[misc] refactor launch API and tensor constructor (#5666)

* [misc] remove config arg from initialize

* [misc] remove old tensor contrusctor

* [plugin] add npu support for ddp

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

for more information, see https://pre-commit.ci

* [devops] fix doc test ci

* [test] fix test launch

* [doc] update launch doc

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

.github/workflows/doc_test_on_pr.yml

@@ -56,7 +56,7 @@ jobs:
     needs: detect-changed-doc
     runs-on: [self-hosted, gpu]
     container:
-      image: hpcaitech/pytorch-cuda:2.0.0-11.7.0
+      image: hpcaitech/pytorch-cuda:2.1.0-12.1.0
       options: --gpus all --rm
     timeout-minutes: 20
     defaults:

applications/Colossal-LLaMA/train.py

@@ -136,7 +136,7 @@ def main() -> None:
     # ==============================
     # Initialize Distributed Training
     # ==============================
-    colossalai.launch_from_torch({})
+    colossalai.launch_from_torch()
     accelerator = get_accelerator()
     coordinator = DistCoordinator()
 

applications/ColossalChat/benchmarks/benchmark_ppo.py

@@ -66,7 +66,7 @@ def benchmark_train(args):
     # ==============================
     # Initialize Distributed Training
     # ==============================
-    colossalai.launch_from_torch({})
+    colossalai.launch_from_torch()
     coordinator = DistCoordinator()
 
     # ======================================================

applications/ColossalChat/examples/training_scripts/train_dpo.py

@@ -37,7 +37,7 @@ def train(args):
     # ==============================
     # Initialize Distributed Training
     # ==============================
-    colossalai.launch_from_torch({})
+    colossalai.launch_from_torch()
     coordinator = DistCoordinator()
 
     # ==============================

applications/ColossalChat/examples/training_scripts/train_ppo.py

@@ -39,7 +39,7 @@ def train(args):
     # ==============================
     # Initialize Distributed Training
     # ==============================
-    colossalai.launch_from_torch({})
+    colossalai.launch_from_torch()
     coordinator = DistCoordinator()
 
     # ======================================================

applications/ColossalChat/examples/training_scripts/train_rm.py

@@ -34,7 +34,7 @@ def train(args):
     # ==============================
     # Initialize Distributed Training
     # ==============================
-    colossalai.launch_from_torch({})
+    colossalai.launch_from_torch()
     coordinator = DistCoordinator()
 
     # ======================================================

applications/ColossalChat/examples/training_scripts/train_sft.py

@@ -29,7 +29,7 @@ def train(args):
     # ==============================
     # Initialize Distributed Training
     # ==============================
-    colossalai.launch_from_torch({})
+    colossalai.launch_from_torch()
     coordinator = DistCoordinator()
 
     # ==============================

applications/ColossalEval/examples/dataset_evaluation/inference.py

@@ -81,7 +81,7 @@ def rm_and_merge(
 
 
 def main(args):
-    colossalai.launch_from_torch(config={}, seed=42)
+    colossalai.launch_from_torch(seed=42)
     accelerator = get_accelerator()
     world_size = dist.get_world_size()
 

applications/ColossalEval/examples/gpt_evaluation/inference.py

@@ -81,7 +81,7 @@ def rm_and_merge(
 
 
 def main(args):
-    colossalai.launch_from_torch(config={}, seed=42)
+    colossalai.launch_from_torch(seed=42)
     world_size = dist.get_world_size()
 
     rank = dist.get_rank()

applications/ColossalMoE/infer.py

@@ -57,7 +57,7 @@ def main():
     args = parse_args()
 
     # Launch ColossalAI
-    colossalai.launch_from_torch(config={}, seed=args.seed)
+    colossalai.launch_from_torch(seed=args.seed)
     coordinator = DistCoordinator()
 
     config = MixtralConfig.from_pretrained(args.model_name)
@@ -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/tests/test_mixtral_layer.py

@@ -50,7 +50,7 @@ def check_mixtral_moe_layer():
 
 
 def run_dist(rank: int, world_size: int, port: int):
-    colossalai.launch({}, rank, world_size, "localhost", port)
+    colossalai.launch(rank, world_size, "localhost", port)
     check_mixtral_moe_layer()
 
 

applications/ColossalMoE/tests/test_moe_checkpoint.py

@@ -133,7 +133,7 @@ def check_mixtral_moe_layer():
 
 
 def run_dist(rank: int, world_size: int, port: int):
-    colossalai.launch({}, rank, world_size, "localhost", port)
+    colossalai.launch(rank, world_size, "localhost", port)
     check_mixtral_moe_layer()
 
 

applications/ColossalMoE/train.py

@@ -145,7 +145,7 @@ def main():
     args = parse_args()
 
     # Launch ColossalAI
-    colossalai.launch_from_torch(config={}, seed=args.seed)
+    colossalai.launch_from_torch(seed=args.seed)
     coordinator = DistCoordinator()
 
     # Set plugin
@@ -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,
     )
 

colossalai/auto_parallel/offload/amp_optimizer.py

@@ -126,7 +126,7 @@ class AMPOptimizer(OptimizerWrapper):
         return self.grad_scaler.scale.item()
 
     def zero_grad(self, *args, **kwargs):
-        self.module.overflow_counter = torch.cuda.IntTensor([0])
+        self.module.overflow_counter = torch.tensor([0], dtype=torch.int, device=get_accelerator().get_current_device())
         return self.optim.zero_grad(set_to_none=True)
 
     def step(self, *args, **kwargs):

colossalai/auto_parallel/offload/base_offload_module.py

@@ -4,7 +4,7 @@ from typing import Optional, Set
 import torch
 import torch.nn as nn
 
-from colossalai.utils import _cast_float
+from colossalai.utils import _cast_float, get_current_device
 from colossalai.utils.common import free_storage
 
 from .region_manager import RegionManager
@@ -25,7 +25,7 @@ class BaseOffloadModule:
         self.model = model
         self.region_manager = region_manager
         self.grad_hook_list = []
-        self.overflow_counter = torch.cuda.IntTensor([0])
+        self.overflow_counter = torch.tensor([0], dtype=torch.int, device=get_current_device())
 
         self.grad_offload_stream = torch.cuda.current_stream() if is_sync else GlobalRuntimeInfo.d2h_stream
 

colossalai/booster/plugin/torch_ddp_plugin.py

@@ -10,6 +10,7 @@ from colossalai.checkpoint_io import CheckpointIO, GeneralCheckpointIO
 from colossalai.cluster import DistCoordinator
 from colossalai.interface import ModelWrapper, OptimizerWrapper
 from colossalai.quantization import BnbQuantizationConfig, quantize_model
+from colossalai.utils import get_current_device
 
 from .dp_plugin_base import DPPluginBase
 
@@ -203,7 +204,7 @@ class TorchDDPPlugin(DPPluginBase):
         return True
 
     def supported_devices(self) -> List[str]:
-        return ["cuda"]
+        return ["cuda", "npu"]
 
     def configure(
         self,
@@ -214,7 +215,7 @@ class TorchDDPPlugin(DPPluginBase):
         lr_scheduler: Optional[LRScheduler] = None,
     ) -> Tuple[nn.Module, OptimizerWrapper, Callable, DataLoader, LRScheduler]:
         # cast model to cuda
-        model = model.cuda()
+        model = model.to(get_current_device())
 
         # convert model to sync bn
         model = nn.SyncBatchNorm.convert_sync_batchnorm(model, None)

colossalai/inference/README.md

@@ -114,7 +114,7 @@ import colossalai
 from transformers import LlamaForCausalLM, LlamaTokenizer
 
 #launch distributed environment
-colossalai.launch_from_torch(config={})
+colossalai.launch_from_torch()
 
 # load original model and tokenizer
 model = LlamaForCausalLM.from_pretrained("/path/to/model")

colossalai/initialize.py

@@ -2,20 +2,15 @@
 # -*- encoding: utf-8 -*-
 
 import os
-import warnings
-from pathlib import Path
-from typing import Dict, Union
 
 import torch.distributed as dist
 
 from colossalai.accelerator import get_accelerator
-from colossalai.context import Config
 from colossalai.logging import get_dist_logger
 from colossalai.utils import set_seed
 
 
 def launch(
-    config: Union[str, Path, Config, Dict],
     rank: int,
     world_size: int,
     host: str,
@@ -44,8 +39,6 @@ def launch(
     Raises:
         Exception: Raise exception when config type is wrong
     """
-    if rank == 0:
-        warnings.warn("`config` is deprecated and will be removed soon.")
 
     cur_accelerator = get_accelerator()
 
@@ -68,7 +61,6 @@ def launch(
 
 
 def launch_from_slurm(
-    config: Union[str, Path, Config, Dict],
     host: str,
     port: int,
     backend: str = "nccl",
@@ -95,7 +87,6 @@ def launch_from_slurm(
         )
 
     launch(
-        config=config,
         rank=rank,
         world_size=world_size,
         host=host,
@@ -107,7 +98,6 @@ def launch_from_slurm(
 
 
 def launch_from_openmpi(
-    config: Union[str, Path, Config, Dict],
     host: str,
     port: int,
     backend: str = "nccl",
@@ -135,7 +125,6 @@ def launch_from_openmpi(
         )
 
     launch(
-        config=config,
         local_rank=local_rank,
         rank=rank,
         world_size=world_size,
@@ -147,9 +136,7 @@ def launch_from_openmpi(
     )
 
 
-def launch_from_torch(
-    config: Union[str, Path, Config, Dict], backend: str = "nccl", seed: int = 1024, verbose: bool = True
-):
+def launch_from_torch(backend: str = "nccl", seed: int = 1024, verbose: bool = True):
     """A wrapper for colossalai.launch for torchrun or torch.distributed.launch by reading rank and world size
     from the environment variables set by PyTorch
 
@@ -171,7 +158,6 @@ def launch_from_torch(
         )
 
     launch(
-        config=config,
         local_rank=local_rank,
         rank=rank,
         world_size=world_size,

colossalai/legacy/inference/dynamic_batching/ray_dist_init.py

@@ -56,7 +56,7 @@ class Worker:
         # initialize a ray collective group, otherwise colossalai distributed env won't be built successfully
         collective.init_collective_group(world_size, rank, "nccl", "default")
         # initialize and set distributed environment
-        colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+        colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
         ray_serve_logger.info(f"Worker with rank {rank} (world size {world_size}) setting up..")
         log_cuda_info("Worker.setup")
 

colossalai/legacy/inference/hybridengine/engine.py

@@ -42,7 +42,7 @@ class CaiInferEngine:
     import colossalai
     from transformers import LlamaForCausalLM, LlamaTokenizer
 
-    colossalai.launch_from_torch(config={})
+    colossalai.launch_from_torch()
 
     model = LlamaForCausalLM.from_pretrained("your_path_to_model")
     tokenizer = LlamaTokenizer.from_pretrained("/home/lczyh/share/models/llama-7b-hf")

colossalai/legacy/inference/pipeline/README.md

@@ -36,7 +36,7 @@ from colossalai.inference.pipeline.policies import LlamaModelInferPolicy
 import colossalai
 from transformers import LlamaForCausalLM, LlamaTokenizer
 
-colossalai.launch_from_torch(config={})
+colossalai.launch_from_torch()
 
 model = LlamaForCausalLM.from_pretrained("/path/to/model")
 tokenizer = LlamaTokenizer.from_pretrained("/path/to/model")
@@ -57,27 +57,27 @@ We conducted multiple benchmark tests to evaluate the performance. We compared t
 ### Llama Throughput (tokens/s) | input length=1024, output length=128
 
 #### A10 7b, fp16
-| batch_size(micro_batch size)| 2(1) | 4(2) | 8(4) | 16(8) | 32(8) | 32(16)|
-| :---: | :---: | :---: | :---: | :---: | :---: | :---:|
-| Pipeline Inference | 40.35 | 77.1 | 139.03 | 232.7 | 257.81 | OOM |
-| Hugging Face |  41.43 | 65.30 | 91.93 | 114.62 | OOM| OOM |
+| batch_size(micro_batch size) | 2(1)  | 4(2)  |  8(4)  | 16(8)  | 32(8)  | 32(16) |
+|:----------------------------:|:-----:|:-----:|:------:|:------:|:------:|:------:|
+|      Pipeline Inference      | 40.35 | 77.1  | 139.03 | 232.7  | 257.81 |  OOM   |
+|         Hugging Face         | 41.43 | 65.30 | 91.93  | 114.62 |  OOM   |  OOM   |
 
 #### A10 13b, fp16
-| batch_size(micro_batch size)| 2(1) | 4(2) | 8(4) | 16(4) |
-| :---: | :---: | :---: | :---: | :---: |
-| Pipeline Inference | 25.39 | 47.09 | 83.7 | 89.46 |
-| Hugging Face | 23.48 | 37.59 | 53.44 | OOM |
+| batch_size(micro_batch size) | 2(1)  | 4(2)  | 8(4)  | 16(4) |
+|:----------------------------:|:-----:|:-----:|:-----:|:-----:|
+|      Pipeline Inference      | 25.39 | 47.09 | 83.7  | 89.46 |
+|         Hugging Face         | 23.48 | 37.59 | 53.44 |  OOM  |
 
 
 #### A800 7b, fp16
-| batch_size(micro_batch size) | 2(1) | 4(2) | 8(4) | 16(8) | 32(16) |
-| :---: | :---: | :---: | :---: | :---: | :---: |
-| Pipeline Inference| 57.97 | 110.13 | 213.33 | 389.86 | 670.12  |
-| Hugging Face  | 42.44 | 76.5 | 151.97 | 212.88 | 256.13 |
+| batch_size(micro_batch size) | 2(1)  |  4(2)  |  8(4)  | 16(8)  | 32(16) |
+|:----------------------------:|:-----:|:------:|:------:|:------:|:------:|
+|      Pipeline Inference      | 57.97 | 110.13 | 213.33 | 389.86 | 670.12 |
+|         Hugging Face         | 42.44 |  76.5  | 151.97 | 212.88 | 256.13 |
 
 
 #### A800 13b, fp16
-| batch_size(micro_batch size) | 2(1) | 4(2) | 8(4) | 16(8) | 32(16) |
-| :---: | :---: | :---: | :---: | :---: | :---: |
-| Pipeline Inference | 41.78 | 94.18 | 172.67| 310.75| 470.15 |
-| Hugging Face   | 36.57 | 68.4 | 105.81 | 139.51 | 166.34 |
+| batch_size(micro_batch size) | 2(1)  | 4(2)  |  8(4)  | 16(8)  | 32(16) |
+|:----------------------------:|:-----:|:-----:|:------:|:------:|:------:|
+|      Pipeline Inference      | 41.78 | 94.18 | 172.67 | 310.75 | 470.15 |
+|         Hugging Face         | 36.57 | 68.4  | 105.81 | 139.51 | 166.34 |

colossalai/legacy/inference/pipeline/benchmark/benchmark.py

@@ -12,7 +12,7 @@ from colossalai.inference.pipeline.policies import LlamaModelInferPolicy
 GIGABYTE = 1024**3
 MEGABYTE = 1024 * 1024
 
-colossalai.launch_from_torch(config={})
+colossalai.launch_from_torch()
 
 
 def data_gen(batch_size: int = 4, seq_len: int = 512):

colossalai/legacy/inference/serving/ray_serve/Colossal_Inference_rayserve.py

@@ -56,7 +56,7 @@ class Worker:
         # initialize a ray collective group, otherwise colossalai distributed env won't be built successfully
         collective.init_collective_group(world_size, rank, "nccl", "default")
         # initialize and set distributed environment
-        colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+        colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
         ray_serve_logger.info(f"Worker with rank {rank} (world size {world_size}) setting up..")
         log_cuda_info("Worker.setup")
 

colossalai/legacy/inference/serving/torch_serve/Colossal_Inference_Handler.py

@@ -98,7 +98,7 @@ class ColossalInferenceHandler(BaseHandler, ABC):
         self.model.cuda()
         self.model.eval()
 
-        colossalai.launch(config={}, rank=rank, world_size=world_size, host=host, port=port, backend="nccl")
+        colossalai.launch(rank=rank, world_size=world_size, host=host, port=port, backend="nccl")
         logger.info("Initializing TPInferEngine ...")
         shard_config = ShardConfig(
             enable_tensor_parallelism=True if self.tp_size > 1 else False, extra_kwargs={"inference_only": True}

colossalai/legacy/pipeline/rpc/utils.py

@@ -114,7 +114,7 @@ def run_worker(rank, args, master_func):
     port = args.master_port
     backend = "nccl" if device == "cuda" else "gloo"
 
-    launch(dict(), rank, world_size, host, int(port), backend, verbose=False)
+    launch(rank, world_size, host, int(port), backend, verbose=False)
     ppg.set_global_info(
         rank=rank,
         world_size=world_size,

colossalai/nn/optimizer/fused_adam.py

@@ -8,7 +8,7 @@ Licensed under the MIT License.
 """
 import torch
 
-from colossalai.utils import multi_tensor_applier
+from colossalai.utils import get_current_device, multi_tensor_applier
 
 
 class FusedAdam(torch.optim.Optimizer):
@@ -75,7 +75,7 @@ class FusedAdam(torch.optim.Optimizer):
             fused_optim = FusedOptimizerLoader().load()
 
             # Skip buffer
-            self._dummy_overflow_buf = torch.cuda.IntTensor([0])
+            self._dummy_overflow_buf = torch.tensor([0], dtype=torch.int, device=get_current_device())
             self.multi_tensor_adam = fused_optim.multi_tensor_adam
         else:
             raise RuntimeError("FusedAdam requires cuda extensions")

colossalai/nn/optimizer/hybrid_adam.py

@@ -3,7 +3,7 @@ from typing import Any, Optional
 import torch
 
 from colossalai.kernel.kernel_loader import FusedOptimizerLoader
-from colossalai.utils import multi_tensor_applier
+from colossalai.utils import get_current_device, multi_tensor_applier
 
 from .cpu_adam import CPUAdam
 
@@ -87,7 +87,7 @@ class HybridAdam(CPUAdam):
         if torch.cuda.is_available():
             fused_optim = FusedOptimizerLoader().load()
             self.gpu_adam_op = fused_optim.multi_tensor_adam
-            self._dummy_overflow_buf = torch.cuda.IntTensor([0])
+            self._dummy_overflow_buf = torch.tensor([0], dtype=torch.int, device=get_current_device())
 
     @torch.no_grad()
     def step(self, closure=None, div_scale: float = -1):

colossalai/shardformer/README.md

@@ -38,7 +38,7 @@ from transformers import BertForMaskedLM
 import colossalai
 
 # launch colossalai
-colossalai.launch_from_torch(config={})
+colossalai.launch_from_torch()
 
 # create model
 config = BertConfig.from_pretrained('bert-base-uncased')

colossalai/shardformer/examples/convergence_benchmark.py

@@ -28,7 +28,7 @@ def to_device(x: Any, device: torch.device) -> Any:
 
 
 def train(args):
-    colossalai.launch_from_torch(config={}, seed=42)
+    colossalai.launch_from_torch(seed=42)
     coordinator = DistCoordinator()
 
     # prepare for data and dataset

colossalai/shardformer/examples/performance_benchmark.py

@@ -1,6 +1,7 @@
 """
 Shardformer Benchmark
 """
+
 import torch
 import torch.distributed as dist
 import transformers
@@ -84,5 +85,5 @@ def bench_shardformer(BATCH, N_CTX, provider, model_func, dtype=torch.float32, d
 # start benchmark, command:
 # torchrun --standalone --nproc_per_node=2 performance_benchmark.py
 if __name__ == "__main__":
-    colossalai.launch_from_torch({})
+    colossalai.launch_from_torch()
     bench_shardformer.run(save_path=".", print_data=dist.get_rank() == 0)

colossalai/shardformer/shard/shardformer.py

@@ -26,7 +26,7 @@ class ShardFormer:
     import colossalai
     import torch
 
-    colossalai.launch_from_torch(config={})
+    colossalai.launch_from_torch()
 
     org_model = BertForMaskedLM.from_pretrained('bert-base-uncased')
     shard_config = ShardConfig()

colossalai/tensor/d_tensor/README.md

@@ -69,7 +69,7 @@ import colossalai
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.tensor.d_tensor import DTensor, ShardingSpec
 
-colossalai.launch_from_torch(config={})
+colossalai.launch_from_torch()
 
 # define your device mesh
 # assume you have 4 GPUs

docs/source/en/advanced_tutorials/train_gpt_using_hybrid_parallelism.md

@@ -75,7 +75,7 @@ WARMUP_FRACTION = 0.1
 we create a distributed environment.
 ```python
 # Launch ColossalAI
-colossalai.launch_from_torch(config={}, seed=42)
+colossalai.launch_from_torch( seed=42)
 coordinator = DistCoordinator()
 ```
 prepare the dataset. You can use `plugin.prepare_dataloader` to generate a dataloader or customize your own dataloader.

docs/source/en/advanced_tutorials/train_vit_with_hybrid_parallelism.md

@@ -71,7 +71,7 @@ PP_SIZE = 2
 Create a distributed environment.
 ```python
 # Launch ColossalAI
-colossalai.launch_from_torch(config={}, seed=SEEDå)
+colossalai.launch_from_torch( seed=SEEDå)
 coordinator = DistCoordinator()
 world_size = coordinator.world_size
 ```

docs/source/en/basics/booster_api.md

@@ -55,7 +55,7 @@ from colossalai.booster.plugin import TorchDDPPlugin
 
 def train():
     # launch colossalai
-    colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost')
+    colossalai.launch(rank=rank, world_size=world_size, port=port, host='localhost')
 
     # create plugin and objects for training
     plugin = TorchDDPPlugin()

docs/source/en/basics/launch_colossalai.md

@@ -87,8 +87,7 @@ import colossalai
 args = colossalai.get_default_parser().parse_args()
 
 # launch distributed environment
-colossalai.launch(config=args.config,
-                  rank=args.rank,
+colossalai.launch(rank=args.rank,
                   world_size=args.world_size,
                   host=args.host,
                   port=args.port,
@@ -106,20 +105,11 @@ First, we need to set the launch method in our code. As this is a wrapper of the
 use `colossalai.launch_from_torch`. The arguments required for distributed environment such as rank, world size, host and port are all set by the PyTorch
 launcher and can be read from the environment variable directly.
 
-config.py
-```python
-BATCH_SIZE = 512
-LEARNING_RATE = 3e-3
-WEIGHT_DECAY = 0.3
-NUM_EPOCHS = 2
-```
 train.py
 ```python
 import colossalai
 
-colossalai.launch_from_torch(
-    config="./config.py",
-)
+colossalai.launch_from_torch()
 ...
 ```
 
@@ -203,7 +193,6 @@ Do this in your training script:
 import colossalai
 
 colossalai.launch_from_slurm(
-    config=<CONFIG>,
     host=args.host,
     port=args.port
 )
@@ -224,7 +213,6 @@ use them to start the distributed backend.
 Do this in your train.py:
 ```python
 colossalai.launch_from_openmpi(
-    config=<CONFIG>,
     host=args.host,
     port=args.port
 )
@@ -238,3 +226,5 @@ mpirun --hostfile <my_hostfile> -np <num_process> python train.py --host <node n
 
 - --hostfile: use this option to specify a list of hosts on which to run
 - --np: set the number of processes (GPUs) to launch in total. For example, if --np 4, 4 python processes will be initialized to run train.py.
+
+<!-- doc-test-command: echo  -->

docs/source/en/features/gradient_accumulation_with_booster.md

@@ -45,7 +45,7 @@ We then need to initialize distributed environment. For demo purpose, we uses `l
 parser = colossalai.get_default_parser()
 args = parser.parse_args()
 # launch from torch
-colossalai.launch_from_torch(config=dict())
+colossalai.launch_from_torch()
 ```
 
 ### Step 3. Create training components

docs/source/en/features/gradient_clipping_with_booster.md

@@ -61,7 +61,7 @@ We then need to initialize distributed environment. For demo purpose, we uses `l
 for other initialization methods.
 
 ```python
-colossalai.launch_from_torch(config=dict())
+colossalai.launch_from_torch()
 logger = get_dist_logger()
 ```
 

docs/source/en/features/lazy_init.md

@@ -29,7 +29,7 @@ from colossalai.booster.plugin import GeminiPlugin
 
 from transformers import LlamaForCausalLM, LlamaConfig, BertForPreTraining
 
-colossalai.launch({})
+colossalai.launch()
 plugin = GeminiPlugin()
 booster = Booster(plugin)
 

docs/source/en/features/mixed_precision_training_with_booster.md

@@ -20,10 +20,10 @@ In Colossal-AI, we have incorporated different implementations of mixed precisio
 3. naive amp
 
 | Colossal-AI    | support tensor parallel | support pipeline parallel | fp16 extent                                                                                          |
-| -------------- | ----------------------- | ------------------------- | ---------------------------------------------------------------------------------------------------- |
-| AMP_TYPE.TORCH | ✅                      | ❌                        | Model parameters, activation, gradients are downcast to fp16 during forward and backward propagation |
-| AMP_TYPE.APEX  | ❌                      | ❌                        | More fine-grained, we can choose opt_level O0, O1, O2, O3                                            |
-| AMP_TYPE.NAIVE | ✅                      | ✅                        | Model parameters, forward and backward operations are all downcast to fp16                           |
+|----------------|-------------------------|---------------------------|------------------------------------------------------------------------------------------------------|
+| AMP_TYPE.TORCH | ✅                       | ❌                         | Model parameters, activation, gradients are downcast to fp16 during forward and backward propagation |
+| AMP_TYPE.APEX  | ❌                       | ❌                         | More fine-grained, we can choose opt_level O0, O1, O2, O3                                            |
+| AMP_TYPE.NAIVE | ✅                       | ✅                         | Model parameters, forward and backward operations are all downcast to fp16                           |
 
 The first two rely on the original implementation of PyTorch (version 1.6 and above) and NVIDIA Apex.
 The last method is similar to Apex O2 level.
@@ -164,7 +164,7 @@ parser = colossalai.get_default_parser()
 args = parser.parse_args()
 
 # launch from torch
-colossalai.launch_from_torch(config=dict())
+colossalai.launch_from_torch()
 
 ```
 

docs/source/en/features/nvme_offload.md

@@ -185,7 +185,7 @@ Then we can train GPT model with Gemini. The placement policy of Gemini should b
 
 ```python
 def train_gemini_cpu(nvme_offload_fraction: float = 0.0):
-    colossalai.launch_from_torch({})
+    colossalai.launch_from_torch()
     config = GPT2Config()
     with ColoInitContext(device=torch.cuda.current_device()):
         model = GPT2LMHeadModel(config)

docs/source/en/features/zero_with_chunk.md

@@ -174,7 +174,7 @@ def main():
     SEQ_LEN = 1024
     VOCAB_SIZE = 50257
     NUM_STEPS = 10
-    colossalai.launch_from_torch(config={})
+    colossalai.launch_from_torch()
 
     # build criterion
     criterion = GPTLMLoss()

docs/source/zh-Hans/advanced_tutorials/train_gpt_using_hybrid_parallelism.md

@@ -62,7 +62,7 @@ plugin = HybridParallelPlugin(
 ## 创建分布式环境.
 ```python
 # Launch ColossalAI
-colossalai.launch_from_torch(config={}, seed=42)
+colossalai.launch_from_torch(seed=42)
 coordinator = DistCoordinator()
 ```
 ## 定义GPT-2模型的训练组件

docs/source/zh-Hans/advanced_tutorials/train_vit_with_hybrid_parallelism.md

@@ -70,7 +70,7 @@ PP_SIZE = 2
 首先我们创建一个分布式环境
 ```python
 # Launch ColossalAI
-colossalai.launch_from_torch(config={}, seed=SEEDå)
+colossalai.launch_from_torch(seed=SEEDå)
 coordinator = DistCoordinator()
 world_size = coordinator.world_size
 ```

docs/source/zh-Hans/basics/booster_api.md

@@ -60,7 +60,7 @@ from colossalai.booster.plugin import TorchDDPPlugin
 
 def train():
     # launch colossalai
-    colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host='localhost')
+    colossalai.launch(rank=rank, world_size=world_size, port=port, host='localhost')
 
     # create plugin and objects for training
     plugin = TorchDDPPlugin()

docs/source/zh-Hans/basics/launch_colossalai.md

@@ -74,8 +74,7 @@ import colossalai
 args = colossalai.get_default_parser().parse_args()
 
 # launch distributed environment
-colossalai.launch(config=args.config,
-                  rank=args.rank,
+colossalai.launch(rank=args.rank,
                   world_size=args.world_size,
                   host=args.host,
                   port=args.port,
@@ -93,20 +92,11 @@ PyTorch自带的启动器需要在每个节点上都启动命令才能启动多
 首先，我们需要在代码里指定我们的启动方式。由于这个启动器是PyTorch启动器的封装，那么我们自然而然应该使用`colossalai.launch_from_torch`。
 分布式环境所需的参数，如 rank, world size, host 和 port 都是由 PyTorch 启动器设置的，可以直接从环境变量中读取。
 
-config.py
-```python
-BATCH_SIZE = 512
-LEARNING_RATE = 3e-3
-WEIGHT_DECAY = 0.3
-NUM_EPOCHS = 2
-```
 train.py
 ```python
 import colossalai
 
-colossalai.launch_from_torch(
-    config="./config.py",
-)
+colossalai.launch_from_torch()
 ...
 ```
 
@@ -186,7 +176,6 @@ colossalai run --nproc_per_node 4 --hostfile ./hostfile --master_addr host1  --e
 import colossalai
 
 colossalai.launch_from_slurm(
-    config=<CONFIG>,
     host=args.host,
     port=args.port
 )
@@ -206,7 +195,6 @@ srun python train.py --host <master_node> --port 29500
 您可以在您的训练脚本中尝试以下操作。
 ```python
 colossalai.launch_from_openmpi(
-    config=<CONFIG>,
     host=args.host,
     port=args.port
 )
@@ -219,3 +207,5 @@ mpirun --hostfile <my_hostfile> -np <num_process> python train.py --host <node n
 
 - --hostfile: 指定一个要运行的主机列表。
 - --np: 设置总共要启动的进程（GPU）的数量。例如，如果 --np 4，4个 python 进程将被初始化以运行 train.py。
+
+<!-- doc-test-command: echo  -->

docs/source/zh-Hans/features/gradient_accumulation_with_booster.md

@@ -46,7 +46,7 @@ parser = colossalai.get_default_parser()
 args = parser.parse_args()
 
 # launch from torch
-colossalai.launch_from_torch(config=dict())
+colossalai.launch_from_torch()
 
 ```
 

docs/source/zh-Hans/features/gradient_clipping_with_booster.md

@@ -61,7 +61,7 @@ from colossalai.nn.lr_scheduler import CosineAnnealingLR
 我们需要初始化分布式环境. 为了快速演示，我们使用`launch_from_torch`. 您可以参考 [Launch Colossal-AI](../basics/launch_colossalai.md)
 
 ```python
-colossalai.launch_from_torch(config=dict())
+colossalai.launch_from_torch()
 logger = get_dist_logger()
 ```
 

docs/source/zh-Hans/features/lazy_init.md

@@ -29,7 +29,7 @@ from colossalai.booster.plugin import GeminiPlugin
 
 from transformers import LlamaForCausalLM, LlamaConfig, BertForPreTraining
 
-colossalai.launch({})
+colossalai.launch()
 plugin = GeminiPlugin()
 booster = Booster(plugin)
 

docs/source/zh-Hans/features/mixed_precision_training_with_booster.md

@@ -19,11 +19,11 @@ AMP 代表自动混合精度训练。
 2. apex.amp
 3. naive amp
 
-| Colossal-AI    | 支持张量并行 | 支持流水并行 | fp16 范围                                                 |
-| -------------- | ------------ | ------------ | --------------------------------------------------------- |
-| AMP_TYPE.TORCH | ✅           | ❌           | 在前向和反向传播期间，模型参数、激活和梯度向下转换至 fp16 |
-| AMP_TYPE.APEX  | ❌           | ❌           | 更细粒度，我们可以选择 opt_level O0, O1, O2, O3           |
-| AMP_TYPE.NAIVE | ✅           | ✅           | 模型参数、前向和反向操作，全都向下转换至 fp16             |
+| Colossal-AI    | 支持张量并行 | 支持流水并行 | fp16 范围                                               |
+|----------------|--------------|--------------|-------------------------------------------------------|
+| AMP_TYPE.TORCH | ✅            | ❌            | 在前向和反向传播期间，模型参数、激活和梯度向下转换至 fp16 |
+| AMP_TYPE.APEX  | ❌            | ❌            | 更细粒度，我们可以选择 opt_level O0, O1, O2, O3          |
+| AMP_TYPE.NAIVE | ✅            | ✅            | 模型参数、前向和反向操作，全都向下转换至 fp16             |
 
 前两个依赖于 PyTorch (1.6 及以上) 和 NVIDIA Apex 的原始实现。最后一种方法类似 Apex O2。在这些方法中，Apex-AMP 与张量并行不兼容。这是因为张量是以张量并行的方式在设备之间拆分的，因此，需要在不同的进程之间进行通信，以检查整个模型权重中是否出现 inf 或 nan。我们修改了 torch amp 实现，使其现在与张量并行兼容。
 
@@ -153,7 +153,7 @@ parser = colossalai.get_default_parser()
 args = parser.parse_args()
 
 # launch from torch
-colossalai.launch_from_torch(config=dict())
+colossalai.launch_from_torch()
 
 ```
 

docs/source/zh-Hans/features/nvme_offload.md

@@ -175,7 +175,7 @@ Mem usage: 4968.016 MB
 
 ```python
 def train_gemini_cpu(nvme_offload_fraction: float = 0.0):
-    colossalai.launch_from_torch({})
+    colossalai.launch_from_torch()
     config = GPT2Config()
     with ColoInitContext(device=torch.cuda.current_device()):
         model = GPT2LMHeadModel(config)

docs/source/zh-Hans/features/zero_with_chunk.md

@@ -174,7 +174,7 @@ def main():
     SEQ_LEN = 1024
     VOCAB_SIZE = 50257
     NUM_STEPS = 10
-    colossalai.launch_from_torch(config={})
+    colossalai.launch_from_torch()
 
     # build criterion
     criterion = GPTLMLoss()

examples/community/roberta/pretraining/run_pretraining.py

@@ -35,12 +35,12 @@ def main():
 
     if args.vscode_debug:
         colossalai.launch(
-            config={}, rank=args.rank, world_size=args.world_size, host=args.host, port=args.port, backend=args.backend
+            rank=args.rank, world_size=args.world_size, host=args.host, port=args.port, backend=args.backend
         )
         args.local_rank = -1
         args.log_interval = 1
     else:
-        colossalai.launch_from_torch(config={})  # args.colossal_config
+        colossalai.launch_from_torch()  # args.colossal_config
         args.local_rank = int(os.environ["LOCAL_RANK"])
         logger.info(
             f"launch_from_torch, world size: {torch.distributed.get_world_size()} | "

examples/images/dreambooth/debug.py

@@ -9,7 +9,7 @@ from colossalai.zero import ColoInitContext
 
 path = "/data/scratch/diffuser/stable-diffusion-v1-4"
 
-colossalai.launch_from_torch(config={})
+colossalai.launch_from_torch()
 with ColoInitContext(device="cpu"):
     vae = AutoencoderKL.from_pretrained(
         path,

examples/images/dreambooth/train_dreambooth_colossalai.py

@@ -372,9 +372,9 @@ def get_full_repo_name(model_id: str, organization: Optional[str] = None, token:
 
 def main(args):
     if args.seed is None:
-        colossalai.launch_from_torch(config={})
+        colossalai.launch_from_torch()
     else:
-        colossalai.launch_from_torch(config={}, seed=args.seed)
+        colossalai.launch_from_torch(seed=args.seed)
 
     local_rank = dist.get_rank()
     world_size = dist.get_world_size()

examples/images/dreambooth/train_dreambooth_colossalai_lora.py

@@ -371,9 +371,9 @@ def get_full_repo_name(model_id: str, organization: Optional[str] = None, token:
 
 def main(args):
     if args.seed is None:
-        colossalai.launch_from_torch(config={})
+        colossalai.launch_from_torch()
     else:
-        colossalai.launch_from_torch(config={}, seed=args.seed)
+        colossalai.launch_from_torch(seed=args.seed)
 
     local_rank = gpc.get_local_rank(ParallelMode.DATA)
     world_size = gpc.get_world_size(ParallelMode.DATA)

examples/images/resnet/train.py

@@ -128,7 +128,7 @@ def main():
     # ==============================
     # Launch Distributed Environment
     # ==============================
-    colossalai.launch_from_torch(config={})
+    colossalai.launch_from_torch()
     coordinator = DistCoordinator()
 
     # update the learning rate with linear scaling

examples/images/vit/vit_benchmark.py

@@ -46,7 +46,7 @@ def main():
     args = parse_benchmark_args()
 
     # Launch ColossalAI
-    colossalai.launch_from_torch(config={}, seed=args.seed)
+    colossalai.launch_from_torch(seed=args.seed)
     coordinator = DistCoordinator()
     world_size = coordinator.world_size
 

examples/images/vit/vit_train_demo.py

@@ -137,7 +137,7 @@ def main():
     args = parse_demo_args()
 
     # Launch ColossalAI
-    colossalai.launch_from_torch(config={}, seed=args.seed)
+    colossalai.launch_from_torch(seed=args.seed)
     coordinator = DistCoordinator()
     world_size = coordinator.world_size
 

examples/inference/benchmark_llama.py

@@ -136,7 +136,7 @@ def benchmark_inference(args):
 
 
 def hybrid_inference(rank, world_size, port, args):
-    colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     benchmark_inference(args)
 
 

examples/inference/run_llama_inference.py

@@ -68,7 +68,7 @@ def run_inference(args):
 
 
 def run_tp_pipeline_inference(rank, world_size, port, args):
-    colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     run_inference(args)
 
 

examples/language/bert/benchmark.py

@@ -81,7 +81,7 @@ def main():
     # ==============================
     # Launch Distributed Environment
     # ==============================
-    colossalai.launch_from_torch(config={}, seed=42)
+    colossalai.launch_from_torch(seed=42)
     coordinator = DistCoordinator()
 
     # local_batch_size = BATCH_SIZE // coordinator.world_size

examples/language/bert/finetune.py

@@ -202,7 +202,7 @@ def main():
     # ==============================
     # Launch Distributed Environment
     # ==============================
-    colossalai.launch_from_torch(config={}, seed=42)
+    colossalai.launch_from_torch(seed=42)
     coordinator = DistCoordinator()
 
     lr = LEARNING_RATE * coordinator.world_size

examples/language/gpt/experiments/auto_offload/train_gpt_offload.py

@@ -94,8 +94,7 @@ def train_gpt(args):
 
 
 def run(rank, world_size, port, args):
-    config = {}
-    colossalai.launch(config=config, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     train_gpt(args)
 
 

examples/language/gpt/experiments/auto_parallel/auto_parallel_with_gpt.py

@@ -47,7 +47,7 @@ def get_data(batch_size, seq_len, vocab_size):
 
 def main():
     disable_existing_loggers()
-    launch_from_torch(config={})
+    launch_from_torch()
     logger = get_dist_logger()
     config = transformers.GPT2Config(n_position=SEQ_LENGTH, n_layer=NUM_LAYERS, n_head=NUM_HEADS, n_embd=HIDDEN_DIM)
     if FP16:

examples/language/gpt/gemini/train_gpt_demo.py

@@ -132,7 +132,7 @@ def main():
     PROF_FLAG = False  # The flag of profiling, False by default
 
     disable_existing_loggers()
-    colossalai.launch_from_torch(config={})
+    colossalai.launch_from_torch()
 
     logger = get_dist_logger()
     logger.info(f"{args.model_type}, {args.distplan}, batch size {BATCH_SIZE}", ranks=[0])

examples/language/gpt/hybridparallelism/benchmark.py

@@ -67,7 +67,7 @@ def main():
     parser.add_argument("--cpu_offload", action="store_true", help="Use gradient checkpointing")
     args = parser.parse_args()
 
-    colossalai.launch_from_torch({})
+    colossalai.launch_from_torch()
     coordinator = DistCoordinator()
 
     def empty_init():

examples/language/gpt/hybridparallelism/finetune.py

@@ -196,7 +196,7 @@ def main():
     # ==============================
     # Launch Distributed Environment
     # ==============================
-    colossalai.launch_from_torch(config={}, seed=42)
+    colossalai.launch_from_torch(seed=42)
     coordinator = DistCoordinator()
 
     # local_batch_size = BATCH_SIZE // coordinator.world_size

examples/language/gpt/titans/train_gpt.py

@@ -36,9 +36,9 @@ def main():
     args = parser.parse_args()
     disable_existing_loggers()
     if args.from_torch:
-        colossalai.launch_from_torch(config=args.config)
+        colossalai.launch_from_torch()
     else:
-        colossalai.launch_from_slurm(config=args.config, host=args.host, port=29500, seed=42)
+        colossalai.launch_from_slurm(host=args.host, port=29500, seed=42)
     logger = get_dist_logger()
 
     data_path = None if args.use_dummy_dataset else os.environ["DATA"]

examples/language/grok-1/inference_tp.py

@@ -16,7 +16,7 @@ if __name__ == "__main__":
     parser = get_default_parser()
     args = parser.parse_args()
     start = time.time()
-    colossalai.launch_from_torch({})
+    colossalai.launch_from_torch()
     coordinator = DistCoordinator()
     plugin = HybridParallelPlugin(
         tp_size=coordinator.world_size,

examples/language/llama/benchmark.py

@@ -78,7 +78,7 @@ def main():
     parser.add_argument("--custom-ckpt", action="store_true", help="Customize checkpoint", default=False)
     args = parser.parse_args()
 
-    colossalai.launch_from_torch({})
+    colossalai.launch_from_torch()
     coordinator = DistCoordinator()
 
     def empty_init():

examples/language/openmoe/benchmark/benchmark_cai.py

@@ -146,7 +146,7 @@ def main():
     args = parse_args()
 
     # Launch ColossalAI
-    colossalai.launch_from_torch(config={}, seed=args.seed)
+    colossalai.launch_from_torch(seed=args.seed)
     coordinator = DistCoordinator()
 
     # Set plugin

examples/language/openmoe/train.py

@@ -207,7 +207,7 @@ def main():
     args = parse_args()
 
     # Launch ColossalAI
-    colossalai.launch_from_torch(config={}, seed=args.seed)
+    colossalai.launch_from_torch(seed=args.seed)
     coordinator = DistCoordinator()
     test_mode = args.model_name == "test"
 

examples/language/opt/opt_benchmark.py

@@ -46,7 +46,7 @@ def main():
     args = parse_benchmark_args()
 
     # Launch ColossalAI
-    colossalai.launch_from_torch(config={}, seed=args.seed)
+    colossalai.launch_from_torch(seed=args.seed)
     coordinator = DistCoordinator()
     world_size = coordinator.world_size
 

examples/language/opt/opt_train_demo.py

@@ -64,7 +64,7 @@ def main():
     args = parse_demo_args()
 
     # Launch ColossalAI
-    colossalai.launch_from_torch(config={}, seed=args.seed)
+    colossalai.launch_from_torch(seed=args.seed)
     coordinator = DistCoordinator()
     world_size = coordinator.world_size
 

examples/language/palm/train.py

@@ -102,7 +102,7 @@ args = parse_args()
 if args.distplan not in ["colossalai", "pytorch"]:
     raise TypeError(f"{args.distplan} is error")
 disable_existing_loggers()
-colossalai.launch_from_torch(config={})
+colossalai.launch_from_torch()
 logger = get_dist_logger()
 
 

examples/tutorial/auto_parallel/auto_ckpt_batchsize_test.py

@@ -20,7 +20,7 @@ def _benchmark(rank, world_size, port):
     only result in minor performance drop. So at last we might be able to find better training batch size for our
     model (combine with large batch training optimizer such as LAMB).
     """
-    colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     model = tm.resnet152()
     gm = symbolic_trace(model)
     raw_graph = deepcopy(gm.graph)

examples/tutorial/auto_parallel/auto_ckpt_solver_test.py

@@ -17,7 +17,7 @@ def _benchmark(rank, world_size, port, args):
     The benchmark will sample in a range of memory budget for each model and output the benchmark summary and
     data visualization of peak memory vs. budget memory and relative step time vs. peak memory.
     """
-    colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     if args.model == "resnet50":
         model = tm.resnet50()
         data_gen = partial(data_gen_resnet, batch_size=128, shape=(3, 224, 224))

examples/tutorial/new_api/cifar_resnet/train.py

@@ -128,7 +128,7 @@ def main():
     # ==============================
     # Launch Distributed Environment
     # ==============================
-    colossalai.launch_from_torch(config={})
+    colossalai.launch_from_torch()
     coordinator = DistCoordinator()
 
     # update the learning rate with linear scaling

examples/tutorial/new_api/cifar_vit/train.py

@@ -148,7 +148,7 @@ def main():
     # ==============================
     # Launch Distributed Environment
     # ==============================
-    colossalai.launch_from_torch(config={})
+    colossalai.launch_from_torch()
     coordinator = DistCoordinator()
 
     # update the learning rate with linear scaling

examples/tutorial/new_api/glue_bert/finetune.py

@@ -125,7 +125,7 @@ def main():
     # ==============================
     # Launch Distributed Environment
     # ==============================
-    colossalai.launch_from_torch(config={}, seed=42)
+    colossalai.launch_from_torch(seed=42)
     coordinator = DistCoordinator()
 
     # local_batch_size = BATCH_SIZE // coordinator.world_size

examples/tutorial/opt/opt/run_clm.py

@@ -289,7 +289,7 @@ class DummyDataloader:
 def main():
     args = parse_args()
     disable_existing_loggers()
-    colossalai.legacy.launch_from_torch(config=dict())
+    colossalai.legacy.launch_from_torch()
     logger = get_dist_logger()
     is_main_process = dist.get_rank() == 0
 

tests/test_auto_parallel/test_ckpt_solvers/test_C_solver_consistency.py

@@ -27,7 +27,7 @@ except:
 
 
 def _run_C_solver_consistency_test(rank, world_size, port):
-    colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
 
     for M, mem_budget in [(tm.resnet50, 4000), (tm.densenet121, 8080)]:
         model = M()

tests/test_auto_parallel/test_ckpt_solvers/test_ckpt_torchvision.py

@@ -75,7 +75,7 @@ def check_backward_consistency(
 
 
 def _run_ckpt_solver(rank, world_size, port):
-    colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     MODEL_LIST = [tm.densenet121]
 
     torch.backends.cudnn.deterministic = True
@@ -111,7 +111,7 @@ def test_ckpt_solver():
 
 
 def _run_ckpt_solver_torch11(rank, world_size, port):
-    colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     MODEL_LIST = [tm.densenet121]
 
     torch.backends.cudnn.deterministic = True

tests/test_auto_parallel/test_offload/test_perf.py

@@ -141,8 +141,7 @@ def exam_fwd_bwd(model_name: str, memory_budget: float, solver_name: str):
 
 
 def run_dist(rank, world_size, port):
-    config = {}
-    colossalai.launch(config=config, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    colossalai.launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     exam_fwd_bwd()
 
 

tests/test_auto_parallel/test_tensor_shard/test_bias_addition_forward.py

@@ -42,7 +42,7 @@ class ConvModel(torch.nn.Module):
 
 def check_linear_module(rank, world_size, port):
     disable_existing_loggers()
-    launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     model = LinearModel(4, 8).cuda()
     input = torch.rand(4, 4).cuda()
     output_compare = model(input)
@@ -59,7 +59,7 @@ def check_linear_module(rank, world_size, port):
 
 def check_conv_module(rank, world_size, port):
     disable_existing_loggers()
-    launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     model = ConvModel(3, 6, 2).cuda()
     input = torch.rand(4, 3, 64, 64).cuda()
     output_compare = model(input)

tests/test_auto_parallel/test_tensor_shard/test_checkpoint.py

@@ -39,7 +39,7 @@ class GPT2MLPWithCkpt(nn.Module):
 
 def check_act_ckpt(rank, world_size, port):
     disable_existing_loggers()
-    launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     model = GPT2MLPWithCkpt(intermediate_size=4 * HIDDEN_SIZE, hidden_size=HIDDEN_SIZE)
     torch.rand(1, 64, HIDDEN_SIZE)
     input_sample = {

tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_ddp.py

@@ -32,7 +32,7 @@ class MLP(torch.nn.Module):
 
 def check_compatibility_with_ddp(rank, world_size, port):
     disable_existing_loggers()
-    launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     model = MLP(4).cuda()
     if rank in [0, 1]:
         input = torch.arange(0, 16, dtype=torch.float).reshape(4, 4).cuda()

tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_gemini.py

@@ -34,7 +34,7 @@ class MLP(torch.nn.Module):
 
 def check_auto_parallel_with_gemini(rank, world_size, port):
     disable_existing_loggers()
-    launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     model = MLP(4).half().cuda()
     if rank in [0, 1]:
         input = torch.arange(0, 16).reshape(4, 4).half().cuda()

tests/test_auto_parallel/test_tensor_shard/test_gpt/test_runtime_with_gpt_modules.py

@@ -73,7 +73,7 @@ def _check_module_grad(
 
 def check_attention_layer(rank, model_cls, world_size, port):
     disable_existing_loggers()
-    launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
 
     config = transformers.GPT2Config(n_position=64, n_layer=2, n_head=16, n_embd=HIDDEN_DIM)
 

tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_binary_elementwise_metainfo.py

@@ -31,7 +31,7 @@ def _binary_elementwise_mem_test(rank, world_size, port):
         port: port for initializing process group
     """
     disable_existing_loggers()
-    launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     model = BinaryElementwiseOpModule(token=torch.add, shape=1024).cuda()
     input = torch.rand(32, 1024).cuda()
     input.requires_grad = True

tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_conv_metainfo.py

@@ -31,7 +31,7 @@ def _conv_module_mem_test(rank, world_size, port, bias):
         port: port for initializing process group
     """
     disable_existing_loggers()
-    launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     model = nn.Sequential(nn.Conv2d(4, 64, 3, padding=1, bias=bias)).cuda()
     input = torch.rand(4, 4, 64, 64).cuda()
     input.requires_grad = True
@@ -72,7 +72,7 @@ def _conv_function_mem_test(rank, world_size, port):
         port: port for initializing process group
     """
     disable_existing_loggers()
-    launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     model = ConvFunctionModule().cuda()
     input = torch.rand(4, 4, 64, 64).cuda()
     input.requires_grad = True

tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_linear_metainfo.py

@@ -30,7 +30,7 @@ def _linear_module_mem_test(rank, world_size, port):
         port: port for initializing process group
     """
     disable_existing_loggers()
-    launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     model = nn.Sequential(nn.Linear(64, 128, bias=False)).cuda()
     input = torch.rand(8, 8, 16, 64).cuda()
     input.requires_grad = True
@@ -68,7 +68,7 @@ def _linear_function_mem_test(rank, world_size, port):
         port: port for initializing process group
     """
     disable_existing_loggers()
-    launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     model = MyModule().cuda()
     input = torch.rand(8, 8, 16, 64).cuda()
     input.requires_grad = True

tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_norm_metainfo.py

@@ -25,7 +25,7 @@ def _batchnorm_module_mem_test(rank, world_size, port):
         port: port for initializing process group
     """
     disable_existing_loggers()
-    launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     model = nn.Sequential(nn.BatchNorm2d(128)).cuda()
     input = torch.rand(4, 128, 64, 64).cuda()
     input.requires_grad = True

tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_pooling_metainfo.py

@@ -21,7 +21,7 @@ def _adaptiveavgpool_module_mem_test(rank, world_size, port):
         port: port for initializing process group
     """
     disable_existing_loggers()
-    launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     model = nn.Sequential(nn.AdaptiveAvgPool2d((16, 16))).cuda()
     input = torch.rand(4, 128, 64, 64).cuda()
     input.requires_grad = True
@@ -62,7 +62,7 @@ def _maxpool_module_mem_test(rank, world_size, port):
         port: port for initializing process group
     """
     disable_existing_loggers()
-    launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     model = nn.Sequential(nn.MaxPool2d((16, 16))).cuda()
     input = torch.rand(4, 128, 64, 64).cuda()
     input.requires_grad = True

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_addbmm_handler.py

@@ -40,7 +40,7 @@ class AddBMMTorchFunctionModule(nn.Module):
 
 def check_2d_device_mesh(rank, world_size, port, module, bias_shape, using_kwargs):
     disable_existing_loggers()
-    launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     model = module(using_kwargs).cuda()
     physical_mesh_id = torch.arange(0, 4)
     mesh_shape = (2, 2)
@@ -150,7 +150,7 @@ def check_2d_device_mesh(rank, world_size, port, module, bias_shape, using_kwarg
 
 def check_1d_device_mesh(rank, module, bias_shape, using_kwargs, world_size, port):
     disable_existing_loggers()
-    launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     physical_mesh_id = torch.arange(0, 4)
     mesh_shape = (1, 4)
     device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_addmm_handler.py

@@ -40,7 +40,7 @@ class AddmmModel_with_param(nn.Module):
 
 def check_addmm_function_handler(rank, world_size, port, input_shape, model_cls):
     disable_existing_loggers()
-    launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     if model_cls == AddmmModel:
         model = AddmmModel().cuda()
     else:

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_batch_norm_handler.py

@@ -16,7 +16,7 @@ from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import n
 
 def check_bn_module_handler(rank, world_size, port):
     disable_existing_loggers()
-    launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     model = nn.Sequential(nn.BatchNorm2d(16)).cuda()
 
     physical_mesh_id = torch.arange(0, 4)

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bias_linear_function_node.py

@@ -34,7 +34,7 @@ class LinearModule(torch.nn.Module):
 
 def check_linear_module_handler(rank, world_size, port):
     disable_existing_loggers()
-    launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     model = LinearModule(weight_shape=WEIGHT_SHAPE).cuda()
 
     physical_mesh_id = torch.arange(0, 4)

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bias_linear_module_node.py

@@ -30,7 +30,7 @@ class LinearModule(torch.nn.Module):
 
 def check_linear_module_handler(rank, world_size, port, bias):
     disable_existing_loggers()
-    launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    launch(rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
     model = LinearModule(16, 32, bias=bias).cuda()
 
     physical_mesh_id = torch.arange(0, 4)