from transformers import PretrainedConfig

class MyPretrainConfig(PretrainedConfig):
    model_type = "myllm"

    def __init__(
            self,
            dim: int = 512,
            n_layers: int = 8,
            n_heads: int = 16,
            n_kv_heads: int = 8,
            vocab_size: int = 6400,
            hidden_dim: int = None,
            multiple_of: int = 64,
            norm_eps: float = 1e-5,
            max_seq_len: int = 512,
            dropout: float = 0.0,
            flash_attn: bool = True,
            use_moe: bool = False,
            num_experts_per_tok=2,
            n_routed_experts=4,
            n_shared_experts: bool = True,
            scoring_func='softmax',
            aux_loss_alpha=0.01,
            seq_aux=True,
            norm_topk_prob=True,
            **kwargs,
    ):
        self.dim = dim
        self.n_layers = n_layers
        self.n_heads = n_heads
        self.n_kv_heads = n_kv_heads
        self.vocab_size = vocab_size
        self.hidden_dim = hidden_dim
        self.multiple_of = multiple_of
        self.norm_eps = norm_eps
        self.max_seq_len = max_seq_len
        self.dropout = dropout
        self.flash_attn = flash_attn
        self.num_experts_per_tok = num_experts_per_tok  # 每個(gè)token選擇的專家數(shù)量
        self.n_routed_experts = n_routed_experts        # 總的專家數(shù)量
        self.n_shared_experts = n_shared_experts        # 共享專家
        self.scoring_func = scoring_func                # 評(píng)分函數(shù)，默認(rèn)為'softmax'
        self.aux_loss_alpha = aux_loss_alpha            # 輔助損失的alpha參數(shù)
        self.seq_aux = seq_aux                          # 是否在序列級(jí)別上計(jì)算輔助損失
        self.norm_topk_prob = norm_topk_prob            # 是否標(biāo)準(zhǔn)化top-k概率
        super().__init__(**kwargs)

data_path_list = [f'./pretrain_data.bin']
train_ds = PretrainDataset(data_path_list, max_length=max_seq_len, memmap=True)
train_sampler = None
num_workers = 16  # 可以根據(jù)系統(tǒng)的 CPU 核心數(shù)來(lái)調(diào)整
train_loader = DataLoader(
    train_ds,
    batch_size=batch_size,
    pin_memory=True,
    drop_last=False,
    shuffle=False,
    num_workers=num_workers,
    sampler=train_sampler
)

class PretrainDataset(Dataset):
    def __init__(self, data_path_lst, max_length=512, memmap=False):
        super().__init__()
        if memmap:
            with open(data_path_lst[0], 'r') as f:
                nbytes = f.seek(0, 2)
                flen = f.tell() // np.dtype('uint16').itemsize
            self.data = np.memmap(data_path_lst[0], dtype=np.dtype('uint16'), shape=(flen // max_length, max_length))
        else:
            data_lst = []
            for data_path in data_path_lst:
                with open(data_path, 'rb') as f:
                    data = np.fromfile(f, dtype=np.uint16)
                    data_lst.append(data)
            data = np.concatenate(data_lst)
            data = data[:max_length * int(len(data) / max_length)]
            self.data = data.reshape(-1, max_length)
        print("memmap:{} train data.shape:{}".format(memmap, self.data.shape))
        print("downloading finished.....")

    def __len__(self):
        return self.data.shape[0]

    def __getitem__(self, index: int):
        sample = self.data[index]
        X = np.array(sample[:-1]).astype(np.int64)
        Y = np.array(sample[1:]).astype(np.int64)

        return torch.from_numpy(X), torch.from_numpy(Y)

class Transformer(PreTrainedModel):
    last_loss: Optional[torch.Tensor]

    def __init__(self, params: MyPretrainConfig):
        super().__init__(params)
        self.params = params
        self.vocab_size = params.vocab_size
        self.n_layers = params.n_layers

        self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim)
        self.dropout = nn.Dropout(params.dropout)
        self.layers = torch.nn.ModuleList()
        for layer_id in range(params.n_layers):
            self.layers.append(TransformerBlock(layer_id, params))
        self.norm = RMSNorm(params.dim, eps=params.norm_eps)
        self.output = nn.Linear(params.dim, params.vocab_size, bias=False)

        # share the unembedding parameters with the embedding parameters
        self.tok_embeddings.weight = self.output.weight # https://paperswithcode.com/method/weight-tying

        # some useful precompute for the RoPE relative positional embeddings
        freqs_cos, freqs_sin = precompute_freqs_cis(self.params.dim // self.params.n_heads, self.params.max_seq_len)
        self.register_buffer("freqs_cos", freqs_cos, persistent=False)
        self.register_buffer("freqs_sin", freqs_sin, persistent=False)

        # init all weights
        self.apply(self._init_weights)
        # apply special scaled init to the residual projections, per GPT-2 paper
        for pn, p in self.named_parameters():
            if pn.endswith('w3.weight') or pn.endswith('wo.weight'):
                torch.nn.init.normal_(p, mean=0.0, std=0.02/math.sqrt(2 * params.n_layers))

        # Initialize attribute for the loss of the last forward call. This will be set if the forward is called with a targets tensor.
        self.last_loss = None
        self.OUT = CausalLMOutputWithPast()

    def _init_weights(self, module):
        if isinstance(module, nn.Linear):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
            if module.bias is not None:
                torch.nn.init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)

    def forward(self, tokens: torch.Tensor, targets: Optional[torch.Tensor] = None) -> torch.Tensor:
        _bsz, seqlen = tokens.shape
        h = self.tok_embeddings(tokens)
        h = self.dropout(h)
        freqs_cos = self.freqs_cos[:seqlen]
        freqs_sin = self.freqs_sin[:seqlen]

        for layer in self.layers:
            h = layer(h, freqs_cos, freqs_sin)
        h = self.norm(h)

        if targets is not None:
            # if we are given some desired targets also calculate the loss
            logits = self.output(h)
            self.last_loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
        else:
            # inference-time mini-optimization: only forward the output on the very last position
            logits = self.output(h[:, [-1], :]) # note: using list [-1] to preserve the time dim
            self.last_loss = None

        self.OUT.__setitem__('logits', logits)
        self.OUT.__setitem__('last_loss', self.last_loss)
        return self.OUT
...

def init_model():
    def count_parameters(model):
        return sum(p.numel() for p in model.parameters() if p.requires_grad)

    model = Transformer(lm_config).to(device)
    print(f'LLM總參數(shù)量：{count_parameters(model) / 1e6:.3f} 百萬(wàn)')
    return model

model = init_model()
print(model)

Transformer(
  (tok_embeddings): Embedding(6400, 512)
  (dropout): Dropout(p=0.0, inplace=False)
  (layers): ModuleList(
    (0-7): 8 x TransformerBlock(
      (attention): Attention(
        (wq): Linear(in_features=512, out_features=512, bias=False)
        (wk): Linear(in_features=512, out_features=256, bias=False)
        (wv): Linear(in_features=512, out_features=256, bias=False)
        (wo): Linear(in_features=512, out_features=512, bias=False)
        (attn_dropout): Dropout(p=0.0, inplace=False)
        (resid_dropout): Dropout(p=0.0, inplace=False)
      )
      (feed_forward): FeedForward(
        (w1): Linear(in_features=512, out_features=1408, bias=False)
        (w2): Linear(in_features=1408, out_features=512, bias=False)
        (w3): Linear(in_features=512, out_features=1408, bias=False)
        (dropout): Dropout(p=0.0, inplace=False)
      )
      (attention_norm): RMSNorm()
      (ffn_norm): RMSNorm()
    )
  )
  (norm): RMSNorm()
  (output): Linear(in_features=512, out_features=6400, bias=False)
)

scaler = torch.cuda.amp.GradScaler(enabled=(dtype == dtype))
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# 反向傳播
scaler.scale(loss).backward()

# 梯度剪裁和更新參數(shù)
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
scaler.step(optimizer)
scaler.update()

# 清零梯度
optimizer.zero_grad(set_to_none=True)

for epoch in range(epochs):
    start_time = time.time()

    for step, (X, Y) in enumerate(train_loader):
        X = X.to(device)
        Y = Y.to(device)

        # 設(shè)置學(xué)習(xí)率
        lr = get_lr(epoch * iter_per_epoch + step, epochs * iter_per_epoch)
        for param_group in optimizer.param_groups:
            param_group['lr'] = lr

        # 前向傳播和損失計(jì)算
        with ctx:
            out = model(X, Y)
            loss = out.last_loss

        # 反向傳播
        scaler.scale(loss).backward()

        # 梯度剪裁和更新參數(shù)
        if (step + 1) % accumulation_steps == 0:
            scaler.unscale_(optimizer)
            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
            scaler.step(optimizer)
            scaler.update()

        # 清零梯度
        optimizer.zero_grad(set_to_none=True)

        if step % 100 == 0:
            spend_time = time.time() - start_time
            print(
                'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.7f} epoch_Time:{}min:'.format(
                    epoch,
                    epochs,
                    step,
                    iter_per_epoch,
                    loss.item(),
                    optimizer.param_groups[-1]['lr'],
                    spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60))
            model.eval()
            ckp = f'{save_dir}/pretrain_{lm_config.dim}.pth'
            state_dict = model.state_dict()
            torch.save(state_dict, ckp)
            model.train()

import os
import time
import math
import warnings
import inspect
import numpy as np
import torch
from torch import optim
from torch.utils.data import DataLoader
from contextlib import nullcontext
from model.model import Transformer
from torch.utils.data import Dataset
from transformers import PretrainedConfig
from typing import Any, Optional, Tuple
import torch.nn.functional as F
from torch import nn
from transformers import PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast
os.environ["TOKENIZERS_PARALLELISM"] = "false"

warnings.filterwarnings('ignore')
basepath = "../datasets"

class MyPretrainConfig(PretrainedConfig):
    model_type = "myllm"

    def __init__(
            self,
            dim: int = 512,
            n_layers: int = 8,
            n_heads: int = 16,
            n_kv_heads: int = 8,
            vocab_size: int = 6400,
            hidden_dim: int = None,
            multiple_of: int = 64,
            norm_eps: float = 1e-5,
            max_seq_len: int = 512,
            dropout: float = 0.0,
            flash_attn: bool = True,
            num_experts_per_tok=2,
            n_routed_experts=4,
            n_shared_experts: bool = True,
            scoring_func='softmax',
            aux_loss_alpha=0.01,
            seq_aux=True,
            norm_topk_prob=True,
            **kwargs,
    ):
        self.dim = dim
        self.n_layers = n_layers
        self.n_heads = n_heads
        self.n_kv_heads = n_kv_heads
        self.vocab_size = vocab_size
        self.hidden_dim = hidden_dim
        self.multiple_of = multiple_of
        self.norm_eps = norm_eps
        self.max_seq_len = max_seq_len
        self.dropout = dropout
        self.flash_attn = flash_attn
        self.num_experts_per_tok = num_experts_per_tok  # 每個(gè)token選擇的專家數(shù)量
        self.n_routed_experts = n_routed_experts        # 總的專家數(shù)量
        self.n_shared_experts = n_shared_experts        # 共享專家
        self.scoring_func = scoring_func                # 評(píng)分函數(shù)，默認(rèn)為'softmax'
        self.aux_loss_alpha = aux_loss_alpha            # 輔助損失的alpha參數(shù)
        self.seq_aux = seq_aux                          # 是否在序列級(jí)別上計(jì)算輔助損失
        self.norm_topk_prob = norm_topk_prob            # 是否標(biāo)準(zhǔn)化top-k概率
        super().__init__(**kwargs)

class PretrainDataset(Dataset):
    def __init__(self, data_path_lst, max_length=512, memmap=False):
        super().__init__()
        if memmap:
            with open(data_path_lst[0], 'r') as f:
                nbytes = f.seek(0, 2)
                flen = f.tell() // np.dtype('uint16').itemsize
            self.data = np.memmap(data_path_lst[0], dtype=np.dtype('uint16'), shape=(flen // max_length, max_length))
        else:
            data_lst = []
            for data_path in data_path_lst:
                with open(data_path, 'rb') as f:
                    data = np.fromfile(f, dtype=np.uint16)
                    data_lst.append(data)
            data = np.concatenate(data_lst)
            data = data[:max_length * int(len(data) / max_length)]
            self.data = data.reshape(-1, max_length)
        print("memmap:{} train data.shape:{}".format(memmap, self.data.shape))
        print("downloading finished.....")

    def __len__(self):
        return self.data.shape[0]

    def __getitem__(self, index: int):
        sample = self.data[index]
        X = np.array(sample[:-1]).astype(np.int64)
        Y = np.array(sample[1:]).astype(np.int64)

        return torch.from_numpy(X), torch.from_numpy(Y)
    
class RMSNorm(torch.nn.Module):
    def __init__(self, dim: int, eps: float):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))

    def _norm(self, x):
        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)

    def forward(self, x):
        output = self._norm(x.float()).type_as(x)
        return output * self.weight


def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
    t = torch.arange(end, device=freqs.device)  # type: ignore
    freqs = torch.outer(t, freqs).float()  # type: ignore
    freqs_cos = torch.cos(freqs)  # real part
    freqs_sin = torch.sin(freqs)  # imaginary part
    return freqs_cos, freqs_sin

def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
    ndim = x.ndim
    assert 0 <= 1 < ndim
    assert freqs_cis.shape == (x.shape[1], x.shape[-1])
    shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
    return freqs_cis.view(shape)

def apply_rotary_emb(
    xq: torch.Tensor,
    xk: torch.Tensor,
    freqs_cos: torch.Tensor,
    freqs_sin: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:

    # reshape xq and xk to match the complex representation
    xq_r, xq_i = xq.float().reshape(xq.shape[:-1] + (-1, 2)).unbind(-1)
    xk_r, xk_i = xk.float().reshape(xk.shape[:-1] + (-1, 2)).unbind(-1)

    # reshape freqs_cos and freqs_sin for broadcasting
    freqs_cos = reshape_for_broadcast(freqs_cos, xq_r)
    freqs_sin = reshape_for_broadcast(freqs_sin, xq_r)

    # apply rotation using real numbers
    xq_out_r = xq_r * freqs_cos - xq_i * freqs_sin
    xq_out_i = xq_r * freqs_sin + xq_i * freqs_cos
    xk_out_r = xk_r * freqs_cos - xk_i * freqs_sin
    xk_out_i = xk_r * freqs_sin + xk_i * freqs_cos

    # flatten last two dimensions
    xq_out = torch.stack([xq_out_r, xq_out_i], dim=-1).flatten(3)
    xk_out = torch.stack([xk_out_r, xk_out_i], dim=-1).flatten(3)

    return xq_out.type_as(xq), xk_out.type_as(xk)

def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
    """torch.repeat_interleave(x, dim=2, repeats=n_rep)"""
    bs, slen, n_kv_heads, head_dim = x.shape
    if n_rep == 1:
        return x
    return (
        x[:, :, :, None, :]
        .expand(bs, slen, n_kv_heads, n_rep, head_dim)
        .reshape(bs, slen, n_kv_heads * n_rep, head_dim)
    )

class Attention(nn.Module):
    def __init__(self, args: MyPretrainConfig):
        super().__init__()
        self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads
        assert args.n_heads % self.n_kv_heads == 0
        model_parallel_size = 1
        self.n_local_heads = args.n_heads // model_parallel_size
        self.n_local_kv_heads = self.n_kv_heads // model_parallel_size
        self.n_rep = self.n_local_heads // self.n_local_kv_heads
        self.head_dim = args.dim // args.n_heads
        self.wq = nn.Linear(args.dim, args.n_heads * self.head_dim, bias=False)
        self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
        self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
        self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False)
        self.attn_dropout = nn.Dropout(args.dropout)
        self.resid_dropout = nn.Dropout(args.dropout)
        self.dropout = args.dropout

        # use flash attention or a manual implementation?
        self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
        if not self.flash:
            print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")
            mask = torch.full((1, 1, args.max_seq_len, args.max_seq_len), float("-inf"))
            mask = torch.triu(mask, diagonal=1)
            self.register_buffer("mask", mask)

    def forward(
        self,
        x: torch.Tensor,
        freqs_cos: torch.Tensor,
        freqs_sin: torch.Tensor,
    ):
        bsz, seqlen, _ = x.shape

        # QKV
        xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
        xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)
        xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
        xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)

        # RoPE relative positional embeddings
        xq, xk = apply_rotary_emb(xq, xk, freqs_cos, freqs_sin)

        # grouped multiquery attention: expand out keys and values
        xk = repeat_kv(xk, self.n_rep)  # (bs, seqlen, n_local_heads, head_dim)
        xv = repeat_kv(xv, self.n_rep)  # (bs, seqlen, n_local_heads, head_dim)

        # make heads into a batch dimension
        xq = xq.transpose(1, 2)  # (bs, n_local_heads, seqlen, head_dim)
        xk = xk.transpose(1, 2)
        xv = xv.transpose(1, 2)

        # flash implementation
        if self.flash:
            output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None, dropout_p=self.dropout if self.training else 0.0, is_causal=True)
        else:
            # manual implementation
            scores = torch.matmul(xq, xk.transpose(2, 3)) / math.sqrt(self.head_dim)
            assert hasattr(self, 'mask')
            scores = scores + self.mask[:, :, :seqlen, :seqlen]   # (bs, n_local_heads, seqlen, cache_len + seqlen)
            scores = F.softmax(scores.float(), dim=-1).type_as(xq)
            scores = self.attn_dropout(scores)
            output = torch.matmul(scores, xv)  # (bs, n_local_heads, seqlen, head_dim)

        # restore time as batch dimension and concat heads
        output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1)

        # final projection into the residual stream
        output = self.wo(output)
        output = self.resid_dropout(output)
        return output

class FeedForward(nn.Module):
    def __init__(self, dim: int, hidden_dim: int, multiple_of: int, dropout: float):
        super().__init__()
        if hidden_dim is None:
            hidden_dim = 4 * dim
            hidden_dim = int(2 * hidden_dim / 3)
            hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
        self.w1 = nn.Linear(dim, hidden_dim, bias=False)
        self.w2 = nn.Linear(hidden_dim, dim, bias=False)
        self.w3 = nn.Linear(dim, hidden_dim, bias=False)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x)))

class TransformerBlock(nn.Module):
    def __init__(self, layer_id: int, args: MyPretrainConfig):
        super().__init__()
        self.n_heads = args.n_heads
        self.dim = args.dim
        self.head_dim = args.dim // args.n_heads
        self.attention = Attention(args)
        self.feed_forward = FeedForward(
            dim=args.dim,
            hidden_dim=args.hidden_dim,
            multiple_of=args.multiple_of,
            dropout=args.dropout,
        )
        self.layer_id = layer_id
        self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
        self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)

    def forward(self, x, freqs_cos, freqs_sin):
        h = x + self.attention.forward(self.attention_norm(x), freqs_cos, freqs_sin)
        out = h + self.feed_forward.forward(self.ffn_norm(h))
        return out

class Transformer(PreTrainedModel):
    last_loss: Optional[torch.Tensor]

    def __init__(self, params: MyPretrainConfig):
        super().__init__(params)
        self.params = params
        self.vocab_size = params.vocab_size
        self.n_layers = params.n_layers

        self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim)
        self.dropout = nn.Dropout(params.dropout)
        self.layers = torch.nn.ModuleList()
        for layer_id in range(params.n_layers):
            self.layers.append(TransformerBlock(layer_id, params))
        self.norm = RMSNorm(params.dim, eps=params.norm_eps)
        self.output = nn.Linear(params.dim, params.vocab_size, bias=False)

        # share the unembedding parameters with the embedding parameters
        self.tok_embeddings.weight = self.output.weight # https://paperswithcode.com/method/weight-tying

        # some useful precompute for the RoPE relative positional embeddings
        freqs_cos, freqs_sin = precompute_freqs_cis(self.params.dim // self.params.n_heads, self.params.max_seq_len)
        self.register_buffer("freqs_cos", freqs_cos, persistent=False)
        self.register_buffer("freqs_sin", freqs_sin, persistent=False)

        # init all weights
        self.apply(self._init_weights)
        # apply special scaled init to the residual projections, per GPT-2 paper
        for pn, p in self.named_parameters():
            if pn.endswith('w3.weight') or pn.endswith('wo.weight'):
                torch.nn.init.normal_(p, mean=0.0, std=0.02/math.sqrt(2 * params.n_layers))

        # Initialize attribute for the loss of the last forward call. This will be set if the forward is called with a targets tensor.
        self.last_loss = None
        self.OUT = CausalLMOutputWithPast()

    def _init_weights(self, module):
        if isinstance(module, nn.Linear):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
            if module.bias is not None:
                torch.nn.init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)

    def forward(self, tokens: torch.Tensor, targets: Optional[torch.Tensor] = None) -> torch.Tensor:
        _bsz, seqlen = tokens.shape
        h = self.tok_embeddings(tokens)
        h = self.dropout(h)
        freqs_cos = self.freqs_cos[:seqlen]
        freqs_sin = self.freqs_sin[:seqlen]

        for layer in self.layers:
            h = layer(h, freqs_cos, freqs_sin)
        h = self.norm(h)

        if targets is not None:
            # if we are given some desired targets also calculate the loss
            logits = self.output(h)
            self.last_loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
        else:
            # inference-time mini-optimization: only forward the output on the very last position
            logits = self.output(h[:, [-1], :]) # note: using list [-1] to preserve the time dim
            self.last_loss = None

        self.OUT.__setitem__('logits', logits)
        self.OUT.__setitem__('last_loss', self.last_loss)
        return self.OUT

    def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):
        # start with all of the candidate parameters
        param_dict = {pn: p for pn, p in self.named_parameters()}
        # filter out those that do not require grad
        param_dict = {pn: p for pn, p in param_dict.items() if p.requires_grad}
        # create optim groups. Any parameters that is 2D will be weight decayed, otherwise no.
        # i.e. all weight tensors in matmuls + embeddings decay, all biases and layernorms don't.
        decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]
        nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2]
        optim_groups = [
            {'params': decay_params, 'weight_decay': weight_decay},
            {'params': nodecay_params, 'weight_decay': 0.0}
        ]
        num_decay_params = sum(p.numel() for p in decay_params)
        num_nodecay_params = sum(p.numel() for p in nodecay_params)
        print(f"num decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters")
        print(f"num non-decayed parameter tensors: {len(nodecay_params)}, with {num_nodecay_params:,} parameters")
        # Create AdamW optimizer and use the fused version if it is available
        fused_available = 'fused' in inspect.signature(torch.optim.AdamW).parameters
        use_fused = fused_available and device_type == 'cuda'
        extra_args = dict(fused=True) if use_fused else dict()
        optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=betas, **extra_args)
        print(f"using fused AdamW: {use_fused}")

        return optimizer

    def estimate_mfu(self, fwdbwd_per_iter, dt):
        """ estimate model flops utilization (MFU) in units of A100 bfloat16 peak FLOPS """
        # first estimate the number of flops we do per iteration.
        # see PaLM paper Appendix B as ref: https://arxiv.org/abs/2204.02311
        N = sum(p.numel() for p in self.parameters())
        cfg = self.params
        L, H, Q, T = cfg.n_layers, cfg.n_heads, cfg.dim//cfg.n_heads, cfg.max_seq_len
        flops_per_token = 6*N + 12*L*H*Q*T
        flops_per_fwdbwd = flops_per_token * T
        flops_per_iter = flops_per_fwdbwd * fwdbwd_per_iter
        # express our flops throughput as ratio of A100 bfloat16 peak flops
        flops_achieved = flops_per_iter * (1.0/dt) # per second
        flops_promised = 312e12 # A100 GPU bfloat16 peak flops is 312 TFLOPS
        mfu = flops_achieved / flops_promised
        return mfu

    @torch.inference_mode()
    def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
        """
        Take a conditioning sequence of indices idx (LongTensor of shape (b,t)) and complete
        the sequence max_new_tokens times, feeding the predictions back into the model each time.
        Most likely you'll want to make sure to be in model.eval() mode of operation for this.
        Also note this is a super inefficient version of sampling with no key/value cache.
        """
        for _ in range(max_new_tokens):
            # if the sequence context is growing too long we must crop it at block_size
            idx_cond = idx if idx.size(1) <= self.params.max_seq_len else idx[:, -self.params.max_seq_len:]
            # forward the model to get the logits for the index in the sequence
            logits = self(idx_cond)
            logits = logits[:, -1, :] # crop to just the final time step
            if temperature == 0.0:
                # "sample" the single most likely index
                _, idx_next = torch.topk(logits, k=1, dim=-1)
            else:
                # pluck the logits at the final step and scale by desired temperature
                logits = logits / temperature
                # optionally crop the logits to only the top k options
                if top_k is not None:
                    v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
                    logits[logits < v[:, [-1]]] = -float('Inf')
                # apply softmax to convert logits to (normalized) probabilities
                probs = F.softmax(logits, dim=-1)
                idx_next = torch.multinomial(probs, num_samples=1)
            # append sampled index to the running sequence and continue
            idx = torch.cat((idx, idx_next), dim=1)

        return idx

def get_lr(it, all):
    warmup_iters = 0
    lr_decay_iters = all
    min_lr = learning_rate / 10

    if it < warmup_iters:
        return learning_rate * it / warmup_iters
    if it > lr_decay_iters:
        return min_lr
    decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters)
    assert 0 <= decay_ratio <= 1
    coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))
    return min_lr + coeff * (learning_rate - min_lr)

def init_model():
    def count_parameters(model):
        return sum(p.numel() for p in model.parameters() if p.requires_grad)

    model = Transformer(lm_config).to(device)
    print(f'LLM總參數(shù)量：{count_parameters(model) / 1e6:.3f} 百萬(wàn)')
    return model


if __name__ == "__main__":
    # -----------------------------------------------------------------------------
    lm_config = MyPretrainConfig()
    max_seq_len = lm_config.max_seq_len
    out_dir = 'out'
    epochs = 20             # 訓(xùn)練輪數(shù)
    batch_size = 8          # batch_size
    learning_rate = 1e-4    # 學(xué)習(xí)率
    device = 'cuda:0'       # or cpu
    dtype = 'bfloat16'
    save_dir = os.path.join(out_dir)
    os.makedirs(save_dir, exist_ok=True)
    os.makedirs(out_dir, exist_ok=True)
    tokens_per_iter = batch_size * max_seq_len
    torch.manual_seed(1337)
    device_type = device if "cuda" in device else "cpu"
    print(f"device_type: {device_type}")
    ctx = (
        nullcontext()
        if device_type == "cpu"
        else torch.cuda.amp.autocast()
    )
    # -----------------------------------------------------------------------------

    # -----init dataloader------
    data_path_list = [f'{basepath}/pretrain_data.bin']
    train_ds = PretrainDataset(data_path_list, max_length=max_seq_len, memmap=True)
    train_sampler = None
    num_workers = 16  # 可以根據(jù)系統(tǒng)的 CPU 核心數(shù)來(lái)調(diào)整
    train_loader = DataLoader(
        train_ds,
        batch_size=batch_size,
        pin_memory=True,
        drop_last=False,
        shuffle=False,
        num_workers=num_workers,
        sampler=train_sampler
    )

    # init model
    model = init_model()
    print(model)
    scaler = torch.cuda.amp.GradScaler(enabled=(dtype == dtype))
    optimizer = optim.Adam(model.parameters(), lr=learning_rate)

    # training loop
    accumulation_steps = 8
    iter_per_epoch = len(train_loader)
    for epoch in range(epochs):
        start_time = time.time()

        for step, (X, Y) in enumerate(train_loader):
            X = X.to(device)
            Y = Y.to(device)

            # 設(shè)置學(xué)習(xí)率
            lr = get_lr(epoch * iter_per_epoch + step, epochs * iter_per_epoch)
            for param_group in optimizer.param_groups:
                param_group['lr'] = lr

            # 前向傳播和損失計(jì)算
            with ctx:
                out = model(X, Y)
                loss = out.last_loss

            # 反向傳播
            scaler.scale(loss).backward()

            # 梯度剪裁和更新參數(shù)
            if (step + 1) % accumulation_steps == 0:
                scaler.unscale_(optimizer)
                torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
                scaler.step(optimizer)
                scaler.update()

            # 清零梯度
            optimizer.zero_grad(set_to_none=True)

            if step % 100 == 0:
                spend_time = time.time() - start_time
                print(
                    'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.7f} epoch_Time:{}min:'.format(
                        epoch,
                        epochs,
                        step,
                        iter_per_epoch,
                        loss.item(),
                        optimizer.param_groups[-1]['lr'],
                        spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60))
                model.eval()
                ckp = f'{save_dir}/pretrain_{lm_config.dim}.pth'
                state_dict = model.state_dict()
                torch.save(state_dict, ckp)
                model.train()