56 lines
1.4 KiB
C++
56 lines
1.4 KiB
C++
#include "models.h"
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llm_build_mamba::llm_build_mamba(const llama_model & model, const llm_graph_params & params) : llm_graph_context_mamba(params) {
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ggml_tensor * cur;
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ggml_tensor * inpL;
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// {n_embd, n_tokens}
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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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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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// norm
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cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
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cb(cur, "attn_norm", il);
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if (model.arch == LLM_ARCH_MAMBA2) {
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cur = build_mamba2_layer(rs_inp, cur, model, ubatch, il);
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} else {
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cur = build_mamba_layer(rs_inp, cur, model, ubatch, 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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inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
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}
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// residual
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cur = ggml_add(ctx0, cur, inpL);
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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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// final rmsnorm
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cur = build_norm(inpL, model.output_norm, NULL, 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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// lm_head
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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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