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https://github.com/ikawrakow/ik_llama.cpp.git
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3c9680fd3c
The graph builder for Minimax M3 (build_minimaxm3.cpp) was not passing model.layers[il].ffn_up_gate_exps to llm_build_std_moe_ffn, unlike Minimax M2 and all other MoE model graph builders. When -muge (merge_up_gate_experts) is enabled, the merge creates a single ffn_up_gate_exps tensor with ffn_up_exps and ffn_gate_exps as views. Only the parent merged tensor gets the split 'extra' pointer set. Without passing it as up_gate_exps parameter, the function sees null split pointers for up/gate (the views) while split_down_exps is valid, causing the assertion at llama-build-context.cpp:1453 to fail.
70 lines
2.7 KiB
C++
70 lines
2.7 KiB
C++
#include "../llama-build-context.h"
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#include "../llama-model.h"
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#include "../llama-context.h"
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ggml_cgraph* llm_build_context::build_minimaxm3() {
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ggml_cgraph * gf = new_graph_custom();
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const int64_t n_embd_head = hparams.n_embd_head_v(0);
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k(0));
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ggml_tensor * cur;
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ggml_tensor * inpL;
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inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
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ggml_tensor * inp_pos = build_inp_pos();
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ggml_tensor * inp_out_ids = n_tokens > 1 ? build_inp_out_ids() : nullptr;
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ggml_tensor * KQ_mask = build_inp_KQ_mask();
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for (int il = 0; il < n_layer; ++il) {
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ggml_tensor * ffn_inp = build_std_attention(gf, model.layers[il].attn_norm, inpL,
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inp_pos, il == n_layer - 1 ? inp_out_ids : nullptr, nullptr,
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KQ_mask, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), 0.0f, 0,
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il, true, false, true);
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if ((uint32_t) il < hparams.n_layer_dense_lead) {
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cur = llm_build_ffn(ctx0, lctx, model.layers[il].ffn_norm, ffn_inp,
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model.layers[il].ffn_up, nullptr, nullptr,
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model.layers[il].ffn_gate, nullptr, nullptr,
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model.layers[il].ffn_down, nullptr, nullptr,
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nullptr,
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LLM_FFN_SWIGLU_OAI, LLM_FFN_PAR, cb, il, gf, true);
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} else {
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cur = llm_build_std_moe_ffn(ctx0, lctx, model.layers[il].ffn_norm, ffn_inp,
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model.layers[il].ffn_gate_inp,
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nullptr,
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model.layers[il].ffn_up_exps,
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nullptr,
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model.layers[il].ffn_gate_exps,
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nullptr,
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model.layers[il].ffn_down_exps,
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nullptr,
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model.layers[il].ffn_exp_probs_b,
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model.layers[il].ffn_up_shexp,
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nullptr,
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model.layers[il].ffn_gate_shexp,
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nullptr,
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model.layers[il].ffn_down_shexp,
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nullptr,
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n_expert, n_expert_used,
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LLM_FFN_SWIGLU_OAI,
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hparams.expert_weights_norm,
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hparams.expert_weights_scale != 0.0f, hparams.expert_weights_scale,
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(llm_expert_gating_func_type) hparams.expert_gating_func,
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LLM_FFN_SWIGLU_OAI,
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cb, il, gf, true, model.layers[il].ffn_up_gate_exps);
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}
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cur = lctx.cvec.apply_to(ctx0, cur, il);
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cb(cur, "l_out", il);
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inpL = cur;
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}
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cur = build_output(lctx, ctx0, inpL, model.output, model.output_norm, cb);
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cb(cur, "result_output", -1);
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ggml_build_forward_expand(gf, cur);
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return gf;
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}
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