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applications/Colossal-LLaMA-2/colossal_llama2/dataset/loader.py

@@ -80,15 +80,19 @@ def __call__(self, instances: Sequence[Dict[str, List[int]]]) -> Dict[str, torch
 
         # `List[torch.Tensor]`
         batch_input_ids = [
-            torch.LongTensor(instance["input_ids"][: self.max_length])
-            if len(instance["input_ids"]) > self.max_length
-            else torch.LongTensor(instance["input_ids"])
+            (
+                torch.LongTensor(instance["input_ids"][: self.max_length])
+                if len(instance["input_ids"]) > self.max_length
+                else torch.LongTensor(instance["input_ids"])
+            )
             for instance in instances
         ]
         batch_labels = [
-            torch.LongTensor(instance["labels"][: self.max_length])
-            if len(instance["labels"]) > self.max_length
-            else torch.LongTensor(instance["labels"])
+            (
+                torch.LongTensor(instance["labels"][: self.max_length])
+                if len(instance["labels"]) > self.max_length
+                else torch.LongTensor(instance["labels"])
+            )
             for instance in instances
         ]
 

applications/Colossal-LLaMA-2/train.py

@@ -253,9 +253,11 @@ def main() -> None:
     coordinator.print_on_master(f"Model params: {format_numel_str(model_numel)}")
 
     optimizer = HybridAdam(
-        model_params=filter(lambda p: p.requires_grad, model.parameters())
-        if args.freeze_non_embeds_params
-        else model.parameters(),
+        model_params=(
+            filter(lambda p: p.requires_grad, model.parameters())
+            if args.freeze_non_embeds_params
+            else model.parameters()
+        ),
         lr=args.lr,
         betas=(0.9, 0.95),
         weight_decay=args.weight_decay,

applications/ColossalChat/coati/dataset/loader.py

@@ -89,15 +89,19 @@ def __call__(self, instances: Sequence[Dict[str, List[int]]]) -> Dict[str, torch
 
         # `List[torch.Tensor]`
         batch_input_ids = [
-            torch.LongTensor(instance["input_ids"][: self.max_length])
-            if len(instance["input_ids"]) > self.max_length
-            else torch.LongTensor(instance["input_ids"])
+            (
+                torch.LongTensor(instance["input_ids"][: self.max_length])
+                if len(instance["input_ids"]) > self.max_length
+                else torch.LongTensor(instance["input_ids"])
+            )
             for instance in instances
         ]
         batch_labels = [
-            torch.LongTensor(instance["labels"][: self.max_length])
-            if len(instance["labels"]) > self.max_length
-            else torch.LongTensor(instance["labels"])
+            (
+                torch.LongTensor(instance["labels"][: self.max_length])
+                if len(instance["labels"]) > self.max_length
+                else torch.LongTensor(instance["labels"])
+            )
             for instance in instances
         ]
         if self.tokenizer.padding_side == "right":

applications/ColossalChat/coati/models/loss.py

@@ -1,6 +1,7 @@
 """
 loss functions
 """
+
 from typing import Optional, Tuple
 
 import torch

applications/ColossalChat/coati/models/reward_model.py

@@ -1,6 +1,7 @@
 """
 reward model
 """
+
 from typing import Optional
 
 import torch

applications/ColossalChat/coati/trainer/utils.py

@@ -1,6 +1,7 @@
 """
 Training utilities for Coati.
 """
+
 from typing import Any
 
 import torch

applications/ColossalEval/colossal_eval/dataset/agieval.py

@@ -78,7 +78,9 @@ def get_prompt(line: Dict, dataset_name: str, logger: DistributedLogger) -> Dict
             option_string = "ABCDEFG"
             count = len(line["options"])
 
-            input = "问题：" + line["question"] + " " + "从以下选项中选择：" + " ".join(line["options"]) + "\n" + "答案："
+            input = (
+                "问题：" + line["question"] + " " + "从以下选项中选择：" + " ".join(line["options"]) + "\n" + "答案："
+            )
 
             all_classes = list(option_string[0:count])
 
@@ -150,7 +152,15 @@ def combine_prompt(prompt_path, dataset_name, load_explanation=True, chat_mode=F
             )
         elif dataset_name in chinese_qa_datasets:
             question_input = (
-                "问题：" + passage + " " + question + "\n" + "从以下选项中选择：" + " ".join(options) + "\n" + "答案：{}".format(label)
+                "问题："
+                + passage
+                + " "
+                + question
+                + "\n"
+                + "从以下选项中选择："
+                + " ".join(options)
+                + "\n"
+                + "答案：{}".format(label)
             )
         elif dataset_name in english_cloze_datasets:
             question_input = "Question: ".format(idx + 1) + question + "\n" + "Answer: {}".format(answer)

applications/ColossalEval/colossal_eval/dataset/ceval.py

@@ -57,7 +57,11 @@
     "urban_and_rural_planner": ["Urban and Rural Planner", "注册城乡规划师", "Other"],
     "accountant": ["Accountant", "注册会计师", "Other"],
     "fire_engineer": ["Fire Engineer", "注册消防工程师", "Other"],
-    "environmental_impact_assessment_engineer": ["Environmental Impact Assessment Engineer", "环境影响评价工程师", "Other"],
+    "environmental_impact_assessment_engineer": [
+        "Environmental Impact Assessment Engineer",
+        "环境影响评价工程师",
+        "Other",
+    ],
     "tax_accountant": ["Tax Accountant", "税务师", "Other"],
     "physician": ["Physician", "医师资格", "Other"],
 }

applications/ColossalEval/colossal_eval/dataset/mtbench.py

@@ -56,9 +56,11 @@ def load(path: str, logger: DistributedLogger, few_shot: bool) -> List[Dict]:
                     "instruction": question["turns"],
                     "input": "",
                     "output": [],
-                    "target": [""] * turn_number
-                    if question["question_id"] not in reference
-                    else reference[question["question_id"]],
+                    "target": (
+                        [""] * turn_number
+                        if question["question_id"] not in reference
+                        else reference[question["question_id"]]
+                    ),
                 }
 
                 if category in dataset["test"]:

applications/ColossalEval/colossal_eval/models/huggingface.py

@@ -77,7 +77,9 @@ def _get_choices_indices(self, language: str):
             self.indices_for_choices[0].append(
                 self.tokenizer(f"Answer: {choice}", add_special_tokens=False).input_ids[-1]
             )
-            self.indices_for_choices[1].append(self.tokenizer(f"答案：{choice}", add_special_tokens=False).input_ids[-1])
+            self.indices_for_choices[1].append(
+                self.tokenizer(f"答案：{choice}", add_special_tokens=False).input_ids[-1]
+            )
 
     def _load_tokenizer(self, path: str, tokenizer_path: Optional[str], tokenizer_kwargs: dict):
         """

applications/ColossalMoE/infer.py

@@ -96,7 +96,11 @@ def main():
     if coordinator.rank == 0:
         text = ["Hello my name is"]
     else:
-        text = ["What's the largest country in the world?", "How many people live in China?", "帮我续写这首诗：离离原上草"]
+        text = [
+            "What's the largest country in the world?",
+            "How many people live in China?",
+            "帮我续写这首诗：离离原上草",
+        ]
     tokenizer.pad_token = tokenizer.unk_token
     inputs = tokenizer(text, return_tensors="pt", padding=True).to(torch.cuda.current_device())
 

applications/ColossalMoE/train.py

@@ -195,9 +195,9 @@ def main():
     lr_scheduler = CosineAnnealingWarmupLR(
         optimizer=optimizer,
         total_steps=args.num_epochs * len(dataloader),
-        warmup_steps=args.warmup_steps
-        if args.warmup_steps is not None
-        else int(args.num_epochs * len(dataloader) * 0.025),
+        warmup_steps=(
+            args.warmup_steps if args.warmup_steps is not None else int(args.num_epochs * len(dataloader) * 0.025)
+        ),
         eta_min=0.1 * args.lr,
     )
 

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

@@ -7,6 +7,7 @@
 https://github.com/langchain-ai/langchain
 The original code is licensed under the MIT license.
 """
+
 from __future__ import annotations
 
 import copy

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

@@ -8,6 +8,7 @@
 https://github.com/langchain-ai/langchain
 The original code is licensed under the MIT license.
 """
+
 import copy
 from typing import Any, Mapping, Optional, Protocol
 

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

@@ -7,6 +7,7 @@
 https://github.com/langchain-ai/langchain
 The original code is licensed under the MIT license.
 """
+
 import copy
 from typing import Any, List
 

applications/ColossalQA/colossalqa/data_loader/table_dataloader.py

@@ -2,7 +2,6 @@
 Class for loading table type data. please refer to Pandas-Input/Output for file format details.
 """
 
-
 import glob
 import os
 

applications/ColossalQA/colossalqa/local/colossalcloud_llm.py

@@ -20,6 +20,7 @@
 print(resp)  # super-heavyweight awesome-natured yawning Australian creature!
 
 """
+
 import json
 from typing import Any, Mapping
 

applications/ColossalQA/colossalqa/local/llm.py

@@ -12,6 +12,7 @@
 logger.info(llm(TEST_PROMPT_CHATGLM, max_new_tokens=100), verbose=True)
 
 """
+
 from typing import Any, List, Mapping, Optional
 
 import torch

applications/ColossalQA/colossalqa/local/utils.py

@@ -1,6 +1,7 @@
 """
 Generation utilities
 """
+
 import json
 from typing import List
 

applications/ColossalQA/colossalqa/memory.py

@@ -2,6 +2,7 @@
 Implement a memory class for storing conversation history
 Support long term and short term memory
 """
+
 from typing import Any, Dict, List
 
 from colossalqa.chain.memory.summary import ConversationSummaryMemory

applications/ColossalQA/colossalqa/mylogging.py

@@ -1,6 +1,7 @@
 """
 Class for logging with extra control for debugging
 """
+
 import logging
 
 

applications/ColossalQA/colossalqa/retrieval_conversation_en.py

@@ -1,6 +1,7 @@
 """
 Script for Chinese retrieval based conversation system backed by ChatGLM
 """
+
 from typing import Tuple
 
 from colossalqa.chain.retrieval_qa.base import RetrievalQA

applications/ColossalQA/colossalqa/retrieval_conversation_universal.py

@@ -1,6 +1,7 @@
 """
 Multilingual retrieval based conversation system
 """
+
 from typing import List
 
 from colossalqa.data_loader.document_loader import DocumentLoader

applications/ColossalQA/colossalqa/retrieval_conversation_zh.py

@@ -1,6 +1,7 @@
 """
 Script for Chinese retrieval based conversation system backed by ChatGLM
 """
+
 from typing import Tuple
 
 from colossalqa.chain.retrieval_qa.base import RetrievalQA

applications/ColossalQA/colossalqa/retriever.py

@@ -1,6 +1,7 @@
 """
 Code for custom retriver with incremental update
 """
+
 import copy
 import hashlib
 import os

applications/ColossalQA/colossalqa/text_splitter/chinese_text_splitter.py

@@ -1,6 +1,7 @@
 """
 Code for Chinese text splitter
 """
+
 from typing import Any, List, Optional
 
 from colossalqa.text_splitter.utils import get_cleaned_paragraph

applications/ColossalQA/examples/retrieval_conversation_en.py

@@ -1,6 +1,7 @@
 """
 Script for English retrieval based conversation system backed by LLaMa2
 """
+
 import argparse
 import os
 

applications/ColossalQA/examples/retrieval_conversation_en_customer_service.py

@@ -1,6 +1,7 @@
 """
 Script for English retrieval based conversation system backed by LLaMa2
 """
+
 import argparse
 import json
 import os

applications/ColossalQA/examples/retrieval_conversation_zh.py

@@ -1,6 +1,7 @@
 """
 Script for Chinese retrieval based conversation system backed by ChatGLM
 """
+
 import argparse
 import os
 

applications/ColossalQA/examples/retrieval_intent_classification_zh_customer_service.py

@@ -1,6 +1,7 @@
 """
 Script for English retrieval based conversation system backed by LLaMa2
 """
+
 import argparse
 import os
 

colossalai/auto_parallel/meta_profiler/meta_registry/conv.py

@@ -107,20 +107,22 @@ def convnd_meta_info(*args, **kwargs) -> Tuple[TrainCycleItem, TrainCycleItem, L
     # NOTE: currently in SPMD solver we always believe that there will be a new tensor created in forward
     fwd_memory_cost = MemoryCost(
         activation=compute_size_in_bytes([input_tensor, output_tensor]),
-        parameter=compute_size_in_bytes([weight_tensor, bias_tensor])
-        if has_bias
-        else compute_size_in_bytes(weight_tensor),
+        parameter=(
+            compute_size_in_bytes([weight_tensor, bias_tensor]) if has_bias else compute_size_in_bytes(weight_tensor)
+        ),
         temp=0,
         buffer=0,
     )
 
     bwd_memory_cost = MemoryCost(
-        activation=compute_size_in_bytes([input_tensor, weight_tensor, bias_tensor])
-        if has_bias
-        else compute_size_in_bytes([input_tensor, weight_tensor]),
-        parameter=compute_size_in_bytes([weight_tensor, bias_tensor])
-        if has_bias
-        else compute_size_in_bytes(weight_tensor),
+        activation=(
+            compute_size_in_bytes([input_tensor, weight_tensor, bias_tensor])
+            if has_bias
+            else compute_size_in_bytes([input_tensor, weight_tensor])
+        ),
+        parameter=(
+            compute_size_in_bytes([weight_tensor, bias_tensor]) if has_bias else compute_size_in_bytes(weight_tensor)
+        ),
         temp=0,
         buffer=0,
     )

colossalai/inference/engine/modeling/_utils.py

@@ -1,6 +1,7 @@
 """
 Utils for model inference
 """
+
 import os
 
 import torch

colossalai/inference/kv_cache/batch_infer_state.py

@@ -14,6 +14,7 @@ class BatchInferState:
     Information to be passed and used for a batch of inputs during
     a single model forward
     """
+
     batch_size: int
     max_len_in_batch: int
 

colossalai/inference/kv_cache/kvcache_manager.py

@@ -4,6 +4,7 @@
 https://github.com/ModelTC/lightllm/blob/050af3ce65edca617e2f30ec2479397d5bb248c9/lightllm/common/mem_manager.py
 we slightly changed it to make it suitable for our colossal-ai shardformer TP-engine design.
 """
+
 import torch
 from transformers.utils import logging
 

colossalai/legacy/context/process_group_initializer/initializer_2d.py

@@ -138,9 +138,7 @@ def __init__(self, *args, **kwargs):
         self.num_group = self.world_size // self.tensor_parallel_size
         self.summa_dim = int(math.sqrt(self.tensor_parallel_size))
 
-        assert (
-            self.tensor_parallel_size == self.summa_dim**2
-        ), "2D summa dim should equal to tensor parallel size ^ 0.5"
+        assert self.tensor_parallel_size == self.summa_dim**2, "2D summa dim should equal to tensor parallel size ^ 0.5"
         _check_summa_env_var(self.summa_dim)
 
         self.col_initializer = Initializer_2D_Col(self.num_group, self.summa_dim, *args, **kwargs)