[hotfix] hotfix Gemini for no leaf modules bug (#2043)

colossalai/utils/model/colo_init_context.py

@@ -1,10 +1,10 @@
-from typing import Dict, Iterator, Optional, Tuple, Union
+from typing import Any, Dict, Iterator, Optional, Tuple, Union
 
 import torch
 from torch import nn
 
 from colossalai.nn.parallel.layers import ColoEmbedding, ColoLinear, register_colo_module
-from colossalai.tensor import ColoParameter, ColoTensor, ProcessGroup, ShardSpec
+from colossalai.tensor import ColoParameter, ColoTensor, ProcessGroup
 
 from .utils import InsertPostInitMethodToModuleSubClasses
 
@@ -26,6 +26,34 @@ def _named_params_with_replica(
             yield name, val
 
 
+def _convert_to_coloparam(param: torch.nn.Parameter,
+                          device: torch.device,
+                          dtype=torch.float,
+                          default_pg: Optional[ProcessGroup] = None,
+                          default_dist_spec: Optional[Any] = None) -> ColoParameter:
+
+    if isinstance(param, ColoParameter):
+        return param
+    # detaching tensor is necessary for optimizers.
+    requires_grad = param.requires_grad
+    # param is the global tensor.
+    colo_param = ColoParameter(param.to(device=device, dtype=dtype), requires_grad=requires_grad)
+
+    # if default_shard_plan exists, shard the param during initialization.
+    # This can reduce the model size after initialization.
+    # NOTE() embedding usually can not be correctly sharded. So I use except to handle
+    # the param that can not be sharded by the default plan
+    if default_pg is not None:
+        colo_param.set_process_group(default_pg)
+
+    if default_dist_spec is not None:
+        try:
+            colo_param.set_dist_spec(default_dist_spec)
+        except:
+            pass
+    return colo_param
+
+
 def ColoModulize(module):
     """
     Replacing the parameters() and named_parameters() with our customized ones
@@ -94,26 +122,8 @@ class ColoInitContext(InsertPostInitMethodToModuleSubClasses):
             if param in replaced_tensors:
                 colo_param = replaced_tensors[param]
             else:
-                # detaching tensor is necessary for optimizers.
-                requires_grad = param.requires_grad
-
-                # param is the global tensor.
-                colo_param = ColoParameter(param.to(device=self._device, dtype=self._dtype),
-                                           requires_grad=requires_grad)
-
-                # if default_shard_plan exists, shard the param during initialization.
-                # This can reduce the model size after initialization.
-                # NOTE() embedding usually can not be correctly sharded. So I use except to handle
-                # the param that can not be sharded by the default plan
-                if self._default_pg is not None:
-                    colo_param.set_process_group(self._default_pg)
-
-                if self._default_dist_spec is not None:
-                    try:
-                        colo_param.set_dist_spec(self._default_dist_spec)
-                    except:
-                        pass
-
+                colo_param = _convert_to_coloparam(param, self._device, self._dtype, self._default_pg,
+                                                   self._default_dist_spec)
                 replaced_tensors[param] = colo_param
             delattr(submodule, param_name)
             setattr(submodule, param_name, colo_param)
@@ -121,3 +131,39 @@ class ColoInitContext(InsertPostInitMethodToModuleSubClasses):
 
         module.to(self._device)
         ColoModulize(module)
+
+
+def post_process_colo_init_ctx(model: torch.nn.Module,
+                               device: torch.device = torch.device('cpu'),
+                               dtype: torch.dtype = torch.float,
+                               default_pg: Optional[ProcessGroup] = None,
+                               default_dist_spec=None):
+    """post_process_colo_init_ctx
+
+    This function is called after `ColoInitContext`.
+
+    Args:
+        model (torch.nn.module): the model
+        device (torch.device, optional): device type of the model params. Defaults to torch.device('cpu').
+        dtype (torch.dtype, optional): dtype of the model params. Defaults to torch.float.
+        default_pg (Optional[ProcessGroup], optional): default process group. Defaults to None. Inidicates a DP-only process group.
+        default_dist_spec (Any, optional): default dist spec of params. Defaults to None.
+
+    Raises:
+        RuntimeError: raise error if
+    """
+
+    torch_params = []
+    for n, p in model.named_parameters():
+        if not isinstance(p, ColoParameter):
+            print(f"{n} is not a ColoParameter. We are going to converting it to ColoParameter")
+            torch_params.append((n, p))
+
+    for (n, param) in torch_params:
+        delattr(model, n)
+        setattr(model, n, _convert_to_coloparam(param, device, dtype, default_pg, default_dist_spec))
+
+    del torch_params
+    for n, p in model.named_parameters():
+        if not isinstance(p, ColoTensor):
+            raise RuntimeError

tests/test_gemini/update/test_optim.py

@@ -15,10 +15,11 @@ from colossalai.gemini.gemini_mgr import GeminiManager
 from colossalai.nn.optimizer import HybridAdam
 from colossalai.nn.optimizer.zero_optimizer import ZeroOptimizer
 from colossalai.nn.parallel import ZeroDDP
+from colossalai.tensor import ColoParameter, ColoTensor
 from colossalai.testing import parameterize, rerun_if_address_is_in_use
 from colossalai.utils import free_port
 from colossalai.utils.cuda import get_current_device
-from colossalai.utils.model.colo_init_context import ColoInitContext
+from colossalai.utils.model.colo_init_context import ColoInitContext, post_process_colo_init_ctx
 from tests.components_to_test import run_fwd_bwd
 from tests.components_to_test.registry import non_distributed_component_funcs
 from tests.test_tensor.common_utils import debug_print, set_seed
@@ -40,8 +41,7 @@ def check_param(model: ZeroDDP, torch_model: torch.nn.Module):
 
 
 # 'gpt2', 'bert',
-TEST_MODELS = ['gpt2', 'bert']
-EXAMPLE_MODELS = ['simple_net']
+TEST_MODELS = ['no_leaf_module', 'gpt2', 'bert', 'simple_net', 'nested_model', 'repeated_computed_layers']
 
 
 @parameterize('placement_policy', ['cuda', 'cpu', 'auto', 'const'])
@@ -57,8 +57,12 @@ def exam_model_step(placement_policy, model_name: str):
     torch_model, torch_optim = convert_to_apex_amp(torch_model, torch_optim, amp_config)
     torch_model = DDP(torch_model, device_ids=[dist.get_rank()])
 
-    with ColoInitContext(device=get_current_device()):
+    init_dev = get_current_device()
+    with ColoInitContext(device=init_dev):
         model = model_builder()
+
+    post_process_colo_init_ctx(model, device=init_dev)
+
     for torch_p, p in zip(torch_model.parameters(), model.parameters()):
         p.data.copy_(torch_p.data)
 
@@ -99,7 +103,7 @@ def exam_model_step(placement_policy, model_name: str):
 
 
 @parameterize('placement_policy', ['cuda', 'cpu'])
-@parameterize('model_name', EXAMPLE_MODELS)
+@parameterize('model_name', TEST_MODELS)
 def exam_tiny_example(placement_policy, model_name: str):
     set_seed(2008)
     get_components_func = non_distributed_component_funcs.get_callable(model_name)
@@ -111,8 +115,12 @@ def exam_tiny_example(placement_policy, model_name: str):
     torch_model, torch_optim = convert_to_apex_amp(torch_model, torch_optim, amp_config)
     torch_model = DDP(torch_model, device_ids=[dist.get_rank()])
 
-    with ColoInitContext(device=get_current_device()):
+    init_dev = get_current_device()
+    with ColoInitContext(device=init_dev):
         model = model_builder()
+
+    post_process_colo_init_ctx(model, device=init_dev)
+
     for torch_p, p in zip(torch_model.parameters(), model.parameters()):
         p.data.copy_(torch_p.data)