[DTensor] refactor CommSpec (#3034)

tests/test_tensor/test_dtensor/test_comm_spec.py

@@ -0,0 +1,190 @@
+from functools import partial
+
+import pytest
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+from torch.distributed import ReduceOp
+
+from colossalai.core import global_context as gpc
+from colossalai.device.device_mesh import DeviceMesh
+from colossalai.initialize import launch
+from colossalai.logging import disable_existing_loggers
+from colossalai.tensor.d_tensor.comm_spec import CollectiveCommPattern, CommSpec
+from colossalai.tensor.d_tensor.sharding_spec import ShardingSpec
+from colossalai.testing import rerun_if_address_is_in_use
+from colossalai.utils import free_port
+
+
+def check_all_gather(process_groups_dict, rank):
+    # tensor to comm
+    if rank in (0, 2):
+        sharded_tensor_to_comm = torch.ones(2, 2).cuda()
+    else:
+        sharded_tensor_to_comm = torch.zeros(2, 2).cuda()
+
+    # tensor to check
+    tensor_to_check = torch.cat((torch.ones(2, 2), torch.zeros(2, 2)), 1).cuda()
+
+    # CommSpec:(comm_pattern:allgather, gather_dim:1, logical_process_axis:1)
+    comm_spec = CommSpec(CollectiveCommPattern.GATHER_FWD_SPLIT_BWD,
+                         process_groups_dict,
+                         gather_dim=1,
+                         logical_process_axis=1)
+    sharded_tensor_to_comm = sharded_tensor_to_comm = comm_spec.covert_spec_to_action(sharded_tensor_to_comm)
+
+    assert sharded_tensor_to_comm.equal(tensor_to_check)
+
+
+def check_shard(process_groups_dict, rank):
+    # tensor to comm
+    sharded_tensor_to_comm_0 = torch.zeros(2, 2).cuda()
+    sharded_tensor_to_comm_1 = torch.ones(2, 2).cuda()
+    # tensor([[0., 0., 1., 1.],
+    #         [0., 0., 1., 1.]])
+    tensor_to_shard = torch.cat((sharded_tensor_to_comm_0, sharded_tensor_to_comm_1), 1)
+
+    # CommSpec:(comm_pattern:shard, shard_dim:1, logical_process_axis:1)
+    comm_spec = CommSpec(CollectiveCommPattern.SPLIT_FWD_GATHER_BWD,
+                         process_groups_dict,
+                         shard_dim=1,
+                         logical_process_axis=1)
+    tensor_to_shard = comm_spec.covert_spec_to_action(tensor_to_shard)
+
+    if rank in (0, 2):
+        assert tensor_to_shard.equal(sharded_tensor_to_comm_0)
+    if rank in (1, 3):
+        assert tensor_to_shard.equal(sharded_tensor_to_comm_1)
+
+
+def check_all_to_all(process_groups_dict, rank):
+    # tensor to comm
+    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()
+
+    # CommSpec:(comm_pattern:shard, shard_dim:1, logical_process_axis:1)
+    comm_spec = CommSpec(CollectiveCommPattern.ALL2ALL_FWD_ALL2ALL_BWD,
+                         process_groups_dict,
+                         gather_dim=0,
+                         shard_dim=1,
+                         logical_process_axis=0)
+    tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
+
+    assert tensor_to_comm.equal(tensor_to_check)
+
+
+def check_all_reduce_fwd(process_groups_dict, rank):
+    # tensor to comm
+    tensor_to_comm = torch.ones(2, 2).cuda() * rank
+
+    # reduce through logical process axis 0
+    # tensor to check
+    if rank in (0, 2):
+        # tensor([[2., 2.],
+        #         [2., 2.]])
+        tensor_to_check = torch.tensor([[2, 2], [2, 2]], dtype=tensor_to_comm.dtype).cuda()
+    if rank in (1, 3):
+        # tensor([[4., 4.],
+        #         [4., 4.]])
+        tensor_to_check = torch.tensor([[4, 4], [4, 4]], dtype=tensor_to_comm.dtype).cuda()
+
+    comm_spec = CommSpec(CollectiveCommPattern.ALLREDUCE_FWD_IDENTITY_BWD, process_groups_dict, logical_process_axis=0)
+    tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
+
+    assert tensor_to_comm.equal(tensor_to_check)
+
+
+def check_all_reduce_bwd(process_groups_dict, rank):
+    # tensor to comm
+    tensor_to_comm = torch.ones(2, 2).cuda() * rank
+
+    tensor_to_check = torch.ones(2, 2).cuda() * rank
+
+    comm_spec = CommSpec(CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD, process_groups_dict, logical_process_axis=0)
+    tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
+
+    assert tensor_to_comm.equal(tensor_to_check)
+
+
+def check_all_reduce_in_flatten_device_mesh(process_groups_dict, rank):
+    # tensor to comm
+    tensor_to_comm = torch.ones(2, 2).cuda() * rank
+
+    # reduce through logical process axis 0 at flatten device mesh
+    # tensor to check
+    # tensor([[6., 6.],
+    #         [6., 6.]])
+    tensor_to_check = torch.tensor([[6, 6], [6, 6]], dtype=tensor_to_comm.dtype).cuda()
+
+    # CommSpec:(comm_pattern:all_reduce, logical_process_axis:[0, 1])
+    comm_spec = CommSpec(CollectiveCommPattern.ALLREDUCE_FWD_IDENTITY_BWD, process_groups_dict, logical_process_axis=0)
+    tensor_to_comm = comm_spec.covert_spec_to_action(tensor_to_comm)
+
+    assert tensor_to_comm.equal(tensor_to_check)
+
+
+def check_comm(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)
+    assert rank == gpc.get_global_rank()
+
+    mesh_shape = (2, 2)
+    # [[0, 1,
+    #  [2, 3]]
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
+    process_groups_dict = device_mesh.process_groups_dict
+
+    # test all gather
+    check_all_gather(process_groups_dict, rank)
+
+    # test shard
+    check_shard(process_groups_dict, rank)
+
+    # test all to all
+    check_all_to_all(process_groups_dict, rank)
+
+    # test all reduce
+    check_all_reduce_fwd(process_groups_dict, rank)
+    check_all_reduce_bwd(process_groups_dict, rank)
+
+    flatten_process_groups_dict = device_mesh.flatten_device_mesh.process_groups_dict
+    # test all reduce in 1D flatten device mesh
+    check_all_reduce_in_flatten_device_mesh(flatten_process_groups_dict, rank)
+    gpc.destroy()
+
+
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+def test_comm_spec():
+    world_size = 4
+    run_func = partial(check_comm, world_size=world_size, port=free_port())
+    mp.spawn(run_func, nprocs=world_size)
+
+
+if __name__ == '__main__':
+    test_comm_spec()