Merge branch 'main' into feature/shardformer

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/booster/plugin/low_level_zero_plugin.py

@@ -3,6 +3,7 @@ import os
 import warnings
 from functools import partial
 from pathlib import Path
+from types import MethodType
 from typing import Callable, Iterator, List, Optional, Tuple, Union
 
 import torch
@@ -25,9 +26,9 @@ from colossalai.checkpoint_io.utils import (
     sharded_optimizer_loading_epilogue,
     unwrap_optimizer,
 )
-from colossalai.interface import ModelWrapper, OptimizerWrapper
+from colossalai.interface import AMPModelMixin, ModelWrapper, OptimizerWrapper
 from colossalai.utils import get_current_device
-from colossalai.zero import LowLevelZeroOptimizer, zero_model_wrapper, zero_optim_wrapper
+from colossalai.zero import LowLevelZeroOptimizer
 
 from .dp_plugin_base import DPPluginBase
 from .torch_ddp_plugin import TorchDDPCheckpointIO
@@ -44,6 +45,34 @@ def _convert_floating_point(x, dtype: torch.dtype = torch.float16):
 SUPPORTED_PRECISION = ['fp16', 'bf16', 'fp32']
 
 
+class LowLevelZeroModel(ModelWrapper, AMPModelMixin):
+
+    def __init__(self, module: nn.Module, precision: str) -> None:
+        super().__init__(module)
+        self.dtype = None
+        if precision == 'fp16':
+            self.dtype = torch.float16
+        elif precision == 'bf16':
+            self.dtype = torch.bfloat16
+        if self.dtype is not None:
+            module = module.to(self.dtype)
+        module = module.to(get_current_device())
+        self.module = module
+        self.convert_fn = None
+        if self.dtype is not None:
+            self.convert_fn = partial(_convert_floating_point, dtype=self.dtype)
+
+    def forward(self, *args, **kwargs):
+        if self.convert_fn is not None:
+            args = tree_map(self.convert_fn, args)
+            kwargs = tree_map(self.convert_fn, kwargs)
+        return super().forward(*args, **kwargs)
+
+    def unwrap(self):
+        # TODO(ver217): this is a workaround for loading model
+        return self
+
+
 class LowLevelZeroCheckpointIO(TorchDDPCheckpointIO):
 
     def save_unsharded_optimizer(self, optimizer: OptimizerWrapper, checkpoint: str, gather_dtensor: bool = False):
@@ -165,30 +194,36 @@ class LowLevelZeroCheckpointIO(TorchDDPCheckpointIO):
 
         sharded_optimizer_loading_epilogue(optimizer)
 
+    def save_unsharded_model(self, model: LowLevelZeroModel, checkpoint: str, gather_dtensor: bool,
+                             use_safetensors: bool):
+        assert isinstance(model, LowLevelZeroModel)
+        super().save_unsharded_model(model.module, checkpoint, gather_dtensor, use_safetensors)
 
-class LowLevelZeroModel(ModelWrapper):
+    def save_sharded_model(self,
+                           model: nn.Module,
+                           checkpoint_path: str,
+                           gather_dtensor: bool = True,
+                           prefix: Optional[str] = None,
+                           max_shard_size: int = 1024,
+                           use_safetensors: bool = False):
+        assert isinstance(model, LowLevelZeroModel)
+        super().save_sharded_model(model.module, checkpoint_path, gather_dtensor, prefix, max_shard_size,
+                                   use_safetensors)
 
-    def __init__(self, module: nn.Module, stage: int, precision: str) -> None:
-        super().__init__(module)
-        self.dtype = None
-        if precision == 'fp16':
-            self.dtype = torch.float16
-        elif precision == 'bf16':
-            self.dtype = torch.bfloat16
-        module = zero_model_wrapper(module, zero_stage=stage)
-        if self.dtype is not None:
-            module = module.to(self.dtype)
-        module = module.to(get_current_device())
-        self.module = module
-        self.convert_fn = None
-        if self.dtype is not None:
-            self.convert_fn = partial(_convert_floating_point, dtype=self.dtype)
+    def load_unsharded_model(self, model: LowLevelZeroModel, checkpoint: str, strict: bool = True):
+        assert isinstance(model, LowLevelZeroModel)
+        super().load_unsharded_model(model.module, checkpoint, strict)
+        model.update_master_params()
 
-    def forward(self, *args, **kwargs):
-        if self.convert_fn is not None:
-            args = tree_map(self.convert_fn, args)
-            kwargs = tree_map(self.convert_fn, kwargs)
-        return super().forward(*args, **kwargs)
+    def load_sharded_model(self,
+                           model: LowLevelZeroModel,
+                           checkpoint_index_file: Path,
+                           strict: bool = False,
+                           use_safetensors: bool = False,
+                           load_sub_module: bool = True):
+        assert isinstance(model, LowLevelZeroModel)
+        super().load_sharded_model(model.module, checkpoint_index_file, strict, use_safetensors, load_sub_module)
+        model.update_master_params()
 
 
 class LowLevelZeroPlugin(DPPluginBase):
@@ -248,22 +283,24 @@ class LowLevelZeroPlugin(DPPluginBase):
         super().__init__()
         assert stage in (1, 2), f'LowLevelZeroPlugin only supports stage 1/2 training'
         assert precision in SUPPORTED_PRECISION, f'LowLevelZeroPlugin only supports {SUPPORTED_PRECISION} training'
-
+        assert norm_type == 2.0, f'LowLevelZeroPlugin only supports norm_type=2.0 now'
         self.stage = stage
         self.precision = precision
-        self.zero_optim_config = dict(reduce_bucket_size=reduce_bucket_size_in_m * 1024 * 1024,
-                                      communication_dtype=communication_dtype,
-                                      overlap_communication=overlap_communication,
-                                      cpu_offload=cpu_offload)
-        self.optim_kwargs = dict(initial_scale=initial_scale,
-                                 growth_factor=growth_factor,
-                                 backoff_factor=backoff_factor,
-                                 growth_interval=growth_interval,
-                                 hysteresis=hysteresis,
-                                 min_scale=min_scale,
-                                 max_scale=max_scale,
-                                 max_norm=max_norm,
-                                 norm_type=norm_type)
+        self.zero_optim_kwargs = dict(
+            initial_scale=initial_scale,
+            growth_factor=growth_factor,
+            backoff_factor=backoff_factor,
+            growth_interval=growth_interval,
+            hysteresis=hysteresis,
+            min_scale=min_scale,
+            max_scale=max_scale,
+            clip_grad_norm=max_norm,
+            reduce_bucket_size=reduce_bucket_size_in_m * 1024 * 1024,
+            communication_dtype=communication_dtype,
+            overlap_communication=overlap_communication,
+            cpu_offload=cpu_offload,
+            partition_grad=(stage == 2),
+        )
         self.verbose = verbose
 
         # set class name with stage, for better error message
@@ -294,15 +331,15 @@ class LowLevelZeroPlugin(DPPluginBase):
     ) -> Tuple[nn.Module, OptimizerWrapper, Callable, DataLoader, LRScheduler]:
 
         if not isinstance(model, ModelWrapper):
-            model = LowLevelZeroModel(model, self.stage, self.precision)
+            model = LowLevelZeroModel(model, self.precision)
 
         if optimizer is not None and \
                 not isinstance(optimizer, OptimizerWrapper):
-            optimizer = zero_optim_wrapper(model.unwrap(),
-                                           optimizer,
-                                           optim_config=self.zero_optim_config,
-                                           **self.optim_kwargs,
-                                           verbose=self.verbose)
+            optimizer: LowLevelZeroOptimizer = LowLevelZeroOptimizer(optimizer,
+                                                                     **self.zero_optim_kwargs,
+                                                                     verbose=self.verbose)
+            # inject update_master_params
+            model.update_master_params = MethodType(optimizer.update_master_params, model)
 
         return model, optimizer, criterion, dataloader, lr_scheduler
 

colossalai/context/parallel_context.py

@@ -15,8 +15,8 @@ from colossalai.constants import ALLOWED_MODES, INITIALIZER_MAPPING
 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 math
 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 math
 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 torch.utils.data import DataLoader
 
 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/interface/__init__.py

@@ -1,4 +1,4 @@
-from .model import ModelWrapper
+from .model import AMPModelMixin, ModelWrapper
 from .optimizer import OptimizerWrapper
 
-__all__ = ['OptimizerWrapper', 'ModelWrapper']
+__all__ = ['OptimizerWrapper', 'ModelWrapper', 'AMPModelMixin']

colossalai/interface/model.py

@@ -23,3 +23,14 @@ class ModelWrapper(nn.Module):
 
     def forward(self, *args, **kwargs):
         return self.module(*args, **kwargs)
+
+
+class AMPModelMixin:
+    """This mixin class defines the interface for AMP training.
+    """
+
+    def update_master_params(self):
+        """
+        Update the master parameters for AMP training.
+        """
+        pass

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/legacy/engine/_base_engine.py

@@ -8,11 +8,17 @@ from torch import Tensor
 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/legacy/engine/gradient_accumulation/__init__.py

@@ -4,7 +4,7 @@ import torch.nn as nn
 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/legacy/engine/gradient_accumulation/_gradient_accumulation.py

@@ -10,7 +10,7 @@ from torch.optim import Optimizer
 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/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/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/legacy/engine/gradient_handler/_pipeline_parallel_gradient_handler.py

@@ -7,7 +7,7 @@ import torch.distributed as dist
 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/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/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/legacy/engine/schedule/_base_schedule.py

@@ -95,7 +95,7 @@ class BaseSchedule(ABC):
         """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/legacy/engine/schedule/_non_pipeline_schedule.py

@@ -54,7 +54,7 @@ class NonPipelineSchedule(BaseSchedule):
         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/legacy/engine/schedule/_pipeline_schedule.py

@@ -236,7 +236,7 @@ class PipelineSchedule(BaseSchedule):
         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 @@ class PipelineSchedule(BaseSchedule):
         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 @@ class PipelineSchedule(BaseSchedule):
         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 @@ class InterleavedPipelineSchedule(PipelineSchedule):
         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 @@ class InterleavedPipelineSchedule(PipelineSchedule):
         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/legacy/engine/schedule/_pipeline_schedule_v2.py

@@ -69,7 +69,7 @@ class PipelineScheduleV2(PipelineSchedule):
         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/legacy/registry/registry.py

@@ -6,7 +6,7 @@ from typing import List
 
 
 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 @@ class Registry:
         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/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/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.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
 
 

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.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.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.legacy.registry import HOOKS
+
 from ._metric_hook import LearningRateMetric, MetricHook
 
 

colossalai/legacy/trainer/hooks/_metric_hook.py

@@ -6,10 +6,11 @@ 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
-from colossalai.registry import HOOKS
+from colossalai.legacy.registry import HOOKS
 from colossalai.utils import get_current_device, is_no_pp_or_last_stage
 
 from ._base_hook import BaseHook
@@ -19,8 +20,8 @@ from ._commons_ import _format_number
 class Metric(ABC):
     """A basic class of metric collectors. It collects a specific
     metric during training or evaluation and would always be used with
-    :class:`MetricHook` to help it update its states and show the 
-    metric. So please use corresponding hook class to make the metric 
+    :class:`MetricHook` to help it update its states and show the
+    metric. So please use corresponding hook class to make the metric
     collector works.
 
     Args:
@@ -220,9 +221,9 @@ class AccuracyMetric(Metric):
 
 
 class MetricHook(BaseHook):
-    """Specialized hook classes for :class:`Metric`. 
-    Some help metric collectors initialize, reset and 
-    update their states. Others are used to display and 
+    """Specialized hook classes for :class:`Metric`.
+    Some help metric collectors initialize, reset and
+    update their states. Others are used to display and
     record the metric.
 
     Args:
@@ -355,7 +356,7 @@ class ThroughputMetric(Metric):
             self.last_step_num_samples *= gpc.get_world_size(ParallelMode.DATA)
         else:
             self.last_step_used_time = all_reduce(self.last_step_used_time, ParallelMode.DATA) / \
-                 gpc.get_world_size(ParallelMode.DATA)
+                gpc.get_world_size(ParallelMode.DATA)
             self.last_step_num_samples = all_reduce(self.last_step_num_samples, ParallelMode.DATA)
 
         sample_per_sec = _format_number(self.last_step_num_samples / (self.last_step_used_time + 1e-12).item())
@@ -366,7 +367,7 @@ class ThroughputMetric(Metric):
             self.last_step_num_samples *= gpc.get_world_size(ParallelMode.DATA)
         else:
             self.last_step_used_time = all_reduce(self.last_step_used_time, ParallelMode.DATA) / \
-                 gpc.get_world_size(ParallelMode.DATA)
+                gpc.get_world_size(ParallelMode.DATA)
             self.last_step_num_samples = all_reduce(self.last_step_num_samples, ParallelMode.DATA)
 
         sample_per_sec = _format_number(self.last_step_num_samples / (self.last_step_used_time + 1e-12).item())

colossalai/nn/layer/parallel_1d/layers.py

@@ -15,8 +15,8 @@ from colossalai.context import ParallelMode, seed
 from colossalai.core import global_context as gpc
 from colossalai.global_variables import tensor_parallel_env as env
 from colossalai.kernel import LayerNorm
+from colossalai.legacy.registry import LAYERS
 from colossalai.nn import init as init
-from colossalai.registry import LAYERS
 from colossalai.utils.checkpointing import (
     broadcast_state_dict,
     gather_tensor_parallel_state_dict,

colossalai/nn/layer/parallel_2d/layers.py

@@ -5,21 +5,30 @@ from typing import Callable
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+from torch import Tensor
+from torch.nn import Parameter
+
 from colossalai.communication import broadcast
 from colossalai.context import ParallelMode, seed
 from colossalai.core import global_context as gpc
 from colossalai.global_variables import tensor_parallel_env as env
+from colossalai.legacy.registry import LAYERS
 from colossalai.nn import init as init
-from colossalai.registry import LAYERS
 from colossalai.utils.checkpointing import gather_tensor_parallel_state_dict, partition_tensor_parallel_state_dict
 from colossalai.utils.cuda import get_current_device
-from torch import Tensor
-from torch.nn import Parameter
 
 from ..base_layer import ParallelLayer
 from ..utils import divide, set_tensor_parallel_attribute_by_partition, to_2tuple
-from ._operation import (Matmul_AB_2D, Matmul_ABT_2D, add_bias_2d, all_gather_tensor_2d, classifier_2d, layernorm_2d,
-                         reduce_scatter_tensor_2d, split_batch_2d)
+from ._operation import (
+    Matmul_AB_2D,
+    Matmul_ABT_2D,
+    add_bias_2d,
+    all_gather_tensor_2d,
+    classifier_2d,
+    layernorm_2d,
+    reduce_scatter_tensor_2d,
+    split_batch_2d,
+)
 from ._utils import assert_summa_initialization, get_summa_dim_from_env
 
 

colossalai/nn/layer/parallel_2p5d/layers.py

@@ -5,22 +5,34 @@ from typing import Callable
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+from torch import Tensor
+from torch.nn import Parameter
+
 from colossalai.communication import broadcast
 from colossalai.context import ParallelMode, seed
 from colossalai.core import global_context as gpc
 from colossalai.global_variables import tensor_parallel_env as env
+from colossalai.legacy.registry import LAYERS
 from colossalai.nn import init as init
-from colossalai.registry import LAYERS
-from colossalai.utils.checkpointing import (broadcast_state_dict, gather_tensor_parallel_state_dict,
-                                            partition_tensor_parallel_state_dict)
+from colossalai.utils.checkpointing import (
+    broadcast_state_dict,
+    gather_tensor_parallel_state_dict,
+    partition_tensor_parallel_state_dict,
+)
 from colossalai.utils.cuda import get_current_device
-from torch import Tensor
-from torch.nn import Parameter
 
 from ..base_layer import ParallelLayer
 from ..utils import divide, set_tensor_parallel_attribute_by_partition, to_2tuple
-from ._operation import (Matmul_AB_2p5D, Matmul_ABT_2p5D, add_bias_2p5d, all_gather_tensor_2p5d, classifier_2p5d,
-                         layernorm_2p5d, reduce_scatter_tensor_2p5d, split_batch_2p5d)
+from ._operation import (
+    Matmul_AB_2p5D,
+    Matmul_ABT_2p5D,
+    add_bias_2p5d,
+    all_gather_tensor_2p5d,
+    classifier_2p5d,
+    layernorm_2p5d,
+    reduce_scatter_tensor_2p5d,
+    split_batch_2p5d,
+)
 from ._utils import assert_tesseract_initialization, get_tesseract_dim_dep_from_env
 
 

colossalai/nn/layer/parallel_3d/layers.py

@@ -13,9 +13,9 @@ from colossalai.constants import INPUT_GROUP_3D, INPUT_X_WEIGHT_3D, OUTPUT_GROUP
 from colossalai.context import ParallelMode, seed
 from colossalai.core import global_context as gpc
 from colossalai.global_variables import tensor_parallel_env as env
+from colossalai.legacy.registry import LAYERS
 from colossalai.nn import init as init
 from colossalai.nn.layer.base_layer import ParallelLayer
-from colossalai.registry import LAYERS
 from colossalai.utils.checkpointing import (
     broadcast_state_dict,
     gather_tensor_parallel_state_dict,

colossalai/nn/layer/parallel_sequence/layers.py

@@ -2,20 +2,20 @@
 # -*- encoding: utf-8 -*-
 
 import math
-import colossalai
 
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from torch.nn import Parameter
 
+import colossalai
+from colossalai.context import seed
 from colossalai.context.parallel_mode import ParallelMode
 from colossalai.core import global_context as gpc
-from colossalai.nn.layer.parallel_sequence._operation import RingQK, RingAV
-from colossalai.registry import LAYERS
-from colossalai.kernel.cuda_native.scaled_softmax import AttnMaskType
 from colossalai.kernel import FusedScaleMaskSoftmax
-from colossalai.context import seed
+from colossalai.kernel.cuda_native.scaled_softmax import AttnMaskType
+from colossalai.legacy.registry import LAYERS
+from colossalai.nn.layer.parallel_sequence._operation import RingAV, RingQK
 
 
 @LAYERS.register_module

colossalai/nn/layer/vanilla/layers.py

@@ -8,8 +8,8 @@ from torch import nn as nn
 from torch.nn.parameter import Parameter
 
 from colossalai.context import seed
+from colossalai.legacy.registry import LAYERS
 from colossalai.nn import init as init
-from colossalai.registry import LAYERS
 from colossalai.utils.cuda import get_current_device
 
 from ..utils import to_2tuple

colossalai/nn/loss/loss_1d.py

@@ -1,105 +1,106 @@
-import torch
-import torch.distributed as dist
-from colossalai.context import ParallelMode
-from colossalai.core import global_context as gpc
-from colossalai.registry import LOSSES
-from torch.cuda.amp import custom_bwd, custom_fwd
-from torch.nn.modules.loss import _Loss
-
-
-class _VocabParallelCrossEntropy1D(torch.autograd.Function):
-
-    @staticmethod
-    @custom_fwd(cast_inputs=torch.float32)
-    def forward(ctx, vocab_parallel_logits, targets, process_group):
-        if process_group is None:
-            process_group = gpc.get_group(ParallelMode.PARALLEL_1D)
-
-        # Maximum value along vocab dimension across all GPUs.
-        logits_max = torch.max(vocab_parallel_logits, dim=-1)[0]
-        torch.distributed.all_reduce(logits_max, op=torch.distributed.ReduceOp.MAX, group=process_group)
-        # Subtract the maximum value.
-        vocab_parallel_logits.sub_(logits_max.unsqueeze(dim=-1))
-
-        # Get the partition's vocab indices
-        partition_vocab_size = vocab_parallel_logits.size()[-1]
-        rank = dist.get_rank(process_group)
-        vocab_start_index = partition_vocab_size * rank
-        vocab_end_index = vocab_start_index + partition_vocab_size
-
-        # Create a mask of valid vocab ids (1 means it needs to be masked).
-        target_mask = (targets < vocab_start_index) | (targets >= vocab_end_index)
-        masked_target = targets.clone() - vocab_start_index
-        masked_target[target_mask] = 0
-
-        # Get predicted-logits = logits[target].
-        # For Simplicity, we convert logits to a 2-D tensor with size
-        # [*, partition-vocab-size] and target to a 1-D tensor of size [*].
-        logits_2d = vocab_parallel_logits.view(-1, partition_vocab_size)
-        masked_target_1d = masked_target.view(-1)
-        arange_1d = torch.arange(start=0, end=logits_2d.size()[0], device=logits_2d.device)
-        predicted_logits_1d = logits_2d[arange_1d, masked_target_1d]
-        predicted_logits_1d = predicted_logits_1d.clone().contiguous()
-        predicted_logits = predicted_logits_1d.view_as(targets)
-        predicted_logits[target_mask] = 0.0
-        # All reduce is needed to get the chunks from other GPUs.
-        torch.distributed.all_reduce(predicted_logits, op=torch.distributed.ReduceOp.SUM, group=process_group)
-
-        # Sum of exponential of logits along vocab dimension across all GPUs.
-        exp_logits = torch.exp(vocab_parallel_logits)
-        sum_exp_logits = exp_logits.sum(dim=-1)
-        torch.distributed.all_reduce(sum_exp_logits, op=torch.distributed.ReduceOp.SUM, group=process_group)
-
-        # Loss = log(sum(exp(logits))) - predicted-logit.
-        loss = torch.log(sum_exp_logits) - predicted_logits
-        # Store softmax, target-mask and masked-target for backward pass.
-        exp_logits.div_(sum_exp_logits.unsqueeze(dim=-1))
-        ctx.save_for_backward(exp_logits, target_mask, masked_target_1d)
-        return loss
-
-    @staticmethod
-    @custom_bwd
-    def backward(ctx, grad_output):
-
-        # Retrieve tensors from the forward path.
-        softmax, target_mask, masked_target_1d = ctx.saved_tensors
-
-        # All the inputs have softmax as their gradient.
-        grad_input = softmax
-        # For simplicity, work with the 2D gradient.
-        partition_vocab_size = softmax.size()[-1]
-        grad_2d = grad_input.view(-1, partition_vocab_size)
-
-        # Add the gradient from matching classes.
-        arange_1d = torch.arange(start=0, end=grad_2d.size()[0], device=grad_2d.device)
-        grad_2d[arange_1d, masked_target_1d] -= (1.0 - target_mask.view(-1).float())
-
-        # Finally elementwise multiplication with the output gradients.
-        grad_input.mul_(grad_output.unsqueeze(dim=-1))
-
-        return grad_input, None, None
-
-
-@LOSSES.register_module
-class VocabParallelCrossEntropyLoss1D(_Loss):
-    """Vocab parallel cross entropy loss for 1D parallelism.
-
-    Args:
-        reduction (bool, optional): whether to average the loss, defaults to True.
-    """
-
-    def __init__(self, reduction=True):
-        super().__init__()
-        self.reduction_mean = reduction
-
-    def forward(self, logits, targets, process_group=None):
-        """Calculate loss between logits and targets.
-
-        Args:
-            logits (:class:`torch.tensor`): Predicted unnormalized scores (often referred to as logits).
-            targets (:class:`torch.tensor`): Ground truth class indices or class probabilities.
-        """
-        loss = _VocabParallelCrossEntropy1D.apply(logits, targets, process_group)
-        if self.reduction_mean:
-            loss = loss.mean()
-        return loss
+import torch
+import torch.distributed as dist
+from torch.cuda.amp import custom_bwd, custom_fwd
+from torch.nn.modules.loss import _Loss
+
+from colossalai.context import ParallelMode
+from colossalai.core import global_context as gpc
+from colossalai.legacy.registry import LOSSES
+
+
+class _VocabParallelCrossEntropy1D(torch.autograd.Function):
+
+    @staticmethod
+    @custom_fwd(cast_inputs=torch.float32)
+    def forward(ctx, vocab_parallel_logits, targets, process_group):
+        if process_group is None:
+            process_group = gpc.get_group(ParallelMode.PARALLEL_1D)
+
+        # Maximum value along vocab dimension across all GPUs.
+        logits_max = torch.max(vocab_parallel_logits, dim=-1)[0]
+        torch.distributed.all_reduce(logits_max, op=torch.distributed.ReduceOp.MAX, group=process_group)
+        # Subtract the maximum value.
+        vocab_parallel_logits.sub_(logits_max.unsqueeze(dim=-1))
+
+        # Get the partition's vocab indices
+        partition_vocab_size = vocab_parallel_logits.size()[-1]
+        rank = dist.get_rank(process_group)
+        vocab_start_index = partition_vocab_size * rank
+        vocab_end_index = vocab_start_index + partition_vocab_size
+
+        # Create a mask of valid vocab ids (1 means it needs to be masked).
+        target_mask = (targets < vocab_start_index) | (targets >= vocab_end_index)
+        masked_target = targets.clone() - vocab_start_index
+        masked_target[target_mask] = 0
+
+        # Get predicted-logits = logits[target].
+        # For Simplicity, we convert logits to a 2-D tensor with size
+        # [*, partition-vocab-size] and target to a 1-D tensor of size [*].
+        logits_2d = vocab_parallel_logits.view(-1, partition_vocab_size)
+        masked_target_1d = masked_target.view(-1)
+        arange_1d = torch.arange(start=0, end=logits_2d.size()[0], device=logits_2d.device)
+        predicted_logits_1d = logits_2d[arange_1d, masked_target_1d]
+        predicted_logits_1d = predicted_logits_1d.clone().contiguous()
+        predicted_logits = predicted_logits_1d.view_as(targets)
+        predicted_logits[target_mask] = 0.0
+        # All reduce is needed to get the chunks from other GPUs.
+        torch.distributed.all_reduce(predicted_logits, op=torch.distributed.ReduceOp.SUM, group=process_group)
+
+        # Sum of exponential of logits along vocab dimension across all GPUs.
+        exp_logits = torch.exp(vocab_parallel_logits)
+        sum_exp_logits = exp_logits.sum(dim=-1)
+        torch.distributed.all_reduce(sum_exp_logits, op=torch.distributed.ReduceOp.SUM, group=process_group)
+
+        # Loss = log(sum(exp(logits))) - predicted-logit.
+        loss = torch.log(sum_exp_logits) - predicted_logits
+        # Store softmax, target-mask and masked-target for backward pass.
+        exp_logits.div_(sum_exp_logits.unsqueeze(dim=-1))
+        ctx.save_for_backward(exp_logits, target_mask, masked_target_1d)
+        return loss
+
+    @staticmethod
+    @custom_bwd
+    def backward(ctx, grad_output):
+
+        # Retrieve tensors from the forward path.
+        softmax, target_mask, masked_target_1d = ctx.saved_tensors
+
+        # All the inputs have softmax as their gradient.
+        grad_input = softmax
+        # For simplicity, work with the 2D gradient.
+        partition_vocab_size = softmax.size()[-1]
+        grad_2d = grad_input.view(-1, partition_vocab_size)
+
+        # Add the gradient from matching classes.
+        arange_1d = torch.arange(start=0, end=grad_2d.size()[0], device=grad_2d.device)
+        grad_2d[arange_1d, masked_target_1d] -= (1.0 - target_mask.view(-1).float())
+
+        # Finally elementwise multiplication with the output gradients.
+        grad_input.mul_(grad_output.unsqueeze(dim=-1))
+
+        return grad_input, None, None
+
+
+@LOSSES.register_module
+class VocabParallelCrossEntropyLoss1D(_Loss):
+    """Vocab parallel cross entropy loss for 1D parallelism.
+
+    Args:
+        reduction (bool, optional): whether to average the loss, defaults to True.
+    """
+
+    def __init__(self, reduction=True):
+        super().__init__()
+        self.reduction_mean = reduction
+
+    def forward(self, logits, targets, process_group=None):
+        """Calculate loss between logits and targets.
+
+        Args:
+            logits (:class:`torch.tensor`): Predicted unnormalized scores (often referred to as logits).
+            targets (:class:`torch.tensor`): Ground truth class indices or class probabilities.
+        """
+        loss = _VocabParallelCrossEntropy1D.apply(logits, targets, process_group)
+        if self.reduction_mean:
+            loss = loss.mean()
+        return loss

colossalai/nn/loss/loss_2d.py

@@ -1,15 +1,16 @@
 import torch
 import torch.distributed as dist
-from colossalai.context import ParallelMode
-from colossalai.core import global_context as gpc
-from colossalai.nn.layer.parallel_2d import reduce_by_batch_2d, split_batch_2d
-from colossalai.nn.layer.parallel_2d._utils import assert_summa_initialization
-from colossalai.registry import LOSSES
-from colossalai.utils import get_current_device
 from torch.cuda.amp import custom_bwd, custom_fwd
 from torch.nn.functional import cross_entropy
 from torch.nn.modules.loss import _Loss
 
+from colossalai.context import ParallelMode
+from colossalai.core import global_context as gpc
+from colossalai.legacy.registry import LOSSES
+from colossalai.nn.layer.parallel_2d import reduce_by_batch_2d, split_batch_2d
+from colossalai.nn.layer.parallel_2d._utils import assert_summa_initialization
+from colossalai.utils import get_current_device
+
 
 @LOSSES.register_module
 class CrossEntropyLoss2D(_Loss):

colossalai/nn/loss/loss_2p5d.py

@@ -1,15 +1,16 @@
 import torch
 import torch.distributed as dist
-from colossalai.context import ParallelMode
-from colossalai.core import global_context as gpc
-from colossalai.nn.layer.parallel_2p5d import reduce_by_batch_2p5d, split_batch_2p5d
-from colossalai.nn.layer.parallel_2p5d._utils import assert_tesseract_initialization
-from colossalai.registry import LOSSES
-from colossalai.utils import get_current_device
 from torch.cuda.amp import custom_bwd, custom_fwd
 from torch.nn.functional import cross_entropy
 from torch.nn.modules.loss import _Loss
 
+from colossalai.context import ParallelMode
+from colossalai.core import global_context as gpc
+from colossalai.legacy.registry import LOSSES
+from colossalai.nn.layer.parallel_2p5d import reduce_by_batch_2p5d, split_batch_2p5d
+from colossalai.nn.layer.parallel_2p5d._utils import assert_tesseract_initialization
+from colossalai.utils import get_current_device
+
 
 @LOSSES.register_module
 class CrossEntropyLoss2p5D(_Loss):

colossalai/nn/loss/loss_3d.py

@@ -1,15 +1,16 @@
 import torch
 import torch.distributed as dist
-from colossalai.constants import INPUT_GROUP_3D, WEIGHT_GROUP_3D, OUTPUT_GROUP_3D
-from colossalai.core import global_context as gpc
-from colossalai.nn.layer.parallel_3d import reduce_by_batch_3d, split_tensor_3d
-from colossalai.nn.layer.parallel_3d._utils import get_parallel_mode_from_env
-from colossalai.registry import LOSSES
-from colossalai.utils import get_current_device
 from torch.cuda.amp import custom_bwd, custom_fwd
 from torch.nn.functional import cross_entropy
 from torch.nn.modules.loss import _Loss
 
+from colossalai.constants import INPUT_GROUP_3D, OUTPUT_GROUP_3D, WEIGHT_GROUP_3D
+from colossalai.core import global_context as gpc
+from colossalai.legacy.registry import LOSSES
+from colossalai.nn.layer.parallel_3d import reduce_by_batch_3d, split_tensor_3d
+from colossalai.nn.layer.parallel_3d._utils import get_parallel_mode_from_env
+from colossalai.utils import get_current_device
+
 
 @LOSSES.register_module
 class CrossEntropyLoss3D(_Loss):

colossalai/nn/loss/loss_moe.py

@@ -1,80 +1,81 @@
-import torch.nn as nn
-from colossalai.registry import LOSSES
-from torch.nn.modules.loss import _Loss
-from colossalai.context.moe_context import MOE_CONTEXT
-
-
-@LOSSES.register_module
-class MoeCrossEntropyLoss(_Loss):
-    r"""torch.nn.CrossEntropyLoss added with auxiliary loss.
-
-    Args:
-        input (:class:`torch.tensor`): Predicted unnormalized scores (often referred to as logits).
-        target (:class:`torch.tensor`): Ground truth class indices or class probabilities.
-        aux_weight (float, optional): Weight of auxiliary loss in total loss.Defaults 0.01.
-
-    The ``args`` and ``kwargs`` should include parameters below:
-    ::
-
-        weight (Tensor, optional)
-        size_average (bool, optional)
-        ignore_index (int, optional)
-        reduce (bool, optional)
-        reduction (str, optional)
-        label_smoothing (float, optional)
-
-    More details about ``args``, ``kwargs`` and ``torch.nn.functional.cross_entropy`` could be found in
-    `Cross_entropy <https://pytorch.org/docs/stable/generated/torch.nn.functional.cross_entropy.html#torch.nn.functional.cross_entropy>`_.
-    """
-
-    def __init__(self, aux_weight: float = 0.01, *args, **kwargs):
-        super().__init__()
-        self.loss = nn.CrossEntropyLoss(*args, **kwargs)
-        self.aux_weight = aux_weight
-
-    def forward(self, *args):
-        """
-        The ``args`` should at least include parameters below:
-        ::
-
-            input (:class:`torch.tensor`): Predicted unnormalized scores (often referred to as logits).
-            target (:class:`torch.tensor`): Ground truth class indices or class probabilities.
-
-        More details about ``args``, ``kwargs`` and ``torch.nn.functional.cross_entropy`` could be found in
-        `Cross_entropy <https://pytorch.org/docs/stable/generated/torch.nn.functional.cross_entropy.html#torch.nn.functional.cross_entropy>`_.
-        """
-        main_loss = self.loss(*args)
-        aux_loss = MOE_CONTEXT.get_loss()
-        return main_loss + self.aux_weight * aux_loss
-
-
-@LOSSES.register_module
-class MoeLoss(_Loss):
-    """A wrapper class for any loss module to add with auxiliary loss.
-
-    Args:
-        aux_weight (float): Weight of auxiliary loss in total loss.
-        loss_fn (``Callable``): Loss function.
-        args (list): Args in loss function.
-        kwargs (dict): Kwargs in loss function
-    """
-
-    def __init__(self, aux_weight: float, loss_fn, *args, **kwargs):
-        super().__init__()
-        self.loss_fn = loss_fn(*args, **kwargs)
-        self.aux_weight = aux_weight
-
-    def forward(self, *args, **kwargs):
-        """
-        The ``args`` and ``kwargs`` should at least include parameters below:
-        ::
-
-            input (:class:`torch.tensor`): Predicted unnormalized scores (often referred to as logits).
-            target (:class:`torch.tensor`): Ground truth class indices or class probabilities.
-
-        Note:
-            The ``args`` and ``kwargs`` may include different parameters varying with different loss function.
-        """
-        main_loss = self.loss_fn(*args, **kwargs)
-        aux_loss = MOE_CONTEXT.get_loss()
-        return main_loss + self.aux_weight * aux_loss
+import torch.nn as nn
+from torch.nn.modules.loss import _Loss
+
+from colossalai.context.moe_context import MOE_CONTEXT
+from colossalai.legacy.registry import LOSSES
+
+
+@LOSSES.register_module
+class MoeCrossEntropyLoss(_Loss):
+    r"""torch.nn.CrossEntropyLoss added with auxiliary loss.
+
+    Args:
+        input (:class:`torch.tensor`): Predicted unnormalized scores (often referred to as logits).
+        target (:class:`torch.tensor`): Ground truth class indices or class probabilities.
+        aux_weight (float, optional): Weight of auxiliary loss in total loss.Defaults 0.01.
+
+    The ``args`` and ``kwargs`` should include parameters below:
+    ::
+
+        weight (Tensor, optional)
+        size_average (bool, optional)
+        ignore_index (int, optional)
+        reduce (bool, optional)
+        reduction (str, optional)
+        label_smoothing (float, optional)
+
+    More details about ``args``, ``kwargs`` and ``torch.nn.functional.cross_entropy`` could be found in
+    `Cross_entropy <https://pytorch.org/docs/stable/generated/torch.nn.functional.cross_entropy.html#torch.nn.functional.cross_entropy>`_.
+    """
+
+    def __init__(self, aux_weight: float = 0.01, *args, **kwargs):
+        super().__init__()
+        self.loss = nn.CrossEntropyLoss(*args, **kwargs)
+        self.aux_weight = aux_weight
+
+    def forward(self, *args):
+        """
+        The ``args`` should at least include parameters below:
+        ::
+
+            input (:class:`torch.tensor`): Predicted unnormalized scores (often referred to as logits).
+            target (:class:`torch.tensor`): Ground truth class indices or class probabilities.
+
+        More details about ``args``, ``kwargs`` and ``torch.nn.functional.cross_entropy`` could be found in
+        `Cross_entropy <https://pytorch.org/docs/stable/generated/torch.nn.functional.cross_entropy.html#torch.nn.functional.cross_entropy>`_.
+        """
+        main_loss = self.loss(*args)
+        aux_loss = MOE_CONTEXT.get_loss()
+        return main_loss + self.aux_weight * aux_loss
+
+
+@LOSSES.register_module
+class MoeLoss(_Loss):
+    """A wrapper class for any loss module to add with auxiliary loss.
+
+    Args:
+        aux_weight (float): Weight of auxiliary loss in total loss.
+        loss_fn (``Callable``): Loss function.
+        args (list): Args in loss function.
+        kwargs (dict): Kwargs in loss function
+    """
+
+    def __init__(self, aux_weight: float, loss_fn, *args, **kwargs):
+        super().__init__()
+        self.loss_fn = loss_fn(*args, **kwargs)
+        self.aux_weight = aux_weight
+
+    def forward(self, *args, **kwargs):
+        """
+        The ``args`` and ``kwargs`` should at least include parameters below:
+        ::
+
+            input (:class:`torch.tensor`): Predicted unnormalized scores (often referred to as logits).
+            target (:class:`torch.tensor`): Ground truth class indices or class probabilities.
+
+        Note:
+            The ``args`` and ``kwargs`` may include different parameters varying with different loss function.
+        """
+        main_loss = self.loss_fn(*args, **kwargs)
+        aux_loss = MOE_CONTEXT.get_loss()
+        return main_loss + self.aux_weight * aux_loss

colossalai/nn/lr_scheduler/cosine.py

@@ -1,6 +1,7 @@
 from torch.optim.lr_scheduler import CosineAnnealingLR as _CosineAnnealingLR
 
-from colossalai.registry import LR_SCHEDULERS
+from colossalai.legacy.registry import LR_SCHEDULERS
+
 from .delayed import DelayerScheduler, WarmupDelayerScheduler, WarmupScheduler
 
 

colossalai/nn/lr_scheduler/linear.py

@@ -1,6 +1,6 @@
 from torch.optim.lr_scheduler import _LRScheduler
 
-from colossalai.registry import LR_SCHEDULERS
+from colossalai.legacy.registry import LR_SCHEDULERS
 
 
 @LR_SCHEDULERS.register_module

colossalai/nn/lr_scheduler/multistep.py

@@ -2,7 +2,8 @@ from typing import List
 
 from torch.optim.lr_scheduler import MultiStepLR as _MultiStepLR
 
-from colossalai.registry import LR_SCHEDULERS
+from colossalai.legacy.registry import LR_SCHEDULERS
+
 from .delayed import WarmupScheduler
 
 

colossalai/nn/lr_scheduler/onecycle.py

@@ -1,6 +1,6 @@
 from torch.optim.lr_scheduler import OneCycleLR as _OneCycleLR
 
-from colossalai.registry import LR_SCHEDULERS
+from colossalai.legacy.registry import LR_SCHEDULERS
 
 
 @LR_SCHEDULERS.register_module

colossalai/nn/lr_scheduler/poly.py

@@ -1,6 +1,7 @@
 from torch.optim.lr_scheduler import _LRScheduler
 
-from colossalai.registry import LR_SCHEDULERS
+from colossalai.legacy.registry import LR_SCHEDULERS
+
 from .delayed import WarmupScheduler
 
 

colossalai/nn/lr_scheduler/torch.py

@@ -1,9 +1,9 @@
+from torch.optim.lr_scheduler import ExponentialLR as _ExponentialLR
 from torch.optim.lr_scheduler import LambdaLR as _LambdaLR
 from torch.optim.lr_scheduler import MultiplicativeLR as _MultiplicativeLR
 from torch.optim.lr_scheduler import StepLR as _StepLR
-from torch.optim.lr_scheduler import ExponentialLR as _ExponentialLR
 
-from colossalai.registry import LR_SCHEDULERS
+from colossalai.legacy.registry import LR_SCHEDULERS
 
 
 @LR_SCHEDULERS.register_module

colossalai/nn/optimizer/cpu_adam.py

@@ -4,7 +4,7 @@ from typing import Optional
 import torch
 
 from colossalai.kernel.op_builder import CPUAdamBuilder
-from colossalai.registry import OPTIMIZERS
+from colossalai.legacy.registry import OPTIMIZERS
 
 from .nvme_optimizer import NVMeOptimizer
 

colossalai/nn/optimizer/fused_adam.py

@@ -8,7 +8,7 @@ Licensed under the MIT License.
 '''
 import torch
 
-from colossalai.registry import OPTIMIZERS
+from colossalai.legacy.registry import OPTIMIZERS
 from colossalai.utils import multi_tensor_applier
 
 

colossalai/nn/optimizer/fused_lamb.py

@@ -1,7 +1,7 @@
 # modified from https://github.com/NVIDIA/apex/blob/master/apex/optimizers/fused_lamb.py
 import torch
 
-from colossalai.registry import OPTIMIZERS
+from colossalai.legacy.registry import OPTIMIZERS
 from colossalai.utils import multi_tensor_applier
 
 

colossalai/nn/optimizer/fused_sgd.py

@@ -2,7 +2,7 @@
 import torch
 from torch.optim.optimizer import Optimizer, required
 
-from colossalai.registry import OPTIMIZERS
+from colossalai.legacy.registry import OPTIMIZERS
 from colossalai.utils import multi_tensor_applier
 
 

colossalai/nn/optimizer/hybrid_adam.py

@@ -4,7 +4,7 @@ import torch
 from torch.optim import Adam
 
 from colossalai.kernel.op_builder import FusedOptimBuilder
-from colossalai.registry import OPTIMIZERS
+from colossalai.legacy.registry import OPTIMIZERS
 from colossalai.utils import multi_tensor_applier
 
 from .cpu_adam import CPUAdam

colossalai/nn/optimizer/lamb.py

@@ -5,7 +5,7 @@ Adapted from the pytorch-lamb library at https://github.com/cybertronai/pytorch-
 import torch
 from torch.optim import Optimizer
 
-from colossalai.registry import OPTIMIZERS
+from colossalai.legacy.registry import OPTIMIZERS
 
 
 @OPTIMIZERS.register_module

colossalai/nn/optimizer/lars.py

@@ -5,7 +5,7 @@ from typing import Iterable
 import torch
 from torch.optim import Optimizer
 
-from colossalai.registry import OPTIMIZERS
+from colossalai.legacy.registry import OPTIMIZERS
 
 
 @OPTIMIZERS.register_module
@@ -22,28 +22,24 @@ class Lars(Optimizer):
         weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
     """
 
-    def __init__(
-            self,
-            params: Iterable[torch.nn.Parameter],
-            lr=1e-3,
-            momentum=0,
-            eeta=1e-3,
-            weight_decay=0,
-            epsilon=0.0
-    ) -> None:
+    def __init__(self,
+                 params: Iterable[torch.nn.Parameter],
+                 lr=1e-3,
+                 momentum=0,
+                 eeta=1e-3,
+                 weight_decay=0,
+                 epsilon=0.0) -> None:
         if not isinstance(lr, float) or lr < 0.0:
             raise ValueError("Invalid learning rate: {}".format(lr))
         if momentum < 0.0:
             raise ValueError("Invalid momentum value: {}".format(momentum))
         if weight_decay < 0.0:
-            raise ValueError(
-                "Invalid weight_decay value: {}".format(weight_decay))
+            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
         if eeta <= 0 or eeta > 1:
             raise ValueError("Invalid eeta value: {}".format(eeta))
         if epsilon < 0:
             raise ValueError("Invalid epsilon value: {}".format(epsilon))
-        defaults = dict(lr=lr, momentum=momentum,
-                        weight_decay=weight_decay, eeta=eeta, epsilon=epsilon, lars=True)
+        defaults = dict(lr=lr, momentum=momentum, weight_decay=weight_decay, eeta=eeta, epsilon=epsilon, lars=True)
 
         super().__init__(params, defaults)
 
@@ -76,11 +72,9 @@ class Lars(Optimizer):
                 if lars:
                     w_norm = torch.norm(p)
                     g_norm = torch.norm(p.grad)
-                    trust_ratio = torch.where(
-                        w_norm > 0 and g_norm > 0,
-                        eeta * w_norm / (g_norm + weight_decay * w_norm + eps),
-                        torch.ones_like(w_norm)
-                    )
+                    trust_ratio = torch.where(w_norm > 0 and g_norm > 0,
+                                              eeta * w_norm / (g_norm + weight_decay * w_norm + eps),
+                                              torch.ones_like(w_norm))
                     trust_ratio.clamp_(0.0, 50)
                     scaled_lr *= trust_ratio.item()
                     if weight_decay != 0:
@@ -90,8 +84,7 @@ class Lars(Optimizer):
                 if momentum != 0:
                     param_state = self.state[p]
                     if 'momentum_buffer' not in param_state:
-                        buf = param_state['momentum_buffer'] = torch.clone(
-                            decayed_grad).detach()
+                        buf = param_state['momentum_buffer'] = torch.clone(decayed_grad).detach()
                     else:
                         buf = param_state['momentum_buffer']
                         buf.mul_(momentum).add_(decayed_grad)

colossalai/utils/data_sampler/data_parallel_sampler.py

@@ -4,15 +4,15 @@
 
 import math
 import random
-import numpy as np
-from typing import TypeVar, Iterator
+from typing import Iterator, TypeVar
 
+import numpy as np
 import torch
-from torch.utils.data import Sampler, Dataset, DataLoader
+from torch.utils.data import DataLoader, Dataset, Sampler
 
 from colossalai.context.parallel_mode import ParallelMode
 from colossalai.core import global_context as gpc
-from colossalai.registry import DATA_SAMPLERS
+from colossalai.legacy.registry import DATA_SAMPLERS
 
 T_co = TypeVar('T_co', covariant=True)
 
@@ -30,11 +30,7 @@ class DataParallelSampler(Sampler):
             the batch size, then the last batch will be smaller, defaults to False.
     """
 
-    def __init__(self,
-                 dataset: Dataset,
-                 shuffle: bool = False,
-                 seed: int = 0,
-                 drop_last: bool = False) -> None:
+    def __init__(self, dataset: Dataset, shuffle: bool = False, seed: int = 0, drop_last: bool = False) -> None:
         self.dataset = dataset
         self.num_replicas = gpc.get_world_size(ParallelMode.DATA)
         self.rank = gpc.get_local_rank(ParallelMode.DATA)
@@ -54,8 +50,7 @@ class DataParallelSampler(Sampler):
                 self.num_replicas  # type: ignore[arg-type]
             )
         else:
-            self.num_samples = math.ceil(
-                len(self.dataset) / self.num_replicas)  # type: ignore[arg-type]
+            self.num_samples = math.ceil(len(self.dataset) / self.num_replicas)    # type: ignore[arg-type]
         self.total_size = self.num_samples * self.num_replicas
         self.shuffle = shuffle
         self.seed = seed
@@ -72,7 +67,7 @@ class DataParallelSampler(Sampler):
             # set_epoch manually
             self.epoch += 1
         else:
-            indices = list(range(len(self.dataset)))  # type: ignore[arg-type]
+            indices = list(range(len(self.dataset)))    # type: ignore[arg-type]
 
         if not self.drop_last:
             # add extra samples to make it evenly divisible
@@ -80,8 +75,7 @@ class DataParallelSampler(Sampler):
             if padding_size <= len(indices):
                 indices += indices[:padding_size]
             else:
-                indices += (indices * math.ceil(padding_size /
-                            len(indices)))[:padding_size]
+                indices += (indices * math.ceil(padding_size / len(indices)))[:padding_size]
         else:
             # remove tail of data to make it evenly divisible.
             indices = indices[:self.total_size]
@@ -109,8 +103,8 @@ class DataParallelSampler(Sampler):
 
 def get_dataloader(dataset,
                    shuffle=False,
-                   seed=1024, 
-                   add_sampler=True, 
+                   seed=1024,
+                   add_sampler=True,
                    drop_last=False,
                    pin_memory=False,
                    num_workers=0,

colossalai/utils/profiler/profiler.py

@@ -1,17 +1,17 @@
-import os
-from typing import List
-from colossalai.engine import Engine
-from torch.profiler import profile as torch_profile
-from torch.profiler.profiler import ProfilerAction
-from typing import Any, Callable, Iterable, Optional
-from torch.autograd import ProfilerActivity
+import gzip
 import json
 import os
 import tempfile
-import gzip
+from typing import Any, Callable, Iterable, List, Optional
+
+from torch.autograd import ProfilerActivity
+from torch.profiler import profile as torch_profile
+from torch.profiler.profiler import ProfilerAction
+
+from colossalai.legacy.engine import Engine
+from colossalai.logging import get_dist_logger
 from colossalai.utils.profiler.extention import ProfilerExtension
 from colossalai.utils.profiler.stateful_tensor_mem_extention import StatefulTensorMemoryProfilerExtention
-from colossalai.logging import get_dist_logger
 
 
 class profile(torch_profile):

colossalai/utils/profiler/stateful_tensor_mem_extention.py

@@ -1,12 +1,14 @@
 import os
 import threading
 import time
-import torch
 from enum import Enum
 from typing import List
-from colossalai.gemini.stateful_tensor import StatefulTensor
+
+import torch
+
 from colossalai.gemini.ophooks import BaseOpHook
-from colossalai.engine import Engine
+from colossalai.gemini.stateful_tensor import StatefulTensor
+from colossalai.legacy.engine import Engine
 from colossalai.utils.profiler.extention import ProfilerExtension
 
 

colossalai/zero/legacy/gemini/ophooks/_shard_grad_ophook.py

@@ -1,6 +1,6 @@
 import torch
 
-from colossalai.registry import OPHOOKS
+from colossalai.legacy.registry import OPHOOKS
 
 from . import BaseOpHook
 

colossalai/zero/legacy/gemini/ophooks/_shard_param_ophook.py

@@ -1,6 +1,6 @@
 import torch
 
-from colossalai.registry import OPHOOKS
+from colossalai.legacy.registry import OPHOOKS
 
 from . import BaseOpHook
 

colossalai/zero/legacy/sharded_model/zero_hook.py

@@ -3,8 +3,8 @@ from typing import Optional
 import torch
 import torch.distributed as dist
 
+from colossalai.legacy.registry import OPHOOKS
 from colossalai.logging import get_dist_logger
-from colossalai.registry import OPHOOKS
 from colossalai.utils import get_current_device
 from colossalai.zero.gemini.memory_tracer import MemStatsCollector
 from colossalai.zero.legacy.gemini.ophooks import BaseOpHook

colossalai/zero/low_level/low_level_optim.py

@@ -6,6 +6,7 @@ from typing import Dict, Iterator, Optional, Tuple
 
 import torch
 import torch.distributed as dist
+import torch.nn as nn
 from torch.distributed import ProcessGroup
 from torch.optim import Optimizer
 
@@ -617,3 +618,19 @@ class LowLevelZeroOptimizer(OptimizerWrapper):
             ret_block_size += current_block_size
 
         yield ret_block, ret_block_size
+
+    def update_master_params(self, model: nn.Module) -> None:
+        """Update master params from working params
+
+        Args:
+            model (nn.Module): The model to update master params
+        """
+        for p in model.parameters():
+            p_id = id(p)
+            if p_id in self._param_store.working_to_master_param:
+                master_param = self._param_store.working_to_master_param[p_id]
+                padding_size = self._param_store.get_param_padding_size(p)
+                working_param = p.data.view(-1)
+                if padding_size > 0:
+                    working_param = torch.nn.functional.pad(working_param, [0, padding_size])
+                master_param.copy_(working_param.chunk(self._world_size)[self._local_rank])

docs/source/en/advanced_tutorials/add_your_parallel.md

@@ -92,14 +92,14 @@ follow the steps below to create a new distributed initialization.
 
 Gradient handlers are objects which execute the all-reduce operations on parameters' gradients. As different all-reduce
 strategies may be executed for different kinds of parallelism, users can
-inherit `colossalai.engine.gradient_handler.BaseGradientHandler` to implement their strategies. Currently, the library
+inherit `colossalai.legacy.engine.gradient_handler.BaseGradientHandler` to implement their strategies. Currently, the library
 uses the normal data parallel gradient handler which all-reduces the gradients across data parallel ranks. The data
 parallel gradient handler is added to the engine automatically if data parallel is detected. You can add your own
 gradient handler like below:
 
 ```python
-from colossalai.registry import GRADIENT_HANDLER
-from colossalai.engine import BaseGradientHandler
+from colossalai.legacy.registry import GRADIENT_HANDLER
+from colossalai.legacy.engine import BaseGradientHandler
 
 @GRADIENT_HANDLER.register_module
 class YourGradientHandler(BaseGradientHandler):
@@ -121,4 +121,5 @@ gradient_handlers = [
 
 Schedule entails how to execute a forward and backward pass. Currently, Colossal-AI provides pipeline and non-pipeline
 schedules. If you want to modify how the forward and backward passes are executed, you can
-inherit `colossalai.engine.schedule.BaseSchedule` and implement the `forward_back_step` function.
+inherit `colossalai.legacy.engine.schedule.BaseSchedule` and implement the `forward_back_step` function.
+<!-- doc-test-command: echo  -->

docs/source/en/advanced_tutorials/train_gpt_using_hybrid_parallelism.md

@@ -36,14 +36,14 @@ import torch
 import torch.nn as nn
 from colossalai import nn as col_nn
 from colossalai.amp import AMP_TYPE
-from colossalai.builder.pipeline import partition_uniform
+from colossalai.legacy.builder.pipeline import partition_uniform
 from colossalai.context.parallel_mode import ParallelMode
 from colossalai.core import global_context as gpc
-from colossalai.engine.schedule import (InterleavedPipelineSchedule,
+from colossalai.legacy.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

@@ -34,11 +34,11 @@ import colossalai
 import colossalai.nn as col_nn
 import torch
 import torch.nn as nn
-from colossalai.builder import build_pipeline_model
-from colossalai.engine.schedule import (InterleavedPipelineSchedule,
+from colossalai.legacy.builder import build_pipeline_model
+from colossalai.legacy.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
@@ -51,17 +51,17 @@ from torchvision.datasets import CIFAR10
 
 Generally, we provide 3 ways to build a pipelined model:
 
-1. `colossalai.builder.build_pipeline_model_from_cfg`
-2. `colossalai.builder.build_pipeline_model`
+1. `colossalai.legacy.builder.build_pipeline_model_from_cfg`
+2. `colossalai.legacy.builder.build_pipeline_model`
 3. Split the model by stages by yourself
 
 When your memory can fit the model, you can use the first two methods to build your model, otherwise you must split the model by yourself. The first two methods first build the whole model on CPU, then split the model, and finally you can just move the corresponding part of model to GPU.
 
-`colossalai.builder.build_pipeline_model_from_cfg()` receives a config file of model, and it can split the model uniformly (by layer) or balanced (by parameter size).
+`colossalai.legacy.builder.build_pipeline_model_from_cfg()` receives a config file of model, and it can split the model uniformly (by layer) or balanced (by parameter size).
 
-If you are familiar with `PyTorch`, you can use  `colossalai.builder.build_pipeline_model()` which receives a `torch.nn.Sequential` model and split it by layer uniformly.
+If you are familiar with `PyTorch`, you can use  `colossalai.legacy.builder.build_pipeline_model()` which receives a `torch.nn.Sequential` model and split it by layer uniformly.
 
-In this tutorial, we will modify [TIMM/ViT](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py) to `torch.nn.Sequential` and then use `colossalai.builder.build_pipeline_model()` to build the pipelined model.
+In this tutorial, we will modify [TIMM/ViT](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py) to `torch.nn.Sequential` and then use `colossalai.legacy.builder.build_pipeline_model()` to build the pipelined model.
 
 When the data is **one** `Tensor`, you can use the positional argument in `forward()` of your model to get the data tensor. For the first stage of pipeline, the first positional argument of `forward()` is the data tensor loaded from data loader. For other stages, the first positional argument of `forward()` is the output tensor from the previous stage. Note that if the stage is not the last stage, the return of `forward()` must be a `Tensor`.
 
@@ -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
@@ -273,8 +273,8 @@ SEQ_LENGTH = (IMG_SIZE // PATCH_SIZE) ** 2 + 1  # add 1 for cls token
 
 ### Build pipeline model (`/hybrid_parallel/model/vit.py`)
 Colossal-AI provides two methods to build a pipeline model from the existing model.
-- `colossalai.builder.build_pipeline_model_from_cfg`
-- `colossalai.builder.build_pipeline_model`
+- `colossalai.legacy.builder.build_pipeline_model_from_cfg`
+- `colossalai.legacy.builder.build_pipeline_model`
 
 Besides, you can also build a pipeline model from scratch with Colossal-AI.
 ```python
@@ -284,11 +284,11 @@ from typing import Callable
 import inspect
 import torch
 from colossalai import nn as col_nn
-from colossalai.registry import LAYERS, MODELS
+from colossalai.legacy.registry import LAYERS, MODELS
 from colossalai.logging import get_dist_logger
 from colossalai.core import global_context as gpc
 from colossalai.context import ParallelMode
-from colossalai.builder.pipeline import partition_uniform
+from colossalai.legacy.builder.pipeline import partition_uniform
 from torch import dtype, nn
 from model_zoo.vit.vit import ViTBlock, ViTEmbedding, ViTHead
 
@@ -415,7 +415,7 @@ def build_pipeline_vit(num_layers, num_chunks, device=torch.device('cuda'), **kw
 
 #### Import modules
 ```python
-from colossalai.engine.schedule import (InterleavedPipelineSchedule,
+from colossalai.legacy.engine.schedule import (InterleavedPipelineSchedule,
                                         PipelineSchedule)
 from colossalai.utils import MultiTimer
 import os
@@ -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/gradient_handler.md

@@ -28,8 +28,8 @@ To implement a customized gradient handler, you need to follow these steps.
 3. implement `handle_gradient` method.
 
 ```python
-from colossalai.registry import GRADIENT_HANDLER
-from colossalai.engine.gradient_handler import BaseGradientHandler
+from colossalai.legacy.registry import GRADIENT_HANDLER
+from colossalai.legacy.engine.gradient_handler import BaseGradientHandler
 
 
 @GRADIENT_HANDLER.register_module
@@ -61,3 +61,4 @@ to demonstrate the use of gradient handler. In this example, we used `DataParall
 ```shell
 python -m torch.distributed.launch --nproc_per_node 4 --master_addr localhost --master_port 29500  train_with_engine.py
 ```
+<!-- 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/add_your_parallel.md

@@ -81,14 +81,14 @@ Colossal-AI 为用户提供了一个全局 context，使他们能够轻松地管
 ## 梯度 Handler
 
 梯度 handler 是对参数的梯度执行 all-reduce 操作的对象。由于不同的 all-reduce 策略或许在不同的并行中被执行，用户可以继承
-`colossalai.engine.gradient_handler.BaseGradientHandler` 来实现其策略。目前，Colossal-AI 使用普通的数据并行梯度 handler 在数据并行的 rank 间 all-reduce 梯度。
+`colossalai.legacy.engine.gradient_handler.BaseGradientHandler` 来实现其策略。目前，Colossal-AI 使用普通的数据并行梯度 handler 在数据并行的 rank 间 all-reduce 梯度。
 如果数据并行被检测到，梯度 handler 会被自动添加进 engine。
 
 你可以添加你自己的梯度 handler，如下所示：
 
 ```python
-from colossalai.registry import GRADIENT_HANDLER
-from colossalai.engine import BaseGradientHandler
+from colossalai.legacy.registry import GRADIENT_HANDLER
+from colossalai.legacy.engine import BaseGradientHandler
 
 @GRADIENT_HANDLER.register_module
 class YourGradientHandler(BaseGradientHandler):
@@ -109,4 +109,5 @@ gradient_handlers = [
 ## Schedule
 
 Schedule 包含了如何执行前向和后向计算。目前， Colossal-AI 提供了流水和非流水的 schedule。
-如果你想修改前向和后向计算的执行方式，你可以继承 `colossalai.engine.schedule.BaseSchedule` 并实现 `forward_back_step` 函数。
+如果你想修改前向和后向计算的执行方式，你可以继承 `colossalai.legacy.engine.schedule.BaseSchedule` 并实现 `forward_back_step` 函数。
+<!-- doc-test-command: echo  -->

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

@@ -36,14 +36,14 @@ import torch
 import torch.nn as nn
 from colossalai import nn as col_nn
 from colossalai.amp import AMP_TYPE
-from colossalai.builder.pipeline import partition_uniform
+from colossalai.legacy.builder.pipeline import partition_uniform
 from colossalai.context.parallel_mode import ParallelMode
 from colossalai.core import global_context as gpc
-from colossalai.engine.schedule import (InterleavedPipelineSchedule,
+from colossalai.legacy.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

@@ -32,11 +32,11 @@ import colossalai
 import colossalai.nn as col_nn
 import torch
 import torch.nn as nn
-from colossalai.builder import build_pipeline_model
-from colossalai.engine.schedule import (InterleavedPipelineSchedule,
+from colossalai.legacy.builder import build_pipeline_model
+from colossalai.legacy.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
@@ -48,17 +48,17 @@ from torchvision.datasets import CIFAR10
 
 总的来说, 我们提供3种方法来建立一个流水并行的模型:
 
-1. `colossalai.builder.build_pipeline_model_from_cfg`
-2. `colossalai.builder.build_pipeline_model`
+1. `colossalai.legacy.builder.build_pipeline_model_from_cfg`
+2. `colossalai.legacy.builder.build_pipeline_model`
 3. 自己按阶段拆分模型
 
 当你的内存能够容纳模型时，你可以使用前两种方法来建立你的模型，否则你必须自己分割模型。前两种方法首先在 CPU 上建立整个模型，然后分割模型，最后你可以直接把模型的相应部分移到 GPU 上。
 
-`colossalai.builder.build_pipeline_model_from_cfg()` 接收一个模型的配置文件，它可以均匀地（按层）或平衡地（按参数大小）分割模型。
+`colossalai.legacy.builder.build_pipeline_model_from_cfg()` 接收一个模型的配置文件，它可以均匀地（按层）或平衡地（按参数大小）分割模型。
 
-如果你熟悉 `PyTorch`, 你可以使用 `colossalai.builder.build_pipeline_model()` 它接收一个 `torch.nn.Sequential` 模型并按层均匀分割。
+如果你熟悉 `PyTorch`, 你可以使用 `colossalai.legacy.builder.build_pipeline_model()` 它接收一个 `torch.nn.Sequential` 模型并按层均匀分割。
 
-在本教程中，我们将修改 [TIMM/ViT](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py) to `torch.nn.Sequential`，然后使用 `colossalai.builder.build_pipeline_model()` 来建立流水线模型。
+在本教程中，我们将修改 [TIMM/ViT](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py) to `torch.nn.Sequential`，然后使用 `colossalai.legacy.builder.build_pipeline_model()` 来建立流水线模型。
 
 当数据是 **一个** `Tensor`, 你可以使用你的模型 `forward()` 中的位置参数来获得数据张量。对于流水线的第一阶段，`forward()` 的第一个位置参数是从数据加载器加载的数据张量。对于其他阶段，`forward()` 的第一个位置参数是上一阶段的输出张量。注意，如果该阶段不是最后一个阶段，则 `forward()` 的返回必须是一个 `Tensor`。
 
@@ -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
@@ -256,8 +256,8 @@ SEQ_LENGTH = (IMG_SIZE // PATCH_SIZE) ** 2 + 1  # add 1 for cls token
 
 ### 构建流水线模型 (`/hybrid_parallel/model/vit.py`)
 Colossal-AI 提供了两种从现有模型构建流水线模型的方法。
-- `colossalai.builder.build_pipeline_model_from_cfg`
-- `colossalai.builder.build_pipeline_model`
+- `colossalai.legacy.builder.build_pipeline_model_from_cfg`
+- `colossalai.legacy.builder.build_pipeline_model`
 
 此外，您还可以使用 Colossal-AI 从头开始构建流水线模型。
 ```python
@@ -266,11 +266,11 @@ from typing import Callable
 import inspect
 import torch
 from colossalai import nn as col_nn
-from colossalai.registry import LAYERS, MODELS
+from colossalai.legacy.registry import LAYERS, MODELS
 from colossalai.logging import get_dist_logger
 from colossalai.core import global_context as gpc
 from colossalai.context import ParallelMode
-from colossalai.builder.pipeline import partition_uniform
+from colossalai.legacy.builder.pipeline import partition_uniform
 from torch import dtype, nn
 from model_zoo.vit.vit import ViTBlock, ViTEmbedding, ViTHead
 @MODELS.register_module
@@ -380,7 +380,7 @@ def build_pipeline_vit(num_layers, num_chunks, device=torch.device('cuda'), **kw
 
 #### 导入模块
 ```python
-from colossalai.engine.schedule import (InterleavedPipelineSchedule,
+from colossalai.legacy.engine.schedule import (InterleavedPipelineSchedule,
                                         PipelineSchedule)
 from colossalai.utils import MultiTimer
 import os
@@ -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/gradient_handler.md

@@ -25,8 +25,8 @@
 3. 实现 `handle_gradient`
 
 ```python
-from colossalai.registry import GRADIENT_HANDLER
-from colossalai.engine.gradient_handler import BaseGradientHandler
+from colossalai.legacy.registry import GRADIENT_HANDLER
+from colossalai.legacy.engine.gradient_handler import BaseGradientHandler
 
 
 @GRADIENT_HANDLER.register_module
@@ -57,3 +57,4 @@ gradient_handler = [dict(type='MyGradientHandler')]
 ```shell
 python -m torch.distributed.launch --nproc_per_node 4 --master_addr localhost --master_port 29500  train_with_engine.py
 ```
+<!-- 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/dataset/webtext.py

@@ -6,7 +6,7 @@ import torch
 from torch.utils.data import Dataset
 from transformers import GPT2Tokenizer
 
-from colossalai.registry import DATASETS
+from colossalai.legacy.registry import DATASETS
 
 
 @DATASETS.register_module

examples/language/gpt/titans/model/embed.py

@@ -8,11 +8,11 @@ from torch.nn.parameter import Parameter
 
 from colossalai.context import ParallelMode, seed
 from colossalai.core import global_context as gpc
+from colossalai.legacy.registry import LAYERS, LOSSES, MODELS
 from colossalai.nn.layer.base_layer import ParallelLayer
 from colossalai.nn.layer.parallel_1d._utils import gather_forward_split_backward, reduce_grad, reduce_input
 from colossalai.nn.layer.parallel_1d.layers import Linear1D_Row
 from colossalai.nn.layer.utils import divide
-from colossalai.registry import LAYERS, LOSSES, MODELS
 from colossalai.utils import get_current_device
 
 

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

examples/tutorial/sequence_parallel/data/datasets/indexed_dataset.py

@@ -3,17 +3,16 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-
 # copied from fairseq/fairseq/data/indexed_dataset.py
 # Removed IndexedRawTextDataset since it relied on Fairseq dictionary
 # other slight modifications to remove fairseq dependencies
 # Added document index to index file and made it accessible.
 #    An empty sentence no longer separates documents.
 
-from functools import lru_cache
 import os
 import shutil
 import struct
+from functools import lru_cache
 from itertools import accumulate
 
 import numpy as np
@@ -88,16 +87,7 @@ def write_longs(f, a):
     f.write(np.array(a, dtype=np.int64))
 
 
-dtypes = {
-    1: np.uint8,
-    2: np.int8,
-    3: np.int16,
-    4: np.int32,
-    5: np.int64,
-    6: np.float,
-    7: np.double,
-    8: np.uint16
-}
+dtypes = {1: np.uint8, 2: np.int8, 3: np.int16, 4: np.int32, 5: np.int64, 6: float, 7: np.double, 8: np.uint16}
 
 
 def code(dtype):
@@ -136,10 +126,8 @@ class IndexedDataset(torch.utils.data.Dataset):
     def read_index(self, path):
         with open(index_file_path(path), 'rb') as f:
             magic = f.read(8)
-            assert magic == self._HDR_MAGIC, (
-                'Index file doesn\'t match expected format. '
-                'Make sure that --dataset-impl is configured properly.'
-            )
+            assert magic == self._HDR_MAGIC, ('Index file doesn\'t match expected format. '
+                                              'Make sure that --dataset-impl is configured properly.')
             version = f.read(8)
             assert struct.unpack('<Q', version) == (1,)
             code, self.element_size = struct.unpack('<QQ', f.read(16))
@@ -198,13 +186,11 @@ class IndexedDataset(torch.utils.data.Dataset):
 
     @staticmethod
     def exists(path):
-        return (
-            os.path.exists(index_file_path(path)) and os.path.exists(data_file_path(path))
-        )
+        return (os.path.exists(index_file_path(path)) and os.path.exists(data_file_path(path)))
 
     @property
     def supports_prefetch(self):
-        return False  # avoid prefetching to save memory
+        return False    # avoid prefetching to save memory
 
 
 class IndexedCachedDataset(IndexedDataset):
@@ -233,7 +219,7 @@ class IndexedCachedDataset(IndexedDataset):
         for i in indices:
             self.cache_index[i] = ptx
             size = self.data_offsets[i + 1] - self.data_offsets[i]
-            a = self.cache[ptx: ptx + size]
+            a = self.cache[ptx:ptx + size]
             self.data_file.seek(self.data_offsets[i] * self.element_size)
             self.data_file.readinto(a)
             ptx += size
@@ -250,7 +236,7 @@ class IndexedCachedDataset(IndexedDataset):
             tensor_size = self.sizes[self.dim_offsets[i]:self.dim_offsets[i + 1]]
             a = np.empty(tensor_size, dtype=self.dtype)
             ptx = self.cache_index[i]
-            np.copyto(a, self.cache[ptx: ptx + a.size])
+            np.copyto(a, self.cache[ptx:ptx + a.size])
             return a
         elif isinstance(idx, slice):
             # Hack just to make this work, can optimizer later if necessary
@@ -261,15 +247,7 @@ class IndexedCachedDataset(IndexedDataset):
 
 
 class IndexedDatasetBuilder(object):
-    element_sizes = {
-        np.uint8: 1,
-        np.int8: 1,
-        np.int16: 2,
-        np.int32: 4,
-        np.int64: 8,
-        np.float: 4,
-        np.double: 8
-    }
+    element_sizes = {np.uint8: 1, np.int8: 1, np.int16: 2, np.int32: 4, np.int64: 8, float: 4, np.double: 8}
 
     def __init__(self, out_file, dtype=np.int32):
         self.out_file = open(out_file, 'wb')
@@ -332,12 +310,15 @@ def _warmup_mmap_file(path):
 
 
 class MMapIndexedDataset(torch.utils.data.Dataset):
+
     class Index(object):
         _HDR_MAGIC = b'MMIDIDX\x00\x00'
 
         @classmethod
         def writer(cls, path, dtype):
+
             class _Writer(object):
+
                 def __enter__(self):
                     self._file = open(path, 'wb')
 
@@ -384,10 +365,8 @@ class MMapIndexedDataset(torch.utils.data.Dataset):
         def __init__(self, path, skip_warmup=False):
             with open(path, 'rb') as stream:
                 magic_test = stream.read(9)
-                assert self._HDR_MAGIC == magic_test, (
-                    'Index file doesn\'t match expected format. '
-                    'Make sure that --dataset-impl is configured properly.'
-                )
+                assert self._HDR_MAGIC == magic_test, ('Index file doesn\'t match expected format. '
+                                                       'Make sure that --dataset-impl is configured properly.')
                 version = struct.unpack('<Q', stream.read(8))
                 assert (1,) == version
 
@@ -406,16 +385,16 @@ class MMapIndexedDataset(torch.utils.data.Dataset):
             self._bin_buffer_mmap = np.memmap(path, mode='r', order='C')
             self._bin_buffer = memoryview(self._bin_buffer_mmap)
             print("    reading sizes...")
-            self._sizes = np.frombuffer(
-                self._bin_buffer,
-                dtype=np.int32,
-                count=self._len,
-                offset=offset)
+            self._sizes = np.frombuffer(self._bin_buffer, dtype=np.int32, count=self._len, offset=offset)
             print("    reading pointers...")
-            self._pointers = np.frombuffer(self._bin_buffer, dtype=np.int64, count=self._len,
+            self._pointers = np.frombuffer(self._bin_buffer,
+                                           dtype=np.int64,
+                                           count=self._len,
                                            offset=offset + self._sizes.nbytes)
             print("    reading document index...")
-            self._doc_idx = np.frombuffer(self._bin_buffer, dtype=np.int64, count=self._doc_count,
+            self._doc_idx = np.frombuffer(self._bin_buffer,
+                                          dtype=np.int64,
+                                          count=self._doc_count,
                                           offset=offset + self._sizes.nbytes + self._pointers.nbytes)
 
         def __del__(self):
@@ -480,8 +459,7 @@ class MMapIndexedDataset(torch.utils.data.Dataset):
     def __getitem__(self, idx):
         if isinstance(idx, int):
             ptr, size = self._index[idx]
-            np_array = np.frombuffer(self._bin_buffer, dtype=self._index.dtype,
-                                     count=size, offset=ptr)
+            np_array = np.frombuffer(self._bin_buffer, dtype=self._index.dtype, count=size, offset=ptr)
             return np_array
         elif isinstance(idx, slice):
             start, stop, step = idx.indices(len(self))
@@ -491,8 +469,7 @@ class MMapIndexedDataset(torch.utils.data.Dataset):
             sizes = self._index._sizes[idx]
             offsets = list(accumulate(sizes))
             total_size = sum(sizes)
-            np_array = np.frombuffer(self._bin_buffer, dtype=self._index.dtype,
-                                     count=total_size, offset=ptr)
+            np_array = np.frombuffer(self._bin_buffer, dtype=self._index.dtype, count=total_size, offset=ptr)
             sents = np.split(np_array, offsets[:-1])
             return sents
 
@@ -506,8 +483,7 @@ class MMapIndexedDataset(torch.utils.data.Dataset):
         if length is None:
             length = size - offset
         ptr += offset * np.dtype(self._index.dtype).itemsize
-        np_array = np.frombuffer(self._bin_buffer, dtype=self._index.dtype,
-                                 count=length, offset=ptr)
+        np_array = np.frombuffer(self._bin_buffer, dtype=self._index.dtype, count=length, offset=ptr)
         return np_array
 
     @property
@@ -530,12 +506,11 @@ class MMapIndexedDataset(torch.utils.data.Dataset):
 
     @staticmethod
     def exists(path):
-        return (
-            os.path.exists(index_file_path(path)) and os.path.exists(data_file_path(path))
-        )
+        return (os.path.exists(index_file_path(path)) and os.path.exists(data_file_path(path)))
 
 
 class MMapIndexedDatasetBuilder(object):
+
     def __init__(self, out_file, dtype=np.int64):
         self._data_file = open(out_file, 'wb')
         self._dtype = dtype

examples/tutorial/sequence_parallel/requirements.txt

@@ -1,2 +1,3 @@
 colossalai
 torch
+six