Fix parameter count in ModelSummary when parameters are DTensors (#20163)

src/lightning/fabric/utilities/distributed.py

@@ -14,10 +14,11 @@ import torch.nn.functional as F
 from lightning_utilities.core.imports import package_available
 from torch import Tensor
 from torch.utils.data import Dataset, DistributedSampler, Sampler
-from typing_extensions import Self, override
+from typing_extensions import Self, TypeGuard, override
 
 from lightning.fabric.utilities.cloud_io import _is_local_file_protocol
 from lightning.fabric.utilities.data import _num_cpus_available
+from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_4
 from lightning.fabric.utilities.rank_zero import rank_zero_info
 from lightning.fabric.utilities.types import _PATH, ReduceOp
 
@@ -30,6 +31,8 @@ else:
 
 
 if TYPE_CHECKING:
+    from torch.distributed._tensor import DTensor
+
     from lightning.fabric.plugins import ClusterEnvironment
     from lightning.fabric.strategies import Strategy
 
@@ -427,3 +430,11 @@ class _InfiniteBarrier:
         self.barrier()
         if self.group is not None:
             torch.distributed.destroy_process_group(self.group)
+
+
+def _is_dtensor(tensor: Tensor) -> TypeGuard["DTensor"]:
+    if _TORCH_GREATER_EQUAL_2_4:
+        from torch.distributed._tensor import DTensor
+
+        return isinstance(tensor, DTensor)
+    return False

src/lightning/pytorch/CHANGELOG.md

@@ -49,6 +49,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed `_LoggerConnector`'s `_ResultMetric` to move all registered keys to the device of the logged value if needed ([#19814](https://github.com/Lightning-AI/pytorch-lightning/issues/19814))
 
+- Fixed parameter counts in `ModelSummary` when model has distributed parameters (DTensor) ([#20163](https://github.com/Lightning-AI/pytorch-lightning/pull/20163))
 
 
 ## [2.3.0] - 2024-06-13

src/lightning/pytorch/utilities/model_summary/model_summary.py

@@ -25,6 +25,7 @@ from torch import Tensor
 from torch.utils.hooks import RemovableHandle
 
 import lightning.pytorch as pl
+from lightning.fabric.utilities.distributed import _is_dtensor
 from lightning.pytorch.utilities.model_helpers import _ModuleMode
 from lightning.pytorch.utilities.rank_zero import WarningCache
 
@@ -135,7 +136,7 @@ class LayerSummary:
     @property
     def num_parameters(self) -> int:
         """Returns the number of parameters in this module."""
-        return sum(math.prod(p.shape) if not _is_lazy_weight_tensor(p) else 0 for p in self._module.parameters())
+        return sum(p.numel() if not _tensor_has_shape(p) else 0 for p in self._module.parameters())
 
     @property
     def training(self) -> bool:
@@ -264,13 +265,11 @@ class ModelSummary:
 
     @property
     def total_parameters(self) -> int:
-        return sum(p.numel() if not _is_lazy_weight_tensor(p) else 0 for p in self._model.parameters())
+        return sum(p.numel() if not _tensor_has_shape(p) else 0 for p in self._model.parameters())
 
     @property
     def trainable_parameters(self) -> int:
-        return sum(
-            p.numel() if not _is_lazy_weight_tensor(p) else 0 for p in self._model.parameters() if p.requires_grad
-        )
+        return sum(p.numel() if not _tensor_has_shape(p) else 0 for p in self._model.parameters() if p.requires_grad)
 
     @property
     def total_layer_params(self) -> int:
@@ -470,10 +469,11 @@ def get_human_readable_count(number: int) -> str:
     return f"{number:,.1f} {labels[index]}"
 
 
-def _is_lazy_weight_tensor(p: Tensor) -> bool:
+def _tensor_has_shape(p: Tensor) -> bool:
     from torch.nn.parameter import UninitializedParameter
 
-    if isinstance(p, UninitializedParameter):
+    # DTensor is a subtype of `UninitializedParameter`, but the shape is known
+    if isinstance(p, UninitializedParameter) and not _is_dtensor(p):
         warning_cache.warn(
             "The total number of parameters detected may be inaccurate because the model contains"
             " an instance of `UninitializedParameter`. To get an accurate number, set `self.example_input_array`"

src/lightning/pytorch/utilities/model_summary/model_summary_deepspeed.py

@@ -25,7 +25,7 @@ from lightning.pytorch.utilities.model_summary.model_summary import (
     NOT_APPLICABLE,
     LayerSummary,
     ModelSummary,
-    _is_lazy_weight_tensor,
+    _tensor_has_shape,
     get_human_readable_count,
 )
 
@@ -40,7 +40,7 @@ class DeepSpeedLayerSummary(LayerSummary):
     @override
     def num_parameters(self) -> int:
         """Returns the number of parameters in this module."""
-        return sum(deepspeed_param_size(p) if not _is_lazy_weight_tensor(p) else 0 for p in self._module.parameters())
+        return sum(deepspeed_param_size(p) if not _tensor_has_shape(p) else 0 for p in self._module.parameters())
 
     @property
     def average_shard_parameters(self) -> int:
@@ -49,7 +49,7 @@ class DeepSpeedLayerSummary(LayerSummary):
         def partitioned_size(p: Parameter) -> int:
             return p.partitioned_size() if RequirementCache("deepspeed<0.6.6") else p.partition_numel()
 
-        return sum(partitioned_size(p) if not _is_lazy_weight_tensor(p) else 0 for p in self._module.parameters())
+        return sum(partitioned_size(p) if not _tensor_has_shape(p) else 0 for p in self._module.parameters())
 
 
 class DeepSpeedSummary(ModelSummary):
@@ -71,13 +71,13 @@ class DeepSpeedSummary(ModelSummary):
     @property
     @override
     def total_parameters(self) -> int:
-        return sum(deepspeed_param_size(p) if not _is_lazy_weight_tensor(p) else 0 for p in self._model.parameters())
+        return sum(deepspeed_param_size(p) if not _tensor_has_shape(p) else 0 for p in self._model.parameters())
 
     @property
     @override
     def trainable_parameters(self) -> int:
         return sum(
-            deepspeed_param_size(p) if not _is_lazy_weight_tensor(p) else 0
+            deepspeed_param_size(p) if not _tensor_has_shape(p) else 0
             for p in self._model.parameters()
             if p.requires_grad
         )

tests/tests_fabric/utilities/test_distributed.py

@@ -3,7 +3,9 @@ import os
 from functools import partial
 from pathlib import Path
 from unittest import mock
+from unittest.mock import Mock
 
+import lightning.fabric
 import pytest
 import torch
 from lightning.fabric.accelerators import CPUAccelerator, CUDAAccelerator, MPSAccelerator
@@ -15,6 +17,7 @@ from lightning.fabric.utilities.distributed import (
     _gather_all_tensors,
     _InfiniteBarrier,
     _init_dist_connection,
+    _is_dtensor,
     _set_num_threads_if_needed,
     _suggested_max_num_threads,
     _sync_ddp,
@@ -234,3 +237,14 @@ def test_init_dist_connection_registers_destruction_handler(_, atexit_mock):
     atexit_mock.reset_mock()
     _init_dist_connection(LightningEnvironment(), "gloo")
     atexit_mock.register.assert_not_called()
+
+
+@RunIf(min_torch="2.4")
+def test_is_dtensor(monkeypatch):
+    from torch.distributed._tensor import DTensor
+
+    assert _is_dtensor(Mock(spec=DTensor))
+    assert not _is_dtensor(torch.zeros(2, 2))
+
+    monkeypatch.setattr(lightning.fabric.utilities.distributed, "_TORCH_GREATER_EQUAL_2_4", False)
+    assert not _is_dtensor(Mock(spec=DTensor))

tests/tests_pytorch/utilities/test_model_summary.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 from collections import OrderedDict
 from typing import Any
+from unittest import mock
 
 import pytest
 import torch
@@ -345,6 +346,18 @@ def test_lazy_model_summary():
         assert summary.trainable_parameters == 0
 
 
+@mock.patch("lightning.pytorch.utilities.model_summary.model_summary._is_dtensor", return_value=True)
+def test_dtensor_model_summary(_):
+    """Test that the model summary can work with layers that have DTensor parameters."""
+    # We mock the `_is_dtensor` to pretend parameters are DTensors, because testing with real DTensors
+    # would require setting up distributed
+    dtensor_model = UnorderedModel()
+    summary = ModelSummary(dtensor_model)
+    assert summary.total_layer_params > 0
+    assert summary.total_parameters > 0
+    assert summary.trainable_parameters > 0
+
+
 @pytest.mark.parametrize("max_depth", [-1, 0, 1, 3, 999])
 def test_max_depth_param(max_depth):
     """Test that only the modules up to the desired depth are shown."""