#include "models.h" llm_build_phi2::llm_build_phi2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) { const int64_t n_embd_head = hparams.n_embd_head_v; const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); ggml_tensor * cur; ggml_tensor * attn_norm_output; ggml_tensor * ffn_output; ggml_tensor * inpL; inpL = build_inp_embd(model.tok_embd); // inp_pos - contains the positions ggml_tensor * inp_pos = build_inp_pos(); auto * inp_attn = build_attn_inp_kv(); ggml_tensor * inp_out_ids = build_inp_out_ids(); for (int il = 0; il < n_layer; ++il) { attn_norm_output = build_norm(inpL, model.layers[il].attn_norm, model.layers[il].attn_norm_b, LLM_NORM, il); cb(attn_norm_output, "attn_norm", il); // self-attention { ggml_tensor * Qcur = nullptr; ggml_tensor * Kcur = nullptr; ggml_tensor * Vcur = nullptr; if (model.layers[il].wqkv) { cur = build_lora_mm(model.layers[il].wqkv, attn_norm_output); cb(cur, "wqkv", il); cur = ggml_add(ctx0, cur, model.layers[il].bqkv); cb(cur, "bqkv", il); Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd)); Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 1*sizeof(float)*(n_embd)); Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)); } else { Qcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wq, attn_norm_output), model.layers[il].bq); Kcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wk, attn_norm_output), model.layers[il].bk); Vcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wv, attn_norm_output), model.layers[il].bv); Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens); } Qcur = ggml_rope_ext( ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); Kcur = ggml_rope_ext( ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); // with phi2, we scale the Q to avoid precision issues // ref: https://github.com/ml-explore/mlx-examples/blob/08e862336ade809bc37d1035f94b359e7d1a5152/phi2/phi2.py#L64-L66 Qcur = ggml_scale(ctx0, Qcur, 1.0f/sqrtf(float(n_embd_head))); cur = build_attn(inp_attn, model.layers[il].wo, model.layers[il].bo, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il); } if (il == n_layer - 1 && inp_out_ids) { cur = ggml_get_rows(ctx0, cur, inp_out_ids); inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); attn_norm_output = ggml_get_rows(ctx0, attn_norm_output, inp_out_ids); } // FF { ffn_output = build_ffn(attn_norm_output, model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL, NULL, NULL, NULL, model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL, NULL, LLM_FFN_GELU, LLM_FFN_SEQ, il); cb(ffn_output, "ffn_out", il); } cur = ggml_add(ctx0, cur, ffn_output); cur = ggml_add(ctx0, cur, inpL); cur = build_cvec(cur, il); cb(cur, "l_out", il); // input for next layer inpL = cur; } cur = build_norm(inpL, model.output_norm, model.output_norm_b, LLM_NORM, -1); cb(cur, "result_norm", -1); res->t_embd = cur; cur = build_lora_mm(model.output, cur); cb(cur, "result_output_no_bias", -1); cur = ggml_add(ctx0, cur, model.output_b); cb(cur, "result_output", -1); res->t_logits = cur; ggml_build_forward_expand(gf, cur); }