[utils] fixed lazy init context (#1867)

colossalai/utils/model/lazy_init_context.py

 #!/usr/bin/env python
 # coding: utf-8
 
+import inspect
+import types
+from typing import Callable, List
+
 import torch
 import torch.nn as nn
-from colossalai.tensor import ColoParameter, ColoTensor
 
-import types
-import inspect
-from typing import List, Callable
+from colossalai.tensor import ColoParameter, ColoTensor
 from colossalai.utils.model.utils import substitute_init_recursively
 
 
@@ -35,14 +36,15 @@ class LazyInitContext():
         assert not model.weight.is_meta and torch.all(model.weight == 0)
 
     Args:
-        to_meta (bool): optional, whether to initialize the model with meta tensors, default is False.
+        to_meta (bool): optional, whether to initialize the model with meta tensors, default is True. This
+            argument exists for now because some corner cases such as self.weight = torch.zeros(...) cannot be captured yet.
         extra_torch_tensor_func (List[str]): extra torch tensor functions related
             to value setting, such as `zero_` and `triu_`. `zero_` is pre-added by default.
     """
 
     tensor_set_value_func = ['zero_', 'fill_']
 
-    def __init__(self, to_meta: bool = False, extra_torch_tensor_func: List[str] = None):
+    def __init__(self, to_meta: bool = True, extra_torch_tensor_func: List[str] = None):
         # TODO: hijack the torch constructor functions as well
         self._to_meta = to_meta
         self._intercepted_nn_init_func_cache = {}
@@ -212,18 +214,19 @@ class LazyInitContext():
                 materialized_tensor = torch.empty_like(tensor, device=device)
                 # if this tensor is a meta tensor, it must have an init function
                 assert tensor in self._intercepted_nn_init_func_cache
-                tensor = materialized_tensor
+            else:
+                materialized_tensor = tensor
 
             # apply init function
             if tensor in self._intercepted_nn_init_func_cache:
                 init_func, args, kwargs = self._intercepted_nn_init_func_cache[tensor][-1]
-                init_func(tensor, *args, **kwargs)
+                init_func(materialized_tensor, *args, **kwargs)
 
             # convert it to ColoTensor or ColoParameter
             if is_param:
-                tensor = ColoParameter.from_torch_tensor(tensor, requires_grad=tensor.requires_grad)
+                tensor = ColoParameter.from_torch_tensor(materialized_tensor, requires_grad=tensor.requires_grad)
             else:
-                tensor = ColoTensor.from_torch_tensor(tensor)
+                tensor = ColoTensor.from_torch_tensor(materialized_tensor)
 
             # override the original tensor
             with torch.no_grad():

tests/test_fx/test_complete_workflow.py

-import colossalai
-import torch
-import torch.nn as nn
+from functools import partial
+
 import pytest
-import torch.multiprocessing as mp
+import torch
 import torch.distributed as dist
-from colossalai.testing import rerun_if_address_is_in_use
-from functools import partial
+import torch.multiprocessing as mp
+import torch.nn as nn
+
+import colossalai
 from colossalai.fx import ColoTracer
-from colossalai.utils.model.lazy_init_context import LazyInitContext
 from colossalai.fx.passes.shard_1d_pass import transformer_mlp_pass
-from colossalai.utils import free_port
 from colossalai.tensor import ProcessGroup
+from colossalai.testing import rerun_if_address_is_in_use
+from colossalai.utils import free_port
+from colossalai.utils.model.lazy_init_context import LazyInitContext
 
 
 class MLP(torch.nn.Module):
@@ -35,6 +37,9 @@ def run_workflow(world_size):
     with LazyInitContext() as ctx:
         model = MLP(16)
 
+    for param in model.parameters():
+        assert param.is_meta
+
     # tracing
     tracer = ColoTracer()
     graph = tracer.trace(model)
@@ -46,6 +51,8 @@ def run_workflow(world_size):
 
     # materialization and sharding
     ctx.lazy_init_parameters(annotated_gm)
+    for param in model.parameters():
+        assert not param.is_meta
 
     # # check sharding
     assert list(model.linear1.weight.shape) == [16 // world_size, 16]
@@ -57,7 +64,7 @@ def run_workflow(world_size):
     data = torch.rand(4, 16)
     non_fx_out = model(data)
     fx_out = annotated_gm(data)
-    assert torch.equal(non_fx_out, fx_out)
+    assert torch.equal(non_fx_out, fx_out), f'{non_fx_out} vs {fx_out}'
 
 
 def run_dist(rank, world_size, port):
@@ -74,4 +81,4 @@ def test_complete_workflow(world_size):
 
 
 if __name__ == '__main__':
-    test_complete_workflow(2)
+    test_complete_workflow(1)