[fx]add autoparallel passes (#1121)

* [CLI] add CLI launcher

* Revert "[CLI] add CLI launcher"

This reverts commit df7e6506d4.

* feature/add autoparallel passes

tests/test_fx/test_parallel_1d.py

@@ -0,0 +1,63 @@
+#!/usr/bin/env python
+# -*- encoding: utf-8 -*-
+
+from functools import partial
+
+import pytest
+import torch
+import torch.multiprocessing as mp
+from colossalai.core import global_context as gpc
+from colossalai.logging import disable_existing_loggers
+from colossalai.initialize import launch
+from colossalai.utils import free_port
+from colossalai.testing import rerun_if_address_is_in_use
+from torch.fx import symbolic_trace
+from colossalai.fx.passes import column_shard_linear_pass
+
+
+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
+
+
+CONFIG = dict(parallel=dict(tensor=dict(mode='1d', size=2)))
+
+
+def check_layer(rank, world_size, port):
+    disable_existing_loggers()
+    launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    input_tensor = torch.rand(2, 16).cuda()
+    model = MLP(16).cuda()
+    symbolic_traced = symbolic_trace(model)
+    output = model(input_tensor)
+    splitted_gm = column_shard_linear_pass(symbolic_traced)
+    new_output = splitted_gm(input_tensor)
+
+    assert output.equal(new_output)
+
+    gpc.destroy()
+    torch.cuda.empty_cache()
+
+
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+def test_1d():
+    world_size = 2
+    run_func = partial(check_layer, world_size=world_size, port=free_port())
+    mp.spawn(run_func, nprocs=world_size)
+
+
+if __name__ == '__main__':
+    test_1d()