125 lines
4.2 KiB
C++
125 lines
4.2 KiB
C++
#include "models.h"
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llm_build_openai_moe_iswa::llm_build_openai_moe_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
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ggml_tensor * cur;
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ggml_tensor * inpL;
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inpL = build_inp_embd(model.tok_embd);
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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_kv_iswa();
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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 * inpSA = inpL;
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// norm
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cur = build_norm(inpL,
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model.layers[il].attn_norm, nullptr,
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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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ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
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cb(Qcur, "Qcur", il);
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if (model.layers[il].bq) {
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Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
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cb(Qcur, "Qcur", il);
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}
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ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
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cb(Kcur, "Kcur", il);
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if (model.layers[il].bk) {
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Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
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cb(Kcur, "Kcur", il);
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}
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ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
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cb(Vcur, "Vcur", il);
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if (model.layers[il].bv) {
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Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
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cb(Vcur, "Vcur", il);
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}
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Qcur = ggml_reshape_3d(ctx0, Qcur, n_rot, n_head, n_tokens);
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Kcur = ggml_reshape_3d(ctx0, Kcur, n_rot, n_head_kv, n_tokens);
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Vcur = ggml_reshape_3d(ctx0, Vcur, n_rot, n_head_kv, n_tokens);
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Qcur = ggml_rope_ext(
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ctx0, Qcur, inp_pos, nullptr,
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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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);
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Kcur = ggml_rope_ext(
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ctx0, Kcur, inp_pos, nullptr,
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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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);
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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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cur = build_attn(inp_attn,
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model.layers[il].wo, model.layers[il].bo,
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Qcur, Kcur, Vcur, nullptr, model.layers[il].attn_sinks, nullptr, 1.0f/sqrtf(float(n_rot)), il);
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cb(cur, "attn_out", il);
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}
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if (il == n_layer - 1) {
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// skip computing output for unused tokens
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cur = ggml_get_rows(ctx0, cur, inp_out_ids);
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inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
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}
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ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
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cb(ffn_inp, "ffn_inp", il);
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cur = ffn_inp;
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cur = build_norm(cur,
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model.layers[il].attn_post_norm, nullptr,
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LLM_NORM_RMS, il);
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cb(cur, "attn_post_norm", il);
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// MoE branch
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cur = build_moe_ffn(cur,
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model.layers[il].ffn_gate_inp, model.layers[il].ffn_gate_inp_b,
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model.layers[il].ffn_up_exps, model.layers[il].ffn_up_exps_b,
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model.layers[il].ffn_gate_exps, model.layers[il].ffn_gate_exps_b,
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model.layers[il].ffn_down_exps, model.layers[il].ffn_down_exps_b,
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nullptr,
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n_expert, n_expert_used,
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LLM_FFN_SWIGLU_OAI_MOE, false,
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false, 0.0,
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LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX_WEIGHT,
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il);
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cb(cur, "ffn_moe_out", il);
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cur = ggml_add(ctx0, cur, ffn_inp);
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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,
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model.output_norm, NULL,
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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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// lm_head
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cur = build_lora_mm(model.output, cur);
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cb(cur, "result_output", -1);
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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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