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#include "models.h"
llm_build_rwkv6::llm_build_rwkv6(const llama_model & model, const llm_graph_params & params) :
llm_build_rwkv6_base(model, params) {
GGML_ASSERT(hparams.token_shift_count == 2);
ggml_tensor * cur;
ggml_tensor * inpL;
inpL = build_inp_embd(model.tok_embd);
inpL = build_norm(inpL, model.tok_norm, model.tok_norm_b, LLM_NORM, -1);
auto * rs_inp = build_rs_inp();
const auto n_embd = hparams.n_embd;
const auto n_seq_tokens = ubatch.n_seq_tokens;
const auto n_seqs = ubatch.n_seqs;
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
const llama_layer * layer = &model.layers[il];
inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);
ggml_tensor * token_shift = build_rwkv_token_shift_load(rs_inp, ubatch, il);
ggml_tensor * att_shift =
ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1], token_shift->nb[2], 0);
ggml_tensor * ffn_shift = ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1],
token_shift->nb[2], n_embd * ggml_element_size(token_shift));
ggml_tensor * att_norm = build_norm(inpL, layer->attn_norm, layer->attn_norm_b, LLM_NORM, il);
cb(att_norm, "attn_norm", il);
ggml_tensor * x_prev = ggml_concat(
ctx0, att_shift,
ggml_view_3d(ctx0, att_norm, n_embd, n_seq_tokens - 1, n_seqs, att_norm->nb[1], att_norm->nb[2], 0), 1);
cur = build_rwkv6_time_mix(rs_inp, att_norm, x_prev, ubatch, il);
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
cb(ffn_inp, "ffn_inp", il);
ggml_tensor * ffn_norm = build_norm(ffn_inp, layer->attn_norm_2, layer->attn_norm_2_b, LLM_NORM, il);
cb(ffn_norm, "ffn_norm", il);
x_prev = ggml_concat(
ctx0, ffn_shift,
ggml_view_3d(ctx0, ffn_norm, n_embd, n_seq_tokens - 1, n_seqs, ffn_norm->nb[1], ffn_norm->nb[2], 0), 1);
token_shift = ggml_concat(ctx0,
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)),
ggml_view_3d(ctx0, ffn_norm, n_embd, 1, n_seqs, ffn_norm->nb[1], ffn_norm->nb[2],
(n_seq_tokens - 1) * n_embd * ggml_element_size(ffn_norm)),
1);
ggml_build_forward_expand(gf, build_rwkv_token_shift_store(token_shift, ubatch, il));
ffn_inp = ggml_reshape_2d(ctx0, ffn_inp, n_embd, n_tokens);
ffn_norm = ggml_reshape_2d(ctx0, ffn_norm, n_embd, n_tokens);
x_prev = ggml_reshape_2d(ctx0, x_prev, n_embd, n_tokens);
cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
if (il == n_layer - 1 && inp_out_ids) {
ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
ffn_norm = ggml_get_rows(ctx0, ffn_norm, inp_out_ids);
x_prev = ggml_get_rows(ctx0, x_prev, inp_out_ids);
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
}
cur = build_rwkv6_channel_mix(layer, ffn_norm, x_prev, LLM_ARCH_RWKV6);
cur = ggml_add(ctx0, cur, ffn_inp);
if (hparams.rescale_every_n_layers != 0 && (il + 1) % hparams.rescale_every_n_layers == 0) {
cur = ggml_scale(ctx0, cur, 0.5F);
}
cur = build_cvec(cur, il);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
cur = build_norm(cur, 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", -1);
res->t_logits = cur;
ggml_build_forward_expand(gf, cur);
}
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