125 lines
5.0 KiB
C++
125 lines
5.0 KiB
C++
#include "../llama-build-context.h"
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#include "../llama-model.h"
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#include "../llama-context.h"
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ggml_cgraph * llm_build_context::build_gemma3() {
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ggml_cgraph * gf = new_graph_custom();
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struct ggml_tensor * cur;
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struct ggml_tensor * inpL;
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inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
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// important: do not normalize weights for raw embeddings input (i.e. encoded image emdeddings)
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if (batch.token) {
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inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
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cb(inpL, "inp_scaled", -1);
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}
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// inp_pos - contains the positions
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struct ggml_tensor * inp_pos = build_inp_pos();
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// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
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// gemma3 requires different mask for layers using sliding window (SWA)
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struct ggml_tensor * KQ_mask = build_inp_KQ_mask(true);
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struct ggml_tensor * KQ_mask_swa = build_inp_KQ_mask_swa(true);
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// "5-to-1 interleaved attention"
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// 5 layers of local attention followed by 1 layer of global attention
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static const int sliding_window_pattern = 6;
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ggml_tensor * rope_cache = nullptr;
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ggml_tensor * rope_cache_l = nullptr;
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if (cparams.rope_cache && (rope_type == LLAMA_ROPE_TYPE_NEOX || rope_type == LLAMA_ROPE_TYPE_NORM)) {
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rope_cache = ggml_rope_cache(ctx0, inp_pos, nullptr, n_rot, 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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rope_cache_l = ggml_rope_cache(ctx0, inp_pos, nullptr, n_rot, n_rot, rope_type, n_ctx_orig, 10000.0f, 1.0f,
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ext_factor, attn_factor, beta_fast, beta_slow);
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}
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for (int il = 0; il < n_layer; ++il) {
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const bool is_sliding = (il + 1) % sliding_window_pattern;
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const float freq_base_l = is_sliding ? 10000.0f : freq_base;
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const float freq_scale_l = is_sliding ? 1.0f : freq_scale;
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struct ggml_tensor * KQ_mask_l = is_sliding ? KQ_mask_swa : KQ_mask;
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// norm
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cur = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, cb, il);
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cb(cur, "attn_norm", il);
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// self-attention
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{
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auto [Qcur, Kcur, Vcur] = llm_build_mul_mat_qkv(gf, cur,
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model.layers[il].wqkv, nullptr,
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model.layers[il].wqk, nullptr,
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model.layers[il].wq, nullptr, model.layers[il].wk, nullptr, model.layers[il].wv, nullptr,
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model.layers[il].attn_q_norm, model.layers[il].attn_k_norm, 0, il);
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if (rope_cache) {
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auto rcache = is_sliding ? rope_cache_l : rope_cache;
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Qcur = ggml_rope_fast(ctx0, Qcur, rcache);
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Kcur = ggml_rope_fast(ctx0, Kcur, rcache);
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} else {
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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 = 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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}
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cb(Qcur, "Qcur", il);
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cb(Kcur, "Kcur", il);
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cur = llm_build_kv(ctx0, lctx, kv_self, gf, model.layers[il].wo, NULL,
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Kcur, Vcur, Qcur, KQ_mask_l, n_tokens, kv_head, n_kv, hparams.f_attention_scale, cb, il, nullptr,
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KQ_mask_l == KQ_mask_swa ? hparams.n_swa : 0);
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}
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cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, cb, il);
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cb(cur, "attn_post_norm", il);
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if (il == n_layer - 1) {
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// skip computing output for unused tokens
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struct ggml_tensor * inp_out_ids = build_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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struct ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);
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cb(sa_out, "sa_out", il);
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// feed-forward network
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cur = llm_build_ffn(ctx0, lctx, model.layers[il].ffn_norm, sa_out,
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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,
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LLM_FFN_GELU, LLM_FFN_PAR, cb, il);
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cb(cur, "ffn_out", il);
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cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].ffn_post_norm, NULL, LLM_NORM_RMS, cb, -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 = lctx.cvec.apply_to(ctx0, 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 = llm_build_norm(ctx0, cur, hparams, model.output_norm, NULL, LLM_NORM_RMS, cb, -1);
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cb(cur, "result_norm", -1);
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// lm_head
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cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
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cb(cur, "result_output", -1);
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ggml_build_forward_expand(gf, cur);
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return gf;
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}
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