[legacy] move trainer to legacy (#4545)

* [legacy] move trainer to legacy

* [doc] update docs related to trainer

* [test] ignore legacy test

colossalai/legacy/trainer/_trainer.py

@@ -1,14 +1,13 @@
-from typing import Union, List, Any
+from typing import Any, List, Union
 
 import torch
 from torch.utils.data import DataLoader
 from tqdm import tqdm
 
 from colossalai.engine import Engine
+from colossalai.legacy.trainer.hooks import BaseHook
 from colossalai.logging import DistributedLogger
-from colossalai.utils import MultiTimer
-from colossalai.utils import is_dp_rank_0, is_tp_rank_0, is_no_pp_or_last_stage
-from colossalai.trainer.hooks import BaseHook
+from colossalai.utils import MultiTimer, is_dp_rank_0, is_no_pp_or_last_stage, is_tp_rank_0
 
 
 class Trainer:

colossalai/legacy/trainer/hooks/__init__.py

@@ -1,7 +1,12 @@
 from ._base_hook import BaseHook
 from ._checkpoint_hook import SaveCheckpointHook
-from ._log_hook import (LogMemoryByEpochHook, LogMetricByEpochHook, LogMetricByStepHook, LogTimingByEpochHook,
-                        TensorboardHook)
+from ._log_hook import (
+    LogMemoryByEpochHook,
+    LogMetricByEpochHook,
+    LogMetricByStepHook,
+    LogTimingByEpochHook,
+    TensorboardHook,
+)
 from ._lr_scheduler_hook import LRSchedulerHook
 from ._metric_hook import AccuracyHook, LossHook, MetricHook, ThroughputHook
 

colossalai/legacy/trainer/hooks/_checkpoint_hook.py

@@ -1,11 +1,12 @@
 #!/usr/bin/env python
 # -*- encoding: utf-8 -*-
 import torch
-from colossalai.logging import get_dist_logger
 
+from colossalai.legacy.trainer.hooks import BaseHook
+from colossalai.logging import get_dist_logger
 from colossalai.registry import HOOKS
-from colossalai.trainer.hooks import BaseHook
 from colossalai.utils.checkpointing import save_checkpoint
+
 from ._lr_scheduler_hook import LRSchedulerHook
 
 

colossalai/legacy/trainer/hooks/_log_hook.py

@@ -3,17 +3,17 @@
 
 import os
 import os.path as osp
-
 from typing import List
+
 from colossalai.context import ParallelMode
 from colossalai.core import global_context as gpc
-from colossalai.registry import HOOKS
+from colossalai.legacy.trainer.hooks._metric_hook import ThroughputMetric
 from colossalai.logging import DistributedLogger
-from colossalai.utils import report_memory_usage, is_dp_rank_0, \
-    is_tp_rank_0, is_no_pp_or_last_stage, MultiTimer
+from colossalai.registry import HOOKS
+from colossalai.utils import MultiTimer, is_dp_rank_0, is_no_pp_or_last_stage, is_tp_rank_0, report_memory_usage
+
 from ._base_hook import BaseHook
 from ._commons_ import _format_number
-from colossalai.trainer.hooks._metric_hook import ThroughputMetric
 
 
 class LogByEpochHook(BaseHook):

colossalai/legacy/trainer/hooks/_lr_scheduler_hook.py

@@ -1,6 +1,7 @@
-from colossalai.registry import HOOKS
 from torch import Tensor
 
+from colossalai.registry import HOOKS
+
 from ._metric_hook import LearningRateMetric, MetricHook
 
 

colossalai/legacy/trainer/hooks/_metric_hook.py

@@ -6,6 +6,7 @@ from typing import Callable
 
 import torch
 import torch.distributed as dist
+
 from colossalai.communication import all_reduce
 from colossalai.context import ParallelMode
 from colossalai.core import global_context as gpc

docs/source/en/advanced_tutorials/train_gpt_using_hybrid_parallelism.md

@@ -43,7 +43,7 @@ from colossalai.engine.schedule import (InterleavedPipelineSchedule,
                                         PipelineSchedule)
 from colossalai.logging import disable_existing_loggers, get_dist_logger
 from colossalai.nn.layer.wrapper import PipelineSharedModuleWrapper
-from colossalai.trainer import Trainer, hooks
+from colossalai.legacy.trainer import Trainer, hooks
 from colossalai.utils.timer import MultiTimer
 from model_zoo.gpt import GPTLMLoss
 from torch.nn import functional as F
@@ -268,3 +268,4 @@ def train():
         return_output_label=False,
     )
 ```
+<!-- doc-test-command: echo  -->

docs/source/en/advanced_tutorials/train_vit_using_pipeline_parallelism.md

@@ -38,7 +38,7 @@ from colossalai.builder import build_pipeline_model
 from colossalai.engine.schedule import (InterleavedPipelineSchedule,
                                         PipelineSchedule)
 from colossalai.logging import disable_existing_loggers, get_dist_logger
-from colossalai.trainer import Trainer, hooks
+from colossalai.legacy.trainer import Trainer, hooks
 from colossalai.utils import MultiTimer, get_dataloader
 from timm.models import vision_transformer as vit
 from torchvision import transforms
@@ -245,3 +245,4 @@ def train():
                 hooks=hook_list,
                 display_progress=True)
 ```
+<!-- doc-test-command: echo  -->

docs/source/en/advanced_tutorials/train_vit_with_hybrid_parallelism.md

@@ -79,7 +79,7 @@ from colossalai.core import global_context as gpc
 from colossalai.logging import disable_existing_loggers, get_dist_logger
 from colossalai.nn.lr_scheduler import LinearWarmupLR
 from colossalai.nn.metric import Accuracy
-from colossalai.trainer import Trainer, hooks
+from colossalai.legacy.trainer import Trainer, hooks
 ```
 
 - Other modules
@@ -644,3 +644,4 @@ torchrun --standalone --nproc_per_node <NUM_GPUs>  train_hybrid.py --config ./co
 # If your torch >= 1.9.0
 # python -m torch.distributed.run --standalone --nproc_per_node= <NUM_GPUs> train_hybrid.py --config ./configs/config_hybrid_parallel.py
 ```
+<!-- doc-test-command: echo  -->

docs/source/en/basics/engine_trainer.md

@@ -64,7 +64,7 @@ Trainer is a more high-level wrapper for the user to execute training with fewer
 
 ```python
 from colossalai.logging import get_dist_logger
-from colossalai.trainer import Trainer, hooks
+from colossalai.legacy.trainer import Trainer, hooks
 
 # build components and initialize with colossalai.initialize
 ...
@@ -107,7 +107,7 @@ If you want to customize your own hook class, you can inherit `hooks.BaseHook` a
 
 ```python
 from colossalai.logging import get_dist_logger
-from colossalai.trainer import hooks
+from colossalai.legacy.trainer import hooks
 
 class LogMessageHook(hooks.BaseHook):
 
@@ -345,7 +345,7 @@ If you wish to train with a trainer object, you can follow the code snippet belo
 
 ```python
 from colossalai.nn.metric import Accuracy
-from colossalai.trainer import Trainer, hooks
+from colossalai.legacy.trainer import Trainer, hooks
 
 
 # create a trainer object
@@ -387,3 +387,4 @@ python -m torch.distributed.launch --nproc_per_node <num_gpus> --master_addr loc
 # with trainer
 python -m torch.distributed.launch --nproc_per_node <num_gpus> --master_addr localhost --master_port 29500 run_resnet_cifar10_with_trainer.py
 ```
+<!-- doc-test-command: echo  -->

docs/source/en/basics/model_checkpoint.md

@@ -41,7 +41,7 @@ for epoch in range(num_epochs):
 
 #### Save when using trainer
 ```python
-from colossalai.trainer import Trainer, hooks
+from colossalai.legacy.trainer import Trainer, hooks
 model = ...
 engine, _, _, _ = colossalai.initialize(model=model, ...)
 trainer = Trainer(engine, ...)
@@ -61,3 +61,4 @@ model = ...
 load_checkpoint('xxx.pt', model)
 ... # train or test
 ```
+<!-- doc-test-command: echo  -->

docs/source/en/features/mixed_precision_training.md

@@ -267,7 +267,7 @@ from pathlib import Path
 from colossalai.core import global_context as gpc
 from colossalai.logging import get_dist_logger
 from colossalai.utils import get_dataloader
-from colossalai.trainer import Trainer, hooks
+from colossalai.legacy.trainer import Trainer, hooks
 from colossalai.nn.lr_scheduler import LinearWarmupLR
 from timm.models import vit_base_patch16_224
 from torchvision import datasets, transforms

docs/source/en/features/pipeline_parallel.md

@@ -79,7 +79,7 @@ import colossalai.nn as col_nn
 
 from colossalai.core import global_context as gpc
 from colossalai.logging import disable_existing_loggers, get_dist_logger
-from colossalai.trainer import Trainer, hooks
+from colossalai.legacy.trainer import Trainer, hooks
 from colossalai.utils import MultiTimer, get_dataloader
 from colossalai.context import ParallelMode
 from colossalai.pipeline.pipelinable import PipelinableContext
@@ -157,3 +157,4 @@ trainer.fit(train_dataloader=train_dataloader,
 ```
 
 We use `2` pipeline stages and the batch will be split into `4` micro batches.
+<!-- doc-test-command: echo  -->

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

@@ -43,7 +43,7 @@ from colossalai.engine.schedule import (InterleavedPipelineSchedule,
                                         PipelineSchedule)
 from colossalai.logging import disable_existing_loggers, get_dist_logger
 from colossalai.nn.layer.wrapper import PipelineSharedModuleWrapper
-from colossalai.trainer import Trainer, hooks
+from colossalai.legacy.trainer import Trainer, hooks
 from colossalai.utils.timer import MultiTimer
 from model_zoo.gpt import GPTLMLoss
 from torch.nn import functional as F
@@ -273,3 +273,4 @@ def train():
         return_output_label=False,
     )
 ```
+<!-- doc-test-command: echo  -->

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

@@ -36,7 +36,7 @@ from colossalai.builder import build_pipeline_model
 from colossalai.engine.schedule import (InterleavedPipelineSchedule,
                                         PipelineSchedule)
 from colossalai.logging import disable_existing_loggers, get_dist_logger
-from colossalai.trainer import Trainer, hooks
+from colossalai.legacy.trainer import Trainer, hooks
 from colossalai.utils import MultiTimer, get_dataloader
 from timm.models import vision_transformer as vit
 from torchvision import transforms
@@ -244,3 +244,4 @@ def train():
                 hooks=hook_list,
                 display_progress=True)
 ```
+<!-- doc-test-command: echo  -->

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

@@ -74,7 +74,7 @@ from colossalai.core import global_context as gpc
 from colossalai.logging import disable_existing_loggers, get_dist_logger
 from colossalai.nn.lr_scheduler import LinearWarmupLR
 from colossalai.nn.metric import Accuracy
-from colossalai.trainer import Trainer, hooks
+from colossalai.legacy.trainer import Trainer, hooks
 ```
 
 - 其他模块
@@ -589,3 +589,4 @@ torchrun --standalone --nproc_per_node <NUM_GPUs>  train_hybrid.py --config ./co
 # If your torch >= 1.9.0
 # python -m torch.distributed.run --standalone --nproc_per_node= <NUM_GPUs> train_hybrid.py --config ./configs/config_hybrid_parallel.py
 ```
+<!-- doc-test-command: echo  -->

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

@@ -61,7 +61,7 @@ Trainer 的参数 `schedule` 默认值是 `None` 。在大多数情况下，除
 
 ```python
 from colossalai.logging import get_dist_logger
-from colossalai.trainer import Trainer, hooks
+from colossalai.legacy.trainer import Trainer, hooks
 
 # build components and initialize with colossalai.initialize
 ...
@@ -104,7 +104,7 @@ trainer.fit(
 
 ```python
 from colossalai.logging import get_dist_logger
-from colossalai.trainer import hooks
+from colossalai.legacy.trainer import hooks
 
 class LogMessageHook(hooks.BaseHook):
 
@@ -341,7 +341,7 @@ for epoch in range(gpc.config.NUM_EPOCHS):
 
 ```python
 from colossalai.nn.metric import Accuracy
-from colossalai.trainer import Trainer, hooks
+from colossalai.legacy.trainer import Trainer, hooks
 
 
 # create a trainer object
@@ -384,3 +384,4 @@ python -m torch.distributed.launch --nproc_per_node <num_gpus> --master_addr loc
 # with trainer
 python -m torch.distributed.launch --nproc_per_node <num_gpus> --master_addr localhost --master_port 29500 run_resnet_cifar10_with_trainer.py
 ```
+<!-- doc-test-command: echo  -->

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

@@ -41,7 +41,7 @@ for epoch in range(num_epochs):
 
 #### 用 trainer 保存
 ```python
-from colossalai.trainer import Trainer, hooks
+from colossalai.legacy.trainer import Trainer, hooks
 model = ...
 engine, _, _, _ = colossalai.initialize(model=model, ...)
 trainer = Trainer(engine, ...)
@@ -61,3 +61,4 @@ model = ...
 load_checkpoint('xxx.pt', model)
 ... # train or test
 ```
+<!-- doc-test-command: echo  -->

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

@@ -245,7 +245,7 @@ from pathlib import Path
 from colossalai.core import global_context as gpc
 from colossalai.logging import get_dist_logger
 from colossalai.utils import get_dataloader
-from colossalai.trainer import Trainer, hooks
+from colossalai.legacy.trainer import Trainer, hooks
 from colossalai.nn.lr_scheduler import LinearWarmupLR
 from timm.models import vit_base_patch16_224
 from torchvision import datasets, transforms

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

@@ -78,7 +78,7 @@ import colossalai.nn as col_nn
 
 from colossalai.core import global_context as gpc
 from colossalai.logging import disable_existing_loggers, get_dist_logger
-from colossalai.trainer import Trainer, hooks
+from colossalai.legacy.trainer import Trainer, hooks
 from colossalai.utils import MultiTimer, get_dataloader
 from colossalai.context import ParallelMode
 from colossalai.pipeline.pipelinable import PipelinableContext
@@ -156,3 +156,4 @@ trainer.fit(train_dataloader=train_dataloader,
 ```
 
 我们使用 `2` 个流水段，并且 batch 将被切分为 `4` 个 micro batches。
+<!-- doc-test-command: echo  -->

examples/language/gpt/titans/train_gpt.py

@@ -10,9 +10,9 @@ import colossalai
 import colossalai.utils as utils
 from colossalai.context.parallel_mode import ParallelMode
 from colossalai.core import global_context as gpc
+from colossalai.legacy.trainer import Trainer, hooks
 from colossalai.logging import disable_existing_loggers, get_dist_logger
 from colossalai.nn import LinearWarmupLR
-from colossalai.trainer import Trainer, hooks
 from colossalai.utils import colo_set_process_memory_fraction, is_using_pp
 from colossalai.utils.timer import MultiTimer
 from colossalai.zero.legacy.init_ctx import ZeroInitContext

pytest.ini

@@ -4,4 +4,4 @@ markers =
     gpu: tests which requires a single GPU
     dist: tests which are run in a multi-GPU or multi-machine environment
     experiment: tests for experimental features
-addopts = --ignore=tests/test_analyzer --ignore=tests/test_auto_parallel --ignore=tests/test_autochunk --ignore=tests/test_moe --ignore=tests/test_fx
+addopts = --ignore=tests/test_analyzer --ignore=tests/test_auto_parallel --ignore=tests/test_autochunk --ignore=tests/test_moe --ignore=tests/test_fx --ignore=tests/test_legacy

tests/test_data_pipeline_tensor_parallel/test_cifar_with_data_pipeline_tensor.py

@@ -1,100 +0,0 @@
-import os
-from pathlib import Path
-
-import pytest
-import torch
-from torchvision import transforms
-from torchvision.datasets import CIFAR10
-
-import colossalai
-from colossalai.amp import AMP_TYPE
-from colossalai.context import ParallelMode
-from colossalai.core import global_context as gpc
-from colossalai.logging import get_dist_logger
-from colossalai.nn import CrossEntropyLoss
-from colossalai.nn.lr_scheduler import CosineAnnealingWarmupLR
-from colossalai.pipeline.pipelinable import PipelinableContext
-from colossalai.testing import rerun_if_address_is_in_use, skip_if_not_enough_gpus, spawn
-from colossalai.trainer import Trainer, hooks
-from colossalai.utils import get_dataloader
-
-BATCH_SIZE = 4
-NUM_EPOCHS = 60
-WARMUP_EPOCHS = 5
-CONFIG = dict(NUM_MICRO_BATCHES=2,
-              parallel=dict(pipeline=2, tensor=dict(size=2, mode='1d')),
-              fp16=dict(mode=AMP_TYPE.NAIVE),
-              gradient_accumulation=2)
-
-
-def run_trainer(rank, world_size, port):
-    colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
-
-    logger = get_dist_logger()
-
-    # get logger
-    logger = get_dist_logger()
-
-    pipelinable = PipelinableContext()
-    try:
-        from titans.model.vit import vit_tiny_patch4_32
-    except ImportError:
-        logger.warning('skip the test_cifar_with_data_pipeline_tensor test because titan is not installed')
-        logger.warning('please install titan from https://github.com/hpcaitech/Titans')
-        return
-    with pipelinable:
-        model = vit_tiny_patch4_32()
-    pipelinable.to_layer_list()
-    pipelinable.policy = "uniform"
-    model = pipelinable.partition(1, gpc.pipeline_parallel_size, gpc.get_local_rank(ParallelMode.PIPELINE))
-
-    # create dataloaders
-    root = Path(os.environ['DATA'])
-    transform_train = transforms.Compose([
-        transforms.RandomCrop(32, padding=4, pad_if_needed=True),
-        transforms.AutoAugment(policy=transforms.AutoAugmentPolicy.CIFAR10),
-        transforms.ToTensor(),
-        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
-    ])
-    train_dataset = CIFAR10(root=root, train=True, download=True, transform=transform_train)
-    train_dataloader = get_dataloader(dataset=train_dataset, shuffle=True, batch_size=BATCH_SIZE, pin_memory=True)
-
-    # create loss function
-    criterion = CrossEntropyLoss(label_smoothing=0.1)
-
-    # create optimizer
-    optimizer = torch.optim.AdamW(model.parameters(), lr=0.001, weight_decay=0)
-
-    # create lr scheduler
-    lr_scheduler = CosineAnnealingWarmupLR(optimizer=optimizer, total_steps=NUM_EPOCHS, warmup_steps=WARMUP_EPOCHS)
-
-    # initialize
-    engine, train_dataloader, *_ = colossalai.initialize(model=model,
-                                                         optimizer=optimizer,
-                                                         criterion=criterion,
-                                                         train_dataloader=train_dataloader)
-
-    logger = get_dist_logger()
-
-    trainer = Trainer(engine=engine, logger=logger)
-
-    hook_list = [
-        hooks.LRSchedulerHook(lr_scheduler=lr_scheduler, by_epoch=False),
-    ]
-
-    trainer.fit(train_dataloader=train_dataloader,
-                epochs=NUM_EPOCHS,
-                max_steps=2,
-                hooks=hook_list,
-                display_progress=True)
-
-
-@pytest.mark.dist
-@skip_if_not_enough_gpus(min_gpus=8)
-@rerun_if_address_is_in_use()
-def test_hybrid_parallel():
-    spawn(run_trainer, 8)
-
-
-if __name__ == '__main__':
-    test_hybrid_parallel()

tests/test_legacy/test_trainer/test_trainer_with_non_pipe_schedule.py

@@ -3,9 +3,9 @@ import torch
 
 import colossalai
 from colossalai.amp.amp_type import AMP_TYPE
+from colossalai.legacy.trainer import Trainer
 from colossalai.logging import get_dist_logger
 from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
-from colossalai.trainer import Trainer
 from colossalai.utils import MultiTimer
 from tests.components_to_test.registry import non_distributed_component_funcs
 

tests/test_legacy/test_trainer/test_trainer_with_pipe_schedule.py

@@ -12,9 +12,9 @@ from torchvision.models import resnet18
 import colossalai
 from colossalai.context.parallel_mode import ParallelMode
 from colossalai.core import global_context as gpc
+from colossalai.legacy.trainer import Trainer
 from colossalai.logging import get_dist_logger
 from colossalai.testing import rerun_if_address_is_in_use, spawn
-from colossalai.trainer import Trainer
 from colossalai.utils import MultiTimer, get_dataloader
 
 BATCH_SIZE = 4

tests/test_pipeline/test_cuda_rpc_performance.py

@@ -1,25 +1,16 @@
 import os
-from typing import Callable, List, Optional, Type, Union
 import time
 
 import pytest
 import torch
 import torch.nn as nn
+from rpc_test_utils import parse_args, rpc_run
 from titans.dataloader.cifar10 import build_cifar
 from torchvision.models import resnet50
-from torchvision.models.resnet import BasicBlock, Bottleneck, conv1x1
 from tqdm import tqdm
 
-from rpc_test_utils import rpc_run, parse_args
-import colossalai
-import colossalai.nn as col_nn
-from colossalai.logging import disable_existing_loggers, get_dist_logger
-from colossalai.trainer import Trainer, hooks
-from colossalai.utils import MultiTimer, get_dataloader
-from colossalai.context import ParallelMode
-from colossalai.pipeline.pipelinable import PipelinableContext, PipelinableModel
-from colossalai.pipeline.rpc import OneFOneBPipelineEngine, ChimeraPipelineEngine
-from colossalai.pipeline.pipeline_process_group import ppg
+from colossalai.pipeline.pipelinable import PipelinableContext
+from colossalai.pipeline.rpc import OneFOneBPipelineEngine
 
 
 def flatten(x):