84 lines
3.1 KiB
C++
84 lines
3.1 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_mistral3() {
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ggml_cgraph * gf = new_graph_custom();
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const int64_t n_embd_head = hparams.n_embd_head_v(0);
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k(0));
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GGML_ASSERT(n_embd_head == hparams.n_rot);
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ggml_tensor * cur;
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ggml_tensor * inpL;
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inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
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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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// (optional) temperature tuning
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ggml_tensor * inp_attn_scale = nullptr;
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if (hparams.f_attn_temp_scale != 0.0f) {
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inp_attn_scale = build_input_scale(n_tokens);
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}
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ggml_tensor * KQ_mask = build_inp_KQ_mask();
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ggml_tensor * inp_out_ids = n_tokens > 1 ? build_inp_out_ids() : nullptr;
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//const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
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const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : 1.f;
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for (int il = 0; il < n_layer; ++il) {
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auto rope_factors = build_rope_factors(il);
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cur = build_std_attention(gf, model.layers[il].attn_norm, inpL, inp_pos, il == n_layer - 1 ? inp_out_ids : nullptr,
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rope_factors, KQ_mask, nullptr, inp_attn_scale, kq_scale, hparams.f_attention_scale, 0, il, true, false, true);
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// feed-forward network (non-MoE)
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if (model.layers[il].ffn_gate_inp == nullptr) {
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// non-MoE
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cur = llm_build_ffn(ctx0, lctx, model.layers[il].ffn_norm, cur,
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model.layers[il].ffn_up, model.layers[il].ffn_up_b, nullptr,
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model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, nullptr,
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model.layers[il].ffn_down, model.layers[il].ffn_down_b, nullptr,
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NULL,
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LLM_FFN_SILU, LLM_FFN_PAR, cb, il, gf, true);
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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 = llm_build_std_moe_ffn(ctx0, lctx, model.layers[il].ffn_norm, cur,
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model.layers[il].ffn_gate_inp, nullptr,
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model.layers[il].ffn_up_exps, nullptr,
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model.layers[il].ffn_gate_exps, nullptr,
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model.layers[il].ffn_down_exps, nullptr,
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model.layers[il].ffn_exp_probs_b,
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nullptr, nullptr, // we don't have shared experts
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nullptr, nullptr,
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nullptr, nullptr,
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n_expert, n_expert_used,
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LLM_FFN_SILU, true, false, 0.0f,
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LLM_EXPERT_GATING_FUNC_SOFTMAX,
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LLM_FFN_SILU, cb, il, gf, true);
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}
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cb(cur, "ffn_out", il);
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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 = build_output(lctx, ctx0, cur, model.output, model.output_norm, cb);
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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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