diff --git a/model/model_extra.py b/model/model_extra.py
new file mode 100644
index 0000000..fca8c54
--- /dev/null
+++ b/model/model_extra.py
@@ -0,0 +1,745 @@
+import math
+import struct
+import inspect
+import time
+import gc
+#子空间二维分解+梯度更新
+from .LMConfig import LMConfig
+from typing import Any, Optional, Tuple, List, Union
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        return self.weight * self._norm(x.float()).type_as(x)
+
+
+def precompute_pos_cis(dim: int, end: int = int(32 * 1024), theta: float = 1e6):
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+    t = torch.arange(end, device=freqs.device)  # type: ignore
+    freqs = torch.outer(t, freqs).float()  # type: ignore
+    pos_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64
+    return pos_cis
+
+
+def apply_rotary_emb(xq, xk, pos_cis):
+    def unite_shape(pos_cis, x):
+        ndim = x.ndim
+        assert 0 <= 1 < ndim
+        assert pos_cis.shape == (x.shape[1], x.shape[-1])
+        shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+        return pos_cis.view(*shape)
+
+    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+    xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
+    pos_cis = unite_shape(pos_cis, xq_)
+    xq_out = torch.view_as_real(xq_ * pos_cis).flatten(3)
+    xk_out = torch.view_as_real(xk_ * pos_cis).flatten(3)
+    return xq_out.type_as(xq), xk_out.type_as(xk)
+
+class KnowledgeDataset(nn.Module):
+    def __init__(self, params, tok_embeddings, is_train=True):
+        super().__init__()
+        self.is_train = is_train
+        self.params = params
+        self.tok_embeddings = tok_embeddings
+
+        # 嵌入参数
+        self.knowledge_dim = params.knowledge_dim
+        self.key_dim = self.knowledge_dim // 2
+        self.to_queries = nn.Sequential(
+                nn.Linear(params.dim, self.knowledge_dim, bias=False),
+        )
+
+        ## 数据库参数
+        self.knowledge_num = params.knowledge_num
+        self.knowledge_length = params.knowledge_length
+        
+        # 修改键存储为二维分解空间，设置为可训练参数
+        self.num_keys = int(math.sqrt(self.knowledge_num))
+        # 确保keys是可训练参数
+        self.keys = nn.Parameter(torch.randn(self.num_keys, 2, self.key_dim) * 0.02, requires_grad=True)
+        self.product_key_topk = min(16, self.num_keys)
+        
+        # 知识库存储 - 使用register_buffer因为这是整数索引，不需要梯度
+        self.register_buffer('knowledge_dataset', 
+            torch.randint(low=0, high=params.vocab_size, size=(self.knowledge_num, self.knowledge_length), dtype=torch.long))
+
+        # 计算step数目，用于动态调整权重
+        self.step_counter = 0
+
+        # 移除批次计数器和更新频率相关代码
+
+    def intelligent_selection(self, query, all_scores, all_indices):
+        """智能分层选择策略"""
+        if self.is_train == False:
+            return all_scores, all_indices
+        
+        batch_size = all_scores.size(0)
+        device = all_scores.device
+        dtype = all_scores.dtype
+
+        # 记录进入智能选择前的内存状态
+        if hasattr(self, 'step_counter'):
+            self.step_counter += 1
+            # 禁用GPU内存监控记录以提高性能
+            # if self.step_counter % 50 == 0:  # 每50次调用记录一次
+            #     if torch.cuda.is_available():
+            #         allocated_before = torch.cuda.memory_allocated() / (1024**3)
+            #         print(f"[INTEL_SELECT_ENTER] Step {self.step_counter}: GPU Memory: {allocated_before:.2f}GB")
+
+        # 对每个batch进行分层选择
+        enhanced_scores = all_scores.clone()
+        query_features = query.mean(dim=1)  # [batch_size, dim]
+
+        # 预先计算所有候选条目的嵌入（批量优化）
+        all_candidate_indices = torch.cat([all_indices[i] for i in range(batch_size)], dim=0)
+        unique_indices, inverse_indices = torch.unique(all_candidate_indices, return_inverse=True)
+
+        # 批量计算唯一候选条目的嵌入
+        candidate_tokens = self.knowledge_dataset[unique_indices]
+        flat_tokens = candidate_tokens.view(-1)
+        flat_embeddings = self.tok_embeddings(flat_tokens)
+        
+        # 获取flat_tokens对应的index（保留这些变量以便其他地方使用）
+        pre_update_indices = unique_indices.view(-1)
+        pre_update_embeddings = flat_embeddings.view(
+            len(unique_indices), self.knowledge_length, -1
+        )
+
+        unique_candidate_features = flat_embeddings.view(
+            len(unique_indices), self.knowledge_length, -1
+        ).mean(dim=1)  # [num_unique_candidates, dim]
+            
+        # 归一化候选特征（优化相似度计算）
+        normalized_candidates = F.normalize(unique_candidate_features, dim=-1)
+        normalized_queries = F.normalize(query_features, dim=-1)
+
+        # 收集所有batch的best_tokens
+        batch_best_tokens = []
+        batch_best_tokens_embeddings = []
+
+        for batch_idx in range(batch_size):
+            indices = all_indices[batch_idx]
+                
+            # 获取当前batch候选条目对应的特征索引
+            start_idx = batch_idx * len(indices)
+            end_idx = start_idx + len(indices)
+            batch_inverse_indices = inverse_indices[start_idx:end_idx]
+                
+            # 使用预计算的归一化特征进行优化相似度计算
+            batch_candidate_features = normalized_candidates[batch_inverse_indices]
+            query_feature = normalized_queries[batch_idx]
+                
+            # 使用矩阵乘法计算余弦相似度
+            similarity_scores = torch.mv(batch_candidate_features, query_feature)
+            
+            # 找到最大相似度分数的索引
+            max_similarity_idx = torch.argmax(similarity_scores)
+            
+            # 获取最大相似度对应的候选条目索引
+            best_candidate_idx = indices[max_similarity_idx]
+            
+            # 获取对应的tokens
+            best_tokens = self.knowledge_dataset[best_candidate_idx]
+            best_tokens_embeddings = self.tok_embeddings(best_tokens)
+            
+            # 将当前batch的best_tokens添加到列表中
+            batch_best_tokens.append(best_tokens)
+            batch_best_tokens_embeddings.append(best_tokens_embeddings)
+
+        # 将所有batch的best_tokens堆叠成一个张量
+        # [batch_size, knowledge_length]
+        all_best_tokens = torch.stack(batch_best_tokens, dim=0)
+        all_best_tokens_embeddings = torch.stack(batch_best_tokens_embeddings, dim=0)
+
+        # 清理中间张量以防止内存泄漏
+        del all_candidate_indices, unique_indices, inverse_indices
+        del unique_candidate_features, normalized_candidates, normalized_queries
+        del batch_best_tokens, batch_best_tokens_embeddings
+        del flat_tokens, flat_embeddings, pre_update_embeddings
+        
+        # 记录退出智能选择后的内存状态（已禁用以提高性能）
+        # if hasattr(self, 'step_counter') and self.step_counter % 50 == 0:
+        #     if torch.cuda.is_available():
+        #         allocated_after = torch.cuda.memory_allocated() / (1024**3)
+        #         print(f"[INTEL_SELECT_EXIT] Step {self.step_counter}: GPU Memory: {allocated_after:.2f}GB")
+                
+        # 强制垃圾回收（仅在监控步骤）
+        if hasattr(self, 'step_counter') and self.step_counter % 100 == 0:
+            gc.collect()
+            # if torch.cuda.is_available():
+            #     torch.cuda.empty_cache()
+
+        return all_best_tokens, all_best_tokens_embeddings
+    
+        
+    
+    def search_index(self, x):
+        batch_size, seq_len, dim = x.shape
+
+        # 1. 序列维度平均
+        x_flat = x.mean(dim=1)  # [batch_size, dim]
+
+        # 2. 生成查询向量并重塑为两个子查询
+        queries = self.to_queries(x_flat)  # [batch_size, knowledge_dim]
+        queries = queries.reshape(batch_size, 2, self.key_dim)  # [batch_size, 2, key_dim]
+        # 调整维度顺序，使子空间维度位于首位
+        queries = queries.permute(1, 0, 2)  # [2, batch_size, key_dim]
+
+        # 3. 计算每个子空间的相似度
+        sim = torch.einsum('p b d, k p d -> p b k', queries, self.keys)
+
+        # 4. 在两个子空间分别做top-k
+        scores_and_indices = [sim[p].topk(self.product_key_topk, dim=-1) for p in range(2)]
+        scores_x, scores_y = scores_and_indices[0][0], scores_and_indices[1][0]
+        indices_x, indices_y = scores_and_indices[0][1], scores_and_indices[1][1]
+
+        # 5. 组合两个子空间的结果
+        all_scores = scores_x.unsqueeze(-1) + scores_y.unsqueeze(-2) # [batch_size, topk, topk]
+        all_indices = (indices_x.unsqueeze(-1) * self.num_keys) + indices_y.unsqueeze(-2)  # [batch_size, topk, topk]
+        
+        # 6. 将结果重塑为二维
+        all_scores = all_scores.reshape(batch_size, -1)  # [batch_size, topk*topk]
+        all_indices = all_indices.reshape(batch_size, -1)  # [batch_size, topk*topk]
+        
+        # 7. 选择最终的top-k结果
+        scores, indices_of_indices = all_scores.topk(self.product_key_topk, dim=-1)
+        indices = torch.gather(all_indices, 1, indices_of_indices)
+
+        # 8. 应用智能分层选择策略
+        best_tokens, best_tokens_embeddings = self.intelligent_selection(x, scores, indices)
+
+
+        return best_tokens, best_tokens_embeddings
+
+class CrossAttention(nn.Module):
+    def __init__(
+        self,
+        config
+    ):
+        super().__init__()
+        self.config = config
+        self.num_heads = 8
+        self.head_dim = self.config.dim // self.num_heads
+        self.to_q = nn.Linear(self.config.dim, self.config.dim, bias=False)
+        self.to_k = nn.Linear(self.config.dim, self.config.dim, bias=False)
+        self.to_v = nn.Linear(self.config.dim, self.config.dim, bias=False)
+
+        self.to_out = nn.Linear(self.config.dim, self.config.dim, bias=False)
+
+    def forward(self, x, db, context_mask=None, pos_emb=None):
+        batch_size = x.size(0)
+        
+        # 监控交叉注意力开始时的内存（已禁用以提高性能）
+        if not hasattr(self, 'call_counter'):
+            self.call_counter = 0
+        self.call_counter += 1
+        
+        # 禁用GPU内存监控记录以提高性能
+        # if self.call_counter % 100 == 0 and torch.cuda.is_available():
+        #     allocated_before = torch.cuda.memory_allocated() / (1024**3)
+        #     print(f"[CROSS_ATTN_ENTER] Call {self.call_counter}: GPU Memory: {allocated_before:.2f}GB")
+
+        # 分离多头
+        q = self.to_q(x).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        k = self.to_k(db).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        v = self.to_v(db).view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+
+        if pos_emb is not None:
+            pos_emb = pos_emb.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+            q = q + pos_emb
+            k = k + pos_emb
+            v = v + pos_emb
+
+        attn_scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
+
+        if context_mask is not None:
+            expanded_mask = context_mask.unsqueeze(1).expand(-1, self.num_heads, -1, -1)
+            attn_scores = attn_scores.masked_fill(expanded_mask == 0, -1e10)
+
+        attn_weights = F.softmax(attn_scores, dim=-1)
+
+        context = torch.matmul(attn_weights, v)
+
+        context = context.transpose(1, 2).contiguous().view(batch_size, -1, self.config.dim)
+
+        context = self.to_out(context)
+        
+        # 清理中间张量
+        del q, k, v, attn_scores, attn_weights
+        
+        # 监控交叉注意力结束时的内存（已禁用以提高性能）
+        # if self.call_counter % 100 == 0 and torch.cuda.is_available():
+        #     allocated_after = torch.cuda.memory_allocated() / (1024**3)
+        #     print(f"[CROSS_ATTN_EXIT] Call {self.call_counter}: GPU Memory: {allocated_after:.2f}GB")
+
+        return context
+
+class Attention(nn.Module):
+    def __init__(self, args: LMConfig):
+        super().__init__()
+        self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads
+        assert args.n_heads % self.n_kv_heads == 0
+        self.n_local_heads = args.n_heads
+        self.n_local_kv_heads = self.n_kv_heads
+        self.n_rep = self.n_local_heads // self.n_local_kv_heads
+        self.head_dim = args.dim // args.n_heads
+        self.wq = nn.Linear(args.dim, args.n_heads * self.head_dim, bias=False)
+        self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
+        self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
+        self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False)
+        self.attn_dropout = nn.Dropout(args.dropout)
+        self.resid_dropout = nn.Dropout(args.dropout)
+        self.dropout = args.dropout
+        self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and args.flash_attn
+        # print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")
+        mask = torch.full((1, 1, args.max_seq_len, args.max_seq_len), float("-inf"))
+        mask = torch.triu(mask, diagonal=1)
+        self.register_buffer("mask", mask, persistent=False)
+
+    def forward(self,
+                x: torch.Tensor,
+                pos_cis: torch.Tensor):
+        bsz, seq_len, _ = x.shape
+        xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
+        xq = xq.view(bsz, seq_len, self.n_local_heads, self.head_dim)
+        xk = xk.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim)
+        xv = xv.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim)
+
+        xq, xk = apply_rotary_emb(xq, xk, pos_cis)
+        if self.flash and seq_len != 1:
+            dropout_p = self.dropout if self.training else 0.0
+            output = F.scaled_dot_product_attention(
+                xq, xk, xv,
+                attn_mask=None,
+                dropout_p=dropout_p,
+                is_causal=True
+            )
+        else:
+            scores = (xq @ xk.transpose(-2, -1)) / math.sqrt(self.head_dim)
+            scores += self.mask[:, :, :seq_len, :seq_len]
+            scores = F.softmax(scores.float(), dim=-1).type_as(xq)
+            scores = self.attn_dropout(scores)
+            output = scores @ xv
+
+        output = output.transpose(1, 2).reshape(bsz, seq_len, -1)
+        output = self.resid_dropout(self.wo(output))
+        return output
+
+
+class FeedForward(nn.Module):
+    def __init__(self, config: LMConfig):
+        super().__init__()
+        if config.hidden_dim is None:
+            hidden_dim = 4 * config.dim
+            hidden_dim = int(2 * hidden_dim / 3)
+            config.hidden_dim = config.multiple_of * ((hidden_dim + config.multiple_of - 1) // config.multiple_of)
+        self.w1 = nn.Linear(config.dim, config.hidden_dim, bias=False)
+        self.w2 = nn.Linear(config.hidden_dim, config.dim, bias=False)
+        self.w3 = nn.Linear(config.dim, config.hidden_dim, bias=False)
+        self.dropout = nn.Dropout(config.dropout)
+
+    def forward(self, x):
+        return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x)))
+
+
+class MoEGate(nn.Module):
+    def __init__(self, config: LMConfig):
+        super().__init__()
+        self.config = config
+        self.top_k = config.num_experts_per_tok
+        self.n_routed_experts = config.n_routed_experts
+
+        self.scoring_func = config.scoring_func
+        self.alpha = config.aux_loss_alpha
+        self.seq_aux = config.seq_aux
+
+        self.norm_topk_prob = config.norm_topk_prob
+        self.gating_dim = config.dim
+        self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim)))
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        import torch.nn.init as init
+        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+
+    def forward(self, hidden_states):
+        bsz, seq_len, h = hidden_states.shape
+        hidden_states = hidden_states.view(-1, h)
+        logits = F.linear(hidden_states, self.weight, None)
+        if self.scoring_func == 'softmax':
+            scores = logits.softmax(dim=-1)
+        else:
+            raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}')
+
+        topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False)
+
+        if self.top_k > 1 and self.norm_topk_prob:
+            denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
+            topk_weight = topk_weight / denominator
+
+        if self.training and self.alpha > 0.0:
+            scores_for_aux = scores
+            aux_topk = self.top_k
+            topk_idx_for_aux_loss = topk_idx.view(bsz, -1)
+            if self.seq_aux:
+                scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1)
+                ce = torch.zeros(bsz, self.n_routed_experts, device=hidden_states.device)
+                ce.scatter_add_(1, topk_idx_for_aux_loss,
+                                torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device)).div_(
+                    seq_len * aux_topk / self.n_routed_experts)
+                aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(dim=1).mean() * self.alpha
+            else:
+                mask_ce = F.one_hot(topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts)
+                ce = mask_ce.float().mean(0)
+                Pi = scores_for_aux.mean(0)
+                fi = ce * self.n_routed_experts
+                aux_loss = (Pi * fi).sum() * self.alpha
+        else:
+            aux_loss = 0
+        return topk_idx, topk_weight, aux_loss
+
+
+class MOEFeedForward(nn.Module):
+    def __init__(self, config: LMConfig):
+        super().__init__()
+        self.config = config
+        self.experts = nn.ModuleList([
+            FeedForward(config)
+            for _ in range(config.n_routed_experts)
+        ])
+        self.gate = MoEGate(config)
+        if config.n_shared_experts is not None:
+            self.shared_experts = FeedForward(config)
+
+    def forward(self, x):
+        identity = x
+        orig_shape = x.shape
+        bsz, seq_len, _ = x.shape
+        # 使用门控机制选择专家
+        topk_idx, topk_weight, aux_loss = self.gate(x)
+        x = x.view(-1, x.shape[-1])
+        flat_topk_idx = topk_idx.view(-1)
+        if self.training:
+            x = x.repeat_interleave(self.config.num_experts_per_tok, dim=0)
+            y = torch.empty_like(x, dtype=torch.float16)
+            for i, expert in enumerate(self.experts):
+                y[flat_topk_idx == i] = expert(x[flat_topk_idx == i]).to(y.dtype)  # 确保类型一致
+            y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
+            y = y.view(*orig_shape)
+        else:
+            y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape)
+        if self.config.n_shared_experts is not None:
+            y = y + self.shared_experts(identity)
+        self.aux_loss = aux_loss
+        return y
+
+    @torch.no_grad()
+    def moe_infer(self, x, flat_expert_indices, flat_expert_weights):
+        expert_cache = torch.zeros_like(x)
+        idxs = flat_expert_indices.argsort()
+        tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0)
+        token_idxs = idxs // self.config.num_experts_per_tok
+        # 当tokens_per_expert = [6, 15, 20, 26]，tokens_per_expert.shape[0]即为专家数量（此时为4）
+        # 且token_idxs = [3, 7, 19, 21, 24, 25,  4,  5,  6, 10, 11, 12...] 时
+        # 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）
+        # 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
+        for i, end_idx in enumerate(tokens_per_expert):
+            start_idx = 0 if i == 0 else tokens_per_expert[i - 1]
+            if start_idx == end_idx:
+                continue
+            expert = self.experts[i]
+            exp_token_idx = token_idxs[start_idx:end_idx]
+            expert_tokens = x[exp_token_idx]
+            expert_out = expert(expert_tokens).to(expert_cache.dtype)
+            expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]])
+            expert_cache.scatter_add_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out)
+
+        return expert_cache
+
+
+class TripleExtractionHead(nn.Module):
+    """三元组提取任务头"""
+    def __init__(self, config: LMConfig):
+        super().__init__()
+        self.config = config
+        
+        # 三元组长度超参数
+        self.max_subject_len = config.max_subject_len
+        self.max_predicate_len = config.max_predicate_len
+        self.max_object_len = config.max_object_len
+        
+        # 自注意力机制
+        self.self_attention = Attention(config)
+        self.self_attn_norm = RMSNorm(config.dim, eps=config.norm_eps)
+        
+        # 交叉注意力机制（用于主语和宾语提取）
+        self.cross_attention_subject = CrossAttention(config)
+        self.cross_attention_object = CrossAttention(config)
+        
+        # 归一化层
+        self.subject_norm = RMSNorm(config.dim, eps=config.norm_eps)
+        self.object_norm = RMSNorm(config.dim, eps=config.norm_eps)
+        
+        # Feed Forward 网络
+        self.predicate_ff = FeedForward(config)
+        self.subject_ff = FeedForward(config)
+        self.object_ff = FeedForward(config)
+        
+        # 输出投影层 - 修改为支持序列预测
+        self.predicate_output = nn.Linear(config.dim, self.max_predicate_len *config.dim, bias=False)
+        self.subject_output = nn.Linear(config.dim, self.max_subject_len * config.dim, bias=False)
+        self.object_output = nn.Linear(config.dim, self.max_object_len * config.dim, bias=False)
+        
+        print(f"三元组提取任务头配置:")
+        print(f"- 主语最大长度: {self.max_subject_len}")
+        print(f"- 谓语最大长度: {self.max_predicate_len}")
+        print(f"- 宾语最大长度: {self.max_object_len}")
+        
+    def forward(self, h, pos_cis):
+        """
+        Args:
+            h: [batch_size, seq_len, dim] - 来自transformer层的隐藏状态
+            pos_cis: 位置编码
+        Returns:
+            predicate_logits: [batch_size, seq_len, max_predicate_len, vocab_size] - 谓语序列预测
+            subject_logits: [batch_size, seq_len, max_subject_len, vocab_size] - 主语序列预测  
+            object_logits: [batch_size, seq_len, max_object_len, vocab_size] - 宾语序列预测
+        """
+        batch_size, seq_len, dim = h.shape
+        
+        # 1. h通过自注意力得到h1
+        h1 = self.self_attention(self.self_attn_norm(h), pos_cis)
+        h1 = h + h1  # 残差连接
+        
+        # 2. h1通过feed_forward得到谓语输出
+        predicate_features = self.predicate_ff(h1)
+        predicate_features = predicate_features.mean(dim=1)
+        predicate_raw = self.predicate_output(predicate_features)  # [batch_size, max_predicate_len * vocab_size]
+        predicate_logits = predicate_raw.view(batch_size, self.max_predicate_len, -1)
+        
+        # 3. h1通过交叉注意力（k,v都是h）得到h2
+        h2 = self.cross_attention_subject(h1, h)  # query是h1，key和value都是h
+        h2 = h1 + h2  # 残差连接
+        
+        # 4. h2通过feed_forward得到主语输出
+        subject_features = self.subject_ff(self.subject_norm(h2))
+        subject_features = subject_features.mean(dim=1)
+        subject_raw = self.subject_output(subject_features)  # [batch_size, max_subject_len * vocab_size]
+        subject_logits = subject_raw.view(batch_size, self.max_subject_len, -1)
+        
+        # 5. h2通过交叉注意力（k,v都是h）得到h3
+        h3 = self.cross_attention_object(h2, h)  # query是h2，key和value都是h
+        h3 = h2 + h3  # 残差连接
+        
+        # 6. h3通过feed_forward得到宾语输出
+        object_features = self.object_ff(self.object_norm(h3))
+        object_features = object_features.mean(dim=1)
+        object_raw = self.object_output(object_features)  # [batch_size, max_object_len * vocab_size]
+        object_logits = object_raw.view(batch_size, self.max_object_len, -1)
+        
+        return predicate_logits, subject_logits, object_logits
+
+
+class MiniMindBlock(nn.Module):
+    def __init__(self, layer_id: int, config: LMConfig, knowledge_dataset: KnowledgeDataset):
+        super().__init__()
+        self.n_heads = config.n_heads
+        self.dim = config.dim
+        self.head_dim = config.dim // config.n_heads
+        self.self_attention = Attention(config)
+        self.cross_attention = CrossAttention(config)
+        self.knowledge_dataset = knowledge_dataset
+
+        self.layer_id = layer_id
+        self.attention_norm = RMSNorm(config.dim, eps=config.norm_eps)
+        self.ffn_norm = RMSNorm(config.dim, eps=config.norm_eps)
+        self.feed_forward = FeedForward(config) if not config.use_moe else MOEFeedForward(config)
+
+    def forward(self, x, pos_cis):
+        h_attn = self.self_attention(
+            self.attention_norm(x),
+            pos_cis
+        )
+        db, db_embeddings = self.knowledge_dataset.search_index(h_attn)
+        h_attn = self.cross_attention(h_attn, db_embeddings)
+        h = x + h_attn
+        out = h + self.feed_forward(self.ffn_norm(h))
+        return out
+
+
+class MiniMindLM(PreTrainedModel):
+    config_class = LMConfig
+
+    def __init__(self, params: LMConfig = None,mode="triple"):
+        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)
+        self.knowledge_dataset = KnowledgeDataset(params, self.tok_embeddings)
+        self.layers = nn.ModuleList([MiniMindBlock(l, params, self.knowledge_dataset) 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
+        
+        # 添加三元组提取任务头（可训练）
+        self.triple_extraction_head = TripleExtractionHead(params)
+        self.register_buffer("pos_cis",
+                             precompute_pos_cis(dim=params.dim // params.n_heads, theta=params.rope_theta),
+                             persistent=False)
+        self.OUT = CausalLMOutputWithPast()
+        self.freeze_embedding = False
+
+        self.mode = mode
+        
+        # 冻结所有指定组件的权重
+        self._freeze_components()
+
+    def _freeze_components(self):
+        """冻结指定组件的权重"""
+        # 冻结词嵌入层
+        for param in self.tok_embeddings.parameters():
+            param.requires_grad = False
+        
+        # 冻结知识数据库
+        for param in self.knowledge_dataset.parameters():
+            param.requires_grad = False
+        
+        # 冻结所有transformer层
+        for param in self.layers.parameters():
+            param.requires_grad = False
+        
+        # 冻结输出层
+        for param in self.output.parameters():
+            param.requires_grad = False
+        
+        # pos_cis是buffer，本身就不需要梯度，但为了明确起见
+        # (实际上buffer默认就是requires_grad=False)
+        if hasattr(self, 'pos_cis'):
+            self.pos_cis.requires_grad = False
+        
+        print("已冻结以下组件的权重：")
+        print("- tok_embeddings")
+        print("- knowledge_dataset") 
+        print("- layers (所有transformer层)")
+        print("- output")
+        print("- pos_cis")
+        print("注意：triple_extraction_head 保持可训练状态")
+
+    def forward(self,
+                input_ids: Optional[torch.Tensor] = None,
+                logits_to_keep: Union[int, torch.Tensor] = 0,
+                step: int = 0,
+                **args):
+        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)]
+        for l, layer in enumerate(self.layers):
+            h = layer(
+                h, pos_cis
+            )
+
+        # 应用三元组提取任务头
+        predicate_logits, subject_logits, object_logits = self.triple_extraction_head(h, pos_cis)
+        predicate_logits = predicate_logits.reshape(input_ids.size(0)*self.params.max_predicate_len, -1)
+        subject_logits = subject_logits.reshape(input_ids.size(0)*self.params.max_subject_len, -1)
+        object_logits = object_logits.reshape(input_ids.size(0)*self.params.max_object_len, -1)
+
+        predicate_logits = self.output(predicate_logits)
+        subject_logits = self.output(subject_logits)
+        object_logits = self.output(object_logits)
+
+        predicate_logits = predicate_logits.reshape(input_ids.size(0), self.params.max_predicate_len, -1)
+        subject_logits = subject_logits.reshape(input_ids.size(0), self.params.max_subject_len, -1)
+        object_logits = object_logits.reshape(input_ids.size(0), self.params.max_object_len, -1)
+
+        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))
+
+        # 进一步简化，只保留必要的参数
+        output = CausalLMOutputWithPast(
+            logits=logits,
+        )
+        output.hidden_states = h
+        output.aux_loss = aux_loss
+        
+        # 添加三元组提取结果
+        # 注意：现在的维度是 [batch_size, seq_len, max_len, vocab_size]
+        output.predicate_logits = predicate_logits
+        output.subject_logits = subject_logits
+        output.object_logits = object_logits
+
+        return output
+
+    @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):
+        # 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):
+        start, first_seq, past_kvs = input_ids.shape[1], True, None
+        while input_ids.shape[1] < max_new_tokens - 1:
+            if first_seq:
+                out, first_seq = self(input_ids, **args), False
+            else:
+                out = self(input_ids[:, -1:],
+                           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
+
diff --git a/model/model_original.py b/model/model_original.py
new file mode 100644
index 0000000..6e2dcb7
--- /dev/null
+++ b/model/model_original.py
@@ -0,0 +1,385 @@
+import math
+import struct
+import inspect
+import time
+
+from .LMConfig import LMConfig
+from typing import Any, Optional, Tuple, List, Union
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim))
+
+    def _norm(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        return self.weight * self._norm(x.float()).type_as(x)
+
+
+def precompute_pos_cis(dim: int, end: int = int(32 * 1024), theta: float = 1e6):
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+    t = torch.arange(end, device=freqs.device)  # type: ignore
+    freqs = torch.outer(t, freqs).float()  # type: ignore
+    pos_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64
+    return pos_cis
+
+
+def apply_rotary_emb(xq, xk, pos_cis):
+    def unite_shape(pos_cis, x):
+        ndim = x.ndim
+        assert 0 <= 1 < ndim
+        assert pos_cis.shape == (x.shape[1], x.shape[-1])
+        shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+        return pos_cis.view(*shape)
+
+    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+    xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
+    pos_cis = unite_shape(pos_cis, xq_)
+    xq_out = torch.view_as_real(xq_ * pos_cis).flatten(3)
+    xk_out = torch.view_as_real(xk_ * pos_cis).flatten(3)
+    return xq_out.type_as(xq), xk_out.type_as(xk)
+
+
+def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """torch.repeat_interleave(x, dim=2, repeats=n_rep)"""
+    bs, slen, n_kv_heads, head_dim = x.shape
+    if n_rep == 1:
+        return x
+    return (
+        x[:, :, :, None, :]
+        .expand(bs, slen, n_kv_heads, n_rep, head_dim)
+        .reshape(bs, slen, n_kv_heads * n_rep, head_dim)
+    )
+
+
+class Attention(nn.Module):
+    def __init__(self, args: LMConfig):
+        super().__init__()
+        self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads
+        assert args.n_heads % self.n_kv_heads == 0
+        self.n_local_heads = args.n_heads
+        self.n_local_kv_heads = self.n_kv_heads
+        self.n_rep = self.n_local_heads // self.n_local_kv_heads
+        self.head_dim = args.dim // args.n_heads
+        self.wq = nn.Linear(args.dim, args.n_heads * self.head_dim, bias=False)
+        self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
+        self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
+        self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False)
+        self.attn_dropout = nn.Dropout(args.dropout)
+        self.resid_dropout = nn.Dropout(args.dropout)
+        self.dropout = args.dropout
+        self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and args.flash_attn
+        # print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")
+        mask = torch.full((1, 1, args.max_seq_len, args.max_seq_len), float("-inf"))
+        mask = torch.triu(mask, diagonal=1)
+        self.register_buffer("mask", mask, persistent=False)
+
+    def forward(self,
+                x: torch.Tensor,
+                pos_cis: torch.Tensor,
+                past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+                use_cache=False):
+        bsz, seq_len, _ = x.shape
+        xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
+        xq = xq.view(bsz, seq_len, self.n_local_heads, self.head_dim)
+        xk = xk.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim)
+        xv = xv.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim)
+
+        xq, xk = apply_rotary_emb(xq, xk, pos_cis)
+        # kv_cache实现
+        if past_key_value is not None:
+            xk = torch.cat([past_key_value[0], xk], dim=1)
+            xv = torch.cat([past_key_value[1], xv], dim=1)
+        past_kv = (xk, xv) if use_cache else None
+
+        xq, xk, xv = (
+            xq.transpose(1, 2),
+            repeat_kv(xk, self.n_rep).transpose(1, 2),
+            repeat_kv(xv, self.n_rep).transpose(1, 2)
+        )
+        if self.flash and seq_len != 1:
+            dropout_p = self.dropout if self.training else 0.0
+            output = F.scaled_dot_product_attention(
+                xq, xk, xv,
+                attn_mask=None,
+                dropout_p=dropout_p,
+                is_causal=True
+            )
+        else:
+            scores = (xq @ xk.transpose(-2, -1)) / math.sqrt(self.head_dim)
+            scores += self.mask[:, :, :seq_len, :seq_len]
+            scores = F.softmax(scores.float(), dim=-1).type_as(xq)
+            scores = self.attn_dropout(scores)
+            output = scores @ xv
+
+        output = output.transpose(1, 2).reshape(bsz, seq_len, -1)
+        output = self.resid_dropout(self.wo(output))
+        return output, past_kv
+
+
+class FeedForward(nn.Module):
+    def __init__(self, config: LMConfig):
+        super().__init__()
+        if config.hidden_dim is None:
+            hidden_dim = 4 * config.dim
+            hidden_dim = int(2 * hidden_dim / 3)
+            config.hidden_dim = config.multiple_of * ((hidden_dim + config.multiple_of - 1) // config.multiple_of)
+        self.w1 = nn.Linear(config.dim, config.hidden_dim, bias=False)
+        self.w2 = nn.Linear(config.hidden_dim, config.dim, bias=False)
+        self.w3 = nn.Linear(config.dim, config.hidden_dim, bias=False)
+        self.dropout = nn.Dropout(config.dropout)
+
+    def forward(self, x):
+        return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x)))
+
+
+class MoEGate(nn.Module):
+    def __init__(self, config: LMConfig):
+        super().__init__()
+        self.config = config
+        self.top_k = config.num_experts_per_tok
+        self.n_routed_experts = config.n_routed_experts
+
+        self.scoring_func = config.scoring_func
+        self.alpha = config.aux_loss_alpha
+        self.seq_aux = config.seq_aux
+
+        self.norm_topk_prob = config.norm_topk_prob
+        self.gating_dim = config.dim
+        self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim)))
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        import torch.nn.init as init
+        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+
+    def forward(self, hidden_states):
+        bsz, seq_len, h = hidden_states.shape
+        hidden_states = hidden_states.view(-1, h)
+        logits = F.linear(hidden_states, self.weight, None)
+        if self.scoring_func == 'softmax':
+            scores = logits.softmax(dim=-1)
+        else:
+            raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}')
+
+        topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False)
+
+        if self.top_k > 1 and self.norm_topk_prob:
+            denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
+            topk_weight = topk_weight / denominator
+
+        if self.training and self.alpha > 0.0:
+            scores_for_aux = scores
+            aux_topk = self.top_k
+            topk_idx_for_aux_loss = topk_idx.view(bsz, -1)
+            if self.seq_aux:
+                scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1)
+                ce = torch.zeros(bsz, self.n_routed_experts, device=hidden_states.device)
+                ce.scatter_add_(1, topk_idx_for_aux_loss,
+                                torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device)).div_(
+                    seq_len * aux_topk / self.n_routed_experts)
+                aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(dim=1).mean() * self.alpha
+            else:
+                mask_ce = F.one_hot(topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts)
+                ce = mask_ce.float().mean(0)
+                Pi = scores_for_aux.mean(0)
+                fi = ce * self.n_routed_experts
+                aux_loss = (Pi * fi).sum() * self.alpha
+        else:
+            aux_loss = 0
+        return topk_idx, topk_weight, aux_loss
+
+
+class MOEFeedForward(nn.Module):
+    def __init__(self, config: LMConfig):
+        super().__init__()
+        self.config = config
+        self.experts = nn.ModuleList([
+            FeedForward(config)
+            for _ in range(config.n_routed_experts)
+        ])
+        self.gate = MoEGate(config)
+        if config.n_shared_experts is not None:
+            self.shared_experts = FeedForward(config)
+
+    def forward(self, x):
+        identity = x
+        orig_shape = x.shape
+        bsz, seq_len, _ = x.shape
+        # 使用门控机制选择专家
+        topk_idx, topk_weight, aux_loss = self.gate(x)
+        x = x.view(-1, x.shape[-1])
+        flat_topk_idx = topk_idx.view(-1)
+        if self.training:
+            x = x.repeat_interleave(self.config.num_experts_per_tok, dim=0)
+            y = torch.empty_like(x, dtype=torch.float16)
+            for i, expert in enumerate(self.experts):
+                y[flat_topk_idx == i] = expert(x[flat_topk_idx == i]).to(y.dtype)  # 确保类型一致
+            y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
+            y = y.view(*orig_shape)
+        else:
+            y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape)
+        if self.config.n_shared_experts is not None:
+            y = y + self.shared_experts(identity)
+        self.aux_loss = aux_loss
+        return y
+
+    @torch.no_grad()
+    def moe_infer(self, x, flat_expert_indices, flat_expert_weights):
+        expert_cache = torch.zeros_like(x)
+        idxs = flat_expert_indices.argsort()
+        tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0)
+        token_idxs = idxs // self.config.num_experts_per_tok
+        # 当tokens_per_expert = [6, 15, 20, 26]，tokens_per_expert.shape[0]即为专家数量（此时为4）
+        # 且token_idxs = [3, 7, 19, 21, 24, 25,  4,  5,  6, 10, 11, 12...] 时
+        # 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）
+        # 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
+        for i, end_idx in enumerate(tokens_per_expert):
+            start_idx = 0 if i == 0 else tokens_per_expert[i - 1]
+            if start_idx == end_idx:
+                continue
+            expert = self.experts[i]
+            exp_token_idx = token_idxs[start_idx:end_idx]
+            expert_tokens = x[exp_token_idx]
+            expert_out = expert(expert_tokens).to(expert_cache.dtype)
+            expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]])
+            expert_cache.scatter_add_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out)
+
+        return expert_cache
+
+
+class MiniMindBlock(nn.Module):
+    def __init__(self, layer_id: int, config: LMConfig):
+        super().__init__()
+        self.n_heads = config.n_heads
+        self.dim = config.dim
+        self.head_dim = config.dim // config.n_heads
+        self.attention = Attention(config)
+
+        self.layer_id = layer_id
+        self.attention_norm = RMSNorm(config.dim, eps=config.norm_eps)
+        self.ffn_norm = RMSNorm(config.dim, eps=config.norm_eps)
+        self.feed_forward = FeedForward(config) if not config.use_moe else MOEFeedForward(config)
+
+    def forward(self, x, pos_cis, past_key_value=None, use_cache=False):
+        h_attn, past_kv = self.attention(
+            self.attention_norm(x),
+            pos_cis,
+            past_key_value=past_key_value,
+            use_cache=use_cache
+        )
+        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)
+        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)
+        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
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