delete unused files

colossalai/nn/layer/__init__.py

-from .colossalai_layer import *
-from .parallel_1d import *
-from .parallel_2d import *
-from .parallel_2p5d import *
-from .parallel_3d import *
-from .parallel_sequence import *
-from .utils import *
-from .vanilla import *
-from .wrapper import *

colossalai/nn/layer/base_layer.py

-#!/usr/bin/env python
-# -*- encoding: utf-8 -*-
-
-import torch.nn as nn
-
-from colossalai.context import ParallelMode
-from colossalai.core import global_context as gpc
-
-
-class ParallelLayer(nn.Module):
-
-    def __init__(self):
-        super().__init__()
-        self.data_parallel_rank = 0 if not gpc.is_initialized(ParallelMode.DATA) else gpc.get_local_rank(
-            ParallelMode.DATA)
-        self.data_parallel_size = 1 if not gpc.is_initialized(ParallelMode.DATA) else gpc.get_world_size(
-            ParallelMode.DATA)
-
-        self.tensor_parallel_rank = 0 if not gpc.is_initialized(ParallelMode.TENSOR) else gpc.get_local_rank(
-            ParallelMode.TENSOR)
-        self.tensor_parallel_size = 1 if not gpc.is_initialized(ParallelMode.TENSOR) else gpc.get_world_size(
-            ParallelMode.TENSOR)
-
-        self.pipeline_parallel_rank = 0 if not gpc.is_initialized(ParallelMode.PIPELINE) else gpc.get_local_rank(
-            ParallelMode.PIPELINE)
-        self.pipeline_parallel_size = 1 if not gpc.is_initialized(ParallelMode.PIPELINE) else gpc.get_world_size(
-            ParallelMode.PIPELINE)

colossalai/nn/layer/colossalai_layer/__init__.py

-from ._utils import partition_batch
-from .dropout import Dropout
-from .embedding import Embedding, PatchEmbedding
-from .linear import Classifier, Linear
-from .normalization import LayerNorm
-
-__all__ = ['Linear', 'Classifier', 'Embedding', 'PatchEmbedding', 'LayerNorm', 'Dropout', 'partition_batch']

colossalai/nn/layer/colossalai_layer/_utils.py

-from torch import Tensor
-
-from ..parallel_2d._operation import split_tensor_2d
-from ..parallel_2p5d._operation import split_tensor_2p5d
-from ..parallel_3d._operation import split_batch_3d
-from ..utils import get_tensor_parallel_mode
-
-_parallel_split_batch = {'2d': split_tensor_2d, '2.5d': split_tensor_2p5d, '3d': split_batch_3d}
-
-
-def partition_batch(input_) -> Tensor:
-    tensor_parallel_mode = get_tensor_parallel_mode()
-    if tensor_parallel_mode in _parallel_split_batch:
-        if isinstance(input_, dict):
-            return {k: _parallel_split_batch[tensor_parallel_mode](v) for k, v in input_.items()}
-        else:
-            return _parallel_split_batch[tensor_parallel_mode](input_)
-    else:
-        return input_