101 lines
3.5 KiB
C++
101 lines
3.5 KiB
C++
#include "models.h"
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llm_build_cogvlm::llm_build_cogvlm(const llama_model & model, const llm_graph_params & params) :
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llm_graph_context(params) {
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const int64_t n_embd_head = hparams.n_embd_head_v;
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float kq_scale = 1.0f / sqrtf(float(n_embd_head));
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
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GGML_ASSERT(n_embd_head == hparams.n_rot);
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ggml_tensor *inpL, *cur;
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inpL = build_inp_embd(model.tok_embd);
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ggml_tensor * inp_pos = build_inp_pos();
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auto * inp_attn = build_attn_inp_kv();
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// check ubatch to see if we have input tokens (text)
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// or an input embedding vector (image)
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bool is_text;
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if (ubatch.token) {
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is_text = true;
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} else {
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is_text = false;
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}
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for (int il = 0; il < n_layer; ++il) {
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// get either the text or image weight tensors
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ggml_tensor *wqkv, *wo;
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ggml_tensor *ffn_gate, *ffn_down, *ffn_up;
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if (is_text) {
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wqkv = model.layers[il].wqkv;
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wo = model.layers[il].wo;
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ffn_gate = model.layers[il].ffn_gate;
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ffn_down = model.layers[il].ffn_down;
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ffn_up = model.layers[il].ffn_up;
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} else {
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wqkv = model.layers[il].visexp_attn_wqkv;
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wo = model.layers[il].visexp_attn_wo;
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ffn_gate = model.layers[il].visexp_ffn_gate;
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ffn_down = model.layers[il].visexp_ffn_down;
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ffn_up = model.layers[il].visexp_ffn_up;
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}
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ggml_tensor * inpSA = inpL;
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cur = build_norm(inpSA, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
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// build self attention
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{
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ggml_tensor * qkv = build_lora_mm(wqkv, cur);
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// split qkv into Q, K, V along the first dimension
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ggml_tensor * Qcur =
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ggml_view_3d(ctx0, qkv, n_embd_head, n_head, n_tokens, n_embd_head * sizeof(float), qkv->nb[1], 0);
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ggml_tensor * Kcur = ggml_view_3d(ctx0, qkv, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float),
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qkv->nb[1], n_embd * ggml_element_size(qkv));
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ggml_tensor * Vcur = ggml_view_3d(ctx0, qkv, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float),
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qkv->nb[1], 2 * n_embd * ggml_element_size(qkv));
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Qcur = ggml_rope(ctx0, Qcur, inp_pos, n_embd_head, rope_type);
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Kcur = ggml_rope(ctx0, Kcur, inp_pos, n_embd_head, rope_type);
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cur = build_attn(inp_attn,
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wo, nullptr,
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Qcur, Kcur, Vcur,
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nullptr, nullptr, nullptr,
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kq_scale, il);
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cb(cur, "attn_out", il);
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}
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ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
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cb(ffn_inp, "ffn_inp", il);
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cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, 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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ffn_up, NULL, NULL,
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ffn_gate, NULL, NULL,
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ffn_down, NULL, NULL,
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NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
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cur = ggml_add(ctx0, cur, ffn_inp);
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cb(cur, "ffn_out", il);
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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, 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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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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