[Tensor] get named parameters for model using ColoTensors (#874)

colossalai/tensor/__init__.py

@@ -2,8 +2,10 @@ from .spec import ComputePattern, ParallelAction, TensorSpec
 from .op_wrapper import (
     colo_op_impl,)
 from .colo_tensor import ColoTensor
-from .utils import convert_parameter
+from .utils import convert_parameter, named_params_with_colotensor
 from ._ops import *
 
-__all__ = ['ColoTensor', 'convert_parameter', 'colo_op_impl', 'ComputePattern',
-            'TensorSpec', 'ParallelAction']
+__all__ = [
+    'ColoTensor', 'convert_parameter', 'colo_op_impl', 'ComputePattern', 'TensorSpec', 'ParallelAction',
+    'named_params_with_colotensor'
+]

colossalai/tensor/utils.py

@@ -2,6 +2,57 @@ import torch
 
 from colossalai.tensor.colo_tensor import ColoTensor
 
+from typing import Iterator, Tuple, Union
+import torch.nn as nn
+from colossalai.tensor import ColoTensor
+
+
+# The function is credited to PyTorch Team
+def named_params_with_colotensor(
+    module: nn.Module,
+    prefix: str = '',
+    recurse: bool = True,
+) -> Iterator[Tuple[str, Union[nn.Parameter, ColoTensor]]]:
+    r"""Returns an iterator over module parameters (together with the
+    ColoTensor parameters), yielding both the name of the parameter
+    as well as the parameter itself. This is typically passed to a
+    :class:torchshard._shard.sharded_optim.ShardedOptimizer
+
+    Args:
+        prefix (str): prefix to prepend to all parameter names.
+        recurse (bool): if True, then yields parameters of this module
+            and all submodules. Otherwise, yields only parameters that
+            are direct members of this module.
+
+    Yields:
+        (string, Union[Tensor, ColoTensor]): Tuple containing
+            the name and parameter (or ColoTensor parameter)
+
+    Example::
+
+        >>> model = torch.nn.Linear(*linear_size)
+        >>> delattr(model.weight)
+        >>> setattr(model.weight, ColoTensor(...))
+        >>> for name, param in named_params_with_colotensor(model):
+        >>>    if name in ['weight']:
+        >>>        print(param.size())
+
+    """
+    modules = module.named_modules(prefix=prefix) if recurse else [(prefix, module)]
+
+    memo = set()
+    for mod_prefix, mod in modules:
+        # find all sharded tensor params
+        for name, val in vars(mod).items():
+            if isinstance(val, ColoTensor) and val not in memo:
+                memo.add(val)
+                name = mod_prefix + ('.' if mod_prefix else '') + name
+                yield name, val
+
+    # find all nn.Parameters
+    for name, val in module.named_parameters():
+        yield name, val
+
 
 def _convert_tensor(tensor: torch.Tensor) -> ColoTensor:
     return ColoTensor(tensor)