diff --git a/config/ds_block_base.sh b/config/ds_block_base.sh
index 93ea290..441779e 100644
--- a/config/ds_block_base.sh
+++ b/config/ds_block_base.sh
@@ -18,7 +18,7 @@ gpt_options=" \
        --train-iters 150000 \
        --resume-dataloader \
        --train-data bert-base \
-       --lazy-loader \
+       --no-lazy-loader \
        --tokenizer-type BertWordPieceTokenizer \
        --tokenizer-model-type bert-base-uncased \
        --split 949,50,1 \
diff --git a/config/ds_block_tiny.sh b/config/ds_block_tiny.sh
new file mode 100644
index 0000000..baf1dec
--- /dev/null
+++ b/config/ds_block_tiny.sh
@@ -0,0 +1,37 @@
+#! /bin/bash
+
+script_path=$(realpath $BASH_SOURCE)
+script_dir=$(dirname $script_path)
+
+config_json="$script_dir/config_block_base.json"
+gpt_options=" \
+       --block-lm \
+       --bert-prob 1.0 \
+       --experiment-name blocklm-blank \
+       --model-parallel-size ${MP_SIZE} \
+       --num-layers 4 \
+       --hidden-size 768 \
+       --num-attention-heads 12 \
+       --seq-length 512 \
+       --max-position-embeddings 512 \
+       --save /root/data/checkpoints \
+       --train-iters 150000 \
+       --resume-dataloader \
+       --train-data test \
+       --no-lazy-loader \
+       --tokenizer-type BertWordPieceTokenizer \
+       --tokenizer-model-type bert-base-uncased \
+       --split 949,50,1 \
+       --distributed-backend nccl \
+       --lr-decay-style cosine \
+       --lr-decay-iters 120000 \
+       --lr-decay-ratio 0.05 \
+       --warmup .05 \
+       --checkpoint-activations \
+       --deepspeed-activation-checkpointing \
+       --fp16 \
+"
+gpt_options="${gpt_options}
+               --deepspeed \
+               --deepspeed_config ${config_json} \
+"
diff --git a/data_utils/corpora.py b/data_utils/corpora.py
index 4374946..defd6b7 100755
--- a/data_utils/corpora.py
+++ b/data_utils/corpora.py
@@ -97,7 +97,16 @@ class DataReader:
     TASK_QUEUE_LIMIT = 10000000
     DONE_QUEUE_LIMIT = 10000000
 
-    def tokenize_worker(self, input, output, info, tokenizer, tokenize):
+    def tokenize_worker(self, q_in: Queue, q_out: Queue, q_info: Queue, tokenizer, tokenize: bool):
+        """
+        此函数为多进程的主入口
+        Args:
+            q_in: 输入队列，一个 item 为一行数据
+            q_out: 结果队列，item 同上
+            q_info: 日志队列，一个 item 为一条日志
+            tokenizer: 来自 configure_data.prepare_tokenizer 的返回值，class 为 data_utils.tokenization.Tokenizer
+            tokenize: 来自 args 的 --no-pre-tokenize
+        """
         raise NotImplementedError
 
     def print_info(self, info):
@@ -111,6 +120,9 @@ def __init__(self, writers, tokenizer=None, tokenize=False, **kwargs):
         self.writers = writers
 
     def process(self):
+        """
+        采用生产者、消费者的逻辑，将数据文件按照条进行拆分，详细的逻辑在下面
+        """
         if os.path.isdir(self.PATH):
             paths = [os.path.join(top, name) for top, _, names in os.walk(self.PATH) for name in names]
             # paths = [entry.path for entry in os.scandir(self.PATH) if
@@ -120,6 +132,7 @@ def process(self):
         task_queue, done_queue, info_queue = Queue(maxsize=self.TASK_QUEUE_LIMIT), Queue(
             maxsize=self.DONE_QUEUE_LIMIT), Queue()
         processes = []
+        # 定义消费者
         for i in range(NUM_PROCESSES):
             process = Process(target=self.tokenize_worker,
                               args=(task_queue, done_queue, info_queue, self.tokenizer, self.tokenize))
@@ -141,11 +154,13 @@ def read_input_to_queue():
             for i in range(len(processes)):
                 task_queue.put('STOP')
 
+        # 定义生产者
         process = Process(target=read_input_to_queue)
         process.start()
         count = len(processes)
         progress_bar = tqdm.tqdm()
         while True:
+            # 将消费者处理好的数据写至 writer
             data = done_queue.get()
             if data == 'COMPLETE':
                 count -= 1
@@ -187,16 +202,20 @@ def process_line(self, data, tokenizer, tokenize):
 class PromptReader(DataReader):
     is_json = True
 
-    def tokenize_worker(self, input, output, info, tokenizer, tokenize):
-        for row in iter(input.get, 'STOP'):
+    def tokenize_worker(self, q_in, q_out, q_info, tokenizer, tokenize):
+        """
+        对于 PromptReader 这个类来说，每行为一个 JSON String
+        """
+        for row in iter(q_in.get, 'STOP'):
             if row:
                 if self.is_json:
                     row = row.rstrip()
                     row = json.loads(row)
+                # 处理每行数据，需要子类实现
                 prompts, texts = self.process_line(row, tokenizer, tokenize)
                 for prompt, text in zip(prompts, texts):
-                    output.put((prompt, text))
-        output.put("COMPLETE")
+                    q_out.put((prompt, text))
+        q_out.put("COMPLETE")
 
     @staticmethod
     def write_result(data, writers):
@@ -230,12 +249,12 @@ def process_line(self, data, tokenizer, tokenize):
             text_mask.append(len(content))
         return (summary, summary_mask), (text, text_mask)
 
-    def tokenize_worker(self, input, output, info, tokenizer, tokenize):
-        for row in iter(input.get, 'STOP'):
+    def tokenize_worker(self, q_in, q_out, q_info, tokenizer, tokenize):
+        for row in iter(q_in.get, 'STOP'):
             data = json.loads(row)
             summary, content = self.process_line(data, tokenizer, tokenize)
-            output.put((summary, content))
-        output.put("COMPLETE")
+            q_out.put((summary, content))
+        q_out.put("COMPLETE")
 
     @staticmethod
     def write_result(data, writers):
@@ -349,6 +368,11 @@ def process_line(self, data, tokenizer, tokenize):
 
 
 class TestDataset(PromptReader):
+    """
+    当 Dataset 有 2 条数据时，/root/data/test.json 的内容应该形似
+    {"prompt":"", "text":""}
+    {"prompt":"", "text":""}
+    """
     PATH = '/root/data/test.json'
     assert_str = "make sure to set PATH for wikipedia data_utils/corpora.py"
 
@@ -433,9 +457,9 @@ def print_info(self, info):
                 break
         print_rank_0(total_dict)
 
-    def tokenize_worker(self, input, output, info, tokenizer, tokenize):
+    def tokenize_worker(self, q_in, q_out, q_info, tokenizer, tokenize):
         source_dict = defaultdict(int)
-        for row in iter(input.get, 'STOP'):
+        for row in iter(q_in.get, 'STOP'):
             row = row.rstrip()
             if row:
                 if self.is_json:
@@ -444,11 +468,11 @@ def tokenize_worker(self, input, output, info, tokenizer, tokenize):
                 length = 0
                 for prompt, text in zip(prompts, texts):
                     length += len(text)
-                    output.put((prompt, text))
+                    q_out.put((prompt, text))
                 if source:
                     source_dict[source] += length
-        output.put("COMPLETE")
-        info.put(source_dict)
+        q_out.put("COMPLETE")
+        q_info.put(source_dict)
 
     def process_line(self, data, tokenizer, tokenize):
         source = data["meta"].get("pile_set_name", None)
@@ -511,6 +535,7 @@ def process_line(self, item, tokenizer, tokenize):
         return prompts, texts
 
 
+# 数据集的字典
 NAMED_CORPORA = {
     'wikipedia': wikipedia,
     'wikipedia-key': KeyReader,
diff --git a/scripts/ds_pretrain_nvidia.sh b/scripts/ds_pretrain_nvidia.sh
index eac912f..29820e1 100644
--- a/scripts/ds_pretrain_nvidia.sh
+++ b/scripts/ds_pretrain_nvidia.sh
@@ -13,7 +13,7 @@ DATESTR=$(date +"%m-%d-%H-%M")
 OPTIONS_NCCL="NCCL_DEBUG=info NCCL_IB_DISABLE=0 NCCL_NET_GDR_LEVEL=2"
 HOST_FILE_PATH="/workspace/hostfile"
 
-mkdir logs
+mkdir -p logs
 run_cmd="${OPTIONS_NCCL} deepspeed --master_port ${MASTER_PORT} --num_nodes ${NUM_WORKERS} --num_gpus ${NUM_GPUS_PER_WORKER} --hostfile ${HOST_FILE_PATH} pretrain_glm.py ${gpt_options} 2>&1 | tee logs/log-${DATESTR}.txt"
 echo ${run_cmd}
 eval ${run_cmd}