932 lines
44 KiB
Python
932 lines
44 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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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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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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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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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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"""Self attention module without KV cache"""
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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, x: torch.Tensor, pos_cis: torch.Tensor):
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"""Forward pass without KV cache"""
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bsz, seq_len, _ = x.shape
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xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
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xq = xq.view(bsz, seq_len, self.n_local_heads, self.head_dim)
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xk = xk.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim)
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xv = xv.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim)
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xq, xk = apply_rotary_emb(xq, xk, pos_cis)
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# 注意：完全去除了KV cache相关代码
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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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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
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class MemoryGate(nn.Module):
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"""Product Key Memory-based gate mechanism for memory selection with Gumbel-Softmax"""
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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.dim = config.dim
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self.knowledge_num = config.knowledge_num
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self.knowledge_dim = config.knowledge_dim
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self.num_candidates = getattr(config, 'num_candidates', 32) # Generate 32 candidates
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self.num_selected = getattr(config, 'num_selected', 1) # Select 1 best from candidates
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# 确保知识库数量是完全平方数
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assert int(self.knowledge_num ** 0.5) ** 2 == self.knowledge_num, \
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f"knowledge_num ({self.knowledge_num}) must be a perfect square for product key memory"
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self.num_keys = int(self.knowledge_num ** 0.5)
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# 查询投影：将输入维度映射到knowledge_dim * 2（用于两个product key）
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self.gate_proj = nn.Linear(self.dim, self.knowledge_dim, bias=False)
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# Product Key Memory: 两个独立的键集合
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self.keys = nn.Parameter(torch.randn(2, self.num_keys, self.knowledge_dim // 2))
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self.dropout = nn.Dropout(config.dropout)
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def forward(self, x: torch.Tensor):
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"""
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Args:
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x: [batch_size, seq_len, dim]
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Returns:
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memory_indices: [batch_size, seq_len, num_selected]
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memory_scores: [batch_size, seq_len, num_selected]
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balance_loss: 平衡损失（KL散度 + 基尼系数）
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stats: 监控统计信息字典
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"""
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bsz, seq_len, _ = x.shape
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# 生成查询向量
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queries = self.gate_proj(x) # [batch, seq_len, knowledge_dim]
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# 分割为两部分用于product key
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q1 = queries[:, :, :self.knowledge_dim // 2] # [batch, seq_len, knowledge_dim // 2]
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q2 = queries[:, :, self.knowledge_dim // 2:] # [batch, seq_len, knowledge_dim // 2]
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# 计算与两个键集合的相似度
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scores_1 = torch.einsum('bsd,kd->bsk', q1, self.keys[0]) # [batch, seq_len, num_keys]
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scores_2 = torch.einsum('bsd,kd->bsk', q2, self.keys[1]) # [batch, seq_len, num_keys]
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# 获取top-k candidates (now using num_candidates instead of num_selected)
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topk_scores_1, topk_indices_1 = scores_1.topk(self.num_candidates, dim=-1)
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topk_scores_2, topk_indices_2 = scores_2.topk(self.num_candidates, dim=-1)
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# 组合product key的结果
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combined_scores = topk_scores_1.unsqueeze(-1) + topk_scores_2.unsqueeze(-2) # [batch, seq_len, num_candidates, num_candidates]
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combined_indices = topk_indices_1.unsqueeze(-1) * self.num_keys + topk_indices_2.unsqueeze(-2) # [batch, seq_len, num_candidates, num_candidates]
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# 展平并选择最终的top-k candidates
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combined_scores = combined_scores.view(bsz, seq_len, -1)
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combined_indices = combined_indices.view(bsz, seq_len, -1)
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candidate_scores, candidate_pk_indices = combined_scores.topk(self.num_candidates, dim=-1)
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candidate_indices = combined_indices.gather(-1, candidate_pk_indices) # [batch, seq_len, num_candidates]
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# 归一化候选分数
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candidate_scores = F.softmax(candidate_scores, dim=-1)
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candidate_scores = self.dropout(candidate_scores)
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# 返回候选项用于后续的相似度选择
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# 注意：这里返回候选项，在MiniMindBlock中进行相似度选择和多样性损失计算
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return candidate_indices, candidate_scores, None, {}
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def _compute_balance_loss_and_stats(self, memory_indices, memory_scores):
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"""
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计算平衡损失和监控统计信息
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Args:
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memory_indices: [batch_size, seq_len, num_selected]
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memory_scores: [batch_size, seq_len, num_selected]
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Returns:
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balance_loss: 标量张量
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stats: 统计信息字典
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"""
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bsz, seq_len, num_selected = memory_indices.shape
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device = memory_indices.device
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# 1. 计算记忆选择分布
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# 将所有选择的记忆索引展平
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flat_indices = memory_indices.view(-1) # [batch_size * seq_len * num_selected]
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# 统计每个记忆条目被选中的次数
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memory_counts = torch.zeros(self.knowledge_num, device=device)
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memory_counts.scatter_add_(0, flat_indices, torch.ones_like(flat_indices, dtype=torch.float))
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# 计算选择概率分布
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total_selections = bsz * seq_len * num_selected
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memory_probs = memory_counts / total_selections
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# 2. 计算KL散度损失（与均匀分布的KL散度）
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uniform_prob = 1.0 / self.knowledge_num
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# 避免log(0)的问题
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memory_probs_safe = memory_probs + 1e-10
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kl_loss = F.kl_div(
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torch.log(memory_probs_safe),
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torch.full_like(memory_probs, uniform_prob),
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reduction='sum'
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)
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# 3. 计算基尼系数损失（衡量分布不平等程度）
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sorted_probs, _ = torch.sort(memory_probs)
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n = self.knowledge_num
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index = torch.arange(1, n + 1, device=device, dtype=torch.float)
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gini_coeff = (2 * torch.sum(index * sorted_probs) / (n * torch.sum(sorted_probs))) - (n + 1) / n
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gini_loss = gini_coeff # 基尼系数越大，分布越不均匀
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# 4. 组合平衡损失
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balance_loss = 0.5 * kl_loss + 0.5 * gini_loss
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# 5. 计算监控统计信息
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with torch.no_grad():
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# 记忆覆盖率：被选中的记忆条目占总数的比例
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coverage_rate = (memory_counts > 0).float().mean().item()
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# 热点记忆：选择次数前10%的记忆条目
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top10_threshold = torch.quantile(memory_counts, 0.9)
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hot_memories = (memory_counts >= top10_threshold).sum().item()
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# 死记忆：从未被选中的记忆条目
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dead_memories = (memory_counts == 0).sum().item()
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# 记忆选择方差（衡量不平衡程度）
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selection_variance = memory_counts.var().item()
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stats = {
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'gini_coefficient': gini_coeff.item(),
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'kl_divergence': kl_loss.item(),
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'coverage_rate': coverage_rate,
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'hot_memories': hot_memories,
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'dead_memories': dead_memories,
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'selection_variance': selection_variance,
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'max_selections': memory_counts.max().item(),
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'min_selections': memory_counts.min().item(),
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}
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return balance_loss, stats
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class GatedMemoryFusion(nn.Module):
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"""Gated MLP fusion for concatenated h_attn and selected memories"""
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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.dim = config.dim
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self.knowledge_dim = config.knowledge_dim
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self.num_selected = getattr(config, 'num_selected', 1) # Now we select 1 best memory
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# 输入维度：dim (h_attn) + num_selected * dim (选中的记忆，现在只有1个)
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# 实验1.4.9：修改为只选择1个最佳记忆
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concat_dim = self.dim + self.num_selected * self.dim
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# 类似SwiGLU的门控MLP结构
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self.gate_proj = nn.Linear(concat_dim, self.dim, bias=False)
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self.up_proj = nn.Linear(concat_dim, self.dim, bias=False)
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self.down_proj = nn.Linear(self.dim, self.dim, bias=False)
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self.dropout = nn.Dropout(config.dropout)
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def forward(self, h_attn: torch.Tensor, selected_memory: torch.Tensor):
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"""
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Args:
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h_attn: [batch_size, seq_len, dim] - Self attention output
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selected_memory: [batch_size, seq_len, dim] - Selected single best memory
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Returns:
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output: [batch_size, seq_len, dim]
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"""
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bsz, seq_len, _ = h_attn.shape
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# 拼接h_attn和最佳记忆
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concat_input = torch.cat([h_attn, selected_memory], dim=-1) # [batch, seq_len, dim + dim]
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# 门控MLP处理（类似SwiGLU）
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gate = F.silu(self.gate_proj(concat_input)) # [batch, seq_len, dim]
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up = self.up_proj(concat_input) # [batch, seq_len, dim]
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fusion_output = gate * up # Element-wise multiplication
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# 输出投影
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output = self.down_proj(fusion_output) # [batch, seq_len, dim]
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output = self.dropout(output)
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return output
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class MiniMindBlock(nn.Module):
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"""Transformer block with memory-based cross attention instead of FFN"""
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def __init__(self, layer_id: int, config: LMConfig):
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super().__init__()
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self.config = config # 保存config引用
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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.memory_norm = RMSNorm(config.dim, eps=config.norm_eps)
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# 记忆相关模块
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self.memory_gate = MemoryGate(config)
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self.gated_memory_fusion = GatedMemoryFusion(config)
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# Gumbel-Softmax参数
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self.gumbel_temperature = getattr(config, 'gumbel_temperature', 1.0)
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# self.attentionpool = nn.Linear(config.dim, 1)
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def gumbel_softmax_selection(self, similarity_scores, temperature=1.0, hard=True):
|
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"""
|
||
使用Gumbel-Softmax进行可微分的离散选择
|
||
|
||
Args:
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similarity_scores: [batch_size, seq_len, num_candidates] - 相似度分数
|
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temperature: Gumbel-Softmax温度参数
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hard: 是否使用硬选择（one-hot）
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|
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Returns:
|
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selection_weights: [batch_size, seq_len, num_candidates] - 选择权重
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selected_indices: [batch_size, seq_len] - 选中的索引（用于统计）
|
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"""
|
||
# 添加Gumbel噪声
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gumbel_noise = -torch.log(-torch.log(torch.rand_like(similarity_scores) + 1e-20) + 1e-20)
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logits = (similarity_scores + gumbel_noise) / temperature
|
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|
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# Softmax
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soft_weights = F.softmax(logits, dim=-1)
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if hard:
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# 硬选择：创建one-hot向量
|
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_, max_indices = soft_weights.max(dim=-1, keepdim=True)
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hard_weights = torch.zeros_like(soft_weights).scatter_(-1, max_indices, 1.0)
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# 使用straight-through estimator
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selection_weights = hard_weights - soft_weights.detach() + soft_weights
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selected_indices = max_indices.squeeze(-1) # [batch_size, seq_len]
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else:
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# 软选择
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||
selection_weights = soft_weights
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||
selected_indices = torch.argmax(soft_weights, dim=-1)
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return selection_weights, selected_indices
|
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|
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def compute_diversity_loss(self, candidate_memories):
|
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"""
|
||
计算候选集内部多样性损失（鼓励候选项之间的差异性）
|
||
|
||
Args:
|
||
candidate_memories: [batch_size, seq_len, num_candidates, dim]
|
||
|
||
Returns:
|
||
diversity_loss: 标量张量
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||
"""
|
||
bsz, seq_len, num_candidates, dim = candidate_memories.shape
|
||
|
||
# 计算候选项之间的相似度矩阵
|
||
# 归一化候选记忆用于计算余弦相似度
|
||
normalized_memories = F.normalize(candidate_memories, p=2, dim=-1) # [batch, seq_len, num_candidates, dim]
|
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|
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# 计算相似度矩阵: [batch, seq_len, num_candidates, num_candidates]
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similarity_matrix = torch.matmul(normalized_memories, normalized_memories.transpose(-2, -1))
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# 移除对角线（自相似度=1）
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||
mask = torch.eye(num_candidates, device=candidate_memories.device).bool()
|
||
mask = mask.unsqueeze(0).unsqueeze(0).expand(bsz, seq_len, -1, -1)
|
||
|
||
# 计算非对角线元素的平均相似度（希望越小越好，表示越多样）
|
||
off_diagonal_similarities = similarity_matrix.masked_select(~mask)
|
||
avg_similarity = off_diagonal_similarities.mean()
|
||
|
||
# 多样性损失：相似度越高，损失越大
|
||
diversity_loss = avg_similarity
|
||
|
||
return diversity_loss
|
||
|
||
def forward(self, x, pos_cis, memory_bank, tok_embeddings, collect_ema_stats=False):
|
||
"""
|
||
实验1.4.9: Gumbel-Softmax + 多样性损失 + 可微分相似度损失
|
||
|
||
Args:
|
||
x: [batch_size, seq_len, dim]
|
||
pos_cis: positional encoding
|
||
memory_bank: [knowledge_num, knowledge_length] - shared memory bank with token IDs
|
||
tok_embeddings: token embedding layer
|
||
collect_ema_stats: 是否收集EMA更新统计信息
|
||
|
||
Returns:
|
||
out: [batch_size, seq_len, dim]
|
||
balance_loss: 该层的平衡损失 (从候选项计算)
|
||
similarity_loss: 相似度损失 (可微分)
|
||
diversity_loss: 多样性损失
|
||
layer_stats: 该层的监控统计信息
|
||
ema_stats: EMA更新统计信息（如果collect_ema_stats=True）
|
||
cosine_stats: 查找向量与候选记忆条目的余弦相似度统计信息
|
||
"""
|
||
# Self attention
|
||
h_attn = self.attention(self.attention_norm(x), pos_cis)
|
||
h = x + h_attn
|
||
|
||
# 使用h_attn作为门控和交叉注意力的输入（核心：self attention的输出）
|
||
h_for_memory = self.memory_norm(h_attn)
|
||
|
||
# 🔥 新架构：生成32个候选项
|
||
candidate_indices, candidate_scores, _, _ = self.memory_gate(h_for_memory)
|
||
# candidate_indices: [batch, seq_len, num_candidates]
|
||
# candidate_scores: [batch, seq_len, num_candidates]
|
||
|
||
bsz, seq_len, num_candidates = candidate_indices.shape
|
||
|
||
# 解码候选token_ids为特征向量
|
||
candidate_indices_flat = candidate_indices.view(-1) # [batch * seq_len * num_candidates]
|
||
candidate_token_ids = memory_bank[candidate_indices_flat] # [batch * seq_len * num_candidates, knowledge_length]
|
||
|
||
# 解码为embeddings并池化
|
||
candidate_embeddings = tok_embeddings(candidate_token_ids) # [batch * seq_len * num_candidates, knowledge_length, dim]
|
||
candidate_memories = candidate_embeddings.mean(dim=1) # [batch * seq_len * num_candidates, dim]
|
||
candidate_memories = candidate_memories.view(bsz, seq_len, num_candidates, self.dim) # [batch, seq_len, num_candidates, dim]
|
||
|
||
# 🔥 核心改进: 计算可微分的相似度分数 (移除no_grad)
|
||
h_expanded = h_for_memory.unsqueeze(2).expand(-1, -1, num_candidates, -1) # [batch, seq_len, num_candidates, dim]
|
||
similarity_scores = F.cosine_similarity(h_expanded, candidate_memories, dim=-1) # [batch, seq_len, num_candidates]
|
||
|
||
# 🔥 使用Gumbel-Softmax选择最佳候选项
|
||
selection_weights, selected_indices = self.gumbel_softmax_selection(
|
||
similarity_scores,
|
||
temperature=self.gumbel_temperature,
|
||
hard=True
|
||
) # selection_weights: [batch, seq_len, num_candidates], selected_indices: [batch, seq_len]
|
||
|
||
# 🔥 计算相似度损失 (现在是可微分的!)
|
||
# 相似度损失：希望选中的记忆与查询向量相似度尽可能高
|
||
selected_similarities = (similarity_scores * selection_weights).sum(dim=-1) # [batch, seq_len]
|
||
similarity_loss = -selected_similarities.mean() # 负号：相似度越高，损失越小
|
||
|
||
# 🔥 计算候选集多样性损失
|
||
diversity_loss = self.compute_diversity_loss(candidate_memories)
|
||
|
||
# 🔥 使用selection_weights进行加权选择最终记忆
|
||
batch, seq_len, num_candidates, dim = candidate_memories.shape
|
||
selected_memory = (candidate_memories * selection_weights.unsqueeze(-1)) # [batch, seq_len, dim]
|
||
selected_memory = weighted_memories.reshape(batch_size, seq_len * num_candidates, dim)
|
||
|
||
# 门控MLP融合：只融合选中的单个最佳记忆
|
||
memory_output = self.gated_memory_fusion(h_for_memory, selected_memory)
|
||
|
||
# 残差连接
|
||
out = h + memory_output
|
||
|
||
# 🔥 计算平衡损失和统计信息 (基于候选项的选择分布)
|
||
balance_loss, layer_stats = self._compute_candidate_balance_stats(candidate_indices, selection_weights)
|
||
|
||
# 🔥 计算详细的相似度统计信息
|
||
cosine_stats = {
|
||
'similarity_scores': similarity_scores, # [batch, seq_len, num_candidates]
|
||
'selected_similarities': selected_similarities, # [batch, seq_len]
|
||
'avg_similarity': similarity_scores.mean().item(), # 平均相似度
|
||
'max_similarity': similarity_scores.max().item(), # 最大相似度
|
||
'min_similarity': similarity_scores.min().item(), # 最小相似度
|
||
'selected_avg_similarity': selected_similarities.mean().item(), # 选中记忆的平均相似度
|
||
'selection_entropy': -torch.sum(selection_weights * torch.log(selection_weights + 1e-10), dim=-1).mean().item() # 选择熵
|
||
}
|
||
|
||
# 收集EMA更新统计信息（现在基于选中的记忆）
|
||
ema_stats = None
|
||
if collect_ema_stats and self.training:
|
||
# 扩展选中的索引以匹配EMA更新的期望格式
|
||
selected_memory_indices = candidate_indices.gather(2, selected_indices.unsqueeze(-1)) # [batch, seq_len, 1]
|
||
ema_stats = {
|
||
'memory_indices': selected_memory_indices, # [batch, seq_len, 1]
|
||
'memory_scores': torch.ones_like(selected_memory_indices.float()), # [batch, seq_len, 1] - 选中的权重为1
|
||
'h_for_memory': h_for_memory, # [batch, seq_len, dim]
|
||
'selected_memory': selected_memory.unsqueeze(2), # [batch, seq_len, 1, dim]
|
||
}
|
||
|
||
if collect_ema_stats:
|
||
return out, balance_loss, similarity_loss, diversity_loss, layer_stats, ema_stats, cosine_stats
|
||
else:
|
||
return out, balance_loss, similarity_loss, diversity_loss, layer_stats, cosine_stats
|
||
|
||
def _compute_candidate_balance_stats(self, candidate_indices, selection_weights):
|
||
"""
|
||
计算基于候选项选择的平衡损失和统计信息
|
||
|
||
Args:
|
||
candidate_indices: [batch_size, seq_len, num_candidates]
|
||
selection_weights: [batch_size, seq_len, num_candidates] - Gumbel-Softmax权重
|
||
|
||
Returns:
|
||
balance_loss: 标量张量
|
||
stats: 统计信息字典
|
||
"""
|
||
bsz, seq_len, num_candidates = candidate_indices.shape
|
||
device = candidate_indices.device
|
||
|
||
# 使用加权统计每个记忆条目被选中的概率
|
||
flat_indices = candidate_indices.view(-1) # [batch * seq_len * num_candidates]
|
||
flat_weights = selection_weights.view(-1) # [batch * seq_len * num_candidates]
|
||
|
||
# 统计每个记忆条目被选中的加权次数
|
||
memory_counts = torch.zeros(self.config.knowledge_num, device=device)
|
||
memory_counts.scatter_add_(0, flat_indices, flat_weights)
|
||
|
||
# 计算选择概率分布
|
||
total_selections = memory_counts.sum()
|
||
memory_probs = memory_counts / (total_selections + 1e-10)
|
||
|
||
# 计算KL散度损失（与均匀分布的KL散度）
|
||
uniform_prob = 1.0 / self.config.knowledge_num
|
||
memory_probs_safe = memory_probs + 1e-10
|
||
kl_loss = F.kl_div(
|
||
torch.log(memory_probs_safe),
|
||
torch.full_like(memory_probs, uniform_prob),
|
||
reduction='sum'
|
||
)
|
||
|
||
# 计算基尼系数损失
|
||
sorted_probs, _ = torch.sort(memory_probs)
|
||
n = self.config.knowledge_num
|
||
index = torch.arange(1, n + 1, device=device, dtype=torch.float)
|
||
gini_coeff = (2 * torch.sum(index * sorted_probs) / (n * torch.sum(sorted_probs))) - (n + 1) / n
|
||
gini_loss = gini_coeff
|
||
|
||
# 组合平衡损失
|
||
balance_loss = 0.5 * kl_loss + 0.5 * gini_loss
|
||
|
||
# 计算统计信息
|
||
with torch.no_grad():
|
||
coverage_rate = (memory_counts > 0.01).float().mean().item() # 被选中概率>1%的记忆比例
|
||
top10_threshold = torch.quantile(memory_counts, 0.9)
|
||
hot_memories = (memory_counts >= top10_threshold).sum().item()
|
||
dead_memories = (memory_counts < 0.01).sum().item() # 几乎从未被选中的记忆
|
||
selection_variance = memory_counts.var().item()
|
||
|
||
stats = {
|
||
'gini_coefficient': gini_coeff.item(),
|
||
'kl_divergence': kl_loss.item(),
|
||
'coverage_rate': coverage_rate,
|
||
'hot_memories': hot_memories,
|
||
'dead_memories': dead_memories,
|
||
'selection_variance': selection_variance,
|
||
'max_selections': memory_counts.max().item(),
|
||
'min_selections': memory_counts.min().item(),
|
||
}
|
||
|
||
return balance_loss, stats
|
||
|
||
|
||
class MiniMindLM(PreTrainedModel):
|
||
config_class = LMConfig
|
||
|
||
def __init__(self, params: LMConfig = None):
|
||
self.params = params
|
||
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)
|
||
self.layers = nn.ModuleList([MiniMindBlock(l, params) 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)
|
||
|
||
# 初始化共享记忆库 - 实验1.4.6：存储token_id而非特征向量
|
||
# VQ-VAE风格：memory_bank作为codebook，使用EMA更新而非梯度更新
|
||
if params.use_ema_update:
|
||
self.memory_bank = nn.Parameter(
|
||
torch.randint(0, params.vocab_size, (params.knowledge_num, params.knowledge_length)),
|
||
requires_grad=False # 禁用梯度更新，使用EMA更新
|
||
)
|
||
else:
|
||
self.memory_bank = nn.Parameter(
|
||
torch.randint(0, params.vocab_size, (params.knowledge_num, params.knowledge_length)),
|
||
requires_grad=True # 传统梯度更新
|
||
)
|
||
|
||
# EMA更新相关缓冲区
|
||
if params.use_ema_update:
|
||
# 记录每个memory条目的更新统计
|
||
self.register_buffer('ema_update_count', torch.zeros(params.knowledge_num), persistent=False)
|
||
# 注意：现在memory_bank存储token_id，但EMA在特征空间进行，所以不需要sum_buffer了
|
||
# self.register_buffer('ema_sum_buffer', torch.zeros_like(self.memory_bank), persistent=False)
|
||
# EMA更新频率计数器
|
||
self.register_buffer('ema_step_counter', torch.zeros(1, dtype=torch.long), persistent=False)
|
||
|
||
# 记录上一步的记忆库状态，用于计算更新统计
|
||
self.register_buffer('prev_memory_bank', torch.zeros_like(self.memory_bank), persistent=False)
|
||
|
||
# 🔥 新增: 冻结mask - 标记哪些memory_bank条目被冻结（不更新）
|
||
if params.freeze_ratio > 0.0:
|
||
freeze_num = int(params.knowledge_num * params.freeze_ratio)
|
||
freeze_mask = torch.zeros(params.knowledge_num, dtype=torch.bool)
|
||
# 固定冻结前面的条目
|
||
freeze_mask[:freeze_num] = True
|
||
self.register_buffer('freeze_mask', freeze_mask, persistent=False)
|
||
print(f"🔥 Memory bank freezing enabled: {freeze_num}/{params.knowledge_num} entries ({params.freeze_ratio*100:.1f}%) frozen", flush=True)
|
||
import sys; sys.stdout.flush()
|
||
else:
|
||
self.register_buffer('freeze_mask', torch.zeros(params.knowledge_num, dtype=torch.bool), persistent=False)
|
||
print(f"🔥 Memory bank freezing disabled: all entries can be updated", flush=True)
|
||
import sys; sys.stdout.flush()
|
||
|
||
self.OUT = CausalLMOutputWithPast()
|
||
|
||
def get_memory_update_stats(self):
|
||
"""
|
||
计算记忆库更新统计信息
|
||
|
||
Returns:
|
||
update_stats: 包含更新统计的字典
|
||
"""
|
||
with torch.no_grad():
|
||
if hasattr(self, 'prev_memory_bank') and self.prev_memory_bank.numel() > 0:
|
||
# 计算L2距离变化
|
||
l2_distance = torch.norm(self.memory_bank - self.prev_memory_bank, p=2, dim=-1)
|
||
avg_l2_distance = l2_distance.mean().item()
|
||
max_l2_distance = l2_distance.max().item()
|
||
|
||
# 计算余弦相似度
|
||
cos_sim = F.cosine_similarity(
|
||
self.memory_bank.view(-1),
|
||
self.prev_memory_bank.view(-1),
|
||
dim=0
|
||
).item()
|
||
|
||
# 计算更新率（发生显著变化的记忆条目比例）
|
||
threshold = 0.01 # 更新阈值
|
||
updated_memories = (l2_distance > threshold).sum().item()
|
||
update_rate = updated_memories / self.memory_bank.size(0)
|
||
|
||
update_stats = {
|
||
'memory_avg_l2_change': avg_l2_distance,
|
||
'memory_max_l2_change': max_l2_distance,
|
||
'memory_cosine_similarity': cos_sim,
|
||
'memory_update_rate': update_rate,
|
||
'memory_updated_count': updated_memories
|
||
}
|
||
else:
|
||
# 第一次调用时的默认值
|
||
update_stats = {
|
||
'memory_avg_l2_change': 0.0,
|
||
'memory_max_l2_change': 0.0,
|
||
'memory_cosine_similarity': 1.0,
|
||
'memory_update_rate': 0.0,
|
||
'memory_updated_count': 0
|
||
}
|
||
|
||
# 更新prev_memory_bank
|
||
self.prev_memory_bank.copy_(self.memory_bank)
|
||
|
||
return update_stats
|
||
|
||
def forward(self,
|
||
input_ids: Optional[torch.Tensor] = None,
|
||
**args):
|
||
"""Forward pass without KV cache support"""
|
||
start_pos = args.get('start_pos', 0)
|
||
collect_ema_stats = args.get('collect_ema_stats', self.params.use_ema_update and self.training)
|
||
|
||
h = self.dropout(self.tok_embeddings(input_ids))
|
||
pos_cis = self.pos_cis[start_pos:start_pos + input_ids.size(1)]
|
||
|
||
# 收集所有层的损失和统计信息 - 实验1.4.9: 四损失系统
|
||
total_balance_loss = 0
|
||
total_similarity_loss = 0
|
||
total_diversity_loss = 0
|
||
all_layer_stats = {}
|
||
all_ema_stats = {}
|
||
all_cosine_stats = {}
|
||
|
||
for layer_idx, layer in enumerate(self.layers):
|
||
if collect_ema_stats:
|
||
h, balance_loss, similarity_loss, diversity_loss, layer_stats, ema_stats, cosine_stats = layer(h, pos_cis, self.memory_bank, self.tok_embeddings, collect_ema_stats=True)
|
||
all_ema_stats[f'layer_{layer_idx}'] = ema_stats
|
||
else:
|
||
h, balance_loss, similarity_loss, diversity_loss, layer_stats, cosine_stats = layer(h, pos_cis, self.memory_bank, self.tok_embeddings, collect_ema_stats=False)
|
||
|
||
# 累加四种损失
|
||
total_balance_loss += balance_loss
|
||
total_similarity_loss += similarity_loss
|
||
total_diversity_loss += diversity_loss
|
||
|
||
# 为每层的统计信息添加前缀
|
||
for key, value in layer_stats.items():
|
||
all_layer_stats[f'layer_{layer_idx}_{key}'] = value
|
||
|
||
# 为每层的余弦相似度统计信息添加前缀
|
||
for key, value in cosine_stats.items():
|
||
all_cosine_stats[f'layer_{layer_idx}_{key}'] = value
|
||
|
||
logits = self.output(self.norm(h))
|
||
|
||
# 🔥 新的四损失结构
|
||
aux_loss = {
|
||
'balance_loss': total_balance_loss,
|
||
'similarity_loss': total_similarity_loss,
|
||
'diversity_loss': total_diversity_loss,
|
||
}
|
||
|
||
self.OUT.__setitem__('last_hidden_state', h)
|
||
self.OUT.__setitem__('logits', logits)
|
||
self.OUT.__setitem__('aux_loss', aux_loss)
|
||
self.OUT.__setitem__('layer_stats', all_layer_stats) # 添加层级统计信息
|
||
self.OUT.__setitem__('ema_stats', all_ema_stats if collect_ema_stats else None) # 添加EMA统计信息
|
||
self.OUT.__setitem__('cosine_stats', all_cosine_stats) # 添加余弦相似度统计信息
|
||
self.OUT.__setitem__('past_key_values', None) # 不支持KV cache
|
||
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., pad_token_id=0, num_return_sequences=1, **args):
|
||
"""Generate without KV cache"""
|
||
# 流式生成
|
||
if stream:
|
||
return self._stream(input_ids, eos_token_id, max_new_tokens, temperature, top_p, rp, **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, **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, **args):
|
||
"""Stream generation without KV cache - regenerates full sequence each time"""
|
||
start = input_ids.shape[1]
|
||
while input_ids.shape[1] < start + max_new_tokens:
|
||
# 每次都重新计算整个序列（因为没有KV cache）
|
||
out = self(input_ids, **args)
|
||
logits = out.logits[:, -1, :]
|
||
|
||
# 重复惩罚
|
||
logits[:, list(set(input_ids.tolist()[0]))] /= rp
|
||
logits /= (temperature + 1e-9)
|
||
|
||
# Top-p采样
|
||
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
|
||
|
||
def apply_ema_update(self, ema_stats):
|
||
"""
|
||
应用token-based EMA更新到memory_bank
|
||
实验1.4.6：批量化tensor操作优化版本
|
||
|
||
Args:
|
||
ema_stats: 从forward pass收集的EMA统计信息，格式为：
|
||
{'layer_0': {'memory_indices': ..., 'h_for_memory': ...}, 'layer_1': ...}
|
||
"""
|
||
if not self.params.use_ema_update:
|
||
return {}
|
||
|
||
# 增加EMA步数计数器
|
||
self.ema_step_counter += 1
|
||
|
||
# 检查是否需要进行EMA更新
|
||
if self.ema_step_counter % self.params.ema_update_freq != 0:
|
||
return {'ema_update_applied': False, 'reason': 'frequency_check_failed'}
|
||
|
||
with torch.no_grad():
|
||
device = self.memory_bank.device
|
||
knowledge_num, knowledge_length = self.memory_bank.shape
|
||
dim = self.params.dim
|
||
|
||
# 🚀 批量收集所有层的数据（避免字典操作）
|
||
all_indices = []
|
||
all_features = []
|
||
total_selections = 0
|
||
total_layers = 0
|
||
|
||
# 收集所有层的EMA统计信息
|
||
for layer_ema_stats in ema_stats.values():
|
||
if layer_ema_stats is None:
|
||
continue
|
||
|
||
total_layers += 1
|
||
memory_indices = layer_ema_stats['memory_indices'] # [batch, seq_len, num_selected]
|
||
h_for_memory = layer_ema_stats['h_for_memory'] # [batch, seq_len, dim]
|
||
|
||
bsz, seq_len, num_selected = memory_indices.shape
|
||
total_selections += bsz * seq_len * num_selected
|
||
|
||
# 展平索引和对应的h_for_memory
|
||
flat_indices = memory_indices.view(-1) # [batch * seq_len * num_selected]
|
||
|
||
# 为每个选择位置复制对应的h_for_memory
|
||
h_expanded = h_for_memory.unsqueeze(2).expand(-1, -1, num_selected, -1) # [batch, seq_len, num_selected, dim]
|
||
flat_h = h_expanded.reshape(-1, dim) # [batch * seq_len * num_selected, dim]
|
||
|
||
all_indices.append(flat_indices)
|
||
all_features.append(flat_h)
|
||
|
||
if not all_indices:
|
||
return {'ema_update_applied': False, 'reason': 'no_ema_stats'}
|
||
|
||
# 🚀 合并所有数据
|
||
all_indices = torch.cat(all_indices, dim=0) # [total_selections]
|
||
all_features = torch.cat(all_features, dim=0) # [total_selections, dim]
|
||
|
||
# 🚀 批量计算每个memory的平均特征（避免循环）
|
||
unique_indices, inverse_indices = torch.unique(all_indices, return_inverse=True)
|
||
|
||
# 使用scatter_add批量聚合（确保数据类型一致）
|
||
aggregated_features = torch.zeros(unique_indices.size(0), dim, device=device, dtype=all_features.dtype)
|
||
count_per_memory = torch.zeros(unique_indices.size(0), device=device, dtype=all_features.dtype)
|
||
|
||
aggregated_features.scatter_add_(0, inverse_indices.unsqueeze(1).expand(-1, dim), all_features)
|
||
count_per_memory.scatter_add_(0, inverse_indices, torch.ones_like(inverse_indices, dtype=all_features.dtype))
|
||
|
||
# 计算平均值
|
||
avg_features = aggregated_features / count_per_memory.unsqueeze(1) # [unique_count, dim]
|
||
|
||
# 🚀 分批EMA更新（控制显存使用）
|
||
batch_size = 4096 # 每批处理4096个memory，控制显存
|
||
updated_memories = 0
|
||
|
||
for i in range(0, unique_indices.size(0), batch_size):
|
||
end_i = min(i + batch_size, unique_indices.size(0))
|
||
batch_indices = unique_indices[i:end_i]
|
||
batch_avg_features = avg_features[i:end_i]
|
||
|
||
# 当前批次的token解码
|
||
current_tokens_batch = self.memory_bank[batch_indices] # [batch_size, knowledge_length]
|
||
current_embeddings_batch = self.tok_embeddings(current_tokens_batch.view(-1)).view(
|
||
batch_indices.size(0), knowledge_length, dim) # [batch_size, knowledge_length, dim]
|
||
|
||
old_features_batch = current_embeddings_batch.view(batch_indices.size(0), -1) # [batch_size, knowledge_length * dim]
|
||
expanded_new_features = batch_avg_features.repeat(1, knowledge_length) # [batch_size, knowledge_length * dim]
|
||
|
||
# EMA更新：new = γ * old + (1-γ) * new_avg
|
||
updated_features_batch = (
|
||
self.params.ema_decay * old_features_batch +
|
||
(1 - self.params.ema_decay) * expanded_new_features
|
||
)
|
||
|
||
# 分批编码为token_ids（关键：控制输出层的输入大小）
|
||
updated_reshaped = updated_features_batch.view(-1, dim) # [batch_size * knowledge_length, dim]
|
||
logits_batch = self.output(updated_reshaped) # [batch_size * knowledge_length, vocab_size]
|
||
new_token_ids_batch = torch.argmax(logits_batch, dim=-1).view(batch_indices.size(0), knowledge_length)
|
||
|
||
# 🔥 新增: 应用冻结mask，只更新未冻结的条目
|
||
# 检查哪些batch_indices对应的条目没有被冻结
|
||
unfrozen_mask_batch = ~self.freeze_mask[batch_indices] # [batch_size] - True表示未冻结
|
||
|
||
# 只更新未冻结的条目
|
||
if unfrozen_mask_batch.any():
|
||
unfrozen_indices = batch_indices[unfrozen_mask_batch]
|
||
unfrozen_tokens = new_token_ids_batch[unfrozen_mask_batch]
|
||
self.memory_bank[unfrozen_indices] = unfrozen_tokens
|
||
updated_memories += unfrozen_indices.size(0)
|
||
else:
|
||
# 如果这个batch中的所有条目都被冻结，则跳过更新
|
||
pass
|
||
|
||
update_ratio = updated_memories / knowledge_num
|
||
|
||
# 🔥 新增: 计算冻结统计信息
|
||
frozen_count = self.freeze_mask.sum().item()
|
||
total_memories = knowledge_num
|
||
|
||
update_stats = {
|
||
'ema_update_applied': True,
|
||
'ema_step': self.ema_step_counter.item(),
|
||
'total_selections': total_selections,
|
||
'total_layers': total_layers,
|
||
'updated_memories': updated_memories,
|
||
'update_ratio': update_ratio,
|
||
'frozen_memories': frozen_count,
|
||
'frozen_ratio': frozen_count / total_memories,
|
||
'ema_decay': self.params.ema_decay,
|
||
'selected_memory_coverage': updated_memories / knowledge_num,
|
||
}
|
||
|
||
return update_stats |