503 lines
23 KiB
Python
503 lines
23 KiB
Python
import math
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import struct
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import inspect
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import time
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from .LMConfig import LMConfig
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from typing import Any, Optional, Tuple, List, Union
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import numpy as np
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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
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from transformers.modeling_outputs import CausalLMOutputWithPast
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# RMSNorm 类定义了一个用于归一化输入张量的模块。
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class RMSNorm(torch.nn.Module):
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def __init__(self, dim: int, eps: float = 1e-6):
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super().__init__()
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self.eps = eps
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self.weight = nn.Parameter(torch.ones(dim))
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def _norm(self, x):
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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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return self.weight * self._norm(x.float()).type_as(x)
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# precompute_pos_cis 函数用于预计算位置编码。
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def precompute_pos_cis(dim: int, end: int = int(32 * 1024), theta: float = 1e6):
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freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
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t = torch.arange(end, device=freqs.device) # type: ignore
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freqs = torch.outer(t, freqs).float() # type: ignore
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pos_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64
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return pos_cis
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# apply_rotary_emb 函数用于应用旋转位置编码。
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def apply_rotary_emb(xq, xk, pos_cis):
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def unite_shape(pos_cis, x):
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ndim = x.ndim
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assert 0 <= 1 < ndim
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assert pos_cis.shape == (x.shape[1], x.shape[-1])
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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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return pos_cis.view(*shape)
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xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
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xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
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pos_cis = unite_shape(pos_cis, xq_)
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xq_out = torch.view_as_real(xq_ * pos_cis).flatten(3)
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xk_out = torch.view_as_real(xk_ * pos_cis).flatten(3)
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return xq_out.type_as(xq), xk_out.type_as(xk)
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# repeat_kv 函数用于重复键值对。
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def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
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"""torch.repeat_interleave(x, dim=2, repeats=n_rep)"""
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bs, slen, n_kv_heads, head_dim = x.shape
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if n_rep == 1:
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return x
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return (
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x[:, :, :, None, :]
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.expand(bs, slen, n_kv_heads, n_rep, head_dim)
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.reshape(bs, slen, n_kv_heads * n_rep, head_dim)
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)
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class Attention(nn.Module):
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def __init__(self, args: LMConfig):
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super().__init__()
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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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assert args.n_heads % self.n_kv_heads == 0
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self.n_local_heads = args.n_heads
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self.n_local_kv_heads = self.n_kv_heads
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self.n_rep = self.n_local_heads // self.n_local_kv_heads
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self.head_dim = args.dim // args.n_heads
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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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self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False)
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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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self.dropout = args.dropout
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self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and args.flash_attn
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# print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")
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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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self.register_buffer("mask", mask, persistent=False)
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def forward(self,
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x: torch.Tensor,
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pos_cis: torch.Tensor,
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past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
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use_cache=False,
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db_value=None):
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bsz, seq_len, _ = x.shape #bsz: 批量大小, seq_len: 序列长度, _: 隐藏维度
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xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) #将输入张量x分别通过线性层wq, wk, wv进行变换，得到查询、键和值。
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xq = xq.view(bsz, seq_len, self.n_local_heads, self.head_dim) #将变换后的张量xq重塑为形状为(bsz, seq_len, n_local_heads, head_dim)的形状。
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xk = xk.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim) #将变换后的张量xk重塑为形状为(bsz, seq_len, n_local_kv_heads, head_dim)的形状。
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xv = xv.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim) #将变换后的张量xv重塑为形状为(bsz, seq_len, n_local_kv_heads, head_dim)的形状。
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# 应用旋转位置编码
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xq, xk = apply_rotary_emb(xq, xk, pos_cis)
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# kv_cache实现
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if past_key_value is not None:
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xk = torch.cat([past_key_value[0], xk], dim=1)
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xv = torch.cat([past_key_value[1], xv], dim=1)
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past_kv = (xk, xv) if use_cache else None
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# 重复键值对
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xq, xk, xv = (
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xq.transpose(1, 2),
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repeat_kv(xk, self.n_rep).transpose(1, 2),
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repeat_kv(xv, self.n_rep).transpose(1, 2)
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)
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# 如果提供了db_value，根据头的数量调整它的形状并与xv合并
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if db_value is not None:
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# 确保db_value的形状与xv兼容，假设db_value形状为[B, N, H, D]
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if db_value.ndim == 4: # [B, N, H, D]
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db_value = db_value.transpose(1, 2) # -> [B, H, N, D]
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# 检查是否需要调整D维度
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if db_value.shape[-1] != xv.shape[-1]:
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# 如果db_value的维度与xv不同，可以添加一个投影层
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# 或者在这里使用简单的调整方法
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# 这里我们简单地通过均值池化或重复来调整维度
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if db_value.shape[-1] > xv.shape[-1]:
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# 降维
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factor = db_value.shape[-1] // xv.shape[-1]
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db_value = db_value.view(bsz, self.n_local_heads, seq_len, factor, xv.shape[-1])
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db_value = db_value.mean(dim=3)
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else:
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# 升维
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factor = xv.shape[-1] // db_value.shape[-1]
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db_value = db_value.unsqueeze(-1).repeat(1, 1, 1, 1, factor)
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db_value = db_value.view(bsz, self.n_local_heads, seq_len, xv.shape[-1])
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# 将db_value与xv相加或融合
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# 这里我们简单地将它们相加，但你也可以使用其他融合方法
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xv = xv + db_value
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# 使用Flash Attention
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if self.flash and seq_len != 1:
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dropout_p = self.dropout if self.training else 0.0
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output = F.scaled_dot_product_attention(
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xq, xk, xv,
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attn_mask=None,
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dropout_p=dropout_p,
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is_causal=True
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)
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else:
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scores = (xq @ xk.transpose(-2, -1)) / math.sqrt(self.head_dim)
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scores += self.mask[:, :, :seq_len, :seq_len]
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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 = scores @ xv
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output = output.transpose(1, 2).reshape(bsz, seq_len, -1)
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output = self.resid_dropout(self.wo(output))
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return output, past_kv
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class FeedForward(nn.Module):
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def __init__(self, config: LMConfig):
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super().__init__()
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if config.hidden_dim is None:
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hidden_dim = 4 * config.dim
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hidden_dim = int(2 * hidden_dim / 3)
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config.hidden_dim = config.multiple_of * ((hidden_dim + config.multiple_of - 1) // config.multiple_of)
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self.w1 = nn.Linear(config.dim, config.hidden_dim, bias=False)
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self.w2 = nn.Linear(config.hidden_dim, config.dim, bias=False)
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self.w3 = nn.Linear(config.dim, config.hidden_dim, bias=False)
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self.dropout = nn.Dropout(config.dropout)
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def forward(self, x):
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return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x)))
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class MoEGate(nn.Module):
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def __init__(self, config: LMConfig):
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super().__init__()
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self.config = config
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self.top_k = config.num_experts_per_tok
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self.n_routed_experts = config.n_routed_experts
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self.scoring_func = config.scoring_func
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self.alpha = config.aux_loss_alpha
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self.seq_aux = config.seq_aux
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self.norm_topk_prob = config.norm_topk_prob
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self.gating_dim = config.dim
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self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim)))
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self.reset_parameters()
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def reset_parameters(self) -> None:
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import torch.nn.init as init
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init.kaiming_uniform_(self.weight, a=math.sqrt(5))
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def forward(self, hidden_states):
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bsz, seq_len, h = hidden_states.shape
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hidden_states = hidden_states.view(-1, h)
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logits = F.linear(hidden_states, self.weight, None)
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if self.scoring_func == 'softmax':
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scores = logits.softmax(dim=-1)
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else:
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raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}')
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topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False)
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if self.top_k > 1 and self.norm_topk_prob:
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denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
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topk_weight = topk_weight / denominator
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if self.training and self.alpha > 0.0:
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scores_for_aux = scores
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aux_topk = self.top_k
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topk_idx_for_aux_loss = topk_idx.view(bsz, -1)
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if self.seq_aux:
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scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1)
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ce = torch.zeros(bsz, self.n_routed_experts, device=hidden_states.device)
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ce.scatter_add_(1, topk_idx_for_aux_loss,
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torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device)).div_(
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seq_len * aux_topk / self.n_routed_experts)
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aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(dim=1).mean() * self.alpha
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else:
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mask_ce = F.one_hot(topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts)
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ce = mask_ce.float().mean(0)
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Pi = scores_for_aux.mean(0)
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fi = ce * self.n_routed_experts
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aux_loss = (Pi * fi).sum() * self.alpha
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else:
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aux_loss = 0
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return topk_idx, topk_weight, aux_loss
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class MOEFeedForward(nn.Module):
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def __init__(self, config: LMConfig):
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super().__init__()
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self.config = config
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self.experts = nn.ModuleList([
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FeedForward(config)
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for _ in range(config.n_routed_experts)
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])
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self.gate = MoEGate(config)
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if config.n_shared_experts is not None:
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self.shared_experts = FeedForward(config)
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def forward(self, x):
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identity = x
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orig_shape = x.shape
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bsz, seq_len, _ = x.shape
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# 使用门控机制选择专家
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topk_idx, topk_weight, aux_loss = self.gate(x)
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x = x.view(-1, x.shape[-1])
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flat_topk_idx = topk_idx.view(-1)
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if self.training:
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x = x.repeat_interleave(self.config.num_experts_per_tok, dim=0)
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y = torch.empty_like(x, dtype=torch.float16)
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for i, expert in enumerate(self.experts):
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y[flat_topk_idx == i] = expert(x[flat_topk_idx == i]).to(y.dtype) # 确保类型一致
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y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
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y = y.view(*orig_shape)
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else:
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y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape)
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if self.config.n_shared_experts is not None:
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y = y + self.shared_experts(identity)
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self.aux_loss = aux_loss
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return y
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@torch.no_grad()
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def moe_infer(self, x, flat_expert_indices, flat_expert_weights):
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expert_cache = torch.zeros_like(x)
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idxs = flat_expert_indices.argsort()
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tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0)
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token_idxs = idxs // self.config.num_experts_per_tok
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# 当tokens_per_expert = [6, 15, 20, 26]，tokens_per_expert.shape[0]即为专家数量（此时为4）
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# 且token_idxs = [3, 7, 19, 21, 24, 25, 4, 5, 6, 10, 11, 12...] 时
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# 意味token_idxs[:6] -> [3, 7, 19, 21, 24, 25]这6个位置属于专家0处理的token（每个token有可能被多个专家处理，这取决于num_experts_per_tok）
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# 接下来9个位置token_idxs[6:15] -> [4, 5, 6, 10, 11, 12...]属于专家1处理的token...依此类推
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for i, end_idx in enumerate(tokens_per_expert):
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start_idx = 0 if i == 0 else tokens_per_expert[i - 1]
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if start_idx == end_idx:
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continue
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expert = self.experts[i]
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exp_token_idx = token_idxs[start_idx:end_idx]
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expert_tokens = x[exp_token_idx]
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expert_out = expert(expert_tokens).to(expert_cache.dtype)
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expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]])
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expert_cache.scatter_add_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out)
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return expert_cache
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class MiniMindBlock(nn.Module):
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def __init__(self, layer_id: int, config: LMConfig, weight_down_embed=None):
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super().__init__()
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self.n_heads = config.n_heads
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self.dim = config.dim
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self.head_dim = config.dim // config.n_heads
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self.attention = Attention(config)
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self.layer_id = layer_id
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self.attention_norm = RMSNorm(config.dim, eps=config.norm_eps)
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self.ffn_norm = RMSNorm(config.dim, eps=config.norm_eps)
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self.feed_forward = FeedForward(config) if not config.use_moe else MOEFeedForward(config)
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# Product Key 相关参数
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self.weight_down_embed = weight_down_embed
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# 假设num_experts是已定义的总专家数量的平方根
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self.num_keys = int(math.sqrt(self.weight_down_embed.num_embeddings)) if weight_down_embed is not None else 0
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# 查询生成的参数
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self.dim_key = config.dim // 2 # 一般用特征维度的一半
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# 创建查询生成模块
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if weight_down_embed is not None:
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self.to_queries = nn.Sequential(
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nn.Linear(config.dim, self.dim_key * self.n_heads * 2, bias=False),
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nn.Unflatten(2, (2, self.n_heads, self.dim_key)) # 替代Rearrange
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)
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# 存储Product Keys
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self.keys = nn.Parameter(torch.randn(self.n_heads, self.num_keys, 2, self.dim_key) * 0.02)
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# 超参数
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self.product_key_topk = min(16, self.num_keys) # 确保不超过num_keys
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self.num_experts_per_head_topk = 1 # 最终每个头选取的专家数
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def forward(self, x, pos_cis, past_key_value=None, use_cache=False):
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db_value = None
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# 如果有weight_down_embed，使用Product Key机制
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if self.weight_down_embed is not None:
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# 1. 生成queries
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queries = self.to_queries(x) # [b, n, 2, h, d]
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queries = queries.permute(2, 0, 1, 3, 4) # [2, b, n, h, d]
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# 2. 计算queries与keys的相似度
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sim = torch.einsum('p b n h d, h k p d -> p b n h k', queries, self.keys)
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# 3. 在两个子空间分别做top-k
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scores_and_indices = [sim[p].topk(self.product_key_topk, dim=-1) for p in range(2)]
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scores_x, scores_y = scores_and_indices[0][0], scores_and_indices[1][0]
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indices_x, indices_y = scores_and_indices[0][1], scores_and_indices[1][1]
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# 4. 组合两个子空间的分数和索引
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all_scores = scores_x.unsqueeze(-1) + scores_y.unsqueeze(-2)
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all_scores = all_scores.view(*all_scores.shape[:-2], -1)
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all_indices = (indices_x.unsqueeze(-1) * self.num_keys) + indices_y.unsqueeze(-2)
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all_indices = all_indices.view(*all_indices.shape[:-2], -1)
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# 5. 最终top-k选择
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scores, pk_indices = all_scores.topk(self.num_experts_per_head_topk, dim=-1)
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indices = all_indices.gather(-1, pk_indices)
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# 6. 从embedding中获取专家值
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# [b, n, h, k] -> [b, n, h, k, 1] -> [b, n, h, k, d]
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indices_expanded = indices.unsqueeze(-1).expand(-1, -1, -1, -1, self.weight_down_embed.embedding_dim)
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# 将索引从3D展平为1D以便gather操作
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batch_size, seq_len = x.shape[0], x.shape[1]
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||
flat_indices = indices.view(-1)
|
||
|
||
# 从embedding中获取值
|
||
db_values = self.weight_down_embed(flat_indices)
|
||
|
||
# 重塑回原始形状
|
||
db_value = db_values.view(batch_size, seq_len, self.n_heads,
|
||
self.num_experts_per_head_topk, -1)
|
||
|
||
# 使用分数加权
|
||
db_value = db_value * F.relu(scores.unsqueeze(-1))
|
||
|
||
# 合并多个专家的输出（如果每个头有多个专家）
|
||
if self.num_experts_per_head_topk > 1:
|
||
db_value = db_value.sum(dim=3) # [b, n, h, d]
|
||
|
||
# 注意力计算
|
||
h_attn, past_kv = self.attention(
|
||
self.attention_norm(x),
|
||
pos_cis,
|
||
past_key_value=past_key_value,
|
||
use_cache=use_cache,
|
||
db_value=db_value
|
||
)
|
||
h = x + h_attn
|
||
out = h + self.feed_forward(self.ffn_norm(h))
|
||
return out, past_kv
|
||
|
||
|
||
class MiniMindLM(PreTrainedModel):
|
||
config_class = LMConfig
|
||
|
||
def __init__(self, params: LMConfig = None):
|
||
self.params = params or LMConfig()
|
||
super().__init__(self.params)
|
||
self.vocab_size, self.n_layers = params.vocab_size, params.n_layers
|
||
self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim)
|
||
self.dropout = nn.Dropout(params.dropout)
|
||
|
||
# 修改专家数量和知识维度，确保能开方
|
||
self.num_experts = 1000 * 1000 # 1M专家，确保是完全平方数
|
||
# 将knowledge_dim设置为与head_dim相同，以便在attention中直接使用
|
||
self.head_dim = params.dim // params.n_heads
|
||
self.knowledge_dim = self.head_dim
|
||
|
||
# 定义weight_down_embed，用于存储专家知识
|
||
self.weight_down_embed = nn.Embedding(self.num_experts, self.knowledge_dim)
|
||
# 初始化embedding权重
|
||
nn.init.normal_(self.weight_down_embed.weight, std=0.02)
|
||
|
||
# 将self.weight_down_embed传递给每个MiniMindBlock
|
||
self.layers = nn.ModuleList([MiniMindBlock(l, params, self.weight_down_embed) for l in range(self.n_layers)])
|
||
self.norm = RMSNorm(params.dim, eps=params.norm_eps)
|
||
self.output = nn.Linear(params.dim, params.vocab_size, bias=False)
|
||
self.tok_embeddings.weight = self.output.weight
|
||
self.register_buffer("pos_cis",
|
||
precompute_pos_cis(dim=params.dim // params.n_heads, theta=params.rope_theta),
|
||
persistent=False)
|
||
self.OUT = CausalLMOutputWithPast()
|
||
|
||
def forward(self,
|
||
input_ids: Optional[torch.Tensor] = None,
|
||
past_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None,
|
||
use_cache: bool = False,
|
||
logits_to_keep: Union[int, torch.Tensor] = 0,
|
||
**args):
|
||
past_key_values = past_key_values or [None] * len(self.layers)
|
||
start_pos = args.get('start_pos', 0)
|
||
h = self.dropout(self.tok_embeddings(input_ids))
|
||
pos_cis = self.pos_cis[start_pos:start_pos + input_ids.size(1)]
|
||
past_kvs = []
|
||
for l, layer in enumerate(self.layers):
|
||
h, past_kv = layer(
|
||
h, pos_cis,
|
||
past_key_value=past_key_values[l],
|
||
use_cache=use_cache
|
||
)
|
||
past_kvs.append(past_kv)
|
||
|
||
slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
|
||
logits = self.output(self.norm(h)[:, slice_indices, :])
|
||
aux_loss = sum(l.feed_forward.aux_loss for l in self.layers if isinstance(l.feed_forward, MOEFeedForward))
|
||
self.OUT.__setitem__('last_hidden_state', h)
|
||
self.OUT.__setitem__('logits', logits)
|
||
self.OUT.__setitem__('aux_loss', aux_loss)
|
||
self.OUT.__setitem__('past_key_values', past_kvs)
|
||
return self.OUT
|
||
|
||
@torch.inference_mode()
|
||
def generate(self, input_ids, eos_token_id=2, max_new_tokens=1024, temperature=0.75, top_p=0.90,
|
||
stream=False, rp=1., use_cache=True, pad_token_id=0, num_return_sequences=1, **args):
|
||
# 流式生成
|
||
if stream:
|
||
return self._stream(input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, use_cache, **args)
|
||
|
||
# 直接生成
|
||
generated = []
|
||
for i in range(input_ids.size(0)):
|
||
non_pad = input_ids[i][input_ids[i] != pad_token_id].unsqueeze(0)
|
||
for _ in range(num_return_sequences):
|
||
out = self._stream(non_pad, eos_token_id, max_new_tokens, temperature, top_p, rp, use_cache, **args)
|
||
tokens_list = [tokens[:, -1:] for tokens in out]
|
||
gen = torch.cat(tokens_list, dim=-1) if tokens_list else non_pad
|
||
full_sequence = torch.cat([non_pad, gen], dim=-1)
|
||
generated.append(full_sequence)
|
||
|
||
max_length = max(seq.size(1) for seq in generated)
|
||
generated = [
|
||
torch.cat(
|
||
[seq, torch.full((1, max_length - seq.size(1)), pad_token_id, dtype=seq.dtype, device=seq.device)],
|
||
dim=-1)
|
||
for seq in generated
|
||
]
|
||
output = torch.cat(generated, dim=0)
|
||
res = output.view(input_ids.size(0) * num_return_sequences, -1)
|
||
return res
|
||
|
||
def _stream(self, input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, use_cache, **args):
|
||
start, first_seq, past_kvs = input_ids.shape[1], True, None
|
||
while input_ids.shape[1] < max_new_tokens - 1:
|
||
if first_seq or not use_cache:
|
||
out, first_seq = self(input_ids, past_key_values=past_kvs, use_cache=use_cache, **args), False
|
||
else:
|
||
out = self(input_ids[:, -1:], past_key_values=past_kvs, use_cache=use_cache,
|
||
start_pos=input_ids.shape[1] - 1, **args)
|
||
logits, past_kvs = out.logits[:, -1, :], out.past_key_values
|
||
logits[:, list(set(input_ids.tolist()[0]))] /= rp
|
||
logits /= (temperature + 1e-9)
|
||
if top_p is not None and top_p < 1.0:
|
||
sorted_logits, sorted_indices = torch.sort(logits, descending=True, dim=-1)
|
||
sorted_probs = F.softmax(sorted_logits, dim=-1)
|
||
cumulative_probs = torch.cumsum(sorted_probs, dim=-1)
|
||
sorted_indices_to_remove = cumulative_probs > top_p
|
||
sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone()
|
||
sorted_indices_to_remove[:, 0] = False
|
||
indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
|
||
logits[indices_to_remove] = -float('Inf')
|
||
input_ids_next = torch.multinomial(F.softmax(logits, dim=-1), num_samples=1)
|
||
input_ids = torch.cat((input_ids, input_ids_next), dim=1)
|
||
yield input_ids[:, start:]
|
||
if input_ids_next.item() == eos_token_id:
|
||
break
|