[pipelinable]use ColoTensor to replace dummy tensor. (#853)

colossalai/tensor/colo_tensor.py

@@ -53,6 +53,22 @@ class ColoTensor(object):
     def size(self):
         return self._size
 
+    @property
+    def shape(self):
+        return torch.Size(self._size)
+
+    def size(self, dim=None):
+        if dim is None:
+            return self.shape
+        return self._size[dim]
+
+    def dim(self):
+        return len(self._size)
+
+    def normal_(self, mean=0., std=1.):
+        torch_tensor = self.torch_tensor()
+        return torch_tensor.normal_(mean=mean, std=std)
+
     def numel(self):
         return product(self._size)
 

colossalai/utils/model/pipelinable.py

@@ -3,6 +3,7 @@ import functools
 from colossalai.utils.model.utils import _substitute_init_recursively, InsertPostInitMethodToModuleSubClasses, call_to_str
 from colossalai.builder.pipeline import partition_uniform, partition_balanced
 from colossalai.core import global_context as gpc
+from colossalai.tensor import ColoTensor
 
 
 class PipelinableContext(InsertPostInitMethodToModuleSubClasses):
@@ -64,8 +65,15 @@ class PipelinableContext(InsertPostInitMethodToModuleSubClasses):
         layer_spec = LayerSpec(module.__class__, *modified_args, **kwargs)
         layer_spec.set_children(module.children())
         self._layer_spec_dict[module_id] = layer_spec
-        for param in module.parameters(recurse=False):
+        name_list = []
-            param.data = torch.rand(1, 1)
+        for name, param in module.named_parameters():
+            if isinstance(param, ColoTensor):
+                continue
+            name_list.append((name, param))
+
+        for name, param in name_list:
+            delattr(module, name)
+            setattr(module, name, ColoTensor.init_from_torch_tensor(tensor=param, save_payload=False))
 
     def to_layer_list(self, exec_seq=None):
         """