[hotfix] fix unit test test_module_spec (#1321)

colossalai/nn/parallel/layers/module_utils.py

@@ -88,7 +88,7 @@ def init_colo_module(module: torch.nn.Module,
     compute_pattern = compute_spec.compute_pattern
     if is_colo_module(module):
         # for each param
-        # set DistSpec and ComputeSpec
+        # set its process_group, dist_spec and compute_spec
         colo_module = get_colo_module(module)
         colo_module.register(compute_pattern, pg)
         if not colo_module.has_compute_pattern_with_mode(compute_pattern, mode=mode):
@@ -101,6 +101,7 @@ def init_colo_module(module: torch.nn.Module,
                 continue
             param = module.get_parameter(param_name)
             if isinstance(param, ColoParameter):
+                param.set_process_group(pg)
                 param.set_dist_spec(dist_spec)
                 param.compute_spec = compute_spec
                 for mod in param.shared_param_modules:

colossalai/tensor/colo_tensor.py

@@ -18,7 +18,7 @@ def _get_my_nowrap_functions() -> Set[Callable]:
         Tensor._base.__get__,
         Tensor.grad.__get__,
         Tensor._grad.__get__,
-        Tensor.data.__get__,    # make .data returns torch.Tensor rather than ColoTensor
+        Tensor.data.__get__,  # make .data returns torch.Tensor rather than ColoTensor
     }
 
 
@@ -121,11 +121,13 @@ class ColoTensor(torch.Tensor):
             RuntimeError: 
         """
         assert isinstance(pg, ProcessGroup), f"pg as type {type(pg)} is invalid"
-        if self.process_group.tp_world_size() != 1:
-            raise RuntimeError("can not set_process_group on a ColoTensor whose process_group has tp world group")
-
-        if self.dist_spec.placement.value != 'r':
-            raise RuntimeError("can not set_process_group on a ColoTensor whose dist spec is not REPLICATE")
+        # if the new pg is the same as the old pg, just returns
+        if self.process_group == pg:
+            return
+        assert self.process_group.tp_world_size() == 1, \
+            "Can not set_process_group on a ColoTensor whose process_group has tp world group"
+        assert self.dist_spec.placement.value == 'r', \
+            "Can not set_process_group on a ColoTensor whose dist spec is not REPLICATE"
 
         self.process_group = pg
 
@@ -290,17 +292,17 @@ class ColoTensor(torch.Tensor):
 
     def is_replicate(self):
         return self.dist_spec.placement == DistPlacementPattern.REPLICATE \
-            or (len(self.dist_spec.num_partitions) == 1
-                and self.dist_spec.num_partitions[0] == 1) \
-            or (self.process_group.tp_world_size() == 1)
+               or (len(self.dist_spec.num_partitions) == 1
+                   and self.dist_spec.num_partitions[0] == 1) \
+               or (self.process_group.tp_world_size() == 1)
 
     def is_shard_1dcol(self):
         return self.dist_spec.placement == DistPlacementPattern.SHARD \
-            and len(self.dist_spec.dims) == 1 and self.dist_spec.dims[0] == -1
+               and len(self.dist_spec.dims) == 1 and self.dist_spec.dims[0] == -1
 
     def is_shard_1drow(self):
         return self.dist_spec.placement == DistPlacementPattern.SHARD \
-            and len(self.dist_spec.dims) == 1 and self.dist_spec.dims[0] == 0
+               and len(self.dist_spec.dims) == 1 and self.dist_spec.dims[0] == 0
 
     def is_sharded(self):
         return self.dist_spec.placement == DistPlacementPattern.SHARD

tests/test_tensor/test_module_spec.py

-from copy import copy
+from copy import deepcopy
 import pytest
 from functools import partial
 
 import torch
 import torch.multiprocessing as mp
 
-from colossalai.tensor import ColoTensorSpec, ComputePattern, ComputeSpec, ShardSpec, ReplicaSpec
+from colossalai.tensor import ColoTensor, ComputePattern, ComputeSpec, ShardSpec, ColoTensorSpec
 from colossalai.nn.parallel.layers import init_colo_module, check_colo_module
 from _utils import tensor_equal, tensor_shard_equal, set_seed
 
@@ -112,21 +112,25 @@ def run_linear_with_spec(mode):
     with ColoInitContext(device=get_current_device()):
         model = torch.nn.Linear(4, 8)
 
-    model_handy = copy(model)
+    model_handy = deepcopy(model)
     world_size = torch.distributed.get_world_size()
     pg = ProcessGroup(tp_degree=world_size)
     compute_spec = ComputeSpec(ComputePattern.TP1D)
     init_colo_module(model, compute_spec, pg=pg, recursive=True, mode=mode)
 
     x = torch.rand(2, 4).cuda()
+    colo_x = ColoTensor.from_torch_tensor(x, ColoTensorSpec(pg))
+
     out = model(x)
-    colo_out = model_handy(x)
+    colo_out = model_handy(colo_x)
     assert tensor_equal(out, colo_out)
+
     grad = torch.rand_like(out)
     out.backward(grad)
     colo_out.backward(grad)
-    assert tensor_shard_equal(model.weight.grad, model_handy.weight.grad, pg.tp_local_rank(), pg.tp_world_size())
-    assert tensor_shard_equal(model.bias.grad, model_handy.bias.grad, pg.tp_local_rank(), pg.tp_world_size())
+
+    assert tensor_shard_equal(model_handy.weight.grad, model.weight.grad, pg.tp_local_rank(), pg.tp_world_size())
+    assert tensor_shard_equal(model_handy.bias.grad, model.bias.grad, pg.tp_local_rank(), pg.tp_world_size())
 
 
 def run_check_shared_param():
@@ -196,7 +200,7 @@ def run_dist_check(rank, world_size, port):
 
 @pytest.mark.dist
 @pytest.mark.parametrize('world_size', [1, 4])
-@pytest.mark.skip("under development lazy init ColoParameter in Context")
+@pytest.mark.skip("for higher testing speed")
 @rerun_if_address_is_in_use()
 def test_module_linear_1d(world_size):
     run_func = partial(run_dist, world_size=world_size, port=free_port())
@@ -205,7 +209,7 @@ def test_module_linear_1d(world_size):
 
 @pytest.mark.dist
 @pytest.mark.parametrize('world_size', [1, 4])
-@pytest.mark.skip("under development lazy init ColoParameter in Context")
+@pytest.mark.skip("for higher testing speed")
 @rerun_if_address_is_in_use()
 def test_module_model(world_size):
     run_func = partial(run_dist_model, world_size=world_size, port=free_port())
@@ -214,7 +218,7 @@ def test_module_model(world_size):
 
 @pytest.mark.dist
 @pytest.mark.parametrize('world_size', [1, 2])
-@pytest.mark.skip("under development lazy init ColoParameter in Context")
+@pytest.mark.skip("for higher testing speed")
 @rerun_if_address_is_in_use()
 def test_module_check(world_size):
     run_func = partial(run_dist_check, world_size=world_size, port=free_port())
@@ -222,4 +226,4 @@ def test_module_check(world_size):
 
 
 if __name__ == '__main__':
-    test_module_check(2)
+    test_module_linear_1d(4)