[tensor] support runtime ShardingSpec apply (#1453)

* [tensor] support runtime ShardingSpec apply

* polish code

* polish code

tests/test_tensor/test_shape_consistency_apply.py

@@ -0,0 +1,81 @@
+from functools import partial
+import pytest
+import torch
+import torch.multiprocessing as mp
+
+from colossalai.tensor.sharding_spec import ShardingSpec
+from colossalai.device.device_mesh import DeviceMesh
+from colossalai.initialize import launch
+from colossalai.utils import free_port
+from colossalai.testing import rerun_if_address_is_in_use
+from colossalai.logging import disable_existing_loggers
+from colossalai.tensor.shape_consistency import ShapeConsistencyManager, CollectiveCommPattern
+
+
+def check_apply(rank, world_size, port):
+    disable_existing_loggers()
+    launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+
+    physical_mesh_id = torch.arange(0, 4)
+    mesh_shape = (2, 2)
+    # [[0, 1,
+    #  [2, 3]]
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
+    entire_shape = torch.Size((4, 2))
+    shape_consistency_manager = ShapeConsistencyManager()
+    dim_partition_source = {0: [0]}
+    dim_partition_target = {1: [0]}
+
+    # DistSpec:
+    #     shard_sequence: S0,R
+    #     device_mesh_shape: (2, 2)
+    sharding_spec_source = ShardingSpec(device_mesh, entire_shape, dim_partition_source)
+
+    # DistSpec:
+    #     shard_sequence: R,S0
+    #     device_mesh_shape: (2, 2)
+    sharding_spec_target = ShardingSpec(device_mesh, entire_shape, dim_partition_target)
+
+    if rank in (0, 1):
+        sharded_tensor_0 = torch.zeros(2, 1)
+        sharded_tensor_1 = torch.ones(2, 1)
+        # tensor([[0., 1.],
+        #         [0., 1.]])
+        tensor_to_comm = torch.cat((sharded_tensor_0, sharded_tensor_1), 1).cuda()
+    if rank in (2, 3):
+        sharded_tensor_0 = torch.ones(2, 1) * 2
+        sharded_tensor_1 = torch.ones(2, 1) * 3
+        # tensor([[2., 3.],
+        #         [2., 3.]])
+        tensor_to_comm = torch.cat((sharded_tensor_0, sharded_tensor_1), 1).cuda()
+
+    if rank in (0, 1):
+        # tensor([[0.],
+        #         [0.],
+        #         [2.],
+        #         [2.]])
+        tensor_to_check = torch.tensor([[0], [0], [2], [2]], dtype=tensor_to_comm.dtype).cuda()
+    if rank in (2, 3):
+        # tensor([[1.],
+        #         [1.],
+        #         [3.],
+        #         [3.]])
+        tensor_to_check = torch.tensor([[1], [1], [3], [3]], dtype=tensor_to_comm.dtype).cuda()
+
+    tensor_to_comm.sharding_spec = sharding_spec_source
+    shape_consistency_manager.apply(tensor_to_comm, sharding_spec_target)
+    print(tensor_to_comm)
+    assert tensor_to_comm.equal(tensor_to_check)
+    assert str(tensor_to_comm.sharding_spec.sharding_sequence) == str(sharding_spec_target.sharding_sequence)
+
+
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+def test_apply():
+    world_size = 4
+    run_func = partial(check_apply, world_size=world_size, port=free_port())
+    mp.spawn(run_func, nprocs=world_size)
+
+
+if __name__ == '__main__':
+    test_apply()