import math
import struct
import inspect
from .LMConfig import LMConfig
from typing import Any, Optional, Tuple
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from transformers import PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast
# 定义 RMSNorm 类，实现一种归一化方法，类似于 LayerNorm，但计算方式不同
class RMSNorm(torch.nn.Module):
def __init__(self, dim: int, eps: float):
super().__init__()
self.eps = eps # 设置 epsilon，防止除零错误
self.weight = nn.Parameter(torch.ones(dim)) # 初始化权重参数
def _norm(self, x):
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) # 计算 RMSNorm
def forward(self, x):
output = self._norm(x.float()).type_as(x) # 应用 RMSNorm
return output * self.weight # 乘以权重参数
# 定义 precompute_pos_cis 函数，用于预计算位置编码的复数形式
def precompute_pos_cis(dim: int, end: int, theta: float = 10000.0):
freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) # 计算频率
t = torch.arange(end, device=freqs.device) # 生成时间序列
freqs = torch.outer(t, freqs).float() # 计算外积
pos_cis = torch.polar(torch.ones_like(freqs), freqs) # 计算复数形式的位置编码
return pos_cis
# 定义 apply_rotary_emb 函数，用于应用旋转位置编码
def apply_rotary_emb(xq, xk, pos_cis):
def unite_shape(pos_cis, x):
ndim = x.ndim
assert 0 <= 1 < ndim
assert pos_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 pos_cis.view(*shape)
xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) # 将 xq 转换为复数形式
xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) # 将 xk 转换为复数形式
pos_cis = unite_shape(pos_cis, xq_) # 调整 pos_cis 的形状
xq_out = torch.view_as_real(xq_ * pos_cis).flatten(3) # 应用旋转位置编码
xk_out = torch.view_as_real(xk_ * pos_cis).flatten(3) # 应用旋转位置编码
return xq_out.type_as(xq), xk_out.type_as(xk) # 返回结果
# 定义 repeat_kv 函数，用于重复 KV 头的值
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)
)
# 定义 Attention 类，实现自注意力机制
class Attention(nn.Module):
def __init__(self, args: LMConfig):
super().__init__()
self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads # 设置 KV 头的数量
assert args.n_heads % self.n_kv_heads == 0 # 确保 KV 头的数量是总头数的因数
self.n_local_heads = args.n_heads # 设置本地头的数量
self.n_local_kv_heads = self.n_kv_heads # 设置本地 KV 头的数量
self.n_rep = self.n_local_heads // self.n_local_kv_heads # 计算重复次数
self.head_dim = args.dim // args.n_heads # 计算每个头的维度
self.wq = nn.Linear(args.dim, args.n_heads * self.head_dim, bias=False) # 初始化 Q 矩阵
self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False) # 初始化 K 矩阵
self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False) # 初始化 V 矩阵
self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False) # 初始化输出矩阵
self.k_cache, self.v_cache = None, None # 初始化 KV 缓存
self.attn_dropout = nn.Dropout(args.dropout) # 初始化注意力 dropout
self.resid_dropout = nn.Dropout(args.dropout) # 初始化残差 dropout
self.dropout = args.dropout # 设置 dropout 概率
self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and args.flash_attn # 判断是否使用 Flash 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")) # 初始化掩码
mask = torch.triu(mask, diagonal=1) # 生成上三角掩码
self.register_buffer("mask", mask) # 注册掩码
def forward(self, x: torch.Tensor, pos_cis: torch.Tensor, use_kv_cache=False):
bsz, seqlen, _ = x.shape
if use_kv_cache and self.eval(): # 如果使用 KV 缓存且在评估模式下
if self.k_cache is None or self.k_cache.shape[1] != x.shape[1] - 1:
xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) # 计算 Q, K, V
else:
token = x[:, -1:, :] # 获取最后一个 token
xq = torch.cat((torch.zeros_like(x[:, :-1, :]), self.wq(token)), dim=1) # 更新 Q
xk = torch.cat((self.k_cache, self.wk(token)), dim=1) # 更新 K
xv = torch.cat((self.v_cache, self.wv(token)), dim=1) # 更新 V
self.k_cache, self.v_cache = xk, xv # 更新 KV 缓存
else:
xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) # 计算 Q, K, V
xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim) # 调整 Q 的形状
xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim) # 调整 K 的形状
xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim) # 调整 V 的形状
xq, xk = apply_rotary_emb(xq, xk, pos_cis) # 应用旋转位置编码
xk = repeat_kv(xk, self.n_rep) # 重复 K 的值
xv = repeat_kv(xv, self.n_rep) # 重复 V 的值
xq = xq.transpose(1, 2) # 调整 Q 的形状
xk = xk.transpose(1, 2) # 调整 K 的形状
xv = xv.transpose(1, 2) # 调整 V 的形状
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) # 使用 Flash Attention
else:
scores = torch.matmul(xq, xk.transpose(2, 3)) / math.sqrt(self.head_dim) # 计算注意力分数
assert hasattr(self, 'mask')
scores = scores + self.mask[:, :, :seqlen, :seqlen] # 应用掩码
scores = F.softmax(scores.float(), dim=-1).type_as(xq) # 计算 softmax
scores = self.attn_dropout(scores) # 应用注意力 dropout
output = torch.matmul(scores, xv) # 计算输出
output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1) # 调整输出的形状
output = self.wo(output) # 应用输出矩阵
output = self.resid_dropout(output) # 应用残差 dropout
return output # 返回输出
# 定义 FeedForward 类，实现前馈神经网络
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 # 设置隐藏层维度
hidden_dim = int(2 * hidden_dim / 3) # 调整隐藏层维度
hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) # 调整隐藏层维度
self.w1 = nn.Linear(dim, hidden_dim, bias=False) # 初始化第一层线性变换
self.w2 = nn.Linear(hidden_dim, dim, bias=False) # 初始化第二层线性变换
self.w3 = nn.Linear(dim, hidden_dim, bias=False) # 初始化第三层线性变换
self.dropout = nn.Dropout(dropout) # 初始化 dropout
def forward(self, x):
return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x))) # 前向传播
# 定义 MoEGate 类，实现专家混合（MoE）的门控机制
class MoEGate(nn.Module):
def __init__(self, config: LMConfig):
super().__init__()
self.config = config
self.top_k = config.num_experts_per_tok # 设置每个 token 选择的专家数量
self.n_routed_experts = config.n_routed_experts # 设置路由专家的数量
self.scoring_func = config.scoring_func # 设置评分函数
self.alpha = config.aux_loss_alpha # 设置辅助损失的权重
self.seq_aux = config.seq_aux # 设置序列辅助损失
self.norm_topk_prob = config.norm_topk_prob # 设置是否归一化 top-k 概率
self.gating_dim = config.dim # 设置门控维度
self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim))) # 初始化权重参数
self.reset_parameters() # 重置参数
def reset_parameters(self) -> None:
import torch.nn.init as init
init.kaiming_uniform_(self.weight, a=math.sqrt(5)) # 使用 Kaiming 初始化权重
def forward(self, hidden_states):
bsz, seq_len, h = hidden_states.shape
hidden_states = hidden_states.view(-1, h) # 调整隐藏状态的形状
logits = F.linear(hidden_states, self.weight, None) # 计算 logits
if self.scoring_func == 'softmax':
scores = logits.softmax(dim=-1) # 计算 softmax 评分
else:
raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}')
topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False) # 选择 top-k 专家
if self.top_k > 1 and self.norm_topk_prob:
denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20 # 计算归一化分母
topk_weight = topk_weight / denominator # 归一化 top-k 概率
if self.training and self.alpha > 0.0:
scores_for_aux = scores
aux_topk = self.top_k
topk_idx_for_aux_loss = topk_idx.view(bsz, -1)
if self.seq_aux:
scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1)
ce = torch.zeros(bsz, self.n_routed_experts, device=hidden_states.device)
ce.scatter_add_(1, topk_idx_for_aux_loss,
torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device)).div_(
seq_len * aux_topk / self.n_routed_experts)
aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(dim=1).mean() * self.alpha
else:
mask_ce = F.one_hot(topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts)
ce = mask_ce.float().mean(0)
Pi = scores_for_aux.mean(0)
fi = ce * self.n_routed_experts
aux_loss = (Pi * fi).sum() * self.alpha
else:
aux_loss = None
return topk_idx, topk_weight, aux_loss # 返回 top-k 专家索引、权重和辅助损失
# 定义 MOEFeedForward 类，实现专家混合（MoE）的前馈神经网络
class MOEFeedForward(nn.Module):
def __init__(self, config: LMConfig):
super().__init__()
self.config = config
self.experts = nn.ModuleList([
FeedForward(
dim=config.dim,
hidden_dim=config.hidden_dim,
multiple_of=config.multiple_of,
dropout=config.dropout,
)
for _ in range(config.n_routed_experts)
]) # 初始化专家列表
self.gate = MoEGate(config) # 初始化门控机制
if config.n_shared_experts is not None:
self.shared_experts = FeedForward(
dim=config.dim,
hidden_dim=config.hidden_dim,
multiple_of=config.multiple_of,
dropout=config.dropout,
) # 初始化共享专家
def forward(self, x):
identity = x
orig_shape = x.shape
bsz, seq_len, _ = x.shape
# 使用门控机制选择专家
topk_idx, topk_weight, aux_loss = self.gate(x)
x = x.view(-1, x.shape[-1])
flat_topk_idx = topk_idx.view(-1)
if self.training:
# 训练模式下，重复输入数据
x = x.repeat_interleave(self.config.num_experts_per_tok, dim=0)
y = torch.empty_like(x, dtype=torch.float16)
for i, expert in enumerate(self.experts):
y[flat_topk_idx == i] = expert(x[flat_topk_idx == i])
y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
y = y.view(*orig_shape)
else:
# 推理模式下，只选择最优专家
y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape)
if self.config.n_shared_experts is not None:
y = y + self.shared_experts(identity)
return y
@torch.no_grad()
def moe_infer(self, x, flat_expert_indices, flat_expert_weights):
expert_cache = torch.zeros_like(x)
idxs = flat_expert_indices.argsort()
tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0)
token_idxs = idxs // self.config.num_experts_per_tok
# 例如当tokens_per_expert=[6, 15, 20, 26, 33, 38, 46, 52]
# 当token_idxs=[3, 7, 19, 21, 24, 25, 4, 5, 6, 10, 11, 12...]
# 意味着当token_idxs[:6] -> [3, 7, 19, 21, 24, 25, 4]位置的token都由专家0处理，token_idxs[6:15]位置的token都由专家1处理......
for i, end_idx in enumerate(tokens_per_expert):
start_idx = 0 if i == 0 else tokens_per_expert[i - 1]
if start_idx == end_idx:
continue
expert = self.experts[i]
exp_token_idx = token_idxs[start_idx:end_idx]
expert_tokens = x[exp_token_idx]
expert_out = expert(expert_tokens)
expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]])
# 使用 scatter_add_ 进行 sum 操作
expert_cache.scatter_add_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out)
return expert_cache
# 定义 TransformerBlock 类，实现 Transformer 的一个块，包括自注意力和前馈神经网络
class TransformerBlock(nn.Module):
def __init__(self, layer_id: int, args: LMConfig):
super().__init__()
self.n_heads = args.n_heads
self.dim = args.dim
self.head_dim = args.dim // args.n_heads
self.attention = Attention(args) # 初始化自注意力机制
self.layer_id = layer_id
self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps) # 初始化注意力归一化
self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps) # 初始化前馈神经网络归一化
if args.use_moe:
self.feed_forward = MOEFeedForward(args) # 初始化专家混合前馈神经网络
else:
self.feed_forward = FeedForward(
dim=args.dim,
hidden_dim=args.hidden_dim,
multiple_of=args.multiple_of,
dropout=args.dropout,
) # 初始化前馈神经网络
def forward(self, x, pos_cis, use_kv_cache=False):
h = x + self.attention(self.attention_norm(x), pos_cis, use_kv_cache) # 计算自注意力
out = h + self.feed_forward(self.ffn_norm(h)) # 计算前馈神经网络
return out # 返回输出
# 定义 Transformer 类，实现整个 Transformer 模型
class Transformer(PreTrainedModel):
config_class = LMConfig
last_loss: Optional[torch.Tensor]
def __init__(self, params: LMConfig = None):
super().__init__(params)
if not params:
params = LMConfig()
self.params = params
self.vocab_size = params.vocab_size
self.n_layers = params.n_layers
class Transformer(PreTrainedModel):
config_class = LMConfig
last_loss: Optional[torch.Tensor]
def __init__(self, params: LMConfig = None):
super().__init__(params)
if not params:
params = LMConfig()
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) # 初始化词嵌入层
self.dropout = nn.Dropout(params.dropout) # 初始化 dropout 层
self.layers = torch.nn.ModuleList() # 初始化 Transformer 块列表
for layer_id in range(self.n_layers):
self.layers.append(TransformerBlock(layer_id, params)) # 添加 Transformer 块
self.norm = RMSNorm(params.dim, eps=params.norm_eps) # 初始化归一化层
self.output = nn.Linear(params.dim, params.vocab_size, bias=False) # 初始化输出层
self.tok_embeddings.weight = self.output.weight # 共享词嵌入和输出层的权重
pos_cis = precompute_pos_cis(self.params.dim // self.params.n_heads, self.params.max_seq_len) # 预计算位置编码
self.register_buffer("pos_cis", pos_cis, persistent=False) # 注册位置编码缓冲区
self.apply(self._init_weights) # 初始化模型权重
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)) # 对特定权重进行初始化
self.last_loss = None # 初始化最后一个损失
self.OUT = CausalLMOutputWithPast() # 初始化输出对象
def _init_weights(self, module):
if isinstance(module, nn.Linear):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) # 初始化线性层的权重
if module.bias is not None:
torch.nn.init.zeros_(module.bias) # 初始化线性层的偏置
elif isinstance(module, nn.Embedding):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) # 初始化嵌入层的权重
def forward(self, tokens: Optional[torch.Tensor] = None, targets: Optional[torch.Tensor] = None,
use_kv_cache=False, **keyargs):
if 'input_ids' in keyargs:
tokens = keyargs['input_ids'] # 如果传入了 input_ids，则使用 input_ids
if 'attention_mask' in keyargs:
targets = keyargs['attention_mask'] # 如果传入了 attention_mask，则使用 attention_mask
_bsz, seqlen = tokens.shape # 获取批量大小和序列长度
h = self.tok_embeddings(tokens) # 获取词嵌入
h = self.dropout(h) # 应用 dropout
pos_cis = self.pos_cis[:seqlen] # 获取对应序列长度的位置编码
for idx, layer in enumerate(self.layers):
h = layer(h, pos_cis, use_kv_cache) # 逐层应用 Transformer 块
h = self.norm(h) # 应用归一化
if targets is not None:
logits = self.output(h) # 计算 logits
self.last_loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1) # 计算交叉熵损失
else:
logits = self.output(h[:, [-1], :]) # 计算最后一个 token 的 logits
self.last_loss = None # 没有目标时，损失为 None
self.OUT.__setitem__('logits', logits) # 设置输出对象的 logits
self.OUT.__setitem__('last_loss', self.last_loss) # 设置输出对象的 last_loss
return self.OUT # 返回输出对象
@torch.inference_mode() # 推理模式
def generate(self, idx, eos, max_new_tokens, temperature=0.7, top_k=None, stream=True, repetition_penalty=1.,
use_kv_cache=True):
index = idx.shape[1] # 获取当前序列长度
while idx.shape[1] < max_new_tokens - 1: # 当生成的 token 数量小于最大数量时
inference_res = self(idx, use_kv_cache=use_kv_cache) # 进行前向传播
logits = inference_res.logits # 获取 logits
logits = logits[:, -1, :] # 获取最后一个 token 的 logits
for token in set(idx.tolist()[0]): # 对重复 token 进行惩罚
logits[:, token] /= repetition_penalty
if temperature == 0.0: # 如果温度为 0，直接选择概率最高的 token
_, idx_next = torch.topk(logits, k=1, dim=-1)
else:
logits = logits / temperature # 调整 logits
if top_k is not None: # 如果设置了 top-k 采样
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
logits[logits < v[:, [-1]]] = -float('Inf') # 将小于 top-k 的 logits 设为负无穷
probs = F.softmax(logits, dim=-1) # 计算概率
idx_next = torch.multinomial(probs, num_samples=1, generator=None) # 采样下一个 token
if idx_next == eos: # 如果生成的 token 是结束符，停止生成
break
idx = torch.cat((idx, idx_next), dim=1) # 将生成的 token 添加到序列中
if stream: # 如果需要流式输出
yield idx[:, index:] # 返回生成的 token
if not stream: # 如果不需要流式输出
yield idx[:, index:] # 返回生成的 token
@torch.inference_mode() # 推理模式
def eval_answer(self, idx):
idx_cond = idx if idx.size(1) <= self.params.max_seq_len else idx[:, -self.params.max_seq_len:] # 截取序列
inference_res = self(idx_cond) # 进行前向传播
logits = inference_res.logits # 获取 logits
logits = logits[:, -1, :] # 获取最后一个 token 的 logits
return logits # 返回 logits