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Diffstat (limited to 'llama.cpp/src/models/step35-iswa.cpp')
| -rw-r--r-- | llama.cpp/src/models/step35-iswa.cpp | 168 |
1 files changed, 168 insertions, 0 deletions
diff --git a/llama.cpp/src/models/step35-iswa.cpp b/llama.cpp/src/models/step35-iswa.cpp new file mode 100644 index 0000000..f873781 --- /dev/null +++ b/llama.cpp/src/models/step35-iswa.cpp @@ -0,0 +1,168 @@ +#include "models.h" + +llm_build_step35_iswa::llm_build_step35_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { + ggml_tensor * cur; + ggml_tensor * inpL; + + inpL = build_inp_embd(model.tok_embd); + ggml_tensor * inp_pos = build_inp_pos(); + auto * inp_attn = build_attn_inp_kv_iswa(); + ggml_tensor * inp_out_ids = build_inp_out_ids(); + + for (int il = 0; il < n_layer; ++il) { + ggml_tensor * inpSA = inpL; + + const uint32_t n_head_l = hparams.n_head(il); + const uint32_t n_head_kv_l = hparams.n_head_kv(il); + + const float freq_base_l = model.get_rope_freq_base(cparams, il); + const float freq_scale_l = model.get_rope_freq_scale(cparams, il); + + cur = inpL; + + // dump pre-attn RMSNorm input to pinpoint layer boundary issues + cb(cur, "attn_norm_in", il); + + // self-attention + { + cur = build_norm(cur, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il); + cb(cur, "attn_norm", il); + ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur); + ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur); + ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head_l, n_tokens); + Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv_l, n_tokens); + Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head_v, n_head_kv_l, n_tokens); + + // Q/K per-head RMSNorm (Step35 q_norm / k_norm) + if (model.layers[il].attn_q_norm) { + Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il); + cb(Qcur, "Qcur_normed", il); + } + if (model.layers[il].attn_k_norm) { + Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il); + cb(Kcur, "Kcur_normed", il); + } + + // RoPE (partial rotary factors per layer) + const bool is_swa = hparams.is_swa(il); + ggml_tensor * rope_factors = is_swa ? nullptr : model.get_rope_factors(cparams, il); + const int64_t n_rot_l = is_swa ? hparams.n_rot : (hparams.n_rot / 2); + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, rope_factors, + n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l, + ext_factor, attn_factor, beta_fast, beta_slow + ); + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, rope_factors, + n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur_pos", il); + cb(Kcur, "Kcur_pos", il); + + const float kq_scale = 1.0f / sqrtf(float(n_embd_head_k)); + ggml_tensor * attn_out = build_attn(inp_attn, + nullptr, nullptr, + Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il); + cb(attn_out, "attn_out", il); + // head-wise attention gate: sigmoid(g_proj(x)) in torch + if (model.layers[il].wqkv_gate) { + ggml_tensor * gate = build_lora_mm(model.layers[il].wqkv_gate, cur); // [n_head_l, n_tokens] + cb(gate, "attn_gate", il); + + gate = ggml_sigmoid(ctx0, gate); + cb(gate, "attn_gate_sigmoid", il); + + // reshape + broadcast to [n_embd_head_v, n_head_l, n_tokens] + ggml_tensor * attn_3d = ggml_reshape_3d(ctx0, attn_out, n_embd_head_v, n_head_l, n_tokens); + ggml_tensor * gate_3d = ggml_reshape_3d(ctx0, gate, 1, n_head_l, n_tokens); + cb(gate_3d, "attn_gate_3d", il); + + attn_3d = ggml_mul(ctx0, attn_3d, gate_3d); + cb(attn_3d, "attn_gated_3d", il); + + attn_out = ggml_reshape_2d(ctx0, attn_3d, n_embd_head_v * n_head_l, n_tokens); + cb(attn_out, "attn_gated", il); + } + + // output projection + cur = build_lora_mm(model.layers[il].wo, attn_out); + cb(cur, "attn_proj", il); + } + + if (il == n_layer - 1 && inp_out_ids) { + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + cur = build_norm(ffn_inp, model.layers[il].ffn_norm, nullptr, LLM_NORM_RMS, il); + cb(cur, "ffn_norm", il); + + // feed-forward + if (model.layers[il].ffn_gate_inp == nullptr) { + // dense MLP + cur = build_ffn(cur, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, nullptr, + model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, nullptr, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, nullptr, + nullptr, + LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(cur, "ffn_out", il); + } else { + // MoE routed experts + const bool norm_w = hparams.expert_weights_norm; + const float w_scale = hparams.expert_weights_scale; + const bool scale_w = w_scale != 0.0f; + ggml_tensor * moe_out = build_moe_ffn(cur, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, + model.layers[il].ffn_exp_probs_b, + n_expert, n_expert_used, + LLM_FFN_SILU, + norm_w, scale_w, w_scale, + (llama_expert_gating_func_type) hparams.expert_gating_func, + il); + cb(moe_out, "ffn_moe_out", il); + + // shared expert MLP (always added on MoE layers in Step35) + ggml_tensor * sh_out = build_ffn(cur, + model.layers[il].ffn_up_shexp, nullptr, nullptr, + model.layers[il].ffn_gate_shexp, nullptr, nullptr, + model.layers[il].ffn_down_shexp, nullptr, nullptr, + nullptr, + LLM_FFN_SILU, LLM_FFN_PAR, il); + cb(sh_out, "ffn_shared_out", il); + + cur = ggml_add(ctx0, moe_out, sh_out); + cb(cur, "ffn_out", il); + } + cur = ggml_add(ctx0, cur, ffn_inp); + cur = build_cvec(cur, il); + cb(cur, "l_out", il); + + inpL = cur; + } + + cur = inpL; + + cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1); + cb(cur, "result_norm", -1); + res->t_embd = cur; + + cur = build_lora_mm(model.output, cur); + cb(cur, "result_output", -1); + res->t_logits = cur; + + ggml_build_forward_expand(gf, cur); +} |