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llama.cpp fork for Ternary Bonsai 2 27B: MTP speculation, suffix cache reuse (SCR), q8 KV, two-slot fairness, fused DeltaNet kernels
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at main
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197#include "models.h"
void llama_model_phi3::load_arch_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer()) { case 24: type = LLM_TYPE_1B; break; case 32: type = LLM_TYPE_3B; break; case 40: type = LLM_TYPE_14B; break; default: type = LLM_TYPE_UNKNOWN; }
const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
if (found_swa && hparams.n_swa > 0) { LLAMA_LOG_WARN("%s: Phi SWA is currently disabled - results might be suboptimal for some models (see %s)\n", __func__, "https://github.com/ggml-org/llama.cpp/pull/13676");
// TODO: fix conversion scripts to correctly populate `n_swa` and `n_swa_pattern` hparams.swa_type = LLAMA_SWA_TYPE_NONE;
hparams.n_swa = 0; hparams.set_swa_pattern(1); }}
void llama_model_phi3::load_arch_tensors(llama_model_loader &) { LLAMA_LOAD_LOCALS;
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }, 0);
// output output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0); output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
// if output is NULL, init from the input tok embed if (output == NULL) { output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED); }
for (int i = 0; i < n_layer; ++i) { auto & layer = layers[i];
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }, 0);
create_tensor_qkv(layer, i, n_embd, n_embd, n_embd_gqa, n_embd_gqa, TENSOR_NOT_REQUIRED); layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }, 0);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }, 0); layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, 2 * n_ff }, 0);
layer.rope_long = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0)); layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), { n_rot/2 }, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0)); }}
std::unique_ptr<llm_graph_context> llama_model_phi3::build_arch_graph(const llm_graph_params & params) const { if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) { return std::make_unique<graph<true>> (*this, params); } else { return std::make_unique<graph<false>>(*this, params); }}
template<bool iswa>llama_model_phi3::graph<iswa>::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
ggml_tensor * cur; ggml_tensor * inpL;
inpL = build_inp_embd(model.tok_embd);
// inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos();
using inp_attn_type = std::conditional_t<iswa, llm_graph_input_attn_kv_iswa, llm_graph_input_attn_kv>; inp_attn_type * inp_attn = nullptr;
if constexpr (iswa) { inp_attn = build_attn_inp_kv_iswa(); } else { inp_attn = build_attn_inp_kv(); } ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) { auto * residual = inpL;
// self-attention { // rope freq factors for 128k context ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
ggml_tensor* attn_norm_output = build_norm(inpL, model.layers[il].attn_norm, model.layers[il].attn_norm_b, LLM_NORM_RMS, il); cb(attn_norm_output, "attn_norm", il);
auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], attn_norm_output, n_embd_head, n_head, n_head_kv, il); Qcur = ggml_rope_ext( ctx0, Qcur, inp_pos, rope_factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow );
Kcur = ggml_rope_ext( ctx0, Kcur, inp_pos, rope_factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow );
cb(Qcur, "Qcur", il); cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il);
Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head))); cb(Qcur, "Qcur", il);
cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il); } if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); residual = ggml_get_rows(ctx0, residual, inp_out_ids); } cur = ggml_add(ctx0, cur, residual); residual = cur;
cur = build_norm(cur, model.layers[il].ffn_norm, model.layers[il].ffn_norm_b, LLM_NORM_RMS, il); cb(cur, "ffn_norm", il);
// feed-forward network if (model.layers[il].ffn_gate_inp == nullptr) { cur = build_ffn(cur, model.layers[il].ffn_up, NULL, NULL, NULL, NULL, NULL, model.layers[il].ffn_down, NULL, NULL, NULL, LLM_FFN_SWIGLU, LLM_FFN_SEQ, il); cb(cur, "ffn_out", il); } else { // MoE branch cur = 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, nullptr, n_expert, n_expert_used, LLM_FFN_SILU, true, hparams.expert_weights_scale, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il); cb(cur, "ffn_moe_out", il); } cur = ggml_add(ctx0, residual, cur);
cur = build_cvec(cur, il); cb(cur, "l_out", il);
// input for next layer inpL = cur; } cur = build_norm(inpL, model.output_norm, model.output_norm_b, LLM_NORM_RMS, -1);
cb(cur, "result_norm", -1); res->t_embd = cur;
cur = build_lora_mm(model.output, cur, model.output_s);
if (model.output_b != nullptr) { cb(cur, "result_output_no_bias", -1); cur = ggml_add(ctx0, cur, model.output_b); } cb(cur, "result_output", -1); res->t_logits = cur;
ggml_build_forward_expand(gf, cur);}
// Explicit template instantiationstemplate struct llama_model_phi3::graph<false>;template struct llama_model_phi3::graph<true>;