Minimind/model/model.py

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)

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
        self.keys = nn.Parameter(torch.randn(self.knowledge_num, self.knowledge_dim) * 0.02, requires_grad=True)
        self.product_key_topk = min(16, self.knowledge_num)
        
        # 使用频率统计 - 使用register_buffer以便在GPU/CPU间正确移动
        self.register_buffer('has_update_keys', torch.zeros(self.knowledge_num))

        # 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

        self.freeze_embedding = False

        

    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

        # 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
        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)

        # 获取
        
        # 使用重新计算的embeddings更新self.keys
        if self.is_train:
            self._update_keys_with_embeddings(pre_update_indices, pre_update_embeddings)
        
        # 更新被修改过的key
        with torch.no_grad():
            self.has_update_keys[pre_update_indices] = 1
        
        return all_best_tokens, all_best_tokens_embeddings
    
    def _update_keys_with_embeddings(self, pre_update_indices, pre_update_embeddings):
        if self.freeze_embedding:
            return
        # 使用pre_update_embeddings更新self.keys
        with torch.no_grad():
            pre_update_embeddings = pre_update_embeddings.mean(dim=1)  # [337, 512]
            pre_update_embeddings = self.to_queries(pre_update_embeddings)
            self.keys[pre_update_indices] = pre_update_embeddings
    
    def search_index(self,x):
        batch_size, seq_len, dim = x.shape

        # collapse sequence dimension by averaging
        x_flat = x.mean(dim=1)  # [batch_size, dim]

        queries = self.to_queries(x_flat) # [batch_size, 2*dim_key]
        # queries = queries.reshape(batch_size, 2, self.key_dim)
        # queries = queries.permute(1, 0, 2)

        # 2. 计算queries与keys的相似度
        sim = torch.einsum('b d, k d -> b k', queries, self.keys)

        # 3. 在两个子空间分别做top-k
        scores_and_indices = sim.topk(self.product_key_topk, dim=-1)
        scores, indices = scores_and_indices[0], scores_and_indices[1]

        # 5. 应用智能分层选择策略
        best_tokens, best_tokens_embeddings = self.intelligent_selection(x, scores, indices)

        # 6. 更新1%的keys
        if self.is_train:
            # 获取未更新过的keys的索引
            not_updated_indices = torch.where(self.has_update_keys == 0)[0]
                
            # 如果有未更新的keys，随机选择num_update_keys个进行更新
            if len(not_updated_indices) > 0:
                num_update_keys = int(self.knowledge_num * 0.01)
                perm = torch.randperm(len(not_updated_indices))[:num_update_keys]
                perm_num = perm.shape[0]
                pre_update_indices = not_updated_indices[perm]
                pre_update_tokens = self.knowledge_dataset[pre_update_indices]
                pre_update_embeddings = self.tok_embeddings(pre_update_tokens.view(-1))
                pre_update_embeddings = pre_update_embeddings.view(perm_num, self.knowledge_length, -1)
                self._update_keys_with_embeddings(pre_update_indices, pre_update_embeddings)
                # 更新被修改过的key
                with torch.no_grad():
                    self.has_update_keys[pre_update_indices] = 1
            else:
                print("all keys are updated")
                # 重置所有keys的更新状态
                self.has_update_keys.zero_()
                # 重新获取所有可更新的索引
                not_updated_indices = torch.arange(len(self.has_update_keys), device=self.has_update_keys.device)
                num_update_keys = int(self.knowledge_num * 0.01)
                perm = torch.randperm(len(not_updated_indices))[:num_update_keys]
                pre_update_indices = not_updated_indices[perm]
                pre_update_tokens = self.knowledge_dataset[pre_update_indices]
                pre_update_embeddings = self.tok_embeddings(pre_update_tokens.view(-1))
                pre_update_embeddings = pre_update_embeddings.view(num_update_keys, self.knowledge_length, -1)
                self._update_keys_with_embeddings(pre_update_indices, pre_update_embeddings)
                # 更新被修改过的key
                with torch.no_grad():
                    self.has_update_keys[pre_update_indices] = 1
            


        
        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)

        # 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)

        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 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):
        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
        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

    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)
        if self.freeze_embedding and step == 0:
            self.tok_embeddings.weight.requires_grad = False
            # 同时冻结KnowledgeDataset的嵌入更新
            self.knowledge_dataset.freeze_embedding = True
            print("tok_embeddings.weight.requires_grad: ", self.tok_embeddings.weight.requires_grad)
            print("knowledge_dataset.freeze_embedding: ", self.knowledge_dataset.freeze_embedding)
        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
            )

        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

        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