//  Copyright (c) 2023 Intel Corporation
//
//  Licensed under the Apache License, Version 2.0 (the "License");
//  you may not use this file except in compliance with the License.
//  You may obtain a copy of the License at
//
//    http://www.apache.org/licenses/LICENSE-2.0
//
//  Unless required by applicable law or agreed to in writing, software
//  distributed under the License is distributed on an "AS IS" BASIS,
//  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
//  See the License for the specific language governing permissions and
//  limitations under the License.
#include <math.h>
#include <stdio.h>
#include <stdlib.h>

#include <algorithm>
#include <cassert>
#include <cinttypes>
#include <cstring>
#include <exception>
#include <fstream>
#include <iterator>
#include <memory>
#include <random>
#include <string>
#include <unordered_map>
#include <vector>

#include "core/data_types.h"
#include "core/ne.h"
#include "core/ne_layers.h"
#include "core/ne_bestla.h"
#include "core/layers/mha_dense.h"
#include "models/model_utils/model_config.h"
#include "models/model_utils/model_utils.h"
#include "models/model_utils/util.h"

// evaluate the transformer
//
//   - lctx:      model context
//   - inputs:    model_input array
//   - n_input    num of model_input
//   - n_threads: number of threads to use
//

static bool bloom_model_eval_internal(model_context* ctx, const model_input* inputs, const int n_input,
                                      const int n_threads) {
  const int64_t t_start_us = ne_time_us();
  model_context& lctx = *ctx;
  // static batching for now
  const int N = inputs->n_tokens;
  const int n_past = inputs->n_past;
  const int n_total = inputs->n_total;

  const int batch_size = lctx.batch_size;
  MODEL_ASSERT(batch_size == n_input);

  const auto& model = lctx.model;
  const auto& hparams = model.hparams;

  const auto& kv_self = model.kv_self;

  MODEL_ASSERT(!!kv_self.ctx);

  const int n_embd = hparams.n_embd;
  const int n_layer = hparams.n_layer;
  const int n_ctx = lctx.n_ctx;
  const int n_keep = lctx.n_keep;
  const int n_head = hparams.n_head;
  const int n_vocab = hparams.n_vocab;
  const int n_rot = hparams.n_rot;
  const int head_dim = hparams.n_embd / hparams.n_head;

  auto& mem_per_token = lctx.mem_per_token;
  auto& buf_compute = lctx.buf_compute;

  struct ne_init_params params = {
      /*.mem_size   =*/buf_compute.size,
      /*.mem_buffer =*/buf_compute.addr,
      /*.no_alloc   =*/false,
  };

  struct ne_context* ctx0 = ne_init(params);

  // for big profalcon, if BLAS is enabled, it is better to use only one thread
  // otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
  ne_cgraph gf = {};
  gf.n_threads = N >= 32 && ne_cpu_has_blas() ? 1 : n_threads;

  struct ne_tensor* embd = d_ne_new_tensor_1d(ctx0, NE_TYPE_I32, N);
  ne_set_name(embd, "embd");
  for (int i = 0; i < batch_size; ++i) {
    memcpy(static_cast<model_token*>(embd->data) + i * N, (inputs + i)->tokens, N * ne_element_size(embd));
  }

  // wte
  struct ne_tensor* inpL = ne_get_rows(ctx0, model.others[0], embd);

  // word embeddings norm
  {
    inpL = ne_norm(ctx0, inpL, hparams.norm_eps);
    inpL = ne_mul(ctx0, ne_repeat(ctx0, model.others[1], inpL), inpL);
    inpL = ne_add(ctx0, ne_repeat(ctx0, model.others[2], inpL), inpL);
  }

  for (int il = 0; il < n_layer; ++il) {
    struct ne_tensor* inpSA = inpL;

    struct ne_tensor* cur;
    lctx.use_buf(ctx0, 0);
    // norm
    {
      cur = ne_norm(ctx0, inpL, hparams.norm_eps);

      // cur = attention_norm*cur
      cur = ne_mul(ctx0, ne_repeat(ctx0, model.layers[il].norm[0], cur), cur);
      cur = ne_add(ctx0, ne_repeat(ctx0, model.layers[il].norm[1], cur), cur);
    }

    // attn
    {
      cur = ne_mul_mat(ctx0, model.layers[il].attn[0], cur);

      cur = ne_add(ctx0, ne_repeat(ctx0, model.layers[il].attn[1], cur), cur);
    }

    // cur = ggml_debug(ctx0, cur);

    // self-attention
    {
      struct ne_tensor* Qcur = ne_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0 * sizeof(float) * n_embd);
      struct ne_tensor* Kcur = ne_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1 * sizeof(float) * n_embd);
      struct ne_tensor* Vcur = ne_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2 * sizeof(float) * n_embd);

      // store key and value to memory
      if (N >= 1) {
        struct ne_tensor* k =
            ne_view_1d(ctx0, kv_self.k, N * n_embd, (ne_element_size(kv_self.k) * n_embd) * (il * n_ctx + n_past));
        struct ne_tensor* v =
            ne_view_1d(ctx0, kv_self.v, N * n_embd, (ne_element_size(kv_self.v) * n_embd) * (il * n_ctx + n_past));

        ne_build_forward_expand(&gf, ne_cpy(ctx0, Kcur, k));
        ne_build_forward_expand(&gf, ne_cpy(ctx0, Vcur, v));
      }

      // Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
      struct ne_tensor* Q = ne_permute(
          ctx0, ne_cpy(ctx0, Qcur, ne_new_tensor_3d(ctx0, NE_TYPE_F32, n_embd / n_head, n_head, N, NE_SIZE_CALC)), 0, 2,
          1, 3);

      // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
      struct ne_tensor* K = ne_permute(ctx0,
                                       ne_reshape_3d(ctx0,
                                                     ne_view_1d(ctx0, kv_self.k, (n_past + N) * n_embd,
                                                                il * n_ctx * ne_element_size(kv_self.k) * n_embd),
                                                     n_embd / n_head, n_head, n_past + N),
                                       0, 2, 1, 3);

      // K * Q
      struct ne_tensor* KQ = ne_mul_mat(ctx0, K, Q);

      // KQ_scaled = KQ / sqrt(n_embd/n_head)
      struct ne_tensor* KQ_scaled =
          ne_scale(ctx0, KQ, ne_new_f32(ctx0, 1.0f / sqrt(static_cast<float>(n_embd) / n_head)));

      // Alibi
      // KQ_scaled_alibi = KQ_scaled + alibi_bias
      struct ne_tensor* KQ_scaled_alibi = ne_alibi(ctx0, KQ_scaled, n_past, n_head, 8);

      // KQ_masked = mask_past(KQ_scaled)
      struct ne_tensor* KQ_masked = ne_diag_mask_inf(ctx0, KQ_scaled_alibi, n_past);

      // KQ = soft_max(KQ_masked)
      struct ne_tensor* KQ_soft_max = ne_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 ne_tensor* V_trans =
          ne_cpy(ctx0,
                 ne_permute(ctx0,
                            ne_reshape_3d(ctx0,
                                          ne_view_1d(ctx0, kv_self.v, (n_past + N) * n_embd,
                                                     il * n_ctx * ne_element_size(kv_self.v) * n_embd),
                                          n_embd / n_head, n_head, n_past + N),
                            1, 2, 0, 3),
                 ne_new_tensor_3d(ctx0, kv_self.v->type, n_past + N, n_embd / n_head, n_head, NE_SIZE_CALC));
      // KQV = transpose(V) * KQ_soft_max
      struct ne_tensor* KQV = ne_mul_mat(ctx0, V_trans, KQ_soft_max);

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

      // cur = KQV_merged.contiguous().view(n_embd, N)
      cur = ne_cpy(ctx0, KQV_merged, ne_new_tensor_2d(ctx0, NE_TYPE_F32, n_embd, N, NE_SIZE_CALC));

      // projection
      cur = ne_mul_mat(ctx0, model.layers[il].attn[2], cur);
      cur = ne_add(ctx0, ne_repeat(ctx0, model.layers[il].attn[3], cur), cur);
    }

    struct ne_tensor* inpFF = ne_add(ctx0, cur, inpSA);

    // feed-forward network
    {
      // norm
      {
        cur = ne_norm(ctx0, inpFF, hparams.norm_eps);

        // cur = ffn_norm*cur + ffn_norm_b
        cur = ne_mul(ctx0, ne_repeat(ctx0, model.layers[il].ffn[0], cur), cur);
        cur = ne_add(ctx0, ne_repeat(ctx0, model.layers[il].ffn[1], cur), cur);
      }
      if (bestla_fusion_FFN_Add_GeLu_f32f32_support(model.layers[il].ffn[2]->data, model.layers[il].ffn[4]->data,
                                                    N * batch_size, cur->ne[0], model.layers[il].ffn[0]->ne[1],
                                                    model.layers[il].ffn[2]->ne[1])) {
        cur = ne_ffn_add_gelu(ctx0, model.layers[il].ffn[2], model.layers[il].ffn[4], model.layers[il].ffn[3],
                              model.layers[il].ffn[5], cur);
      } else {
        cur = ne_mul_mat(ctx0, model.layers[il].ffn[2], cur);

        cur = ne_add(ctx0, ne_repeat(ctx0, model.layers[il].ffn[3], cur), cur);

        cur = ne_gelu(ctx0, cur);

        cur = ne_mul_mat(ctx0, model.layers[il].ffn[4], cur);

        cur = ne_add(ctx0, ne_repeat(ctx0, model.layers[il].ffn[5], cur), cur);
      }
    }

    cur = ne_add(ctx0, cur, inpFF);

    // input for next layer
    inpL = cur;
  }

  lctx.use_buf(ctx0, 0);
  // used at the end to optionally extract the embeddings
  struct ne_tensor* embeddings = nullptr;

  lctx.use_buf(ctx0, -1);
  // norm
  {
    inpL = ne_norm(ctx0, inpL, hparams.norm_eps);

    // inpL = norm*inpL
    inpL = ne_mul(ctx0, ne_repeat(ctx0, model.others[3], inpL), inpL);

    inpL = ne_add(ctx0, ne_repeat(ctx0, model.others[4], inpL), inpL);
  }

  // lm_head
  { inpL = ne_mul_mat(ctx0, model.others[5], inpL); }

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

  // run the computation
  ne_build_forward_expand(&gf, inpL);
  ne_graph_compute(ctx0, &gf);

  if (ns_log_level() == 0 || ns_log_level() == 2) {
    ne_graph_profiling(&gf);
  }

  // update kv token count
  lctx.model.kv_self.n = n_past + N;

  // extract logits
  {
    auto& logits_out = lctx.logits;

    if (lctx.logits_all) {
      logits_out.resize(n_vocab * N);
      memcpy(logits_out.data(), reinterpret_cast<float*>(ne_get_data(inpL)), sizeof(float) * n_vocab * N);
    } else {
      // return result for just the last token
      logits_out.resize(n_vocab);
      memcpy(logits_out.data(), reinterpret_cast<float*>(ne_get_data(inpL)) + (n_vocab * (N - 1)),
             sizeof(float) * n_vocab);
    }
  }

  // extract embeddings
  if (!lctx.embedding.empty()) {
    auto& embedding_out = lctx.embedding;

    embedding_out.resize(n_embd);
    memcpy(embedding_out.data(), reinterpret_cast<float*>(ne_get_data(embeddings)) + (n_embd * (N - 1)),
           sizeof(float) * n_embd);
  }

  if (mem_per_token == 0) {
    mem_per_token = ne_used_mem(ctx0) / N;
  }

  ne_free(ctx0);

  // measure the performance only for the single-token evals
  int64_t time_interval = ne_time_us() - t_start_us;
  if (N == 1) {
    lctx.t_eval_us += time_interval;
    lctx.n_eval++;
  } else if (N > 1) {
    lctx.t_p_eval_us += time_interval;
    lctx.n_p_eval += N;
  }
  lctx.eval_times.push_back(time_interval);

  return true;
}

int model_eval(struct model_context* ctx, const model_input* inputs, const int n_input, int n_threads) {
  if (!bloom_model_eval_internal(ctx, inputs, n_input, n_threads)) {
    fprintf(stderr, "%s: failed to eval\n", __func__);
    return 1;
  }

  // get a more accurate load time, upon first eval

  if (!ctx->has_evaluated_once) {
    ctx->t_load_us = ne_time_us() - ctx->t_start_us;
    ctx->has_evaluated_once = true;
  }

  return 0;
}
