[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>

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()