[tutorial] edited hands-on practices (#1899)

* Add handson to ColossalAI.

* Change names of handsons and edit sequence parallel example.

* Edit wrong folder name

* resolve conflict

* delete readme

examples/tutorial/sequence_parallel/model/layers/linear.py

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+import torch
+import torch.nn as nn
+from torch.nn import Parameter
+import torch.nn.functional as F
+import torch.nn.init as init
+
+
+class Linear(nn.Module):
+    """Linear layer with column parallelism.
+    The linear layer is defined as Y = XA + b. A is parallelized along
+    its second dimension as A = [A_1, ..., A_p].
+    Arguments:
+        input_size: first dimension of matrix A.
+        output_size: second dimension of matrix A.
+        bias: If true, add bias
+        init_method: method to initialize weights. Note that bias is always set
+                     to zero.
+        stride: For the strided linear layers.
+        keep_master_weight_for_test: This was added for testing and should be
+                                     set to False. It returns the master weights
+                                     used for initialization.
+        skip_bias_add: This was added to enable performance optimations where bias
+                       can be fused with other elementwise operations. we skip
+                       adding bias but instead return it.
+    """
+
+    def __init__(self,
+                 input_size,
+                 output_size,
+                 bias=True,
+                 skip_bias_add=False):
+        super(Linear, self).__init__()
+
+        # Keep input parameters
+        self.input_size = input_size
+        self.output_size = output_size
+        self.skip_bias_add = skip_bias_add
+
+        self.weight = Parameter(torch.empty(self.output_size,
+                                            self.input_size,
+                                            ))
+        init.normal_(self.weight)
+        if bias:
+            self.bias = Parameter(torch.empty(self.output_size))
+            # Always initialize bias to zero.
+            with torch.no_grad():
+                self.bias.zero_()
+        else:
+            self.register_parameter('bias', None)
+
+    def forward(self, input_):
+        # Matrix multiply.
+        bias = self.bias if not self.skip_bias_add else None
+        output = F.linear(input_, self.weight, bias)
+
+        if self.skip_bias_add:
+            return output, self.bias
+        else:
+            return output
+
+    def __repr__(self):
+        return f'Linear(in_features={self.input_size}, out_features={self.output_size}, ' + \
+            f'bias={self.bias is not None}, skip_bias_add={self.skip_bias_add})'