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ckpts/universal/global_step120/zero/14.mlp.dense_h_to_4h_swiglu.weight/fp32.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fa08d8aebd7e783ad6b9bfb1f029b49070b388941e8b783eeabed1b09c7107b9
+size 33555533

venv/lib/python3.10/site-packages/torch/distributed/_composable/fsdp/__init__.py

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+from ._fsdp_api import MixedPrecisionPolicy
+from .fully_shard import FSDP, fully_shard

venv/lib/python3.10/site-packages/torch/distributed/_composable/fsdp/_fsdp_collectives.py

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+from typing import List, NamedTuple, Optional, Tuple
+
+import torch
+import torch.distributed as dist
+from torch.distributed.distributed_c10d import ReduceOp
+from ._fsdp_common import (
+    _get_dim0_padded_size,
+    _raise_assert_with_print,
+    _to_dtype_if_needed,
+)
+from ._fsdp_param import FSDPParam
+
+
+class AllGatherResult(NamedTuple):
+    all_gather_output: torch.Tensor
+    all_gather_event: Optional[torch.cuda.Event]
+    all_gather_work: Optional[dist.distributed_c10d.Work]
+    all_gather_input_numels: List[int]
+
+
+@torch.no_grad()
+def foreach_all_gather(
+    fsdp_params: List[FSDPParam],
+    group: dist.ProcessGroup,
+    async_op: bool,
+    all_gather_copy_in_stream: torch.cuda.Stream,
+    all_gather_stream: torch.cuda.Stream,
+    device: torch.device,
+) -> Optional[AllGatherResult]:
+    world_size, rank = group.size(), group.rank()
+    # - Copy in
+    with torch.cuda.stream(all_gather_copy_in_stream):
+        param_all_gather_inputs = [
+            fsdp_param.all_gather_input for fsdp_param in fsdp_params
+        ]
+        dtype = param_all_gather_inputs[0].dtype
+        if not all(t.dtype == dtype for t in param_all_gather_inputs):
+            raise NotImplementedError(
+                f"Mixed dtype not supported yet: {[t.dtype for t in param_all_gather_inputs]}"
+            )
+        inp_split_sizes = [inp.numel() for inp in param_all_gather_inputs]
+        all_gather_input_numel = sum(inp_split_sizes)
+        all_gather_output = torch.empty(
+            (all_gather_input_numel * world_size,), dtype=dtype, device=device
+        )
+        all_gather_input = all_gather_output.narrow(
+            0, all_gather_input_numel * rank, all_gather_input_numel
+        )
+        foreach_copy_dsts = torch.split(all_gather_input, inp_split_sizes)
+        torch._foreach_copy_(foreach_copy_dsts, param_all_gather_inputs)
+        del param_all_gather_inputs
+    all_gather_stream.wait_stream(all_gather_copy_in_stream)
+    with torch.cuda.stream(all_gather_stream):
+        # - All-gather
+        all_gather_work = dist.all_gather_into_tensor(
+            output_tensor=all_gather_output,
+            input_tensor=all_gather_input,
+            group=group,
+            async_op=async_op,
+        )
+        all_gather_event = all_gather_stream.record_event()
+        return AllGatherResult(
+            all_gather_output, all_gather_event, all_gather_work, inp_split_sizes
+        )
+
+
+@torch.no_grad()
+def foreach_all_gather_copy_out(
+    all_gather_result: AllGatherResult,
+    fsdp_params: List[FSDPParam],
+    group: dist.ProcessGroup,
+) -> None:
+    (
+        all_gather_output,
+        all_gather_event,
+        all_gather_work,
+        all_gather_input_numels,
+    ) = all_gather_result
+    if all_gather_event is not None:  # sync op
+        torch.cuda.current_stream().wait_event(all_gather_event)
+    if all_gather_work is not None:  # async op
+        all_gather_work.wait()
+    world_size = group.size()
+    dtype, device = all_gather_output.dtype, all_gather_output.device
+    for all_gather_input_numel, fsdp_param in zip(all_gather_input_numels, fsdp_params):
+        fsdp_param.init_all_gather_output(
+            all_gather_input_numel, world_size, dtype, device
+        )  # no-op after 1st call
+        fsdp_param.alloc_all_gather_output()
+    all_gather_output = all_gather_output.view(world_size, -1)
+    out = [
+        fsdp_param.all_gather_output.view(world_size, -1) for fsdp_param in fsdp_params
+    ]
+    torch.split_with_sizes_copy(
+        all_gather_output, all_gather_input_numels, dim=1, out=out
+    )
+
+
+@torch.no_grad()
+def foreach_reduce_scatter(
+    fsdp_params: List[FSDPParam],
+    unsharded_grads: List[torch.Tensor],
+    group: dist.ProcessGroup,
+    reduce_scatter_stream: torch.cuda.Stream,
+    orig_dtype: torch.dtype,
+    reduce_dtype: Optional[torch.dtype],
+    device: torch.device,
+    divide_factors: Optional[Tuple[float, float]],
+) -> torch.cuda.Event:
+    """
+    ``unsharded_grads`` owns the references to the gradients computed by
+    autograd, so clearing the list frees the gradients.
+    """
+    grad_dtypes = {grad.dtype for grad in unsharded_grads}
+    if len(grad_dtypes) != 1:
+        # Check this at runtime since it could be a real runtime error if e.g.
+        # fp8 weights do not produce the correct higher precision gradients
+        _raise_assert_with_print(
+            f"FSDP reduce-scatter expects uniform gradient dtype but got {grad_dtypes}"
+        )
+    grad_dtype = unsharded_grads[0].dtype
+    reduce_dtype = reduce_dtype or grad_dtype
+    world_size = group.size()
+    padded_unsharded_sizes = tuple(
+        _get_dim0_padded_size(grad.size(), world_size) for grad in unsharded_grads
+    )
+    reduce_scatter_input_numel = sum(s.numel() for s in padded_unsharded_sizes)
+    reduce_scatter_output_numel = reduce_scatter_input_numel // world_size
+    current_stream = torch.cuda.current_stream()
+    reduce_scatter_stream.wait_stream(current_stream)
+    with torch.cuda.stream(reduce_scatter_stream):
+        reduce_scatter_input = torch.empty(
+            (reduce_scatter_input_numel,), dtype=reduce_dtype, device=device
+        )
+        foreach_reduce_scatter_copy_in(
+            unsharded_grads, reduce_scatter_input, world_size
+        )
+        # Only after the copy-in finishes can we free the gradients, which were
+        # computed in the default stream
+        current_stream.wait_stream(reduce_scatter_stream)
+        unsharded_grads.clear()
+        reduce_scatter_output = reduce_scatter_input.new_empty(
+            (reduce_scatter_output_numel,)
+        )
+        _reduce_scatter(
+            reduce_scatter_output, reduce_scatter_input, group, divide_factors
+        )
+        reduce_scatter_output = _to_dtype_if_needed(reduce_scatter_output, orig_dtype)
+        # - View out and accumulate
+        flat_grad_offset = 0  # [0, reduce_scatter_output_numel - 1]
+        for padded_unsharded_size, fsdp_param in zip(
+            padded_unsharded_sizes, fsdp_params
+        ):
+            new_sharded_grad = torch.as_strided(
+                reduce_scatter_output,
+                size=fsdp_param.sharded_size,
+                stride=fsdp_param.contiguous_sharded_stride,
+                storage_offset=flat_grad_offset,
+            )
+            to_accumulate_grad = fsdp_param.sharded_param.grad is not None
+            new_sharded_dtensor_grad = fsdp_param.to_sharded_dtensor(new_sharded_grad)
+            if to_accumulate_grad:
+                fsdp_param.sharded_param.grad += new_sharded_dtensor_grad
+            else:
+                fsdp_param.sharded_param.grad = new_sharded_dtensor_grad
+            padded_sharded_numel = padded_unsharded_size.numel() // world_size
+            flat_grad_offset += padded_sharded_numel
+        reduce_scatter_view_out_event = reduce_scatter_stream.record_event()
+    # The RS output is allocated in the RS stream and used in the default
+    # stream (for optimizer). To ensure its memory is not reused for later
+    # RSs, we do not need extra synchronization since the sharded parameters
+    # hold refs through the end of backward.
+    return reduce_scatter_view_out_event
+
+
+def foreach_reduce_scatter_copy_in(
+    unsharded_grads: List[torch.Tensor],
+    reduce_scatter_input: torch.Tensor,
+    world_size: int,
+) -> None:
+    grad_views: List[torch.Tensor] = []
+    grads_to_copy: List[torch.Tensor] = []
+    padded_grad_slices: List[torch.Tensor] = []
+    for grad in unsharded_grads:
+        grad_size = grad.size()
+        dim0_padded_size = _get_dim0_padded_size(grad_size, world_size)
+        if dim0_padded_size != grad_size:
+            padded_grad = grad.new_empty(dim0_padded_size)
+            padded_grad_slices.append(padded_grad[: grad.size(0)])
+            grads_to_copy.append(grad)
+            grad = padded_grad
+        grad_views.append(grad.view(world_size, -1))
+    if padded_grad_slices:
+        torch._foreach_copy_(padded_grad_slices, grads_to_copy)
+    torch.cat(grad_views, dim=-1, out=reduce_scatter_input.view(world_size, -1))
+
+
+def _reduce_scatter(
+    output: torch.Tensor,
+    input: torch.Tensor,
+    group: dist.ProcessGroup,
+    divide_factors: Optional[Tuple[float, float]],
+) -> None:
+    if divide_factors:
+        predivide_factor, postdivide_factor = divide_factors
+        _div_if_needed(input, predivide_factor)
+        dist.reduce_scatter_tensor(output, input, group=group)
+        _div_if_needed(output, postdivide_factor)
+    else:
+        # Using NCCL's reduce-scatter to do the division by world size saves
+        # extra memory read/write from a separate division kernel
+        dist.reduce_scatter_tensor(output, input, op=ReduceOp.AVG, group=group)
+
+
+def _div_if_needed(tensor: torch.Tensor, div_factor: float) -> None:
+    if div_factor > 1:
+        tensor.div_(div_factor)

venv/lib/python3.10/site-packages/torch/distributed/_composable/fsdp/_fsdp_init.py

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+import itertools
+from typing import List, Optional, Set, Tuple, Union
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+
+from torch.distributed._tensor import DeviceMesh, DTensor, init_device_mesh
+from torch.distributed.device_mesh import _get_device_handle
+from ._fsdp_common import _is_composable_with_fsdp, FSDPMeshInfo, HSDPMeshInfo
+from ._fsdp_state import _get_module_fsdp_state
+
+
+def _get_post_forward_mesh_info(
+    reshard_after_forward: Union[bool, int], mesh_info: FSDPMeshInfo
+) -> Optional[FSDPMeshInfo]:
+    shard_mesh_size = mesh_info.shard_mesh_size
+    if not isinstance(reshard_after_forward, (bool, int)):
+        raise ValueError(
+            "reshard_after_forward should be a bool or an int representing the "
+            f"group size to reshard to, not {reshard_after_forward}"
+        )
+    # NOTE: `isinstance(False, int)` returns `True`.
+    if not isinstance(reshard_after_forward, bool) and isinstance(
+        reshard_after_forward, int
+    ):
+        if (
+            reshard_after_forward < 1
+            or reshard_after_forward > shard_mesh_size
+            or shard_mesh_size % reshard_after_forward != 0
+        ):
+            raise ValueError(
+                "If passing reshard_after_forward as an int, it should be a "
+                f"factor of {shard_mesh_size}, not {reshard_after_forward}"
+            )
+        elif reshard_after_forward == 1:
+            reshard_after_forward = False
+        elif reshard_after_forward == shard_mesh_size:
+            reshard_after_forward = True
+    post_forward_mesh_info = None
+    if reshard_after_forward is True:
+        post_forward_mesh_info = mesh_info
+    elif reshard_after_forward is not False:  # int case
+        # For HSDP, we can flatten the two replicate dims into the 0th dim
+        post_forward_mesh_tensor = mesh_info.mesh.mesh.view(-1, reshard_after_forward)
+        post_forward_mesh = DeviceMesh(
+            mesh_info.mesh.device_type, post_forward_mesh_tensor
+        )
+        post_forward_mesh_info = HSDPMeshInfo(
+            post_forward_mesh, shard_mesh_dim=1, replicate_mesh_dim=0
+        )
+    return post_forward_mesh_info
+
+
+def _init_default_fully_shard_mesh() -> DeviceMesh:
+    """Default to global CUDA mesh if possible else global CPU mesh."""
+    if not dist.distributed_c10d.is_initialized():
+        dist.distributed_c10d.init_process_group()
+    default_pg = dist.distributed_c10d._get_default_group()
+    device_type = "cuda" if torch.cuda.is_available() else "cpu"
+    mesh = init_device_mesh(device_type, mesh_shape=(default_pg.size(),))
+    return mesh
+
+
+def _get_device_from_mesh(mesh: DeviceMesh) -> torch.device:
+    if mesh.device_type == "cpu":
+        return torch.device("cpu")
+    device_handle = _get_device_handle(mesh.device_type)
+    return torch.device(mesh.device_type, device_handle.current_device())
+
+
+def _get_managed_modules(root_module: nn.Module) -> List[nn.Module]:
+    modules: List[nn.Module] = []
+    # Track visisted modules to avoid visiting shared modules multiple times
+    visited_modules: Set[nn.Module] = set()
+
+    def dfs(module: nn.Module) -> None:
+        """
+        Runs a DFS to collect managed modules, not recursing into modules with
+        a non-composable API or ``fully_shard`` already applied.
+        """
+        if not _is_composable_with_fsdp(module):
+            return
+        elif module is not root_module and _get_module_fsdp_state(module) is not None:
+            return  # nested `fully_shard` module
+        visited_modules.add(module)
+        for submodule in module.children():
+            if submodule not in visited_modules:
+                dfs(submodule)
+        modules.append(module)
+
+    dfs(root_module)
+    return modules
+
+
+def _get_managed_states(
+    modules: List[nn.Module],
+) -> Tuple[List[nn.Parameter], List[torch.Tensor]]:
+    params: List[nn.Parameter] = []
+    buffers: List[torch.Tensor] = []
+    # Track visited parameters/buffers to avoid visiting shared parameters and
+    # buffers multiple times
+    visited_params: Set[nn.Parameter] = set()
+    visited_buffers: Set[torch.Tensor] = set()
+    for module in modules:
+        for param in module.parameters(recurse=False):
+            if param not in visited_params:
+                params.append(param)
+                visited_params.add(param)
+        for buffer in module.buffers(recurse=False):
+            if buffer not in visited_buffers:
+                buffers.append(buffer)
+                visited_buffers.add(buffer)
+    return params, buffers
+
+
+def _move_states_to_device(
+    params: List[nn.Parameter],
+    buffers: List[torch.Tensor],
+    device: torch.device,
+    mesh_info: FSDPMeshInfo,
+) -> None:
+    """
+    We have FSDP move states to device for simpler and faster initialization
+    since FSDP almost always uses CUDA for training. We move parameters/buffers
+    rather than modules since modules to support ignoring parameters/buffers in
+    the future.
+    """
+    # TODO: De-duplicate with `_apply` after `swap_tensors` path lands:
+    # https://github.com/pytorch/pytorch/issues/115792
+    for tensor in itertools.chain(params, buffers):
+        if tensor.device == device or tensor.device.type == "meta":
+            # Keep meta-device tensors on meta device for deferred init
+            continue
+        if isinstance(tensor, DTensor):
+            if (dtensor_mesh_type := tensor._spec.mesh.device_type) != device.type:
+                raise ValueError(
+                    "Requires DTensor to have mesh of the same type as the FSDP mesh "
+                    f"but got {dtensor_mesh_type} for DTensor and {device.type} for FSDP"
+                )
+            raise AssertionError(
+                f"Expects DTensor to be moved to {dtensor_mesh_type} but got {tensor.device}"
+            )
+        tensor.data = tensor.to(device)

venv/lib/python3.10/site-packages/torch/distributed/_composable/fsdp/_fsdp_param.py

@@ -0,0 +1,438 @@
+from dataclasses import dataclass, field
+from enum import auto, Enum
+from typing import cast, List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+
+from torch._prims_common import make_contiguous_strides_for
+from torch.distributed._functional_collectives import AsyncCollectiveTensor
+from torch.distributed._tensor import DTensor, Placement, Replicate, Shard
+from torch.distributed._tensor.device_mesh import _mesh_resources
+from torch.distributed._tensor.placement_types import DTensorSpec
+from ._fsdp_api import MixedPrecisionPolicy
+from ._fsdp_common import (
+    _chunk_with_empty,
+    _from_local_no_grad,
+    _get_dim0_chunked_size,
+    _raise_assert_with_print,
+    _to_dtype_if_needed,
+    FSDPMeshInfo,
+    HSDPMeshInfo,
+)
+
+"""
+[Note: FSDP tensors]
+FSDP considers the following tensors:
+- Original parameter: parameter passed to :class:`FSDPParam`, i.e. the one
+  on the module when applying FSDP
+- Sharded parameter: sharding the original parameter on dim-0 as a DTensor
+  over the main mesh
+- All-gather input: the ``torch.Tensor`` passed to all-gather, derived from the
+  sharded parameter
+- All-gather output: the ``torch.Tensor`` resulting from all-gathering the
+  all-gather input
+- Unsharded parameter: parameter used for forward/backward computation, derived
+  from the all-gather output; autograd leaf
+
+We define these tensors to describe the general framework that can accomodate
+extensions, where:
+- all-gather-input = pre-all-gather-transform(sharded-parameter)
+- unsharded-parameter = post-all-gather-transform(all-gather-output)
+
+For the default ``torch.Tensor`` case, the sharded parameter and all-gather
+input share the same underlying tensor data, meaning that they can be thought
+of as the same tensors. The same applies for the all-gather output and
+unsharded parameter. For non-``torch.Tensor`` extensions, these equivalences
+may no longer hold due to the pre/post-all-gather transforms.
+
+[Note: FSDP and autograd]
+FSDP dynamically frees and allocates the unsharded parameter. Since autograd
+can pack a reference to it or a view to save for backward, we use storage
+resizing to implement the freeing/allocation since that preserves the aliasing.
+This implies that we construct the unsharded parameter object once and write to
+it in-place thereafter. For the default ``torch.Tensor` original parameter
+case, the all-gather output and unsharded parameter share the same
+data, so we use storage resizing on the all-gather output.
+"""
+
+
+class ShardedState(Enum):
+    """
+    - ``SHARDED``: The sharded parameter is registered to the module. It is the
+      only contributor to parameter memory.
+    - ``SHARDED_POST_FORWARD``: The unsharded parameter is resharded to a
+      smaller world size. Since this data should not be used for computation,
+      we do not register it to the module. Users should reshard the module
+      before any in-place modifications. Both it and the sharded parameter
+      contribute to parameter memory.
+    - ``UNSHARDED``: The unsharded parameter is registered to the module. Both
+      it and the sharded parameter contribute to parameter memory.
+    """
+
+    SHARDED = auto()
+    SHARDED_POST_FORWARD = auto()
+    UNSHARDED = auto()
+
+
+@dataclass
+class ParamModuleInfo:
+    """
+    For a parameter, this stores the module and the parameter name to be able
+    to do a parameter swap via ``setattr(module, param_name, ...)`` or to get
+    the parameter via ``getattr(module, param_name)``. We additionally save
+    shared modules and shared parameter names to update them accordingly.
+    """
+
+    # Parameter names are unprefixed, e.g. "weight", not "lin.weight"
+    module: nn.Module
+    param_name: str
+    shared_modules: List[nn.Module] = field(default_factory=list)
+    shared_param_names: List[str] = field(default_factory=list)
+
+
+class FSDPParam:
+    """
+    This class manages a parameter with FSDP or FSDP variants applied,
+    implementing dim-0 per-parameter sharding.
+    """
+
+    orig_dtype: torch.dtype
+    param_dtype: Optional[torch.dtype]
+    reduce_dtype: Optional[torch.dtype]
+    _orig_size: torch.Size  # ND
+    _contiguous_orig_stride: Tuple[int, ...]
+    sharded_size: torch.Size  # ND
+    contiguous_sharded_stride: Tuple[int, ...]
+    padded_sharded_param_size: torch.Size  # ND
+    sharded_post_forward_size: torch.Size  # ND
+    contiguous_sharded_post_forward_stride: Tuple[int, ...]
+    _sharded_param_data: torch.Tensor  # 1D
+    sharded_param: nn.Parameter  # ND
+    _sharded_post_forward_param_data: Optional[torch.Tensor]  # 1D
+    _sharded_post_forward_param: Optional[nn.Parameter]  # ND
+    _unsharded_param: nn.Parameter  # ND
+    _global_placements: Tuple[Placement, ...]
+    _global_size: torch.Size
+    _global_stride: Tuple[int, ...]
+    # DTensor attributes (only defined for DTensor `param`):
+    _tp_spec: DTensorSpec
+
+    def __init__(
+        self,
+        param: nn.Parameter,
+        module_info: ParamModuleInfo,
+        mesh_info: FSDPMeshInfo,
+        post_forward_mesh_info: Optional[FSDPMeshInfo],
+        device: torch.device,
+        mp_policy: MixedPrecisionPolicy,
+    ):
+        self._module_info: ParamModuleInfo = module_info
+        self.mesh_info = mesh_info
+        self.post_forward_mesh_info = post_forward_mesh_info
+        self.device = device
+        self._init_sharded_param(param, device)
+        if self.post_forward_mesh_info:
+            self._init_sharded_post_forward_param_metadata(param)
+        self.all_gather_output = torch.empty(0)
+        self._param_fqn: Optional[str] = None  # prefixed from root module
+
+    @torch.no_grad()
+    def _init_sharded_param(self, param: nn.Parameter, device: torch.device):
+        if param.device != device and param.device.type != "meta":
+            raise AssertionError(
+                f"Expects the parameter to already be moved to device {device} but got {param.device}"
+            )
+        # TODO: Replace the sharded DTensor parameter construction logic with
+        # `distribute_tensor` after https://github.com/pytorch/pytorch/issues/116101
+        # TODO: Simplify the following sharded parameter padding logic after
+        # https://github.com/pytorch/pytorch/issues/113045
+        self.is_dtensor = isinstance(param, DTensor)
+        if self.is_dtensor:
+            self._tp_spec = cast(DTensor, param)._spec
+            if (
+                self.mesh_info.shard_mesh_dim != 0
+                or self.mesh_info.replicate_mesh_dim is not None
+            ):
+                raise NotImplementedError("Using TP with HSDP is not supported")
+            dp_mesh, tp_mesh = (self.mesh_info.mesh, self._tp_spec.mesh)
+            dp_global_mesh = _mesh_resources.get_parent_mesh(dp_mesh)
+            tp_global_mesh = _mesh_resources.get_parent_mesh(tp_mesh)
+            if dp_global_mesh != tp_global_mesh or (
+                dp_global_mesh is None or tp_global_mesh is None
+            ):
+                raise AssertionError(
+                    "FSDP requires the DP and TP mesh to have the same parent mesh but got: \n"
+                    f"DP's global mesh: {dp_global_mesh}\nTP's global mesh: {tp_global_mesh}"
+                )
+            self._global_mesh = dp_global_mesh
+            if len(self._tp_spec.placements) != 1:
+                raise NotImplementedError(
+                    f"FSDP only supports 1D TP, not {self._tp_spec.placements}"
+                )
+            global_placements: List[Placement] = [Replicate(), Replicate()]
+            global_dp_mesh_dim = _mesh_resources.get_parent_mesh_dim(dp_mesh)
+            global_tp_mesh_dim = _mesh_resources.get_parent_mesh_dim(tp_mesh)
+            assert global_dp_mesh_dim is not None  # mypy
+            assert global_tp_mesh_dim is not None  # mypy
+            # TODO: Hard code FSDP + TP; need to support HSDP + TP
+            global_placements[global_dp_mesh_dim] = Shard(0)
+            global_placements[global_tp_mesh_dim] = self._tp_spec.placements[0]
+            self._global_placements = tuple(global_placements)
+            self._global_size = param.size()
+            self._global_stride = param.stride()
+            param_data = cast(DTensor, param)._local_tensor
+        else:
+            self._global_mesh = self.mesh_info.mesh
+            self._global_placements = (Shard(0),)
+            self._global_size = param.size()
+            self._global_stride = param.stride()
+            param_data = param
+        self._orig_size = param_data.size()
+        self._contiguous_orig_stride = make_contiguous_strides_for(self._orig_size)
+        shard_rank = self.mesh_info.shard_mesh_rank
+        shard_world_size = self.mesh_info.shard_mesh_size
+        chunks = _chunk_with_empty(param_data, shard_world_size, dim=0)
+        sharded_param = chunks[shard_rank]
+        self.sharded_size = _get_dim0_chunked_size(sharded_param, param_data.size())
+        self.contiguous_sharded_stride = make_contiguous_strides_for(self.sharded_size)
+        padded_sharded_size = chunks[0].size()  # 0th always padded
+        padded_sharded_param = param_data.new_zeros(padded_sharded_size)
+        self.padded_sharded_param_size = padded_sharded_param.size()
+        if sharded_param.numel() > 0:
+            padded_sharded_param[: sharded_param.size(0)].copy_(sharded_param)
+        self._sharded_param_data = padded_sharded_param.view(-1)
+        self.sharded_param = nn.Parameter(
+            self.to_sharded_dtensor(padded_sharded_param[: sharded_param.size(0)])
+        )
+        self.sharded_param.requires_grad_(param.requires_grad)
+        # Let `param_data` be freed normally when its ref count reaches 0 when
+        # the `fully_shard` call returns to allow provided parameters to alias
+        self._setattr_on_modules(self.sharded_param)
+        self.sharded_state = ShardedState.SHARDED
+
+    def _init_sharded_post_forward_param_metadata(self, param: torch.Tensor) -> None:
+        mesh_info = self.post_forward_mesh_info
+        assert mesh_info is not None  # mypy
+        param_data = param._local_tensor if isinstance(param, DTensor) else param
+        chunks = _chunk_with_empty(param_data, mesh_info.shard_mesh_size, dim=0)
+        self.sharded_post_forward_size = _get_dim0_chunked_size(
+            chunks[mesh_info.shard_mesh_rank], param_data.size()
+        )
+        self.contiguous_sharded_post_forward_stride = make_contiguous_strides_for(
+            self.sharded_post_forward_size
+        )
+
+    def init_dtype_attrs(self, mp_policy: MixedPrecisionPolicy):
+        param_dtype, reduce_dtype = (mp_policy.param_dtype, mp_policy.reduce_dtype)
+        self.orig_dtype = self.sharded_param.dtype
+        # Clamp `param_dtype` to `None` if no casting is required
+        if param_dtype == self.orig_dtype:
+            param_dtype = None
+        self.param_dtype = param_dtype
+        self.reduce_dtype = reduce_dtype
+        # None indicates that the mixed precision is not enabled
+
+    def init_all_gather_output(
+        self,
+        all_gather_input_numel: int,
+        world_size: int,
+        dtype: torch.dtype,
+        device: torch.device,
+    ):
+        if self.all_gather_output.numel() > 0:
+            return  # already initialized
+        all_gather_output_size = torch.Size([all_gather_input_numel * world_size])
+        self.all_gather_output = torch.empty(
+            all_gather_output_size, dtype=dtype, device=device
+        )
+
+    def init_unsharded_param(self):
+        if hasattr(self, "_unsharded_param"):
+            return  # already initialized
+        # For the default path (no post-all-gather), the all-gather output
+        # gives the unsharded parameter data directly
+        unsharded_param = torch.as_strided(
+            self.all_gather_output,
+            self._orig_size,
+            self._contiguous_orig_stride,
+            storage_offset=0,
+        )
+        if self.is_dtensor:
+            unsharded_param = _from_local_no_grad(
+                unsharded_param,
+                self._tp_spec.mesh,
+                self._tp_spec.placements,
+                self._global_size,
+                self._global_stride,
+            )
+        self._unsharded_param = nn.Parameter(unsharded_param)
+        self._unsharded_param.requires_grad_(self.sharded_param.requires_grad)
+
+    def to_sharded(self) -> None:
+        self._setattr_on_modules(self.sharded_param)
+        self.free_all_gather_output()
+        self.sharded_state = ShardedState.SHARDED
+
+    def to_sharded_post_forward(self) -> None:
+        if self.is_dtensor:
+            raise NotImplementedError(
+                "Resharding to smaller mesh with TP is not supported yet"
+            )
+        self._assert_in_states(ShardedState.UNSHARDED)
+        assert self.post_forward_mesh_info is not None  # mypy
+        shard_world_size = self.post_forward_mesh_info.shard_mesh_size
+        if (numel := self.all_gather_output.numel()) % shard_world_size != 0:
+            _raise_assert_with_print(
+                f"All-gather output size ({numel}) must be divisible by the shard "
+                f"world size ({shard_world_size})"
+            )
+        shard_rank = self.post_forward_mesh_info.shard_mesh_rank
+        sharded_numel = numel // shard_world_size
+        self._sharded_post_forward_param_data = (
+            self.all_gather_output.narrow(0, sharded_numel * shard_rank, sharded_numel)
+        ).clone()  # clone to be able to free all-gather output
+        sharded_post_forward_tensor = torch.as_strided(
+            self._sharded_post_forward_param_data,
+            size=self.sharded_post_forward_size,
+            stride=self.contiguous_sharded_post_forward_stride,
+            storage_offset=0,
+        )
+        self._sharded_post_forward_param = nn.Parameter(
+            self.to_sharded_post_forward_dtensor(sharded_post_forward_tensor)
+        )
+        self._setattr_on_modules(self._sharded_post_forward_param)
+        self.free_all_gather_output()
+        self.sharded_state = ShardedState.SHARDED_POST_FORWARD
+
+    def to_unsharded(self) -> None:
+        # Assume that the data has been allocated and all-gathered
+        set_requires_grad_if_needed(self.sharded_param, self._unsharded_param)
+        self._setattr_on_modules(self._unsharded_param)
+        if self.sharded_state == ShardedState.SHARDED_POST_FORWARD:
+            # The data is allocated in the default stream via the post-forward
+            # reshard and must be kept alive for the next all-gather copy-in.
+            # Since we call this method after the copy-out, the data's lifetime
+            # is ensured without further synchronization.
+            self._sharded_post_forward_param = None
+            self._sharded_post_forward_param_data = None  # free
+        self.sharded_state = ShardedState.UNSHARDED
+
+    def _setattr_on_modules(self, param: nn.Parameter) -> None:
+        unsafe_setattr_param(
+            self._module_info.module, self._module_info.param_name, param
+        )
+        for shared_module, shared_param_name in zip(
+            self._module_info.shared_modules, self._module_info.shared_param_names
+        ):
+            unsafe_setattr_param(shared_module, shared_param_name, param)
+
+    def to_sharded_dtensor(self, tensor: torch.Tensor) -> DTensor:
+        """
+        Converts a local tensor representing either the sharded parameter or
+        sharded gradient to DTensor.
+        """
+        if tensor.shape != self.sharded_size:
+            _raise_assert_with_print(
+                f"Expects size {self.sharded_size} but got {tensor.shape}"
+            )
+        return _from_local_no_grad(
+            tensor,
+            self._global_mesh,
+            self._global_placements,
+            self._global_size,
+            self._global_stride,
+        )
+
+    def to_sharded_post_forward_dtensor(self, tensor: torch.Tensor) -> DTensor:
+        if tensor.shape != self.sharded_post_forward_size:
+            _raise_assert_with_print(
+                f"Expects size {self.sharded_post_forward_size} but got {tensor.shape}"
+            )
+        assert isinstance(self.post_forward_mesh_info, HSDPMeshInfo)
+        # TODO: Prefer this DTensor to be read-only and generalize the
+        # placement once we support TP.
+        return _from_local_no_grad(
+            tensor,
+            self.post_forward_mesh_info.mesh,
+            (Replicate(), Shard(0)),
+            self._global_size,
+            self._global_stride,
+        )
+
+    def alloc_all_gather_output(self) -> None:
+        unsafe_alloc_storage(self.all_gather_output)
+
+    def free_all_gather_output(self) -> None:
+        unsafe_free_storage(self.all_gather_output)
+
+    @property
+    def all_gather_input(self) -> torch.Tensor:  # 1D
+        self._assert_in_states(ShardedState.SHARDED, ShardedState.SHARDED_POST_FORWARD)
+        if self.sharded_state == ShardedState.SHARDED:
+            return _to_dtype_if_needed(self._sharded_param_data, self.param_dtype)
+        elif self.sharded_state == ShardedState.SHARDED_POST_FORWARD:
+            return _to_dtype_if_needed(
+                cast(torch.Tensor, self._sharded_post_forward_param_data),
+                self.param_dtype,
+            )
+        return torch.empty(0)  # mypy
+
+    @property
+    def unsharded_param(self) -> nn.Parameter:  # ND
+        self._assert_in_states(ShardedState.UNSHARDED)
+        return self._unsharded_param
+
+    @property
+    def unsharded_grad_data(self) -> torch.Tensor:
+        grad = self.unsharded_param.grad
+        assert grad is not None, "Expects unsharded_param.grad to not be None"
+        return self._get_grad_inner_tensor(grad)
+
+    def _get_grad_inner_tensor(self, grad: torch.Tensor) -> torch.Tensor:
+        if self.is_dtensor:
+            if isinstance(grad, AsyncCollectiveTensor):
+                grad = grad.wait()
+            grad = cast(DTensor, grad)._local_tensor
+        return grad
+
+    def _assert_in_states(self, *states: ShardedState) -> None:
+        if self.sharded_state not in states:
+            _raise_assert_with_print(
+                f"Expects to be in one of {states}, not {self.sharded_state}"
+            )
+
+
+# NOTE: Unsafe here refers to not checking whether the storage is already
+# allocated or freed, respectively. We should be safe to use them since we
+# explicitly manage the state transition.
+def unsafe_alloc_storage(tensor: torch.Tensor) -> None:
+    # Skip the already-allocated check and assume that `tensor` is the base
+    # tensor to save CPU overhead
+    tensor.untyped_storage().resize_(tensor.numel() * tensor.itemsize)
+
+
+def unsafe_free_storage(tensor: torch.Tensor) -> None:
+    # Skip the already-freed check to save CPU overhead
+    tensor.untyped_storage().resize_(0)
+
+
+# NOTE: These bypass `nn.Module.__setattr__` checks, which incur non-trivial
+# CPU overhead, if the module did not override it. For FSDP, we know we do not
+# need those checks when transitioning between sharded/unsharded parameters.
+def unsafe_setattr_param(
+    module: nn.Module, param_name: str, param: nn.Parameter
+) -> None:
+    if getattr(module.__setattr__, "__func__", None) is nn.Module.__setattr__:
+        module._parameters[param_name] = param
+    else:  # slow path
+        setattr(module, param_name, param)
+
+
+def set_requires_grad_if_needed(
+    src_tensor: torch.Tensor, dst_tensor: torch.Tensor
+) -> None:
+    # Only call `requires_grad_` if needed to avoid the Python <> C++ context
+    # switch overhead
+    if src_tensor.requires_grad != dst_tensor.requires_grad:
+        dst_tensor.requires_grad_(src_tensor.requires_grad)

venv/lib/python3.10/site-packages/torch/distributed/_composable/fsdp/_fsdp_param_group.py

@@ -0,0 +1,506 @@
+import contextlib
+
+from typing import Any, cast, Dict, List, NamedTuple, Optional, Set, Tuple
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+
+from torch.autograd.graph import Node
+from torch.distributed.fsdp._common_utils import _named_parameters_with_duplicates
+from torch.utils._pytree import tree_flatten, tree_unflatten
+from torch.utils.hooks import RemovableHandle
+from ._fsdp_api import MixedPrecisionPolicy
+from ._fsdp_collectives import (
+    AllGatherResult,
+    foreach_all_gather,
+    foreach_all_gather_copy_out,
+    foreach_reduce_scatter,
+)
+from ._fsdp_common import FSDPMeshInfo, HSDPMeshInfo, TrainingState
+from ._fsdp_param import FSDPParam, ParamModuleInfo, ShardedState
+
+_ModuleToHandleDict = Dict[nn.Module, RemovableHandle]  # for state dict
+
+
+"""
+[Note: Overlapping all-gather copy-in and all-gather]
+For implicit forward prefetching, we want to overlap the next copy-in with the
+current all-gather. We do so using a separate copy-in stream. However, since
+we have the all-gather input as a view into the output, we must make sure to
+copy into different memory from the current all-gather's output. Thus, we keep
+a reference to the current all-gather's output and have the next FSDP parameter
+group free it after its copy-in. Finally, we have the last FSDP state flush the
+reference to avoid holding onto memory after forward.
+"""
+
+
+class FSDPCommContext:
+    """This has the communication state shared across FSDP states/parameter groups."""
+
+    def init(self):
+        # Setting the all-gather/reduce-scatter streams to be higher priority
+        # can help avoid some issues where their copies in/out are delayed and
+        # block computation
+        high_priority = -1
+        # All-gather state and copy-in stream allow overlapping the next
+        # copy-in with the current all-gather in forward; copy-in overlaps with
+        # reduce-scatter in backward without the separate copy-in stream
+        self.all_gather_copy_in_stream = torch.cuda.Stream(priority=high_priority)
+        self.all_gather_state: Optional[AllGatherState] = None
+        # All-gather stream allows overlapping next all-gather with current
+        # forward compute
+        self.all_gather_stream = torch.cuda.Stream(priority=high_priority)
+        # Reduce-scatter stream gives separate execution "thread" for post-
+        # backward logic like pre/post-gradient division and reduce-scatter
+        self.reduce_scatter_stream = torch.cuda.Stream(priority=high_priority)
+        # Post-forward order for explicit backward prefetching
+        self.post_forward_order: List[FSDPParamGroup] = []  # will cause ref cycles
+
+    def get_all_gather_streams(
+        self, training_state: TrainingState
+    ) -> Tuple[torch.cuda.Stream, torch.cuda.Stream]:
+        if training_state in (TrainingState.FORWARD, TrainingState.PRE_BACKWARD):
+            # Use separate streams for implicit prefetching
+            return self.all_gather_copy_in_stream, self.all_gather_stream
+        current_stream = torch.cuda.current_stream()
+        return current_stream, current_stream
+
+
+# See [Note: Overlapping all-gather copy-in and all-gather]
+class AllGatherState(NamedTuple):
+    all_gather_result: AllGatherResult
+    event: torch.cuda.Event  # all-gather copy-out
+
+
+class FSDPParamGroup:
+    """This class represents a parameter group to communicate together."""
+
+    _orig_dtype: torch.dtype
+    _reduce_dtype: Optional[torch.dtype]
+
+    def __init__(
+        self,
+        params: List[nn.Parameter],
+        module: nn.Module,
+        mesh_info: FSDPMeshInfo,
+        post_forward_mesh_info: Optional[FSDPMeshInfo],
+        device: torch.device,
+        mp_policy: MixedPrecisionPolicy,
+    ):
+        self.module = module  # permit ref cycle because 1:1 lifetime
+        param_module_infos = _get_param_module_infos(params, module)
+        self.fsdp_params = [
+            FSDPParam(
+                param, module_info, mesh_info, post_forward_mesh_info, device, mp_policy
+            )
+            for param, module_info in zip(params, param_module_infos)
+        ]
+        self.mesh_info = mesh_info
+        self.post_forward_mesh_info = post_forward_mesh_info
+        self.device = device
+        self.mp_policy = mp_policy
+        self._training_state = TrainingState.IDLE
+        # Group's sharded state always matches its parameters' sharded states
+        self._sharded_state = ShardedState.SHARDED
+        self._module_fqn: Optional[str] = None  # prefixed from root module
+
+        # - Hook state
+        self._module_to_pre_save_state_dict_hook_handle: _ModuleToHandleDict = {}
+        self._module_to_pre_load_state_dict_hook_handle: _ModuleToHandleDict = {}
+
+        # - Communication and communication/computation overlap
+        self.comm_ctx = FSDPCommContext()
+        # Group's indices in the shared post-forward order
+        self._post_forward_indices: List[int] = []
+        # Used to avoid mistargeted backward prefetches when the module is used
+        # in forward but not in backward: for each forward, we record a tuple
+        # of the output's grad fns and later query the autograd engine whether
+        # any grad fn will execute in the current backward to know to prefetch.
+        self.all_forward_output_grad_fns: Set[Tuple[Node, ...]] = set()
+        # Whether to reduce-scatter or all-reduce gradients, respectively
+        # (can be set to false to save communication during gradient
+        # accumulation); all-reducing without reduce-scatter is disallowed
+        self.reduce_scatter_grads: bool = True
+        self.all_reduce_grads: bool = True
+
+        # - CUDA events for stream synchronization
+        # Holds the all-gather output buffer, sync objects, and metadata
+        self._all_gather_result: Optional[AllGatherResult] = None
+        # Holds the reduce-scatter view-out CUDA event that marks the end of
+        # the group's post-backward (e.g. reduce-scatter and div), which should
+        # be waited on at the end of backward
+        self._reduce_scatter_view_out_event: Optional[torch.cuda.Event] = None
+        # Holds the reshard-after-forward CUDA event when resharding to a
+        # different world size, which should be waited on in the next unshard
+        self._reshard_after_forward_event: Optional[torch.cuda.Event] = None
+
+    # Initialization #
+    def _init_mp_dtypes(self) -> None:
+        for fsdp_param in self.fsdp_params:
+            fsdp_param.init_dtype_attrs(self.mp_policy)
+        orig_dtypes = {fsdp_param.orig_dtype for fsdp_param in self.fsdp_params}
+        if len(orig_dtypes) != 1:
+            # This can be relaxed if we copy-out for the reduce-scatter
+            raise AssertionError(
+                f"FSDP expects uniform original parameter dtype but got {orig_dtypes}"
+            )
+        self._orig_dtype = next(iter(orig_dtypes))
+        reduce_dtypes = {fsdp_param.reduce_dtype for fsdp_param in self.fsdp_params}
+        if len(reduce_dtypes) != 1:
+            # This can be relaxed if we issue one reduce-scatter per reduce
+            # dtype (but we would need a way for users to specify multiple
+            # reduce dtypes)
+            raise AssertionError(
+                f"FSDP expects uniform reduce dtype but got {reduce_dtypes}"
+            )
+        self._reduce_dtype = next(iter(reduce_dtypes))
+
+    def _init_grad_divide_factors(self):
+        data_parallel_world_size = 1
+        data_parallel_world_size *= self.mesh_info.shard_mesh_size
+        if isinstance(self.mesh_info, HSDPMeshInfo):
+            data_parallel_world_size *= self.mesh_info.replicate_mesh_size
+        if self._reduce_dtype == torch.float32:
+            # Use NCCL's AVG op to divide after reduction since it is more
+            # performant and fp32 has sufficient precision
+            self._grad_divide_factors: Optional[Tuple[float, float]] = None
+            return
+        # For N data parallel workers, each worker computes g_i, and they
+        # collectively reduce (g_1 + ... + g_N) / N. To avoid overflow and
+        # underflow, we divide by ~sqrt(N) before and after the reduction.
+        factor: int = 1
+        while (
+            data_parallel_world_size % factor == 0
+            and data_parallel_world_size / factor > factor
+        ):
+            factor *= 2
+        factor = float(factor)
+        self._grad_divide_factors = (factor, data_parallel_world_size / factor)
+
+    def lazy_init(self):
+        param_names_on_meta = [
+            fsdp_param._param_fqn
+            for fsdp_param in self.fsdp_params
+            if fsdp_param.sharded_param.device.type == "meta"
+        ]
+        if param_names_on_meta:
+            raise RuntimeError(
+                "FSDP parameters should be materialized from meta device before training, "
+                f"but the following were still on meta device: {param_names_on_meta}\n"
+                "For example, call module.to_empty(device) to materialize to device and "
+                "call module.reset_parameters() on each module to initialize values."
+            )
+        # Initialize mixed precision attributes lazily in case the user changes
+        # the parameter dtypes after construction time but before forward
+        self._init_mp_dtypes()
+        self._init_grad_divide_factors()
+        self._register_state_dict_hooks()
+
+    # Runtime #
+    def unshard(self, async_op: bool = False):
+        if self._all_gather_result is not None:  # already called, pending wait
+            return
+        if self.is_unsharded:
+            return  # no-op
+        if self._reshard_after_forward_event is not None:
+            # Resharded parameter data is allocated in the default stream and
+            # used in the all-gather streams
+            self._wait_all_gather_streams_on_event(self._reshard_after_forward_event)
+            self._reshard_after_forward_event = None
+        self._all_gather_result = foreach_all_gather(
+            self.fsdp_params,
+            self._all_gather_process_group,
+            async_op,
+            *self.comm_ctx.get_all_gather_streams(self._training_state),
+            self.device,
+        )
+
+    def wait_for_unshard(self):
+        """
+        1. In forward with implict prefetching, to overlap the current copy-out
+        with the next all-gather, we save a reference to the current all-gather
+        result to free after the next copy-out.
+        2. Otherwise (explicit prefetching or in backward), we free the
+        all-gather result immediately after the current copy-out since we can
+        already overlap the current copy-out with the previous reduce-scatter.
+        """
+        if not self._all_gather_result:
+            return  # no preceding unshard
+        if self._training_state == TrainingState.FORWARD:  # implicit prefetch
+            if prev_all_gather_state := self.comm_ctx.all_gather_state:
+                self._wait_all_gather_streams_on_event(prev_all_gather_state.event)
+                self.comm_ctx.all_gather_state = None  # free the all-gather result
+        foreach_all_gather_copy_out(
+            self._all_gather_result, self.fsdp_params, self._all_gather_process_group
+        )
+        for fsdp_param in self.fsdp_params:
+            fsdp_param.init_unsharded_param()  # no-op after 1st call
+        self._to_unsharded()
+        all_gather_copy_out_event = torch.cuda.Event()
+        all_gather_copy_out_event.record()
+        if self._training_state == TrainingState.FORWARD:
+            self.comm_ctx.all_gather_state = AllGatherState(
+                self._all_gather_result, all_gather_copy_out_event
+            )
+        else:
+            self._wait_all_gather_streams_on_event(all_gather_copy_out_event)
+        self._all_gather_result = None  # free unless saved in `all_gather_state`
+
+    def _wait_all_gather_streams_on_event(self, event: torch.cuda.Event):
+        self.comm_ctx.all_gather_copy_in_stream.wait_event(event)
+        self.comm_ctx.all_gather_stream.wait_event(event)
+
+    def reshard(self):
+        if self._training_state == TrainingState.FORWARD:
+            if not self._reshard_after_forward:
+                return
+            if self._use_post_forward_mesh:
+                self._to_sharded_post_forward()
+                self._reshard_after_forward_event = torch.cuda.Event()
+                self._reshard_after_forward_event.record()
+                return
+        self._to_sharded()
+
+    def pre_forward(
+        self, module: nn.Module, args: Tuple[Any, ...], kwargs: Dict[str, Any]
+    ) -> Tuple[Tuple[Any, ...], Dict[str, Any]]:
+        with torch.profiler.record_function("FSDP::pre_forward"):
+            self._training_state = TrainingState.FORWARD
+            self.unshard()
+            self.wait_for_unshard()
+            args, kwargs = self._register_post_backward_hook(args, kwargs)
+            return args, kwargs
+
+    def post_forward(self, module: nn.Module, input: Any, output: Any):
+        with torch.profiler.record_function("FSDP::post_forward"):
+            self.reshard()
+            self._record_post_forward()
+            self._training_state = TrainingState.IDLE
+            return output
+
+    def _record_post_forward(self) -> None:
+        # Since a group has one pre-backward unshard for each forward call
+        # before the backward, we record each usage (with multiplicity)
+        post_forward_index = len(self.comm_ctx.post_forward_order)
+        self.comm_ctx.post_forward_order.append(self)
+        self._post_forward_indices.append(post_forward_index)
+
+    def pre_backward(self, forward_grad_fns: Tuple[Any, ...], *unused: Any):
+        with torch.profiler.record_function("FSDP::pre_backward"):
+            self._training_state = TrainingState.PRE_BACKWARD
+            self.unshard()  # no-op if prefetched
+            self.wait_for_unshard()
+            # Can be already removed if running multiple `backward`s
+            self.all_forward_output_grad_fns.discard(forward_grad_fns)
+            self._prefetch_unshard()
+
+    def post_backward(self, *unused: Any):
+        self._training_state = TrainingState.POST_BACKWARD
+        with torch.profiler.record_function("FSDP::post_backward_reshard"):
+            if not self.reduce_scatter_grads:
+                self.reshard()
+                return
+            # Save the autograd-computed gradients before resharding to only
+            # access the unsharded parameters when their data is present
+            fsdp_params_with_grad: List[FSDPParam] = []
+            unsharded_grads: List[torch.Tensor] = []
+            for fsdp_param in self.fsdp_params:
+                if fsdp_param.unsharded_param.grad is not None:
+                    fsdp_params_with_grad.append(fsdp_param)
+                    unsharded_grads.append(fsdp_param.unsharded_grad_data)
+                    fsdp_param.unsharded_param.grad = None
+            self.reshard()
+        if len(fsdp_params_with_grad) == 0:
+            return
+        with torch.profiler.record_function("FSDP::post_backward_reduce"):
+            self._reduce_scatter_view_out_event = foreach_reduce_scatter(
+                fsdp_params_with_grad,
+                unsharded_grads,
+                self._reduce_scatter_process_group,
+                self.comm_ctx.reduce_scatter_stream,
+                self._orig_dtype,
+                self._reduce_dtype,
+                self.device,
+                self._grad_divide_factors,
+            )
+
+    def finalize_backward(self):
+        if self._reduce_scatter_view_out_event is not None:
+            torch.cuda.current_stream().wait_event(self._reduce_scatter_view_out_event)
+            self._reduce_scatter_view_out_event = None
+        self._training_state = TrainingState.IDLE
+        self._post_forward_indices.clear()
+        self.all_forward_output_grad_fns.clear()
+
+    def _prefetch_unshard(self):
+        if self._training_state == TrainingState.PRE_BACKWARD:
+            if not self._post_forward_indices:
+                # Can be cleared if running multiple `backward`s
+                return
+            curr_index = self._post_forward_indices.pop()
+            if (target_index := curr_index - 1) < 0:
+                return
+            target_fsdp_param_group = self.comm_ctx.post_forward_order[target_index]
+            if any(
+                torch._C._will_engine_execute_node(grad_fn)  # type: ignore[attr-defined]
+                for grad_fns in target_fsdp_param_group.all_forward_output_grad_fns
+                for grad_fn in grad_fns
+            ):
+                with torch.profiler.record_function(
+                    "FSDP::backward_prefetch"
+                ), target_fsdp_param_group.use_training_state(
+                    TrainingState.PRE_BACKWARD
+                ):
+                    target_fsdp_param_group.unshard()
+
+    # Utilities #
+    def _to_sharded(self):
+        if not self.is_sharded:
+            for fsdp_param in self.fsdp_params:
+                fsdp_param.to_sharded()
+            self._sharded_state = ShardedState.SHARDED
+
+    def _to_sharded_post_forward(self):
+        if not self.is_sharded_post_forward:
+            for fsdp_param in self.fsdp_params:
+                fsdp_param.to_sharded_post_forward()
+            self._sharded_state = ShardedState.SHARDED_POST_FORWARD
+
+    def _to_unsharded(self):
+        if not self.is_unsharded:
+            for fsdp_param in self.fsdp_params:
+                fsdp_param.to_unsharded()
+            self._sharded_state = ShardedState.UNSHARDED
+
+    @property
+    def is_sharded(self) -> bool:
+        return self._sharded_state == ShardedState.SHARDED
+
+    @property
+    def is_sharded_post_forward(self) -> bool:
+        return self._sharded_state == ShardedState.SHARDED_POST_FORWARD
+
+    @property
+    def is_unsharded(self) -> bool:
+        return self._sharded_state == ShardedState.UNSHARDED
+
+    @contextlib.contextmanager
+    def use_training_state(self, training_state: TrainingState):
+        old_training_state = self._training_state
+        self._training_state = training_state
+        try:
+            yield
+        finally:
+            self._training_state = old_training_state
+
+    # Hook Registration #
+    def _register_post_backward_hook(
+        self, args: Tuple[Any, ...], kwargs: Dict[str, Any]
+    ) -> Tuple[Tuple[Any, ...], Dict[str, Any]]:
+        if not torch.is_grad_enabled():
+            return args, kwargs
+        args_list, args_spec = tree_flatten(args)
+        kwargs_list, kwargs_spec = tree_flatten(kwargs)
+        args_kwargs_list = list(args_list) + list(kwargs_list)
+        inp_tensor_indices: List[int] = []
+        inp_tensors: List[torch.Tensor] = []
+        for i, obj in enumerate(args_kwargs_list):
+            if torch.is_tensor(obj) and obj.requires_grad:
+                inp_tensor_indices.append(i)
+                inp_tensors.append(obj)
+        if len(inp_tensors) == 0:
+            return args, kwargs  # no tensors that require gradients
+        inp_tensors = RegisterPostBackwardFunction.apply(self, *inp_tensors)
+        for inp_tensor_idx, inp_tensor in zip(inp_tensor_indices, inp_tensors):
+            args_kwargs_list[inp_tensor_idx] = inp_tensor
+        args_list = args_kwargs_list[: len(args_list)]
+        kwargs_list = args_kwargs_list[len(args_list) :]
+        args = tree_unflatten(args_list, args_spec)
+        kwargs = tree_unflatten(kwargs_list, kwargs_spec)
+        return args, kwargs
+
+    def _register_state_dict_hooks(self) -> None:
+        assert len(self._module_to_pre_save_state_dict_hook_handle) == 0
+        assert len(self._module_to_pre_load_state_dict_hook_handle) == 0
+        modules_with_fsdp_params: Set[nn.Module] = {
+            fsdp_param._module_info.module for fsdp_param in self.fsdp_params
+        }
+
+        def to_sharded_hook(*args: Any, **kwargs: Any) -> None:
+            self._to_sharded()
+
+        for module in modules_with_fsdp_params:
+            self._module_to_pre_save_state_dict_hook_handle[
+                module
+            ] = module.register_state_dict_pre_hook(to_sharded_hook)
+            self._module_to_pre_load_state_dict_hook_handle[
+                module
+            ] = module._register_load_state_dict_pre_hook(to_sharded_hook)
+
+    # Properties #
+    @property
+    def _reshard_after_forward(self) -> bool:
+        return self.post_forward_mesh_info is not None
+
+    @property
+    def _use_post_forward_mesh(self) -> bool:
+        return (
+            self._reshard_after_forward
+            and self.mesh_info != self.post_forward_mesh_info
+        )
+
+    @property
+    def _all_gather_process_group(self) -> dist.ProcessGroup:
+        mesh_info = (
+            cast(FSDPMeshInfo, self.post_forward_mesh_info)
+            if self.is_sharded_post_forward
+            else self.mesh_info
+        )
+        assert isinstance(mesh_info, FSDPMeshInfo)
+        return mesh_info.shard_process_group
+
+    @property
+    def _reduce_scatter_process_group(self) -> dist.ProcessGroup:
+        mesh_info = self.mesh_info
+        assert isinstance(mesh_info, FSDPMeshInfo)
+        return mesh_info.shard_process_group
+
+
+def _get_param_module_infos(
+    params: List[nn.Parameter], module: nn.Module
+) -> List[ParamModuleInfo]:
+    """
+    Shared parameter: lin1.weight = lin2.weight
+    Shared module: mlp.lin1 = mlp.lin2
+    We do not remove duplicates when traversing both modules and parameters to
+    find shared modules' parameters and shared parameters within a module.
+    """
+    params_set = set(params)
+    param_to_module_info: Dict[nn.Parameter, ParamModuleInfo] = {}
+    for _, submodule in module.named_modules(remove_duplicate=False):
+        for param_name, param in _named_parameters_with_duplicates(
+            submodule, recurse=False
+        ):
+            if param in params_set:
+                if param not in param_to_module_info:
+                    param_to_module_info[param] = ParamModuleInfo(submodule, param_name)
+                else:
+                    param_to_module_info[param].shared_modules.append(submodule)
+                    param_to_module_info[param].shared_param_names.append(param_name)
+    if len(param_to_module_info) != len(params):
+        raise AssertionError(f"Some parameters are not in the module tree of {module}")
+    return [param_to_module_info[param] for param in params]
+
+
+class RegisterPostBackwardFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, param_group: FSDPParamGroup, *inputs: torch.Tensor):
+        # All tensors in `inputs` should require gradient
+        ctx.param_group = param_group
+        return inputs
+
+    @staticmethod
+    def backward(ctx, *grads: torch.Tensor):
+        ctx.param_group.post_backward()
+        return (None,) + grads

venv/lib/python3.10/site-packages/torch/distributed/_composable/fsdp/_fsdp_state.py

@@ -0,0 +1,246 @@
+import functools
+
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+from torch.autograd.graph import Node, register_multi_grad_hook
+from torch.distributed._composable_state import (
+    _get_module_state,
+    _insert_module_state,
+    _State,
+)
+from torch.distributed.utils import _to_kwargs
+from torch.utils._pytree import tree_flatten, tree_map
+from torch.utils.hooks import RemovableHandle
+from ._fsdp_api import MixedPrecisionPolicy
+from ._fsdp_common import _cast_fp_tensor, TrainingState
+from ._fsdp_param import FSDPParam
+from ._fsdp_param_group import FSDPCommContext, FSDPParamGroup
+
+
+class FSDPStateContext:
+    """This has state shared across FSDP states."""
+
+    def __init__(self):
+        # All FSDP states in the root state's module tree
+        self.all_states: List[FSDPState] = []
+        # Iteration's forward root runs the once-per-forward logic; this root
+        # may not be the overall root set by lazy initialization in cases where
+        # only a submodule runs forward (e.g. encoder-only for eval)
+        self.iter_forward_root: Optional[FSDPState] = None
+        # Final callback should only be queued once per backward
+        self.post_backward_final_callback_queued: bool = False
+        # Whether to finalize backward in this backward's final callback
+        self.is_last_backward: bool = True
+
+
+class FSDPState(_State):
+    def __init__(self):
+        super().__init__()
+        self._fsdp_param_group: Optional[FSDPParamGroup] = None
+        self._is_root: Optional[bool] = None  # root set during lazy init
+        self._state_ctx = FSDPStateContext()
+        self._comm_ctx = FSDPCommContext()
+        self._training_state: TrainingState = TrainingState.IDLE
+        self._pre_backward_hook_handles: List[RemovableHandle] = []
+
+    # Define a separate init since `__init__` is called in the contract
+    def init(
+        self, module: nn.Module, device: torch.device, mp_policy: MixedPrecisionPolicy
+    ) -> None:
+        _insert_module_state(module, self)
+        self._module = module
+        self._device = device
+        self._mp_policy = mp_policy
+        self._pre_forward_hook_handle = module.register_forward_pre_hook(
+            self._pre_forward, prepend=True, with_kwargs=True
+        )
+        self._post_forward_hook_handle = module.register_forward_hook(
+            self._post_forward, prepend=False
+        )
+
+    def _root_pre_forward(
+        self, module: nn.Module, args: Tuple[Any, ...], kwargs: Dict[str, Any]
+    ) -> Tuple[Tuple[Any, ...], Dict[str, Any]]:
+        self._lazy_init()
+        if self._state_ctx.iter_forward_root is not None:
+            return args, kwargs
+        self._state_ctx.iter_forward_root = self
+        with torch.profiler.record_function("FSDP::root_pre_forward"):
+            # Wait for optimizer before implicitly prefetched all-gathers
+            current_stream = torch.cuda.current_stream()
+            self._comm_ctx.all_gather_copy_in_stream.wait_stream(current_stream)
+            self._comm_ctx.all_gather_stream.wait_stream(current_stream)
+            if self._device.type == "cuda":
+                with torch.profiler.record_function("FSDP::inputs_to_device"):
+                    args_tuple, kwargs_tuple = _to_kwargs(
+                        args, kwargs, self._device, False
+                    )  # same as DDP
+                args, kwargs = args_tuple[0], kwargs_tuple[0]
+        return args, kwargs
+
+    def _lazy_init(self) -> None:
+        """
+        Lazy initialization represents when all modules' parallelisms have
+        finalized (e.g. FSDP has been applied to all desired modules). This
+        means that we can determine which state is the root, and we do so by
+        the 1st state to run forward.
+        """
+        if self._is_root is not None:
+            return  # no-op: already initialized
+        self._is_root = True
+        root_module = self._module
+        for module_name, module in root_module.named_modules():
+            if (state := _get_module_fsdp_state(module)) is None:
+                continue
+            if module is not root_module:
+                if state._is_root is not None:
+                    raise RuntimeError(
+                        "FSDP state has already been lazily initialized for "
+                        f"{module_name}\nFSDP requires running forward through "
+                        "the root module first"
+                    )
+                state._is_root = False
+            self._state_ctx.all_states.append(state)
+        if self._fsdp_param_group:
+            # For the root, do not reshard after forward since for training,
+            # the parameters would be freed and all-gathered immediately
+            self._fsdp_param_group.post_forward_mesh_info = None
+        self._init_fqns()
+        self._init_shared_state()
+        # Run parameter group lazy inits after initializing FQNs for improved
+        # error messages
+        for state in self._state_ctx.all_states:
+            if state._fsdp_param_group:
+                state._fsdp_param_group.lazy_init()
+
+    def _init_shared_state(self) -> None:
+        self._comm_ctx.init()
+        for state in self._state_ctx.all_states:
+            state._state_ctx = self._state_ctx
+            state._comm_ctx = self._comm_ctx
+            if fsdp_param_group := state._fsdp_param_group:
+                fsdp_param_group.comm_ctx = self._comm_ctx
+
+    def _init_fqns(self) -> None:
+        """Sets module and parameter FQN attributes for debugging."""
+        assert self._is_root
+        root_module = self._module
+        param_to_fsdp_param: Dict[nn.Parameter, FSDPParam] = {}
+        module_to_fsdp_param_group: Dict[nn.Module, FSDPParamGroup] = {}
+        for state in self._state_ctx.all_states:
+            if fsdp_param_group := state._fsdp_param_group:
+                for fsdp_param in fsdp_param_group.fsdp_params:
+                    param_to_fsdp_param[fsdp_param.sharded_param] = fsdp_param
+                module_to_fsdp_param_group[fsdp_param_group.module] = fsdp_param_group
+        for param_name, param in root_module.named_parameters():
+            if param in param_to_fsdp_param:
+                param_to_fsdp_param[param]._param_fqn = param_name
+        for module_name, module in root_module.named_modules():
+            if module in module_to_fsdp_param_group:
+                module_to_fsdp_param_group[module]._module_fqn = module_name
+
+    def _pre_forward(
+        self, module: nn.Module, args: Tuple[Any, ...], kwargs: Dict[str, Any]
+    ) -> Tuple[Tuple[Any, ...], Dict[str, Any]]:
+        # When composing with module-hook-based activation checkpointing, the
+        # the pre-backward hook is responsible for the unshard
+        if self._training_state == TrainingState.PRE_BACKWARD:
+            return args, kwargs
+        self._training_state = TrainingState.FORWARD
+        args, kwargs = self._root_pre_forward(module, args, kwargs)
+        if self._mp_policy.cast_forward_inputs and self._mp_policy.param_dtype:
+            with torch.profiler.record_function("FSDP::cast_forward_inputs"):
+                cast_fn = functools.partial(
+                    _cast_fp_tensor, self._mp_policy.param_dtype
+                )
+                args, kwargs = tree_map(cast_fn, args), tree_map(cast_fn, kwargs)
+        if self._fsdp_param_group:
+            args, kwargs = self._fsdp_param_group.pre_forward(module, args, kwargs)
+        return args, kwargs
+
+    def _post_forward(self, module: nn.Module, input: Any, output: Any) -> Any:
+        # When composing with module-hook-based activation checkpointing, the
+        # post-backward hook is responsible for the reshard
+        if self._training_state == TrainingState.PRE_BACKWARD:
+            return output
+        if self._fsdp_param_group:
+            output = self._fsdp_param_group.post_forward(module, input, output)
+        output = self._register_pre_backward_hook(output)
+        self._training_state = TrainingState.IDLE
+        if self._state_ctx.iter_forward_root is self:
+            if all_gather_state := self._comm_ctx.all_gather_state:
+                # Free the last all-gather result if needed; refer to
+                # [Note: Overlapping all-gather copy-in and all-gather]
+                self._comm_ctx.all_gather_copy_in_stream.wait_event(
+                    all_gather_state.event
+                )
+                self._comm_ctx.all_gather_stream.wait_event(all_gather_state.event)
+                self._comm_ctx.all_gather_state = None  # free the all-gather result
+            self._state_ctx.iter_forward_root = None
+        if self._mp_policy.output_dtype is not None:
+            with torch.profiler.record_function("FSDP::cast_forward_outputs"):
+                output = tree_map(
+                    functools.partial(_cast_fp_tensor, self._mp_policy.output_dtype),
+                    output,
+                )
+        return output
+
+    def _pre_backward(self, forward_grad_fns: Tuple[Node, ...], *unused: Any) -> None:
+        self._training_state = TrainingState.PRE_BACKWARD
+        self._register_root_post_backward_final_callback()
+        if self._fsdp_param_group:
+            self._fsdp_param_group.pre_backward(forward_grad_fns, *unused)
+
+    def _root_post_backward_final_callback(self) -> None:
+        with torch.profiler.record_function("FSDP::root_post_backward_callback"):
+            for state in self._state_ctx.all_states:
+                if state._fsdp_param_group and state._fsdp_param_group.is_unsharded:
+                    # Run post-backward in case forward inputs did not require
+                    # gradient so the autograd backward did not run
+                    state._fsdp_param_group.post_backward()
+                if self._state_ctx.is_last_backward:
+                    state._finalize_backward()
+            if self._state_ctx.is_last_backward:
+                self._comm_ctx.post_forward_order.clear()
+            self._state_ctx.post_backward_final_callback_queued = False
+
+    def _finalize_backward(self) -> None:
+        self._training_state = TrainingState.IDLE
+        for handle in self._pre_backward_hook_handles:
+            handle.remove()
+        self._pre_backward_hook_handles.clear()
+        if self._fsdp_param_group:
+            self._fsdp_param_group.finalize_backward()
+
+    def _register_pre_backward_hook(self, output: Any) -> Any:
+        if not torch.is_grad_enabled():
+            return output
+
+        flat_outputs, _ = tree_flatten(output)
+        tensors = tuple(t for t in flat_outputs if t.requires_grad)
+        if tensors:
+            grad_fns = tuple(t.grad_fn for t in tensors if t.grad_fn is not None)
+            pre_backward = functools.partial(self._pre_backward, grad_fns)
+            handle = register_multi_grad_hook(tensors, pre_backward, mode="any")
+            self._pre_backward_hook_handles.append(handle)
+            if self._fsdp_param_group:
+                self._fsdp_param_group.all_forward_output_grad_fns.add(grad_fns)
+        return output
+
+    def _register_root_post_backward_final_callback(self):
+        if self._state_ctx.post_backward_final_callback_queued:
+            return
+        self._state_ctx.post_backward_final_callback_queued = True
+        Variable._execution_engine.queue_callback(
+            self._root_post_backward_final_callback
+        )
+
+
+def _get_module_fsdp_state(module: nn.Module) -> Optional[FSDPState]:
+    state = _get_module_state(module)
+    if isinstance(state, FSDPState):
+        return state
+    return None

venv/lib/python3.10/site-packages/torch/distributed/_composable/fsdp/fully_shard.py

@@ -0,0 +1,246 @@
+from typing import Any, cast, Optional, Union
+
+import typing_extensions
+
+import torch
+import torch.nn as nn
+
+from torch.distributed._composable import contract
+from torch.distributed._tensor import DeviceMesh, DTensor
+
+from ._fsdp_api import MixedPrecisionPolicy
+from ._fsdp_common import FSDPMeshInfo, HSDPMeshInfo
+from ._fsdp_init import (
+    _get_device_from_mesh,
+    _get_managed_modules,
+    _get_managed_states,
+    _get_post_forward_mesh_info,
+    _init_default_fully_shard_mesh,
+    _move_states_to_device,
+)
+from ._fsdp_param_group import FSDPParamGroup
+from ._fsdp_state import _get_module_fsdp_state, FSDPState
+
+
+# The decorator adds a state object to `module` that can be accessed via
+# `fully_shard.state(module)`. The state object and module are 1:1.
+@contract(state_cls=FSDPState)
+def fully_shard(
+    module: nn.Module,
+    *,
+    mesh: Optional[DeviceMesh] = None,
+    reshard_after_forward: Union[bool, int] = True,
+    mp_policy: MixedPrecisionPolicy = MixedPrecisionPolicy(),
+):
+    """
+    Shard module parameters across data parallel workers.
+
+    This function applies fully sharded data parallelism (FSDP) or a variant to
+    ``module``, a technique for memory savings at the cost of communication.
+    Parameters are sharded across ``mesh``, and in turn, so are their gradients
+    and optimizer states.
+
+    The sharded parameters are all-gathered to construct the unsharded
+    parameters for forward or backward computation. The unsharded parameters
+    are freed after computation to save memory. The gradients are reduced
+    across the mesh and divided by the mesh size for data parallelism. The
+    optimizer step runs on the sharded parameters.
+
+    Each call to ``fully_shard`` constructs one communication group that
+    includes the parameters in ``module.parameters()`` except those already
+    assigned to a group from a nested call. Each group's parameters and its
+    gradients are communicated together in one collective, respectively.
+    Constructing multiple groups across the model (e.g. "layer by layer")
+    allows for peak memory savings and communication/computation overlap.
+
+    Implementation-wise, the sharded parameters are represented as
+    :class:`DTensor` s, sharded on dim-0, and the unsharded parameters are
+    represented as :class:`Tensor` s. A module forward pre-hook all-gathers the
+    parameters, and a module forward hook frees them. Similar backward hooks
+    gather parameters and later free parameters/reduce gradients.
+
+    Args:
+        mesh (Optional[DeviceMesh]): This data parallel mesh defines the
+            sharding and device. If 1D, then parameters are fully sharded
+            across the 1D mesh (FSDP). If 2D, then parameters are sharded
+            across the 0th dim and replicated across the 1st dim (HSDP). The
+            mesh's device type gives the device type used for communication;
+            if a CUDA or CUDA-like device type, then we use the current device.
+        reshard_after_forward (Union[bool, int]): This controls the parameter
+            behavior after forward and can trade off memory and communication:
+            - If ``True``, then this reshards parameters after forward and
+            all-gathers in backward.
+            - If ``False``, then this keeps the unsharded parameters in memory
+            after forward and avoids the all-gather in backward.
+            - If an ``int``, then this represents the world size to reshard to
+            after forward. It should be a non-trivial divisor of the ``mesh``
+            shard dim size (i.e. excluding 1 and the dim size itself). A choice
+            may be the intra-node size (e.g. ``torch.cuda.device_count()``).
+            This allows the all-gather in backward to be over a smaller world
+            size at the cost of higher memory usage than setting to ``True``.
+            - The root FSDP state has its value specially set to ``False`` as a
+            heuristic since its parameters would typically be immediately
+            all-gathered for backward.
+            - After forward, the parameters registered to the module depend on
+            to this: The registered parameters are the sharded parameters if
+            ``True``; unsharded parameters if ``False``; and the paramters
+            resharded to the smaller mesh otherwise. To modify the parameters
+            between forward and backward, the registered parameters must be the
+            sharded parameters. For ``False`` or an ``int``, this can be done
+            by manually resharding via :meth:`reshard`.
+        mp_policy (MixedPrecisionPolicy): This controls the mixed precision
+            policy, which offers parameter/reduction mixed precision for this
+            module. See :class:`MixedPrecisionPolicy` for details.
+    """
+    if isinstance(module, (nn.ModuleList, nn.ModuleDict)):
+        raise ValueError(
+            f"fully_shard does not support containers that do not implement forward: {module}"
+        )
+    mesh = mesh or _init_default_fully_shard_mesh()
+    if mesh.ndim not in (1, 2):
+        raise ValueError(f"fully_shard expects a 1D or 2D DeviceMesh but got {mesh}")
+    elif mesh.ndim == 1:
+        mesh_info = FSDPMeshInfo(mesh, shard_mesh_dim=0)
+    else:
+        mesh_info = HSDPMeshInfo(mesh, shard_mesh_dim=1, replicate_mesh_dim=0)
+    device = _get_device_from_mesh(mesh)
+    post_forward_mesh_info = _get_post_forward_mesh_info(
+        reshard_after_forward, mesh_info
+    )
+
+    state = fully_shard.state(module)
+    state.init(module, device, mp_policy)
+
+    managed_modules = _get_managed_modules(module)
+    params, buffers = _get_managed_states(managed_modules)
+    _move_states_to_device(params, buffers, device, mesh_info)
+    if params:
+        state._fsdp_param_group = FSDPParamGroup(
+            params, module, mesh_info, post_forward_mesh_info, device, mp_policy
+        )
+
+    # for dynamo
+    for module in managed_modules:
+        module._is_fsdp_managed_module = True  # type: ignore[assignment]
+        module._fsdp_use_orig_params = True  # type: ignore[assignment]
+
+    # Place FSDP leftmost for highest priority in the method resolution order
+    cls = module.__class__
+    dct = {"__deepcopy__": unimplemented_deepcopy}
+    new_cls = type(f"FSDP{cls.__name__}", (FSDP, cls), dct)
+    module.__class__ = new_cls
+    return module
+
+
+def unimplemented_deepcopy(*args: Any, **kwargs: Any) -> typing_extensions.Never:
+    raise AssertionError(
+        "FSDP does not support deepcopy. Please use state dict for serialization."
+    )
+
+
+class FSDP:
+    def __new__(cls, *args, **kwargs):
+        """
+        Override ``__new__`` to remove the FSDP class and directly construct
+        the original class for cases like indexing into a container module.
+        """
+        # Use index 2 since 0 is the dynamically constructed `FSDP<...>` class
+        # and index 1 is the `FSDP` class itself
+        orig_cls = cls.__mro__[2]
+        self = orig_cls.__new__(orig_cls, *args, **kwargs)
+        self.__init__(*args, **kwargs)
+        return self
+
+    def reshard(self) -> None:
+        """
+        Reshards the module's parameters, registering the sharded parameters
+        to the module and freeing the unsharded parameters if needed. This
+        method is *not* recursive.
+        """
+        state = self._get_fsdp_state()
+        if fsdp_param_group := state._fsdp_param_group:
+            fsdp_param_group.reshard()
+
+    def set_is_last_backward(self, is_last_backward: bool) -> None:
+        """
+        Sets whether the next backward is the last one, meaning that FSDP
+        should wait for gradient reduction to finish and clear internal data
+        structures used for explicit prefetching.
+        """
+        state = self._get_fsdp_state()
+        state._state_ctx.is_last_backward = is_last_backward
+
+    def set_requires_gradient_sync(
+        self, requires_gradient_sync: bool, recurse: bool = True
+    ) -> None:
+        """
+        Sets if the module should sync gradients. This can be used to implement
+        gradient accumulation without communication. For HSDP, this controls
+        both reduce-scatter and all-reduce together.
+
+        Args:
+            requires_gradient_sync (bool): Whether to reduce gradients for the
+                module's parameters.
+            recurse (bool): Whether to set for all submodules or just the
+                passed-in module.
+        """
+        for module in cast(nn.Module, self).modules():
+            if isinstance(module, FSDP):
+                state = module._get_fsdp_state()
+                if fsdp_param_group := state._fsdp_param_group:
+                    fsdp_param_group.reduce_scatter_grads = requires_gradient_sync
+                    fsdp_param_group.all_reduce_grads = requires_gradient_sync
+
+    def set_requires_all_reduce(self, requires_all_reduce: bool, recurse: bool = True):
+        """
+        Sets if the module should all-reduce gradients. This can be used to
+        implement gradient accumulation with only reduce-scatter but not
+        all-reduce for HSDP.
+        """
+        for module in cast(nn.Module, self).modules():
+            if isinstance(module, FSDP):
+                state = module._get_fsdp_state()
+                if fsdp_param_group := state._fsdp_param_group:
+                    fsdp_param_group.all_reduce_grads = requires_all_reduce
+
+    def _get_fsdp_state(self) -> FSDPState:
+        if (state := _get_module_fsdp_state(cast(nn.Module, self))) is None:
+            raise AssertionError(f"No FSDP state found on {self}")
+        return state
+
+    def _apply(self, *args: Any, **kwargs: Any) -> Any:
+        # Reshard to ensure that sharded parameters are registered
+        self.reshard()
+        ret = super()._apply(*args, **kwargs)  # type: ignore[misc]
+        state = self._get_fsdp_state()
+        if not (fsdp_param_group := state._fsdp_param_group):
+            return ret
+        # TODO: Remove this padding logic once DTensor pads the local tensor:
+        # https://github.com/pytorch/pytorch/issues/113045
+        with torch.no_grad():
+            for fsdp_param in fsdp_param_group.fsdp_params:
+                module_info = fsdp_param._module_info
+                new_param = getattr(module_info.module, module_info.param_name)
+                if new_param is not fsdp_param.sharded_param:
+                    if torch.__future__.get_swap_module_params_on_conversion():
+                        raise AssertionError(
+                            "Expects swap_tensors to preserve object but got "
+                            f"{new_param} instead of {fsdp_param.sharded_param}"
+                        )
+                    else:
+                        raise AssertionError(
+                            "Please set torch.__future__.set_swap_module_params_on_conversion(True) "
+                            "to use _apply methods with FSDP"
+                        )
+                local_tensor = new_param._local_tensor
+                padded_sharded_size = fsdp_param.padded_sharded_param_size
+                if local_tensor.size() != padded_sharded_size:
+                    padded_local_tensor = local_tensor.new_zeros(padded_sharded_size)
+                    padded_local_tensor[: local_tensor.size(0)].copy_(local_tensor)
+                    local_tensor = padded_local_tensor
+                fsdp_param._sharded_param_data = local_tensor.view(-1)
+                assert isinstance(fsdp_param.sharded_param, DTensor)  # mypy
+                fsdp_param.sharded_param._local_tensor = local_tensor[
+                    : fsdp_param.sharded_size[0]
+                ]
+        return ret

venv/lib/python3.10/site-packages/torch/distributed/checkpoint/__init__.py

@@ -0,0 +1,15 @@
+from .api import CheckpointException
+from .default_planner import DefaultLoadPlanner, DefaultSavePlanner
+from .filesystem import FileSystemReader, FileSystemWriter
+from .fsspec import FsspecReader, FsspecWriter
+from .metadata import (
+    BytesStorageMetadata,
+    ChunkStorageMetadata,
+    Metadata,
+    TensorStorageMetadata,
+)
+from .optimizer import load_sharded_optimizer_state_dict
+from .planner import LoadPlan, LoadPlanner, ReadItem, SavePlan, SavePlanner, WriteItem
+from .state_dict_loader import load, load_state_dict
+from .state_dict_saver import async_save, save, save_state_dict
+from .storage import StorageReader, StorageWriter

venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_dedup_save_plans.py

@@ -0,0 +1,49 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import dataclasses
+from collections import defaultdict
+from typing import Dict, List, Set
+
+from torch.distributed.checkpoint.metadata import MetadataIndex
+from torch.distributed.checkpoint.planner import SavePlan, WriteItem
+
+__all__ = ["dedup_save_plans"]
+
+
+def dedup_save_plans(all_plans: List[SavePlan]) -> List[SavePlan]:
+    """
+    Removes duplicate entries from appearing on multiple SavePlans. For each duplicate across
+    a set of SavePlans, only the smallest SavePlan in terms of planned storage keeps the entry.
+    """
+
+    write_item_to_plan_indices: Dict[MetadataIndex, Set[int]] = defaultdict(set)
+    write_item_idx_to_write_item: Dict[MetadataIndex, WriteItem] = {}
+    for plan_idx, plan in enumerate(all_plans):
+        for write_item in plan.items:
+            # map each write item to its plan
+            write_item_to_plan_indices[write_item.index].add(plan_idx)
+            write_item_idx_to_write_item[write_item.index] = write_item
+
+    # put item in the plan with the smallest size and remove it from the other plan_indices
+    to_remove: List[Set] = [set() for _ in range(len(all_plans))]
+    plan_to_size = [0] * len(all_plans)
+    for write_item_idx, plan_indices in write_item_to_plan_indices.items():
+        select_plan_idx = min(plan_indices, key=lambda plan_idx: plan_to_size[plan_idx])
+
+        write_item = write_item_idx_to_write_item[write_item_idx]
+        # essentially ignores the storage size of anything that is not a tensor, since
+        # we don't know how much storage they represent
+        plan_to_size[select_plan_idx] += write_item.tensor_storage_size() or 1
+
+        plan_indices.remove(select_plan_idx)
+        for plan_idx in plan_indices:
+            to_remove[plan_idx].add(write_item_idx)
+
+    for plan_idx, remove_set in enumerate(to_remove):
+        new_items = [
+            write_item
+            for write_item in all_plans[plan_idx].items
+            if write_item.index not in remove_set
+        ]
+        all_plans[plan_idx] = dataclasses.replace(all_plans[plan_idx], items=new_items)
+
+    return all_plans

venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_dedup_tensors.py

@@ -0,0 +1,59 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+import dataclasses
+import logging
+from typing import Dict, List
+
+from torch.distributed.checkpoint.metadata import MetadataIndex
+from torch.distributed.checkpoint.planner import SavePlan
+
+__all__ = ["dedup_tensors"]
+
+
+def init_logger() -> logging.Logger:
+    logger = logging.getLogger(__name__)
+    level = logging.INFO
+    logger.setLevel(level)
+    console = logging.StreamHandler()
+    formatter = logging.Formatter(
+        "%(asctime)s %(filename)s:%(lineno)s %(levelname)s p:%(processName)s t:%(threadName)s: %(message)s"
+    )
+    console.setFormatter(formatter)
+    console.setLevel(level)
+    logger.addHandler(console)
+    logger.propagate = False
+    return logger
+
+
+logger = init_logger()
+
+
+# TODO add docstring for dedup_tensors
+def dedup_tensors(all_plans: List[SavePlan]) -> List[SavePlan]:
+    all_plans = list(all_plans)
+    key_to_plan: Dict[MetadataIndex, List[int]] = {}
+    for plan_idx, plan in enumerate(all_plans):
+        for write_item in plan.items:
+            key_to_plan.setdefault(write_item.index, []).append(plan_idx)
+
+    replicated_items = {k: v for k, v in key_to_plan.items() if len(v) > 1}
+
+    # Remove duplicates by always keeping the first entry.
+    # Compute the per-rank remove set.
+    plan_to_keys: Dict[int, List[MetadataIndex]] = {}
+    for key, plans in replicated_items.items():
+        for plan_idx in plans[1:]:
+            plan_to_keys.setdefault(plan_idx, []).append(key)
+    if len(plan_to_keys) > 0:
+        logger.info("Duplicate keys to remove: %s", plan_to_keys)
+
+    for plan_idx, keys in plan_to_keys.items():
+        key_set = set(keys)
+        # rewrite items and remove elements
+        new_items = [
+            write_item
+            for write_item in all_plans[plan_idx].items
+            if write_item.index not in key_set
+        ]
+        all_plans[plan_idx] = dataclasses.replace(all_plans[plan_idx], items=new_items)
+
+    return all_plans

venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_fsspec_filesystem.py

@@ -0,0 +1,15 @@
+# Mypy will not try inferring the types of any 3rd party libraries installed.
+# mypy: ignore-errors
+
+import logging
+
+from torch.distributed.checkpoint.fsspec import (  # noqa: F401  # noqa: F401
+    FsspecReader,
+    FsspecWriter,
+)
+
+log = logging.getLogger(__name__)
+log.warning(
+    "FSSpec Filesystem has been made public, please update your "
+    "import to torch.distributed.checkpoint"
+)

venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_nested_dict.py

@@ -0,0 +1,53 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+from typing import Dict, Tuple
+
+from torch.distributed.checkpoint.metadata import STATE_DICT_TYPE
+
+from ._traverse import OBJ_PATH, set_element, STATE_DICT_ITEM, traverse_state_dict
+
+"""
+TODO:
+Need to add ability to handle tuple, OrderedDict, NamedTuple.
+Update mappings from dict to a class.
+Change set_element to recreate the right type for tuple, OrderedDict, and NamedTuple.
+"""
+
+
+FLATTEN_MAPPING = Dict[str, OBJ_PATH]
+
+
+# TODO: Update Docstring for nested_dict.py
+def flatten_state_dict(
+    state_dict: STATE_DICT_TYPE,
+) -> Tuple[STATE_DICT_TYPE, FLATTEN_MAPPING]:
+    """
+    Flatten ``state_dict`` made of nested dicts and lists into a top level dictionary.
+
+    Use ``unflatten_state_dict`` to revert this process.
+    Returns:
+        A tuple with the flatten state_dict and a mapping from original to new state_dict.
+    N.B. The new keys are derived from the object paths, joined by dot.
+        For example: ``{ 'a': {'b':...}}`` results in the key `a.b`.
+    """
+    flattened: STATE_DICT_TYPE = {}
+    mappings: FLATTEN_MAPPING = {}
+
+    def flat_copy(path: OBJ_PATH, value: STATE_DICT_ITEM) -> None:
+        new_fqn = ".".join(map(str, path))
+        if new_fqn in flattened:
+            raise ValueError(f"duplicated flatten key {new_fqn}")
+        flattened[new_fqn] = value
+        mappings[new_fqn] = path
+
+    traverse_state_dict(state_dict, flat_copy)
+    return flattened, mappings
+
+
+def unflatten_state_dict(
+    state_dict: STATE_DICT_TYPE, mapping: FLATTEN_MAPPING
+) -> STATE_DICT_TYPE:
+    """Restore the original nested state_dict according to ``mapping`` and the flattened ``state_dict``."""
+    nested: STATE_DICT_TYPE = {}
+    for key, value in state_dict.items():
+        set_element(nested, mapping[key], value)
+    return nested

venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_sharded_tensor_utils.py

@@ -0,0 +1,103 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+
+import copy
+
+import torch.distributed as dist
+from torch.distributed._shard.sharded_tensor import Shard, ShardedTensor, ShardMetadata
+from torch.distributed._shard.sharded_tensor.metadata import ShardedTensorMetadata
+from torch.distributed.checkpoint.metadata import STATE_DICT_TYPE
+from torch.distributed.remote_device import _remote_device
+
+from ._traverse import OBJ_PATH, set_element, STATE_DICT_ITEM, traverse_state_dict
+from .utils import _element_wise_add, _normalize_device_info
+
+
+# TODO: We need to refactor this code.
+def _flatten_sharded_tensors(state_dict: STATE_DICT_TYPE) -> STATE_DICT_TYPE:
+    r"""
+    Transform ``state_dict`` by flattening all nested ShardedTensor instances found.
+
+    The resulting ShardedTensor instances are only correct regarding the local shard and
+    MUST not be used for any other purpose but checkpointing, as no operator will work with them.
+
+    This function should be used in conjunction with a state_dict produced by FSDP's
+    StateDictType.SHARDED_STATE_DICT methods.
+    """
+    new_state_dict: STATE_DICT_TYPE = {}
+
+    def rewrite_dict(path: OBJ_PATH, value: STATE_DICT_ITEM) -> None:
+        if not isinstance(value, ShardedTensor):
+            set_element(new_state_dict, path, value)
+            return
+        shards = value.local_shards()
+
+        if len(shards) == 0:
+            return
+        if len(shards) != 1:
+            set_element(new_state_dict, path, value)
+            return
+
+        outer_shard = shards[0]
+
+        inner_st = outer_shard.tensor
+        if not isinstance(inner_st, ShardedTensor):
+            set_element(new_state_dict, path, value)
+            return
+
+        if len(inner_st.local_shards()) != 1:
+            raise ValueError("Cannot handle inner tensor with more than 1 shard")
+        inner_shard = inner_st.local_shards()[0]
+
+        local_shards = [
+            Shard(
+                tensor=inner_shard.tensor,
+                metadata=ShardMetadata(
+                    shard_offsets=_element_wise_add(
+                        outer_shard.metadata.shard_offsets,
+                        inner_shard.metadata.shard_offsets,
+                    ),
+                    shard_sizes=inner_shard.metadata.shard_sizes,
+                    placement=f"rank:{dist.get_rank()}/{inner_shard.tensor.device}",
+                ),
+            )
+        ]
+
+        st_meta: ShardedTensorMetadata = copy.deepcopy(value.metadata())
+        other_rank = 0 if dist.get_rank() > 0 else 1
+        device_info = _normalize_device_info(inner_shard.tensor.device.type, 0)
+
+        # Remove the outer ST shard the inner ST covers
+        for i, shard_md in enumerate(st_meta.shards_metadata):
+            if shard_md.shard_offsets == outer_shard.metadata.shard_offsets:
+                st_meta.shards_metadata.pop(i)
+                break
+
+        # Attribute other rank for the other shards
+        for shard_md in st_meta.shards_metadata:
+            shard_md.placement = _remote_device(f"rank:{other_rank}/{device_info}")
+
+        # Add other inner shards from the inner tensor
+        for inner_md in inner_st.metadata().shards_metadata:
+            if inner_md.shard_offsets != inner_shard.metadata.shard_offsets:
+                st_meta.shards_metadata.append(
+                    ShardMetadata(
+                        shard_offsets=_element_wise_add(
+                            outer_shard.metadata.shard_offsets,
+                            inner_md.shard_offsets,
+                        ),
+                        shard_sizes=inner_md.shard_sizes,
+                        placement=f"rank:{other_rank}/{device_info}",
+                    )
+                )
+
+        # Finally add this shard
+        st_meta.shards_metadata.append(local_shards[0].metadata)
+
+        st = ShardedTensor._init_from_local_shards_and_global_metadata(
+            local_shards=local_shards,
+            sharded_tensor_metadata=st_meta,
+        )
+        set_element(new_state_dict, path, st)
+
+    traverse_state_dict(state_dict, rewrite_dict)
+    return new_state_dict

venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_storage_utils.py

@@ -0,0 +1,50 @@
+import os
+from typing import List, Type, Union
+
+from .filesystem import FileSystemReader, FileSystemWriter
+
+from .storage import StorageReader, StorageWriter
+
+
+def _storage_setup(
+    storage: Union[StorageReader, StorageWriter, None],
+    checkpoint_id: Union[str, os.PathLike, None],
+    reader: bool = False,
+) -> Union[None, StorageReader, StorageWriter]:
+    if storage:
+        if checkpoint_id is not None:
+            storage.reset(checkpoint_id)
+        return storage
+
+    if not checkpoint_id:
+        raise RuntimeError(
+            "`checkpoint_id` must be specificed if "
+            "storage_reader/storage_writer is None."
+        )
+
+    targets: List[Type[Union[StorageReader, StorageWriter]]] = []
+    if reader:
+        targets = [
+            FileSystemReader,
+        ]
+    else:
+        targets = [
+            FileSystemWriter,
+        ]
+    try:
+        from .fsspec import FsspecReader, FsspecWriter
+
+        targets.append(FsspecReader if reader else FsspecWriter)
+    except Exception:
+        pass
+
+    for target in targets:
+        if target.validate_checkpoint_id(checkpoint_id):
+            storage = target(checkpoint_id)  # type: ignore[call-arg]
+            storage.reset(checkpoint_id)
+            return storage
+
+    raise RuntimeError(
+        "Cannot detect which StorageReader or StorageWriter to use. "
+        "Please specify the storage_reader/storage_writer."
+    )

venv/lib/python3.10/site-packages/torch/distributed/checkpoint/_traverse.py

@@ -0,0 +1,167 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates
+from typing import (
+    Callable,
+    cast,
+    Collection,
+    List,
+    Mapping,
+    MutableMapping,
+    Optional,
+    Tuple,
+    TypeVar,
+    Union,
+)
+
+import torch
+from torch.distributed._shard.sharded_tensor.api import ShardedTensor
+from torch.distributed._tensor import DTensor
+from torch.distributed.checkpoint.metadata import STATE_DICT_TYPE
+
+PATH_ITEM = Union[str, int]
+OBJ_PATH = Tuple[PATH_ITEM, ...]
+T = TypeVar("T")
+
+STATE_DICT_ITEM = object
+CONTAINER_TYPE = MutableMapping[PATH_ITEM, STATE_DICT_ITEM]
+
+__all__ = ["traverse_state_dict", "set_element", "get_element", "print_tensor"]
+
+
+def _keep_visiting_tensors(value: STATE_DICT_ITEM) -> bool:
+    return isinstance(value, torch.Tensor)
+
+
+# TODO: update docstring for traverse.py
+def traverse_state_dict(
+    state_dict: STATE_DICT_TYPE,
+    visitor: Callable[[OBJ_PATH, STATE_DICT_ITEM], None],
+    keep_traversing: Callable[[STATE_DICT_ITEM], bool] = _keep_visiting_tensors,
+) -> None:
+    """
+    Invoke ``visitor`` for each value recursively in ``state_dict``.
+
+    Traversal is short-circuited when if finds a collection for which ``keep_visiting_tensors`` evaluates
+    to false for all elements.
+    By default, all collections with at least one ``torch.Tensor`` element are traversed.
+    Visitor takes a path argument that is a tuple of the keys used to reach it.
+    """
+
+    # a value is terminal if it has no other containers values inside it
+    def _is_terminal(value: STATE_DICT_ITEM) -> bool:
+        values: Collection[STATE_DICT_ITEM]
+        if isinstance(value, Mapping):
+            values = value.values()
+        elif isinstance(value, list):
+            values = value
+        else:
+            return True
+
+        for entry in values:
+            if isinstance(entry, (Mapping, list)) and not _is_terminal(entry):
+                return False
+            if keep_traversing is not None and keep_traversing(entry):
+                return False
+        return True
+
+    def _traverse_obj(path: OBJ_PATH, value: STATE_DICT_ITEM) -> None:
+        if _is_terminal(value):
+            visitor(path, value)
+        elif isinstance(value, Mapping):
+            for k, v in value.items():
+                _traverse_obj(path + (str(k),), v)
+        elif isinstance(value, list):
+            for i, v in enumerate(value):
+                _traverse_obj(path + (i,), v)
+
+    for key, value in state_dict.items():
+        _traverse_obj((str(key),), value)
+
+
+def set_element(
+    root_dict: STATE_DICT_TYPE, path: OBJ_PATH, value: STATE_DICT_ITEM
+) -> None:
+    """Set ``value`` in ``root_dict`` along the ``path`` object path."""
+    cur_container = cast(CONTAINER_TYPE, root_dict)
+
+    def extend_list(lst: List[STATE_DICT_ITEM], idx: int) -> None:
+        while len(lst) <= idx:
+            lst.append(None)
+
+    for i in range(1, len(path)):
+        prev_key = path[i - 1]
+        key = path[i]
+        def_val = cast(STATE_DICT_ITEM, {} if type(key) == str else [])
+
+        if isinstance(cur_container, Mapping):
+            cur_container = cast(
+                CONTAINER_TYPE, cur_container.setdefault(prev_key, def_val)
+            )
+        else:
+            extend_list(cur_container, prev_key)
+            if cur_container[prev_key] is None:
+                cur_container[prev_key] = def_val
+            cur_container = cur_container[prev_key]
+
+    key = path[-1]
+    if type(key) == int:
+        extend_list(cast(List[STATE_DICT_ITEM], cur_container), key)
+
+    cur_container[key] = value
+
+
+def get_element(
+    root_dict: STATE_DICT_TYPE,
+    path: OBJ_PATH,
+    default_value: Optional[T] = None,
+) -> Optional[T]:
+    """Retrieve the value at ``path``from ``root_dict``, returning ``default_value`` if not found."""
+    cur_value = cast(CONTAINER_TYPE, root_dict)
+    for part in path:
+        if type(part) is int:
+            if not isinstance(cur_value, list) or len(cur_value) < part:
+                return default_value
+        elif not isinstance(cur_value, Mapping) or part not in cur_value:
+            return default_value
+
+        cur_value = cast(CONTAINER_TYPE, cur_value[part])
+    return cast(Optional[T], cur_value)
+
+
+def _print_nested(
+    value: STATE_DICT_ITEM,
+    prefix: str = "",
+    print_fun: Callable[[str], None] = print,
+) -> None:
+    if type(value) is ShardedTensor:
+        print_fun(f"{prefix} ShardedTensor size: {value.size()}")
+        for shard in value.local_shards():
+            _print_nested(
+                shard.tensor,
+                f"{shard.metadata.shard_offsets} ",
+                print_fun=print_fun,
+            )
+    elif type(value) is (DTensor):
+        print_fun(f"{prefix} DistributedTensor size: {value.size()}")
+        # TODO: add local offset for _local_tensor in print_nested.
+        _print_nested(
+            value._local_tensor,
+            print_fun=print_fun,
+        )
+    elif isinstance(value, torch.Tensor):
+        print_fun(f"{prefix} Tensor size: {value.size()}")
+    else:
+        print_fun(f"{prefix} Type: {type(value)}")
+
+
+def print_tensor(
+    path: OBJ_PATH,
+    value: STATE_DICT_ITEM,
+    print_fun: Callable[[str], None] = print,
+) -> None:
+    """
+    Use this callback with traverse_state_dict to print its content.
+
+    By default the content is printed using the builtin ``print`` but this can
+    be change by passing a different ``print_fun` callable.
+    """
+    _print_nested(value, prefix=str(path), print_fun=print_fun)

venv/lib/python3.10/site-packages/torch/distributed/checkpoint/api.py

@@ -0,0 +1,41 @@
+import traceback as tb
+from typing import Any, Dict, Tuple
+
+WRAPPED_EXCEPTION = Tuple[BaseException, tb.StackSummary]
+
+__all__ = ["CheckpointException"]
+
+
+def _wrap_exception(exc: BaseException) -> WRAPPED_EXCEPTION:
+    return (exc, tb.extract_tb(exc.__traceback__))
+
+
+def _is_wrapped_exception(obj: Any) -> bool:
+    if not isinstance(obj, tuple):
+        return False
+    if len(obj) != 2:
+        return False
+    return isinstance(obj[0], BaseException) and isinstance(obj[1], tb.StackSummary)
+
+
+class CheckpointException(BaseException):
+    """Exception raised if failure was detected as part of a checkpoint load or save."""
+
+    def __init__(self, msg: str, failures: Dict[int, WRAPPED_EXCEPTION]):
+        super().__init__(msg, failures)
+        self._failures = failures
+
+    @property
+    def failures(self) -> Dict[int, WRAPPED_EXCEPTION]:
+        """Return a dictionary mapping node ranks to their associated exceptions in case of failure."""
+        return self._failures
+
+    def __str__(self):
+        str = f"CheckpointException ranks:{self._failures.keys()}\n"
+        for rank, exc_pair in self._failures.items():
+            exc, trace = exc_pair
+            str += f"Traceback (most recent call last): (RANK {rank})\n"
+            if trace is not None:
+                str += "".join(tb.format_list(trace))
+            str += "".join(tb.format_exception_only(type(exc), value=exc))
+        return str