87 lines
2.8 KiB
C++
87 lines
2.8 KiB
C++
#include "models.h"
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llm_build_rwkv6qwen2::llm_build_rwkv6qwen2(const llama_model & model, const llm_graph_params & params) : llm_build_rwkv6_base(model, params) {
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GGML_ASSERT(n_embd == hparams.n_embd_r());
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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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auto * rs_inp = build_rs_inp();
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const auto n_embd = hparams.n_embd;
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const auto n_seq_tokens = ubatch.n_seq_tokens;
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const auto n_seqs = ubatch.n_seqs;
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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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const llama_layer * layer = &model.layers[il];
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inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);
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ggml_tensor * token_shift = build_rwkv_token_shift_load(rs_inp, ubatch, il);
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ggml_tensor * att_norm = build_norm(inpL, layer->attn_norm, layer->attn_norm_b, LLM_NORM_RMS, il);
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cb(att_norm, "attn_norm", il);
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ggml_tensor * x_prev = ggml_concat(
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ctx0,
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token_shift,
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ggml_view_3d(ctx0, att_norm, n_embd, n_seq_tokens - 1, n_seqs, att_norm->nb[1], att_norm->nb[2], 0),
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1
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);
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cur = build_rwkv6_time_mix(rs_inp, att_norm, x_prev, ubatch, il);
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token_shift = ggml_view_3d(ctx0, att_norm, n_embd, 1, n_seqs, att_norm->nb[1], att_norm->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(att_norm));
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ggml_build_forward_expand(gf, build_rwkv_token_shift_store(token_shift, ubatch, il));
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ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
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cb(ffn_inp, "ffn_inp", il);
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cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
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ffn_inp = ggml_reshape_2d(ctx0, ffn_inp, n_embd, n_tokens);
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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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ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
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}
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// feed-forward network
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cur = build_norm(ffn_inp,
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model.layers[il].ffn_norm, NULL,
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LLM_NORM_RMS, il);
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cb(cur, "ffn_norm", il);
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cur = build_ffn(cur,
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model.layers[il].ffn_up, NULL, NULL,
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model.layers[il].ffn_gate, NULL, NULL,
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model.layers[il].ffn_down, NULL, NULL,
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NULL,
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LLM_FFN_SILU, LLM_FFN_PAR, il);
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cb(cur, "ffn_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, model.output_norm, model.output_norm_b, 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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