[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/mlp.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .linear import Linear
+from colossalai.kernel.jit import bias_gelu_impl
+
+
+class TransformerMLP(nn.Module):
+    """MLP.
+    MLP will take the input with h hidden state, project it to 4*h
+    hidden dimension, perform nonlinear transformation, and project the
+    state back into h hidden dimension. At the end, dropout is also
+    applied.
+    """
+
+    def __init__(self, hidden_size, mlp_ratio, fuse_gelu=True):
+        super(TransformerMLP, self).__init__()
+
+        # Project to 4h.
+        self.dense_h_to_4h = Linear(
+            hidden_size,
+            int(hidden_size*mlp_ratio),
+            skip_bias_add=True)
+
+        self.bias_gelu_fusion = fuse_gelu
+        self.activation_func = F.gelu
+
+        # Project back to h.
+        self.dense_4h_to_h = Linear(
+            int(hidden_size*mlp_ratio),
+            hidden_size,
+            skip_bias_add=True)
+
+    def forward(self, hidden_states):
+        # hidden states should be in the shape of [s, b, h]
+        # it will be projects into [s, b, 4h]
+        # and projected back to [s, b, h]
+        intermediate_parallel, bias_parallel = self.dense_h_to_4h(hidden_states)
+
+        if self.bias_gelu_fusion:
+            intermediate_parallel = \
+                bias_gelu_impl(intermediate_parallel, bias_parallel)
+        else:
+            intermediate_parallel = \
+                self.activation_func(intermediate_parallel + bias_parallel)
+
+        # [s, b, h]
+        output, output_bias = self.dense_4h_to_h(intermediate_parallel)
+        return output, output_bias