[pre-commit.ci] auto fixes from pre-commit.com hooks

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applications/ColossalChat/coati/dataset/conversation.py

@@ -25,7 +25,9 @@ def from_config(cls, tokenizer: PreTrainedTokenizer, config: Dict):
         Setup the conversation template from config
         """
         tokenizer.chat_template = config["chat_template"]
-        conv = cls(tokenizer, config["system_message"], config["chat_template"], config["stop_ids"], config["end_of_assistant"])
+        conv = cls(
+            tokenizer, config["system_message"], config["chat_template"], config["stop_ids"], config["end_of_assistant"]
+        )
         conv.clear()
         return conv
 

applications/ColossalChat/coati/dataset/tokenization_utils.py

@@ -97,16 +97,17 @@ def supervised_tokenize_sft(
 
     target_turn = turns[target_turn_index - 1]
     prompt = template.get_prompt(2 * target_turn)
-    chunks, require_loss = split_templated_prompt_into_chunks(template.messages[: 2 * target_turn], prompt, 
-        conversation_template.end_of_assistant)
+    chunks, require_loss = split_templated_prompt_into_chunks(
+        template.messages[: 2 * target_turn], prompt, conversation_template.end_of_assistant
+    )
     tokenized, starts, ends = tokenize_and_concatenate(tokenizer, chunks, require_loss)
 
     labels = [ignore_index] * len(tokenized)
     for start, end in zip(starts, ends):
         if end == len(tokenized):
             tokenized = tokenized + [tokenizer.eos_token_id]
             labels = labels + [ignore_index]
-        labels[start : end] = tokenized[start : end]
+        labels[start:end] = tokenized[start:end]
 
     # truncate the sequence at the last token that requires loss calculation
     to_truncate_len = 0
@@ -139,14 +140,14 @@ def supervised_tokenize_sft(
         label_decode = []
         for i in range(len(labels)):
             if labels[i] == ignore_index:
-                if start!=end:
-                    label_decode.append(tokenizer.decode(labels[start+1:i], skip_special_tokens=False))
+                if start != end:
+                    label_decode.append(tokenizer.decode(labels[start + 1 : i], skip_special_tokens=False))
                 start = i
                 end = i
             else:
                 end = i
                 if i == len(labels) - 1:
-                    label_decode.append(tokenizer.decode(labels[start+1:], skip_special_tokens=False))
+                    label_decode.append(tokenizer.decode(labels[start + 1 :], skip_special_tokens=False))
 
     except TypeError as e:
         raise TypeError(str(e) + f"\nUnable to decode input_ids: {tokenized}")
@@ -216,8 +217,9 @@ def tokenize_prompt_dataset(
 
     # Prepare data
     prompt = template.get_prompt(target_turn, add_generation_prompt=True)
-    chunks, require_loss = split_templated_prompt_into_chunks(template.messages[: target_turn], prompt, 
-        conversation_template.end_of_assistant)
+    chunks, require_loss = split_templated_prompt_into_chunks(
+        template.messages[:target_turn], prompt, conversation_template.end_of_assistant
+    )
     tokenized, starts, ends = tokenize_and_concatenate(tokenizer, chunks, require_loss)
     if tokenizer.bos_token_id is not None:
         if tokenized[0] != tokenizer.bos_token_id:
@@ -246,8 +248,9 @@ def apply_rlhf_data_format(
 ):
     target_turn = int(len(template.messages) / 2)
     prompt = template.get_prompt(target_turn * 2)
-    chunks, require_loss = split_templated_prompt_into_chunks(template.messages[: 2 * target_turn], prompt, 
-        template.end_of_assistant)
+    chunks, require_loss = split_templated_prompt_into_chunks(
+        template.messages[: 2 * target_turn], prompt, template.end_of_assistant
+    )
     tokenized, starts, ends = tokenize_and_concatenate(tokenizer, chunks, require_loss)
     loss_mask = [0] * len(tokenized)
     mask_token = tokenizer.eos_token_id or tokenizer.pad_token_id
@@ -260,8 +263,8 @@ def apply_rlhf_data_format(
         if end == len(tokenized):
             tokenized = tokenized + [tokenizer.eos_token_id]
             loss_mask = loss_mask + [1]
-        loss_mask[start : end] = [1] * len(loss_mask[start : end])
-        label_decode.append(tokenizer.decode(tokenized[start : end], skip_special_tokens=False))
+        loss_mask[start:end] = [1] * len(loss_mask[start:end])
+        label_decode.append(tokenizer.decode(tokenized[start:end], skip_special_tokens=False))
     if tokenizer.bos_token_id is not None:
         if tokenized[0] != tokenizer.bos_token_id:
             tokenized = [tokenizer.bos_token_id] + tokenized

applications/ColossalChat/coati/dataset/utils.py

@@ -121,8 +121,10 @@ def split_templated_prompt_into_chunks(messages: List[Dict[str, str]], prompt: s
     for line in messages:
         content_length = len(line["content"])
         first_occur = prompt.find(line["content"], start_idx)
-        if line["role"].lower() == "assistant" and end_of_assistant in prompt[first_occur + content_length:]:
-            content_length = prompt.find(end_of_assistant, first_occur + content_length) + len(end_of_assistant) - first_occur
+        if line["role"].lower() == "assistant" and end_of_assistant in prompt[first_occur + content_length :]:
+            content_length = (
+                prompt.find(end_of_assistant, first_occur + content_length) + len(end_of_assistant) - first_occur
+            )
         if prompt[first_occur - 1] != " ":
             chunks.append(prompt[start_idx:first_occur])
             chunks.append(prompt[first_occur : first_occur + content_length])

applications/ColossalChat/coati/models/critic.py

@@ -37,4 +37,4 @@ def get_input_embeddings(self):
         return self.model.get_input_embeddings()
 
     def get_output_embeddings(self):
-        return self.model.get_output_embeddings()
+        return self.model.get_output_embeddings()

applications/ColossalChat/config/conversation_template/01-ai_Yi-1.5-9B-Chat.json

@@ -5,4 +5,4 @@
         7
     ],
     "end_of_assistant": "<|im_end|>"
-}
+}

applications/ColossalChat/config/conversation_template/Qwen_Qwen1.5-110B-Chat.json

@@ -6,4 +6,4 @@
         151643
     ],
     "end_of_assistant": "<|im_end|>"
-}
+}

applications/ColossalChat/config/conversation_template/THUDM_chatglm2-6b.json

@@ -5,4 +5,4 @@
         2
     ],
     "end_of_assistant": "<|im_end|>"
-}
+}

applications/ColossalChat/config/conversation_template/THUDM_chatglm3-6b.json

@@ -5,4 +5,4 @@
         2
     ],
     "end_of_assistant": "<|user|>"
-}
+}

applications/ColossalChat/config/conversation_template/baichuan-inc_Baichuan2-13B-Chat.json

@@ -5,4 +5,4 @@
         2
     ],
     "end_of_assistant": "<|im_end|>"
-}
+}

applications/ColossalChat/config/conversation_template/colossal-llama2.json

@@ -5,4 +5,4 @@
         2
     ],
     "end_of_assistant": "</s>"
-}
+}

applications/ColossalChat/config/conversation_template/deepseek-ai_DeepSeek-V2-Lite.json

@@ -5,4 +5,4 @@
         100001
     ],
     "end_of_assistant": "<｜end▁of▁sentence｜>"
-}
+}

applications/ColossalChat/config/conversation_template/llama2.json

@@ -5,4 +5,4 @@
         2
     ],
     "end_of_assistant": "</s>"
-}
+}

applications/ColossalChat/config/conversation_template/microsoft_phi-2.json

@@ -5,4 +5,4 @@
         50256
     ],
     "end_of_assistant": "<|im_end|>"
-}
+}

applications/ColossalChat/config/conversation_template/mistralai_Mixtral-8x7B-Instruct-v0.1.json

@@ -5,4 +5,4 @@
         2
     ],
     "end_of_assistant": "</s>"
-}
+}

applications/ColossalChat/examples/data_preparation_scripts/prepare_dataset.py

@@ -226,7 +226,7 @@ def main():
                 "max_length": args.max_length,
             },
             keep_in_memory=False,
-            num_proc= min(len(dataset), cpu_count()),
+            num_proc=min(len(dataset), cpu_count()),
         )
 
         dataset = dataset.filter(

applications/ColossalChat/tests/llama.json

@@ -6,4 +6,4 @@
         2
     ],
     "end_of_assistant": "</s>"
-}
+}

applications/ColossalChat/tests/test_chat_template.py

@@ -1,36 +1,41 @@
+import json
+import os
+
 from coati.dataset import setup_conversation_template
-from coati.dataset.conversation import Conversation
 from coati.dataset.tokenization_utils import supervised_tokenize_sft
 from transformers import AutoTokenizer
-import json
-import os
 
 model_data_mapping = {
-    'THUDM/chatglm2-6b': 'THUDM_chatglm2-6b.json', 
-    'THUDM/chatglm3-6b': 'THUDM_chatglm3-6b.json',
-    'baichuan-inc/Baichuan2-13B-Chat': 'baichuan-inc_Baichuan2-13B-Chat.json',
-    '01-ai/Yi-1.5-9B-Chat': '01-ai_Yi-1.5-9B-Chat.json',
-    '01-ai/Yi-34B': '01-ai_Yi-34B.json',
-    'deepseek-ai/DeepSeek-V2-Lite': 'deepseek-ai_DeepSeek-V2-Lite.json',
-    'microsoft/phi-2': 'microsoft_phi-2.json',
-    'mistralai/Mixtral-8x7B-Instruct-v0.1': 'mistralai_Mixtral-8x7B-Instruct-v0.1.json'
-} 
-chat_template_config_path = './config/conversation_template'
+    "THUDM/chatglm2-6b": "THUDM_chatglm2-6b.json",
+    "THUDM/chatglm3-6b": "THUDM_chatglm3-6b.json",
+    "baichuan-inc/Baichuan2-13B-Chat": "baichuan-inc_Baichuan2-13B-Chat.json",
+    "01-ai/Yi-1.5-9B-Chat": "01-ai_Yi-1.5-9B-Chat.json",
+    "01-ai/Yi-34B": "01-ai_Yi-34B.json",
+    "deepseek-ai/DeepSeek-V2-Lite": "deepseek-ai_DeepSeek-V2-Lite.json",
+    "microsoft/phi-2": "microsoft_phi-2.json",
+    "mistralai/Mixtral-8x7B-Instruct-v0.1": "mistralai_Mixtral-8x7B-Instruct-v0.1.json",
+}
+chat_template_config_path = "./config/conversation_template"
 
 
 def test_tokenization_sft():
     for model in model_data_mapping:
         print(f"#############{model}#############")
-        conversation_template_config = os.path.join(chat_template_config_path, model_data_mapping[model])  
-        messages = [{"from": "human", "content": "What are the three primary colors?"}, 
+        conversation_template_config = os.path.join(chat_template_config_path, model_data_mapping[model])
+        messages = [
+            {"from": "human", "content": "What are the three primary colors?"},
             {"from": "assistant", "content": "The three primary colors are red, blue, and yellow."},
-            {"from": "human", "content": "解释个人电脑和服务器之间的区别。"}, 
-            {"from": "assistant", "content": "个人电脑和服务器是两种不同类型的计算机系统，它们的主要区别在于用途、硬件配置和性能。 个人电脑，顾名思义，是为个人使用而设计的计算机。它们通常用于日常的工作、娱乐和学习，可以运行各种各样的应用程序和游戏。个人电脑的硬件配置一般是按照标准配置来设计的，不过也可以根据个人需求进行定制。 而服务器是为了满足大量用户的需求而设计的计算机系统，它们通常用于为用户提供各种网络服务，如网站、电子邮件和文件传输等。服务器通常需要高性能的硬件配置，并且可以承受高负载和长时间的运行。由于服务器需要支持大量用户的访问，它们通常配备多核处理器、大容量内存和大容量硬盘驱动器，以提高系统的运行速度和稳定性。 总之，个人电脑和服务器之间的主要区别在于它们的用途、硬件配置和性能。个人电脑用于个人使用，而服务器用于支持大量用户的访问。服务器的硬件配置通常比个人电脑更高，以保证系统的性能和稳定性。"}]
+            {"from": "human", "content": "解释个人电脑和服务器之间的区别。"},
+            {
+                "from": "assistant",
+                "content": "个人电脑和服务器是两种不同类型的计算机系统，它们的主要区别在于用途、硬件配置和性能。 个人电脑，顾名思义，是为个人使用而设计的计算机。它们通常用于日常的工作、娱乐和学习，可以运行各种各样的应用程序和游戏。个人电脑的硬件配置一般是按照标准配置来设计的，不过也可以根据个人需求进行定制。 而服务器是为了满足大量用户的需求而设计的计算机系统，它们通常用于为用户提供各种网络服务，如网站、电子邮件和文件传输等。服务器通常需要高性能的硬件配置，并且可以承受高负载和长时间的运行。由于服务器需要支持大量用户的访问，它们通常配备多核处理器、大容量内存和大容量硬盘驱动器，以提高系统的运行速度和稳定性。 总之，个人电脑和服务器之间的主要区别在于它们的用途、硬件配置和性能。个人电脑用于个人使用，而服务器用于支持大量用户的访问。服务器的硬件配置通常比个人电脑更高，以保证系统的性能和稳定性。",
+            },
+        ]
         chat_template_config = json.load(open(conversation_template_config, "r", encoding="utf8"))
         tokenizer = AutoTokenizer.from_pretrained(model, use_fast=False, trust_remote_code=True)
         conversation_template = setup_conversation_template(
-                tokenizer, chat_template_config=chat_template_config, save_path=conversation_template_config
-            )
+            tokenizer, chat_template_config=chat_template_config, save_path=conversation_template_config
+        )
 
         output = supervised_tokenize_sft({"messages": messages}, tokenizer, conversation_template)
         with open(f"./tests/test_data/chat_template/{model_data_mapping[model]}", "r", encoding="utf8") as f:

applications/ColossalChat/tests/test_data/chat_template/01-ai_Yi-1.5-9B-Chat.json

@@ -582,4 +582,4 @@
     ],
     "seq_length": 286,
     "seq_category": "None"
-}
+}

applications/ColossalChat/tests/test_data/chat_template/01-ai_Yi-34B.json

@@ -604,4 +604,4 @@
     ],
     "seq_length": 297,
     "seq_category": "None"
-}
+}

applications/ColossalChat/tests/test_data/chat_template/Qwen_Qwen-7B-Chat.json

@@ -600,4 +600,4 @@
     ],
     "seq_length": 295,
     "seq_category": "None"
-}
+}

applications/ColossalChat/tests/test_data/chat_template/THUDM_chatglm2-6b.json

@@ -712,4 +712,4 @@
     ],
     "seq_length": 351,
     "seq_category": "None"
-}
+}

applications/ColossalChat/tests/test_data/chat_template/THUDM_chatglm3-6b.json

@@ -582,4 +582,4 @@
     ],
     "seq_length": 286,
     "seq_category": "None"
-}
+}

applications/ColossalChat/tests/test_data/chat_template/baichuan-inc_Baichuan2-13B-Chat.json

@@ -694,4 +694,4 @@
     ],
     "seq_length": 342,
     "seq_category": "None"
-}
+}

applications/ColossalChat/tests/test_data/chat_template/deepseek-ai_DeepSeek-V2-Lite.json

@@ -578,4 +578,4 @@
     ],
     "seq_length": 284,
     "seq_category": "None"
-}
+}

applications/ColossalChat/tests/test_data/chat_template/microsoft_phi-2.json

@@ -2006,4 +2006,4 @@
     ],
     "seq_length": 998,
     "seq_category": "None"
-}
+}

applications/ColossalChat/tests/test_data/chat_template/mistralai_Mixtral-8x7B-Instruct-v0.1.json

@@ -916,4 +916,4 @@
     ],
     "seq_length": 453,
     "seq_category": "None"
-}
+}