129 lines
4.6 KiB
C++
129 lines
4.6 KiB
C++
#include "models.h"
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template <bool iswa>
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llm_build_plamo3<iswa>::llm_build_plamo3(const llama_model & model, const llm_graph_params & params) :
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llm_graph_context(params) {
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const int64_t head_dim_q = hparams.n_embd_head_k;
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const int64_t head_dim_v = hparams.n_embd_head_v;
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ggml_tensor * cur;
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ggml_tensor * inpL = build_inp_embd(model.tok_embd);
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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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ggml_tensor * residual = inpL;
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float freq_base_l = 0.0f;
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float freq_scale_l = 0.0f;
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if constexpr (iswa) {
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freq_base_l = model.get_rope_freq_base (cparams, il);
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freq_scale_l = model.get_rope_freq_scale(cparams, il);
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} else {
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freq_base_l = freq_base;
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freq_scale_l = freq_scale;
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}
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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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ggml_tensor * qkv = build_lora_mm(model.layers[il].wqkv, cur);
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cb(cur, "wqkv", il);
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const int32_t n_head = hparams.n_head(il);
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const int32_t n_head_kv = hparams.n_head_kv(il);
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const int64_t q_offset = 0;
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const int64_t k_offset = head_dim_q * n_head;
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const int64_t v_offset = k_offset + head_dim_q * n_head_kv;
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ggml_tensor * Qcur = ggml_view_3d(ctx0, qkv, head_dim_q, n_head, n_tokens,
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head_dim_q * sizeof(float), qkv->nb[1], q_offset * ggml_element_size(qkv));
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ggml_tensor * Kcur = ggml_view_3d(ctx0, qkv, head_dim_q, n_head_kv, n_tokens,
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head_dim_q * sizeof(float), qkv->nb[1], k_offset * ggml_element_size(qkv));
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ggml_tensor * Vcur = ggml_view_3d(ctx0, qkv, head_dim_v, n_head_kv, n_tokens,
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head_dim_v * sizeof(float), qkv->nb[1], v_offset * ggml_element_size(qkv));
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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 = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
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cb(Qcur, "attn_q_norm", il);
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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, "attn_k_norm", il);
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Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr,
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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 = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr,
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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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const float attn_scale = 1.0f / sqrtf(float(head_dim_q));
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cur = build_attn(inp_attn,
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model.layers[il].wo, NULL,
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Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, attn_scale, il);
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cb(cur, "attn_out", il);
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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 = 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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cur = ggml_add(ctx0, cur, residual);
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cb(cur, "attn_residual", il);
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residual = cur;
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cur = build_norm(cur, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
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cb(cur, "ffn_norm", il);
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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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cur = build_norm(cur, model.layers[il].ffn_post_norm, NULL, LLM_NORM_RMS, il);
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cb(cur, "ffn_post_norm", il);
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cur = ggml_add(ctx0, cur, residual);
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cb(cur, "ffn_residual", il);
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cur = build_cvec(cur, il);
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cb(cur, "l_out", il);
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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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res->t_embd = cur;
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cur = build_lora_mm(model.output, cur);
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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 llm_build_plamo3<false>;
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template struct llm_build_plamo3<true>;
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