Coverage Report

Created: 2026-07-16 06:35

next uncovered line (L), next uncovered region (R), next uncovered branch (B)
/src/llama.cpp/src/models/mamba2.cpp
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#include "models.h"
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void llama_model_mamba2::load_arch_hparams(llama_model_loader & ml) {
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    ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
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    ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
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    ml.get_key(LLM_KV_SSM_STATE_SIZE,     hparams.ssm_d_state);
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    ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
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    ml.get_key(LLM_KV_SSM_GROUP_COUNT,    hparams.ssm_n_group);
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    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
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    switch (hparams.n_layer()) {
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        case 24:
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            switch (hparams.n_embd) {
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                case 768: type = LLM_TYPE_SMALL; break;
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                default: type = LLM_TYPE_UNKNOWN;
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            } break;
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        case 48:
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            switch (hparams.n_embd) {
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                case 1024: type = LLM_TYPE_MEDIUM; break;
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                case 1536: type = LLM_TYPE_LARGE; break;
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                case 2048: type = LLM_TYPE_XL; break;
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                default: type = LLM_TYPE_UNKNOWN;
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            } break;
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        case 64:
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            switch (hparams.n_embd) {
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                case 2560: type = LLM_TYPE_3B; break;
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                case 4096: type = LLM_TYPE_7B; break;
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                default: type = LLM_TYPE_UNKNOWN;
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            } break;
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        default: type = LLM_TYPE_UNKNOWN;
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    }
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}
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void llama_model_mamba2::load_arch_tensors(llama_model_loader &) {
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    LLAMA_LOAD_LOCALS;
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    const int64_t d_conv  = hparams.ssm_d_conv;
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    const int64_t d_inner = hparams.ssm_d_inner;
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    const int64_t d_state = hparams.ssm_d_state;
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    const int64_t n_group = hparams.ssm_n_group;
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    const int64_t dt_rank  = hparams.ssm_dt_rank;
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    const int64_t conv_dim = d_inner + 2 * n_group * d_state;
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    const int64_t d_in_proj = d_inner + conv_dim + dt_rank;
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    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
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    // output
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    {
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        output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
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        output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
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        // if output is NULL, init from the input tok embed, duplicated to allow offloading
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        if (output == NULL) {
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            output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
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        }
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    }
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    for (int i = 0; i < n_layer; ++i) {
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        auto & layer = layers[i];
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        // norm
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        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
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        layer.ssm_in = create_tensor(tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, d_in_proj}, 0);
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        layer.ssm_conv1d = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner + 2*n_group*d_state}, 0);
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        layer.ssm_conv1d_b = create_tensor(tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner + 2*n_group*d_state}, 0);
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        layer.ssm_dt_b = create_tensor(tn(LLM_TENSOR_SSM_DT, "bias", i), {dt_rank}, 0);
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        // no "weight" suffix for these
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        layer.ssm_a = create_tensor(tn(LLM_TENSOR_SSM_A, i), {1, dt_rank}, 0);
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        layer.ssm_d = create_tensor(tn(LLM_TENSOR_SSM_D, i), {1, dt_rank}, 0);
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        layer.ssm_norm = create_tensor(tn(LLM_TENSOR_SSM_NORM, "weight", i), {d_inner / n_group, n_group}, 0);
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        // out_proj
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        layer.ssm_out = create_tensor(tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd}, 0);
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    }
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}
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std::unique_ptr<llm_graph_context> llama_model_mamba2::build_arch_graph(const llm_graph_params & params) const {
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    return std::make_unique<graph>(*this, params);
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}
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