import math
import struct
import inspect
import time
#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
# 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)
return all_best_tokens, all_best_tokens_embeddings
with torch.no_grad():
# 1. 计算token序列的平均嵌入
pre_update_embeddings = pre_update_embeddings.mean(dim=1) # [num_indices, dim]
# 2. 转换维度
pre_update_embeddings = self.to_queries(pre_update_embeddings) # [num_indices, knowledge_dim]
# 3. 将one-hot索引转换为子空间索引
indices_x = pre_update_indices // self.num_keys
indices_y = pre_update_indices % self.num_keys
# 4. 收集需要更新的唯一子键
unique_x = torch.unique(indices_x)
unique_y = torch.unique(indices_y)
# 5. 更新第一个子空间的键
for k1 in unique_x:
# 找出所有使用该子键的索引
mask_k1 = (indices_x == k1)
if mask_k1.sum() == 0:
continue
# 获取所有相关嵌入并计算平均值
k1_embeddings = pre_update_embeddings[mask_k1]
k1_avg_embedding = k1_embeddings.mean(dim=0)
# 拆分为两个子空间并更新第一个子空间
self.keys[k1, 0] = k1_avg_embedding[:self.key_dim]
# 6. 更新第二个子空间的键
for k2 in unique_y:
# 找出所有使用该子键的索引
mask_k2 = (indices_y == k2)
if mask_k2.sum() == 0:
continue
# 获取所有相关嵌入并计算平均值
k2_embeddings = pre_update_embeddings[mask_k2]
k2_avg_embedding = k2_embeddings.mean(dim=0)
# 更新第二个子空间
self.keys[k2, 1] = k2_avg_embedding[self.key_dim:]
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)
# 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
# 移除对knowledge_dataset.freeze_embedding的设置，让键更新由batch_counter控制
# self.knowledge_dataset.freeze_embedding = True
print("tok_embeddings.weight.requires_grad: ", self.tok_embeddings.weight.requires_grad)
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