include llama.cpp since there were some edits
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#include "models.h"
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void llama_model_gemma_embedding::load_arch_hparams(llama_model_loader & ml) {
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hparams.swa_type = LLAMA_SWA_TYPE_SYMMETRIC;
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load_swa_pattern(ml, 6);
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hparams.causal_attn = false; // embeddings do not use causal attention
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ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false);
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ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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//applied only if model converted with --sentence-transformers-dense-modules
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ml.get_key(LLM_KV_DENSE_2_FEAT_IN, hparams.dense_2_feat_in, false);
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ml.get_key(LLM_KV_DENSE_2_FEAT_OUT, hparams.dense_2_feat_out, false);
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ml.get_key(LLM_KV_DENSE_3_FEAT_IN, hparams.dense_3_feat_in, false);
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ml.get_key(LLM_KV_DENSE_3_FEAT_OUT, hparams.dense_3_feat_out, false);
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GGML_ASSERT((hparams.dense_2_feat_in == 0 || hparams.dense_2_feat_in == hparams.n_embd) && "dense_2_feat_in must be equal to n_embd");
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GGML_ASSERT((hparams.dense_3_feat_out == 0 || hparams.dense_3_feat_out == hparams.n_embd) && "dense_3_feat_out must be equal to n_embd");
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switch (hparams.n_layer()) {
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case 24: type = LLM_TYPE_0_3B; break;
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default: type = LLM_TYPE_UNKNOWN;
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}
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hparams.f_attention_scale = 1.0f / std::sqrt(float(hparams.n_embd_head_k()));
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}
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void llama_model_gemma_embedding::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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// Dense linear weights
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dense_2_out_layers = create_tensor(tn(LLM_TENSOR_DENSE_2_OUT, "weight"), {n_embd, hparams.dense_2_feat_out}, TENSOR_NOT_REQUIRED);
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dense_3_out_layers = create_tensor(tn(LLM_TENSOR_DENSE_3_OUT, "weight"), {hparams.dense_3_feat_in, n_embd}, TENSOR_NOT_REQUIRED);
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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_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);
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layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
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layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0);
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layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
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layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 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_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
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layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 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_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0);
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}
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}
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std::unique_ptr<llm_graph_context> llama_model_gemma_embedding::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_gemma_embedding::graph::graph(const llama_model & model, const llm_graph_params & params) :
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llm_graph_context(params) {
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const int64_t 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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// important: do not normalize weights for raw embeddings input (i.e. encoded image embeddings)
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inpL = build_inp_embd(model.tok_embd, sqrtf(n_embd));
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cb(inpL, "inp_scaled", -1);
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// inp_pos - contains the positions
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ggml_tensor * inp_pos = build_inp_pos();
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auto * inp_attn = build_attn_inp_no_cache();
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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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const float freq_base_l = model.get_rope_freq_base(cparams, il);
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const float freq_scale_l = model.get_rope_freq_scale(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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// compute Q and K and RoPE them
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auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,
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n_embd_head, n_head, n_head_kv, il);
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Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
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cb(Qcur, "Qcur_normed", il);
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Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
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ext_factor, attn_factor, beta_fast, beta_slow);
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Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
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cb(Kcur, "Kcur_normed", il);
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Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
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ext_factor, attn_factor, beta_fast, beta_slow);
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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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// ref: https://github.com/google/gemma_pytorch/blob/014acb7ac4563a5f77c76d7ff98f31b568c16508/gemma/model.py#L315
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Qcur = ggml_scale(ctx0, Qcur, hparams.f_attention_scale);
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cur =
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build_attn(inp_attn,
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model.layers[il].wo, NULL, 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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inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
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}
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cur = build_norm(cur, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, il);
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cb(cur, "attn_post_norm", il);
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ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);
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cb(sa_out, "sa_out", il);
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cur = build_norm(sa_out, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
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cb(cur, "ffn_norm", il);
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// feed-forward network
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{
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cur = build_ffn(cur,
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model.layers[il].ffn_up, NULL, NULL,
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model.layers[il].ffn_gate, NULL, NULL,
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model.layers[il].ffn_down, NULL, NULL,
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NULL, LLM_FFN_GELU, LLM_FFN_PAR, il);
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cb(cur, "ffn_out", il);
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}
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cur = build_norm(cur, model.layers[il].ffn_post_norm, NULL, LLM_NORM_RMS, -1);
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cb(cur, "ffn_post_norm", -1);
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cur = ggml_add(ctx0, cur, sa_out);
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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 = inpL;
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cur = build_norm(cur, model.output_norm, NULL, 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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}
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