Buckets:
| void llama_model_gemma_embedding2::load_arch_hparams(llama_model_loader & ml) { | |
| hparams.swa_type = LLAMA_SWA_TYPE_SYMMETRIC; | |
| load_swa_pattern(ml, 6); | |
| hparams.causal_attn = false; // embeddings do not use causal attention | |
| hparams.f_attention_scale = 1.0f; // same as Gemma4, q_norm makes the scaling unnecessary | |
| ml.get_key(LLM_KV_ROPE_FREQ_BASE_SWA, hparams.rope_freq_base_train_swa, false); | |
| ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa); | |
| ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); | |
| ml.get_key(LLM_KV_EMBEDDING_LENGTH_PER_LAYER, hparams.n_embd_per_layer); | |
| switch (hparams.n_layer()) { | |
| case 24: type = LLM_TYPE_0_3B; break; | |
| default: type = LLM_TYPE_UNKNOWN; | |
| } | |
| } | |
| void llama_model_gemma_embedding2::load_arch_tensors(llama_model_loader &) { | |
| LLAMA_LOAD_LOCALS; | |
| const int64_t n_embd_per_layer = hparams.n_embd_per_layer; | |
| const int64_t n_embd_out = hparams.n_embd_out(); | |
| if (n_embd_head_k != n_embd_head_v) { | |
| throw std::runtime_error("EmbeddingGemma2 requires n_embd_head_k == n_embd_head_v"); | |
| } | |
| if (hparams.n_embd_head_k_swa != hparams.n_embd_head_v_swa) { | |
| throw std::runtime_error("EmbeddingGemma2 requires n_embd_head_k_swa == n_embd_head_v_swa"); | |
| } | |
| tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0); | |
| per_layer_model_proj = create_tensor(tn(LLM_TENSOR_PER_LAYER_MODEL_PROJ, "weight", 0), {n_embd, n_embd_per_layer * n_layer}, 0); | |
| per_layer_proj_norm = create_tensor(tn(LLM_TENSOR_PER_LAYER_PROJ_NORM, "weight", 0), {n_embd_per_layer}, 0); | |
| output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0); | |
| // projects the final hidden state to the embedding dimension | |
| output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_embd_out}, 0); | |
| for (int i = 0; i < n_layer; ++i) { | |
| auto & layer = layers[i]; | |
| const int64_t n_head = hparams.n_head(i); | |
| const int64_t n_embd_head = hparams.n_embd_head_k(i); | |
| const int64_t n_embd_k = hparams.n_embd_k_gqa(i); | |
| const int64_t n_embd_v = hparams.n_embd_v_gqa(i); | |
| layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0); | |
| layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head * n_head}, 0); | |
| layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k}, 0); | |
| layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v}, 0); | |
| layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head * n_head, n_embd}, 0); | |
| layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head}, 0); | |
| layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head}, 0); | |
| layer.attn_post_norm = create_tensor(tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd}, 0); | |
| layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0); | |
| layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0); | |
| layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0); | |
| layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0); | |
| layer.ffn_post_norm = create_tensor(tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd}, 0); | |
| layer.per_layer_inp_gate = create_tensor(tn(LLM_TENSOR_PER_LAYER_INP_GATE, "weight", i), {n_embd, n_embd_per_layer}, 0); | |
| layer.per_layer_proj = create_tensor(tn(LLM_TENSOR_PER_LAYER_PROJ, "weight", i), {n_embd_per_layer, n_embd}, 0); | |
| layer.per_layer_post_norm = create_tensor(tn(LLM_TENSOR_PER_LAYER_POST_NORM, "weight", i), {n_embd}, 0); | |
| layer.out_scale = create_tensor(tn(LLM_TENSOR_LAYER_OUT_SCALE, "weight", i), {1u}, 0); | |
| } | |
| } | |
| std::unique_ptr<llm_graph_context> llama_model_gemma_embedding2::build_arch_graph(const llm_graph_params & params) const { | |
| return std::make_unique<graph>(*this, params); | |
| } | |
| llama_model_gemma_embedding2::graph::graph(const llama_model & model, const llm_graph_params & params) : | |
| llm_graph_context(params), | |
| model(model) { | |
| ggml_tensor * cur; | |
| ggml_tensor * inpL; | |
| // important: do not normalize weights for raw embeddings input (i.e. encoded image embeddings) | |
| inpL = build_inp_embd(model.tok_embd, sqrtf(n_embd)); | |
| cb(inpL, "inp_scaled", -1); | |
| // inp_pos - contains the positions | |
| ggml_tensor * inp_pos = build_inp_pos(); | |
| auto * inp_attn = build_attn_inp_no_cache(); | |
| ggml_tensor * inp_out_ids = build_inp_out_ids(); | |
| // inp_per_layer shape: [n_embd_per_layer, n_tokens, n_layer] | |
| ggml_tensor * inp_per_layer = build_inp_per_layer(inpL); | |
| for (int il = 0; il < n_layer; ++il) { | |
| const int64_t n_embd_head = hparams.n_embd_head_k(il); | |
| const int64_t n_head = hparams.n_head(il); | |
| const int64_t n_head_kv = hparams.n_head_kv(il); | |
| const float freq_base_l = model.get_rope_freq_base(cparams, il); | |
| const float freq_scale_l = model.get_rope_freq_scale(cparams, il); | |
| const int n_rot_l = hparams.n_rot(il); | |
| // norm | |
| cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il); | |
| cb(cur, "attn_norm", il); | |
| // self-attention | |
| { | |
| ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur, model.layers[il].wq_s); | |
| ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur, model.layers[il].wk_s); | |
| ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur, model.layers[il].wv_s); | |
| cb(Qcur, "Qcur", il); | |
| cb(Kcur, "Kcur", il); | |
| cb(Vcur, "Vcur", il); | |
| Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); | |
| Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); | |
| Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); | |
| Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il); | |
| Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il); | |
| Vcur = ggml_rms_norm(ctx0, Vcur, hparams.f_norm_rms_eps); | |
| cb(Qcur, "Qcur_normed", il); | |
| cb(Kcur, "Kcur_normed", il); | |
| cb(Vcur, "Vcur_normed", il); | |
| Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l, | |
| ext_factor, attn_factor, beta_fast, beta_slow); | |
| Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l, | |
| ext_factor, attn_factor, beta_fast, beta_slow); | |
| cb(Qcur, "Qcur_pos", il); | |
| cb(Kcur, "Kcur_pos", il); | |
| cur = build_attn(inp_attn, | |
| model.layers[il].wo, nullptr, model.layers[il].wo_s, | |
| Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, hparams.f_attention_scale, il); | |
| } | |
| if (il == n_layer - 1 && inp_out_ids) { | |
| cur = ggml_get_rows(ctx0, cur, inp_out_ids); | |
| inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); | |
| } | |
| cur = build_norm(cur, model.layers[il].attn_post_norm, nullptr, LLM_NORM_RMS, il); | |
| cb(cur, "attn_post_norm", il); | |
| ggml_tensor * attn_out = ggml_add(ctx0, cur, inpL); | |
| cb(attn_out, "attn_out", il); | |
| // feed-forward network | |
| cur = build_norm(attn_out, model.layers[il].ffn_norm, nullptr, LLM_NORM_RMS, il); | |
| cb(cur, "ffn_norm", il); | |
| cur = build_ffn(cur, | |
| model.layers[il].ffn_up, nullptr, model.layers[il].ffn_up_s, | |
| model.layers[il].ffn_gate, nullptr, model.layers[il].ffn_gate_s, | |
| model.layers[il].ffn_down, nullptr, model.layers[il].ffn_down_s, | |
| nullptr, | |
| LLM_FFN_GELU, LLM_FFN_PAR, il); | |
| cb(cur, "ffn_out", il); | |
| cur = build_norm(cur, model.layers[il].ffn_post_norm, nullptr, LLM_NORM_RMS, -1); | |
| cb(cur, "ffn_post_norm", il); | |
| cur = ggml_add(ctx0, cur, attn_out); | |
| // per-layer embedding | |
| { | |
| ggml_tensor * pe_in = cur; | |
| cb(cur, "pe_in", il); | |
| cur = build_lora_mm(model.layers[il].per_layer_inp_gate, cur); // [n_embd_per_layer, n_tokens] | |
| cur = ggml_gelu(ctx0, cur); | |
| ggml_tensor * inp_this_layer = ggml_view_2d(ctx0, inp_per_layer, | |
| inp_per_layer->ne[0], inp_per_layer->ne[1], inp_per_layer->nb[1], il * inp_per_layer->nb[2]); | |
| if (il == n_layer - 1 && inp_out_ids) { | |
| inp_this_layer = ggml_get_rows(ctx0, inp_this_layer, inp_out_ids); | |
| } | |
| cur = ggml_mul(ctx0, cur, inp_this_layer); | |
| cur = build_lora_mm(model.layers[il].per_layer_proj, cur); // [n_embd, n_tokens] | |
| cur = build_norm(cur, model.layers[il].per_layer_post_norm, nullptr, LLM_NORM_RMS, il); | |
| cb(cur, "per_layer_embd_out", il); | |
| cur = ggml_add(ctx0, pe_in, cur); | |
| } | |
| // layer_scalar | |
| cur = ggml_mul(ctx0, cur, model.layers[il].out_scale); | |
| cb(cur, "out_scaled", il); | |
| cur = build_cvec(cur, il); | |
| cb(cur, "l_out", il); | |
| // input for next layer | |
| inpL = cur; | |
| } | |
| cur = build_norm(inpL, model.output_norm, nullptr, LLM_NORM_RMS, -1); | |
| cb(cur, "result_norm", -1); | |
| // projecting per token is equivalent to projecting after mean pooling | |
| cur = build_lora_mm(model.output, cur, model.output_s); | |
| cb(cur, "result_embd", -1); | |
| res->t_embd = cur; | |
| } | |
| // equivalent to EmbeddingGemma2TextPLE in python code | |
| // this model has no per-layer token embeddings, the per-layer inputs come only from the projection | |
| // inpL shape: [n_embd, n_tokens] | |
| // output shape: [n_embd_per_layer, n_tokens, n_layer] | |
| ggml_tensor * llama_model_gemma_embedding2::graph::build_inp_per_layer(ggml_tensor * inpL) { | |
| const int64_t n_embd_per_layer = hparams.n_embd_per_layer; | |
| ggml_tensor * cur = ggml_mul_mat(ctx0, model.per_layer_model_proj, inpL); // [n_embd_per_layer * n_layer, n_tokens] | |
| cur = ggml_scale(ctx0, cur, 1.0f / sqrtf((float) n_embd)); | |
| cur = ggml_reshape_3d(ctx0, cur, n_embd_per_layer, n_layer, n_tokens); | |
| cur = build_norm(cur, model.per_layer_proj_norm, nullptr, LLM_NORM_RMS, -1); | |
| cb(cur, "inp_per_layer", -1); | |
| // permute to shape: [n_embd_per_layer, n_tokens, n_layer] | |
| cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 0, 2, 1, 3)); | |
| return cur; | |
| } | |
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