Fix attribute error on _NotYetLoadedTensor after loading checkpoint…

… into quantized model with `_lazy_load()` (Lightning-AI#20121)

src/lightning/fabric/CHANGELOG.md

@@ -35,7 +35,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
--
+- Fixed an attribute error when loading a checkpoint into a quantized model using the `_lazy_load()` function ([#20121](https://github.com/Lightning-AI/lightning/pull/20121))
+
 
 -
 

src/lightning/fabric/utilities/load.py

@@ -160,7 +160,7 @@ def __getattr__(self, name: str) -> Any:
  return getattr(self.metatensor, name)
 
  # materializing these is needed for quantization (see lit-gpt)
- if name in {"contiguous", "cuda", "half"}:
+ if name in {"contiguous", "cuda", "half", "data"}:
  return getattr(self._load_tensor(), name)
 
  raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'")

tests/tests_fabric/plugins/precision/test_bitsandbytes.py

@@ -22,6 +22,7 @@
 from lightning.fabric.connector import _Connector
 from lightning.fabric.plugins.precision.bitsandbytes import _BITSANDBYTES_AVAILABLE, BitsandbytesPrecision
 from lightning.fabric.utilities.init import _materialize_meta_tensors
+from lightning.fabric.utilities.load import _lazy_load
 
 from tests_fabric.helpers.runif import RunIf
 
@@ -264,3 +265,10 @@ def forward(self, x):
  assert model.linear.weight.shape == (128, 1)
  # Shapes match during forward (weight is being dequantized during forward)
  model(torch.randn(2, 16, device=fabric.device))
+
+ # Test with lazy load (LitGPT uses this)
+ # TODO: Replace `_lazy_load` with `torch.load(..., mmap=True)` in LitGPT
+ state_dict = _lazy_load(tmp_path / "checkpoint.pt")
+ model.load_state_dict(state_dict)
+ assert model.linear.weight.dtype == torch.uint8
+ assert model.linear.weight.shape == (128, 1)