mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2026-09-03 03:21:16 +02:00
Xuan-Son Nguyen
5a32f7b66e
* text: conversion * init impl * address review comments * fix rope * move to a new llama_kv_cache_dsa_iswa
481 lines
24 KiB
C++
481 lines
24 KiB
C++
#include "models.h"
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#include "llama-kv-cache.h"
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#include "llama-kv-cache-dsa.h"
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// note: code adapted from deepseek32.cpp (DSA indexer + absorbed MLA) and step35.cpp (head-wise output gate)
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void llama_model_dots3note::load_arch_hparams(llama_model_loader & ml) {
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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hparams.f_norm_eps = 1e-6; // eps for the indexer k_norm layer norm
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// TODO: use MTP layer
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ml.get_key(LLM_KV_NEXTN_PREDICT_LAYERS, hparams.n_layer_nextn, false);
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GGML_ASSERT(hparams.n_layer_nextn < hparams.n_layer_all && "n_layer_nextn must be < n_layer_all");
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// MoE parameters
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ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
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ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
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ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
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ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
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ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
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ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func);
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// MLA parameters of the full-attention layers
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ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
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ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);
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ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA, hparams.n_embd_head_k_mla_impl);
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ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla_impl);
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// MLA parameters of the sliding-window layers
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ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK_SWA, hparams.n_lora_kv_swa);
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ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH_MLA_SWA, hparams.n_embd_head_k_mla_swa);
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ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA_SWA, hparams.n_embd_head_v_mla_swa);
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hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
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ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
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ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa);
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ml.get_arr(LLM_KV_ATTENTION_SLIDING_WINDOW_PATTERN, hparams.is_swa_impl);
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// DSA parameters
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ml.get_key(LLM_KV_ATTENTION_INDEXER_HEAD_COUNT, hparams.indexer_n_head);
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ml.get_key(LLM_KV_ATTENTION_INDEXER_KEY_LENGTH, hparams.indexer_head_size);
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ml.get_key(LLM_KV_ATTENTION_INDEXER_TOP_K, hparams.indexer_top_k);
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ml.get_arr(LLM_KV_ATTENTION_INDEXER_TYPES, hparams.is_indexer_full_impl);
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switch (hparams.n_layer()) {
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case 46: type = LLM_TYPE_288B_A19B; break;
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default: type = LLM_TYPE_UNKNOWN;
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}
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}
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void llama_model_dots3note::load_arch_tensors(llama_model_loader & ml) {
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LLAMA_LOAD_LOCALS;
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GGML_UNUSED(ml);
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if (!hparams.is_mla()) {
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throw std::runtime_error("DOTS3NOTE architecture requires MLA");
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}
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const int64_t n_embd_head_qk_rope = hparams.n_rot();
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const int64_t q_lora_rank = hparams.n_lora_q;
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const int64_t n_ff_exp = hparams.n_ff_exp;
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const int64_t n_expert_shared = hparams.n_expert_shared;
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tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
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output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
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output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
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if (!output) {
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output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
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}
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for (int i = 0; i < n_layer_all; ++i) {
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auto & layer = layers[i];
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const bool is_mtp = i >= n_layer;
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// the NextN/MTP block uses the sliding-attention geometry
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const bool is_swa = is_mtp || hparams.is_swa(i);
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// MTP tensors are preserved in the GGUF but there is no MTP graph yet
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const int flags = is_mtp ? TENSOR_SKIP | TENSOR_NOT_REQUIRED : 0;
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const int64_t n_head_l = hparams.n_head(i);
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const int64_t kv_lora_rank = is_swa ? hparams.n_lora_kv_swa : hparams.n_lora_kv;
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const int64_t n_embd_head_k_mla = is_swa ? hparams.n_embd_head_k_mla_swa : hparams.n_embd_head_k_mla();
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const int64_t n_embd_head_v_mla = is_swa ? hparams.n_embd_head_v_mla_swa : hparams.n_embd_head_v_mla();
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const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;
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layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, flags);
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layer.attn_q_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}, flags);
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layer.attn_kv_a_norm = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}, flags);
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// norm applied on the shared rope key before rope
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layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_qk_rope}, flags);
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layer.wq_a = create_tensor(tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}, flags);
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layer.wq_b = create_tensor(tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head_l * n_embd_head_k_mla}, flags);
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layer.wkv_a_mqa = create_tensor(tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + n_embd_head_qk_rope}, flags);
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layer.wk_b = create_tensor(tn(LLM_TENSOR_ATTN_K_B, "weight", i), {n_embd_head_qk_nope, kv_lora_rank, n_head_l}, flags);
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layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V_B, "weight", i), {kv_lora_rank, n_embd_head_v_mla, n_head_l}, flags);
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layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head_l * n_embd_head_v_mla, n_embd}, flags);
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// head-wise sigmoid output gate
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layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_head_l}, flags);
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layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, flags);
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// DSA indexer
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if (!is_mtp && hparams.is_indexer_full(i)) {
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layer.indexer_k_norm = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM, "weight", i), {hparams.indexer_head_size}, flags);
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layer.indexer_k_norm_b = create_tensor(tn(LLM_TENSOR_INDEXER_K_NORM, "bias", i), {hparams.indexer_head_size}, flags);
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layer.indexer_proj = create_tensor(tn(LLM_TENSOR_INDEXER_PROJ, "weight", i), {n_embd, hparams.indexer_n_head}, flags);
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layer.indexer_attn_k = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_K, "weight", i), {n_embd, hparams.indexer_head_size}, flags);
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layer.indexer_attn_q_b = create_tensor(tn(LLM_TENSOR_INDEXER_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.indexer_n_head * hparams.indexer_head_size}, flags);
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}
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if (is_mtp || i < (int) hparams.n_layer_dense_lead) {
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layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, flags);
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layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, flags);
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layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, flags);
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} else {
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if (n_expert == 0 || n_expert_used == 0) {
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throw std::runtime_error("n_expert and n_expert_used must be > 0");
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}
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layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, flags);
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layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, flags);
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layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, flags);
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layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}, flags);
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layer.ffn_up_exps = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}, flags);
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layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);
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layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd}, flags);
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layer.ffn_up_shexp = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared}, flags);
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}
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if (is_mtp) {
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layer.nextn.eh_proj = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ, "weight", i), { 2 * n_embd, n_embd }, flags);
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layer.nextn.enorm = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM, "weight", i), { n_embd }, flags);
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layer.nextn.hnorm = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM, "weight", i), { n_embd }, flags);
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layer.nextn.embed_tokens = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS, "weight", i), { n_embd, n_vocab }, flags);
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layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd }, flags);
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}
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}
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}
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std::unique_ptr<llm_graph_context> llama_model_dots3note::build_arch_graph(const llm_graph_params & params) const {
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return std::make_unique<graph>(*this, params);
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}
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llama_model_dots3note::graph::graph(const llama_model & model, const llm_graph_params & params) :
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llm_graph_context(params) {
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GGML_ASSERT(hparams.is_mla());
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const int64_t n_embd_head_qk_rope = hparams.n_rot();
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const int64_t n_indexer_head = hparams.indexer_n_head;
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const int64_t n_embd_indexer_head = hparams.indexer_head_size;
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const uint32_t n_indexer_top_k = hparams.indexer_top_k;
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// the indexer head layout is [rope | nope]
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GGML_ASSERT(hparams.n_rot() <= n_embd_indexer_head);
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ggml_tensor * cur;
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ggml_tensor * inpL;
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inpL = build_inp_embd(model.tok_embd);
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ggml_tensor * inp_pos = build_inp_pos();
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llm_graph_input_attn_k_dsa_iswa * inp_attn = build_attn_inp_k_dsa_iswa();
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ggml_tensor * inp_out_ids = build_inp_out_ids();
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for (int il = 0; il < n_layer; ++il) {
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ggml_tensor * inpSA = inpL;
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const bool is_swa = hparams.is_swa(il);
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const int64_t n_head_l = hparams.n_head(il);
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const int64_t kv_lora_rank = is_swa ? hparams.n_lora_kv_swa : hparams.n_lora_kv;
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const int64_t n_embd_head_k_mla = is_swa ? hparams.n_embd_head_k_mla_swa : hparams.n_embd_head_k_mla();
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const int64_t n_embd_head_v_mla = is_swa ? hparams.n_embd_head_v_mla_swa : hparams.n_embd_head_v_mla();
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const int64_t n_embd_head_qk_nope = n_embd_head_k_mla - n_embd_head_qk_rope;
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const float kq_scale = 1.0f/sqrtf(float(n_embd_head_k_mla));
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const float freq_base_l = model.get_rope_freq_base(cparams, il);
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// norm
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cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
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cb(cur, "attn_norm", il);
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// self_attention
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{
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ggml_tensor * attn_inp = cur;
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ggml_tensor * qr = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur);
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cb(qr, "qr", il);
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qr = build_norm(qr, model.layers[il].attn_q_a_norm, nullptr, LLM_NORM_RMS, il);
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cb(qr, "qr", il);
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ggml_tensor * top_k = nullptr;
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// lightning indexer (full-attention layers only)
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if (!is_swa) {
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ggml_tensor * indexer_q = ggml_mul_mat(ctx0, model.layers[il].indexer_attn_q_b, qr);
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cb(indexer_q, "indexer_q", il);
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// {n_embd_indexer_head, n_indexer_head, n_tokens}
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indexer_q = ggml_reshape_3d(ctx0, indexer_q, n_embd_indexer_head, n_indexer_head, n_tokens);
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indexer_q = ggml_rope_ext(ctx0, indexer_q, inp_pos, nullptr, n_rot,
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LLAMA_ROPE_TYPE_NEOX, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow);
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cb(indexer_q, "indexer_q", il);
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ggml_tensor * indexer_k = ggml_mul_mat(ctx0, model.layers[il].indexer_attn_k, cur);
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cb(indexer_k, "indexer_k", il);
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indexer_k = build_norm(indexer_k, model.layers[il].indexer_k_norm, model.layers[il].indexer_k_norm_b, LLM_NORM, il);
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cb(indexer_k, "indexer_k", il);
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// {n_embd_indexer_head, 1, n_tokens}
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indexer_k = ggml_reshape_3d(ctx0, indexer_k, n_embd_indexer_head, 1, n_tokens);
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indexer_k = ggml_rope_ext(ctx0, indexer_k, inp_pos, nullptr, n_rot,
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LLAMA_ROPE_TYPE_NEOX, n_ctx_orig, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow);
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cb(indexer_k, "indexer_k", il);
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// perform Hadamard transform on indexer q and k
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indexer_q = ggml_mul_mat(ctx0, inp_attn->get_dsa()->self_k_rot_lid, indexer_q);
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cb(indexer_q, "indexer_q", il);
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indexer_k = ggml_mul_mat(ctx0, inp_attn->get_dsa()->self_k_rot_lid, indexer_k);
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cb(indexer_k, "indexer_k", il);
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// store indexer keys to KV cache
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const auto * mctx_lid = inp_attn->get_dsa()->mctx->get_lid();
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const auto & k_idxs_lid = inp_attn->get_dsa()->get_k_idxs_lid();
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ggml_build_forward_expand(gf, mctx_lid->cpy_k(ctx0, indexer_k, k_idxs_lid, il));
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ggml_tensor * indexer_weights = ggml_mul_mat(ctx0, model.layers[il].indexer_proj, cur);
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cb(indexer_weights, "indexer_weights", il);
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indexer_k = mctx_lid->get_k(ctx0, il);
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// split the batch into streams if needed
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const auto n_stream = indexer_k->ne[3];
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indexer_q = ggml_view_4d(ctx0, indexer_q, indexer_q->ne[0], indexer_q->ne[1], indexer_q->ne[2]/n_stream, n_stream, indexer_q->nb[1], indexer_q->nb[2], indexer_q->nb[3]/n_stream, 0);
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indexer_weights = ggml_view_4d(ctx0, indexer_weights, indexer_weights->ne[0], indexer_weights->ne[1]/n_stream, indexer_weights->ne[2], n_stream, indexer_weights->nb[1], indexer_weights->nb[2]/n_stream, indexer_weights->nb[3]/n_stream, 0);
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// pre-scale weights to avoid scaling operations on huge indexer_score tensor
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indexer_weights = ggml_scale(ctx0, indexer_weights, 1.0f / sqrtf(float(n_embd_indexer_head * n_indexer_head)));
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cb(indexer_weights, "indexer_weights", il);
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ggml_tensor * indexer_score = nullptr;
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if (cparams.fused_lid) {
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indexer_score = ggml_lightning_indexer(ctx0, indexer_q, indexer_k, indexer_weights, inp_attn->get_dsa()->get_kq_mask_lid());
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cb(indexer_score, "indexer_score", il);
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res->add_fused_node({LLM_FUSED_OP_LIGHTNING_INDEXER, indexer_score, il});
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} else {
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indexer_q = ggml_permute(ctx0, indexer_q, 0, 2, 1, 3);
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cb(indexer_q, "indexer_q", il);
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indexer_k = ggml_permute(ctx0, indexer_k, 0, 2, 1, 3);
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cb(indexer_k, "indexer_k", il);
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ggml_tensor * indexer_kq = ggml_mul_mat(ctx0, indexer_k, indexer_q);
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cb(indexer_kq, "indexer_kq", il);
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// ReLU requires contiguous tensors
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indexer_kq = ggml_cont(ctx0, ggml_permute(ctx0, indexer_kq, 2, 1, 0, 3));
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cb(indexer_kq, "indexer_kq", il);
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indexer_score = ggml_relu(ctx0, indexer_kq);
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cb(indexer_score, "indexer_score", il);
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indexer_score = ggml_mul(ctx0, indexer_score, indexer_weights);
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cb(indexer_score, "indexer_score", il);
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// sum by q n_indexer_head dimension
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indexer_score = ggml_sum_rows(ctx0, indexer_score);
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cb(indexer_score, "indexer_score", il);
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// permute result to match KQ mask
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indexer_score = ggml_cont(ctx0, ggml_permute(ctx0, indexer_score, 2, 1, 0, 3));
|
|
cb(indexer_score, "indexer_score", il);
|
|
|
|
ggml_tensor * indexer_kq_mask = inp_attn->get_dsa()->get_kq_mask_lid();
|
|
indexer_score = ggml_add(ctx0, indexer_score, indexer_kq_mask);
|
|
cb(indexer_score, "indexer_score", il);
|
|
}
|
|
|
|
// get indices of top k indexer scores
|
|
uint32_t n_top_k = indexer_score->ne[0] < n_indexer_top_k ? indexer_score->ne[0] : n_indexer_top_k;
|
|
top_k = ggml_cont(ctx0, ggml_top_k(ctx0, indexer_score, n_top_k));
|
|
cb(top_k, "top_k", il);
|
|
}
|
|
|
|
ggml_tensor * q = ggml_mul_mat(ctx0, model.layers[il].wq_b, qr);
|
|
cb(q, "q", il);
|
|
|
|
// split into {n_embd_head_qk_nope, n_head_l, n_tokens}
|
|
ggml_tensor * q_nope =
|
|
ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head_l, n_tokens, ggml_row_size(q->type, n_embd_head_k_mla),
|
|
ggml_row_size(q->type, n_embd_head_k_mla) * n_head_l, 0);
|
|
cb(q_nope, "q_nope", il);
|
|
|
|
// and {n_embd_head_qk_rope, n_head_l, n_tokens}
|
|
ggml_tensor * q_pe = ggml_view_3d(
|
|
ctx0, q, n_embd_head_qk_rope, n_head_l, n_tokens, ggml_row_size(q->type, n_embd_head_k_mla),
|
|
ggml_row_size(q->type, n_embd_head_k_mla) * n_head_l, ggml_row_size(q->type, n_embd_head_qk_nope));
|
|
cb(q_pe, "q_pe", il);
|
|
|
|
ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);
|
|
cb(kv_cmpr_pe, "kv_cmpr_pe", il);
|
|
|
|
// split into {kv_lora_rank, n_tokens}
|
|
ggml_tensor * kv_cmpr =
|
|
ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens,
|
|
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0);
|
|
cb(kv_cmpr, "kv_cmpr", il);
|
|
|
|
// and {n_embd_head_qk_rope, 1, n_tokens}
|
|
ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens,
|
|
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
|
|
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
|
|
ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));
|
|
cb(k_pe, "k_pe", il);
|
|
|
|
// norm on the shared rope key, applied before rope
|
|
k_pe = build_norm(k_pe, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
|
|
cb(k_pe, "k_pe", il);
|
|
|
|
q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow);
|
|
cb(q_pe, "q_pe", il);
|
|
|
|
k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale,
|
|
ext_factor, attn_factor, beta_fast, beta_slow);
|
|
cb(k_pe, "k_pe", il);
|
|
|
|
kv_cmpr = build_norm(kv_cmpr, model.layers[il].attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);
|
|
cb(kv_cmpr, "kv_cmpr", il);
|
|
|
|
// MLA attention with the absorption optimization
|
|
{
|
|
// {n_embd_head_qk_nope, n_tokens, n_head_l}
|
|
q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
|
|
cb(q_nope, "q_nope_perm", il);
|
|
|
|
// {n_embd_head_qk_nope, kv_lora_rank, n_head_l} x {n_embd_head_qk_nope, n_tokens, n_head_l}
|
|
ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, model.layers[il].wk_b, q_nope);
|
|
cb(q_nope_absorbed, "q_nope_absorbed", il);
|
|
|
|
// {kv_lora_rank, n_head_l, n_tokens}
|
|
q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);
|
|
cb(q_nope_absorbed, "q_nope_absorbed_perm", il);
|
|
|
|
// {n_embd_head_qk_rope + kv_lora_rank, n_head_l, n_tokens}
|
|
ggml_tensor * Qcur = ggml_concat(ctx0, q_nope_absorbed, q_pe, 0);
|
|
cb(Qcur, "Qcur", il);
|
|
|
|
kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens);
|
|
cb(kv_cmpr, "kv_cmpr_reshape", il);
|
|
|
|
// {n_embd_head_qk_rope + kv_lora_rank, 1, n_tokens}
|
|
ggml_tensor * Kcur = ggml_concat(ctx0, kv_cmpr, k_pe, 0);
|
|
cb(Kcur, "Kcur", il);
|
|
|
|
// {kv_lora_rank, 1, n_tokens}
|
|
ggml_tensor * Vcur = kv_cmpr;
|
|
cb(Vcur, "Vcur", il);
|
|
|
|
// apply the head-wise output gate before o_proj, so wo stays out of build_attn
|
|
if (is_swa) {
|
|
cur = build_attn(inp_attn->get_swa(),
|
|
nullptr, nullptr, nullptr,
|
|
Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, kq_scale, il);
|
|
} else {
|
|
cur = build_attn(inp_attn->get_dsa(),
|
|
nullptr, nullptr, nullptr,
|
|
Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, top_k, kq_scale, il);
|
|
}
|
|
cb(cur, "attn_out", il);
|
|
|
|
ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, attn_inp);
|
|
cb(gate, "attn_gate", il);
|
|
|
|
gate = ggml_sigmoid(ctx0, gate);
|
|
cb(gate, "attn_gate_sigmoid", il);
|
|
|
|
// broadcast the per-head gate over the head dimension
|
|
ggml_tensor * attn_3d = ggml_reshape_3d(ctx0, cur, n_embd_head_v_mla, n_head_l, n_tokens);
|
|
ggml_tensor * gate_3d = ggml_reshape_3d(ctx0, gate, 1, n_head_l, n_tokens);
|
|
attn_3d = ggml_mul(ctx0, attn_3d, gate_3d);
|
|
cb(attn_3d, "attn_gated", il);
|
|
|
|
cur = ggml_reshape_2d(ctx0, attn_3d, n_embd_head_v_mla * n_head_l, n_tokens);
|
|
|
|
cur = build_lora_mm(model.layers[il].wo, cur, model.layers[il].wo_s);
|
|
cb(cur, "attn_output", il);
|
|
}
|
|
}
|
|
|
|
if (il == n_layer - 1 && inp_out_ids) {
|
|
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
|
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
|
}
|
|
|
|
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
|
cb(ffn_inp, "ffn_inp", il);
|
|
|
|
cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
|
|
cb(cur, "ffn_norm", il);
|
|
|
|
if ((uint32_t) il < hparams.n_layer_dense_lead) {
|
|
cur = build_ffn(cur,
|
|
model.layers[il].ffn_up, NULL, model.layers[il].ffn_up_s,
|
|
model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_s,
|
|
model.layers[il].ffn_down, NULL, model.layers[il].ffn_down_s,
|
|
NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
|
|
cb(cur, "ffn_out", il);
|
|
} else {
|
|
ggml_tensor * moe_out = build_moe_ffn(cur,
|
|
model.layers[il].ffn_gate_inp,
|
|
model.layers[il].ffn_up_exps,
|
|
model.layers[il].ffn_gate_exps,
|
|
model.layers[il].ffn_down_exps,
|
|
model.layers[il].ffn_exp_probs_b,
|
|
n_expert, n_expert_used,
|
|
LLM_FFN_SILU, hparams.expert_weights_norm,
|
|
hparams.expert_weights_scale,
|
|
(llama_expert_gating_func_type) hparams.expert_gating_func,
|
|
il,
|
|
nullptr,
|
|
model.layers[il].ffn_gate_up_exps,
|
|
model.layers[il].ffn_up_exps_s,
|
|
model.layers[il].ffn_gate_exps_s,
|
|
model.layers[il].ffn_down_exps_s);
|
|
cb(moe_out, "ffn_moe_out", il);
|
|
|
|
ggml_tensor * ffn_shexp =
|
|
build_ffn(cur,
|
|
model.layers[il].ffn_up_shexp, NULL, model.layers[il].ffn_up_shexp_s,
|
|
model.layers[il].ffn_gate_shexp, NULL, model.layers[il].ffn_gate_shexp_s,
|
|
model.layers[il].ffn_down_shexp, NULL, model.layers[il].ffn_down_shexp_s,
|
|
NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
|
|
cb(ffn_shexp, "ffn_shexp", il);
|
|
|
|
cur = ggml_add(ctx0, moe_out, ffn_shexp);
|
|
cb(cur, "ffn_out", il);
|
|
}
|
|
|
|
cur = ggml_add(ctx0, cur, ffn_inp);
|
|
|
|
cur = build_cvec(cur, il);
|
|
cb(cur, "l_out", il);
|
|
|
|
inpL = cur;
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
|
|
|
|
cb(cur, "result_norm", -1);
|
|
res->t_embd = cur;
|
|
|
|
cur = ggml_mul_mat(ctx0, model.output, cur);
|
|
|
|
cb(cur, "result_output", -1);
|
|
res->t_logits = cur;
|
|
|
|
ggml_build_forward_expand(gf, cur);
|
|
}
|