445 lines
19 KiB
Python
445 lines
19 KiB
Python
import math
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import inspect
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from dataclasses import dataclass
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from typing import Any, Optional, Tuple
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import torch
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import torch.nn.functional as F
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from torch import nn
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from transformers import PreTrainedModel, AutoTokenizer
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from transformers.modeling_outputs import CausalLMOutputWithPast
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from transformers import PretrainedConfig
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class ModelConfig(PretrainedConfig):
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model_type = "Tiny-K"
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def __init__(
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self,
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dim: int = 768,
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n_layers: int = 12,
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n_heads: int = 16,
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n_kv_heads: int = 8,
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vocab_size: int = 6144,
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hidden_dim: int = None,
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multiple_of: int = 64,
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norm_eps: float = 1e-5,
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max_seq_len: int = 512,
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dropout: float = 0.0,
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flash_attn: bool = True,
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**kwargs,
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):
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self.dim = dim
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self.n_layers = n_layers
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self.n_heads = n_heads
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self.n_kv_heads = n_kv_heads
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self.vocab_size = vocab_size
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self.hidden_dim = hidden_dim
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self.multiple_of = multiple_of
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self.norm_eps = norm_eps
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self.max_seq_len = max_seq_len
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self.dropout = dropout
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self.flash_attn = flash_attn
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super().__init__(**kwargs)
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class RMSNorm(nn.Module):
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def __init__(self, dim: int, eps: float):
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super().__init__()
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# eps是为了防止除以0的情况
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self.eps = eps
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# weight是一个可学习的参数，全部初始化为1
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self.weight = nn.Parameter(torch.ones(dim))
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def _norm(self, x):
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# 计算RMSNorm的核心部分
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# x.pow(2).mean(-1, keepdim=True)计算了输入x的平方的均值
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# torch.rsqrt是平方根的倒数，这样就得到了RMSNorm的分母部分，再加上eps防止分母为0
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# 最后乘以x，得到RMSNorm的结果
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return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
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def forward(self, x):
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# forward函数是模型的前向传播
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# 首先将输入x转为float类型，然后进行RMSNorm，最后再转回原来的数据类型
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# 最后乘以weight，这是RMSNorm的一个可学习的缩放因子
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output = self._norm(x.float()).type_as(x)
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return output * self.weight
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# 获得旋转嵌入的实部和虚部
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# 注意：此处的dim应为 dim//n_head，因为我们是对每个head进行旋转嵌入
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def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
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# torch.arange(0, dim, 2)[: (dim // 2)].float()生成了一个从0开始，步长为2的序列，长度为dim的一半
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# 然后每个元素除以dim，再取theta的倒数，得到频率
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freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
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# 生成一个从0到end的序列，长度为end
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t = torch.arange(end, device=freqs.device)
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# 计算外积，得到一个二维矩阵，每一行是t的元素乘以freqs的元素
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freqs = torch.outer(t, freqs).float()
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# 计算频率的余弦值，得到实部
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freqs_cos = torch.cos(freqs)
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# 计算频率的正弦值，得到虚部
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freqs_sin = torch.sin(freqs)
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return freqs_cos, freqs_sin
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# 此函数的作用是将freqs_cis调整为与x的形状相同，以便能够与x进行广播操作
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def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
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# 获取x的维度数
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ndim = x.ndim
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# 断言，确保1在x的维度范围内
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assert 0 <= 1 < ndim
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# 断言，确保freqs_cis的形状与x的第二维和最后一维相同
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assert freqs_cis.shape == (x.shape[1], x.shape[-1])
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# 构造一个新的形状，除了第二维和最后一维，其他维度都为1，这样做是为了能够将freqs_cis与x进行广播操作
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shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
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# 将freqs_cis调整为新的形状，并返回
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return freqs_cis.view(shape)
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def apply_rotary_emb(
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xq: torch.Tensor,
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xk: torch.Tensor,
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freqs_cos: torch.Tensor,
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freqs_sin: torch.Tensor
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) -> Tuple[torch.Tensor, torch.Tensor]:
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# 将查询和键张量转换为浮点数，并重塑形状以分离实部和虚部
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xq_r, xq_i = xq.float().reshape(xq.shape[:-1] + (-1, 2)).unbind(-1)
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xk_r, xk_i = xk.float().reshape(xk.shape[:-1] + (-1, 2)).unbind(-1)
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# 重新塑形频率张量以进行广播
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freqs_cos = reshape_for_broadcast(freqs_cos, xq_r)
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freqs_sin = reshape_for_broadcast(freqs_sin, xq_r)
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# 应用旋转，分别计算旋转后的实部和虚部
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xq_out_r = xq_r * freqs_cos - xq_i * freqs_sin
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xq_out_i = xq_r * freqs_sin + xq_i * freqs_cos
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xk_out_r = xk_r * freqs_cos - xk_i * freqs_sin
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xk_out_i = xk_r * freqs_sin + xk_i * freqs_cos
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# 将最后两个维度合并，并还原为原始张量的形状
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xq_out = torch.stack([xq_out_r, xq_out_i], dim=-1).flatten(3)
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xk_out = torch.stack([xk_out_r, xk_out_i], dim=-1).flatten(3)
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return xq_out.type_as(xq), xk_out.type_as(xk)
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def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
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# 获取输入张量的形状：批量大小、序列长度、键/值对头的数量、每个头的维度大小
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bs, slen, n_kv_heads, head_dim = x.shape
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# 如果重复次数为1，则不需要重复，直接返回原始张量
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if n_rep == 1:
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return x
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# 对张量进行扩展和重塑操作以重复键值对
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return (
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x[:, :, :, None, :] # 在第四个维度（头的维度前）添加一个新的维度
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.expand(bs, slen, n_kv_heads, n_rep, head_dim) # 将新添加的维度扩展到n_rep大小，实现重复的效果
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.reshape(bs, slen, n_kv_heads * n_rep, head_dim) # 重新塑形，合并键/值对头的数量和重复次数的维度
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)
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class Attention(nn.Module):
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def __init__(self, args: ModelConfig):
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super().__init__()
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# 根据是否指定n_kv_heads，确定用于键（key）和值（value）的头的数量。
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self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads
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# 确保总头数可以被键值头数整除。
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assert args.n_heads % self.n_kv_heads == 0
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# 模型并行处理大小，默认为1。
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model_parallel_size = 1
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# 本地计算头数，等于总头数除以模型并行处理大小。
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self.n_local_heads = args.n_heads // model_parallel_size
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# 本地键值头数，等于键值头数除以模型并行处理大小。
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self.n_local_kv_heads = self.n_kv_heads // model_parallel_size
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# 重复次数，用于扩展键和值的尺寸。
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self.n_rep = self.n_local_heads // self.n_local_kv_heads
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# 每个头的维度，等于模型维度除以头的总数。
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self.head_dim = args.dim // args.n_heads
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# 定义权重矩阵。
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self.wq = nn.Linear(args.dim, args.n_heads * self.head_dim, bias=False)
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self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
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self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
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# 输出权重矩阵。
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self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False)
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# 定义dropout。
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self.attn_dropout = nn.Dropout(args.dropout)
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self.resid_dropout = nn.Dropout(args.dropout)
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# 保存dropout概率。
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self.dropout = args.dropout
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# 检查是否使用Flash Attention（需要PyTorch >= 2.0）。
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self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
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if not self.flash:
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# 若不支持Flash Attention，则使用手动实现的注意力机制，并设置mask。
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print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")
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# 创建一个上三角矩阵，用于遮蔽未来信息。
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mask = torch.full((1, 1, args.max_seq_len, args.max_seq_len), float("-inf"))
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mask = torch.triu(mask, diagonal=1)
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# 注册为模型的缓冲区
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self.register_buffer("mask", mask)
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def forward(self, x: torch.Tensor, freqs_cos: torch.Tensor, freqs_sin: torch.Tensor):
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# 获取批次大小和序列长度，[batch_size, seq_len, dim]
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bsz, seqlen, _ = x.shape
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# 计算查询（Q）、键（K）、值（V）。
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xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
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# 调整形状以适应头的维度。
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xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)
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xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
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xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
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# 应用旋转位置嵌入（RoPE）。
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xq, xk = apply_rotary_emb(xq, xk, freqs_cos, freqs_sin)
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# 对键和值进行扩展以适应重复次数。
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xk = repeat_kv(xk, self.n_rep)
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xv = repeat_kv(xv, self.n_rep)
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# 将头作为批次维度处理。
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xq = xq.transpose(1, 2)
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xk = xk.transpose(1, 2)
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xv = xv.transpose(1, 2)
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# 根据是否支持Flash Attention，选择实现方式。
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if self.flash:
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# 使用Flash Attention。
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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)
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else:
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# 使用手动实现的注意力机制。
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scores = torch.matmul(xq, xk.transpose(2, 3)) / math.sqrt(self.head_dim)
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assert hasattr(self, 'mask')
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scores = scores + self.mask[:, :, :seqlen, :seqlen]
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scores = F.softmax(scores.float(), dim=-1).type_as(xq)
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scores = self.attn_dropout(scores)
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output = torch.matmul(scores, xv)
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# 恢复时间维度并合并头。
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output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1)
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# 最终投影回残差流。
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output = self.wo(output)
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output = self.resid_dropout(output)
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return output
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class MLP(nn.Module):
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def __init__(self, dim: int, hidden_dim: int, multiple_of: int, dropout: float):
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super().__init__()
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# 如果没有指定隐藏层的维度，我们将其设置为输入维度的4倍
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# 然后将其减少到2/3，最后确保它是multiple_of的倍数
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if hidden_dim is None:
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hidden_dim = 4 * dim
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hidden_dim = int(2 * hidden_dim / 3)
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hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
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# 定义第一层线性变换，从输入维度到隐藏维度
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self.w1 = nn.Linear(dim, hidden_dim, bias=False)
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# 定义第二层线性变换，从隐藏维度到输入维度
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self.w2 = nn.Linear(hidden_dim, dim, bias=False)
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# 定义第三层线性变换，从输入维度到隐藏维度
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self.w3 = nn.Linear(dim, hidden_dim, bias=False)
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# 定义dropout层，用于防止过拟合
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self.dropout = nn.Dropout(dropout)
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def forward(self, x):
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# 前向传播函数
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# 首先，输入x通过第一层线性变换和SILU激活函数
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# 然后，结果乘以输入x通过第三层线性变换的结果
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# 最后，通过第二层线性变换和dropout层
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return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x)))
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class DecoderLayer(nn.Module):
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def __init__(self, layer_id: int, args: ModelConfig):
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super().__init__()
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# 定义多头注意力的头数
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self.n_heads = args.n_heads
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# 定义输入维度
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self.dim = args.dim
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# 定义每个头的维度，等于输入维度除以头数
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self.head_dim = args.dim // args.n_heads
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# 定义LLaMA2Attention对象，用于进行多头注意力计算
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self.attention = Attention(args)
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# 定义LLaMAMLP对象，用于进行前馈神经网络计算
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self.feed_forward = MLP(
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dim=args.dim,
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hidden_dim=args.hidden_dim,
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multiple_of=args.multiple_of,
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dropout=args.dropout,
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)
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# 定义层的ID
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self.layer_id = layer_id
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# 定义注意力计算的归一化层
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self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
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# 定义前馈神经网络计算的归一化层
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self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)
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def forward(self, x, freqs_cos, freqs_sin):
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# 前向传播函数
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# 首先，输入x经过注意力归一化层，然后进行注意力计算，结果与输入x相加得到h
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# 然后，h经过前馈神经网络归一化层，然后进行前馈神经网络计算，结果与h相加得到输出
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h = x + self.attention.forward(self.attention_norm(x), freqs_cos, freqs_sin)
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out = h + self.feed_forward.forward(self.ffn_norm(h))
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return out
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class Transformer(PreTrainedModel):
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config_class = ModelConfig # 配置类
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last_loss: Optional[torch.Tensor] # 记录最后一次计算的损失
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def __init__(self, args: ModelConfig = None):
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super().__init__(args)
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# 初始化模型参数
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self.args = args
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# 词汇表大小
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self.vocab_size = args.vocab_size
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# 层数
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self.n_layers = args.n_layers
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# 词嵌入层
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self.tok_embeddings = nn.Embedding(args.vocab_size, args.dim)
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# Dropout层
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self.dropout = nn.Dropout(args.dropout)
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# Decoder层
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self.layers = torch.nn.ModuleList()
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for layer_id in range(args.n_layers):
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self.layers.append(DecoderLayer(layer_id, args))
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# 归一化层
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self.norm = RMSNorm(args.dim, eps=args.norm_eps)
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# 输出层
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self.output = nn.Linear(args.dim, args.vocab_size, bias=False)
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# 将词嵌入层的权重与输出层的权重共享
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self.tok_embeddings.weight = self.output.weight
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# 预计算相对位置嵌入的频率
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freqs_cos, freqs_sin = precompute_freqs_cis(self.args.dim // self.args.n_heads, self.args.max_seq_len)
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self.register_buffer("freqs_cos", freqs_cos, persistent=False)
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self.register_buffer("freqs_sin", freqs_sin, persistent=False)
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# 初始化所有权重
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self.apply(self._init_weights)
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# 对残差投影进行特殊的缩放初始化
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for pn, p in self.named_parameters():
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if pn.endswith('w3.weight') or pn.endswith('wo.weight'):
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torch.nn.init.normal_(p, mean=0.0, std=0.02/math.sqrt(2 * args.n_layers))
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# 初始化最后一次前向传播的损失属性
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self.last_loss = None
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self.OUT = CausalLMOutputWithPast() # 输出容器
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self._no_split_modules = [name for name, _ in self.named_modules()] # 不分割的模块列表
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def _init_weights(self, module):
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# 初始化权重的函数
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if isinstance(module, nn.Linear):
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torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
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if module.bias is not None:
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torch.nn.init.zeros_(module.bias)
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elif isinstance(module, nn.Embedding):
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torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
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def forward(self, tokens: torch.Tensor, targets: Optional[torch.Tensor] = None, **keyargs) -> torch.Tensor:
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"""
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- tokens: Optional[torch.Tensor], 输入 token 张量。
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- targets: Optional[torch.Tensor], 目标 token 张量。
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- kv_cache: bool, 是否使用键值缓存。
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- keyargs: 其他关键字参数。
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- self.OUT: CausalLMOutputWithPast, 包含 logits 和损失。
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"""
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if 'input_ids' in keyargs:
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tokens = keyargs['input_ids']
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if 'attention_mask' in keyargs:
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targets = keyargs['attention_mask']
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# 前向传播函数
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_bsz, seqlen = tokens.shape
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||
# 通过词嵌入层和Dropout层
|
||
h = self.tok_embeddings(tokens)
|
||
h = self.dropout(h)
|
||
# 获取相对位置嵌入的频率
|
||
freqs_cos = self.freqs_cos[:seqlen]
|
||
freqs_sin = self.freqs_sin[:seqlen]
|
||
|
||
# 通过Decoder层
|
||
for layer in self.layers:
|
||
h = layer(h, freqs_cos, freqs_sin)
|
||
# 通过归一化层
|
||
h = self.norm(h)
|
||
|
||
if targets is not None:
|
||
# 如果给定了目标，计算损失
|
||
logits = self.output(h)
|
||
self.last_loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=0, reduction='none')
|
||
else:
|
||
# 推理时的小优化：只对最后一个位置的输出进行前向传播
|
||
logits = self.output(h[:, [-1], :])
|
||
self.last_loss = None
|
||
|
||
# 设置输出
|
||
self.OUT.__setitem__('logits', logits)
|
||
self.OUT.__setitem__('last_loss', self.last_loss)
|
||
return self.OUT
|
||
|
||
|
||
@torch.inference_mode()
|
||
def generate(self, idx, stop_id=None, max_new_tokens=256, temperature=1.0, top_k=None):
|
||
"""
|
||
给定输入序列 idx（形状为 (bz,seq_len) 的长整型张量），通过多次生成新 token 来完成序列。
|
||
在 model.eval() 模式下运行。效率较低的采样版本，没有使用键k/v cache。
|
||
"""
|
||
index = idx.shape[1]
|
||
for _ in range(max_new_tokens):
|
||
# 如果序列上下文过长，截断它到最大长度
|
||
idx_cond = idx if idx.size(1) <= self.args.max_seq_len else idx[:, -self.args.max_seq_len:]
|
||
|
||
# 前向传播获取序列中最后一个位置的 logits
|
||
logits = self(idx_cond).logits
|
||
logits = logits[:, -1, :] # 只保留最后一个时间步的输出
|
||
|
||
if temperature == 0.0:
|
||
# 选择最有可能的索引
|
||
_, idx_next = torch.topk(logits, k=1, dim=-1)
|
||
else:
|
||
# 缩放 logits 并应用 softmax
|
||
logits = logits / temperature
|
||
if top_k is not None:
|
||
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
|
||
logits[logits < v[:, [-1]]] = -float('Inf')
|
||
probs = F.softmax(logits, dim=-1)
|
||
idx_next = torch.multinomial(probs, num_samples=1)
|
||
|
||
|
||
if idx_next == stop_id:
|
||
break
|
||
|
||
# 将采样的索引添加到序列中并继续
|
||
idx = torch.cat((idx, idx_next), dim=1)
|
||
|
||
return idx[:, index:] # 只返回生成的token
|
||
|
||
if __name__ == '__main__':
|
||
tokenizer = AutoTokenizer.from_pretrained("tokenizer_k")
|
||
args = ModelConfig(
|
||
dim=1024,
|
||
n_layers=18,
|
||
)
|
||
# 实例化LLaMA2Model
|
||
model = Transformer(args=args)
|
||
# 计算model的全部参数
|
||
num_params = sum(p.numel() for p in model.parameters())
|
||
print(f'LLM总参数量：{num_params / 1e6:.3f} 百万')
|
||
|
||
prompt = "你好呀，今天吃什么呢？你过得怎么样嘞？"
|
||
text = f"{tokenizer.bos_token}{prompt}{tokenizer.eos_token}"
|
||
print(f"Input text: {text}")
|
||
|
||
input_id = tokenizer(text).data['input_ids']
|
||
print("input_ids :", input_id)
|
||
print("dcode_str :", tokenizer.decode(input_id))
|
||
|
||
X = torch.tensor(input_id[:-1]).unsqueeze(0)
|
||
Y = torch.tensor(input_id[1:]).unsqueeze(0)
|
||
print("X shape :", X.shape)
|
||
print("Y shape :", Y.shape)
|
||
|
||
# 将输入张量传入模型
|
||
output = model(X, Y) |