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chore : fix models indent after refactor (#16992)
This commit is contained in:
Sigbjørn Skjæret
@@ -1,107 +1,106 @@
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#include "models.h"
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llm_build_jamba::llm_build_jamba(const llama_model & model, const llm_graph_params & params) : llm_graph_context_mamba(params) {
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const int64_t n_embd_head = hparams.n_embd_head_v;
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const int64_t n_embd_head = hparams.n_embd_head_v;
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ggml_tensor * cur;
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ggml_tensor * inpL;
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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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// {n_embd, n_tokens}
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inpL = build_inp_embd(model.tok_embd);
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auto * inp_hybrid = build_inp_mem_hybrid();
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auto * inp_hybrid = build_inp_mem_hybrid();
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ggml_tensor * inp_out_ids = build_inp_out_ids();
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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 int64_t n_head_kv = hparams.n_head_kv(il);
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for (int il = 0; il < n_layer; ++il) {
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const int64_t n_head_kv = hparams.n_head_kv(il);
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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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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 (n_head_kv == 0) {
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cur = build_mamba_layer(inp_hybrid->get_recr(), cur, model, ubatch, il);
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} else {
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// Attention
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if (n_head_kv == 0) {
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cur = build_mamba_layer(inp_hybrid->get_recr(), cur, model, ubatch, il);
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} else {
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// Attention
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struct ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
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struct ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
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struct ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
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struct ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
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struct ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
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struct ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
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cb(Qcur, "Qcur", il);
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cb(Kcur, "Kcur", il);
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cb(Vcur, "Vcur", il);
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cb(Qcur, "Qcur", il);
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cb(Kcur, "Kcur", il);
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cb(Vcur, "Vcur", il);
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Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
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Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
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Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
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Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
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Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
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Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
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cb(Qcur, "Qcur", il);
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cb(Kcur, "Kcur", il);
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cb(Vcur, "Vcur", il);
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cb(Qcur, "Qcur", il);
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cb(Kcur, "Kcur", il);
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cb(Vcur, "Vcur", il);
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// No RoPE :)
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cur = build_attn(inp_hybrid->get_attn(),
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model.layers[il].wo, NULL,
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Qcur, Kcur, Vcur, NULL, NULL, NULL, 1.0f/sqrtf(float(n_embd_head)), 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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struct ggml_tensor * ffn_inp = ggml_add(ctx0, inpL, cur);
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cb(cur, "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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// feed-forward network
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if (model.layers[il].ffn_gate_inp == nullptr) {
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// FFN
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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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} else {
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// MoE branch
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cur = build_moe_ffn(cur,
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model.layers[il].ffn_gate_inp,
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model.layers[il].ffn_up_exps,
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model.layers[il].ffn_gate_exps,
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model.layers[il].ffn_down_exps,
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nullptr,
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n_expert, n_expert_used,
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LLM_FFN_SILU, false,
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false, 0.0,
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LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
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il);
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cb(cur, "ffn_moe_out", il);
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}
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// residual
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cur = ggml_add(ctx0, ffn_inp, cur);
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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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// No RoPE :)
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cur = build_attn(inp_hybrid->get_attn(),
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model.layers[il].wo, NULL,
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Qcur, Kcur, Vcur, NULL, NULL, NULL, 1.0f/sqrtf(float(n_embd_head)), il);
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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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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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struct ggml_tensor * ffn_inp = ggml_add(ctx0, inpL, cur);
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cb(cur, "ffn_inp", il);
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cb(cur, "result_norm", -1);
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res->t_embd = cur;
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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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// lm_head
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cur = build_lora_mm(model.output, cur);
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// feed-forward network
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if (model.layers[il].ffn_gate_inp == nullptr) {
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// FFN
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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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} else {
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// MoE branch
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cur = build_moe_ffn(cur,
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model.layers[il].ffn_gate_inp,
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model.layers[il].ffn_up_exps,
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model.layers[il].ffn_gate_exps,
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model.layers[il].ffn_down_exps,
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nullptr,
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n_expert, n_expert_used,
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LLM_FFN_SILU, false,
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false, 0.0,
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LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
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il);
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cb(cur, "ffn_moe_out", il);
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}
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// residual
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cur = ggml_add(ctx0, ffn_inp, cur);
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cb(cur, "result_output", -1);
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res->t_logits = cur;
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cur = build_cvec(cur, il);
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cb(cur, "l_out", il);
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ggml_build_forward_expand(gf, cur);
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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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