#include "models.h"

#include <sstream>

void llama_model_granite::load_arch_hparams(llama_model_loader & ml) {

    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);

    ml.get_key(LLM_KV_LOGIT_SCALE,                 hparams.f_logit_scale);

    ml.get_key(LLM_KV_RESIDUAL_SCALE,              hparams.f_residual_scale, false);

    ml.get_key(LLM_KV_EMBEDDING_SCALE,             hparams.f_embedding_scale, false);

    ml.get_key(LLM_KV_ATTENTION_SCALE,             hparams.f_attention_scale, false);

    // Granite4 Vision uses array deepstack_mapping

    ml.get_arr(LLM_KV_DEEPSTACK_MAPPING, hparams.deepstack_mapping_arr, false);

    // Count the unique deepstack input indices

    std::unordered_set<uint32_t> unique_deepstack_idxs;

    for (const auto val : hparams.deepstack_mapping_arr) {

        if (val >= 0) {

            unique_deepstack_idxs.insert(val);

        }

    }

    hparams.n_deepstack_layers = unique_deepstack_idxs.size();

    // Ensure all values are valid (avoid overflow attacks)

    for (const auto val : unique_deepstack_idxs) {

        if (val > hparams.n_deepstack_layers) {

            std::stringstream ss;

            ss << "Invalid deepstack index: " << val << " > " << hparams.n_deepstack_layers;

            throw std::runtime_error(ss.str());

        }

    }

    // Granite uses rope_finetuned as a switch for rope, so default to true

    bool rope_finetuned = true;

    ml.get_key(LLM_KV_ROPE_SCALING_FINETUNED, rope_finetuned, false);

    hparams.rope_finetuned = rope_finetuned;

    switch (hparams.n_layer()) {

        case 32: type = LLM_TYPE_3B; break;

        case 40: type = LLM_TYPE_3B; break;

        // Add additional layer/vocab/etc checks here for other model sizes

        default: type = LLM_TYPE_UNKNOWN;

    }

    // For Granite MoE Shared

    ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, /* required */ false);

}

void llama_model_granite::load_arch_tensors(llama_model_loader &) {

    LLAMA_LOAD_LOCALS;

    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);

    // output

    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);

    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);

    // if output is NULL, init from the input tok embed

    if (output == NULL) {

        output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);

    }

    for (int i = 0; i < n_layer; ++i) {

        auto & layer = layers[i];

        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);

        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head, n_embd_k_gqa, n_embd_v_gqa, 0);

        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);

        // optional bias tensors

        layer.wo_b = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);

        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);

        if (hparams.rope_scaling_type_train == LLAMA_ROPE_SCALING_TYPE_LONGROPE) {

            layer.rope_long  = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_LONG,  "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));

            layer.rope_short = create_tensor(tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));

        }

        else {

            layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED | (i != 0 ? TENSOR_DUPLICATED : 0));

        }

        if (n_expert == 0) {

            layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);

            layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);

            layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);

            // optional MLP bias

            layer.ffn_gate_b = create_tensor(tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);

            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);

            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, TENSOR_NOT_REQUIRED);

        } else {

            layer.ffn_gate_inp  = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), {n_embd, n_expert}, 0);

            layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd,   n_ff, n_expert}, TENSOR_NOT_REQUIRED);

            layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {  n_ff, n_embd, n_expert}, 0);

            layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert}, 0);

            // For Granite MoE Shared

            if (hparams.n_ff_shexp > 0) {

                layer.ffn_gate_shexp = create_tensor(tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, hparams.n_ff_shexp}, 0);

                layer.ffn_up_shexp   = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, hparams.n_ff_shexp}, 0);

                layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, 0);

            }

        }

    }

}

std::unique_ptr<llm_graph_context> llama_model_granite::build_arch_graph(const llm_graph_params & params) const {

    return std::make_unique<graph>(*this, params);

}

llama_model_granite::graph::graph(

    const llama_model & model,

    const llm_graph_params & params)

    : llm_graph_context(params) {

    const int64_t n_embd_head = hparams.n_embd_head_v();

    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());

    GGML_ASSERT(n_embd_head == n_rot);

    ggml_tensor * cur;

    ggml_tensor * inpL;

    inpL = build_inp_embd(model.tok_embd);

    // inp_pos - built only if rope enabled

    ggml_tensor * inp_pos = nullptr;

    if (hparams.rope_finetuned) {

        inp_pos = build_inp_pos();

    }

    auto * inp_attn = build_attn_inp_kv();

    ggml_tensor * inp_out_ids = build_inp_out_ids();

    for (int il = 0; il < n_layer; ++il) {

        // Granite Vision 4.1 deepstack: inject the projector stream that

        // targets decoder layer `il` before the decoder runs.

        // NOTE: skip the first deepstack layer since that's inpL

        const auto & deepstack_emb_idx = hparams.deepstack_mapping_arr[il];

        if (il > 0 && deepstack_emb_idx >= 0) {

            ggml_tensor * ds = ggml_view_2d(ctx0,

                res->t_inp_embd, n_embd, n_tokens,

                res->t_inp_embd->nb[1],

                deepstack_emb_idx * n_embd * sizeof(float));

            inpL = ggml_add(ctx0, inpL, ds);

            cb(inpL, "deepstack_in", il);

        }

        ggml_tensor * inpSA = inpL;

        // norm

        cur = build_norm(inpL,

                model.layers[il].attn_norm, NULL,

                LLM_NORM_RMS, il);

        cb(cur, "attn_norm", il);

        // self-attention

        cur = build_attention_layer(

            cur, inp_pos, inp_attn,

            model, n_embd_head, il);

        if (il == n_layer - 1 && inp_out_ids) {

            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);

            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);

        }

        // ffn

        cur = build_layer_ffn(cur, inpSA, model, il);

        // input for next layer

        inpL = cur;

    }

    cur = inpL;

    cur = build_norm(cur,

            model.output_norm, NULL,

            LLM_NORM_RMS, -1);

    cb(cur, "result_norm", -1);

    res->t_embd = cur;

    // lm_head

    cur = build_lora_mm(model.output, cur, model.output_s);

    // For Granite architectures - scale logits

    cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale);

    cb(cur, "result_output", -1);

    res->t_logits = cur;

    ggml_build_forward_expand(gf, cur);

}

ggml_tensor * llama_model_granite::graph::build_attention_layer(

          ggml_tensor             * cur,

          ggml_tensor             * inp_pos,

          llm_graph_input_attn_kv * inp_attn,

    const llama_model             & model,

    const int64_t                 n_embd_head,

    const int                     il) {

    auto [Qcur, Kcur, Vcur] = build_qkv(model.layers[il], cur,

            n_embd_head, hparams.n_head(il), hparams.n_head_kv(il), il);

    const bool use_rope = hparams.rope_finetuned;

    if (use_rope) {

        ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);

        Qcur = ggml_rope_ext(

                ctx0, Qcur, inp_pos, rope_factors,

                n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,

                ext_factor, attn_factor, beta_fast, beta_slow

                );

        Kcur = ggml_rope_ext(

                ctx0, Kcur, inp_pos, rope_factors,

                n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,

                ext_factor, attn_factor, beta_fast, beta_slow

                );

    }

    cb(Qcur, "Qcur", il);

    cb(Kcur, "Kcur", il);

    cb(Vcur, "Vcur", il);

    const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;

    cur = build_attn(inp_attn,

            model.layers[il].wo, model.layers[il].wo_b, model.layers[il].wo_s,

            Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);

            cb(cur, "attn_out", il);

    return cur;

}

ggml_tensor * llama_model_granite::graph::build_layer_ffn(

          ggml_tensor       * cur,

          ggml_tensor       * inpSA,

    const llama_model       & model,

    const int                 il) {

    // For Granite architectures - scale residual

    if (hparams.f_residual_scale) {

        cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);

    }

    ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);

    cb(ffn_inp, "ffn_inp", il);

    // feed-forward network (non-MoE)

    if (model.layers[il].ffn_gate_inp == nullptr) {

        cur = build_norm(ffn_inp,

                model.layers[il].ffn_norm, NULL,

                LLM_NORM_RMS, il);

                cb(cur, "ffn_norm", il);

        cur = build_ffn(cur,

                model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,

                model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,

                model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,

                NULL,

                LLM_FFN_SILU, LLM_FFN_PAR, il);

                cb(cur, "ffn_out", il);

    } else {

        // MoE branch

        cur = build_norm(ffn_inp,

                model.layers[il].ffn_norm, NULL,

                LLM_NORM_RMS, il);

                cb(cur, "ffn_norm", il);

        ggml_tensor * moe_out = build_moe_ffn(cur,

                model.layers[il].ffn_gate_inp,

                model.layers[il].ffn_up_exps,

                model.layers[il].ffn_gate_exps,

                model.layers[il].ffn_down_exps,

                nullptr,

                n_expert, n_expert_used,

                LLM_FFN_SILU, true,

                hparams.expert_weights_scale,

                LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,

                il);

        cb(moe_out, "ffn_moe_out", il);

        // For Granite MoE Shared

        if (hparams.n_ff_shexp > 0) {

            ggml_tensor * ffn_shexp = build_ffn(cur,

                model.layers[il].ffn_up_shexp,   NULL, NULL,

                model.layers[il].ffn_gate_shexp, NULL, NULL,

                model.layers[il].ffn_down_shexp, NULL, NULL,

                NULL,

                LLM_FFN_SILU, LLM_FFN_PAR, il);

            cb(ffn_shexp, "ffn_shexp", il);

            cur = ggml_add(ctx0, moe_out, ffn_shexp);

            cb(cur, "ffn_out", il);

        } else {

            cur = moe_out;

        }

    }

    // For Granite architectures - scale residual

    if (hparams.f_residual_scale) {

        cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);

    }

    cur = ggml_add(ctx0, cur, ffn_inp);

    cb(cur, "ffn_out", il);

    cur = build_cvec(cur, il);

    cb(cur, "l_out", il);

    return cur;

}