[legacy] move unused and outdated codes to legacy

colossalai/auto_parallel/tensor_shard/node_handler/registry.py

@@ -1,5 +1,5 @@
 class Registry:
-    # TODO: refactor the registry classes used in colossalai.registry, colossalai.fx and here
+    # TODO: refactor the registry classes used in colossalai.legacy.registry, colossalai.fx and here
 
     def __init__(self, name):
         self.name = name

colossalai/context/parallel_context.py

@@ -15,8 +15,8 @@
 from colossalai.context.config import Config
 from colossalai.context.singleton_meta import SingletonMeta
 from colossalai.global_variables import tensor_parallel_env as env
+from colossalai.legacy.registry import DIST_GROUP_INITIALIZER
 from colossalai.logging import get_dist_logger
-from colossalai.registry import DIST_GROUP_INITIALIZER
 
 from .parallel_mode import ParallelMode
 from .random import add_seed, get_seeds, set_mode

colossalai/context/process_group_initializer/initializer_1d.py

@@ -2,8 +2,9 @@
 # -*- encoding: utf-8 -*-
 
 import torch.distributed as dist
+
 from colossalai.global_variables import tensor_parallel_env as env
-from colossalai.registry import DIST_GROUP_INITIALIZER
+from colossalai.legacy.registry import DIST_GROUP_INITIALIZER
 
 from ..parallel_mode import ParallelMode
 from .process_group_initializer import ProcessGroupInitializer

colossalai/context/process_group_initializer/initializer_2d.py

@@ -3,7 +3,7 @@
 import torch.distributed as dist
 
 from colossalai.global_variables import tensor_parallel_env as env
-from colossalai.registry import DIST_GROUP_INITIALIZER
+from colossalai.legacy.registry import DIST_GROUP_INITIALIZER
 
 from ..parallel_mode import ParallelMode
 from .process_group_initializer import ProcessGroupInitializer

colossalai/context/process_group_initializer/initializer_2p5d.py

@@ -4,9 +4,10 @@
 import math
 
 import torch.distributed as dist
+
 from colossalai.context import Config
 from colossalai.global_variables import tensor_parallel_env as env
-from colossalai.registry import DIST_GROUP_INITIALIZER
+from colossalai.legacy.registry import DIST_GROUP_INITIALIZER
 
 from ..parallel_mode import ParallelMode
 from .process_group_initializer import ProcessGroupInitializer

colossalai/context/process_group_initializer/initializer_3d.py

@@ -6,7 +6,7 @@
 import torch.distributed as dist
 
 from colossalai.global_variables import tensor_parallel_env as env
-from colossalai.registry import DIST_GROUP_INITIALIZER
+from colossalai.legacy.registry import DIST_GROUP_INITIALIZER
 
 from ..parallel_mode import ParallelMode
 from .process_group_initializer import ProcessGroupInitializer

colossalai/context/process_group_initializer/initializer_data.py

@@ -3,7 +3,7 @@
 
 from torch import distributed as dist
 
-from colossalai.registry import DIST_GROUP_INITIALIZER
+from colossalai.legacy.registry import DIST_GROUP_INITIALIZER
 
 from ..parallel_mode import ParallelMode
 from .process_group_initializer import ProcessGroupInitializer

colossalai/context/process_group_initializer/initializer_model.py

@@ -2,9 +2,11 @@
 # -*- encoding: utf-8 -*-
 
 import torch.distributed as dist
-from colossalai.registry import DIST_GROUP_INITIALIZER
-from .process_group_initializer import ProcessGroupInitializer
+
+from colossalai.legacy.registry import DIST_GROUP_INITIALIZER
+
 from ..parallel_mode import ParallelMode
+from .process_group_initializer import ProcessGroupInitializer
 
 
 @DIST_GROUP_INITIALIZER.register_module

colossalai/context/process_group_initializer/initializer_pipeline.py

@@ -3,7 +3,7 @@
 
 from torch import distributed as dist
 
-from colossalai.registry import DIST_GROUP_INITIALIZER
+from colossalai.legacy.registry import DIST_GROUP_INITIALIZER
 
 from ..parallel_mode import ParallelMode
 from .process_group_initializer import ProcessGroupInitializer

colossalai/context/process_group_initializer/initializer_sequence.py

@@ -2,7 +2,7 @@
 # -*- encoding: utf-8 -*-
 import torch.distributed as dist
 
-from colossalai.registry import DIST_GROUP_INITIALIZER
+from colossalai.legacy.registry import DIST_GROUP_INITIALIZER
 
 from ..parallel_mode import ParallelMode
 from .initializer_tensor import Initializer_Tensor

colossalai/context/process_group_initializer/initializer_tensor.py

@@ -3,9 +3,10 @@
 
 import torch.distributed as dist
 
-from colossalai.registry import DIST_GROUP_INITIALIZER
-from .process_group_initializer import ProcessGroupInitializer
+from colossalai.legacy.registry import DIST_GROUP_INITIALIZER
+
 from ..parallel_mode import ParallelMode
+from .process_group_initializer import ProcessGroupInitializer
 
 
 @DIST_GROUP_INITIALIZER.register_module

colossalai/initialize.py

@@ -17,13 +17,13 @@
 
 from colossalai.amp import AMP_TYPE, convert_to_amp
 from colossalai.amp.naive_amp import NaiveAMPModel
-from colossalai.builder.builder import build_gradient_handler
 from colossalai.context import Config, ConfigException, ParallelMode
 from colossalai.context.moe_context import MOE_CONTEXT
 from colossalai.core import global_context as gpc
-from colossalai.engine import Engine
-from colossalai.engine.gradient_accumulation import accumulate_gradient
-from colossalai.engine.schedule import (
+from colossalai.legacy.builder.builder import build_gradient_handler
+from colossalai.legacy.engine import Engine
+from colossalai.legacy.engine.gradient_accumulation import accumulate_gradient
+from colossalai.legacy.engine.schedule import (
     InterleavedPipelineSchedule,
     NonPipelineSchedule,
     PipelineSchedule,

colossalai/builder/builder.py → colossalai/legacy/builder/builder.py

@@ -3,7 +3,7 @@
 
 import inspect
 
-from colossalai.registry import *
+from colossalai.legacy.registry import *
 
 
 def build_from_config(module, config: dict):
@@ -71,7 +71,7 @@ def build_gradient_handler(config, model, optimizer):
         optimizer (:class:`torch.optim.Optimizer`): An optimizer object containing parameters for the gradient handler
 
     Returns:
-        An object of :class:`colossalai.engine.BaseGradientHandler`
+        An object of :class:`colossalai.legacy.engine.BaseGradientHandler`
     """
     config_ = config.copy()
     config_['model'] = model

colossalai/engine/_base_engine.py → colossalai/legacy/engine/_base_engine.py

@@ -8,11 +8,17 @@
 from torch.nn import Module
 from torch.nn.modules.loss import _Loss
 
-from colossalai.engine.gradient_handler import BaseGradientHandler
-from colossalai.engine.schedule import BaseSchedule, InterleavedPipelineSchedule, NonPipelineSchedule, PipelineSchedule
+from colossalai.legacy.engine.gradient_handler import BaseGradientHandler
+from colossalai.legacy.engine.schedule import (
+    BaseSchedule,
+    InterleavedPipelineSchedule,
+    NonPipelineSchedule,
+    PipelineSchedule,
+)
 from colossalai.logging import get_dist_logger
-from colossalai.zero.legacy.gemini import BaseOpHook, register_ophooks_recursively
 from colossalai.nn.optimizer import ColossalaiOptimizer
+from colossalai.zero.legacy.gemini import BaseOpHook, register_ophooks_recursively
+
 
 class Engine:
     """Basic engine class for training and evaluation. It runs a specific process method

colossalai/engine/gradient_accumulation/__init__.py → colossalai/legacy/engine/gradient_accumulation/__init__.py

@@ -4,7 +4,7 @@
 from torch.optim import Optimizer
 from torch.optim.lr_scheduler import _LRScheduler
 
-from colossalai.engine import BaseGradientHandler
+from colossalai.legacy.engine import BaseGradientHandler
 
 from ._gradient_accumulation import (
     GradAccumDataloader,
@@ -33,7 +33,7 @@ def accumulate_gradient(model: nn.Module,
         dataloader (:class:`torch.utils.data.DataLoader` or iterable objects):
             your dataloader object, would be called like iter(dataloader)
         accumulate_size (int): the number of steps to accumulate gradients
-        gradient_handlers (List[:class:`colossalai.engine.BaseGradientHandler`]):
+        gradient_handlers (List[:class:`colossalai.legacy.engine.BaseGradientHandler`]):
             list of gradient handler objects. Default is None.
         lr_scheduler (`torch.optim.lr_scheduler` or `colossalai.nn.lr_scheduler`):
             your ``lr_scheduler`` object for gradient accumulation. Defaults to None.

colossalai/engine/gradient_accumulation/_gradient_accumulation.py → colossalai/legacy/engine/gradient_accumulation/_gradient_accumulation.py

@@ -10,7 +10,7 @@
 from torch.optim.lr_scheduler import _LRScheduler
 from torch.utils.data import DataLoader
 
-from colossalai.engine import BaseGradientHandler
+from colossalai.legacy.engine import BaseGradientHandler
 from colossalai.nn.optimizer import ColossalaiOptimizer
 from colossalai.utils import conditional_context
 
@@ -262,7 +262,7 @@ class GradAccumGradientHandler:
     before accumulation size is reached.
 
     Args:
-        grad_handler (:class:`colossalai.engine.BaseGradientHandler`):
+        grad_handler (:class:`colossalai.legacy.engine.BaseGradientHandler`):
             Your ``gradient_handler`` object for gradient accumulation, would be called when achieving `accumulate_size`.
         accumulate_size (int): The number of steps to accumulate gradients.
 

colossalai/engine/gradient_handler/_data_parallel_gradient_handler.py → colossalai/legacy/engine/gradient_handler/_data_parallel_gradient_handler.py

@@ -1,7 +1,7 @@
+from colossalai.context.parallel_mode import ParallelMode
 from colossalai.core import global_context as gpc
-from colossalai.registry import GRADIENT_HANDLER
+from colossalai.legacy.registry import GRADIENT_HANDLER
 
-from ...context.parallel_mode import ParallelMode
 from ._base_gradient_handler import BaseGradientHandler
 from .utils import bucket_allreduce
 

colossalai/engine/gradient_handler/_moe_gradient_handler.py → colossalai/legacy/engine/gradient_handler/_moe_gradient_handler.py

@@ -1,9 +1,9 @@
 from colossalai.context.moe_context import MOE_CONTEXT
+from colossalai.context.parallel_mode import ParallelMode
 from colossalai.core import global_context as gpc
-from colossalai.registry import GRADIENT_HANDLER
+from colossalai.legacy.registry import GRADIENT_HANDLER
 from colossalai.utils.moe import get_moe_epsize_param_dict
 
-from ...context.parallel_mode import ParallelMode
 from ._base_gradient_handler import BaseGradientHandler
 from .utils import bucket_allreduce
 

colossalai/engine/gradient_handler/_pipeline_parallel_gradient_handler.py → colossalai/legacy/engine/gradient_handler/_pipeline_parallel_gradient_handler.py

@@ -7,7 +7,7 @@
 from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
 
 from colossalai.core import global_context as gpc
-from colossalai.registry import GRADIENT_HANDLER
+from colossalai.legacy.registry import GRADIENT_HANDLER
 
 from ._base_gradient_handler import BaseGradientHandler
 

colossalai/engine/gradient_handler/_sequence_parallel_gradient_handler.py → colossalai/legacy/engine/gradient_handler/_sequence_parallel_gradient_handler.py

@@ -1,7 +1,7 @@
+from colossalai.context.parallel_mode import ParallelMode
 from colossalai.core import global_context as gpc
-from colossalai.registry import GRADIENT_HANDLER
+from colossalai.legacy.registry import GRADIENT_HANDLER
 
-from ...context.parallel_mode import ParallelMode
 from ._base_gradient_handler import BaseGradientHandler
 from .utils import bucket_allreduce
 

colossalai/engine/gradient_handler/_zero_gradient_handler.py → colossalai/legacy/engine/gradient_handler/_zero_gradient_handler.py

@@ -1,4 +1,4 @@
-from colossalai.registry import GRADIENT_HANDLER
+from colossalai.legacy.registry import GRADIENT_HANDLER
 
 from ._base_gradient_handler import BaseGradientHandler
 

colossalai/engine/schedule/_base_schedule.py → colossalai/legacy/engine/schedule/_base_schedule.py

@@ -95,7 +95,7 @@ def forward_backward_step(self,
         """The process function over a batch of dataset for training or evaluation.
 
         Args:
-            engine (colossalai.engine.Engine): Colossalai engine for training and inference.
+            engine (colossalai.legacy.engine.Engine): Colossalai engine for training and inference.
             data_iter (Iterable): Data iterator from which get a batch of data, obtained by calling iter(dataloader).
             forward_only (bool): If True, the process won't include backward.
             return_loss (bool, optional): If False, the loss won't be returned.

colossalai/engine/schedule/_non_pipeline_schedule.py → colossalai/legacy/engine/schedule/_non_pipeline_schedule.py

@@ -54,7 +54,7 @@ def forward_backward_step(self,
         The returned labels and loss will None if :attr:`return_loss` is False.
 
         Args:
-            engine (colossalai.engine.Engine): Colossalai engine for training and inference.
+            engine (colossalai.legacy.engine.Engine): Colossalai engine for training and inference.
             data_iter (Iterable): Dataloader as the form of an iterator, obtained by calling iter(dataloader).
             forward_only (bool, optional):
                 If True, the model is run for the forward pass, else back propagation will be executed.

colossalai/engine/schedule/_pipeline_schedule.py → colossalai/legacy/engine/schedule/_pipeline_schedule.py

@@ -236,7 +236,7 @@ def _forward_step(self, engine, input_obj, return_tensors, return_output_label=T
         Returns output tensor. This is a helper function and can be ignored by users.
 
         Args:
-            engine (colossalai.engine.Engine): Colossalai engine for training and inference.
+            engine (colossalai.legacy.engine.Engine): Colossalai engine for training and inference.
             input_obj (Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]): Input tensor for this pipeline stage.
             return_tensors (List[:class:`torch.Tensor`]): A list of tensors to return.
             return_output_label (bool, optional): Whether returns output labels.
@@ -274,7 +274,7 @@ def _backward_step(self, engine, input_obj, output_obj, output_obj_grad):
         This is a helper function and can be ignored by users.
 
         Args:
-            engine (colossalai.engine.Engine): Colossalai engine for training and inference.
+            engine (colossalai.legacy.engine.Engine): Colossalai engine for training and inference.
             input_obj (Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]): input tensor for this pipeline stage.
             output_obj (Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]): output tensor for this pipeline stage.
             output_obj_grad (Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]): gradient of output tensor for this pipeline stage.
@@ -314,7 +314,7 @@ def forward_backward_step(self, engine, data_iter, forward_only=False, return_lo
         Returns a tuple with losses if the last stage, an empty tuple otherwise.
 
         Args:
-            engine (colossalai.engine.Engine): Colossalai engine for training and inference.
+            engine (colossalai.legacy.engine.Engine): Colossalai engine for training and inference.
             data_iter (Iterable): Dataloader as the form of an iterator, obtained by calling iter(dataloader).
             forward_only (bool, optional):
                 Whether run forward step only. Default is false. If true, no backward will be run.
@@ -518,7 +518,7 @@ def _forward_step(self,
         Returns output tensor. This is a helper function and can be ignored by users.
 
         Args:
-            engine (colossalai.engine.Engine): Colossalai engine for training and inference.
+            engine (colossalai.legacy.engine.Engine): Colossalai engine for training and inference.
             model_chunk_id (int): The id of model chunks.
             input_obj (Union[:class:`torch.Tensor`, List[:class:`torch.Tensor`]]): Input tensor for this pipeline stage.
             return_tensors (List[:class:`torch.Tensor`]): A list of tensors to return.
@@ -555,7 +555,7 @@ def forward_backward_step(self, engine, data_iter, forward_only=False, return_lo
         communication between pipeline stages as needed.
 
         Args:
-            engine (colossalai.engine.Engine): Colossalai engine for training and inference.
+            engine (colossalai.legacy.engine.Engine): Colossalai engine for training and inference.
             data_iter (Iterable): Dataloader as the form of an iterator, obtained by calling iter(dataloader).
             forward_only (bool, optional):
                 Whether run forward step only. Default is false. If true, no backward will be run.

colossalai/engine/schedule/_pipeline_schedule_v2.py → colossalai/legacy/engine/schedule/_pipeline_schedule_v2.py

@@ -69,7 +69,7 @@ def forward_backward_step(self,
         Returns a tuple with losses if the last stage, an empty tuple otherwise.
 
         Args:
-            engine (colossalai.engine.Engine): Colossalai engine for training and inference.
+            engine (colossalai.legacy.engine.Engine): Colossalai engine for training and inference.
             data_iter (Iterable): Dataloader as the form of an iterator, obtained by calling iter(dataloader).
             forward_only (bool, optional):
                 Whether run forward step only. Default is false. If true, no backward will be run.

colossalai/registry/registry.py → colossalai/legacy/registry/registry.py

@@ -6,7 +6,7 @@
 
 
 class Registry:
-    """This is a registry class used to register classes and modules so that a universal 
+    """This is a registry class used to register classes and modules so that a universal
     object builder can be enabled.
 
     Args:
@@ -42,7 +42,7 @@ def register_module(self, module_class):
         return module_class
 
     def get_module(self, module_name: str):
-        """Retrieves a module with name `module_name` and returns the module if it has 
+        """Retrieves a module with name `module_name` and returns the module if it has
         already been registered before.
 
         Args:

colossalai/trainer/_trainer.py → 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.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/trainer/hooks/__init__.py → 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/trainer/hooks/_checkpoint_hook.py → 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.registry import HOOKS
-from colossalai.trainer.hooks import BaseHook
+from colossalai.legacy.registry import HOOKS
+from colossalai.legacy.trainer.hooks import BaseHook
+from colossalai.logging import get_dist_logger
 from colossalai.utils.checkpointing import save_checkpoint
+
 from ._lr_scheduler_hook import LRSchedulerHook