#include "models.h"

// get 2D slice view from a 3D tensor, the idx corresponds to the 3rd dim

static ggml_tensor * ggml_view_2d_slice(ggml_context * ctx0, ggml_tensor * x, int idx) {

    GGML_ASSERT(idx < (int) x->ne[2]);

    return ggml_view_2d(ctx0, x, x->ne[0], x->ne[1], ggml_row_size(x->type, x->ne[0]),

                        idx * x->ne[0] * x->ne[1] * ggml_element_size(x));

}

llm_build_gemma4_iswa::llm_build_gemma4_iswa(const llama_model & model, const llm_graph_params & params) :

        llm_graph_context(params),

        model(model),

        n_embd_per_layer(model.hparams.n_embd_per_layer) {

    ggml_tensor * cur;

    ggml_tensor * inpL;

    inpL = build_inp_embd(model.tok_embd);

    // important: do not normalize weights for raw embeddings input (i.e. encoded image emdeddings)

    inpL = ggml_scale(ctx0, inpL, ubatch.token ? sqrtf(n_embd) : 1.0f);

    cb(inpL, "inp_scaled", -1);

    // inp_pos - contains the positions

    ggml_tensor * inp_pos = build_inp_pos();

    // TODO: is causal == true correct? might need some changes

    auto * inp_attn = build_attn_inp_kv_iswa();

    ggml_tensor * inp_out_ids = build_inp_out_ids();

    ggml_tensor * inp_per_layer = nullptr;

    if (model.per_layer_tok_embd) {

        inp_per_layer = build_inp_per_layer();

        ggml_build_forward_expand(gf, inp_per_layer);

        // inp_per_layer shape: [n_embd_per_layer, n_tokens, n_layer]

        inp_per_layer = project_per_layer_inputs(inpL, inp_per_layer);

    }

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

        const int64_t n_embd_head = hparams.n_embd_head_k(il);

        GGML_ASSERT(n_embd_head == hparams.n_embd_head_v(il));

        const int64_t n_head    = hparams.n_head(il);

        const int64_t n_head_kv = hparams.n_head_kv(il);

        const float freq_base_l  = model.get_rope_freq_base(cparams, il);

        const float freq_scale_l = model.get_rope_freq_scale(cparams, il);

        const int   n_rot_l      = hparams.n_rot(il);

        // norm

        cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);

        cb(cur, "attn_norm", il);

        ggml_tensor * freq_factors = nullptr;

        if (!hparams.is_swa(il)) {

            // full_attention layers use rope_freqs for proportional rope

            freq_factors = model.layers[il].rope_freqs;

        }

        // Q projection (shared for both non-KV and KV layers)

        // this is to mirror Gemma4Attention in pytorch code

        ggml_tensor * Qcur;

        {

            Qcur = build_lora_mm(model.layers[il].wq, cur);

            cb(Qcur, "Qcur", il);

            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);

            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, nullptr, LLM_NORM_RMS, il);

            cb(Qcur, "Qcur_normed", il);

            Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, freq_factors, n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,

                                 ext_factor, attn_factor, beta_fast, beta_slow);

            cb(Qcur, "Qcur_pos", il);

        }

        // self-attention

        if (hparams.has_kv(il)) {

            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);

            cb(Kcur, "Kcur", il);

            ggml_tensor * Vcur = model.layers[il].wv

                                    ? build_lora_mm(model.layers[il].wv, cur)

                                    : Kcur; // if v_proj is not present, use Kcur as Vcur

            cb(Vcur, "Vcur", il);

            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);

            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);

            Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, nullptr, LLM_NORM_RMS, il);

            Vcur = ggml_rms_norm(ctx0, Vcur, hparams.f_norm_rms_eps);

            cb(Kcur, "Kcur_normed", il);

            cb(Vcur, "Vcur_normed", il);

            Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, freq_factors, n_rot_l, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,

                                 ext_factor, attn_factor, beta_fast, beta_slow);

            cb(Kcur, "Kcur_pos", il);

            cur = build_attn(inp_attn, model.layers[il].wo,

                    nullptr, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr,

                    hparams.f_attention_scale, il);

        } else {

            // reuse KV cache of earlier layers

            cur = build_attn(inp_attn,

                    model.layers[il].wo, nullptr,

                    Qcur, nullptr, nullptr, nullptr, nullptr, nullptr, hparams.f_attention_scale, il);

        }

        // TODO @ngxson : strip unused token right after the last KV layer to speed up prompt processing

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

            cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);

            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);

        }

        cur = build_norm(cur,

                model.layers[il].attn_post_norm, nullptr,

                LLM_NORM_RMS, il);

        cb(cur, "attn_post_norm", il);

        ggml_tensor * attn_out = ggml_add(ctx0, cur, inpL);

        cb(attn_out, "attn_out", il);

        // feed-forward network

        const bool is_moe_layer = model.layers[il].ffn_gate_inp != nullptr;

        if (is_moe_layer) {

            // MLP (shared exp)

            ggml_tensor * cur_mlp = build_norm(attn_out,

                    model.layers[il].ffn_norm, nullptr,

                    LLM_NORM_RMS, il);

            cb(cur_mlp, "ffn_norm_1", il);

            cur_mlp = build_ffn(cur_mlp,

                    model.layers[il].ffn_up,   nullptr, nullptr,

                    model.layers[il].ffn_gate, nullptr, nullptr,

                    model.layers[il].ffn_down, nullptr, nullptr,

                    nullptr,

                    LLM_FFN_GELU, LLM_FFN_PAR, il);

            cur_mlp = build_norm(cur_mlp,

                    model.layers[il].ffn_post_norm_1, nullptr,

                    LLM_NORM_RMS, il);

            cb(cur_mlp, "ffn_mlp", il);

            // Expert FFN

            ggml_tensor * cur_moe = build_norm(attn_out,

                    model.layers[il].ffn_pre_norm_2, nullptr,

                    LLM_NORM_RMS, il);

            cb(cur_moe, "ffn_norm_2", il);

            // custom MoE logits calculation (router operates on attn_out, not cur)

            ggml_tensor * tmp = ggml_rms_norm(ctx0, attn_out, hparams.f_norm_rms_eps);

            tmp = ggml_scale(ctx0, tmp, 1.0f / sqrtf((float) n_embd));

            tmp = ggml_mul(ctx0, tmp, model.layers[il].ffn_gate_inp_s);

            ggml_tensor * logits = build_lora_mm(model.layers[il].ffn_gate_inp, tmp); // [n_expert, n_tokens]

            cb(logits, "ffn_moe_logits", il);

            cur_moe = build_moe_ffn(cur_moe,

                    nullptr, // gate_inp

                    nullptr, // up_exps

                    nullptr, // gate_exps

                    model.layers[il].ffn_down_exps,

                    nullptr, // exp_probs_b (not used for gemma4)

                    n_expert, n_expert_used,

                    LLM_FFN_GELU, true,

                    1.0f,

                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,

                    il, logits,

                    model.layers[il].ffn_gate_up_exps,

                    nullptr, // up_exps_s

                    nullptr, // gate_exps_s

                    model.layers[il].ffn_down_exps_s);

            cur_moe = build_norm(cur_moe,

                    model.layers[il].ffn_post_norm_2, nullptr,

                    LLM_NORM_RMS, il);

            cb(cur_moe, "ffn_moe", il);

            cur = ggml_add(ctx0, cur_mlp, cur_moe);

            cb(cur, "ffn_moe_combined", il);

        } else {

            cur = build_norm(attn_out,

                    model.layers[il].ffn_norm, nullptr,

                    LLM_NORM_RMS, il);

            cb(cur, "ffn_norm", il);

            cur = build_ffn(cur,

                    model.layers[il].ffn_up,   nullptr, nullptr,

                    model.layers[il].ffn_gate, nullptr, nullptr,

                    model.layers[il].ffn_down, nullptr, nullptr,

                    nullptr,

                    LLM_FFN_GELU, LLM_FFN_PAR, il);

            cb(cur, "ffn_out", il);

        }

        cur = build_norm(cur,

                model.layers[il].ffn_post_norm, nullptr,

                LLM_NORM_RMS, -1);

        cb(cur, "ffn_post_norm", il);

        // residual connection

        cur = ggml_add(ctx0, cur, attn_out);

        // per-layer embedding

        if (inp_per_layer) {

            ggml_tensor * pe_in = cur;

            cb(cur, "pe_in", il);

            cur = build_lora_mm(model.layers[il].per_layer_inp_gate, cur); // [n_embd_per_layer, n_tokens]

            cur = ggml_gelu(ctx0, cur);

            ggml_tensor * inp_this_layer = ggml_view_2d_slice(ctx0, inp_per_layer, il); // [n_embd_per_layer, n_tokens]

            // TODO @ngxson : improve this

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

                inp_this_layer = ggml_get_rows(ctx0, inp_this_layer, inp_out_ids);

            }

            cur = ggml_mul(ctx0, cur, inp_this_layer);

            cur = build_lora_mm(model.layers[il].per_layer_proj, cur); // [n_embd, n_tokens]

            cur = build_norm(cur, model.layers[il].per_layer_post_norm, nullptr, LLM_NORM_RMS, il);

            cb(cur, "per_layer_embd_out", il);

            // residual connection

            cur = ggml_add(ctx0, pe_in, cur);

        }

        // layer_scalar

        if (model.layers[il].out_scale) {

            cur = ggml_mul(ctx0, cur, model.layers[il].out_scale);

            cb(cur, "out_scaled", il);

        }

        cur = build_cvec(cur, il);

        cb(cur, "l_out", il);

        // input for next layer

        inpL = cur;

    }

    cur = inpL;

    cur = build_norm(cur,

            model.output_norm, nullptr,

            LLM_NORM_RMS, -1);

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

    res->t_embd = cur;

    // lm_head

    cur = build_lora_mm(model.output, cur);

    if (hparams.f_final_logit_softcapping) {

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

        cur = ggml_tanh(ctx0, cur);

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

    }

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

    res->t_logits = cur;

    ggml_build_forward_expand(gf, cur);

}

// equivalent to get_per_layer_inputs() in python code

// output shape: [n_embd_per_layer, n_layer, n_tokens]

ggml_tensor * llm_build_gemma4_iswa::build_inp_per_layer() {

    auto inp = std::make_unique<llm_graph_input_embd>(n_embd);

    ggml_tensor * inp_per_layer;

    float tok_embd_scale = sqrtf((float) n_embd_per_layer);

    if (ubatch.token) {

        inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_tokens);

        ggml_set_input(inp->tokens);

        res->t_inp_tokens = inp->tokens;

        inp_per_layer = ggml_get_rows  (ctx0, model.per_layer_tok_embd, inp->tokens);

        inp_per_layer = ggml_reshape_3d(ctx0, inp_per_layer, n_embd_per_layer, n_layer, n_tokens);

        inp_per_layer = ggml_scale     (ctx0, inp_per_layer, tok_embd_scale);

        cb(inp_per_layer, "inp_per_layer_selected", -1);

        res->add_input(std::move(inp));

    } else {

        // Multimodal embedding path: use padding token (ID=0) embedding

        // TODO: verify if this is the correct behavior in transformers implementation

        const int64_t embd_size = model.per_layer_tok_embd->ne[0];  // n_embd_per_layer * n_layer

        // Extract and dequantize padding token embedding (row 0)

        ggml_tensor * padding = ggml_view_1d(ctx0, model.per_layer_tok_embd, embd_size, 0);

        inp_per_layer = ggml_cast (ctx0, padding, GGML_TYPE_F32);

        inp_per_layer = ggml_scale(ctx0, inp_per_layer, tok_embd_scale);

        // Reshape to [n_embd_per_layer, n_layer, 1]

        inp_per_layer = ggml_reshape_3d(ctx0, inp_per_layer, n_embd_per_layer, n_layer, 1);

        cb(inp_per_layer, "inp_per_layer_multimodal", -1);

    }

    return inp_per_layer;

}

// equivalent to project_per_layer_inputs() in python code

// this calculates the per-layer inputs, so the final tensor shape will have n_layer as the last dim

// inp_batch     shape: [n_embd, n_tokens]

// inp_per_layer shape: [n_embd_per_layer, n_layer, n_tokens] (from build_inp_per_layer)

// output shape: [n_embd_per_layer, n_tokens, n_layer]

ggml_tensor * llm_build_gemma4_iswa::project_per_layer_inputs(ggml_tensor * inp_batch, ggml_tensor * inp_per_layer) {

    const float per_layer_projection_scale = 1.0f / sqrtf((float) n_embd);

    const float per_layer_input_scale      = 1.0f / sqrtf(2.0f);

    // note: this matrix multiplication will be performed in the input layer (i.e. on the CPU)

    ggml_tensor * per_layer_proj;

    per_layer_proj = ggml_mul_mat   (ctx0, model.per_layer_model_proj, inp_batch);

    per_layer_proj = ggml_scale     (ctx0, per_layer_proj, per_layer_projection_scale);

    per_layer_proj = ggml_reshape_3d(ctx0, per_layer_proj, n_embd_per_layer, n_layer, n_tokens);

    per_layer_proj = build_norm(per_layer_proj, model.per_layer_proj_norm, nullptr, LLM_NORM_RMS, -1);

    cb(per_layer_proj, "per_layer_proj", -1);

    inp_per_layer = ggml_add  (ctx0, per_layer_proj, inp_per_layer);

    inp_per_layer = ggml_scale(ctx0, inp_per_layer, per_layer_input_scale);

    cb(inp_per_layer, "inp_per_layer", -1);

    // permute to shape: [n_embd_per_layer, n_tokens, n_layer]

    inp_per_layer = ggml_cont(ctx0, ggml_permute(ctx0, inp_per_layer, 0, 2, 1, 3));

    return inp_per_layer;

}