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applications/Colossal-LLaMA-2/colossal_llama2/model/init_model.py

@@ -8,11 +8,10 @@ import argparse
 
 import numpy as np
 import torch
-from transformers import LlamaTokenizer, LlamaForCausalLM
+from transformers import LlamaForCausalLM, LlamaTokenizer
 
 from colossalai.logging import get_dist_logger
 
-
 logger = get_dist_logger()
 
 

applications/Colossal-LLaMA-2/colossal_llama2/utils/ckpt_io.py

@@ -10,8 +10,8 @@ import os
 from typing import Any, Dict, Tuple, Union
 
 import torch
-from torch.optim.optimizer import Optimizer
 from torch.optim.lr_scheduler import _LRScheduler
+from torch.optim.optimizer import Optimizer
 
 from colossalai.booster import Booster
 from colossalai.cluster import DistCoordinator

applications/Colossal-LLaMA-2/colossal_llama2/utils/stream_chat_patch.py

@@ -1,12 +1,11 @@
 from copy import deepcopy
-from typing import Optional, List, Dict, Tuple, Callable, Any
+from typing import Any, Callable, Dict, List, Optional, Tuple
 
 import torch
 from torch import nn
-
 from transformers import PreTrainedTokenizer
-from transformers.utils import logging
 from transformers.generation.utils import GenerationConfig, LogitsProcessorList, StoppingCriteriaList
+from transformers.utils import logging
 
 logger = logging.get_logger(__name__)
 
@@ -48,6 +47,7 @@ def get_prompt_template(
                 prompt += f"{role}: <s>"
     return prompt
 
+
 @torch.inference_mode()
 def streaming_chat(
     model: Any,
@@ -99,14 +99,14 @@ def streaming_chat(
         logits_processor = LogitsProcessorList()
 
     generation_kwargs = {
-        'temperature': temperature,
-        'top_p': top_p,
-        'top_k': top_k,
-        'do_sample': do_sample,
-        'max_new_tokens': max_new_tokens,
-        'length_penalty': length_penalty,
-        'use_cache': True,
-        **kwargs
+        "temperature": temperature,
+        "top_p": top_p,
+        "top_k": top_k,
+        "do_sample": do_sample,
+        "max_new_tokens": max_new_tokens,
+        "length_penalty": length_penalty,
+        "use_cache": True,
+        **kwargs,
     }
 
     prompt_str = get_prompt_template(input_query, history=history, roles=roles)
@@ -116,9 +116,14 @@ def streaming_chat(
     history.append({"role": roles[1], "message": input_query.strip()})
     history.append({"role": roles[2], "message": None})
 
-    for outputs in stream_generate(model, **inputs, past_key_values=past_key_values,
-                            eos_token_id=eos_token_id, return_past_key_values=return_past_key_values,
-                            **generation_kwargs):
+    for outputs in stream_generate(
+        model,
+        **inputs,
+        past_key_values=past_key_values,
+        eos_token_id=eos_token_id,
+        return_past_key_values=return_past_key_values,
+        **generation_kwargs,
+    ):
         if return_past_key_values:
             outputs, past_key_values = outputs
 

applications/Colossal-LLaMA-2/inference_example.py

@@ -15,7 +15,7 @@ def load_model(model_path, device="cuda", **kwargs):
     model.to(device)
 
     try:
-        tokenizer = AutoTokenizer.from_pretrained(model_path, padding_side='left')
+        tokenizer = AutoTokenizer.from_pretrained(model_path, padding_side="left")
     except OSError:
         raise ImportError("Tokenizer not found. Please check if the tokenizer exists or the model path is correct.")
 

applications/Colossal-LLaMA-2/requirements.txt

@@ -12,4 +12,3 @@ flash-attn>=2.0.0,<=2.0.5
 tqdm
 sentencepiece==0.1.99
 protobuf<=3.20.0
-

applications/Colossal-LLaMA-2/stream_chat_example.py

@@ -1,11 +1,11 @@
-import os
 import argparse
 
-from transformers import AutoTokenizer, AutoModelForCausalLM
 from colossal_llama2.utils.stream_chat_patch import streaming_chat
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
 SYSTEM = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions."
 
+
 def main(args):
     model = AutoModelForCausalLM.from_pretrained(args.model_path).cuda().eval()
     tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path)
@@ -27,7 +27,11 @@ def main(args):
         print(f"\n{roles[2]}: ", end="")
         gen_len = 0
         for response, history, past_key_values in streaming_chat(
-            model, tokenizer, input_query, history=history, roles=roles,
+            model,
+            tokenizer,
+            input_query,
+            history=history,
+            roles=roles,
             temperature=args.temperature,
             top_p=args.top_p,
             top_k=args.top_k,
@@ -35,21 +39,22 @@ def main(args):
             length_penalty=args.length_penalty,
             max_new_tokens=args.max_new_tokens,
             past_key_values=past_key_values,
-            return_past_key_values=True):
-
+            return_past_key_values=True,
+        ):
             output = response[gen_len:]
             print(output, end="", flush=True)
             gen_len = len(response)
 
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument('--model_path', type=str, default=None, help="path to chat version model")
-    parser.add_argument('--tokenizer_path', type=str, default=None, help="path to chat version tokenizer")
-    parser.add_argument('--temperature', type=float, default=0.8, help="set temperature")
-    parser.add_argument('--top_p', type=float, default=0.95, help="set top p value")
-    parser.add_argument('--top_k', type=int, default=50, help="set top k value")
-    parser.add_argument('--do_sample', type=bool, default=True, help="whether turn on do_sample or not")
-    parser.add_argument('--length_penalty', type=float, default=1.2, help="set length penalty")
-    parser.add_argument('--max_new_tokens', type=int, default=512, help="set max new tokens")
+    parser.add_argument("--model_path", type=str, default=None, help="path to chat version model")
+    parser.add_argument("--tokenizer_path", type=str, default=None, help="path to chat version tokenizer")
+    parser.add_argument("--temperature", type=float, default=0.8, help="set temperature")
+    parser.add_argument("--top_p", type=float, default=0.95, help="set top p value")
+    parser.add_argument("--top_k", type=int, default=50, help="set top k value")
+    parser.add_argument("--do_sample", type=bool, default=True, help="whether turn on do_sample or not")
+    parser.add_argument("--length_penalty", type=float, default=1.2, help="set length penalty")
+    parser.add_argument("--max_new_tokens", type=int, default=512, help="set max new tokens")
     args = parser.parse_args()
     main(args)

applications/ColossalChat/examples/training_scripts/train_dpo.py

@@ -20,13 +20,13 @@ import colossalai
 from colossalai.booster import Booster
 from colossalai.booster.plugin import GeminiPlugin, HybridParallelPlugin, LowLevelZeroPlugin
 from colossalai.cluster import DistCoordinator
+from colossalai.logging import get_dist_logger
 from colossalai.nn.lr_scheduler import CosineAnnealingWarmupLR
 from colossalai.nn.optimizer import HybridAdam
-from colossalai.utils import get_current_device
-from colossalai.logging import get_dist_logger
 
 logger = get_dist_logger()
 
+
 def train(args):
     # check lora compatibility
     if "gemini" in args.plugin and args.lora_rank > 0:

applications/ColossalEval/examples/dataset_evaluation/inference.py

@@ -3,7 +3,6 @@ import copy
 import os
 from typing import Dict, List
 
-import torch
 import torch.distributed as dist
 from colossal_eval import dataset, models, utils
 

applications/ColossalMoE/infer.py

@@ -106,6 +106,5 @@ def main():
     print(f"[{coordinator.rank}] {outputs}")
 
 
-
 if __name__ == "__main__":
     main()

applications/ColossalQA/colossalqa/chain/retrieval_qa/base.py

@@ -24,6 +24,7 @@ from langchain.pydantic_v1 import Field
 from langchain.schema import BaseRetriever, Document
 from langchain.schema.language_model import BaseLanguageModel
 
+
 class CustomBaseRetrievalQA(BaseRetrievalQA):
     """Base class for question-answering chains."""
 
@@ -98,7 +99,6 @@ class CustomBaseRetrievalQA(BaseRetrievalQA):
             for k, v in inputs.items()
             if k in ["stop", "temperature", "top_k", "top_p", "max_new_tokens", "doc_prefix"]
         }
-        answers = []
         if self.combine_documents_chain.memory is not None:
             buffered_history_backup, summarized_history_temp_backup = copy.deepcopy(
                 self.combine_documents_chain.memory.buffered_history
@@ -117,10 +117,10 @@ class CustomBaseRetrievalQA(BaseRetrievalQA):
             ) = copy.deepcopy(buffered_history_backup), copy.deepcopy(summarized_history_temp_backup)
 
         # if rejection_trigger_keywords is not given, return the response from LLM directly
-        rejection_trigger_keywords = inputs.get('rejection_trigger_keywords', [])
+        rejection_trigger_keywords = inputs.get("rejection_trigger_keywords", [])
         answer = answer if all([rej not in answer for rej in rejection_trigger_keywords]) else None
         if answer is None:
-            answer = inputs.get('rejection_answer', "抱歉，根据提供的信息无法回答该问题。")
+            answer = inputs.get("rejection_answer", "抱歉，根据提供的信息无法回答该问题。")
         if self.combine_documents_chain.memory is not None:
             self.combine_documents_chain.memory.save_context({"question": question}, {"output": answer})
 
@@ -161,10 +161,14 @@ class CustomBaseRetrievalQA(BaseRetrievalQA):
             input_documents=docs, question=question, callbacks=_run_manager.get_child(), **kwargs
         )
         # if rejection_trigger_keywords is not given, return the response from LLM directly
-        rejection_trigger_keywords = inputs.get('rejection_trigger_keywords', [])
-        answer = answer if all([rej not in answer for rej in rejection_trigger_keywords]) or len(rejection_trigger_keywords)==0 else None
+        rejection_trigger_keywords = inputs.get("rejection_trigger_keywords", [])
+        answer = (
+            answer
+            if all([rej not in answer for rej in rejection_trigger_keywords]) or len(rejection_trigger_keywords) == 0
+            else None
+        )
         if answer is None:
-            answer = inputs.get('rejection_answer', "抱歉，根据提供的信息无法回答该问题。")
+            answer = inputs.get("rejection_answer", "抱歉，根据提供的信息无法回答该问题。")
         self.combine_documents_chain.memory.save_context({"question": question}, {"output": answer})
 
         if self.return_source_documents:

applications/ColossalQA/colossalqa/data_loader/table_dataloader.py

@@ -1,32 +1,33 @@
-'''
+"""
 Class for loading table type data. please refer to Pandas-Input/Output for file format details.
-'''
+"""
 
 
-import os
 import glob
+import os
+
 import pandas as pd
-from sqlalchemy import create_engine
-from colossalqa.utils import drop_table
 from colossalqa.mylogging import get_logger
+from colossalqa.utils import drop_table
+from sqlalchemy import create_engine
 
 logger = get_logger()
 
-SUPPORTED_DATA_FORMAT = ['.csv','.xlsx', '.xls','.json','.html','.h5', '.hdf5','.parquet','.feather','.dta']
+SUPPORTED_DATA_FORMAT = [".csv", ".xlsx", ".xls", ".json", ".html", ".h5", ".hdf5", ".parquet", ".feather", ".dta"]
+
 
 class TableLoader:
-    '''
+    """
     Load tables from different files and serve a sql database for database operations
-    '''
-    def __init__(self, files: str, 
-                 sql_path:str='sqlite:///mydatabase.db', 
-                 verbose=False, **kwargs) -> None:
-        '''
+    """
+
+    def __init__(self, files: str, sql_path: str = "sqlite:///mydatabase.db", verbose=False, **kwargs) -> None:
+        """
         Args:
             files: list of files (list[file path, name])
             sql_path: how to serve the sql database
             **kwargs: keyword type arguments, useful for certain document types
-        '''
+        """
         self.data = {}
         self.verbose = verbose
         self.sql_path = sql_path
@@ -49,10 +50,10 @@ class TableLoader:
             self.to_sql(path, dataset_name)
 
     def load_data(self, path):
-        '''
+        """
         Load data and serve the data as sql database.
         Data must be in pandas format
-        '''
+        """
         files = []
         # Handle glob expression
         try:
@@ -67,40 +68,40 @@ class TableLoader:
             for file in files:
                 self.load_data(file)
 
-        if path.endswith('.csv'):
+        if path.endswith(".csv"):
             # Load csv
             self.data[path] = pd.read_csv(path)
-        elif path.endswith('.xlsx') or path.endswith('.xls'):
+        elif path.endswith(".xlsx") or path.endswith(".xls"):
             # Load excel
             self.data[path] = pd.read_excel(path)  # You can adjust the sheet_name as needed
-        elif path.endswith('.json'):
+        elif path.endswith(".json"):
             # Load json
             self.data[path] = pd.read_json(path)
-        elif path.endswith('.html'):
+        elif path.endswith(".html"):
             # Load html
             html_tables = pd.read_html(path)
             # Choose the desired table from the list of DataFrame objects
             self.data[path] = html_tables[0]  # You may need to adjust this index
-        elif path.endswith('.h5') or path.endswith('.hdf5'):
+        elif path.endswith(".h5") or path.endswith(".hdf5"):
             # Load h5
-            self.data[path] = pd.read_hdf(path, key=self.kwargs.get('key', 'data'))  # You can adjust the key as needed
-        elif path.endswith('.parquet'):
+            self.data[path] = pd.read_hdf(path, key=self.kwargs.get("key", "data"))  # You can adjust the key as needed
+        elif path.endswith(".parquet"):
             # Load parquet
-            self.data[path] = pd.read_parquet(path, engine='fastparquet')
-        elif path.endswith('.feather'):
+            self.data[path] = pd.read_parquet(path, engine="fastparquet")
+        elif path.endswith(".feather"):
             # Load feather
             self.data[path] = pd.read_feather(path)
-        elif path.endswith('.dta'):
+        elif path.endswith(".dta"):
             # Load dta
             self.data[path] = pd.read_stata(path)
         else:
             raise ValueError("Unsupported file format")
 
     def to_sql(self, path, table_name):
-        '''
+        """
         Serve the data as sql database.
-        '''
-        self.data[path].to_sql(table_name, con=self.sql_engine, if_exists='replace', index=False)
+        """
+        self.data[path].to_sql(table_name, con=self.sql_engine, if_exists="replace", index=False)
         logger.info(f"Loaded to Sqlite3\nPath: {path}", verbose=self.verbose)
         return self.sql_path
 
@@ -113,7 +114,3 @@ class TableLoader:
             self.sql_engine.dispose()
             del self.data
             del self.sql_engine
-
-
-
-

applications/ColossalQA/colossalqa/local/colossalcloud_llm.py

@@ -21,7 +21,7 @@ print(resp)  # super-heavyweight awesome-natured yawning Australian creature!
 
 """
 import json
-from typing import Any, List, Mapping, Optional
+from typing import Any, Mapping
 
 import requests
 from langchain.llms.base import LLM
@@ -33,11 +33,11 @@ class ColossalCloudLLM(LLM):
     A custom LLM class that integrates LLMs running on the ColossalCloud Platform
 
     """
+
     n: int
     gen_config: dict = None
     auth_config: dict = None
-    valid_gen_para: list = ['max_new_tokens', 'top_k',
-                            'top_p', 'temperature', 'repetition_penalty']
+    valid_gen_para: list = ["max_new_tokens", "top_k", "top_p", "temperature", "repetition_penalty"]
 
     def __init__(self, gen_config=None, **kwargs):
         """
@@ -63,15 +63,15 @@ class ColossalCloudLLM(LLM):
 
     @property
     def _llm_type(self) -> str:
-        return 'ColossalCloudLLM'
+        return "ColossalCloudLLM"
 
     def set_auth_config(self, **kwargs):
         url = get_from_dict_or_env(kwargs, "url", "URL")
         host = get_from_dict_or_env(kwargs, "host", "HOST")
 
         auth_config = {}
-        auth_config['endpoint'] = url
-        auth_config['Host'] = host
+        auth_config["endpoint"] = url
+        auth_config["Host"] = host
         self.auth_config = auth_config
 
     def _call(self, prompt: str, stop=None, **kwargs: Any) -> str:
@@ -86,7 +86,9 @@ class ColossalCloudLLM(LLM):
         # Update the generation arguments
         for key, value in kwargs.items():
             if key not in self.valid_gen_para:
-                raise KeyError(f"Invalid generation parameter: '{key}'. Valid keys are: {', '.join(self.valid_gen_para)}")
+                raise KeyError(
+                    f"Invalid generation parameter: '{key}'. Valid keys are: {', '.join(self.valid_gen_para)}"
+                )
             if key in self.gen_config:
                 self.gen_config[key] = value
 
@@ -98,26 +100,16 @@ class ColossalCloudLLM(LLM):
                     resp_text = resp_text.split(stopping_words)[0]
         return resp_text
 
-
     def text_completion(self, prompt, gen_config, auth_config):
         # Complusory Parameters
-        endpoint = auth_config.pop('endpoint')
-        max_new_tokens = gen_config.pop('max_new_tokens')
+        endpoint = auth_config.pop("endpoint")
+        max_new_tokens = gen_config.pop("max_new_tokens")
         # Optional Parameters
-        optional_params = ['top_k', 'top_p', 'temperature', 'repetition_penalty']  # Self.optional
+        optional_params = ["top_k", "top_p", "temperature", "repetition_penalty"]  # Self.optional
         gen_config = {key: gen_config[key] for key in optional_params if key in gen_config}
         # Define the data payload
-        data = {
-            "max_new_tokens": max_new_tokens,
-            "history": [
-                {"instruction": prompt, "response": ""}
-            ],
-            **gen_config
-        }
-        headers = {
-            "Content-Type": "application/json",
-            **auth_config  # 'Host', 
-        }
+        data = {"max_new_tokens": max_new_tokens, "history": [{"instruction": prompt, "response": ""}], **gen_config}
+        headers = {"Content-Type": "application/json", **auth_config}  # 'Host',
         # Make the POST request
         response = requests.post(endpoint, headers=headers, data=json.dumps(data))
         response.raise_for_status()  # raise error if return code is not 200(success)

applications/ColossalQA/colossalqa/local/llm.py

@@ -193,4 +193,3 @@ class VllmLLM(LLM):
     def _identifying_params(self) -> Mapping[str, int]:
         """Get the identifying parameters."""
         return {"n": self.n}
-

applications/ColossalQA/colossalqa/prompt/prompt.py

@@ -4,7 +4,6 @@ All custom prompt templates are defined here.
 
 from langchain.prompts.prompt import PromptTemplate
 
-
 # Below are Chinese retrieval qa prompts
 
 _CUSTOM_SUMMARIZER_TEMPLATE_ZH = """请递进式地总结所提供的当前对话，将当前对话的摘要内容添加到先前已有的摘要上，返回一个融合了当前对话的新的摘要。

applications/ColossalQA/colossalqa/retriever.py

@@ -99,13 +99,7 @@ class CustomRetriever(BaseRetriever):
     def clear_documents(self):
         """Clear all document vectors from database"""
         for source in self.vector_stores:
-            index(
-                [],
-                self.record_managers[source],
-                self.vector_stores[source],
-                cleanup="full",
-                source_id_key="source"
-            )
+            index([], self.record_managers[source], self.vector_stores[source], cleanup="full", source_id_key="source")
         self.vector_stores = {}
         self.sql_index_database = {}
         self.record_managers = {}

applications/ColossalQA/examples/retrieval_conversation_universal.py

@@ -1,22 +1,27 @@
 import argparse
+
 from colossalqa.retrieval_conversation_universal import UniversalRetrievalConversation
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     # Parse arguments
     parser = argparse.ArgumentParser()
-    parser.add_argument('--en_model_path', type=str, default=None)
-    parser.add_argument('--zh_model_path', type=str, default=None)
-    parser.add_argument('--zh_model_name', type=str, default=None)
-    parser.add_argument('--en_model_name', type=str, default=None)
-    parser.add_argument('--sql_file_path', type=str, default=None, help='path to the a empty folder for storing sql files for indexing')
+    parser.add_argument("--en_model_path", type=str, default=None)
+    parser.add_argument("--zh_model_path", type=str, default=None)
+    parser.add_argument("--zh_model_name", type=str, default=None)
+    parser.add_argument("--en_model_name", type=str, default=None)
+    parser.add_argument(
+        "--sql_file_path", type=str, default=None, help="path to the a empty folder for storing sql files for indexing"
+    )
     args = parser.parse_args()
 
     # Will ask for documents path in running time
-    session = UniversalRetrievalConversation(files_en=None, 
+    session = UniversalRetrievalConversation(
+        files_en=None,
         files_zh=None,
-                zh_model_path=args.zh_model_path, en_model_path=args.en_model_path,
-                zh_model_name=args.zh_model_name, en_model_name=args.en_model_name,
-                sql_file_path=args.sql_file_path
+        zh_model_path=args.zh_model_path,
+        en_model_path=args.en_model_path,
+        zh_model_name=args.zh_model_name,
+        en_model_name=args.en_model_name,
+        sql_file_path=args.sql_file_path,
     )
     session.start_test_session()
-        

applications/ColossalQA/examples/webui_demo/RAG_ChatBot.py

@@ -5,13 +5,7 @@ from colossalqa.chain.retrieval_qa.base import RetrievalQA
 from colossalqa.data_loader.document_loader import DocumentLoader
 from colossalqa.memory import ConversationBufferWithSummary
 from colossalqa.mylogging import get_logger
-from colossalqa.prompt.prompt import (
-    PROMPT_DISAMBIGUATE_ZH,
-    PROMPT_RETRIEVAL_QA_ZH,
-    SUMMARY_PROMPT_ZH,
-    ZH_RETRIEVAL_QA_REJECTION_ANSWER,
-    ZH_RETRIEVAL_QA_TRIGGER_KEYWORDS,
-)
+from colossalqa.prompt.prompt import ZH_RETRIEVAL_QA_REJECTION_ANSWER, ZH_RETRIEVAL_QA_TRIGGER_KEYWORDS
 from colossalqa.retriever import CustomRetriever
 from langchain import LLMChain
 from langchain.embeddings import HuggingFaceEmbeddings

applications/ColossalQA/examples/webui_demo/config.py

@@ -1,58 +1,30 @@
-from colossalqa.prompt.prompt import (
-    PROMPT_DISAMBIGUATE_ZH,
-    PROMPT_RETRIEVAL_QA_ZH,
-    SUMMARY_PROMPT_ZH,
-    ZH_RETRIEVAL_QA_REJECTION_ANSWER,
-    ZH_RETRIEVAL_QA_TRIGGER_KEYWORDS,
-)
+from colossalqa.prompt.prompt import PROMPT_DISAMBIGUATE_ZH, PROMPT_RETRIEVAL_QA_ZH, SUMMARY_PROMPT_ZH
 from colossalqa.text_splitter import ChineseTextSplitter
 
 ALL_CONFIG = {
     "embed": {
         "embed_name": "m3e",  # embedding model name
         "embed_model_name_or_path": "moka-ai/m3e-base",  # path to embedding model, could be a local path or a huggingface path
-        "embed_model_device": {
-            "device": "cpu"
-        }
+        "embed_model_device": {"device": "cpu"},
     },
     "model": {
         "mode": "api",  # "local" for loading models, "api" for using model api
         "model_name": "chatgpt_api",  # local model name, "chatgpt_api" or "pangu_api"
         "model_path": "",  # path to the model, could be a local path or a huggingface path. don't need if using an api
-        "device": {
-            "device": "cuda"
-        }
-    },
-    "splitter": {
-        "name": ChineseTextSplitter
-    },
-    "retrieval": {
-        "retri_top_k": 3,
-        "retri_kb_file_path": "./", # path to store database files
-        "verbose": True
+        "device": {"device": "cuda"},
     },
+    "splitter": {"name": ChineseTextSplitter},
+    "retrieval": {"retri_top_k": 3, "retri_kb_file_path": "./", "verbose": True},  # path to store database files
     "chain": {
         "mem_summary_prompt": SUMMARY_PROMPT_ZH,  # summary prompt template
         "mem_human_prefix": "用户",
         "mem_ai_prefix": "Assistant",
         "mem_max_tokens": 2000,
-        "mem_llm_kwargs": {
-            "max_new_tokens": 50,
-            "temperature": 1,
-            "do_sample": True
-        },
+        "mem_llm_kwargs": {"max_new_tokens": 50, "temperature": 1, "do_sample": True},
         "disambig_prompt": PROMPT_DISAMBIGUATE_ZH,  # disambiguate prompt template
-        "disambig_llm_kwargs": {
-            "max_new_tokens": 30,
-            "temperature": 1,
-            "do_sample": True
-        },
-        "gen_llm_kwargs": {
-            "max_new_tokens": 100,
-            "temperature": 1,
-            "do_sample": True
-        },
+        "disambig_llm_kwargs": {"max_new_tokens": 30, "temperature": 1, "do_sample": True},
+        "gen_llm_kwargs": {"max_new_tokens": 100, "temperature": 1, "do_sample": True},
         "gen_qa_prompt": PROMPT_RETRIEVAL_QA_ZH,  # generation prompt template
-        "verbose": True    
-    }   
+        "verbose": True,
+    },
 }

applications/ColossalQA/examples/webui_demo/server.py

@@ -1,27 +1,18 @@
 import argparse
-import os
 from typing import List, Union
 
-
-from colossalqa.local.llm import ColossalAPI, ColossalLLM
-from colossalqa.data_loader.document_loader import DocumentLoader
-from colossalqa.mylogging import get_logger
-from colossalqa.retrieval_conversation_zh import ChineseRetrievalConversation
-from colossalqa.retriever import CustomRetriever
-from enum import Enum
-from fastapi import FastAPI, Request
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from pydantic import BaseModel, Field
-import uvicorn
-
 import config
+import uvicorn
+from colossalqa.local.llm import ColossalAPI, ColossalLLM
+from colossalqa.mylogging import get_logger
+from fastapi import FastAPI, Request
+from pydantic import BaseModel
 from RAG_ChatBot import RAG_ChatBot
 from utils import DocAction
 
-
 logger = get_logger()
 
+
 def parseArgs():
     parser = argparse.ArgumentParser()
     parser.add_argument("--http_host", default="0.0.0.0")
@@ -36,6 +27,7 @@ class DocUpdateReq(BaseModel):
     doc_files: Union[List[str], str, None] = None
     action: DocAction = DocAction.ADD
 
+
 class GenerationTaskReq(BaseModel):
     user_input: str
 
@@ -84,12 +76,13 @@ if __name__ == "__main__":
                 "user": "User",
                 "max_tokens": all_config["chain"]["disambig_llm_kwargs"]["max_new_tokens"],
                 "temperature": all_config["chain"]["disambig_llm_kwargs"]["temperature"],
-                "n": 1   # the number of responses generated
+                "n": 1,  # the number of responses generated
             }
             llm = Pangu(gen_config=gen_config)
             llm.set_auth_config()  # verify user's auth info here
         elif model_name == "chatgpt_api":
             from langchain.llms import OpenAI
+
             llm = OpenAI()
     else:
         raise ValueError("Unsupported mode.")

applications/ColossalQA/examples/webui_demo/webui.py

@@ -1,24 +1,26 @@
 import argparse
 import json
 import os
-import requests
 
 import gradio as gr
-
+import requests
 from utils import DocAction
 
+
 def parseArgs():
     parser = argparse.ArgumentParser()
     parser.add_argument("--http_host", default="0.0.0.0")
     parser.add_argument("--http_port", type=int, default=13666)
     return parser.parse_args()
 
+
 def get_response(data, url):
     headers = {"Content-type": "application/json"}
     response = requests.post(url, json=data, headers=headers)
     response = json.loads(response.content)
     return response
 
+
 def add_text(history, text):
     history = history + [(text, None)]
     return history, gr.update(value=None, interactive=True)
@@ -28,18 +30,14 @@ def add_file(history, files):
     files_string = "\n".join([os.path.basename(file.name) for file in files])
 
     doc_files = [file.name for file in files]
-    data = {
-        "doc_files": doc_files,
-        "action": DocAction.ADD
-    }
+    data = {"doc_files": doc_files, "action": DocAction.ADD}
     response = get_response(data, update_url)["response"]
     history = history + [(files_string, response)]
     return history
 
+
 def bot(history):
-    data = {
-        "user_input": history[-1][0].strip()
-    }
+    data = {"user_input": history[-1][0].strip()}
     response = get_response(data, gen_url)
 
     if response["error"] != "":
@@ -51,11 +49,8 @@ def bot(history):
 
 def restart(chatbot, txt):
     # Reset the conversation state and clear the chat history
-    data = {
-        "doc_files": "",
-        "action": DocAction.CLEAR
-    }
-    response = get_response(data, update_url)
+    data = {"doc_files": "", "action": DocAction.CLEAR}
+    get_response(data, update_url)
 
     return gr.update(value=None), gr.update(value=None, interactive=True)
 

applications/ColossalQA/tests/test_document_loader.py

@@ -1,21 +1,21 @@
 import os
+
 from colossalqa.data_loader.document_loader import DocumentLoader
 
 
 def test_add_document():
-    PATH = os.environ.get('TEST_DOCUMENT_LOADER_DATA_PATH')
-    files = [[PATH, 'all data']]
+    PATH = os.environ.get("TEST_DOCUMENT_LOADER_DATA_PATH")
+    files = [[PATH, "all data"]]
     document_loader = DocumentLoader(files)
     documents = document_loader.all_data
     all_files = []
     for doc in documents:
         assert isinstance(doc.page_content, str) == True
-        if doc.metadata['source'] not in all_files:
-            all_files.append(doc.metadata['source'])
+        if doc.metadata["source"] not in all_files:
+            all_files.append(doc.metadata["source"])
     print(all_files)
     assert len(all_files) == 6
 
 
-if __name__=='__main__':
+if __name__ == "__main__":
     test_add_document()
-

applications/ColossalQA/tests/test_retrieval_qa.py

@@ -4,56 +4,44 @@ from colossalqa.retrieval_conversation_universal import UniversalRetrievalConver
 
 
 def test_en_retrievalQA():
-    data_path_en = os.environ.get('TEST_DATA_PATH_EN')
-    data_path_zh = os.environ.get('TEST_DATA_PATH_ZH')
-    en_model_path = os.environ.get('EN_MODEL_PATH')
-    zh_model_path = os.environ.get('ZH_MODEL_PATH')
-    zh_model_name = os.environ.get('ZH_MODEL_NAME')
-    en_model_name = os.environ.get('EN_MODEL_NAME')
-    sql_file_path = os.environ.get('SQL_FILE_PATH')
-    qa_session = UniversalRetrievalConversation(files_en=[{
-        'data_path': data_path_en,
-        'name': 'company information',
-        'separator': '\n'
-    }],
-                                                files_zh=[{
-                                                    'data_path': data_path_zh,
-                                                    'name': 'company information',
-                                                    'separator': '\n'
-                                                }],
+    data_path_en = os.environ.get("TEST_DATA_PATH_EN")
+    data_path_zh = os.environ.get("TEST_DATA_PATH_ZH")
+    en_model_path = os.environ.get("EN_MODEL_PATH")
+    zh_model_path = os.environ.get("ZH_MODEL_PATH")
+    zh_model_name = os.environ.get("ZH_MODEL_NAME")
+    en_model_name = os.environ.get("EN_MODEL_NAME")
+    sql_file_path = os.environ.get("SQL_FILE_PATH")
+    qa_session = UniversalRetrievalConversation(
+        files_en=[{"data_path": data_path_en, "name": "company information", "separator": "\n"}],
+        files_zh=[{"data_path": data_path_zh, "name": "company information", "separator": "\n"}],
         zh_model_path=zh_model_path,
         en_model_path=en_model_path,
         zh_model_name=zh_model_name,
         en_model_name=en_model_name,
-                                                sql_file_path=sql_file_path)
-    ans = qa_session.run("which company runs business in hotel industry?", which_language='en')
+        sql_file_path=sql_file_path,
+    )
+    ans = qa_session.run("which company runs business in hotel industry?", which_language="en")
     print(ans)
 
 
 def test_zh_retrievalQA():
-    data_path_en = os.environ.get('TEST_DATA_PATH_EN')
-    data_path_zh = os.environ.get('TEST_DATA_PATH_ZH')
-    en_model_path = os.environ.get('EN_MODEL_PATH')
-    zh_model_path = os.environ.get('ZH_MODEL_PATH')
-    zh_model_name = os.environ.get('ZH_MODEL_NAME')
-    en_model_name = os.environ.get('EN_MODEL_NAME')
-    sql_file_path = os.environ.get('SQL_FILE_PATH')
-    qa_session = UniversalRetrievalConversation(files_en=[{
-        'data_path': data_path_en,
-        'name': 'company information',
-        'separator': '\n'
-    }],
-                                                files_zh=[{
-                                                    'data_path': data_path_zh,
-                                                    'name': 'company information',
-                                                    'separator': '\n'
-                                                }],
+    data_path_en = os.environ.get("TEST_DATA_PATH_EN")
+    data_path_zh = os.environ.get("TEST_DATA_PATH_ZH")
+    en_model_path = os.environ.get("EN_MODEL_PATH")
+    zh_model_path = os.environ.get("ZH_MODEL_PATH")
+    zh_model_name = os.environ.get("ZH_MODEL_NAME")
+    en_model_name = os.environ.get("EN_MODEL_NAME")
+    sql_file_path = os.environ.get("SQL_FILE_PATH")
+    qa_session = UniversalRetrievalConversation(
+        files_en=[{"data_path": data_path_en, "name": "company information", "separator": "\n"}],
+        files_zh=[{"data_path": data_path_zh, "name": "company information", "separator": "\n"}],
         zh_model_path=zh_model_path,
         en_model_path=en_model_path,
         zh_model_name=zh_model_name,
         en_model_name=en_model_name,
-                                                sql_file_path=sql_file_path)
-    ans = qa_session.run("哪家公司在经营酒店业务？", which_language='zh')
+        sql_file_path=sql_file_path,
+    )
+    ans = qa_session.run("哪家公司在经营酒店业务？", which_language="zh")
     print(ans)
 
 

colossalai/__init__.py

@@ -1,5 +1,5 @@
-from .initialize import launch, launch_from_openmpi, launch_from_slurm, launch_from_torch
 from . import accelerator
+from .initialize import launch, launch_from_openmpi, launch_from_slurm, launch_from_torch
 
 try:
     # .version will be created by setup.py

colossalai/booster/plugin/torch_fsdp_plugin.py

@@ -27,7 +27,7 @@ from torch.optim import Optimizer
 from torch.optim.lr_scheduler import _LRScheduler as LRScheduler
 from torch.utils.data import DataLoader
 
-from colossalai.checkpoint_io import CheckpointIO, GeneralCheckpointIO, utils, CheckpointIndexFile
+from colossalai.checkpoint_io import CheckpointIndexFile, CheckpointIO, GeneralCheckpointIO, utils
 from colossalai.cluster import DistCoordinator
 from colossalai.interface import ModelWrapper, OptimizerWrapper
 
@@ -93,9 +93,7 @@ class TorchFSDPCheckpointIO(GeneralCheckpointIO):
 
         Path(checkpoint_path).mkdir(parents=True, exist_ok=True)
         with FSDP.state_dict_type(
-            model.unwrap(),
-            StateDictType.FULL_STATE_DICT,
-            FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
+            model.unwrap(), StateDictType.FULL_STATE_DICT, FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
         ):
             state_dict = model.unwrap().state_dict()
 
@@ -172,7 +170,7 @@ class TorchFSDPCheckpointIO(GeneralCheckpointIO):
         with FSDP.state_dict_type(
             optimizer.unwrap_model().unwrap(),
             StateDictType.FULL_STATE_DICT,
-            FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
+            FullStateDictConfig(offload_to_cpu=True, rank0_only=True),
         ):
             fsdp_optim_state = FSDP.full_optim_state_dict(
                 optimizer.unwrap_model().unwrap(), optim=optimizer, rank0_only=True
@@ -241,7 +239,6 @@ class TorchFSDPCheckpointIO(GeneralCheckpointIO):
             )
             optimizer.load_state_dict(fsdp_state)
 
-
     def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str):
         """
         Save model to checkpoint but only on master process.

colossalai/checkpoint_io/utils.py

@@ -294,6 +294,7 @@ def shard_optimizer_checkpoint(state_dict: dict, max_shard_size: int = 1024) ->
 # Helper functions for saving state dict
 # ======================================
 
+
 def save_state_dict(state_dict: dict, checkpoint_file_path: str, use_safetensors: bool) -> None:
     """
     Save state dict to checkpoint.

colossalai/cluster/process_group_mesh.py

@@ -225,4 +225,3 @@ class ProcessGroupMesh:
             # no need to cache it explicitly, since it will be cached in `create_group_along_axis`
             return self.create_group_along_axis(axis, indices_at_axis, backend=backend)
         return self._ranks_to_group[ranks_in_group]
-    

colossalai/inference/engine/modeling/llama.py

@@ -29,13 +29,17 @@ except:
 
 try:
     from colossalai.kernel.triton.flash_decoding import token_flash_decoding
+
     HAS_TRITON_FLASH_DECODING_KERNEL = True
 except:
-    print("no triton flash decoding support, please install lightllm from https://github.com/ModelTC/lightllm/blob/ece7b43f8a6dfa74027adc77c2c176cff28c76c8")
+    print(
+        "no triton flash decoding support, please install lightllm from https://github.com/ModelTC/lightllm/blob/ece7b43f8a6dfa74027adc77c2c176cff28c76c8"
+    )
     HAS_TRITON_FLASH_DECODING_KERNEL = False
 
 try:
     from flash_attn import flash_attn_with_kvcache
+
     HAS_FLASH_KERNEL = True
 except:
     HAS_FLASH_KERNEL = False
@@ -48,6 +52,7 @@ def rotate_half(x):
     x2 = x[..., x.shape[-1] // 2 :]
     return torch.cat((-x2, x1), dim=-1)
 
+
 def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
     # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
     cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
@@ -96,15 +101,20 @@ def llama_triton_context_attention(
             infer_state.max_len_in_batch,
         )
 
-def llama_triton_token_attention(query_states, attn_output, infer_state, num_key_value_groups=1, q_head_num = -1, head_dim = -1):
+
+def llama_triton_token_attention(
+    query_states, attn_output, infer_state, num_key_value_groups=1, q_head_num=-1, head_dim=-1
+):
     if HAS_TRITON_FLASH_DECODING_KERNEL and q_head_num != -1 and head_dim != -1:
-        token_flash_decoding(q = query_states, 
+        token_flash_decoding(
+            q=query_states,
             o_tensor=attn_output,
             infer_state=infer_state,
             q_head_num=q_head_num,
             head_dim=head_dim,
             cache_k=infer_state.cache_manager.key_buffer[infer_state.decode_layer_id],
-                                cache_v = infer_state.cache_manager.value_buffer[infer_state.decode_layer_id])
+            cache_v=infer_state.cache_manager.value_buffer[infer_state.decode_layer_id],
+        )
         return
 
     if num_key_value_groups == 1:
@@ -459,14 +469,15 @@ class LlamaInferenceForwards:
                 )
 
             if HAS_LIGHTLLM_KERNEL:
-                
                 attn_output = torch.empty_like(query_states)
-                llama_triton_token_attention(query_states = query_states, 
+                llama_triton_token_attention(
+                    query_states=query_states,
                     attn_output=attn_output,
                     infer_state=infer_state,
                     num_key_value_groups=self.num_key_value_groups,
                     q_head_num=q_len * self.num_heads,
-                                             head_dim = self.head_dim)
+                    head_dim=self.head_dim,
+                )
             else:
                 self.num_heads // self.num_key_value_heads
                 cache_k = infer_state.cache_manager.key_buffer[infer_state.decode_layer_id]

colossalai/inference/quant/gptq/cai_gptq/cai_quant_linear.py

@@ -18,15 +18,15 @@ from .gptq_op import CaiGPTQLinearOp
 HAS_GPTQ_CUDA = False
 try:
     from colossalai.kernel.op_builder.gptq import GPTQBuilder
+
     gptq_cuda = GPTQBuilder().load()
     HAS_GPTQ_CUDA = True
 except ImportError:
-    warnings.warn('CUDA gptq is not installed')
+    warnings.warn("CUDA gptq is not installed")
     HAS_GPTQ_CUDA = False
 
 
 class CaiQuantLinear(nn.Module):
-
     def __init__(self, bits, groupsize, infeatures, outfeatures, bias, tp_size=1, tp_rank=0, row_split=False):
         super().__init__()
         if bits not in [2, 4, 8]:
@@ -37,23 +37,28 @@ class CaiQuantLinear(nn.Module):
         self.maxq = 2**self.bits - 1
         self.groupsize = groupsize if groupsize != -1 else infeatures
 
-        self.register_buffer('qweight', torch.zeros((infeatures // 32 * self.bits, outfeatures), dtype=torch.int32))
+        self.register_buffer("qweight", torch.zeros((infeatures // 32 * self.bits, outfeatures), dtype=torch.int32))
         self.register_buffer(
-            'qzeros',
-            torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures // 32 * self.bits), dtype=torch.int32))
-        self.register_buffer('scales',
-                             torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures), dtype=torch.float16))
+            "qzeros",
+            torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures // 32 * self.bits), dtype=torch.int32),
+        )
+        self.register_buffer(
+            "scales", torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures), dtype=torch.float16)
+        )
         if row_split:
             self.register_buffer(
-                'g_idx',
-                torch.tensor([(i + (tp_rank * self.infeatures)) // self.groupsize for i in range(infeatures)],
-                             dtype=torch.int32))
+                "g_idx",
+                torch.tensor(
+                    [(i + (tp_rank * self.infeatures)) // self.groupsize for i in range(infeatures)], dtype=torch.int32
+                ),
+            )
         else:
-            self.register_buffer('g_idx',
-                                 torch.tensor([i // self.groupsize for i in range(infeatures)], dtype=torch.int32))
+            self.register_buffer(
+                "g_idx", torch.tensor([i // self.groupsize for i in range(infeatures)], dtype=torch.int32)
+            )
 
         if bias:
-            self.register_buffer('bias', torch.zeros((outfeatures), dtype=torch.float16))
+            self.register_buffer("bias", torch.zeros((outfeatures), dtype=torch.float16))
         else:
             self.bias = None
 
@@ -66,9 +71,11 @@ class CaiQuantLinear(nn.Module):
         self.row_split = row_split
 
     def pack(self, linear, scales, zeros, g_idx=None):
-
-        g_idx = g_idx.clone() if g_idx is not None else torch.tensor(
-            [i // self.groupsize for i in range(self.infeatures)], dtype=torch.int32)
+        g_idx = (
+            g_idx.clone()
+            if g_idx is not None
+            else torch.tensor([i // self.groupsize for i in range(self.infeatures)], dtype=torch.int32)
+        )
 
         scales = scales.t().contiguous()
         zeros = zeros.t().contiguous()
@@ -79,7 +86,6 @@ class CaiQuantLinear(nn.Module):
         if linear.bias is not None:
             self.bias = linear.bias.clone().half()
 
-        wn = 8
         pbits = 32
         ptype = torch.int32
         unsign_type = np.uint32
@@ -88,9 +94,10 @@ class CaiQuantLinear(nn.Module):
         intweight = []
         for idx in range(self.infeatures):
             intweight.append(
-                torch.round(
-                    (linear.weight.data[:, idx] + scale_zeros[g_idx[idx]]) / half_scales[g_idx[idx]]).to(ptype)[:,
-                                                                                                                None])
+                torch.round((linear.weight.data[:, idx] + scale_zeros[g_idx[idx]]) / half_scales[g_idx[idx]]).to(ptype)[
+                    :, None
+                ]
+            )
         intweight = torch.cat(intweight, dim=1)
         intweight = intweight.t().contiguous()
         intweight = intweight.numpy().astype(unsign_type)
@@ -144,13 +151,16 @@ class CaiQuantLinear(nn.Module):
                 torch.tensor(
                     [(i + (self.tp_rank * self.infeatures)) // self.groupsize for i in range(self.infeatures)],
                     dtype=torch.int32,
-                        device=self.g_idx.device)):
+                    device=self.g_idx.device,
+                ),
+            ):
                 self.g_idx = None
             elif torch.equal(
                 self.g_idx,
-                    torch.tensor([i // self.groupsize for i in range(self.infeatures)],
-                                 dtype=torch.int32,
-                                 device=self.g_idx.device)):
+                torch.tensor(
+                    [i // self.groupsize for i in range(self.infeatures)], dtype=torch.int32, device=self.g_idx.device
+                ),
+            ):
                 self.g_idx = None
 
         if self.g_idx is not None:
@@ -165,7 +175,6 @@ class CaiQuantLinear(nn.Module):
         outshape = x.shape[:-1] + (self.outfeatures,)
 
         if HAS_GPTQ_CUDA and self.bits == 4:
-
             if self.q4 is None:
                 self.init_q4()
 
@@ -191,7 +200,6 @@ class CaiQuantLinear(nn.Module):
 
 
 def split_column_copy(gptq_linear, cai_linear, tp_size=1, tp_rank=0, split_num=1):
-
     qweights = gptq_linear.qweight.split(gptq_linear.out_features // split_num, dim=-1)
     qzeros = gptq_linear.qzeros.split(gptq_linear.out_features // (32 // cai_linear.bits) // split_num, dim=-1)
     scales = gptq_linear.scales.split(gptq_linear.out_features // split_num, dim=-1)
@@ -203,24 +211,24 @@ def split_column_copy(gptq_linear, cai_linear, tp_size=1, tp_rank=0, split_num=1
     zero_split_block = cai_linear.outfeatures // (32 // cai_linear.bits) // split_num
 
     for i in range(split_num):
-        cai_linear.qweight[:, i * cai_split_out_features:(i + 1) *
-                           cai_split_out_features] = qweights[i][:, tp_rank * cai_split_out_features:(tp_rank + 1) *
-                                                                 cai_split_out_features]
-        cai_linear.qzeros[:, i * zero_split_block:(i + 1) *
-                          zero_split_block] = qzeros[i][:, tp_rank * zero_split_block:(tp_rank + 1) * zero_split_block]
-        cai_linear.scales[:, i * cai_split_out_features:(i + 1) *
-                          cai_split_out_features] = scales[i][:, tp_rank * cai_split_out_features:(tp_rank + 1) *
-                                                              cai_split_out_features]
+        cai_linear.qweight[:, i * cai_split_out_features : (i + 1) * cai_split_out_features] = qweights[i][
+            :, tp_rank * cai_split_out_features : (tp_rank + 1) * cai_split_out_features
+        ]
+        cai_linear.qzeros[:, i * zero_split_block : (i + 1) * zero_split_block] = qzeros[i][
+            :, tp_rank * zero_split_block : (tp_rank + 1) * zero_split_block
+        ]
+        cai_linear.scales[:, i * cai_split_out_features : (i + 1) * cai_split_out_features] = scales[i][
+            :, tp_rank * cai_split_out_features : (tp_rank + 1) * cai_split_out_features
+        ]
         if cai_linear.bias is not None:
-            cai_linear.bias[i * cai_split_out_features:(i + 1) *
-                            cai_split_out_features] = bias[i][tp_rank * cai_split_out_features:(tp_rank + 1) *
-                                                              cai_split_out_features]
+            cai_linear.bias[i * cai_split_out_features : (i + 1) * cai_split_out_features] = bias[i][
+                tp_rank * cai_split_out_features : (tp_rank + 1) * cai_split_out_features
+            ]
 
     cai_linear.g_idx.copy_(g_idx)
 
 
 def split_row_copy(gptq_linear, cai_linear, tp_rank=0, split_num=1):
-
     qweights = gptq_linear.qweight.split(gptq_linear.in_features // split_num, dim=0)
     qzeros = gptq_linear.qzeros.split(gptq_linear.in_features // split_num, dim=0)
     scales = gptq_linear.scales.split(gptq_linear.in_features // split_num, dim=0)
@@ -231,47 +239,40 @@ def split_row_copy(gptq_linear, cai_linear, tp_rank=0, split_num=1):
     idx_split_features = cai_linear.infeatures // split_num
 
     for i in range(split_num):
-        cai_linear.qweight[i * cai_split_in_features:(i + 1) *
-                           cai_split_in_features, :] = qweights[i][tp_rank * cai_split_in_features:(tp_rank + 1) *
-                                                                   cai_split_in_features, :]
-        cai_linear.qzeros[i * zero_split_block:(i + 1) *
-                          zero_split_block, :] = qzeros[i][tp_rank * zero_split_block:(tp_rank + 1) *
-                                                           zero_split_block, :]
-        cai_linear.scales[i * zero_split_block:(i + 1) *
-                          zero_split_block, :] = scales[i][tp_rank * zero_split_block:(tp_rank + 1) *
-                                                           zero_split_block, :]
-        cai_linear.g_idx[i * idx_split_features:(i + 1) *
-                         idx_split_features] = g_idxs[i][tp_rank * idx_split_features:(tp_rank + 1) *
-                                                         idx_split_features]
+        cai_linear.qweight[i * cai_split_in_features : (i + 1) * cai_split_in_features, :] = qweights[i][
+            tp_rank * cai_split_in_features : (tp_rank + 1) * cai_split_in_features, :
+        ]
+        cai_linear.qzeros[i * zero_split_block : (i + 1) * zero_split_block, :] = qzeros[i][
+            tp_rank * zero_split_block : (tp_rank + 1) * zero_split_block, :
+        ]
+        cai_linear.scales[i * zero_split_block : (i + 1) * zero_split_block, :] = scales[i][
+            tp_rank * zero_split_block : (tp_rank + 1) * zero_split_block, :
+        ]
+        cai_linear.g_idx[i * idx_split_features : (i + 1) * idx_split_features] = g_idxs[i][
+            tp_rank * idx_split_features : (tp_rank + 1) * idx_split_features
+        ]
     if cai_linear.bias is not None:
         cai_linear.bias.copy_(gptq_linear.bias)
 
 
 class RowCaiQuantLinear(CaiQuantLinear, ParallelModule):
-
     def __init__(self, bits, groupsize, infeatures, outfeatures, bias, tp_size=1, tp_rank=0, row_split=False):
-
-        super().__init__(bits,
-                         groupsize,
-                         infeatures,
-                         outfeatures,
-                         bias,
-                         tp_size=tp_size,
-                         tp_rank=tp_rank,
-                         row_split=row_split)
+        super().__init__(
+            bits, groupsize, infeatures, outfeatures, bias, tp_size=tp_size, tp_rank=tp_rank, row_split=row_split
+        )
         self.process_group = None
 
     @staticmethod
-    def from_native_module(module: nn.Module, process_group: Union[ProcessGroup, List[ProcessGroup]], *args,
-                           **kwargs) -> ParallelModule:
+    def from_native_module(
+        module: nn.Module, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, **kwargs
+    ) -> ParallelModule:
         LazyInitContext.materialize(module)
         # get the attributes
         in_features = module.in_features
 
         # ensure only one process group is passed
         if isinstance(process_group, (list, tuple)):
-            assert len(process_group) == 1, \
-                f'Expected only one process group, got {len(process_group)}.'
+            assert len(process_group) == 1, f"Expected only one process group, got {len(process_group)}."
             process_group = process_group[0]
 
         tp_size = dist.get_world_size(process_group)
@@ -282,15 +283,18 @@ class RowCaiQuantLinear(CaiQuantLinear, ParallelModule):
 
         if in_features % tp_size != 0:
             raise ValueError(
-                f"The size of in_features:{in_features} is not integer multiples of tensor parallel size: {tp_size}!")
-        linear_1d = RowCaiQuantLinear(module.bits,
+                f"The size of in_features:{in_features} is not integer multiples of tensor parallel size: {tp_size}!"
+            )
+        linear_1d = RowCaiQuantLinear(
+            module.bits,
             module.group_size,
             module.in_features // tp_size,
             module.out_features,
             module.bias is not None,
             tp_size=tp_size,
             tp_rank=tp_rank,
-                                      row_split=True)
+            row_split=True,
+        )
         linear_1d.process_group = process_group
 
         split_row_copy(module, linear_1d, tp_rank=tp_rank, **kwargs)
@@ -306,30 +310,23 @@ class RowCaiQuantLinear(CaiQuantLinear, ParallelModule):
 
 
 class ColCaiQuantLinear(CaiQuantLinear, ParallelModule):
-
     def __init__(self, bits, groupsize, infeatures, outfeatures, bias, tp_size=1, tp_rank=0, row_split=False):
-
-        super().__init__(bits,
-                         groupsize,
-                         infeatures,
-                         outfeatures,
-                         bias,
-                         tp_size=tp_size,
-                         tp_rank=tp_rank,
-                         row_split=row_split)
+        super().__init__(
+            bits, groupsize, infeatures, outfeatures, bias, tp_size=tp_size, tp_rank=tp_rank, row_split=row_split
+        )
         self.process_group = None
 
     @staticmethod
-    def from_native_module(module: nn.Module, process_group: Union[ProcessGroup, List[ProcessGroup]], *args,
-                           **kwargs) -> ParallelModule:
+    def from_native_module(
+        module: nn.Module, process_group: Union[ProcessGroup, List[ProcessGroup]], *args, **kwargs
+    ) -> ParallelModule:
         LazyInitContext.materialize(module)
         # get the attributes
         in_features = module.in_features
 
         # ensure only one process group is passed
         if isinstance(process_group, (list, tuple)):
-            assert len(process_group) == 1, \
-                f'Expected only one process group, got {len(process_group)}.'
+            assert len(process_group) == 1, f"Expected only one process group, got {len(process_group)}."
             process_group = process_group[0]
 
         tp_size = dist.get_world_size(process_group)
@@ -340,14 +337,17 @@ class ColCaiQuantLinear(CaiQuantLinear, ParallelModule):
 
         if in_features % tp_size != 0:
             raise ValueError(
-                f"The size of in_features:{in_features} is not integer multiples of tensor parallel size: {tp_size}!")
-        linear_1d = ColCaiQuantLinear(module.bits,
+                f"The size of in_features:{in_features} is not integer multiples of tensor parallel size: {tp_size}!"
+            )
+        linear_1d = ColCaiQuantLinear(
+            module.bits,
             module.group_size,
             module.in_features,
             module.out_features // tp_size,
             module.bias is not None,
             tp_size=tp_size,
-                                      tp_rank=tp_rank)
+            tp_rank=tp_rank,
+        )
         linear_1d.process_group = process_group
 
         split_column_copy(module, linear_1d, tp_rank=tp_rank, **kwargs)

colossalai/kernel/triton/context_attention.py

@@ -5,6 +5,7 @@ import torch
 try:
     import triton
     import triton.language as tl
+
     HAS_TRITON = True
 except ImportError:
     HAS_TRITON = False
@@ -16,6 +17,7 @@ if HAS_TRITON:
     https://github.com/ModelTC/lightllm/blob/f093edc20683ac3ea1bca3fb5d8320a0dd36cf7b/lightllm/models/llama/triton_kernel/context_flashattention_nopad.py#L10
     """
     if triton.__version__ < "2.1.0":
+
         @triton.jit
         def _context_flash_attention_kernel(
             Q,
@@ -131,23 +133,41 @@ if HAS_TRITON:
                 m_i = m_i_new
 
             off_o = (
-                (cur_batch_start_index + offs_m[:, None]) * stride_obs + cur_head * stride_oh + offs_d[None, :] * stride_od
+                (cur_batch_start_index + offs_m[:, None]) * stride_obs
+                + cur_head * stride_oh
+                + offs_d[None, :] * stride_od
             )
             out_ptrs = Out + off_o
             tl.store(out_ptrs, acc, mask=offs_m[:, None] < cur_batch_seq_len)
             return
+
     else:
         # this function is modified from https://github.com/ModelTC/lightllm/blob/main/lightllm/models/llama/triton_kernel/context_flashattention_nopad.py#L11
         @triton.jit
         def _context_flash_attention_kernel_2(
-            Q, K, V, sm_scale, Alibi, B_Start_Loc, B_Seqlen,
+            Q,
+            K,
+            V,
+            sm_scale,
+            Alibi,
+            B_Start_Loc,
+            B_Seqlen,
             Out,
             kv_group_num,
-            stride_qbs, stride_qh, stride_qd,
-            stride_kbs, stride_kh, stride_kd,
-            stride_vbs, stride_vh, stride_vd,
-            stride_obs, stride_oh, stride_od,
-            BLOCK_M: tl.constexpr, BLOCK_DMODEL: tl.constexpr,
+            stride_qbs,
+            stride_qh,
+            stride_qd,
+            stride_kbs,
+            stride_kh,
+            stride_kd,
+            stride_vbs,
+            stride_vh,
+            stride_vd,
+            stride_obs,
+            stride_oh,
+            stride_od,
+            BLOCK_M: tl.constexpr,
+            BLOCK_DMODEL: tl.constexpr,
             BLOCK_N: tl.constexpr,
         ):
             cur_batch = tl.program_id(0)
@@ -166,7 +186,11 @@ if HAS_TRITON:
             offs_n = tl.arange(0, BLOCK_N)
             offs_d = tl.arange(0, BLOCK_DMODEL)
             offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
-            off_q = (cur_batch_in_all_start_index + offs_m[:, None]) * stride_qbs + cur_head * stride_qh + offs_d[None, :] * stride_qd
+            off_q = (
+                (cur_batch_in_all_start_index + offs_m[:, None]) * stride_qbs
+                + cur_head * stride_qh
+                + offs_d[None, :] * stride_qd
+            )
             if kv_group_num is None or kv_group_num == 1:
                 off_k = offs_n[None, :] * stride_kbs + cur_head * stride_kh + offs_d[:, None] * stride_kd
                 off_v = offs_n[:, None] * stride_vbs + cur_head * stride_vh + offs_d[None, :] * stride_vd
@@ -191,8 +215,11 @@ if HAS_TRITON:
             for start_n in range(0, block_mask * (start_m + 1) * BLOCK_M, BLOCK_N):
                 start_n = tl.multiple_of(start_n, BLOCK_N)
                 # -- compute qk ----
-                k = tl.load(k_ptrs + (cur_batch_in_all_start_index + start_n) * stride_kbs,
-                            mask=(start_n + offs_n[None, :]) < cur_batch_seq_len, other=0.0)
+                k = tl.load(
+                    k_ptrs + (cur_batch_in_all_start_index + start_n) * stride_kbs,
+                    mask=(start_n + offs_n[None, :]) < cur_batch_seq_len,
+                    other=0.0,
+                )
 
                 qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
                 qk += tl.dot(q, k)
@@ -220,8 +247,11 @@ if HAS_TRITON:
                 acc_scale = l_i / l_i_new * alpha
                 acc = acc * acc_scale[:, None]
                 # update acc
-                v = tl.load(v_ptrs + (cur_batch_in_all_start_index + start_n) * stride_vbs,
-                            mask=(start_n + offs_n[:, None]) < cur_batch_seq_len, other=0.0)
+                v = tl.load(
+                    v_ptrs + (cur_batch_in_all_start_index + start_n) * stride_vbs,
+                    mask=(start_n + offs_n[:, None]) < cur_batch_seq_len,
+                    other=0.0,
+                )
 
                 p = p.to(v.dtype)
                 acc += tl.dot(p, v)
@@ -229,7 +259,11 @@ if HAS_TRITON:
                 l_i = l_i_new
                 m_i = m_i_new
             # initialize pointers to output
-            off_o = (cur_batch_in_all_start_index + offs_m[:, None]) * stride_obs + cur_head * stride_oh + offs_d[None, :] * stride_od
+            off_o = (
+                (cur_batch_in_all_start_index + offs_m[:, None]) * stride_obs
+                + cur_head * stride_oh
+                + offs_d[None, :] * stride_od
+            )
             out_ptrs = Out + off_o
             tl.store(out_ptrs, acc, mask=offs_m[:, None] < cur_batch_seq_len)
             return
@@ -286,7 +320,13 @@ if HAS_TRITON:
             )
         else:
             _context_flash_attention_kernel_2[grid](
-                q, k, v, sm_scale, alibi, b_start_loc, b_seq_len,
+                q,
+                k,
+                v,
+                sm_scale,
+                alibi,
+                b_start_loc,
+                b_seq_len,
                 o,
                 None,
                 q.stride(0),
@@ -388,6 +428,7 @@ if HAS_TRITON:
                 BLOCK_DMODEL=Lk,
                 BLOCK_N=BLOCK,
                 num_warps=num_warps,
-                num_stages=1,)
+                num_stages=1,
+            )
 
         return

colossalai/kernel/triton/flash_decoding.py

@@ -1,8 +1,10 @@
 # adepted from https://github.com/ModelTC/lightllm/blob/ece7b43f8a6dfa74027adc77c2c176cff28c76c8/lightllm/models/llama/triton_kernel/flash_decoding.py
 import torch
+
 try:
     from lightllm.models.llama.triton_kernel.flash_decoding_stage1 import flash_decode_stage1
     from lightllm.models.llama.triton_kernel.flash_decoding_stage2 import flash_decode_stage2
+
     HAS_LIGHTLLM_KERNEL = True
 except:
     print("install lightllm from https://github.com/ModelTC/lightllm/blob/ece7b43f8a6dfa74027adc77c2c176cff28c76c8")
@@ -10,31 +12,29 @@ except:
 
 
 if HAS_LIGHTLLM_KERNEL:
+
     def token_flash_decoding(q, o_tensor, infer_state, q_head_num, head_dim, cache_k, cache_v):
         BLOCK_SEQ = 256
         batch_size = infer_state.batch_size
         max_len_in_batch = infer_state.max_len_in_batch
 
-
         calcu_shape1 = (batch_size, q_head_num, head_dim)
 
-        if getattr(infer_state, 'mid_o', None) is None:
-            infer_state.mid_o = torch.empty([batch_size, 
-                                            q_head_num, 
-                                            max_len_in_batch // BLOCK_SEQ + 1, 
-                                            head_dim], 
+        if getattr(infer_state, "mid_o", None) is None:
+            infer_state.mid_o = torch.empty(
+                [batch_size, q_head_num, max_len_in_batch // BLOCK_SEQ + 1, head_dim],
                 dtype=torch.float32,
-                                            device="cuda")
-            infer_state.mid_o_logexpsum = torch.empty([batch_size, 
-                                            q_head_num,
-                                            max_len_in_batch // BLOCK_SEQ + 1], 
-                                            dtype=torch.float32, 
-                                            device="cuda")
+                device="cuda",
+            )
+            infer_state.mid_o_logexpsum = torch.empty(
+                [batch_size, q_head_num, max_len_in_batch // BLOCK_SEQ + 1], dtype=torch.float32, device="cuda"
+            )
 
         mid_o = infer_state.mid_o
         mid_o_logexpsum = infer_state.mid_o_logexpsum
 
-        flash_decode_stage1(q.view(calcu_shape1),
+        flash_decode_stage1(
+            q.view(calcu_shape1),
             cache_k,
             cache_v,
             infer_state.block_loc,
@@ -42,9 +42,6 @@ if HAS_LIGHTLLM_KERNEL:
             infer_state.max_len_in_batch,
             mid_o,
             mid_o_logexpsum,
-                                    BLOCK_SEQ)
-        flash_decode_stage2(mid_o,
-                            mid_o_logexpsum, 
-                            infer_state.seq_len, 
-                            o_tensor.view(calcu_shape1), 
-                            BLOCK_SEQ)
+            BLOCK_SEQ,
+        )
+        flash_decode_stage2(mid_o, mid_o_logexpsum, infer_state.seq_len, o_tensor.view(calcu_shape1), BLOCK_SEQ)

colossalai/kernel/triton/llama_act_combine_kernel.py

@@ -8,6 +8,7 @@ from torch.cuda.amp import custom_bwd, custom_fwd
 try:
     import triton
     import triton.language as tl
+
     HAS_TRITON = True
 except ImportError:
     HAS_TRITON = False
@@ -41,9 +42,9 @@ if HAS_TRITON:
         for off in range(0, N, BLOCK_SIZE):
             cols = off + tl.arange(0, BLOCK_SIZE)
             mask = cols < N
-            x_gate1 = tl.load(X_GATE1 + cols, mask=mask, other=0.)
-            x_gate2 = tl.load(X_GATE2 + cols, mask=mask, other=0.)
-            x_up = tl.load(X_UP + cols, mask=mask, other=0.)
+            x_gate1 = tl.load(X_GATE1 + cols, mask=mask, other=0.0)
+            x_gate2 = tl.load(X_GATE2 + cols, mask=mask, other=0.0)
+            x_up = tl.load(X_UP + cols, mask=mask, other=0.0)
             x_gate2_sigmoid = tl.sigmoid(x_gate2.to(tl.float32)).to(x_gate2.dtype)
             y = x_gate1 * x_gate2 * x_gate2_sigmoid * x_up
             # Write output
@@ -76,10 +77,10 @@ if HAS_TRITON:
         for off in range(0, N, BLOCK_SIZE):
             cols = off + tl.arange(0, BLOCK_SIZE)
             mask = cols < N
-            x_gate1 = tl.load(X_GATE1 + cols, mask=mask, other=0.)
-            x_gate2 = tl.load(X_GATE2 + cols, mask=mask, other=0.)
-            x_up = tl.load(X_UP + cols, mask=mask, other=0.)
-            y_grad = tl.load(Y_GRAD + cols, mask=mask, other=0.)
+            x_gate1 = tl.load(X_GATE1 + cols, mask=mask, other=0.0)
+            x_gate2 = tl.load(X_GATE2 + cols, mask=mask, other=0.0)
+            x_up = tl.load(X_UP + cols, mask=mask, other=0.0)
+            y_grad = tl.load(Y_GRAD + cols, mask=mask, other=0.0)
 
             # forward: y = x_gate1 * x_gate2 * tl.sigmoid(x_gate2) * x_up
             x_gate2_sigmoid = tl.sigmoid(x_gate2.to(tl.float32)).to(x_gate2.dtype)
@@ -147,14 +148,9 @@ if HAS_TRITON:
             # restore setting
             ctx.M, ctx.N, ctx.BLOCK_SIZE, ctx.num_warps = M, N, BLOCK_SIZE, num_warps
             # enqueue kernel
-            _llama_act_combine_forward[(M,)](x_gate1,
-                                             x_gate2,
-                                             x_up,
-                                             y,
-                                             x_up.stride(-2),
-                                             N,
-                                             BLOCK_SIZE=BLOCK_SIZE,
-                                             num_warps=num_warps)
+            _llama_act_combine_forward[(M,)](
+                x_gate1, x_gate2, x_up, y, x_up.stride(-2), N, BLOCK_SIZE=BLOCK_SIZE, num_warps=num_warps
+            )
             return y
 
         @staticmethod
@@ -166,11 +162,15 @@ if HAS_TRITON:
 
             # init grad
             y_grad = grad_outputs[0]
-            x_gate1_grad, x_gate2_grad, x_up_grad = torch.empty_like(x_gate1), torch.empty_like(
-                x_gate2), torch.empty_like(x_up)
+            x_gate1_grad, x_gate2_grad, x_up_grad = (
+                torch.empty_like(x_gate1),
+                torch.empty_like(x_gate2),
+                torch.empty_like(x_up),
+            )
 
             # enqueue kernel
-            _llama_act_combine_backward[(M,)](x_gate1,
+            _llama_act_combine_backward[(M,)](
+                x_gate1,
                 x_gate2,
                 x_up,
                 x_gate1_grad,
@@ -180,6 +180,7 @@ if HAS_TRITON:
                 x_up.stride(-2),
                 N,
                 BLOCK_SIZE=BLOCK_SIZE,
-                                              num_warps=num_warps)
+                num_warps=num_warps,
+            )
             x_gate_grad = torch.cat([x_gate1_grad, x_gate2_grad], dim=-1)
             return x_gate_grad, x_up_grad, None, None

colossalai/kernel/triton/token_attention_kernel.py

@@ -13,10 +13,18 @@ except ImportError:
     print("please install triton from https://github.com/openai/triton")
 
 try:
-    from lightllm.models.llama.triton_kernel.token_attention_nopad_reduceV import token_att_fwd2 as lightllm_llama_token_att_fwd2
-    from lightllm.models.llama.triton_kernel.token_attention_nopad_att1 import token_att_fwd as lightllm_llama_token_att_fwd
-    from lightllm.models.llama.triton_kernel.token_attention_nopad_softmax import token_softmax_fwd as lightllm_llama_token_softmax_fwd
-    from lightllm.models.bloom.triton_kernel.token_attention_nopad_att1 import token_att_fwd as lightllm_bloom_token_att_fwd
+    from lightllm.models.bloom.triton_kernel.token_attention_nopad_att1 import (
+        token_att_fwd as lightllm_bloom_token_att_fwd,
+    )
+    from lightllm.models.llama.triton_kernel.token_attention_nopad_att1 import (
+        token_att_fwd as lightllm_llama_token_att_fwd,
+    )
+    from lightllm.models.llama.triton_kernel.token_attention_nopad_reduceV import (
+        token_att_fwd2 as lightllm_llama_token_att_fwd2,
+    )
+    from lightllm.models.llama.triton_kernel.token_attention_nopad_softmax import (
+        token_softmax_fwd as lightllm_llama_token_softmax_fwd,
+    )
 
     HAS_TRITON_TOKEN_ATTENTION = True
 except ImportError:
@@ -205,9 +213,7 @@ class Llama2TokenAttentionForwards:
 
         if triton.__version__ == "2.0.0":
             prob = torch.empty_like(att_m_tensor)
-            lightllm_llama_token_softmax_fwd(
-                att_m_tensor, kv_cache_start_loc, kv_cache_seq_len, prob, max_len_in_batch
-            )
+            lightllm_llama_token_softmax_fwd(att_m_tensor, kv_cache_start_loc, kv_cache_seq_len, prob, max_len_in_batch)
             att_m_tensor = None
 
             lightllm_llama_token_att_fwd2(

colossalai/legacy/inference/tensor_parallel/modeling/llama.py

@@ -8,7 +8,9 @@ from transformers.models.llama.modeling_llama import LlamaAttention, LlamaDecode
 from colossalai.inference.tensor_parallel.batch_infer_state import BatchInferState
 from colossalai.kernel.triton import llama_context_attn_fwd, token_attention_fwd
 from colossalai.kernel.triton.token_attention_kernel import Llama2TokenAttentionForwards
+
 from ._utils import copy_kv_to_mem_cache
+
 try:
     from lightllm.models.llama.triton_kernel.context_flashattention_nopad import (
         context_attention_fwd as lightllm_llama_context_attention_fwd,

colossalai/tensor/d_tensor/__init__.py

@@ -2,13 +2,13 @@ from .api import (
     compute_global_numel,
     customized_distributed_tensor_to_param,
     distribute_tensor,
-    init_as_dtensor,
     distribute_tensor_with_customization,
-    init_tensor_as_customization_distributed,
     get_device_mesh,
     get_global_shape,
     get_layout,
     get_sharding_spec,
+    init_as_dtensor,
+    init_tensor_as_customization_distributed,
     is_customized_distributed_tensor,
     is_distributed_tensor,
     is_sharded,

colossalai/tensor/d_tensor/api.py

@@ -128,7 +128,10 @@ def distribute_tensor(tensor: torch.Tensor, device_mesh: DeviceMesh, sharding_sp
 
     return sharded_tensor
 
-def init_as_dtensor(tensor: torch.Tensor, device_mesh: DeviceMesh, sharding_spec: ShardingSpec, global_shape: torch.Size) -> torch.Tensor:
+
+def init_as_dtensor(
+    tensor: torch.Tensor, device_mesh: DeviceMesh, sharding_spec: ShardingSpec, global_shape: torch.Size
+) -> torch.Tensor:
     assert not is_distributed_tensor(tensor), "The input tensor is already a distributed tensor."
     dist_layout = Layout(device_mesh=device_mesh, sharding_spec=sharding_spec, global_shape=global_shape)
 
@@ -140,6 +143,7 @@ def init_as_dtensor(tensor: torch.Tensor, device_mesh: DeviceMesh, sharding_spec
 
     return tensor
 
+
 def redistribute(dtensor: torch.Tensor, device_mesh: DeviceMesh, sharding_spec: ShardingSpec) -> None:
     """
     Convert the layout of the tensor from source_spec to target_spec.
@@ -468,7 +472,6 @@ def init_tensor_as_customization_distributed(tensor: torch.Tensor, shard_fn, gat
     assert callable(gather_fn), "The gather_fn must be callable."
     assert not is_distributed_tensor(tensor), "The input tensor is already a distributed tensor."
 
-
     # set the shard_fn and gather_fn as attributes of the distributed tensor
     tensor.shard_fn = shard_fn
     tensor.gather_fn = gather_fn

examples/images/vit/vit_benchmark.py

@@ -119,9 +119,7 @@ def main():
             if hasattr(booster.plugin, "stage_manager") and booster.plugin.stage_manager is not None:
                 # run pipeline forward backward
                 batch = iter([batch])
-                outputs = booster.execute_pipeline(
-                    batch, model, criterion, optimizer, return_loss=True
-                )
+                outputs = booster.execute_pipeline(batch, model, criterion, optimizer, return_loss=True)
             else:
                 outputs = model(**batch)
                 loss = criterion(outputs, None)

examples/language/llama2/finetune.py

@@ -270,9 +270,7 @@ def main():
         ) as pbar:
             for step in pbar:
                 if use_pipeline:
-                    outputs = booster.execute_pipeline(
-                        dataloader_iter, model, _criterion, optimizer, return_loss=True
-                    )
+                    outputs = booster.execute_pipeline(dataloader_iter, model, _criterion, optimizer, return_loss=True)
                     loss = outputs["loss"]
                 else:
                     batch = next(dataloader_iter)

examples/language/llama2/pretrain.py

@@ -285,9 +285,7 @@ def main():
         ) as pbar:
             for step in pbar:
                 if use_pipeline:
-                    outputs = booster.execute_pipeline(
-                        dataloader_iter, model, _criterion, optimizer, return_loss=True
-                    )
+                    outputs = booster.execute_pipeline(dataloader_iter, model, _criterion, optimizer, return_loss=True)
                     loss = outputs["loss"]
                 else:
                     batch = next(dataloader_iter)

examples/language/openmoe/benchmark/utils.py

@@ -50,7 +50,6 @@ def all_reduce_mean(x: float, world_size: int) -> float:
 
 
 class Timer:
-
     def __init__(self) -> None:
         self.start_time: Optional[float] = None
         self.duration: float = 0.0
@@ -112,7 +111,7 @@ class PerformanceEvaluator:
         batch_size, seq_len = input_ids.shape
 
         self.num_samples += batch_size
-        self.flop += (batch_size * seq_len * self.model_numel * 2 * (3 + int(self.enable_grad_checkpoint)))
+        self.flop += batch_size * seq_len * self.model_numel * 2 * (3 + int(self.enable_grad_checkpoint))
 
     def on_fit_end(self) -> None:
         avg_duration = all_reduce_mean(self.timer.duration, self.world_size)
@@ -122,5 +121,6 @@ class PerformanceEvaluator:
         if dist.get_rank() == 0:
             print(
                 f"num_samples: {self.num_samples}, dp_world_size: {self.dp_world_size}, flop: {self.flop}, avg_duration: {avg_duration}, "
-                f"avg_throughput: {avg_throughput}")
+                f"avg_throughput: {avg_throughput}"
+            )
             print(f"Throughput: {avg_throughput:.2f} samples/sec, TFLOPS per GPU: {avg_tflops_per_gpu:.2f}")

examples/language/openmoe/infer.py

@@ -16,17 +16,15 @@ def inference(args):
     tokenizer = T5Tokenizer.from_pretrained("google/umt5-small")
     if args.model == "test":
         config = LlamaConfig.from_pretrained("hpcai-tech/openmoe-base")
-        set_openmoe_args(config,
-                         num_experts=config.num_experts,
-                         moe_layer_interval=config.moe_layer_interval,
-                         enable_kernel=True)
+        set_openmoe_args(
+            config, num_experts=config.num_experts, moe_layer_interval=config.moe_layer_interval, enable_kernel=True
+        )
         model = OpenMoeForCausalLM(config)
     else:
         config = LlamaConfig.from_pretrained(f"hpcai-tech/openmoe-{args.model}")
-        set_openmoe_args(config,
-                         num_experts=config.num_experts,
-                         moe_layer_interval=config.moe_layer_interval,
-                         enable_kernel=False)
+        set_openmoe_args(
+            config, num_experts=config.num_experts, moe_layer_interval=config.moe_layer_interval, enable_kernel=False
+        )
         model = OpenMoeForCausalLM.from_pretrained(f"hpcai-tech/openmoe-{args.model}", config=config)
     model = model.eval().bfloat16()
     model = model.to(torch.cuda.current_device())

examples/language/openmoe/model/convert_openmoe_ckpt.py

@@ -172,9 +172,9 @@ def make_state_dict(converted_params):
 def load_t5x_weights_in_t5(model, config, t5x_checkpoint_path):
     """Replaces the params in model witht the T5X converted params."""
     variables = checkpoints.load_t5x_checkpoint(t5x_checkpoint_path)
-    converted = convert_t5x_to_pytorch(variables,
-                                       num_layers=config.num_hidden_layers,
-                                       moe_interval=config.moe_layer_interval)
+    converted = convert_t5x_to_pytorch(
+        variables, num_layers=config.num_hidden_layers, moe_interval=config.moe_layer_interval
+    )
     state_dict = make_state_dict(converted)
     model.load_state_dict(state_dict, strict=True)
 
@@ -203,11 +203,9 @@ def convert_t5x_checkpoint_to_pytorch(t5x_checkpoint_path, config_file, pytorch_
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
     # Required parameters
-    parser.add_argument("--t5x_checkpoint_path",
-                        default=None,
-                        type=str,
-                        required=True,
-                        help="Path to the T5X checkpoint.")
+    parser.add_argument(
+        "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
+    )
     parser.add_argument(
         "--config_file",
         default=None,
@@ -215,10 +213,8 @@ if __name__ == "__main__":
         required=True,
         help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
     )
-    parser.add_argument("--pytorch_dump_path",
-                        default=None,
-                        type=str,
-                        required=True,
-                        help="Path to the output PyTorch model.")
+    parser.add_argument(
+        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
+    )
     args = parser.parse_args()
     convert_t5x_checkpoint_to_pytorch(args.t5x_checkpoint_path, args.config_file, args.pytorch_dump_path)

examples/language/opt/opt_train_demo.py

@@ -41,9 +41,7 @@ def train_epoch(epoch, model, optimizer, _criterion, lr_scheduler, dataloader, b
         # Forward pass
         for _ in pbar:
             if use_pipeline:
-                outputs = booster.execute_pipeline(
-                    dataloader, model, _criterion, optimizer, return_loss=True
-                )
+                outputs = booster.execute_pipeline(dataloader, model, _criterion, optimizer, return_loss=True)
                 # Backward and optimize
                 if is_pp_last_stage:
                     loss = outputs["loss"]

extensions/cpu_adam/__init__.py

@@ -1,5 +1,4 @@
 from .cpu_adam_arm import CpuAdamArmExtension
 from .cpu_adam_x86 import CpuAdamX86Extension
 
-__all__ = ['CpuAdamArmExtension', 'CpuAdamX86Extension']
-
+__all__ = ["CpuAdamArmExtension", "CpuAdamX86Extension"]

extensions/moe/__init__.py

@@ -1,3 +1,3 @@
 from .moe_cuda import MoeCudaExtension
 
-__all__ = ['MoeCudaExtension']
+__all__ = ["MoeCudaExtension"]

extensions/optimizer/__init__.py

@@ -1,3 +1,3 @@
 from .fused_optimizer_cuda import FusedOptimizerCudaExtension
 
-__all__ = ['FusedOptimizerCudaExtension']
+__all__ = ["FusedOptimizerCudaExtension"]

extensions/softmax/__init__.py

@@ -1,4 +1,4 @@
 from .scaled_masked_softmax_cuda import ScaledMaskedSoftmaxCudaExtension
 from .scaled_upper_triangle_masked_softmax_cuda import ScaledUpperTriangleMaskedSoftmaxCudaExtension
 
-__all__ = ['ScaledMaskedSoftmaxCudaExtension', 'ScaledUpperTriangleMaskedSoftmaxCudaExtension'] 
+__all__ = ["ScaledMaskedSoftmaxCudaExtension", "ScaledUpperTriangleMaskedSoftmaxCudaExtension"]

tests/kit/model_zoo/__init__.py

@@ -1,33 +1,33 @@
 import os
+
 from . import custom, diffusers, timm, torchaudio, torchvision, transformers
 from .executor import run_fwd, run_fwd_bwd
 from .registry import model_zoo
 
 # We pick a subset of models for fast testing in order to reduce the total testing time
 COMMON_MODELS = [
-    'custom_hanging_param_model',
-    'custom_nested_model',
-    'custom_repeated_computed_layers',
-    'custom_simple_net',
-    'diffusers_clip_text_model',
-    'diffusers_auto_encoder_kl',
-    'diffusers_unet2d_model',
-    'timm_densenet',
-    'timm_resnet',
-    'timm_swin_transformer',
-    'torchaudio_wav2vec2_base',
-    'torchaudio_conformer',
-    'transformers_bert_for_masked_lm',
-    'transformers_bloom_for_causal_lm',
-    'transformers_falcon_for_causal_lm',
-    'transformers_chatglm_for_conditional_generation',
-    'transformers_llama_for_casual_lm',
-    'transformers_vit_for_masked_image_modeling',
-    'transformers_mistral_for_casual_lm'
+    "custom_hanging_param_model",
+    "custom_nested_model",
+    "custom_repeated_computed_layers",
+    "custom_simple_net",
+    "diffusers_clip_text_model",
+    "diffusers_auto_encoder_kl",
+    "diffusers_unet2d_model",
+    "timm_densenet",
+    "timm_resnet",
+    "timm_swin_transformer",
+    "torchaudio_wav2vec2_base",
+    "torchaudio_conformer",
+    "transformers_bert_for_masked_lm",
+    "transformers_bloom_for_causal_lm",
+    "transformers_falcon_for_causal_lm",
+    "transformers_chatglm_for_conditional_generation",
+    "transformers_llama_for_casual_lm",
+    "transformers_vit_for_masked_image_modeling",
+    "transformers_mistral_for_casual_lm",
 ]
 
-IS_FAST_TEST = os.environ.get('FAST_TEST', '0') == '1'
+IS_FAST_TEST = os.environ.get("FAST_TEST", "0") == "1"
 
 
-__all__ = ["model_zoo", "run_fwd", "run_fwd_bwd", 'COMMON_MODELS', 'IS_FAST_TEST']
-
+__all__ = ["model_zoo", "run_fwd", "run_fwd_bwd", "COMMON_MODELS", "IS_FAST_TEST"]

tests/kit/model_zoo/transformers/chatglm2.py

@@ -2,6 +2,7 @@ import torch
 
 from colossalai.shardformer.modeling.chatglm2_6b.configuration_chatglm import ChatGLMConfig
 from colossalai.shardformer.modeling.chatglm2_6b.modeling_chatglm import ChatGLMForConditionalGeneration, ChatGLMModel
+
 from ..registry import ModelAttribute, model_zoo
 
 # ================================

tests/test_checkpoint_io/test_hybrid_parallel_plugin_checkpoint_io.py

@@ -74,9 +74,7 @@ def exam_state_dict(shard: bool, model_name: str, size_per_shard: int, test_conf
     data = data_gen_fn()
     model.train()
     if booster.plugin.stage_manager is not None:
-        booster.execute_pipeline(
-            _preprocess_data(data), model, _criterion, optimizer, return_loss=True
-        )
+        booster.execute_pipeline(_preprocess_data(data), model, _criterion, optimizer, return_loss=True)
     else:
         output = model(**_preprocess_data(data))
         loss = criterion(output)
@@ -108,9 +106,7 @@ def exam_state_dict(shard: bool, model_name: str, size_per_shard: int, test_conf
     data_for_shard = data_gen_fn()
     data_for_origin = data_gen_fn()
     if booster.plugin.stage_manager is not None:
-        booster.execute_pipeline(
-            _preprocess_data(data_for_shard), model, _criterion, optimizer, return_loss=True
-        )
+        booster.execute_pipeline(_preprocess_data(data_for_shard), model, _criterion, optimizer, return_loss=True)
         booster.execute_pipeline(
             _preprocess_data(data_for_origin),
             new_model,

tests/test_checkpoint_io/test_torch_fsdp_checkpoint_io.py

@@ -113,6 +113,7 @@ def check_torch_fsdp_ckpt():
         full_osd = FSDP.full_optim_state_dict(optimizer.unwrap_model().unwrap(), optim=optimizer)
 
         import copy
+
         sharded_osd = copy.deepcopy(full_osd)
 
         run_model()

tests/test_gptq/test_gptq_linear.py

@@ -1,16 +1,8 @@
-import math
-import time
-
-import numpy as np
 import pytest
 import torch
-import torch.nn as nn
-import transformers
 from packaging import version
 
 try:
-    import triton
-    import triton.language as tl
     HAS_TRITON = True
 except ImportError:
     HAS_TRITON = False
@@ -22,6 +14,7 @@ try:
     from exllama_kernels import prepare_buffers, set_tuning_params
 
     from colossalai.inference.quant.gptq import CaiQuantLinear
+
     HAS_AUTO_GPTQ = True
 except:
     HAS_AUTO_GPTQ = False
@@ -32,13 +25,14 @@ import warnings
 HAS_GPTQ_CUDA = False
 try:
     from colossalai.kernel.op_builder.gptq import GPTQBuilder
+
     gptq_cuda = GPTQBuilder().load()
     HAS_GPTQ_CUDA = True
 except ImportError:
-    warnings.warn('CUDA gptq is not installed')
+    warnings.warn("CUDA gptq is not installed")
     HAS_GPTQ_CUDA = False
 
-TRITON_CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse('11.4')
+TRITON_CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse("11.4")
 
 max_inner_outer_dim = 1
 max_input_len = 1
@@ -64,9 +58,9 @@ def init_buffer(cai_linear, use_act_order=False):
         max_input_len = 4096
     # The temp_state buffer is required to reorder X in the act-order case.
     # The temp_dq buffer is required to dequantize weights when using cuBLAS, typically for the prefill.
-    gptq_temp_state_buffer = torch.zeros((max_input_len, max_inner_outer_dim),
-                                         dtype=torch.float16,
-                                         device=torch.cuda.current_device())
+    gptq_temp_state_buffer = torch.zeros(
+        (max_input_len, max_inner_outer_dim), dtype=torch.float16, device=torch.cuda.current_device()
+    )
     gptq_temp_dq_buffer = torch.zeros((1, max_dq_buffer_size), dtype=torch.float16, device=torch.cuda.current_device())
 
     gptq_cuda.prepare_buffers(torch.device(torch.cuda.current_device()), gptq_temp_state_buffer, gptq_temp_dq_buffer)
@@ -77,10 +71,11 @@ def init_buffer(cai_linear, use_act_order=False):
     gptq_cuda.set_tuning_params(matmul_recons_thd, matmul_fused_remap, matmul_no_half2)
 
 
-@pytest.mark.skipif(not TRITON_CUDA_SUPPORT or not HAS_TRITON or not HAS_AUTO_GPTQ,
-                    reason="triton requires cuda version to be higher than 11.4 or not install auto-gptq")
+@pytest.mark.skipif(
+    not TRITON_CUDA_SUPPORT or not HAS_TRITON or not HAS_AUTO_GPTQ,
+    reason="triton requires cuda version to be higher than 11.4 or not install auto-gptq",
+)
 def test_gptq_linear():
-
     infeature = 1024
     outfeature = 1024
     group_size = 128
@@ -120,7 +115,7 @@ def test_gptq_linear():
     max_input_len = 2048
     buffers = {
         "temp_state": torch.zeros((max_input_len, max_inner_outer_dim), dtype=torch.float16, device=device),
-        "temp_dq": torch.zeros((1, max_dq_buffer_size), dtype=torch.float16, device=device)
+        "temp_dq": torch.zeros((1, max_dq_buffer_size), dtype=torch.float16, device=device),
     }
 
     prepare_buffers(device, buffers["temp_state"], buffers["temp_dq"])
@@ -146,5 +141,4 @@ def test_gptq_linear():
 
 
 if __name__ == "__main__":
-
     test_gptq_linear()

tests/test_optimizer/test_nvme.py

@@ -1,5 +1,5 @@
-import torch
 import pytest
+import torch
 
 from colossalai.nn.optimizer import CPUAdam, HybridAdam
 from colossalai.testing import clear_cache_before_run, parameterize
@@ -17,6 +17,7 @@ def check_params_equal(model, torch_model):
     for p, torch_p in zip(model.parameters(), torch_model.parameters()):
         assert torch.allclose(p, torch_p, atol=1e-3), f"diff: {torch.abs(p - torch_p)}"
 
+
 # TODO Something wrong with ci when running this test.
 @pytest.mark.skip(reason="skip because of something wrong with CI")
 @clear_cache_before_run()

tests/test_pipeline/test_schedule/test_interleaved.py

@@ -103,9 +103,7 @@ def run_pp(
     torch_loss = criterion(torch_output)
     torch_loss.backward()
 
-    pp_ret = schedule.forward_backward_step(
-        sharded_model, iter(input_list), criterion, pp_optimizer, return_loss=True
-    )
+    pp_ret = schedule.forward_backward_step(sharded_model, iter(input_list), criterion, pp_optimizer, return_loss=True)
 
     # check loss
     if stage_manager.is_last_stage(ignore_chunk=True):

tests/test_pipeline/test_schedule/test_oneF_oneB.py

@@ -99,9 +99,7 @@ def examine_pp(num_microbatch: int, batch_size: int):
     torch_output = torch_model(input_list[0])
     torch_loss = criterion(torch_output)
     torch_loss.backward()
-    pp_ret = schedule.forward_backward_step(
-        sharded_model, iter(input_list), criterion, pp_optimizer, return_loss=True
-    )
+    pp_ret = schedule.forward_backward_step(sharded_model, iter(input_list), criterion, pp_optimizer, return_loss=True)
 
     # check loss
     if stage_manager.is_last_stage():