diff --git a/colossalai/nn/layer/base_layer.py b/colossalai/nn/layer/base_layer.py
index 5234b6b1a..4a06bdcb7 100644
--- a/colossalai/nn/layer/base_layer.py
+++ b/colossalai/nn/layer/base_layer.py
@@ -10,6 +10,7 @@ from colossalai.core import global_context as gpc
 
 
 class ParallelLayer(nn.Module):
+
     global_state_dict: bool = True
 
     def __init__(self):
diff --git a/colossalai/shardformer/layer/_operation.py b/colossalai/shardformer/layer/_operation.py
index e817ea3eb..208a391c3 100644
--- a/colossalai/shardformer/layer/_operation.py
+++ b/colossalai/shardformer/layer/_operation.py
@@ -54,10 +54,10 @@ class LinearWithAsyncCommunication(torch.autograd.Function):
     """
 
     @staticmethod
-    def forward(ctx, input_, weight, bias, parallel_mode, async_grad_allreduce):
+    def forward(ctx, input_, weight, bias, process_group, async_grad_allreduce):
         ctx.save_for_backward(input_, weight)
         ctx.use_bias = bias is not None
-        ctx.parallel_mode = parallel_mode
+        ctx.process_group = process_group
         ctx.async_grad_allreduce = async_grad_allreduce
 
         output = torch.matmul(input_, weight.t())
@@ -74,12 +74,13 @@ class LinearWithAsyncCommunication(torch.autograd.Function):
         grad_input = grad_output.matmul(weight)
         grad_output = grad_output.contiguous()
         # Convert the tensor shapes to 2D for execution compatibility
-        grad_output = grad_output.view(grad_output.shape[0] * grad_output.shape[1], grad_output.shape[2])
-        total_input = total_input.view(total_input.shape[0] * total_input.shape[1], total_input.shape[2])
+        if len(grad_output.shape) > 2:
+            grad_output = grad_output.view(-1, grad_output.shape[-1])
+            total_input = total_input.view(-1, total_input.shape[-1])
 
         if ctx.async_grad_allreduce:
             # Asynchronous all-reduce
-            handle = dist.all_reduce(grad_input, group=gpc.get_group(ctx.parallel_mode), async_op=True)
+            handle = dist.all_reduce(grad_input, group=ctx.process_group, async_op=True)
             # Delay the start of weight gradient computation shortly (3us) to have
             # all-reduce scheduled first and have GPU resources allocated
             _ = torch.empty(1, device=grad_output.device) + 1
@@ -93,5 +94,123 @@ class LinearWithAsyncCommunication(torch.autograd.Function):
         return grad_input, grad_weight, grad_bias, None, None, None
 
 
-def linear_with_async_comm(input_, weight, bias, parallel_mode, async_grad_allreduce):
-    return LinearWithAsyncCommunication.apply(input_, weight, bias, parallel_mode, async_grad_allreduce)
+class _SplitForwardGatherBackward(torch.autograd.Function):
+    """
+    Split the input and keep only the corresponding chuck to the rank.
+
+    Args:
+        input_ (`torch.Tensor`): input matrix.
+        dim (int): the dimension to perform split and gather
+        process_group (`torch.distributed.ProcessGroup`): the process group used for collective communication
+
+    """
+
+    @staticmethod
+    def forward(ctx, input_, dim, process_group):
+        ctx.process_group = process_group
+        ctx.dim = dim
+        return _split(input_, dim, process_group)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return _gather(grad_output, ctx.dim, ctx.process_group), None, None
+
+
+class _ReduceInput(torch.autograd.Function):
+    """
+    All-reduce the input from the model parallel region.
+
+    Args:
+        input_: input matrix.
+        parallel_mode: parallel mode.
+    """
+
+    @staticmethod
+    def forward(ctx, input_, process_group):
+        return _reduce(input_, process_group)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return grad_output, None
+
+
+def _reduce(input_, process_group):
+    # skip if only one rank involved
+    if dist.get_world_size(process_group) == 1:
+        return input_
+    else:
+        dist.all_reduce(input_, group=process_group)
+        return input_
+
+
+def _split(input_, dim=-1, process_group=None):
+    # skip if only one rank involved
+    world_size = dist.get_world_size(process_group)
+    if world_size == 1:
+        return input_
+
+    # Split along last dimension.
+    dim_size = input_.size(dim)
+    assert dim_size % world_size == 0, \
+        f'The dimension to split ({dim_size}) is not a multiple of world size ({world_size}), ' \
+        f'cannot split tensor evenly'
+
+    tensor_list = torch.split(input_, dim_size // world_size, dim=dim)
+    rank = dist.get_rank(process_group)
+    output = tensor_list[rank].contiguous()
+
+    return output
+
+
+def _gather(input_, dim=-1, process_group=None):
+    # skip if only one rank involved
+    world_size = dist.get_world_size(process_group)
+    if world_size == 1:
+        return input_
+
+    # all gather
+    rank = dist.get_rank(process_group)
+    tensor_list = [torch.empty_like(input_) for _ in range(world_size)]
+    tensor_list[rank] = input_
+    torch.distributed.all_gather(tensor_list, input_, group=process_group)
+
+    # concat
+    output = torch.cat(tensor_list, dim=dim).contiguous()
+
+    return output
+
+
+class _GatherForwardSplitBackward(torch.autograd.Function):
+    """Gather the input from model parallel region and concatenate.
+
+    Args:
+        input_: input matrix.
+        parallel_mode: parallel mode.
+        dim: dimension
+    """
+
+    @staticmethod
+    def forward(ctx, input_, dim, process_group):
+        ctx.process_group = process_group
+        ctx.dim = dim
+        return _gather(input_, dim, process_group)
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return _split(grad_output, ctx.dim, ctx.process_group), None, None
+
+
+def linear_with_async_comm(input_, weight, bias, process_group, async_grad_allreduce):
+    return LinearWithAsyncCommunication.apply(input_, weight, bias, process_group, async_grad_allreduce)
+
+
+def gather_forward_split_backward(input_, dim, process_group):
+    return _GatherForwardSplitBackward.apply(input_, dim, process_group)
+
+
+def split_forward_gather_backward(input_, dim, process_group):
+    return _SplitForwardGatherBackward.apply(input_, dim, process_group)
+
+
+def reduce_input(input_, process_group):
+    return _ReduceInput.apply(input_, process_group)
diff --git a/colossalai/shardformer/layer/dropout.py b/colossalai/shardformer/layer/dropout.py
index 0f653a9be..5d295be6b 100644
--- a/colossalai/shardformer/layer/dropout.py
+++ b/colossalai/shardformer/layer/dropout.py
@@ -1,58 +1,20 @@
-import os
-from contextlib import contextmanager
-
 import torch
+import torch.distributed as dist
 import torch.nn as nn
 
-
-class SeedManager:
-    """
-    This class is a random state manager to change random state for different random seed.
-
-    """
-
-    def __init__(self):
-        original_state = torch.cuda.get_rng_state()
-        # TODO: unify this seed manager with the colossalai.context.random
-        seed = os.getpid()
-        torch.cuda.manual_seed(int(seed))
-        self.dropout_state = torch.cuda.get_rng_state()
-        torch.cuda.set_rng_state(original_state)
-
-    def set_mode(self, rng_state):
-        torch.cuda.set_rng_state(rng_state)
-
-    def get_current_mode(self):
-        current_state = torch.cuda.get_rng_state()
-        return current_state
-
-    @contextmanager
-    def dropout_mode(self):
-        """
-        This is a context manager to change the dropout state and recover the original state.
-
-        Usage:
-        ::
-            >>> with _seed_manager.dropout_mode():
-            >>>     input = super().forward(input)
-        """
-        try:
-            current_mode = self.get_current_mode()
-            yield self.set_mode(self.dropout_state)
-        finally:
-            self.dropout_state = self.get_current_mode()
-            self.set_mode(current_mode)
-
-
-_seed_manager = SeedManager()
+from .utils import create_randomizer_with_offset
 
 
 class Dropout1D(nn.Dropout):
 
-    def __init__(self, p=0.5, inplace=False):
+    def __init__(self, p=0.5, inplace=False, process_group=None):
         super().__init__(p, inplace)
 
+        # offset the seed with randomizer index and rank
+        seed = torch.random.initial_seed()
+        self.randomizer = create_randomizer_with_offset(seed, process_group=process_group)
+
     def forward(self, input):
-        with _seed_manager.dropout_mode():
+        with self.randomizer.fork_rng():
             input = super().forward(input)
         return input
diff --git a/colossalai/shardformer/layer/layers.py b/colossalai/shardformer/layer/layers.py
index a9f3cf5ad..2ad6523c9 100644
--- a/colossalai/shardformer/layer/layers.py
+++ b/colossalai/shardformer/layer/layers.py
@@ -2,12 +2,16 @@
 # -*- encoding: utf-8 -*-
 
 import math
+from abc import ABC, abstractmethod
 from collections import OrderedDict
-from typing import Callable, Tuple
+from typing import Callable, List, Tuple, Union
 
 import torch
+import torch.distributed as dist
+import torch.nn as nn
 import torch.nn.functional as F
 from torch import Tensor
+from torch.distributed import ProcessGroup
 from torch.nn.parameter import Parameter
 
 from colossalai.communication import broadcast
@@ -22,13 +26,11 @@ from colossalai.nn.layer.parallel_1d._utils import (
     gather_forward_split_backward,
     get_parallel_input,
     reduce_grad,
-    reduce_input,
     set_parallel_input,
-    split_forward_gather_backward,
 )
 from colossalai.nn.layer.utils import divide, set_tensor_parallel_attribute_by_partition
 from colossalai.nn.layer.vanilla import VanillaLayerNorm, VanillaPatchEmbedding
-from colossalai.registry import LAYERS
+from colossalai.tensor.d_tensor.api import shard_colwise, shard_rowwise
 from colossalai.utils.checkpointing import (
     broadcast_state_dict,
     gather_tensor_parallel_state_dict,
@@ -36,7 +38,13 @@ from colossalai.utils.checkpointing import (
 )
 from colossalai.utils.cuda import get_current_device
 
-from ._operation import linear_with_async_comm
+from ._operation import (
+    gather_forward_split_backward,
+    linear_with_async_comm,
+    reduce_input,
+    split_forward_gather_backward,
+)
+from .utils import create_randomizer_with_offset
 
 Fast_LN = None
 try:
@@ -46,17 +54,172 @@ except ImportError:
     pass
 
 
-# @LAYERS.register_module
-class Linear1D(ColossalaiModule):
-    r"""Linear layer for 1D parallelism.
+class ParallelModule(nn.Module, ABC):
+
+    @abstractmethod
+    def from_native_module(module: nn.Module,
+                           process_group: Union[ProcessGroup, List[ProcessGroup]] = None) -> "ParallelModule":
+        """
+        Convert a native PyTorch module to a parallelized module.
+
+        Args:
+            module (nn.Module): the module to be converted.
+            process_group (ProcessGroup or list[ProcessGroup]): the process group(s) to be used for communication.
+                If this is a list, the process group at the ith index of the list will correspond to the process group
+                in the ith axis of the device mesh. Defaults to None, which means the global process group.
+        """
+        pass
+
+
+class Linear1D_Col(ParallelModule):
+    r"""Linear layer with column parallelism.
+
+    The linear layer is defined as :math:`Y = XA + b`. A is parallelized along
+    its second dimension as :math:`A = [A_1, ..., A_p]`.
 
     Args:
         in_features (int): size of each input sample.
         out_features (int): size of each output sample.
         bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
-        dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
-        gather_output (bool, optional): Whether to call all-gather on output, defaults to False.
-        skip_bias_add (bool, optional): If set to ``True``, it will skip bias add for linear layer,
+        dtype (`torch.dtype`): The dtype of parameters, defaults to None.
+        device (`torch.device`): The device of parameters, defaults to None.
+        process_group (`torch.distributed.ProcessGroup`): The process group to be used for weight sharding and communication, defaults to None.
+        gather_output (bool, optional): If true, call all-gather on output and make Y available
+                    to all GPUs, otherwise, every GPU will have its output
+                    which is :math:`Y_i = XA_i`, defaults to False
+        skip_bias_add (bool): If set to ``True``, it will skip bias add for linear layer,
+            which is preserved for kernel fusion, defaults to False
+        weight_initializer (`typing.Callable`):
+            The initializer of weight, defaults to kaiming uniform initializer.
+        bias_initializer (`typing.Callable`):
+            The initializer of bias, defaults to xavier uniform initializer.
+
+    More details about ``initializer`` please refer to
+    `init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
+    """
+
+    def __init__(self,
+                 in_features: int,
+                 out_features: int,
+                 bias: bool = True,
+                 dtype: torch.dtype = None,
+                 device: torch.device = None,
+                 process_group: ProcessGroup = None,
+                 gather_output: bool = False,
+                 skip_bias_add: bool = False,
+                 weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
+                 bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
+        super().__init__()
+
+        # Keep input parameters
+        self.in_features = in_features
+        self.out_features = out_features
+        self.gather_output = gather_output
+        self.skip_bias_add = skip_bias_add
+        self.device = device
+        self.process_group = process_group
+        self.num_partitions = dist.get_world_size(self.process_group)
+
+        if skip_bias_add and not bias:
+            raise ValueError('cannot skip bias addition if bias is None')
+
+        self.out_features_per_partition = divide(out_features, self.num_partitions)
+
+        # Parameters.
+        # Initialize weight.
+        if device is None:
+            device = get_current_device()
+        factory_kwargs = {'device': device, 'dtype': dtype}
+        self.weight = Parameter(torch.empty(self.out_features_per_partition, self.in_features, **factory_kwargs))
+
+        if bias:
+            self.bias = Parameter(torch.empty(self.out_features_per_partition, **factory_kwargs))
+        else:
+            self.bias = None
+
+        # offset the seed with randomizer index and rank
+        seed = torch.random.initial_seed()
+        self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group)
+
+        with self.randomizer.fork_rng(enable_cpu=True):
+            self.reset_parameters(weight_initializer, bias_initializer)
+
+    def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], *args,
+                           **kwargs) -> ParallelModule:
+        r"""
+        Convert a native PyTorch linear layer to a parallelized linear layer.
+        """
+        # get the attributes
+        in_features = module.in_features
+        out_features = module.out_features
+        bias = module.bias is not None
+        device = module.weight.device
+
+        # ensure only one process group is passed
+        if isinstance(process_group, (list, tuple)):
+            assert len(process_group) == 1, \
+                f'Expected only one process group, got {len(process_group)}.'
+            process_group = process_group[0]
+
+        linear_1d = Linear1D_Col(in_features=in_features,
+                                 out_features=out_features,
+                                 bias=bias,
+                                 device=device,
+                                 process_group=process_group,
+                                 *args,
+                                 **kwargs)
+
+        # TODO: copy the sharded weights
+        with torch.no_grad():
+            # the weigh to the linear layer is a transpose
+            # thus shard on row is equal to shard on column
+            sharded_weight = shard_rowwise(module.weight.data, process_group)
+            linear_1d.weight.data.copy_(sharded_weight)
+            if bias:
+                sharded_bias = shard_colwise(module.bias.data, process_group)
+                linear_1d.bias.copy_(sharded_bias)
+
+        return linear_1d
+
+    def reset_parameters(self, weight_initializer, bias_initializer) -> None:
+        fan_in, fan_out = self.in_features, self.out_features
+        weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
+        if self.bias is not None:
+            bias_initializer(self.bias, fan_in=fan_in)
+
+    def forward(self, input_: Tensor) -> Tuple[Tensor, Tensor]:
+        assert input_.shape[-1] == self.weight.shape[-1], \
+            'Invalid shapes in Linear1D_Col forward: input={}, weight={}. Expected last dim of input {}.'.format(
+                input_.shape, self.weight.shape, self.weight.shape[-1])
+        # Set up backprop all-reduce.
+        # input_parallel = reduce_grad(input_, ParallelMode.PARALLEL_1D)
+        input_parallel = input_
+        # Matrix multiply.
+        bias = self.bias if not self.skip_bias_add else None
+        output_parallel = linear_with_async_comm(input_parallel, self.weight, bias, self.process_group, True)
+
+        if self.gather_output:
+            # All-gather across the partitions.
+            output = gather_forward_split_backward(output_parallel, dim=-1, process_group=self.process_group)
+        else:
+            output = output_parallel
+
+        if self.skip_bias_add:
+            return output, self.bias
+        else:
+            return output
+
+
+class Linear1D_Row(ParallelModule):
+    r""" Linear layer with row parallelism
+
+    Args:
+        in_features (int): size of each input sample.
+        out_features (int): size of each output sample.
+        bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
+        dtype (`torch.dtype`): The dtype of parameters, defaults to None.
+        parallel_input (bool): If set to ``True``, it's assumed that the input is split, defaults to False.
+        skip_bias_add (bool): If set to ``True``, it will skip bias add for linear layer,
             which is preserved for kernel fusion, defaults to False
         weight_initializer (:class:`typing.Callable`, optional):
             The initializer of weight, defaults to kaiming uniform initializer.
@@ -72,32 +235,149 @@ class Linear1D(ColossalaiModule):
                  out_features: int,
                  bias: bool = True,
                  dtype: torch.dtype = None,
-                 gather_output: bool = False,
+                 device: torch.device = None,
+                 process_group: ProcessGroup = None,
+                 parallel_input: bool = True,
                  skip_bias_add: bool = False,
                  weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
-                 bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
-        parallel_input = get_parallel_input()
-        if not parallel_input and not gather_output:
-            layer = Linear1D_Col(in_features,
-                                 out_features,
-                                 bias=bias,
-                                 dtype=dtype,
-                                 skip_bias_add=skip_bias_add,
-                                 weight_initializer=weight_initializer,
-                                 bias_initializer=bias_initializer)
+                 bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
+                 stream_chunk_num: int = 1):
+        super().__init__()
+
+        self.stream_chunk_num = stream_chunk_num
+
+        # Keep input parameters
+        self.in_features = in_features
+        self.out_features = out_features
+        self.parallel_input = parallel_input
+        self.skip_bias_add = skip_bias_add
+        self.process_group = process_group
+        self.num_partitions = dist.get_world_size(self.process_group)
+
+        if skip_bias_add and not bias:
+            raise ValueError('cannot skip bias addition if bias is None')
+
+        # Divide the weight matrix along the last dimension.
+        self.input_size_per_partition = divide(in_features, self.num_partitions)
+
+        # Parameters.
+        # Initialize weight.
+        if device is None:
+            device = get_current_device()
+
+        factory_kwargs = {'device': device, 'dtype': dtype}
+        self.weight = Parameter(torch.empty(self.out_features, self.input_size_per_partition, **factory_kwargs))
+
+        if self.stream_chunk_num > 1:
+            # TODO() work for inference only
+            self.chunk_weight()
+        if bias:
+            self.bias = Parameter(torch.empty(self.out_features, **factory_kwargs))
         else:
-            layer = Linear1D_Row(in_features,
-                                 out_features,
+            self.bias = None
+
+        # offset the seed with randomizer index and rank
+        seed = torch.random.initial_seed()
+        self.randomizer = create_randomizer_with_offset(seed, process_group=self.process_group)
+
+        with self.randomizer.fork_rng(enable_cpu=True):
+            self.reset_parameters(weight_initializer, bias_initializer)
+
+    @staticmethod
+    def from_native_module(module: nn.Linear, process_group: Union[ProcessGroup, List[ProcessGroup]], *args,
+                           **kwargs) -> ParallelModule:
+        r"""
+        Convert a native PyTorch linear layer to a parallelized linear layer.
+        """
+        # get the attributes
+        in_features = module.in_features
+        out_features = module.out_features
+        bias = module.bias is not None
+        device = module.weight.device
+
+        # ensure only one process group is passed
+        if isinstance(process_group, (list, tuple)):
+            assert len(process_group) == 1, \
+                f'Expected only one process group, got {len(process_group)}.'
+            process_group = process_group[0]
+
+        linear_1d = Linear1D_Row(in_features=in_features,
+                                 out_features=out_features,
                                  bias=bias,
-                                 dtype=dtype,
-                                 parallel_input=parallel_input,
-                                 skip_bias_add=skip_bias_add,
-                                 weight_initializer=weight_initializer,
-                                 bias_initializer=bias_initializer)
-        super().__init__(layer)
+                                 device=device,
+                                 process_group=process_group,
+                                 *args,
+                                 **kwargs)
+
+        # TODO: copy the sharded weights
+        with torch.no_grad():
+            # the weigh to the linear layer is a transpose
+            # thus shard on col is equal to shard on row
+            sharded_weight = shard_colwise(module.weight.data, process_group)
+            linear_1d.weight.data.copy_(sharded_weight)
+
+            if bias:
+                linear_1d.bias.copy_(module.bias.data)
+
+        return linear_1d
+
+    def chunk_weight(self):
+        self.weight_list = torch.chunk(self.weight, self.stream_chunk_num, dim=0)
+
+    def reset_parameters(self, weight_initializer, bias_initializer) -> None:
+        fan_in, fan_out = self.in_features, self.out_features
+        weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
+
+        if self.bias is not None:
+            bias_initializer(self.bias, fan_in=fan_in)
+            if self.process_group is None:
+                src_rank = 0
+            else:
+                src_rank = dist.distributed_c10d._get_global_rank(self.process_group, 0)
+            dist.broadcast(self.bias, src=src_rank, group=self.process_group)
+
+    def forward(self, input_: Tensor) -> Tensor:
+        # Set up backprop all-reduce.
+        if self.parallel_input:
+            assert input_.shape[-1] == self.weight.shape[-1], \
+                'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format(
+                input_.shape, self.weight.shape, self.weight.shape[-1])
+            input_ = input_
+        else:
+            assert divide(input_.shape[-1], self.num_partitions) == self.weight.shape[-1], \
+                'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format(
+                input_.shape, self.weight.shape, self.weight.shape[-1] * self.num_partitions)
+            input_ = split_forward_gather_backward(input_, dim=-1, process_group=self.process_group)
+
+        if self.stream_chunk_num > 1:
+            if self.training:
+                raise RuntimeError("use stream_chunk_num=1 in Linear1D_Row for training!")
+            with torch.no_grad():
+                output_parallel_list = [None for i in range(self.stream_chunk_num)]
+                handle_list = []
+                for i in range(self.stream_chunk_num):
+                    output_parallel_list[i] = F.linear(input_, self.weight_list[i])
+                    handle = torch.distributed.all_reduce(output_parallel_list[i],
+                                                          group=self.process_group,
+                                                          async_op=True)
+                    handle_list.append(handle)
+                    # output_parallel_list[i] = reduce_input(output_parallel_list[i], ParallelMode.PARALLEL_1D)
+                for handle in handle_list:
+                    handle.wait()
+                output = torch.cat(output_parallel_list, dim=-1)
+        else:
+            output_parallel = F.linear(input_, self.weight)
+            # output_parallel = linear_with_async_comm(input_, self.weight, None, ParallelMode.PARALLEL_1D, False)
+            output = reduce_input(output_parallel, self.process_group)
+
+        if not self.skip_bias_add:
+            if self.bias is not None:
+                output = output + self.bias
+            return output
+        else:
+            return output, self.bias
 
 
-# @LAYERS.register_module
 class LayerNorm1D(ColossalaiModule):
     r"""
     Layer Normalization for colossalai
@@ -152,7 +432,6 @@ class LayerNorm1D(ColossalaiModule):
             super()._save_to_state_dict(destination, prefix, keep_vars)
 
 
-# @LAYERS.register_module
 class Classifier1D(ParallelLayer):
     r"""RowLinear with given weight. Classifier of 1D parallelism.
 
@@ -288,7 +567,6 @@ class Classifier1D(ParallelLayer):
         return output
 
 
-# @LAYERS.register_module
 class VocabParallelClassifier1D(ParallelLayer):
     r"""ColLinear with given weight. Classifier of 1D parallelism.
 
@@ -424,317 +702,8 @@ class VocabParallelClassifier1D(ParallelLayer):
 
 
 # @LAYERS.register_module
-class Linear1D_Col(ParallelLayer):
-    r"""Linear layer with column parallelism.
-
-    The linear layer is defined as :math:`Y = XA + b`. A is parallelized along
-    its second dimension as :math:`A = [A_1, ..., A_p]`.
-
-    Args:
-        in_features (int): size of each input sample.
-        out_features (int): size of each output sample.
-        bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
-        dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
-        gather_output (bool, optional): If true, call all-gather on output and make Y available
-                    to all GPUs, otherwise, every GPU will have its output
-                    which is :math:`Y_i = XA_i`, defaults to False
-        skip_bias_add (bool, optional): If set to ``True``, it will skip bias add for linear layer,
-            which is preserved for kernel fusion, defaults to False
-        weight_initializer (:class:`typing.Callable`, optional):
-            The initializer of weight, defaults to kaiming uniform initializer.
-        bias_initializer (:class:`typing.Callable`, optional):
-            The initializer of bias, defaults to xavier uniform initializer.
-
-    More details about ``initializer`` please refer to
-    `init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
-    """
-
-    def __init__(self,
-                 in_features: int,
-                 out_features: int,
-                 bias: bool = True,
-                 dtype: torch.dtype = None,
-                 gather_output: bool = False,
-                 skip_bias_add: bool = False,
-                 weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
-                 bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
-        super().__init__()
-
-        # Keep input parameters
-        self.in_features = in_features
-        self.out_features = out_features
-        self.gather_output = gather_output
-        self.skip_bias_add = skip_bias_add
-
-        if skip_bias_add and not bias:
-            raise ValueError('cannot skip bias addition if bias is None')
-
-        # self.out_features_per_partition = divide(out_features*2, gpc.tensor_parallel_size)
-        self.out_features_per_partition = out_features
-
-        # Parameters.
-        # Initialize weight.
-        factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
-        self.weight = Parameter(torch.empty(self.out_features_per_partition, self.in_features, **factory_kwargs))
-
-        if bias:
-            self.bias = Parameter(torch.empty(self.out_features_per_partition, **factory_kwargs))
-        else:
-            self.bias = None
-        with seed(ParallelMode.TENSOR):
-            self.reset_parameters(weight_initializer, bias_initializer)
-        self._set_tensor_parallel_attributes()
-        is_parallel_output = not self.gather_output
-        set_parallel_input(is_parallel_output)
-
-    def reset_parameters(self, weight_initializer, bias_initializer) -> None:
-        fan_in, fan_out = self.in_features, self.out_features
-        weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
-        if self.bias is not None:
-            bias_initializer(self.bias, fan_in=fan_in)
-
-    def _set_tensor_parallel_attributes(self):
-        num_partition = gpc.get_world_size(ParallelMode.TENSOR)
-        set_tensor_parallel_attribute_by_partition(self.weight, num_partition)
-        if self.bias is not None:
-            set_tensor_parallel_attribute_by_partition(self.bias, num_partition)
-
-    def _load_from_global_state_dict(self, state_dict, prefix, *args):
-        local_state = OrderedDict()
-        weight_key = prefix + 'weight'
-        bias_key = prefix + 'bias'
-        if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
-            # weight
-            weight = state_dict.pop(weight_key, None)
-            if weight is not None:
-                local_state[weight_key] = weight
-            # bias
-            if self.bias is not None:
-                bias = state_dict.pop(bias_key, None)
-                if bias is not None:
-                    local_state[bias_key] = bias
-
-        local_state = partition_tensor_parallel_state_dict(local_state,
-                                                           ParallelMode.PARALLEL_1D,
-                                                           dims={
-                                                               weight_key: 0,
-                                                               bias_key: 0
-                                                           },
-                                                           partition_states={
-                                                               weight_key: True,
-                                                               bias_key: True
-                                                           })
-        super()._load_from_global_state_dict(local_state, prefix, *args)
-
-    def _save_to_global_state_dict(self, destination, prefix, keep_vars):
-        weight_key = prefix + 'weight'
-        bias_key = prefix + 'bias'
-        local_state = OrderedDict({weight_key: self.weight})
-        if self.bias is not None:
-            local_state[bias_key] = self.bias
-        local_state = gather_tensor_parallel_state_dict(local_state,
-                                                        ParallelMode.PARALLEL_1D,
-                                                        dims={
-                                                            weight_key: 0,
-                                                            bias_key: 0
-                                                        },
-                                                        partition_states={
-                                                            weight_key: True,
-                                                            bias_key: True
-                                                        },
-                                                        keep_vars=keep_vars)
-        destination.update(local_state)
-
-    def forward(self, input_: Tensor) -> Tuple[Tensor, Tensor]:
-        assert input_.shape[-1] == self.weight.shape[-1], \
-            'Invalid shapes in Linear1D_Col forward: input={}, weight={}. Expected last dim of input {}.'.format(
-                input_.shape, self.weight.shape, self.weight.shape[-1])
-        # Set up backprop all-reduce.
-        # input_parallel = reduce_grad(input_, ParallelMode.PARALLEL_1D)
-        input_parallel = input_
-        # Matrix multiply.
-        bias = self.bias if not self.skip_bias_add else None
-        # output_parallel = F.linear(input_parallel, self.weight, bias)
-        output_parallel = linear_with_async_comm(input_parallel, self.weight, bias, ParallelMode.PARALLEL_1D, True)
-        if self.gather_output:
-            # All-gather across the partitions.
-            output = gather_forward_split_backward(output_parallel, ParallelMode.PARALLEL_1D, dim=-1)
-        else:
-            output = output_parallel
-
-        if self.skip_bias_add:
-            return output, self.bias
-        else:
-            return output
 
 
-# @LAYERS.register_module
-class Linear1D_Row(ParallelLayer):
-    r""" Linear layer with row parallelism
-
-    Args:
-        in_features (int): size of each input sample.
-        out_features (int): size of each output sample.
-        bias (bool, optional): If set to ``False``, the layer will not learn an additive bias, defaults to ``True``.
-        dtype (:class:`torch.dtype`, optional): The dtype of parameters, defaults to None.
-        parallel_input (bool, optional): If set to ``True``, it's assumed that the input is split, defaults to False.
-        skip_bias_add (bool, optional): If set to ``True``, it will skip bias add for linear layer,
-            which is preserved for kernel fusion, defaults to False
-        weight_initializer (:class:`typing.Callable`, optional):
-            The initializer of weight, defaults to kaiming uniform initializer.
-        bias_initializer (:class:`typing.Callable`, optional):
-            The initializer of bias, defaults to xavier uniform initializer.
-
-    More details about ``initializer`` please refer to
-    `init <https://github.com/hpcaitech/ColossalAI/blob/main/colossalai/nn/init.py>`_.
-    """
-
-    def __init__(self,
-                 in_features: int,
-                 out_features: int,
-                 bias: bool = True,
-                 dtype: torch.dtype = None,
-                 parallel_input: bool = True,
-                 skip_bias_add: bool = False,
-                 weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
-                 bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1),
-                 stream_chunk_num: int = 1):
-        super().__init__()
-
-        self.stream_chunk_num = stream_chunk_num
-
-        # Keep input parameters
-        self.in_features = in_features
-        self.out_features = out_features
-        self.parallel_input = parallel_input
-        self.skip_bias_add = skip_bias_add
-
-        if skip_bias_add and not bias:
-            raise ValueError('cannot skip bias addition if bias is None')
-
-        # Divide the weight matrix along the last dimension.
-        # self.input_size_per_partition = divide(in_features*2, gpc.tensor_parallel_size)
-        self.input_size_per_partition = in_features
-
-        # Parameters.
-        # Initialize weight.
-        factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
-        self.weight = Parameter(torch.empty(self.out_features, self.input_size_per_partition, **factory_kwargs))
-
-        if self.stream_chunk_num > 1:
-            # TODO() work for inference only
-            self.chunk_weight()
-        if bias:
-            self.bias = Parameter(torch.empty(self.out_features, **factory_kwargs))
-        else:
-            self.bias = None
-        with seed(ParallelMode.TENSOR):
-            self.reset_parameters(weight_initializer, bias_initializer)
-        self._set_tensor_parallel_attributes()
-        set_parallel_input(False)
-
-    def chunk_weight(self):
-        self.weight_list = torch.chunk(self.weight, self.stream_chunk_num, dim=0)
-
-    def reset_parameters(self, weight_initializer, bias_initializer) -> None:
-        fan_in, fan_out = self.in_features, self.out_features
-        weight_initializer(self.weight, fan_in=fan_in, fan_out=fan_out)
-        if self.bias is not None:
-            bias_initializer(self.bias, fan_in=fan_in)
-            broadcast(self.bias, gpc.get_ranks_in_group(ParallelMode.PARALLEL_1D)[0], ParallelMode.PARALLEL_1D)
-
-    def _set_tensor_parallel_attributes(self):
-        num_partition = gpc.get_world_size(ParallelMode.TENSOR)
-        set_tensor_parallel_attribute_by_partition(self.weight, num_partition)
-
-    def _load_from_global_state_dict(self, state_dict, prefix, *args):
-        local_state = OrderedDict()
-        weight_key = prefix + 'weight'
-        bias_key = prefix + 'bias'
-        if gpc.get_local_rank(ParallelMode.TENSOR) == 0:
-            # weight
-            weight = state_dict.pop(weight_key, None)
-            if weight is not None:
-                local_state[weight_key] = weight
-            # bias
-            if self.bias is not None:
-                bias = state_dict.pop(bias_key, None)
-                if bias is not None:
-                    local_state[bias_key] = bias
-
-        local_state = partition_tensor_parallel_state_dict(local_state,
-                                                           ParallelMode.PARALLEL_1D,
-                                                           dims={
-                                                               weight_key: -1,
-                                                               bias_key: 0
-                                                           },
-                                                           partition_states={
-                                                               weight_key: True,
-                                                               bias_key: False
-                                                           })
-        super()._load_from_global_state_dict(local_state, prefix, *args)
-
-    def _save_to_global_state_dict(self, destination, prefix, keep_vars):
-        weight_key = prefix + 'weight'
-        bias_key = prefix + 'bias'
-        local_state = OrderedDict({weight_key: self.weight})
-        if self.bias is not None:
-            local_state[bias_key] = self.bias
-        local_state = gather_tensor_parallel_state_dict(local_state,
-                                                        ParallelMode.PARALLEL_1D,
-                                                        dims={
-                                                            weight_key: -1,
-                                                            bias_key: 0
-                                                        },
-                                                        partition_states={
-                                                            weight_key: True,
-                                                            bias_key: False
-                                                        },
-                                                        keep_vars=keep_vars)
-        destination.update(local_state)
-
-    def forward(self, input_: Tensor) -> Tensor:
-        # Set up backprop all-reduce.
-        if self.parallel_input:
-            assert input_.shape[-1] == self.weight.shape[-1], \
-                'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format(
-                input_.shape, self.weight.shape, self.weight.shape[-1])
-            input_ = input_
-        else:
-            assert divide(input_.shape[-1], gpc.tensor_parallel_size) == self.weight.shape[-1], \
-                'Invalid shapes in Linear1D_Row forward: input={}, weight={}. Expected last dim of input {}.'.format(
-                input_.shape, self.weight.shape, self.weight.shape[-1] * gpc.tensor_parallel_size)
-            input_ = split_forward_gather_backward(input_, ParallelMode.PARALLEL_1D, dim=-1)
-
-        if self.stream_chunk_num > 1:
-            if self.training:
-                raise RuntimeError("use stream_chunk_num=1 in Linear1D_Row for training!")
-            with torch.no_grad():
-                output_parallel_list = [None for i in range(self.stream_chunk_num)]
-                handle_list = []
-                for i in range(self.stream_chunk_num):
-                    output_parallel_list[i] = F.linear(input_, self.weight_list[i])
-                    handle = torch.distributed.all_reduce(output_parallel_list[i],
-                                                          group=gpc.get_group(ParallelMode.PARALLEL_1D),
-                                                          async_op=True)
-                    handle_list.append(handle)
-                    # output_parallel_list[i] = reduce_input(output_parallel_list[i], ParallelMode.PARALLEL_1D)
-                for handle in handle_list:
-                    handle.wait()
-                output = torch.cat(output_parallel_list, dim=-1)
-        else:
-            output_parallel = F.linear(input_, self.weight)
-            # output_parallel = linear_with_async_comm(input_, self.weight, None, ParallelMode.PARALLEL_1D, False)
-            output = reduce_input(output_parallel, ParallelMode.PARALLEL_1D)
-        if not self.skip_bias_add:
-            if self.bias is not None:
-                output = output + self.bias
-            return output
-        else:
-            return output, self.bias
-
-
-# @LAYERS.register_module
 class Embedding1D(ParallelLayer):
     r"""Embedding for 1D parallelism.
 
@@ -842,7 +811,6 @@ class Embedding1D(ParallelLayer):
         return output
 
 
-# @LAYERS.register_module
 class VocabParallelEmbedding1D(ParallelLayer):
     r"""Embedding parallelized in the vocabulary dimension.
 
@@ -960,7 +928,6 @@ class VocabParallelEmbedding1D(ParallelLayer):
         return output
 
 
-# @LAYERS.register_module
 class Dropout1D(ParallelLayer):
     """Dropout layer of 1D parallelism.
 
diff --git a/colossalai/shardformer/layer/utils.py b/colossalai/shardformer/layer/utils.py
new file mode 100644
index 000000000..c3d6ab57e
--- /dev/null
+++ b/colossalai/shardformer/layer/utils.py
@@ -0,0 +1,138 @@
+from contextlib import contextmanager
+
+import torch
+import torch.distributed as dist
+from torch.distributed import ProcessGroup
+
+
+class Randomizer:
+    """
+    Randomizer enables the program to be executed under a different seed within the context.
+
+    Example:
+
+    ```python
+    randomizer = Randomizer(seed=1024)
+
+    with randomizer.fork():
+        # do something here with seed 1024
+        do_something()
+    ```
+
+    Args:
+        seed (int): The random seed to set.
+        enable_cpu (bool): fork the CPU RNG state as well.
+        with_index (bool): whether to use the index of the randomizer.
+    """
+
+    _INDEX = 0
+
+    def __init__(self, seed: int):
+        # TODO: remove colossalai.context.random
+
+        self.seed = seed
+
+        # Handle CUDA rng state
+        # 1. get the current rng state
+        # 2. set the seed and store the rng state
+        # 3. recover the original rng state
+        cuda_original_rng_state = torch.cuda.get_rng_state()
+        torch.cuda.manual_seed(seed)
+        self.cuda_rng_state = torch.cuda.get_rng_state()
+        torch.cuda.set_rng_state(cuda_original_rng_state)
+
+        # to the same for cpu rng state
+        cpu_original_rng_state = torch.get_rng_state()
+        torch.manual_seed(seed)
+        self.cpu_rng_state = torch.get_rng_state()
+        torch.set_rng_state(cpu_original_rng_state)
+
+    def _set_cuda_rng_state(self, rng_state):
+        torch.cuda.set_rng_state(rng_state)
+
+    def _get_cuda_rng_state(self):
+        current_state = torch.cuda.get_rng_state()
+        return current_state
+
+    def _set_cpu_rng_state(self, rng_state):
+        torch.set_rng_state(rng_state)
+
+    def _get_cpu_rng_state(self):
+        current_state = torch.get_rng_state()
+        return current_state
+
+    @contextmanager
+    def fork_rng(self, enable_cpu: bool = False):
+        """
+        This is a context manager to change the dropout state and recover the original state.
+
+        Usage:
+        ::
+            >>> with _seed_manager.dropout_mode():
+            >>>     input = super().forward(input)
+        """
+        try:
+            current_cuda_rng_state = self._get_cuda_rng_state()
+            self._set_cuda_rng_state(self.cuda_rng_state)
+
+            if enable_cpu:
+                current_cpu_rng_state = self._get_cpu_rng_state()
+                self._set_cpu_rng_state(self.cpu_rng_state)
+            yield
+        finally:
+            self.cuda_rng_state = self._get_cuda_rng_state()
+            self._set_cuda_rng_state(current_cuda_rng_state)
+
+            if enable_cpu:
+                self.cpu_rng_state = self._get_cpu_rng_state()
+                self._set_cpu_rng_state(current_cpu_rng_state)
+
+    @staticmethod
+    def index():
+        """
+        Return the index of the randomizer. The index is useful when the user wants
+        to introduce some randomness in the program.
+
+        Note:
+        The index will increment by one each time this method is called.
+
+        Example:
+
+        ```python
+        # assume we need a randomizer to init the weight of different layers
+        # we can use the index of the randomizer to do so that
+        # each layer has its own randomizer with a different seed
+        base_seed = torch.random.initial_seed()
+        seed = base_seed + Randomizer.index()
+        randomizer = Randomizer(seed)
+
+        with randomizer.fork():
+            init_weights()
+        ```
+
+        """
+        idx = Randomizer._INDEX
+        Randomizer._INDEX += 1
+        return idx
+
+
+def create_randomizer_with_offset(seed: int, process_group: ProcessGroup = None):
+    """
+    Create a randomizer with an offset. The offset is equal to the rank of the process and the index of the randomizer.
+
+    Args:
+        seed (int): The base random seed to set.
+        enable_cpu (bool): fork the CPU RNG state as well.
+        process_group (ProcessGroup): the process group to get the rank from.
+
+    Returns:
+        Randomizer: the randomizer with offset.
+    """
+    offset = Randomizer.index()
+
+    if dist.is_initialized():
+        rank = dist.get_rank(process_group)
+        offset += rank
+
+    seed += offset
+    return Randomizer(seed=seed)
diff --git a/colossalai/tensor/d_tensor/api.py b/colossalai/tensor/d_tensor/api.py
new file mode 100644
index 000000000..afb1fc003
--- /dev/null
+++ b/colossalai/tensor/d_tensor/api.py
@@ -0,0 +1,44 @@
+from typing import Union
+
+import torch
+import torch.distributed as dist
+from torch.distributed import ProcessGroup
+
+from colossalai.device.device_mesh import DeviceMesh
+
+from .d_tensor import DTensor
+from .sharding_spec import ShardingSpec
+
+
+def shard_rowwise(tensor: torch.Tensor, group_or_device_mesh: Union[ProcessGroup, DeviceMesh] = None) -> DTensor:
+    """
+    Shard the first dim of the given tensor
+    """
+    # if the group_or_device_mesh is None, we shard the tensor with respect to the global process group
+    if group_or_device_mesh is None:
+        group_or_device_mesh = dist.GroupMember.WORLD
+
+    if isinstance(group_or_device_mesh, ProcessGroup):
+        device_mesh = DeviceMesh.from_process_group(group_or_device_mesh)
+    else:
+        assert len(group_or_device_mesh.shape) == 1, 'Only 1D DeviceMesh is accepted for row-wise sharding.'
+        device_mesh = group_or_device_mesh
+    sharding_spec = ShardingSpec(dim_size=tensor.dim(), dim_partition_dict={0: [0]})
+    return DTensor(tensor, device_mesh, sharding_spec)
+
+
+def shard_colwise(tensor: torch.Tensor, group_or_device_mesh: Union[ProcessGroup, DeviceMesh] = None) -> DTensor:
+    """
+    Shard the first dim of the given tensor
+    """
+    # if the group_or_device_mesh is None, we shard the tensor with respect to the global process group
+    if group_or_device_mesh is None:
+        group_or_device_mesh = dist.GroupMember.WORLD
+
+    if isinstance(group_or_device_mesh, ProcessGroup):
+        device_mesh = DeviceMesh.from_process_group(group_or_device_mesh)
+    else:
+        assert len(group_or_device_mesh.shape) == 1, 'Only 1D DeviceMesh is accepted for row-wise sharding.'
+        device_mesh = group_or_device_mesh
+    sharding_spec = ShardingSpec(dim_size=tensor.dim(), dim_partition_dict={-1: [0]})
+    return DTensor(tensor, device_mesh, sharding_spec)
diff --git a/colossalai/tensor/d_tensor/layout.py b/colossalai/tensor/d_tensor/layout.py
index ee7ef74a9..f15956ea3 100644
--- a/colossalai/tensor/d_tensor/layout.py
+++ b/colossalai/tensor/d_tensor/layout.py
@@ -34,7 +34,7 @@ class Layout:
     def get_sharded_shape_per_device(self):
         sharded_shape = list(self.entire_shape)
         for dim, shard_list in self.sharding_spec.dim_partition_dict.items():
-            mesh_list = [self.device_mesh.mesh_shape[mesh_dim] for mesh_dim in shard_list]
+            mesh_list = [self.device_mesh.shape[mesh_dim] for mesh_dim in shard_list]
             shard_partitions = reduce(operator.mul, mesh_list, 1)
             assert sharded_shape[
                 dim] % shard_partitions == 0, f'Cannot shard dimension {dim} into {shard_partitions} partitions.'
@@ -45,14 +45,15 @@ class Layout:
         sharding_spec = self.sharding_spec
 
         # make sure all axes in logical device mesh only be used once
-        dim_check_list = list(range(self.device_mesh.logical_mesh_id.dim()))
-        for dim, shard_list in sharding_spec.dim_partition_dict.items():
-            for element in shard_list:
-                if element in dim_check_list:
-                    dim_check_list.remove(element)
-                else:
-                    raise DuplicatedShardingDimensionError(
-                        f"find an invalid sharding axis {element} in dim_partition_dict in tensor dimension {dim}.")
+        if self.device_mesh.logical_mesh_id is not None:
+            dim_check_list = list(range(self.device_mesh.logical_mesh_id.dim()))
+            for dim, shard_list in sharding_spec.dim_partition_dict.items():
+                for element in shard_list:
+                    if element in dim_check_list:
+                        dim_check_list.remove(element)
+                    else:
+                        raise DuplicatedShardingDimensionError(
+                            f"find an invalid sharding axis {element} in dim_partition_dict in tensor dimension {dim}.")
 
         # make sure that the sharding for a dimension is divisible by the number of devices
         for dim, shard_list in sharding_spec.dim_partition_dict.items():
@@ -60,7 +61,7 @@ class Layout:
             num_devices = 1
 
             for element in shard_list:
-                num_devices *= self.device_mesh.mesh_shape[element]
+                num_devices *= self.device_mesh.shape[element]
 
             if tensor_dim_size % num_devices != 0:
                 raise ShardingNotDivisibleError(
diff --git a/colossalai/tensor/d_tensor/layout_converter.py b/colossalai/tensor/d_tensor/layout_converter.py
index cf02aac30..abc70e19a 100644
--- a/colossalai/tensor/d_tensor/layout_converter.py
+++ b/colossalai/tensor/d_tensor/layout_converter.py
@@ -304,7 +304,7 @@ class LayoutConverter(metaclass=SingletonMeta):
         process_groups_dict = source_layout.device_mesh.process_groups_dict
 
         # legal sharding dims means the mesh_id is still available to use.
-        legal_sharding_dims = [i for i in range(len(source_layout.device_mesh.mesh_shape))]
+        legal_sharding_dims = [i for i in range(len(source_layout.device_mesh.shape))]
         for dim, shard_list in source_spec.dim_partition_dict.items():
             for element in shard_list:
                 legal_sharding_dims.remove(element)
diff --git a/tests/test_shardformer/test_layer/test_linear_1d.py b/tests/test_shardformer/test_layer/test_linear_1d.py
new file mode 100644
index 000000000..449522c64
--- /dev/null
+++ b/tests/test_shardformer/test_layer/test_linear_1d.py
@@ -0,0 +1,67 @@
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+from torch.testing import assert_close
+
+import colossalai
+from colossalai.shardformer.layer.layers import Linear1D_Col, Linear1D_Row
+from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
+
+
+def check_linear_1d_col():
+    linear = nn.Linear(32, 128).cuda()
+    linear_col = Linear1D_Col.from_native_module(linear, process_group=None, gather_output=True)
+
+    assert linear_col.weight.shape == torch.Size([64, 32])
+    assert linear_col.bias.shape == torch.Size([64])
+
+    # check computation correctness
+    x = torch.rand(4, 32).cuda()
+    out = linear(x)
+    gather_out = linear_col(x)
+    assert_close(out, gather_out)
+
+    # check backward correctness
+    out.sum().backward()
+    gather_out.sum().backward()
+
+    rank = dist.get_rank()
+    target_grad = torch.chunk(linear.weight.grad, 2, dim=0)[rank]
+    assert_close(target_grad, linear_col.weight.grad)
+
+
+def check_linear_1d_row():
+    linear = nn.Linear(32, 128).cuda()
+    linear_row = Linear1D_Row.from_native_module(linear, process_group=None, parallel_input=False)
+
+    assert linear_row.weight.shape == torch.Size([128, 16])
+    assert linear_row.bias.shape == torch.Size([128])
+
+    # check computation correctness
+    x = torch.rand(4, 32).cuda()
+    out = linear(x)
+    gather_out = linear_row(x)
+    assert_close(out, gather_out)
+
+    # check backward correctness
+    out.sum().backward()
+    gather_out.sum().backward()
+
+    rank = dist.get_rank()
+    target_grad = torch.chunk(linear.weight.grad, 2, dim=1)[rank]
+    assert_close(target_grad, linear_row.weight.grad)
+
+
+def run_dist(rank, world_size, port):
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    check_linear_1d_col()
+    check_linear_1d_row()
+
+
+@rerun_if_address_is_in_use()
+def test_linear():
+    spawn(run_dist, nprocs=2)
+
+
+if __name__ == '__main__':
+    test_linear()