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