#include "ggml.h"
#include "otherarch.h"

#include "utils.h"

#include <cassert>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <map>
#include <string>
#include <vector>
#include <iostream>

#include "model_adapter.h"



// load the model's weights from a file
bool mpt_model_load(const std::string & fname, mpt_model & model, gpt_vocab & vocab) {
    printf("%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());

    auto fin = std::ifstream(fname, std::ios::binary);
    if (!fin) {
        fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
        return false;
    }

    // verify magic
    {
        uint32_t magic;
        fin.read((char *)&magic, sizeof(magic));
        if (magic != 0x67676d6c) {
            fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
            return false;
        }
    }

    // load hparams
    {
        auto & hparams = model.hparams;

        fin.read((char *) &hparams.d_model,        sizeof(hparams.d_model));
        fin.read((char *) &hparams.max_seq_len,    sizeof(hparams.max_seq_len));
        fin.read((char *) &hparams.n_heads,        sizeof(hparams.n_heads));
        fin.read((char *) &hparams.n_layers,       sizeof(hparams.n_layers));
        fin.read((char *) &hparams.n_vocab,        sizeof(hparams.n_vocab));
        fin.read((char *) &hparams.alibi_bias_max, sizeof(hparams.alibi_bias_max));
        fin.read((char *) &hparams.clip_qkv,       sizeof(hparams.clip_qkv));
        fin.read((char *) &hparams.ftype,          sizeof(hparams.ftype));

        hparams.n_ctx = std::min(hparams.max_seq_len, hparams.n_ctx);

        const int32_t qntvr = hparams.ftype / GGML_QNT_VERSION_FACTOR;

        printf("%s: d_model        = %d\n", __func__, hparams.d_model);
        printf("%s: max_seq_len    = %d\n", __func__, hparams.max_seq_len);
        printf("%s: n_ctx          = %d\n", __func__, hparams.n_ctx);
        printf("%s: n_heads        = %d\n", __func__, hparams.n_heads);
        printf("%s: n_layers       = %d\n", __func__, hparams.n_layers);
        printf("%s: n_vocab        = %d\n", __func__, hparams.n_vocab);
        printf("%s: alibi_bias_max = %f\n", __func__, hparams.alibi_bias_max);
        printf("%s: clip_qkv       = %f\n", __func__, hparams.clip_qkv);
        printf("%s: ftype          = %d\n", __func__, hparams.ftype);
        printf("%s: qntvr          = %d\n", __func__, qntvr);

        hparams.ftype %= GGML_QNT_VERSION_FACTOR;
    }

    // load vocab
    {
        const int32_t n_vocab = model.hparams.n_vocab;

        std::string word;
        std::vector<char> buf(128);

        for (int i = 0; i < n_vocab; i++) {           
            uint32_t len;
            fin.read((char *) &len, sizeof(len));

            buf.resize(len);
            fin.read((char *) buf.data(), len);
            word.assign(buf.data(), len);

            vocab.token_to_id[word] = i;
            vocab.id_to_token[i] = word;
        }
    }

    // for the big tensors, we have the option to store the data in 16-bit
    // floats or quantized in order to save memory and also to speed up the
    // computation
    ggml_type wtype = ggml_ftype_to_ggml_type((ggml_ftype)(model.hparams.ftype));
    if (wtype == GGML_TYPE_COUNT) {
        fprintf(stderr, "%s: invalid model file '%s' (bad ftype value %d)\n", __func__, fname.c_str(),
                model.hparams.ftype);
        return false;
    }

    auto & ctx = model.ctx;

    size_t ctx_size = 0;

    const auto & hparams = model.hparams;
    const size_t n_ctx = hparams.n_ctx;

    {
        const size_t n_embd = hparams.d_model;
        const size_t n_layer = hparams.n_layers;
        const size_t n_vocab = hparams.n_vocab;

        ctx_size += n_embd * n_vocab * ggml_type_sizef(wtype); // wte_weight
        ctx_size += n_embd * ggml_type_sizef(GGML_TYPE_F32);   // norm_f_weight

        ctx_size += n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32));      // ln_1_weight
        ctx_size += n_layer * (3 * n_embd * n_embd * ggml_type_sizef(wtype)); // attn_Wqkv_weight
        ctx_size += n_layer * (n_embd * n_embd * ggml_type_sizef(wtype));     // attn_out_proj_weight
        ctx_size += n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32));      // ln_2_weight
        ctx_size += n_layer * (4 * n_embd * n_embd * ggml_type_sizef(wtype)); // mlp_mlp_up_weight
        ctx_size += n_layer * (n_embd * n_embd * 4 * ggml_type_sizef(wtype)); // mlp_mlp_down_weight

        ctx_size += (n_ctx * n_layer * n_embd * ggml_type_sizef(GGML_TYPE_F16)); // memory_k
        ctx_size += (n_ctx * n_layer * n_embd * ggml_type_sizef(GGML_TYPE_F16)); // memory_v

        ctx_size += (6 + 6 * n_layer) * 512; // object overhead

        printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size / (1024.0 * 1024.0));
    }

    // create the ggml context
    {
        struct ggml_init_params params;
        params.mem_size = ctx_size;
        params.mem_buffer = NULL;
        params.no_alloc = false;

        model.ctx = ggml_init(params);
        if (!model.ctx) {
            fprintf(stderr, "%s: ggml_init() failed\n", __func__);
            return false;
        }
    }

    // prepare memory for the weights
    {
        const auto & hparams = model.hparams;

        const size_t n_embd = hparams.d_model;
        const size_t n_layer = hparams.n_layers;
        const size_t n_vocab = hparams.n_vocab;

        model.layers.resize(n_layer);

        model.wte_weight    = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
        model.norm_f_weight = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

        // map by name
        model.tensors["transformer.wte.weight"]    = model.wte_weight;
        model.tensors["transformer.norm_f.weight"] = model.norm_f_weight;

        for (int i = 0; i < (int) n_layer; ++i) {
            auto & layer = model.layers[i];

            layer.norm_1_weight          = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,     n_embd);
            layer.c_attn_wqkv_weight     = ggml_new_tensor_2d(ctx, wtype,             n_embd, 3 * n_embd);
            layer.c_attn_out_proj_weight = ggml_new_tensor_2d(ctx, wtype,             n_embd,     n_embd);
            layer.norm_2_weight          = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,     n_embd);
            layer.ffn_up_proj            = ggml_new_tensor_2d(ctx, wtype,             n_embd, 4 * n_embd);
            layer.ffn_down_proj          = ggml_new_tensor_2d(ctx, wtype,         4 * n_embd,     n_embd);

            // map by name
            model.tensors["transformer.blocks." + std::to_string(i) + ".norm_1.weight"]        = layer.norm_1_weight;
            model.tensors["transformer.blocks." + std::to_string(i) + ".attn.Wqkv.weight"]     = layer.c_attn_wqkv_weight;
            model.tensors["transformer.blocks." + std::to_string(i) + ".attn.out_proj.weight"] = layer.c_attn_out_proj_weight;
            model.tensors["transformer.blocks." + std::to_string(i) + ".norm_2.weight"]        = layer.norm_2_weight;
            model.tensors["transformer.blocks." + std::to_string(i) + ".ffn.up_proj.weight"]   = layer.ffn_up_proj;
            model.tensors["transformer.blocks." + std::to_string(i) + ".ffn.down_proj.weight"] = layer.ffn_down_proj;
        }
    }

    // key + value memory
    {
        const auto & hparams = model.hparams;

        const size_t n_embd  = hparams.d_model;
        const size_t n_layer = hparams.n_layers;

        const int64_t n_mem      = n_layer * n_ctx;
        const int64_t n_elements = n_embd  * n_mem;

        model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
        model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);

        const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);

        printf("%s: memory_size = %8.2f MB, n_mem = %" PRId64 "\n", __func__, memory_size / 1024.0 / 1024.0, n_mem);
    }

    // load weights
    {
        int n_tensors = 0;
        size_t total_size = 0;

        printf("%s: ", __func__);

        while (true) {
            int32_t n_dims;
            int32_t length;
            int32_t ttype;

            fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
            fin.read(reinterpret_cast<char *>(&length), sizeof(length));
            fin.read(reinterpret_cast<char *>(&ttype), sizeof(ttype));

            if (fin.eof()) {
                break;
            }

            int32_t nelements = 1;
            int32_t ne[2] = {1, 1};
            for (int i = 0; i < n_dims; ++i) {
                fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
                nelements *= ne[i];
            }

            std::string name(length, 0);
            fin.read(&name[0], length);

            if (model.tensors.find(name.data()) == model.tensors.end()) {
                fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
                return false;
            }

            auto tensor = model.tensors[name.data()];
            if (ggml_nelements(tensor) != nelements) {
                fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
                return false;
            }

            if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
                fprintf(stderr,
                        "%s: tensor '%s' has wrong shape in model file: got [%5d, "
                        "%5d], expected [%5d, %5d]\n",
                        __func__, name.data(), (int)tensor->ne[0], (int)tensor->ne[1], ne[0], ne[1]);
                return false;
            }

            // for debugging
            if (0) {
                printf("%24s - [%5d, %5d], type = %6s, %6.2f MB, %9zu bytes\n", name.data(), ne[0], ne[1],
                       ggml_type_name(ggml_type(ttype)), ggml_nbytes(tensor) / 1024.0 / 1024.0, ggml_nbytes(tensor));
            }

            const size_t bpe = ggml_type_size(ggml_type(ttype));

            if ((nelements * bpe) / ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
                fprintf(stderr,
                        "%s: tensor '%s' has wrong size in model file: got %zu, "
                        "expected %zu\n",
                        __func__, name.data(), ggml_nbytes(tensor), nelements * bpe);
                return false;
            }

            fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));

            total_size += ggml_nbytes(tensor);
            if (++n_tensors % 8 == 0) {
                printf(".");
                fflush(stdout);
            }
        }

        printf(" done\n");

        printf("%s: model size = %8.2f MB / num tensors = %d\n", __func__, total_size / 1024.0 / 1024.0, n_tensors);
    }

    fin.close();

    return true;
}

// evaluate the transformer
//
//   - model:     the model
//   - n_threads: number of threads to use
//   - n_past:    the context size so far
//   - embd_inp:  the embeddings of the tokens in the context
//   - embd_w:    the predicted logits for the next token
//
bool mpt_eval(const mpt_model & model, const int n_threads, const int n_past,
              const std::vector<gpt_vocab::id> & embd_inp, std::vector<float> & embd_w, bool logits_all, size_t & mem_per_token) {
    const int N = embd_inp.size();

    const auto & hparams = model.hparams;

    const int n_embd  = hparams.d_model;
    const int n_layer = hparams.n_layers;
    const int n_head  = hparams.n_heads;
    const int n_vocab = hparams.n_vocab;
    const int n_ctx   = hparams.n_ctx;

    static size_t buf_size = 256u * 1024 * 1024;
    static void * buf = malloc(buf_size);

    // use 2 scratch buffers
    // TODO: very hacky solution - reimplement in a more elegant way
    static size_t scr0_size = (n_ctx>2048?1024u:512u)*1024*1024;
    static void * scr0 = malloc(scr0_size);

    static size_t scr1_size = (n_ctx>2048?1024u:512u)*1024*1024;
    static void * scr1 = malloc(scr1_size);

    if (mem_per_token > 0 && mem_per_token * N > buf_size) {
        const size_t buf_size_new = 1.1 * (mem_per_token * N); // add 10% to account for ggml object overhead
        // printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__,
        // buf_size, buf_size_new);
        // reallocate
        buf_size = buf_size_new;
        buf = realloc(buf, buf_size);
        if (buf == nullptr) {
            fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
            return false;
        }
    }

    struct ggml_init_params params;
    params.mem_size   = buf_size;
    params.mem_buffer = buf;
    params.no_alloc   = false;

    struct ggml_context * ctx0 = ggml_init(params);
    struct ggml_cgraph gf = {};
    gf.n_threads = n_threads;

    struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
    memcpy(embd->data, embd_inp.data(), N * ggml_element_size(embd));

    struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.wte_weight, embd);

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

        struct ggml_tensor * cur;

        ggml_set_scratch(ctx0, { 0, scr0_size, scr0, });

        // a = self.ln_1(x)
        {
            cur = ggml_norm(ctx0, inpL);

            cur = ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].norm_1_weight, cur), cur);
        }

        // self-attention
        //  b, _, past_key_value = self.attn(a, past_key_value=past_key_value,
        //  attn_bias=attn_bias, attention_mask=attention_mask,
        //  is_causal=is_causal)
        {
            // compute QKV
            cur = ggml_mul_mat(ctx0, model.layers[il].c_attn_wqkv_weight, cur);

            if (model.hparams.clip_qkv > 0.0f) {
                cur = ggml_clamp(ctx0, cur, -model.hparams.clip_qkv, model.hparams.clip_qkv);
            }

            struct ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0 * sizeof(float) * n_embd);
            struct ggml_tensor * Kcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1 * sizeof(float) * n_embd);
            struct ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2 * sizeof(float) * n_embd);

            // store key and value to memory
            {
                struct ggml_tensor * k =
                    ggml_view_1d(ctx0, model.memory_k, N * n_embd,
                                 (ggml_element_size(model.memory_k) * n_embd) * (il * n_ctx + n_past));
                struct ggml_tensor * v =
                    ggml_view_1d(ctx0, model.memory_v, N * n_embd,
                                 (ggml_element_size(model.memory_v) * n_embd) * (il * n_ctx + n_past));

                ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
                ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
            }

            // Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0,
            // 2, 1, 3) [64, N, 12]
            struct ggml_tensor * Q = ggml_permute(
                ctx0, ggml_cpy(ctx0, Qcur, ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd / n_head, n_head, N)), 0, 2,
                1, 3);

            // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1,
            // 3) [64, n_past + N, 12]
            struct ggml_tensor * K =
                ggml_permute(ctx0,
                             ggml_reshape_3d(ctx0,
                                             ggml_view_1d(ctx0, model.memory_k, (n_past + N) * n_embd,
                                                          il * n_ctx * ggml_element_size(model.memory_k) * n_embd),
                                             n_embd / n_head, n_head, n_past + N),
                             0, 2, 1, 3);
            // K * Q
            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);

            // KQ_scaled = KQ / sqrt(n_embd/n_head)
            struct ggml_tensor * KQ_scaled =
                ggml_scale(ctx0, KQ, ggml_new_f32(ctx0, 1.0f / sqrt(float(n_embd) / n_head)));

            struct ggml_tensor * KQ_scaled_alibi =
                ggml_alibi(ctx0, KQ_scaled, n_past, n_head, model.hparams.alibi_bias_max);

            // KQ_masked = mask_past(KQ_scaled)
            struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled_alibi, n_past);

            // KQ = soft_max(KQ_masked)
            struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);

            // V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1,
            // 2, 0, 3).contiguous() [n_past + N, 64, 12]
            struct ggml_tensor * V_trans = ggml_cpy(
                ctx0,
                ggml_permute(ctx0,
                             ggml_reshape_3d(ctx0,
                                             ggml_view_1d(ctx0, model.memory_v, (n_past + N) * n_embd,
                                                          il * n_ctx * ggml_element_size(model.memory_v) * n_embd),
                                             n_embd / n_head, n_head, n_past + N),
                             1, 2, 0, 3),
                ggml_new_tensor_3d(ctx0, model.memory_v->type, n_past + N, n_embd / n_head, n_head));

            // KQV = transpose(V) * KQ_soft_max
            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);

            // KQV_merged = KQV.permute(0, 2, 1, 3)
            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);

            // cur = KQV_merged.contiguous().view(n_embd, N)
            cur = ggml_cpy(ctx0, KQV_merged, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));

            // projection
            { cur = ggml_mul_mat(ctx0, model.layers[il].c_attn_out_proj_weight, cur); }
        }

        inpL = ggml_add(ctx0, inpL, cur);

        ggml_set_scratch(ctx0, { 0, scr1_size, scr1, });

        // m = self.ln_2(x)
        {
            cur = ggml_norm(ctx0, inpL);

            cur = ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].norm_2_weight, cur), cur);
        }

        // n = self.mlp(m)
        {

            cur = ggml_mul_mat(ctx0, model.layers[il].ffn_up_proj, cur);

            // GELU activation
            cur = ggml_gelu(ctx0, cur);

            // projection
            // cur = proj_w*cur + proj_b
            cur = ggml_mul_mat(ctx0, model.layers[il].ffn_down_proj, cur);
        }

        // x = x + n
        inpL = ggml_add(ctx0, inpL, cur);
    }

    ggml_set_scratch(ctx0, { 0, scr0_size, scr0, });

    // norm
    {
        inpL = ggml_norm(ctx0, inpL);
        // inpL = ln_f_g*inpL
        inpL = ggml_mul(ctx0, ggml_repeat(ctx0, model.norm_f_weight, inpL), inpL);
    }

    ggml_set_scratch(ctx0, { 0, 0, nullptr, });

    // output embedding weight tied to input embedding
    inpL = ggml_mul_mat(ctx0, model.wte_weight, inpL);

    // logits -> probs
    // inpL = ggml_soft_max(ctx0, inpL);

    // run the computation
    ggml_build_forward_expand(&gf, inpL);
    ggml_graph_compute(ctx0, &gf);

    // std::cout << "Qcur" << std::endl;
    // print_tensor(Qcur);

    // if (n_past%100 == 0) {
    // ggml_graph_print(&gf);
    // ggml_graph_dump_dot(&gf, NULL, "mpt-model.dot");
    // }

    if (logits_all) {
        // return result for all tokens
        embd_w.resize(n_vocab *N);
        memcpy(embd_w.data(), (float *)ggml_get_data(inpL) , sizeof(float) * n_vocab * N);
    } else {
        // return result for just the last token
        embd_w.resize(n_vocab);
        memcpy(embd_w.data(), (float *)ggml_get_data(inpL) + (n_vocab * (N - 1)), sizeof(float) * n_vocab);
    }

    if (mem_per_token == 0) {
        mem_per_token = ggml_used_mem(ctx0) / N;
    }
    // printf("used_mem = %zu\n", ggml_used_mem(ctx0));

    ggml_free(ctx0);

    return true;
}