[fx]add uniform policy
diff --git a/colossalai/fx/passes/adding_split_node_pass.py b/colossalai/fx/passes/adding_split_node_pass.py
index 4d34c2b56410..91005fe6bd51 100644
--- a/colossalai/fx/passes/adding_split_node_pass.py
+++ b/colossalai/fx/passes/adding_split_node_pass.py
@@ -32,6 +32,35 @@ def balanced_split_pass(gm: torch.fx.GraphModule, pp_size: int):
     return gm
 
 
+def uniform_split_pass(gm: torch.fx.GraphModule, pp_size: int):
+    mod_graph = gm.graph
+    valid_children_size = 0
+    valid_children = []
+    for module in mod_graph.owning_module.children():
+        valid_children_size += 1
+        valid_children.append(module)
+
+    if valid_children_size < pp_size:
+        # If valid children is not enough to shard, we will use balanced policy instead of uniform policy.
+        return balanced_split_pass(gm, pp_size)
+    layers_per_partition = valid_children_size // pp_size
+    accumulate_layer_amount = 0
+    for node in mod_graph.nodes:
+        if pp_size <= 1:
+            break
+        if node.op == "call_module":
+            target_module = node.graph.owning_module.get_submodule(node.target)
+            if target_module in valid_children:
+                accumulate_layer_amount += 1
+        if accumulate_layer_amount == layers_per_partition:
+            accumulate_layer_amount = 0
+            pp_size -= 1
+            with mod_graph.inserting_after(node):
+                split_node = mod_graph.create_node('call_function', pipe_split)
+    gm.recompile()
+    return gm
+
+
 def split_with_split_nodes_pass(annotated_gm: torch.fx.GraphModule):
     part_idx = 0
 
diff --git a/tests/test_fx/test_pipeline_passes.py b/tests/test_fx/test_pipeline_passes.py
new file mode 100644
index 000000000000..228fcb880344
--- /dev/null
+++ b/tests/test_fx/test_pipeline_passes.py
@@ -0,0 +1,48 @@
+import torch
+import torch.nn as nn
+import colossalai
+import colossalai.nn as col_nn
+from torch.fx import symbolic_trace
+from colossalai.fx.passes.adding_split_node_pass import split_with_split_nodes_pass, balanced_split_pass, \
+                                                        uniform_split_pass
+
+MODEL_DIM = 16
+BATCH_SIZE = 8
+PIPELINE_SIZE = 2
+
+
+class MLP(torch.nn.Module):
+
+    def __init__(self, dim: int):
+        super().__init__()
+        self.linear1 = torch.nn.Linear(dim, dim)
+        self.linear2 = torch.nn.Linear(dim, dim)
+        self.linear3 = torch.nn.Linear(dim, dim)
+        self.linear4 = torch.nn.Linear(dim, dim)
+
+    def forward(self, x):
+        x = self.linear1(x)
+        x = self.linear2(x)
+        x = self.linear3(x)
+        x = self.linear4(x)
+        return x
+
+
+def pipeline_pass_test_helper(model, data, pass_func):
+    origin_output = model(data)
+    symbolic_traced = symbolic_trace(model)
+    annotated_model = pass_func(symbolic_traced, PIPELINE_SIZE)
+    split_model, split_submodules = split_with_split_nodes_pass(annotated_model)
+    output = split_model(data)
+    assert output.equal(origin_output)
+
+
+def test_pipeline_passes():
+    model = MLP(MODEL_DIM)
+    data = torch.rand(BATCH_SIZE, MODEL_DIM)
+    pipeline_pass_test_helper(model, data, balanced_split_pass)
+    pipeline_pass_test_helper(model, data, uniform_split_pass)
+
+
+if __name__ == '__main__':
+    test_pipeline_passes()
