Reverse transform of state_dict per submodule

thunder/core/module.py

@@ -1,7 +1,7 @@
+from __future__ import annotations
 from contextlib import contextmanager
 import itertools
-from typing import Any
-from collections.abc import Mapping
+from typing import TYPE_CHECKING
 import collections
 
 import torch as pytorch
@@ -10,6 +10,22 @@
 import thunder
 from thunder.core.compile_data import get_compile_data
 
+if TYPE_CHECKING:
+    from collections.abc import Mapping
+    from typing import Any
+    from thunder.core.transform_common import Transform
+
+
+def _convert_state_dict_to_per_module(state_dict: dict[str, Any], module_names: set[str]) -> dict[str, dict[str, Any]]:
+    state_dict_per_module = collections.defaultdict(dict)
+    for k, v in state_dict.items():
+        prefix, sep, _ = k.rpartition(".")
+        # not great but should not happen too often / deep
+        while prefix not in module_names:
+            prefix, sep, _ = prefix.rpartition(".")
+        state_dict_per_module[prefix][k[len(prefix) + len(sep) :]] = v
+    return state_dict_per_module
+
 
 class ThunderModule(pytorch.nn.Module):
     """A wrapper nn.Module subclass.
@@ -120,14 +136,7 @@ def _get_shared_names(self):
 
     def load_original_state_dict(self, state_dict):
         # this loads the state dict incrementally to not exhaust memory
-        module_names = {n for n, _ in self._model.named_modules()}
-        sd_per_module = collections.defaultdict(dict)
-        for k, v in state_dict.items():
-            prefix, sep, _ = k.rpartition(".")
-            # not great but should not happen too often / deep
-            while prefix not in module_names:
-                prefix, sep, _ = prefix.rpartition(".")
-            sd_per_module[prefix][k[len(prefix) + len(sep) :]] = v
+        sd_per_module = _convert_state_dict_to_per_module(state_dict, {n for n, _ in self._model.named_modules()})
 
         shared_names = self._get_shared_names()
         processed_names = set()
@@ -182,7 +191,7 @@ def state_dict(self, *, destination=None, prefix="", keep_vars=False):
         Returns the state dict of the (transformed) Thunder module.
 
         Args:
-            destination: if given, use this mutuable mapping as the dict container.
+            destination: if given, use this mutable mapping as the dict container.
             prefix: a prefix for the keys.
             keep_vars: do not detach
 
@@ -215,6 +224,45 @@ def state_dict(self, *, destination=None, prefix="", keep_vars=False):
                 destination[extra_state_key] = self.get_extra_state()
         return destination
 
+    def original_state_dict(
+        self,
+        *,
+        destination: dict[str, Any] | None = None,
+        prefix: str = "",
+        keep_vars: bool = False,
+    ) -> dict[str, Any]:
+        """Returns the state dict of the transformed :class:`ThunderModule` with reverse transform applied.
+
+        For example, :func:`ThunderModule.state_dict` returns a state dict of sharded tensors if
+        a model is :func:`thunder.distributed.fsdp` applied while :func:`ThunderModule.original_state_dict`
+        returns a state dict of unsharded tensors.
+
+        Args:
+            destination: if given, use this mutable mapping as the dict container.
+            prefix: a prefix for the keys.
+            keep_vars: do not detach
+
+        """
+        module_names = {name for name, _ in self._model.named_modules()}
+        state_dict = self.state_dict(destination=destination, prefix=prefix, keep_vars=keep_vars)
+        state_dict_per_submodule = _convert_state_dict_to_per_module(state_dict, module_names)
+
+        _state_dict_keys = list(state_dict.keys())
+        cur_idx: int = 0
+        transform: Transform
+        for submodule_name, submodule_state_dict in state_dict_per_submodule.items():
+            for transform in reversed(self._lc_transforms):
+                submodule_state_dict = transform.reverse_transform_state_dict_for_submodule(
+                    self,
+                    submodule_name,
+                    submodule_state_dict,
+                )
+            state_dict_per_submodule[submodule_name] = submodule_state_dict
+            for v in submodule_state_dict.values():
+                state_dict[_state_dict_keys[cur_idx]] = v
+                cur_idx += 1
+        return state_dict
+
     def load_state_dict(self, state_dict: Mapping[str, Any], strict: bool = True, assign: bool = False):
         """Loads the state dict to a transformed module.
 

thunder/core/transform_common.py

@@ -1,11 +1,9 @@
 from __future__ import annotations
 import time
 from typing import TYPE_CHECKING
-from abc import ABC, abstractmethod
-from collections import defaultdict
+from abc import ABC
 from collections.abc import Sequence
-from collections import defaultdict
-from itertools import filterfalse, chain
+from itertools import filterfalse
 from functools import partial
 
 import thunder
@@ -15,11 +13,11 @@
 from thunder.core.pytree import tree_flatten, tree_iter, tree_map, tree_unflatten
 from thunder.core.symbol import BoundSymbol, BoundSymbolRHS, has_tags
 from thunder.core.trace import from_trace, TraceProvenance, TraceCtx as Trace, tracectx
-from thunder.core.utils import ProxyDict, producers, check, consumers
+from thunder.core.utils import ProxyDict, producers, check
 
 if TYPE_CHECKING:
-    from thunder.core.proxies import ProxyInterface
-    from thunder.core.symbol import Symbol, VariableInterface
+    from typing import Any
+    from thunder.core.module import ThunderModule
 
 
 #
@@ -346,13 +344,16 @@ def transform_traces_pre_prologue(
         # default to noop
         return prologue_trace, computation_trace, epilogue_trace
 
-    def transform_module(self, model: thunder.ThunderModule):
+    def transform_module(self, model: ThunderModule) -> None:
         """Transforms the ThunderModule. This is executed once on application of the transform"""
         pass
 
     def transform_state_dict_for_submodule(
-        self, model: thunder.ThunderModule, submodule_name: str, state_dict: dict
-    ) -> dict:
+        self,
+        model: ThunderModule,
+        submodule_name: str,
+        state_dict: dict[str, Any],
+    ) -> dict[str, Any]:
         """
         Implement this to transform the state dict (mostly parameters and buffers) of a module, e.g. when loading
         from a state dict of the original model.
@@ -370,6 +371,14 @@ def transform_trace_post_optimization(self, computation_trace: Trace, **kwargs):
         """
         return computation_trace
 
+    def reverse_transform_state_dict_for_submodule(
+        self,
+        model: ThunderModule,
+        submodule_name: str,
+        state_dict: dict[str, Any],
+    ) -> dict[str, Any]:
+        return state_dict
+
 
 def order_proxies(bsyms: Sequence[BoundSymbol]) -> dict[str, int]:
     """computes a canonical ordering of proxies in the bound symbols based on the order of appearance

thunder/distributed/__init__.py

@@ -4,7 +4,6 @@
 from itertools import chain
 from contextlib import contextmanager
 from contextvars import ContextVar, Token
-import copy
 from enum import auto, Enum
 from typing import TYPE_CHECKING, Any
 from collections.abc import Generator
@@ -423,13 +422,14 @@ def f(tensor: TensorProxy) -> str:
 
 
 def fsdp(
-    model: torch.nn.Module,
+    model: torch.nn.Module | ThunderModule,
     *,
     device: torch.device | None = None,
     broadcast_from: int | None = None,
     sharding_strategy: FSDPType = FSDPType.ZERO2,
     bucketing_strategy: FSDPBucketingStrategy = FSDPBucketingStrategy.NONE,
-) -> torch.nn.Module:
+    move_state_dict_to_cpu: bool | None = None,
+) -> torch.nn.Module | ThunderModule:
     """Convert ``model`` into Fully Sharded Data Parallel.
 
     This splits ``model``'s parameters in their first dimension into ``world_size`` chunks
@@ -458,20 +458,22 @@ def fsdp(
             from a checkpoint in a single rank.
         sharding_strategy:
         bucketing_strategy:
+        move_state_dict_to_cpu: Move all-gather'ed parameters of :func:`~thunder.core.module.ThunderModule.original_state_dict` to CPU
+            as each all-gather is finished.
 
      Returns:
         :class:`torch.nn.Module`
 
     """
-    import thunder
+    from thunder.core.module import ThunderModule
 
     utils.check(isinstance(sharding_strategy, FSDPType), lambda: f"FSDPType.ZERO2 and FSDPType.ZERO3 are supported.")
     utils.check(
         tdist.is_available(),
         lambda: "fsdp requires torch distributed to be available (but it's not)",
     )
 
-    if isinstance(model, thunder.ThunderModule):
+    if isinstance(model, ThunderModule):
         from thunder.core.transforms import add_transform
         from thunder.distributed.transforms.fsdp_v2 import FSDPTransform
         from thunder.transforms import MaterializationTransform
@@ -488,11 +490,18 @@ def fsdp(
                     sharding_strategy=sharding_strategy,
                     bucketing_strategy=bucketing_strategy,
                     release_original_parameters=True,
+                    move_state_dict_to_cpu=False if move_state_dict_to_cpu is None else move_state_dict_to_cpu,
                 ),
                 MaterializationTransform(device, init=MaterializationTransform.init_from_original_module_init()),
             ],
         )
 
+    if move_state_dict_to_cpu is not None:
+        import warnings
+
+        warnings.warn(
+            "`move_state_dict_to_cpu` is only effective when `model` is `ThunderModule`, i.e., `thunder.jit(model)`"
+        )
     process_group = copy_default_process_group()
     utils.check(process_group is not None, lambda: "The default process group is None")
     model.use_fsdp = True

thunder/distributed/transforms/fsdp_v2.py

@@ -1,7 +1,6 @@
 """Transform for `fsdp(jit(model))` to convert a model to use fsdp."""
 
 from __future__ import annotations
-import copy
 from dataclasses import dataclass
 from dataclasses import field
 from itertools import chain
@@ -27,14 +26,13 @@
     copy_default_process_group,
     FSDPType,
     FSDPBucketingStrategy,
-    _materialize,
-    _shard_param,
     _shard_tensor,
 )
 
 if TYPE_CHECKING:
     from typing import Any
     from torch.distributed import ProcessGroup
+    from thunder.core.module import ThunderModule
     from thunder.core.symbol import BoundSymbol
     from thunder.core.trace import VariableInterface
 
@@ -71,7 +69,7 @@ def __call__(self, bsym: BoundSymbol) -> VISIT_TYPE:
         return VISIT_TYPE.REPLACE
 
 
-# When the user calls fsdp(jitted_module), or applies this Transform direcly, it does the following
+# When the user calls fsdp(jitted_module), or applies this Transform directly, it does the following
 # - It transforms the ThunderModule jitted_module, sharding the parameters as `overrides`
 #   in the ThunderModule.
 # - While doing that, it leaves the original user module alone, except when
@@ -86,21 +84,16 @@ def __call__(self, bsym: BoundSymbol) -> VISIT_TYPE:
 #
 # The thunder.distributed.fsdp function calls FSDPTransform followed by MaterializationTransform, the latter does
 # the materialization of submodules previously on the meta device.
-
-
 class FSDPTransform(Transform):
-    sharded_params: dict[str, Any]
-    process_group: ProcessGroup
-    shared_params_name: dict[str, str]
-
     def __init__(
         self,
         device: torch.device | None = None,
         broadcast_from: int | None = None,
         sharding_strategy: FSDPType = FSDPType.ZERO2,
         bucketing_strategy: FSDPBucketingStrategy = FSDPBucketingStrategy.NONE,
         release_original_parameters: bool = False,
-    ):
+        move_state_dict_to_cpu: bool = False,
+    ) -> None:
         self.device = device
         self.broadcast_from = broadcast_from
         self.sharding_strategy = sharding_strategy
@@ -109,13 +102,13 @@ def __init__(
         self.sharded_params: dict[str, Any] = {}
         self.process_group: ProcessGroup | None = None
         self.shared_params_name: dict[str, str] = {}
+        self.move_state_dict_to_cpu = move_state_dict_to_cpu
 
     def transform_module(
         self,
         thunder_model: ThunderModule,
     ):
         from thunder import compile_data as get_compile_data
-        from thunder.core.transforms import add_transform
         from thunder.core.module import ThunderModule
 
         self.process_group = copy_default_process_group()
@@ -250,8 +243,11 @@ def transform_module(
                     p_orig._thunder_device = self.device
 
     def transform_state_dict_for_submodule(
-        self, model: thunder.ThunderModule, submodule_name: str, state_dict: dict
-    ) -> dict:
+        self,
+        model: ThunderModule,
+        submodule_name: str,
+        state_dict: dict[str, Any],
+    ) -> dict[str, Any]:
         prefix = ""
         if submodule_name:
             prefix = f"{submodule_name}."
@@ -263,6 +259,36 @@ def transform_state_dict_for_submodule(
             new_state_dict[k] = v
         return new_state_dict
 
+    def reverse_transform_state_dict_for_submodule(
+        self,
+        model: ThunderModule,
+        submodule_name: str,
+        state_dict: dict[str, Any],
+    ) -> dict[str, Any]:
+        from thunder.executors.torchex import _all_gather_prim_impl
+
+        for name, tensor in state_dict.items():
+            fqn: str
+            if submodule_name:
+                fqn = f"{submodule_name}.{name}"
+            else:
+                fqn = name
+
+            if fqn not in self.sharded_params:
+                continue
+
+            old_shape, *_ = self.sharded_params[fqn]
+            unsharded_tensor = _all_gather_prim_impl(
+                tensor,
+                group=self.process_group,
+                do_async=False,
+                dim=None,
+            ).narrow(0, 0, old_shape[0])
+            if self.move_state_dict_to_cpu:
+                unsharded_tensor = unsharded_tensor.cpu()
+            state_dict[name] = unsharded_tensor
+        return state_dict
+
     def transform_traces_pre_prologue(self, prologue_trace, computation_trace, epilogue_trace, **kwargs):
         from thunder.distributed import prims as dist_prims
 

thunder/tests/distributed/test_fsdp.py

@@ -685,6 +685,26 @@ def test_load_original_state_dict(self):
 
         torch.testing.assert_close(y_1, y_2)
 
+    @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires 2 devices")
+    def test_original_state_dict(self):
+        device = torch.device("cuda", self.rank)
+
+        for move_state_dict_to_cpu in (False, True):
+            with torch.device("cuda"):
+                model = ToyModel()
+
+            jitted = fsdp(thunder.jit(model), device=device, move_state_dict_to_cpu=move_state_dict_to_cpu)
+            state_dict = jitted.state_dict()
+            original_state_dict = jitted.original_state_dict()
+
+            for key, sharded_param in state_dict.items():
+                unsharded = original_state_dict[key]
+                self.assertEqual(len(sharded_param) * self.world_size, len(unsharded))
+                if move_state_dict_to_cpu:
+                    self.assertEqual(unsharded.device, torch.device("cpu"))
+                else:
+                    self.assertEqual(unsharded.device, device)
+
 
 common_utils.instantiate_parametrized_tests(FSDPTest)