169 lines
6.9 KiB
C++
169 lines
6.9 KiB
C++
#include "models.h"
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llm_build_step35_iswa::llm_build_step35_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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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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const uint32_t n_head_l = hparams.n_head(il);
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const uint32_t n_head_kv_l = hparams.n_head_kv(il);
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const float freq_base_l = model.get_rope_freq_base(cparams, il);
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const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
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cur = inpL;
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// dump pre-attn RMSNorm input to pinpoint layer boundary issues
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cb(cur, "attn_norm_in", il);
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// self-attention
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{
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cur = build_norm(cur, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
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cb(cur, "attn_norm", il);
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ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
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ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
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ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
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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 = ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head_l, n_tokens);
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Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv_l, n_tokens);
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Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head_v, n_head_kv_l, n_tokens);
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// Q/K per-head RMSNorm (Step35 q_norm / k_norm)
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if (model.layers[il].attn_q_norm) {
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Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);
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cb(Qcur, "Qcur_normed", il);
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}
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if (model.layers[il].attn_k_norm) {
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Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);
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cb(Kcur, "Kcur_normed", il);
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}
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// RoPE (partial rotary factors per layer)
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const bool is_swa = hparams.is_swa(il);
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ggml_tensor * rope_factors = is_swa ? nullptr : model.get_rope_factors(cparams, il);
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const int64_t n_rot_l = is_swa ? hparams.n_rot : (hparams.n_rot / 2);
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Qcur = ggml_rope_ext(
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ctx0, Qcur, inp_pos, rope_factors,
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n_rot_l, 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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Kcur = ggml_rope_ext(
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ctx0, Kcur, inp_pos, rope_factors,
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n_rot_l, 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_pos", il);
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cb(Kcur, "Kcur_pos", il);
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const float kq_scale = 1.0f / sqrtf(float(n_embd_head_k));
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ggml_tensor * attn_out = build_attn(inp_attn,
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nullptr, nullptr,
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Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
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cb(attn_out, "attn_out", il);
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// head-wise attention gate: sigmoid(g_proj(x)) in torch
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if (model.layers[il].wqkv_gate) {
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ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, cur); // [n_head_l, n_tokens]
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cb(gate, "attn_gate", il);
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gate = ggml_sigmoid(ctx0, gate);
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cb(gate, "attn_gate_sigmoid", il);
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// reshape + broadcast to [n_embd_head_v, n_head_l, n_tokens]
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ggml_tensor * attn_3d = ggml_reshape_3d(ctx0, attn_out, n_embd_head_v, n_head_l, n_tokens);
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ggml_tensor * gate_3d = ggml_reshape_3d(ctx0, gate, 1, n_head_l, n_tokens);
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cb(gate_3d, "attn_gate_3d", il);
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attn_3d = ggml_mul(ctx0, attn_3d, gate_3d);
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cb(attn_3d, "attn_gated_3d", il);
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attn_out = ggml_reshape_2d(ctx0, attn_3d, n_embd_head_v * n_head_l, n_tokens);
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cb(attn_out, "attn_gated", il);
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}
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// output projection
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cur = build_lora_mm(model.layers[il].wo, attn_out);
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cb(cur, "attn_proj", il);
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}
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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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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 = build_norm(ffn_inp, model.layers[il].ffn_norm, nullptr, LLM_NORM_RMS, il);
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cb(cur, "ffn_norm", il);
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// feed-forward
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if (model.layers[il].ffn_gate_inp == nullptr) {
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// dense MLP
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cur = build_ffn(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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nullptr,
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LLM_FFN_SILU, LLM_FFN_PAR, il);
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cb(cur, "ffn_out", il);
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} else {
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// MoE routed experts
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const bool norm_w = hparams.expert_weights_norm;
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const float w_scale = hparams.expert_weights_scale;
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const bool scale_w = w_scale != 0.0f;
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ggml_tensor * moe_out = build_moe_ffn(cur,
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model.layers[il].ffn_gate_inp,
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model.layers[il].ffn_up_exps,
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model.layers[il].ffn_gate_exps,
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model.layers[il].ffn_down_exps,
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model.layers[il].ffn_exp_probs_b,
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n_expert, n_expert_used,
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LLM_FFN_SILU,
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norm_w, scale_w, w_scale,
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(llama_expert_gating_func_type) hparams.expert_gating_func,
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il);
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cb(moe_out, "ffn_moe_out", il);
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// shared expert MLP (always added on MoE layers in Step35)
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ggml_tensor * sh_out = build_ffn(cur,
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model.layers[il].ffn_up_shexp, nullptr, nullptr,
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model.layers[il].ffn_gate_shexp, nullptr, nullptr,
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model.layers[il].ffn_down_shexp, nullptr, nullptr,
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nullptr,
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LLM_FFN_SILU, LLM_FFN_PAR, il);
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cb(sh_out, "ffn_shared_out", il);
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cur = ggml_add(ctx0, moe_out, sh_out);
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cb(cur, "ffn_out", il);
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}
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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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inpL = cur;
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}
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cur = inpL;
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cur = build_norm(cur, model.output_norm, nullptr, 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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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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