[moe] initialize MoE groups by ProcessGroup (#1640)

colossalai/context/moe_context.py

@@ -3,6 +3,7 @@ import torch.distributed as dist
 
 from colossalai.context.parallel_mode import ParallelMode
 from colossalai.context.singleton_meta import SingletonMeta
+from colossalai.tensor import ProcessGroup
 
 from typing import Tuple
 
@@ -22,41 +23,9 @@ class MoeParallelInfo:
         _check_sanity()
         self.ep_size = ep_size
         self.dp_size = dp_size
-        self.ep_group = None
+        self.pg = ProcessGroup(tp_degree=ep_size, dp_degree=dp_size)
-        # data parallel group for experts, since ep_group is different
+        self.ep_group = self.pg.tp_process_group()
-        # we may have different dp_group from get_group(ParallelMode.DATA)
+        self.dp_group = self.pg.dp_process_group()
-        self.dp_group = None
-
-        # Here we assume tensor parallel size = 1
-        # Otherwise, MoE can't be used
-        # Since TENSOR parallel group and DATA parallel group
-        # have been created, we can use them directly.
-        if ep_size == 1:
-            from colossalai.core import global_context as gpc
-            self.ep_group = gpc.get_group(ParallelMode.TENSOR)
-            self.dp_group = gpc.get_group(ParallelMode.DATA)
-            return
-
-        if dp_size == 1:
-            from colossalai.core import global_context as gpc
-            self.ep_group = gpc.get_group(ParallelMode.DATA)
-            self.dp_group = gpc.get_group(ParallelMode.TENSOR)
-            return
-
-        rank = dist.get_rank()
-        # Create expert parallel group
-        for i in range(dp_size):
-            ranks = [i * ep_size + j for j in range(ep_size)]
-            group = dist.new_group(ranks)
-            if rank in ranks:
-                self.ep_group = group
-
-        # Create data parallel group
-        for j in range(ep_size):
-            ranks = [i * ep_size + j for i in range(dp_size)]
-            group = dist.new_group(ranks)
-            if rank in ranks:
-                self.dp_group = group
 
 
 class MoeContext(metaclass=SingletonMeta):

tests/test_moe/test_moe_colo_init.py

@@ -0,0 +1,63 @@
+from functools import partial
+
+import colossalai
+import pytest
+import torch
+import torch.multiprocessing as mp
+import torch.distributed as dist
+from colossalai.testing import parameterize
+from colossalai.utils import free_port
+from colossalai.context import MOE_CONTEXT
+from colossalai.tensor import ColoParameter
+from colossalai.utils.model.colo_init_context import ColoInitContext
+
+from colossalai.testing import rerun_if_address_is_in_use
+from colossalai.utils import get_current_device
+
+from tests.test_zero.common import CONFIG
+from tests.test_moe.test_moe_zero_init import MoeModel
+from tests.test_tensor.common_utils import debug_print
+
+
+@parameterize("init_device_type", ['cpu', 'cuda'])
+def exam_moe_colo_init(init_device_type):
+    world_size = dist.get_world_size()
+
+    if init_device_type == 'cuda':
+        init_device = get_current_device()
+    elif init_device_type == 'cpu':
+        init_device = torch.device("cpu")
+    else:
+        raise NotImplementedError("Unknown device found.")
+
+    with ColoInitContext(device=init_device):
+        model = MoeModel(checkpoint=True)
+
+    for name, param in model.named_parameters():
+        assert isinstance(param, ColoParameter), "parameter `{}` has an init problem".format(name)
+
+        if hasattr(param, "moe_info"):
+            param.set_process_group(param.moe_info.pg)
+
+        if hasattr(param, "moe_info"):
+            assert param.process_group.dp_world_size() == param.moe_info.dp_size
+        else:
+            assert param.process_group.dp_world_size() == world_size
+
+
+def _run_dist(rank, world_size, port):
+    colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    MOE_CONTEXT.setup(seed=42)
+    exam_moe_colo_init()
+
+
+@pytest.mark.dist
+@pytest.mark.parametrize("world_size", [4])
+@rerun_if_address_is_in_use()
+def test_moe_colo_init(world_size):
+    run_func = partial(_run_dist, world_size=world_size, port=free_port())
+    mp.spawn(run_func, nprocs=world_size)
+
+
+if __name__ == '__main__':
+    test_moe_colo_init(world_size=4)