diff --git a/src/models/kimi-linear.cpp b/src/models/kimi-linear.cpp index 83349cc9ec..1e533fa51b 100644 --- a/src/models/kimi-linear.cpp +++ b/src/models/kimi-linear.cpp @@ -72,9 +72,11 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll // Note: Kimi MLA does NOT use RoPE (rotary_emb=None in vLLM) // So we don't need inp_pos - auto * inp = build_inp_mem_hybrid_k(); - auto * inp_rs = inp->get_recr(); - auto * inp_attn = inp->get_attn(); + auto * inp_kv = !hparams.is_mla() ? build_inp_mem_hybrid() : nullptr; + auto * inp_k = hparams.is_mla() ? build_inp_mem_hybrid_k() : nullptr; + auto * inp_rs = hparams.is_mla() ? inp_k->get_recr() : inp_kv->get_recr(); + auto * inp_attn_kv = !hparams.is_mla() ? inp_kv->get_attn() : nullptr; + auto * inp_attn_k = hparams.is_mla() ? inp_k->get_attn() : nullptr; // Output ids for selecting which tokens to output ggml_tensor * inp_out_ids = build_inp_out_ids(); @@ -272,7 +274,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll ggml_tensor * Vcur = kv_cmpr; cb(Vcur, "Vcur", il); - cur = build_attn(inp_attn, layer.wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, layer.wv_b, kq_scale_mla, il); + cur = build_attn(inp_attn_k, layer.wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, layer.wv_b, kq_scale_mla, il); cb(cur, "mla_out", il); } else { // MLA KV cache disabled. Fall back to MHA KV cache. Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head_k_mla, n_head, n_tokens); @@ -302,7 +304,7 @@ llm_build_kimi_linear::llm_build_kimi_linear(const llama_model & model, const ll // Direct softmax attention (with MHA KV cache) // Use build_attn with inp_attn for proper mask handling - cur = build_attn(inp_attn, layer.wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale_mla, il); + cur = build_attn(inp_attn_kv, layer.wo, NULL, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale_mla, il); cb(cur, "mla_out", il); } } else {