//  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 <iostream>
#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 baichuan_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;  // max number fo tokens to keep in the kv-cache
  const int n_keep = lctx.n_keep;
  const bool shift_roped_k = lctx.shift_roped_k;
  const bool is_ring_full = shift_roped_k && n_total > n_past;
  NE_ASSERT(("Shift-RoPE-K to be implemented for AliBi!", !is_ring_full));

  int n_head = hparams.n_head;
  const int n_vocab = hparams.n_vocab;
  const int head_size = n_embd / n_head;
  const float attn_scale = 1.f / std::sqrt(head_size);
  const int n_rot = hparams.n_rot;
  int baichuan_version = 0;
  if (hparams.n_embd == 4096) {
    baichuan_version = 7;
  } else {
    baichuan_version = 13;
  }

  bool enable_tp = false;
#ifdef NS_TP_MODEL
  parallel_context* p_ctx = init_parallel_context();
  int32_t world_size = get_tp_size(p_ctx);
  int32_t rank = get_tp_rank(p_ctx);
  enable_tp = world_size > 1 ? true : false;

  // after TP the Q K n_head will become 1/world_size
  if (enable_tp) {
    n_head /= world_size;
  }
#endif
  int hidden_size = head_size * 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 probaichuans, 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;

  const bool run_mha_reordered = model.layers[0].k_cache->type == NE_TYPE_BTLA;
  kv_cache_info_t kv_cache_info = {};
  if (run_mha_reordered) {
    NE_ASSERT(("kv cache should be the same dtype", model.layers[0].v_cache->type == NE_TYPE_BTLA));
    attn_shape_t attn_shape = {
        /* .batch_size = */ 1,
        /* .head_num = */ n_head,
        /* .heads_kv = */ n_head,
        /* .head_size = */ head_size,
        /* .sl_q = */ N,  // Note: make sure that bestla reordered attn supports next token inference
        /* .sl_kv = */ n_past + N,
    };

    NE_ASSERT(("bestla managed kv-cache not supported; use `--memory-f16 / --memory-f32` instead",
               bestla_reordered_attn_fp32_support(&attn_shape)));
    kv_shape_t kv_shape{
        /* .heads_kv = */ static_cast<uint32_t>(n_head),
        /* .head_size = */ static_cast<uint32_t>(head_size),
        /* .sl_kv_max = */ static_cast<uint32_t>(n_ctx),
    };
    bestla_reordered_attn_fp32_batch_kv_info(&kv_shape, &kv_cache_info);
  }

  struct ne_tensor* embd = d_ne_new_tensor_1d(ctx0, NE_TYPE_I32, N);

  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));
  }

  struct ne_tensor* inpL = ne_get_rows(ctx0, model.others[0], embd);
  NE_ASSERT(N == inpL->ne[1]);

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

    lctx.use_buf(ctx0, 0);

    struct ne_tensor* residual = inpL;

    // LayerNorm
    cur = ne_rms_norm(ctx0, inpL, hparams.norm_eps);
    cur = ne_mul(ctx0, cur, model.layers[il].norm[0]);
    // SelfAttention
    {
      // Linear::forward compute QKV
      cur = ne_mul_mat(ctx0, model.layers[il].attn[0], cur);
      ne_tensor* query_layer = ne_view_3d(ctx0, cur, head_size, n_head, N, head_size * ne_element_size(cur), cur->nb[1],
                                          0);  // [N, hidden]

      ne_tensor* key_layer = ne_view_3d(ctx0, cur, head_size, n_head, N, head_size * ne_element_size(cur), cur->nb[1],
                                        hidden_size * ne_element_size(cur));

      ne_tensor* value_layer = ne_view_3d(ctx0, cur, head_size, n_head, N, head_size * ne_element_size(cur), cur->nb[1],
                                          2 * hidden_size * ne_element_size(cur));  // [N, heads, head_size]

      // using mode = 2 for neox mode
      if (baichuan_version == 7) {
        query_layer = ne_rope_inplace(ctx0, query_layer, n_past, n_rot, 2, 0, hparams.freq_base, hparams.freq_scale);
        key_layer = ne_rope_inplace(ctx0, key_layer, n_past, n_rot, 2, 0, hparams.freq_base, hparams.freq_scale);
      }

      if (!run_mha_reordered) {
        query_layer = ne_permute(ctx0, query_layer, 0, 2, 1, 3);  // [heads, N, head_size]
        key_layer = ne_permute(ctx0, key_layer, 0, 2, 1, 3);      // [heads, N, head_size]
        value_layer = ne_permute(ctx0, value_layer, 1, 2, 0, 3);  // [heads, head_size, N]

        // store key and value to memory
        {
          struct ne_tensor* k_cache_view =
              ne_view_3d(ctx0, model.layers[il].k_cache, head_size, N, n_head, model.layers[il].k_cache->nb[1],
                         model.layers[il].k_cache->nb[2],
                         n_past * head_size * ne_element_size(model.layers[il].k_cache));  // [kv_heads, N, head_size]

          struct ne_tensor* v_cache_view =
              ne_view_3d(ctx0, model.layers[il].v_cache, N, head_size, n_head, model.layers[il].v_cache->nb[1],
                         model.layers[il].v_cache->nb[2],
                         n_past * ne_element_size(model.layers[il].v_cache));  // [kv_heads, head_size, N]

          ne_build_forward_expand(&gf, ne_cpy(ctx0, key_layer, k_cache_view));
          ne_build_forward_expand(&gf, ne_cpy(ctx0, value_layer, v_cache_view));
        }
        // concat key & value with past kv
        key_layer = ne_view_3d(ctx0, model.layers[il].k_cache, head_size, n_past + N, n_head,
                               model.layers[il].k_cache->nb[1], model.layers[il].k_cache->nb[2],
                               0);  // [kv_heads, klen, head_size]
        value_layer = ne_view_3d(ctx0, model.layers[il].v_cache, n_past + N, head_size, n_head,
                                 model.layers[il].v_cache->nb[1], model.layers[il].v_cache->nb[2],
                                 0);  // [kv_heads, head_size, klen]

        // attention
        struct ne_tensor* attn_scores = ne_mul_mat(ctx0, key_layer, query_layer);  // [heads, N, klen]
        attn_scores = ne_scale_inplace(ctx0, attn_scores, ne_new_f32(ctx0, attn_scale));

        if (baichuan_version == 13) {
          attn_scores = ne_alibi(ctx0, attn_scores, n_past, n_head, 8);
        }

        if (n_past == 0) {
          attn_scores = ne_diag_mask_inf_inplace(ctx0, attn_scores, n_past);
        }
        ne_tensor* attn_probs = ne_soft_max_inplace(ctx0, attn_scores);  // [heads, N, klen]

        // ne_compute_forward_mul_mat_f16_f32
        ne_tensor* context_layer = ne_mul_mat(ctx0, value_layer, attn_probs);  // [heads, N, head_size]
        context_layer = ne_cont(ctx0, ne_permute(ctx0, context_layer, 0, 2, 1, 3));
        context_layer = ne_reshape_2d(ctx0, context_layer, hidden_size, N);

        // F32 mul_mat
        cur = ne_mul_mat(ctx0, model.layers[il].attn[1], context_layer);
      } else {
        const auto k_size = kv_cache_info.k_bytes;
        const auto v_size = kv_cache_info.v_bytes;

        // store key and value to memory
        {
          const auto k_cache = ne_view_3d(ctx0, model.layers[il].k_cache,  // tensor
                                          head_size, n_ctx, n_head,        // ne
                                          0, 0,                            // nb (bestla managed)
                                          0);                              // offset
          ne_build_forward_expand(&gf, ne_flash_attn_update_k(ctx0, k_cache, key_layer, n_past, false));
          const auto v_cache = ne_view_3d(ctx0, model.layers[il].v_cache,  // tensor
                                          head_size, n_ctx, n_head,        // ne
                                          0, 0,                            // nb (bestla managed)
                                          0);                              // offset
          ne_build_forward_expand(&gf, ne_flash_attn_update_v(ctx0, v_cache, value_layer, n_past, false));
        }

        query_layer = ne_permute(ctx0, query_layer, 0, 2, 1, 3);                          // [heads, N, head_size]
        key_layer =                                                                       //
            ne_view_3d(ctx0, model.layers[il].k_cache,                                    // tensor
                       head_size, n_past + N, n_head,                                     // ne
                       kv_cache_info.stride_k_sl, kv_cache_info.stride_k_head_num,        // nb (bestla managed)
                       0);                                                                // offset
        *reinterpret_cast<ATTN_FWD_LAYOUT*>(&key_layer->nb[0]) = kv_cache_info.k_layout;  // us nb0 for layout

        value_layer =                                                                       //
            ne_view_3d(ctx0, model.layers[il].v_cache,                                      // tensor
                       n_past + N, head_size, n_head,                                       // ne
                       kv_cache_info.stride_v_head_size, kv_cache_info.stride_v_head_num,   // nb (bestla managed)
                       0);                                                                  // offset
        *reinterpret_cast<ATTN_FWD_LAYOUT*>(&value_layer->nb[0]) = kv_cache_info.v_layout;  // us nb0 for layout

        ne_attn_flags_t attn_flags = NE_ATTN_FLAG_IS_ALIBI8;
        if (n_past == 0) attn_flags |= NE_ATTN_FLAG_IS_CAUSAL;  // no causal mask on next-token cases
        struct ne_tensor* KQV_Out = ne_flash_attn(ctx0, query_layer, key_layer, value_layer, attn_scale, attn_flags);
        cur = ne_view_2d(ctx0, KQV_Out, hidden_size, N, hidden_size * ne_element_size(KQV_Out), 0);

        // F32 mul_mat
        cur = ne_mul_mat(ctx0, model.layers[il].attn[1], cur);
      }
    }

#ifdef NS_TP_MODEL
    if (enable_tp) {
      cur = ne_all_reduce(ctx0, cur);
    }
#endif

    lctx.use_buf(ctx0, 1);
    cur = ne_add_inplace(ctx0, cur, residual);
    residual = cur;

    // post_attention_layernorm
    struct ne_tensor* hidden_states = ne_rms_norm(ctx0, cur, hparams.norm_eps);
    hidden_states = ne_mul(ctx0, hidden_states, model.layers[il].norm[1]);

    // mlp.forward
    struct ne_tensor* mlp_output;
    if (bestla_fusion_FFN_SiLu_f32f32_support(model.layers[il].ffn[0]->data, model.layers[il].ffn[1]->data,
                                              model.layers[il].ffn[2]->data, N, hidden_states->ne[0],
                                              model.layers[il].ffn[0]->ne[1], model.layers[il].ffn[1]->ne[1])) {
      mlp_output =
          ne_ffn_silu(ctx0, model.layers[il].ffn[0], model.layers[il].ffn[1], model.layers[il].ffn[2], hidden_states);
    } else {
      struct ne_tensor* up = ne_mul_mat(ctx0, model.layers[il].ffn[2], hidden_states);
      struct ne_tensor* gate = ne_mul_mat(ctx0, model.layers[il].ffn[0], hidden_states);
      gate = ne_silu(ctx0, gate);
      mlp_output = ne_mul(ctx0, gate, up);
      mlp_output = ne_mul_mat(ctx0, model.layers[il].ffn[1], mlp_output);
    }
#ifdef NS_TP_MODEL
    if (enable_tp) {
      mlp_output = ne_all_reduce(ctx0, mlp_output);
    }
#endif

    inpL = ne_add_inplace(ctx0, mlp_output, residual);
  }

  lctx.use_buf(ctx0, 0);
  // used at the end to optionally extract the embeddings
  struct ne_tensor* embeddings = nullptr;
  // norm
  {
    inpL = ne_rms_norm(ctx0, inpL, hparams.norm_eps);
    inpL = ne_mul(ctx0, inpL, model.others[1]);
  }

  lctx.use_buf(ctx0, -1);
  if (embd->ne[0] > 1) {
    inpL = ne_view_1d(ctx0, inpL, n_embd, (embd->ne[0] - 1) * n_embd * ne_element_size(inpL));
  }

  // lm_head
  inpL = ne_mul_mat(ctx0, model.others[2], inpL);

  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)), 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 (!baichuan_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;
}
