(3/n) Support 2D Parallelism - Efficient loading of full-state checkp…

…oints (#19870)

* memory-optimized loading of full checkpoints into dist model

* simplify

* handle buffers

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* handle strict loading, buffers, and add test

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* chlog

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

src/lightning/fabric/CHANGELOG.md

@@ -15,7 +15,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Added support for PyTorch 2.3 ([#19708](https://github.com/Lightning-AI/pytorch-lightning/pull/19708))
 
-- Added `ModelParallelStrategy` to support 2D parallelism ([#19846](https://github.com/Lightning-AI/pytorch-lightning/pull/19846), [#19852](https://github.com/Lightning-AI/pytorch-lightning/pull/19852))
+- Added `ModelParallelStrategy` to support 2D parallelism ([#19846](https://github.com/Lightning-AI/pytorch-lightning/pull/19846), [#19852](https://github.com/Lightning-AI/pytorch-lightning/pull/19852), [#19870](https://github.com/Lightning-AI/pytorch-lightning/pull/19870))
 
 
 ### Changed

src/lightning/fabric/strategies/model_parallel.py

@@ -11,11 +11,12 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import itertools
 import shutil
 from contextlib import ExitStack
 from datetime import timedelta
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, Callable, ContextManager, Dict, Literal, Optional, TypeVar, Union
+from typing import TYPE_CHECKING, Any, Callable, ContextManager, Dict, Generator, Literal, Optional, TypeVar, Union
 
 import torch
 from lightning_utilities.core.rank_zero import rank_zero_only as utils_rank_zero_only
@@ -429,7 +430,6 @@ def _load_checkpoint(
         StateDictOptions,
         get_model_state_dict,
         get_optimizer_state_dict,
-        set_model_state_dict,
         set_optimizer_state_dict,
     )
 
@@ -484,13 +484,8 @@ def _load_checkpoint(
         if not _TORCH_GREATER_EQUAL_2_4:
             raise ImportError("Loading a non-distributed checkpoint into a distributed model requires PyTorch >= 2.4.")
 
-        state_dict_options = StateDictOptions(
-            broadcast_from_rank0=True,  # type: ignore[call-arg]
-            full_state_dict=True,
-            strict=strict,
-        )
         checkpoint = torch.load(path, mmap=True, map_location="cpu")
-        set_model_state_dict(module, checkpoint.pop(module_key), options=state_dict_options)
+        _load_raw_module_state(checkpoint.pop(module_key), module, strict=strict)
 
         requested_metadata_keys = state.keys() - modules.keys() - optimizers.keys()
         _validate_keys_for_strict_loading(requested_metadata_keys, checkpoint.keys(), strict=strict)
@@ -525,7 +520,9 @@ def _load_raw_module_state_from_path(path: Path, module: Module, world_size: int
     _load_raw_module_state(state_dict=state_dict, module=module, world_size=world_size, strict=strict)
 
 
-def _load_raw_module_state(state_dict: Dict[str, Any], module: Module, world_size: int, strict: bool = True) -> None:
+def _load_raw_module_state(
+    state_dict: Dict[str, Any], module: Module, world_size: int = 1, strict: bool = True
+) -> None:
     """Loads the state dict into the module by gathering all weights first and then and writing back to each shard."""
     from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
 
@@ -535,11 +532,39 @@ def _load_raw_module_state(state_dict: Dict[str, Any], module: Module, world_siz
 
         from torch.distributed.checkpoint.state_dict import StateDictOptions, set_model_state_dict
 
-        state_dict_options = StateDictOptions(broadcast_from_rank0=True, full_state_dict=True)  # type: ignore[call-arg]
-        set_model_state_dict(module, state_dict, options=state_dict_options)
+        state_dict_options = StateDictOptions(
+            broadcast_from_rank0=True,  # type: ignore[call-arg]
+            full_state_dict=True,
+            strict=strict,  # gets ignored at the moment
+        )
+
+        for submodule_name, submodule in module.named_modules():
+            for param_name, _ in _named_parameters_and_buffers_to_load(submodule):
+                full_param_name = f"{submodule_name}{'.' if submodule_name else ''}{param_name}"
+                if full_param_name not in state_dict:
+                    # Note: PyTorch does not currently respect the `strict` setting in state_dict_options!
+                    if not strict:
+                        continue
+                    raise KeyError(
+                        f"The model contains a key '{full_param_name}' that does not exist in the loaded checkpoint."
+                        " To disable strict loading, set `strict=False`."
+                    )
+                local_state_dict = {param_name: state_dict[full_param_name]}
+                set_model_state_dict(submodule, local_state_dict, options=state_dict_options)
 
     elif isinstance(module, FSDP):
         with _get_full_state_dict_context(module, world_size=world_size, rank0_only=False):
             module.load_state_dict(state_dict, strict=strict)
     else:
         module.load_state_dict(state_dict, strict=strict)
+
+
+def _named_parameters_and_buffers_to_load(module: Module) -> Generator:
+    """Returns parameters and buffers, with non-persistent buffers excluded."""
+    for param_name, param in itertools.chain(
+        module.named_buffers(recurse=False),
+        module.named_parameters(recurse=False),
+    ):
+        if param_name in module._non_persistent_buffers_set:
+            continue
+        yield param_name, param

tests/tests_fabric/strategies/test_model_parallel_integration.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import os
+from copy import deepcopy
 from pathlib import Path
 from unittest import mock
 
@@ -20,7 +21,7 @@
 import torch.nn as nn
 import torch.nn.functional as F
 from lightning.fabric import Fabric
-from lightning.fabric.strategies import ModelParallelStrategy
+from lightning.fabric.strategies.model_parallel import ModelParallelStrategy, _load_raw_module_state
 from lightning.fabric.utilities.load import _load_distributed_checkpoint
 from torch.utils.data import DataLoader, DistributedSampler
 
@@ -675,3 +676,46 @@ def test_save_sharded_and_consolidate_and_load(tmp_path):
 
     state = {"model": model, "steps": 1}
     fabric.load(checkpoint_path_full, state)
+
+
+@RunIf(min_torch="2.4", min_cuda_gpus=2, standalone=True)
+def test_load_raw_module_state():
+    from torch.distributed.device_mesh import init_device_mesh
+    from torch.distributed.tensor.parallel import ColwiseParallel, parallelize_module
+
+    class CustomModel(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.parameter = nn.Parameter(torch.rand(2, 2))
+            self.layer1 = nn.Linear(4, 4)
+            self.layer2 = nn.Linear(4, 4)
+            self.register_buffer("persistent_buffer", torch.rand(2), persistent=True)
+            self.register_buffer("non_persistent_buffer", torch.rand(2), persistent=False)
+
+    fabric = Fabric(accelerator="cuda", devices=2)
+    fabric.launch()
+    fabric.seed_everything(0)
+
+    with fabric.init_module():
+        model = CustomModel()
+
+    state_dict = deepcopy(model.state_dict())
+
+    with fabric.init_module():
+        model = CustomModel()
+
+    device_mesh = init_device_mesh("cuda", mesh_shape=(2,), mesh_dim_names=("tp",))
+    plan = {"layer1": ColwiseParallel()}
+    parallelize_module(model, device_mesh, plan)
+    _load_raw_module_state(state_dict, model, strict=True)
+
+    assert torch.equal(model.parameter, state_dict["parameter"])
+    assert torch.equal(model.layer1.weight.full_tensor(), state_dict["layer1.weight"])
+    assert torch.equal(model.layer2.weight, state_dict["layer2.weight"])
+    assert torch.equal(model.persistent_buffer, state_dict["persistent_buffer"])
+
+    state_dict.pop("parameter")
+    with pytest.raises(KeyError, match="The model contains a key 'parameter' that does not exist"):
+        _load_raw_module_state(state_dict, model, strict=True)
+
+    _load_raw_module_state(state_dict, model, strict=False)