#include "llm.h"

// https://github.com/ggerganov/ggml/blob/master/examples/gpt-j/main.cpp

// default hparams (GPT-J 6B)
struct gptj_hparams {
  int32_t n_vocab = 50400;
  int32_t n_ctx = 2048;
  int32_t n_embd = 4096;
  int32_t n_head = 16;
  int32_t n_layer = 28;
  int32_t n_rot = 64;
  int32_t ftype = 1;
};

struct gptj_layer {
  // normalization
  struct ggml_tensor *ln_1_g;
  struct ggml_tensor *ln_1_b;

  // attention
  struct ggml_tensor *c_attn_q_proj_w;
  struct ggml_tensor *c_attn_k_proj_w;
  struct ggml_tensor *c_attn_v_proj_w;

  struct ggml_tensor *c_attn_proj_w;

  // ff
  struct ggml_tensor *c_mlp_fc_w;
  struct ggml_tensor *c_mlp_fc_b;

  struct ggml_tensor *c_mlp_proj_w;
  struct ggml_tensor *c_mlp_proj_b;
};

struct gptj_model {
  gptj_hparams hparams;

  // normalization
  struct ggml_tensor *ln_f_g;
  struct ggml_tensor *ln_f_b;

  struct ggml_tensor *wte;  // position embedding

  struct ggml_tensor *lmh_g;  // language model head
  struct ggml_tensor *lmh_b;  // language model bias

  std::vector<gptj_layer> layers;

  // key + value memory
  struct ggml_tensor *memory_k;
  struct ggml_tensor *memory_v;

  //
  struct ggml_context *ctx;
  std::map<std::string, struct ggml_tensor *> tensors;
};

// load the model's weights from a file
bool gptj_model_load(const std::string &fname, gptj_model &model,
                     gpt_vocab &vocab) {
  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.n_vocab, sizeof(hparams.n_vocab));
    fin.read((char *)&hparams.n_ctx, sizeof(hparams.n_ctx));
    fin.read((char *)&hparams.n_embd, sizeof(hparams.n_embd));
    fin.read((char *)&hparams.n_head, sizeof(hparams.n_head));
    fin.read((char *)&hparams.n_layer, sizeof(hparams.n_layer));
    fin.read((char *)&hparams.n_rot, sizeof(hparams.n_rot));
    fin.read((char *)&hparams.ftype, sizeof(hparams.ftype));

    const int32_t qntvr = hparams.ftype / GGML_QNT_VERSION_FACTOR;

    hparams.ftype %= GGML_QNT_VERSION_FACTOR;
  }

  // load vocab
  {
    int32_t n_vocab = 0;
    fin.read((char *)&n_vocab, sizeof(n_vocab));

    if (n_vocab != model.hparams.n_vocab) {
      fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
              __func__, fname.c_str(), n_vocab, model.hparams.n_vocab);
      return false;
    }

    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 int n_embd = hparams.n_embd;
    const int n_layer = hparams.n_layer;
    const int n_ctx = hparams.n_ctx;
    const int n_vocab = hparams.n_vocab;

    ctx_size += n_embd * ggml_type_sizef(GGML_TYPE_F32);  // ln_f_g
    ctx_size += n_embd * ggml_type_sizef(GGML_TYPE_F32);  // ln_f_b

    ctx_size += n_embd * n_vocab * ggml_type_sizef(wtype);  // wte

    ctx_size += n_embd * n_vocab * ggml_type_sizef(wtype);  // lmh_g
    ctx_size += n_vocab * ggml_type_sizef(GGML_TYPE_F32);   // lmh_b

    ctx_size += n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32));  // ln_1_g
    ctx_size += n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32));  // ln_1_b

    ctx_size += n_layer *
                (n_embd * n_embd * ggml_type_sizef(wtype));  // c_attn_q_proj_w
    ctx_size += n_layer *
                (n_embd * n_embd * ggml_type_sizef(wtype));  // c_attn_k_proj_w
    ctx_size += n_layer *
                (n_embd * n_embd * ggml_type_sizef(wtype));  // c_attn_v_proj_w

    ctx_size +=
        n_layer * (n_embd * n_embd * ggml_type_sizef(wtype));  // c_attn_proj_w

    ctx_size +=
        n_layer * (4 * n_embd * n_embd * ggml_type_sizef(wtype));  // c_mlp_fc_w
    ctx_size +=
        n_layer * (4 * n_embd * ggml_type_sizef(GGML_TYPE_F32));  // c_mlp_fc_b

    ctx_size += n_layer *
                (4 * n_embd * n_embd * ggml_type_sizef(wtype));  // c_mlp_proj_w
    ctx_size +=
        n_layer * (n_embd * ggml_type_sizef(GGML_TYPE_F32));  // c_mlp_proj_b

    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 += (5 + 10 * n_layer) * 512;  // object overhead
  }

  // create the ggml context
  {
    struct ggml_init_params params = {
        /*.mem_size   =*/ctx_size,
        /*.mem_buffer =*/NULL,
        /*.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 int n_embd = hparams.n_embd;
    const int n_layer = hparams.n_layer;
    const int n_vocab = hparams.n_vocab;

    model.layers.resize(n_layer);

    model.wte = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);

    model.ln_f_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
    model.ln_f_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

    model.lmh_g = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
    model.lmh_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_vocab);

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

    model.tensors["transformer.ln_f.weight"] = model.ln_f_g;
    model.tensors["transformer.ln_f.bias"] = model.ln_f_b;

    model.tensors["lm_head.weight"] = model.lmh_g;
    model.tensors["lm_head.bias"] = model.lmh_b;

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

      layer.ln_1_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
      layer.ln_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

      layer.c_attn_q_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
      layer.c_attn_k_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);
      layer.c_attn_v_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);

      layer.c_attn_proj_w = ggml_new_tensor_2d(ctx, wtype, n_embd, n_embd);

      layer.c_mlp_fc_w = ggml_new_tensor_2d(ctx, wtype, n_embd, 4 * n_embd);
      layer.c_mlp_fc_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4 * n_embd);

      layer.c_mlp_proj_w = ggml_new_tensor_2d(ctx, wtype, 4 * n_embd, n_embd);
      layer.c_mlp_proj_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);

      // map by name
      model.tensors["transformer.h." + std::to_string(i) + ".ln_1.weight"] =
          layer.ln_1_g;
      model.tensors["transformer.h." + std::to_string(i) + ".ln_1.bias"] =
          layer.ln_1_b;

      model.tensors["transformer.h." + std::to_string(i) +
                    ".attn.q_proj.weight"] = layer.c_attn_q_proj_w;
      model.tensors["transformer.h." + std::to_string(i) +
                    ".attn.k_proj.weight"] = layer.c_attn_k_proj_w;
      model.tensors["transformer.h." + std::to_string(i) +
                    ".attn.v_proj.weight"] = layer.c_attn_v_proj_w;

      model.tensors["transformer.h." + std::to_string(i) +
                    ".attn.out_proj.weight"] = layer.c_attn_proj_w;

      model
          .tensors["transformer.h." + std::to_string(i) + ".mlp.fc_in.weight"] =
          layer.c_mlp_fc_w;
      model.tensors["transformer.h." + std::to_string(i) + ".mlp.fc_in.bias"] =
          layer.c_mlp_fc_b;

      model.tensors["transformer.h." + std::to_string(i) +
                    ".mlp.fc_out.weight"] = layer.c_mlp_proj_w;
      model.tensors["transformer.h." + std::to_string(i) + ".mlp.fc_out.bias"] =
          layer.c_mlp_proj_b;
    }
  }

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

    const int n_embd = hparams.n_embd;
    const int n_layer = hparams.n_layer;
    const int n_ctx = hparams.n_ctx;

    const int n_mem = n_layer * n_ctx;
    const int 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);
  }

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

    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 [%d, %d], "
                "expected [%d, %d]\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));

      // printf("%42s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0],
      // ne[1], ttype == 0 ? "float" : "f16",
      // ggml_nbytes(tensor)/1024.0/1024.0);
      total_size += ggml_nbytes(tensor);
    }
  }

  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
//
// The GPT-J model requires about 16MB of memory per input token.
//
bool gptj_eval(const gptj_model &model, const int n_threads, const int n_past,
               const std::vector<gpt_vocab::id> &embd_inp,
               std::vector<float> &embd_w, size_t &mem_per_token) {
  const int N = embd_inp.size();

  const auto &hparams = model.hparams;

  const int n_embd = hparams.n_embd;
  const int n_layer = hparams.n_layer;
  const int n_ctx = hparams.n_ctx;
  const int n_head = hparams.n_head;
  const int n_vocab = hparams.n_vocab;
  const int n_rot = hparams.n_rot;

  static size_t buf_size = 256u * 1024 * 1024;
  static void *buf = malloc(buf_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 = {
      /*.mem_size   =*/buf_size,
      /*.mem_buffer =*/buf,
      /*.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));

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

  for (int il = 0; il < n_layer; ++il) {
    struct ggml_tensor *cur;

    // norm
    {
      cur = ggml_norm(ctx0, inpL);

      // cur = ln_1_g*cur + ln_1_b
      cur = ggml_add(
          ctx0,
          ggml_mul(ctx0, ggml_repeat(ctx0, model.layers[il].ln_1_g, cur), cur),
          ggml_repeat(ctx0, model.layers[il].ln_1_b, cur));
    }

    struct ggml_tensor *inpSA = cur;

    // self-attention
    {
      struct ggml_tensor *Qcur = ggml_rope_inplace(
          ctx0,
          ggml_reshape_3d(
              ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_q_proj_w, cur),
              n_embd / n_head, n_head, N),
          n_past, n_rot, 0);
      struct ggml_tensor *Kcur = ggml_rope_inplace(
          ctx0,
          ggml_reshape_3d(
              ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_k_proj_w, cur),
              n_embd / n_head, n_head, N),
          n_past, n_rot, 0);

      // store key and value to memory
      {
        struct ggml_tensor *Vcur = ggml_transpose(
            ctx0, ggml_mul_mat(ctx0, model.layers[il].c_attn_v_proj_w, cur));

        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_2d(
            ctx0, model.memory_v, N, n_embd,
            (n_ctx)*ggml_element_size(model.memory_v),
            (il * n_ctx) * ggml_element_size(model.memory_v) * n_embd +
                n_past * ggml_element_size(model.memory_v));

        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)
      struct ggml_tensor *Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);

      // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
      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_inplace(
          ctx0, KQ, ggml_new_f32(ctx0, 1.0f / sqrt(float(n_embd) / n_head)));

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

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

      // V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0,
      // 3).contiguous()
      struct ggml_tensor *V = ggml_view_3d(
          ctx0, model.memory_v, n_past + N, n_embd / n_head, n_head,
          n_ctx * ggml_element_size(model.memory_v),
          n_ctx * ggml_element_size(model.memory_v) * n_embd / n_head,
          il * n_ctx * ggml_element_size(model.memory_v) * n_embd);

      // KQV = transpose(V) * KQ_soft_max
      struct ggml_tensor *KQV = ggml_mul_mat(ctx0, V, 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 (no bias)
      cur = ggml_mul_mat(ctx0, model.layers[il].c_attn_proj_w, cur);
    }

    struct ggml_tensor *inpFF = cur;

    // feed-forward network
    // this is independent of the self-attention result, so it could be done in
    // parallel to the self-attention
    {
      // note here we pass inpSA instead of cur
      cur = ggml_mul_mat(ctx0, model.layers[il].c_mlp_fc_w, inpSA);

      cur = ggml_add(ctx0, ggml_repeat(ctx0, model.layers[il].c_mlp_fc_b, cur),
                     cur);

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

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

      cur = ggml_add(
          ctx0, ggml_repeat(ctx0, model.layers[il].c_mlp_proj_b, cur), cur);
    }

    // self-attention + FF
    cur = ggml_add(ctx0, cur, inpFF);

    // input for next layer
    inpL = ggml_add(ctx0, cur, inpL);
  }

  // norm
  {
    inpL = ggml_norm(ctx0, inpL);

    // inpL = ln_f_g*inpL + ln_f_b
    inpL = ggml_add(ctx0,
                    ggml_mul(ctx0, ggml_repeat(ctx0, model.ln_f_g, inpL), inpL),
                    ggml_repeat(ctx0, model.ln_f_b, inpL));
  }

  // lm_head
  {
    inpL = ggml_mul_mat(ctx0, model.lmh_g, inpL);

    inpL = ggml_add(ctx0, ggml_repeat(ctx0, model.lmh_b, inpL), inpL);
  }

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

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

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

  // embd_w.resize(n_vocab*N);
  // memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);

  // 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;
}

REGISTER_LLM(gptj);