add Chinese annotations pretrain

.gitignore

@@ -4,3 +4,5 @@ __pycache__/
 out
 website/
 docs-minimind/
+.idea/
+.vscode/

dataset/lm_dataset.py

@@ -39,6 +39,7 @@ class PretrainDataset(Dataset):
         super().__init__()
         self.tokenizer = tokenizer
         self.max_length = max_length
+        #load_dataset是HuggingFace datasets库上用来加载数据集的函数，可以加载HuggingFace Hub上的数据，也可以加载本地的json/csv/txt等文件
         self.samples = load_dataset('json', data_files=data_path, split='train')
 
     def __len__(self):

model/model_minimind.py

@@ -54,43 +54,76 @@ class RMSNorm(torch.nn.Module):
         self.weight = nn.Parameter(torch.ones(dim))
 
     def norm(self, x):
+        #rsqrt是sqrt的倒数，keepdim是保持维度不变，这里是把求平均的维度位置为1
         return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
 
     def forward(self, x):
+        #self.weight是一个可学习的参数
         return (self.weight * self.norm(x.float())).type_as(x)
 
 def precompute_freqs_cis(dim: int, end: int = int(32 * 1024), rope_base: float = 1e6, rope_scaling: dict = None):
     freqs, attn_factor = 1.0 / (rope_base ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)), 1.0
+
+    """
+    把原本只能支持较短上下文的RoPE，平滑地扩展到更长上下文
+    普通rope的频率是f，位置m的旋转角度是mf，如果上下文长度很长，m很大，角度会转的太快，模型泛化不好
+    YaRN的做法是对一部分频率进行缩放，让他们转的慢一点
+    一部分频率保持原样，保留短上下文能力
+    一部分频率平滑过渡
+    一部分频率缩小，用于支持长上下文
+    """
     if rope_scaling is not None: # YaRN: f'(i) = f(i)((1-γ) + γ/s), where γ∈[0,1] is linear ramp
+        """
+        orig_max是原模型训练时支持的最大位置长度
+        factor是上下文扩展倍数
+        beta_fast和beat_slow用来决定哪些维度的频率保持不变
+        attn_factir是乘在cos/sin上的attention的缩放因子
+        """
         orig_max, factor, beta_fast, beta_slow, attn_factor = (
             rope_scaling.get("original_max_position_embeddings", 2048), rope_scaling.get("factor", 16),
             rope_scaling.get("beta_fast", 32.0), rope_scaling.get("beta_slow", 1.0), rope_scaling.get("attention_factor", 1.0)
         )
+        #只有当你实际预计算的长度end超过原始最大长度时，才需要做rope scaling
         if end / orig_max > 1.0:
+            #inv_dim推理公式，用beta，也就是这里的b控制周期和原始上下文长度之间的关系
             inv_dim = lambda b: (dim * math.log(orig_max / (b * 2 * math.pi))) / (2 * math.log(rope_base))
+            #low，high是两个分界点
+            #0到low的区间，ramp=0，频率不变，low到high的区间，ramp从0平滑加到1，high到dim//2的区间，ramp=1，频率除以factor
             low, high = max(math.floor(inv_dim(beta_fast)), 0), min(math.ceil(inv_dim(beta_slow)), dim // 2 - 1)
+            #torch.clamp是裁剪或限制张量中元素的数值范围，torch.clamp(input,min,max),不满足范围内的用最大最小值代替
             ramp = torch.clamp((torch.arange(dim // 2, device=freqs.device).float() - low) / max(high - low, 0.001), 0, 1)
             freqs = freqs * (1 - ramp + ramp / factor)
     t = torch.arange(end, device=freqs.device)
+    #[end,dim//2]
     freqs = torch.outer(t, freqs).float()
+    #[end,dim]
     freqs_cos = torch.cat([torch.cos(freqs), torch.cos(freqs)], dim=-1) * attn_factor
     freqs_sin = torch.cat([torch.sin(freqs), torch.sin(freqs)], dim=-1) * attn_factor
     return freqs_cos, freqs_sin
 
 def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
+
+    # rope作用在qk上，传统的正弦位置编码一般加在token embedding上，不用作用在v上，因为注意力分数计算只用qk
+    #常见的qk shape是[baych,num_heads,seq_len,head_dim]要对头和seq_len做一个维度交换以适应硬件要求
+    #...表示省略前面所有的维度，前面的维度都按原样保留
     def rotate_half(x): return torch.cat((-x[..., x.shape[-1] // 2:], x[..., : x.shape[-1] // 2]), dim=-1)
     q_embed = ((q * cos.unsqueeze(unsqueeze_dim)) + (rotate_half(q) * sin.unsqueeze(unsqueeze_dim))).to(q.dtype)
     k_embed = ((k * cos.unsqueeze(unsqueeze_dim)) + (rotate_half(k) * sin.unsqueeze(unsqueeze_dim))).to(k.dtype)
     return q_embed, k_embed
 
 def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    用于GQA和MQA attention 把k/v heads复制成和query_heads一样多，方便后面做attention计算
+    """
     bs, slen, num_key_value_heads, head_dim = x.shape
     if n_rep == 1: return x
+    #None是在这里插入一个新维度，expand通常不真的复制维度，只是创建一个广播视图，所以更省内存
     return (x[:, :, :, None, :].expand(bs, slen, num_key_value_heads, n_rep, head_dim).reshape(bs, slen, num_key_value_heads * n_rep, head_dim))
 
 class Attention(nn.Module):
     def __init__(self, config: MiniMindConfig):
         super().__init__()
+        #qk用几个头
         self.num_key_value_heads = config.num_attention_heads if config.num_key_value_heads is None else config.num_key_value_heads
         self.n_local_heads = config.num_attention_heads
         self.n_local_kv_heads = self.num_key_value_heads
@@ -106,6 +139,7 @@ class Attention(nn.Module):
         self.attn_dropout = nn.Dropout(config.dropout)
         self.resid_dropout = nn.Dropout(config.dropout)
         self.dropout = config.dropout
+        #self.flash是布尔值
         self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and config.flash_attn
 
     def forward(self, x, position_embeddings, past_key_value=None, use_cache=False, attention_mask=None):
@@ -121,15 +155,25 @@ class Attention(nn.Module):
             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
+        #这段是在判断，当前情况能不能走Pytorch中内置的高效attention实现，scaled_dot_product_attention
         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) and (not self.is_causal or past_key_value is None) and (attention_mask is None or torch.all(attention_mask == 1)):
             output = F.scaled_dot_product_attention(xq, xk, xv, dropout_p=self.dropout if self.training else 0.0, is_causal=self.is_causal)
         else:
+            #@是矩阵乘法，*是逐元素相乘，两个张量形状必须相同
             scores = (xq @ xk.transpose(-2, -1)) / math.sqrt(self.head_dim)
+            #torch.full是生成一个形状指定，且所有元素都填充为同一个固定值的张量
+            #triu是pytorch中用于提取矩阵上三角的函数，diagonal参数=0时，保留主对角线及以上的元素，>0时保留主对角线上方的第N条对角线及以上的元素，<0时保留主对角线下方及以下的元素,其余用0替代
+            #triu只对张量的后两个维度进行操作
             if self.is_causal: scores[:, :, :, -seq_len:] += torch.full((seq_len, seq_len), float("-inf"), device=scores.device).triu(1)
+            #casual mask和attention_mask管的是两件事
+            #casual_mask是阻止当前位置看到未来token，attention_mask是告诉模型哪些是有效的，哪些token不应该看见
+            #attention_mask是在padding时忽略padding部分的注意力分数
             if attention_mask is not None: scores += (1.0 - attention_mask.unsqueeze(1).unsqueeze(2)) * -1e9
+            #对注意力分数做一个正则化
             output = self.attn_dropout(F.softmax(scores.float(), dim=-1).type_as(xq)) @ xv
         output = output.transpose(1, 2).reshape(bsz, seq_len, -1)
+        #还要对输出结果做一个正则化
         output = self.resid_dropout(self.o_proj(output))
         return output, past_kv
 
@@ -155,20 +199,34 @@ class MOEFeedForward(nn.Module):
 
     def forward(self, x):
         batch_size, seq_len, hidden_dim = x.shape
+        #[bs*seq_len,hidden_dim]
         x_flat = x.view(-1, hidden_dim)
+        #[bs*seq_len,num_experts]
         scores = F.softmax(self.gate(x_flat), dim=-1)
+        #[bs*seq_len,num_experts_per_tok]
         topk_weight, topk_idx = torch.topk(scores, k=self.config.num_experts_per_tok, dim=-1, sorted=False)
         if self.config.norm_topk_prob: topk_weight = topk_weight / (topk_weight.sum(dim=-1, keepdim=True) + 1e-20)
+        #[bs*seq_len,hidden_dim]
         y = torch.zeros_like(x_flat)
         for i, expert in enumerate(self.experts):
+            #[bs*seq_len,num_experts_per_tok]
             mask = (topk_idx == i)  
+            #any()用来判断是否至少有一个元素为真
             if mask.any():
+                #nonzero用来返回非零元素的索引，flatten将结果展成一维向量
+                #[num_selected_tokens] 
                 token_idx = mask.any(dim=-1).nonzero().flatten()
+                #[num_selected_tokens,1]，这里mask是一个和top_weight同维度的布尔矩阵，会把top_weight中和mask矩阵位置相同且为True的部分提取出来
                 weight = topk_weight[mask].view(-1, 1)
+                #x_flat[token_idx]->[num_selected_tokens,hidden_dim]
+                #y[token_idx] += expert_output，index_add_(dim,index,source)
                 y.index_add_(0, token_idx, (expert(x_flat[token_idx]) * weight).to(y.dtype))
             elif self.training:
+                #某个专家没参与计算，也把它挂到计算图上
                 y[0, 0] += 0 * sum(p.sum() for p in expert.parameters())
         if self.training and self.config.router_aux_loss_coef > 0:
+            #F.one_hot(topk_idx, self.config.num_experts)->[bs*seq_len,num_experts_per_tok,num_experts]，会把原来的每一个数字变成一组one-hot向量
+            #做mean(0)后，会在第一个维度做平均，[num_experts_per_tok,num_experts]
             load = F.one_hot(topk_idx, self.config.num_experts).float().mean(0)
             self.aux_loss = (load * scores.mean(0)).sum() * self.config.num_experts * self.config.router_aux_loss_coef
         else:
@@ -202,6 +260,7 @@ class MiniMindModel(nn.Module):
         self.dropout = nn.Dropout(config.dropout)
         self.layers = nn.ModuleList([MiniMindBlock(l, config) for l in range(self.num_hidden_layers)])
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        #[max_position_embeddings,head_dim]
         freqs_cos, freqs_sin = precompute_freqs_cis(dim=config.head_dim, end=config.max_position_embeddings, rope_base=config.rope_theta, rope_scaling=config.rope_scaling)
         self.register_buffer("freqs_cos", freqs_cos, persistent=False)
         self.register_buffer("freqs_sin", freqs_sin, persistent=False)
@@ -228,6 +287,7 @@ class MiniMindModel(nn.Module):
             )
             presents.append(present)
         hidden_states = self.norm(hidden_states)
+        #sum方法中第二个参数为初始值，new_zeros创建与当前张量同类型，同设备的新零张量。里面的参数是形状，设置一个初值0
         aux_loss = sum([l.mlp.aux_loss for l in self.layers if isinstance(l.mlp, MOEFeedForward)], hidden_states.new_zeros(1).squeeze())
         return hidden_states, presents, aux_loss
 
@@ -239,7 +299,9 @@ class MiniMindForCausalLM(PreTrainedModel, GenerationMixin):
         super().__init__(self.config)
         self.model = MiniMindModel(self.config)
         self.lm_head = nn.Linear(self.config.hidden_size, self.config.vocab_size, bias=False)
+        #nn.Embedding的权重矩阵存储是(input_dim,output_dim),nn.Linear的权重矩阵存储是转秩过后的
         if self.config.tie_word_embeddings: self.model.embed_tokens.weight = self.lm_head.weight
+        #在模型所有子模块创建完成后，执行Huggingface规定的后处理初始化逻辑
         self.post_init()
 
     def forward(self, input_ids, attention_mask=None, past_key_values=None, use_cache=False, logits_to_keep=0, labels=None, **kwargs):
@@ -250,11 +312,14 @@ class MiniMindForCausalLM(PreTrainedModel, GenerationMixin):
         if labels is not None:
             x, y = logits[..., :-1, :].contiguous(), labels[..., 1:].contiguous()
             loss = F.cross_entropy(x.view(-1, x.size(-1)), y.view(-1), ignore_index=-100)
+        #把loss，aux_loss，logits等封装成一个标准输出对象，方便训练，推理,以后可以用字典的方式访问
         return MoeCausalLMOutputWithPast(loss=loss, aux_loss=aux_loss, logits=logits, past_key_values=past_key_values, hidden_states=hidden_states)
     
     # https://github.com/jingyaogong/minimind/discussions/611
+    #表示这个函数在推理模式下运行，不计算梯度
     @torch.inference_mode()
     def generate(self, inputs=None, attention_mask=None, max_new_tokens=8192, temperature=0.85, top_p=0.85, top_k=50, eos_token_id=2, streamer=None, use_cache=True, num_return_sequences=1, do_sample=True, repetition_penalty=1.0, **kwargs):
+        #这里的repeat是向量复制，这里是在第0维复制num_return_sequences次，第1维复制1次，也就是不变
         input_ids = kwargs.pop("input_ids", inputs).repeat(num_return_sequences, 1)
         attention_mask = attention_mask.repeat(num_return_sequences, 1) if attention_mask is not None else None
         past_key_values = kwargs.pop("past_key_values", None)
@@ -265,17 +330,21 @@ class MiniMindForCausalLM(PreTrainedModel, GenerationMixin):
             outputs = self.forward(input_ids[:, past_len:], attention_mask, past_key_values, use_cache=use_cache, **kwargs)
             attention_mask = torch.cat([attention_mask, attention_mask.new_ones(attention_mask.shape[0], 1)], -1) if attention_mask is not None else None
             logits = outputs.logits[:, -1, :] / temperature
+            #重复惩罚，如果某个token已经在当前生成序列里出现过，就降低它下一次再次被选中的概率
             if repetition_penalty != 1.0:
+                #torch.unique是去重，给张量
                 for i in range(input_ids.shape[0]):
                     seen = torch.unique(input_ids[i]); score = logits[i, seen]; logits[i, seen] = torch.where(score > 0, score / repetition_penalty, score * repetition_penalty)
             if top_k > 0: 
                 logits[logits < torch.topk(logits, top_k)[0][..., -1, None]] = -float('inf')
+            #按概率从高到低，只保留累计概率达到top_P之前的token
             if top_p < 1.0:
                 sorted_logits, sorted_indices = torch.sort(logits, descending=True)
                 mask = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1) > top_p
                 mask[..., 1:], mask[..., 0] = mask[..., :-1].clone(), 0
                 logits[mask.scatter(1, sorted_indices, mask)] = -float('inf')
-            next_token = torch.multinomial(torch.softmax(logits, dim=-1), num_samples=1) if do_sample else torch.argmax(logits, dim=-1, keepdim=True)
+            next_token = torch.multinomial(torch.softmax(logits, dim=-1),num_samples=1) if do_sample else torch.argmax(logits, dim=-1, keepdim=True)
+            #torch.where是pytorch的条件选择函数，类似三元运算符torch.where(condition,x,y)
             if eos_token_id is not None: next_token = torch.where(finished.unsqueeze(-1), next_token.new_full((next_token.shape[0], 1), eos_token_id), next_token)
             input_ids = torch.cat([input_ids, next_token], dim=-1)
             past_key_values = outputs.past_key_values if use_cache else None

trainer/train_pretrain.py

@@ -35,16 +35,17 @@ def train_epoch(epoch, loader, iters, start_step=0, wandb=None):
             res = model(input_ids, labels=labels)
             loss = res.loss + res.aux_loss
             loss = loss / args.accumulation_steps
-
+        #将loss放大后再反向传播，防止fp16下梯度下溢
         scaler.scale(loss).backward()
 
         if step % args.accumulation_steps == 0:
             scaler.unscale_(optimizer)
             torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
-
+            #step是让优化器更新参数，同时更新优化器内部状态
             scaler.step(optimizer)
+            #动态调整scale值，如果scale太大，loss会出现上溢，scale太小会发生下溢
             scaler.update()
-
+            #把梯度全部设置为None，不用保留全0梯度的tensor，可以节省内存和加速下一次反向传播
             optimizer.zero_grad(set_to_none=True)
 
         if step % args.log_interval == 0 or step == iters:
@@ -64,13 +65,14 @@ def train_epoch(epoch, loader, iters, start_step=0, wandb=None):
             raw_model = model.module if isinstance(model, DistributedDataParallel) else model
             raw_model = getattr(raw_model, '_orig_mod', raw_model)
             state_dict = raw_model.state_dict()
+            #转成fp16，减小文件大小，转到cpu，避免保存gpu tensor
             torch.save({k: v.half().cpu() for k, v in state_dict.items()}, ckp)
             lm_checkpoint(lm_config, weight=args.save_weight, model=model, optimizer=optimizer, scaler=scaler, epoch=epoch, step=step, wandb=wandb, save_dir='../checkpoints')
             model.train()
             del state_dict
 
         del input_ids, labels, res, loss
-
+    #处理还没更新的梯度
     if last_step > start_step and last_step % args.accumulation_steps != 0:
         scaler.unscale_(optimizer)
         torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
@@ -118,6 +120,7 @@ if __name__ == "__main__":
     # ========== 3. 设置混合精度 ==========
     device_type = "cuda" if "cuda" in args.device else "cpu"
     dtype = torch.bfloat16 if args.dtype == "bfloat16" else torch.float16
+    #根据设备自动选择是否转半精度。cuda自动混精度加速
     autocast_ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast(dtype=dtype)
     
     # ========== 4. 配wandb ==========
@@ -127,12 +130,17 @@ if __name__ == "__main__":
         wandb_id = ckp_data.get('wandb_id') if ckp_data else None
         resume = 'must' if wandb_id else None
         wandb_run_name = f"MiniMind-Pretrain-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LearningRate-{args.learning_rate}"
+        #project是实验的项目名称，id是指定或恢复一个实验运行的唯一标识符，resume是当提供的id存在时是否继续运行（'must'表示必须继续，None表示不继续）。如果提供了wandb_id并且resume设置为'must'，则会尝试恢复之前的运行，否则会创建一个新的运行。
+        #id等于none时会认为正在启动一个全新的实验运行
         wandb.init(project=args.wandb_project, name=wandb_run_name, id=wandb_id, resume=resume)
     
     # ========== 5. 定义模型、数据、优化器 ==========
     model, tokenizer = init_model(lm_config, args.from_weight, device=args.device)
     train_ds = PretrainDataset(args.data_path, tokenizer, max_length=args.max_seq_len)
+    #当处于分布式环境时，实例化DistributedSampler会将数据集切分成若干份，确保每个gpu只读取它自己那一部分数据
     train_sampler = DistributedSampler(train_ds) if dist.is_initialized() else None
+    #梯度缩放器，在用fp16计算时，可能会出现梯度下溢现象，GradScaler会将loss乘一个很大的数，从而放大梯度，在优化器更新权重之前，再把放大的梯度恢复到原本的大小
+    #当dtype=float16时启动
     scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype == 'float16'))
     optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate)
     
@@ -147,6 +155,7 @@ if __name__ == "__main__":
     
     # ========== 7. 编译和分布式包装 ==========
     if args.use_compile == 1:
+        #这行代码会对模型进行编译，生成一个优化过的版本，优化过程包括算子融合、生成更高效的底层代码，让模型跑的更快
         model = torch.compile(model)
         Logger('torch.compile enabled')
     if dist.is_initialized():
@@ -154,10 +163,17 @@ if __name__ == "__main__":
     
     # ========== 8. 开始训练 ==========
     for epoch in range(start_epoch, args.epochs):
+        #set_epoch是在每个epoch重新设置数据集的采样顺序，会根据epoch的索引，重新设置随机数种子
+        #使每个epoch产生不同的随机顺序，从而确保每个epoch用不同的样本顺序进行训练
         train_sampler and train_sampler.set_epoch(epoch)
+        #代码中的分号；是python中的语法特性，可以用来在同一行中执行多个语句
+        #torch.randperm是在生成一个随机打乱后的数据索引列表，会生成从0到len(train_ds)的随机排列，然后再转为list
         setup_seed(42 + epoch); indices = torch.randperm(len(train_ds)).tolist()
         skip = start_step if (epoch == start_epoch and start_step > 0) else 0
+        #dataset负责给它一个idx，返回一条数据，Sampler负责决定按上面顺序给dataset提供idx，所以sampler本质上就是一个索引生成器
         batch_sampler = SkipBatchSampler(train_sampler or indices, args.batch_size, skip)
+        #这里传的是batch_sampler，dataloader不再自己决定batch怎么组成，而是听sampler的
+        #根据sampler提供的索引来取数据
         loader = DataLoader(train_ds, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True)
         if skip > 0: 
             Logger(f'Epoch [{epoch + 1}/{args.epochs}]: 跳过前{start_step}个step，从step {start_step + 1}开始')

trainer/train_tokenizer.py

@@ -44,6 +44,7 @@ def train_tokenizer(data_path, tokenizer_dir, vocab_size, special_tokens_num=SPE
         vocab_size=vocab_size,
         show_progress=True,
         initial_alphabet=pre_tokenizers.ByteLevel.alphabet(),
+        #BpeTrainer会优先给special_token分配id，而且一般是按special_tokens列表顺序分配
         special_tokens=all_special_tokens
     )
     texts = get_texts(data_path)
@@ -68,10 +69,15 @@ def train_tokenizer(data_path, tokenizer_dir, vocab_size, special_tokens_num=SPE
         idx = tokenizer.token_to_id(token)
         added_tokens_decoder[str(idx)] = {
             "content": token,
+            #lstrip表示匹配这个token时，是否把它左边的空白字符一起吃掉
             "lstrip": False,
+            #表示这个token按原始字符串精确匹配，不被normalizer改动
             "normalized": False,
+            #是否把右边的字符吃掉
             "rstrip": False,
+            #这个token是否只能作为一个完整单词匹配
             "single_word": False,
+            #被tokenizer的special token逻辑特殊处理
             "special": True if token in special_tokens_list else False
         }
 

trainer/trainer_utils.py

@@ -17,6 +17,7 @@ from model.model_minimind import MiniMindForCausalLM
 
 def get_model_params(model, config):
     total = sum(p.numel() for p in model.parameters()) / 1e6
+    #getattr用于动态获取对象的属性或方法
     n_routed = getattr(config, 'n_routed_experts', getattr(config, 'num_experts', 0))
     n_active = getattr(config, 'num_experts_per_tok', 0)
     n_shared = getattr(config, 'n_shared_experts', 0)
@@ -52,13 +53,13 @@ def init_distributed_mode():
 
 
 def setup_seed(seed: int):
-    random.seed(seed)
+    random.seed(seed)#设置python的随机种子
-    np.random.seed(seed)
+    np.random.seed(seed)#设置numpy的随机种子
-    torch.manual_seed(seed)
+    torch.manual_seed(seed)#设置cpu随机种子
-    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed(seed)#设置pytorch在当前GPU上的随机种子
-    torch.cuda.manual_seed_all(seed)
+    torch.cuda.manual_seed_all(seed)#设置pytorch在所有GPU上的随机种子
-    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.deterministic = True#使用确定性算法
-    torch.backends.cudnn.benchmark = False
+    torch.backends.cudnn.benchmark = False#禁用cudnn的自动优化
 
 def lm_checkpoint(lm_config, weight='full_sft', model=None, optimizer=None, epoch=0, step=0, wandb=None, save_dir='../checkpoints', **kwargs):
     os.makedirs(save_dir, exist_ok=True)
@@ -66,13 +67,16 @@ def lm_checkpoint(lm_config, weight='full_sft', model=None, optimizer=None, epoc
     ckp_path = f'{save_dir}/{weight}_{lm_config.hidden_size}{moe_path}.pth'
     resume_path = f'{save_dir}/{weight}_{lm_config.hidden_size}{moe_path}_resume.pth'
 
-    if model is not None:
+    if model is not None:#存储模型模式
+        #如果模型被DDP包了，真正的模型在model.module里
         raw_model = model.module if isinstance(model, DistributedDataParallel) else model
         raw_model = getattr(raw_model, '_orig_mod', raw_model)
         state_dict = raw_model.state_dict()
+        #half表示转成float16，然后移到cpu上
         state_dict = {k: v.half().cpu() for k, v in state_dict.items()}
         ckp_tmp = ckp_path + '.tmp'
         torch.save(state_dict, ckp_tmp)
+        #os.replace用于将一个文件替换成另一个文件 os.replace(src, dst) 将 src 文件替换为 dst 文件，如果 dst 已经存在，则会被替换掉。这是一个原子操作，确保在替换过程中不会出现文件损坏或丢失的情况。
         os.replace(ckp_tmp, ckp_path)
         wandb_id = None
         if wandb:
@@ -87,6 +91,7 @@ def lm_checkpoint(lm_config, weight='full_sft', model=None, optimizer=None, epoc
             'optimizer': optimizer.state_dict(),
             'epoch': epoch,
             'step': step,
+            #训练时用了几张GPU
             'world_size': dist.get_world_size() if dist.is_initialized() else 1,
             'wandb_id': wandb_id
         }
@@ -102,10 +107,12 @@ def lm_checkpoint(lm_config, weight='full_sft', model=None, optimizer=None, epoc
         resume_tmp = resume_path + '.tmp'
         torch.save(resume_data, resume_tmp)
         os.replace(resume_tmp, resume_path)
+        #删除临时变量，然后清理cuda缓存
         del state_dict, resume_data
         torch.cuda.empty_cache()
     else:  # 加载模式
         if os.path.exists(resume_path):
+            #加载到gpu
             ckp_data = torch.load(resume_path, map_location='cpu')
             saved_ws = ckp_data.get('world_size', 1)
             current_ws = dist.get_world_size() if dist.is_initialized() else 1