);
|
|
layer.ffn_up_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert }, 0);
|
|
|
|
// Shared expert (if present)
|
|
if (hparams.n_ff_shexp > 0) {
|
|
layer.ffn_gate_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, hparams.n_ff_shexp }, 0);
|
|
layer.ffn_down_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { hparams.n_ff_shexp, n_embd }, 0);
|
|
layer.ffn_up_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, hparams.n_ff_shexp }, 0);
|
|
}
|
|
}
|
|
else { // Dense layers
|
|
create_std_ffn(i, tn, layer, n_ff, n_embd, ctx_split);
|
|
}
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_hunyuan_tensors(const LLM_TN & tn) {
|
|
LOADING_PRELUDE
|
|
|
|
create_embd_output(tn, n_embd, n_vocab);
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for (int i = 0; i < n_layer; ++i) {
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ggml_context * ctx_layer = ctx_for_layer(i);
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ggml_context * ctx_split = ctx_for_layer_split(i);
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auto & layer = model.layers[i];
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layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
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|
|
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layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
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layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa}, 0);
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layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa}, 0);
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layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
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|
|
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layer.attn_k_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
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layer.attn_q_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
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|
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layer.ffn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
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|
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layer.ffn_gate_inp = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
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layer.ffn_gate_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0);
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layer.ffn_down_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}, 0);
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layer.ffn_up_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}, 0);
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|
|
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layer.ffn_gate_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
|
|
layer.ffn_up_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);
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|
layer.ffn_down_shexp = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, 0);
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|
}
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|
return use_mmap_buffer;
|
|
}
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|
|
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bool create_tensors_helper::create_openai_moe_tensors(const LLM_TN & tn) {
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LOADING_PRELUDE
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|
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const int64_t n_ff_exp = hparams.n_ff_exp;
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|
|
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model.tok_embd = create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
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|
|
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// output
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|
model.output_norm = create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
|
model.output = create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
|
|
|
for (int i = 0; i < n_layer; ++i) {
|
|
ggml_context * ctx_layer = ctx_for_layer(i);
|
|
ggml_context * ctx_split = ctx_for_layer_split(i);
|
|
auto & layer = model.layers[i];
|
|
|
|
layer.attn_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
|
layer.attn_post_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
|
|
|
|
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_head * n_rot}, 0);
|
|
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_head_kv * n_rot}, 0);
|
|
layer.wv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_head_kv * n_rot}, 0);
|
|
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head * n_rot, n_embd}, 0);
|
|
|
|
layer.attn_sinks = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_SINKS, "weight", i), {n_head}, 0);
|
|
|
|
layer.ffn_gate_inp = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), { n_embd, n_expert}, 0);
|
|
layer.ffn_gate_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0);
|
|
layer.ffn_down_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, 0);
|
|
layer.ffn_up_exps = create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, 0);
|
|
|
|
// bias
|
|
layer.bq = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_head * n_rot}, 0);
|
|
layer.bk = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_head_kv * n_rot}, 0);
|
|
layer.bv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_head_kv * n_rot}, 0);
|
|
layer.bo = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, 0);
|
|
|
|
layer.ffn_gate_inp_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "bias", i), {n_expert}, 0);
|
|
layer.ffn_gate_exps_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_EXPS, "bias", i), {n_ff_exp, n_expert}, 0);
|
|
layer.ffn_down_exps_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN_EXPS, "bias", i), { n_embd, n_expert}, 0);
|
|
layer.ffn_up_exps_b = create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP_EXPS, "bias", i), {n_ff_exp, n_expert}, 0);
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
bool create_tensors_helper::create_tensors() {
|
|
const auto tn = LLM_TN(model.arch);
|
|
bool use_mmap_buffer = true;
|
|
switch (model.arch) {
|
|
case LLM_ARCH_LLAMA:
|
|
case LLM_ARCH_REFACT:
|
|
case LLM_ARCH_MINICPM:
|
|
case LLM_ARCH_GRANITE:
|
|
case LLM_ARCH_GRANITE_MOE:
|
|
use_mmap_buffer = create_llama_tensors(tn); break;
|
|
case LLM_ARCH_DECI:
|
|
use_mmap_buffer = create_deci_tensors(tn); break;
|
|
case LLM_ARCH_LLAMA4:
|
|
use_mmap_buffer = create_llama4_tensors(tn); break;
|
|
case LLM_ARCH_GROK:
|
|
use_mmap_buffer = create_grok_tensors(tn); break;
|
|
case LLM_ARCH_DBRX:
|
|
use_mmap_buffer = create_dbrx_tensors(tn); break;
|
|
case LLM_ARCH_BAICHUAN:
|
|
use_mmap_buffer = create_baichuan_tensors(tn); break;
|
|
case LLM_ARCH_FALCON:
|
|
use_mmap_buffer = create_falcon_tensors(tn); break;
|
|
case LLM_ARCH_STARCODER:
|
|
use_mmap_buffer = create_starcoder_tensors(tn); break;
|
|
case LLM_ARCH_BERT:
|
|
case LLM_ARCH_NOMIC_BERT:
|
|
use_mmap_buffer = create_bert_tensors(tn); break;
|
|
case LLM_ARCH_JINA_BERT_V2:
|
|
use_mmap_buffer = create_jina_bert2_tensors(tn); break;
|
|
case LLM_ARCH_BLOOM:
|
|
use_mmap_buffer = create_bloom_tensors(tn); break;
|
|
case LLM_ARCH_MPT:
|
|
use_mmap_buffer = create_mpt_tensors(tn); break;
|
|
case LLM_ARCH_STABLELM:
|
|
use_mmap_buffer = create_stablelm_tensors(tn); break;
|
|
case LLM_ARCH_QWEN:
|
|
use_mmap_buffer = create_qwen_tensors(tn); break;
|
|
case LLM_ARCH_QWEN2:
|
|
case LLM_ARCH_QWEN2VL:
|
|
use_mmap_buffer = create_qwen2_tensors(tn); break;
|
|
case LLM_ARCH_QWEN2MOE:
|
|
use_mmap_buffer = create_qwen2_moe_tensors(tn); break;
|
|
case LLM_ARCH_QWEN3:
|
|
use_mmap_buffer = create_qwen3_tensors(tn); break;
|
|
case LLM_ARCH_QWEN3MOE:
|
|
use_mmap_buffer = create_qwen3_moe_tensors(tn); break;
|
|
case LLM_ARCH_PHI2:
|
|
use_mmap_buffer = create_phi2_tensors(tn); break;
|
|
case LLM_ARCH_PHI3:
|
|
use_mmap_buffer = create_phi3_tensors(tn); break;
|
|
case LLM_ARCH_PLAMO:
|
|
use_mmap_buffer = create_baichuan_tensors(tn, false); break;
|
|
case LLM_ARCH_GPT2:
|
|
use_mmap_buffer = create_gpt2_tensors(tn); break;
|
|
case LLM_ARCH_CODESHELL:
|
|
use_mmap_buffer = create_codeshell_tensors(tn); break;
|
|
case LLM_ARCH_ORION:
|
|
use_mmap_buffer = create_orion_tensors(tn); break;
|
|
case LLM_ARCH_INTERNLM2:
|
|
use_mmap_buffer = create_internlm_tensors(tn); break;
|
|
case LLM_ARCH_GEMMA:
|
|
use_mmap_buffer = create_gemma_tensors(tn, 1); break;
|
|
case LLM_ARCH_GEMMA2:
|
|
use_mmap_buffer = create_gemma_tensors(tn, 2); break;
|
|
case LLM_ARCH_GEMMA3:
|
|
use_mmap_buffer = create_gemma_tensors(tn, 3); break;
|
|
case LLM_ARCH_STARCODER2:
|
|
use_mmap_buffer = create_starcoder2_tensors(tn); break;
|
|
case LLM_ARCH_MAMBA:
|
|
use_mmap_buffer = create_mamba_tensors(tn); break;
|
|
case LLM_ARCH_XVERSE:
|
|
use_mmap_buffer = create_xverse_tensors(tn); break;
|
|
case LLM_ARCH_COMMAND_R:
|
|
use_mmap_buffer = create_command_r_tensors(tn); break;
|
|
case LLM_ARCH_OLMO: // adapted from LLM_ARCH_LLAMA with norm params removed
|
|
use_mmap_buffer = create_olmo_tensors(tn); break;
|
|
case LLM_ARCH_OPENELM:
|
|
use_mmap_buffer = create_openelm_tensors(tn); break;
|
|
case LLM_ARCH_GPTNEOX:
|
|
use_mmap_buffer = create_gptneox_tensors(tn); break;
|
|
case LLM_ARCH_ARCTIC:
|
|
use_mmap_buffer = create_arctix_tensors(tn); break;
|
|
case LLM_ARCH_DEEPSEEK2:
|
|
use_mmap_buffer = create_deepseek2_tensors(tn); break;
|
|
case LLM_ARCH_GLM4_MOE:
|
|
use_mmap_buffer = create_glm4_moe_tensors(tn); break;
|
|
case LLM_ARCH_BITNET:
|
|
use_mmap_buffer = create_bitnet_tensors(tn); break;
|
|
case LLM_ARCH_BITNET_B158:
|
|
case LLM_ARCH_BITNET_25:
|
|
use_mmap_buffer = create_bitnet2_tensors(tn); break;
|
|
case LLM_ARCH_T5:
|
|
use_mmap_buffer = create_t5_tensors(tn); break;
|
|
case LLM_ARCH_T5ENCODER:
|
|
use_mmap_buffer = create_tsencoder_tensors(tn); break;
|
|
case LLM_ARCH_JAIS:
|
|
use_mmap_buffer = create_jais_tensors(tn); break;
|
|
case LLM_ARCH_CHATGLM:
|
|
use_mmap_buffer = create_chatglm_tensors(tn); break;
|
|
case LLM_ARCH_COHERE2:
|
|
use_mmap_buffer = create_cohere2_tensors(tn); break;
|
|
case LLM_ARCH_GLM4:
|
|
use_mmap_buffer = create_glm4_tensors(tn); break;
|
|
case LLM_ARCH_DOTS1:
|
|
use_mmap_buffer = create_dots1_tensors(tn); break;
|
|
case LLM_ARCH_ERNIE4_5:
|
|
case LLM_ARCH_ERNIE4_5_MOE:
|
|
use_mmap_buffer = create_ernie45_tensors(tn); break;
|
|
case LLM_ARCH_HUNYUAN_MOE:
|
|
use_mmap_buffer = create_hunyuan_tensors(tn); break;
|
|
case LLM_ARCH_OPENAI_MOE:
|
|
use_mmap_buffer = create_openai_moe_tensors(tn); break;
|
|
default:
|
|
throw std::runtime_error("unknown architecture");
|
|
}
|
|
return use_mmap_buffer;
|
|
}
|
|
|
|
std::unique_ptr<create_tensors_helper_interface> create_tensors_helper_interface::instance(llama_model_loader & ml, llama_model & model) {
|
|
return std::make_unique<create_tensors_helper>(ml, model);
|
|
}
|