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 infer_4_47_1/lib/python3.10/site-packages/sympy/stats/__pycache__/crv_types.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
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 infer_4_47_1/lib/python3.10/site-packages/sympy/core/__pycache__/expr.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
+infer_4_47_1/lib/python3.10/site-packages/torch/testing/_internal/__pycache__/common_utils.cpython-310.pyc filter=lfs diff=lfs merge=lfs -text
+infer_4_30_0/lib/python3.10/site-packages/vllm/vllm_flash_attn/_vllm_fa2_C.abi3.so filter=lfs diff=lfs merge=lfs -text

infer_4_30_0/lib/python3.10/site-packages/vllm/vllm_flash_attn/_vllm_fa2_C.abi3.so

@@ -0,0 +1,3 @@
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+oid sha256:c666388d3fc323a1e8ec1a89543d391a531cc405dc112ea1c66c372b620664ea
+size 220831720

infer_4_47_1/lib/python3.10/site-packages/torch/distributed/_composable/__init__.py

@@ -0,0 +1,4 @@
+from .checkpoint_activation import checkpoint
+from .contract import _get_registry, contract
+from .fully_shard import fully_shard
+from .replicate import replicate

infer_4_47_1/lib/python3.10/site-packages/torch/distributed/_composable/checkpoint_activation.py

@@ -0,0 +1,126 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+from contextlib import contextmanager, nullcontext
+from typing import Any, ContextManager, Dict, Optional, Tuple
+
+import torch
+import torch.nn as nn
+from torch.utils.checkpoint import (
+    _checkpoint_without_reentrant_generator,
+    _DEFAULT_DETERMINISM_MODE,
+)
+
+from .contract import contract
+
+
+@contextmanager
+def _no_hook(module: nn.Module, user_ctx: Optional[ContextManager] = None):
+    r"""
+    Disable hooks installed by checkpoint to avoid unintentional recursion
+    during backward recomputation.
+    """
+
+    with user_ctx if user_ctx else nullcontext():
+        orig_enable_hook = checkpoint.state(module).enable_hook
+        checkpoint.state(module).enable_hook = False
+        try:
+            yield
+        finally:
+            checkpoint.state(module).enable_hook = orig_enable_hook
+
+
+@contract()
+def checkpoint(module: nn.Module, **kwargs) -> nn.Module:
+    r"""
+    This is a composable activation checkpointing API. Unlike functional
+    activation checkpointing APIs, this one does not require changing model
+    source code. Unlike ``nn.Module`` wrapper activation checkpointing APIs,
+    this one does not modify model structure or fully-qualified names either.
+    Under the hood, it registers activation checkpointing logic as pre- and
+    post-forward hooks. Hence, this API can be easily applied to any model or
+    sub-modules in the model.
+
+    Args:
+        module (nn.Module): the target model or sub-module to apply activation
+            checkpointing.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> import torch.nn as nn
+        >>>
+        >>> class MyModel(nn.Module):
+        >>>     def __init__(self) -> None:
+        >>>         super().__init__()
+        >>>         self.l1 = nn.Linear(10, 10)
+        >>>         self.l2 = nn.Linear(10, 10)
+        >>>
+        >>>     def forward(self, x):
+        >>>         return self.l2(self.l1(x))
+        >>>
+        >>> model = MyModel()
+        >>> checkpoint(model.l1)  # apply activation checkpointing only to l1
+        >>> model(torch.zeros(2, 10)).sum().backward()
+
+    """
+    torch._C._log_api_usage_once("torch.distributed.checkpoint")
+
+    use_reentrant = kwargs.pop("use_reentrant", False)
+    if use_reentrant:
+        raise NotImplementedError(
+            "use_reentrant=True is not supported in composable checkpoint. "
+            "Please use torch.utils.checkpoint.checkpoint instead."
+        )
+    preserve_rng_state = kwargs.pop("preserve_rng_state", True)
+    user_context_fns = kwargs.pop("context_fn", None)
+    determinism_check = kwargs.pop("determinism_check", _DEFAULT_DETERMINISM_MODE)
+    debug = kwargs.pop("debug", False)
+
+    if kwargs:
+        raise ValueError(
+            "Unexpected keyword arguments: " + ",".join(arg for arg in kwargs)
+        )
+
+    def forward_pre_hook(
+        module: nn.Module, args: Tuple[Any, ...], kwargs: Dict[str, Any]
+    ) -> None:
+        if checkpoint.state(module).enable_hook:
+
+            def context_fns():
+                if user_context_fns is not None:
+                    ctx1, ctx2 = user_context_fns()
+                    return ctx1, _no_hook(module, ctx2)
+                else:
+                    return nullcontext(), _no_hook(module)
+
+            checkpoint.state(
+                module
+            )._ac_generator = _checkpoint_without_reentrant_generator(
+                module,
+                preserve_rng_state,
+                context_fns,
+                determinism_check,
+                debug,
+                *args,
+                **kwargs,
+            )
+            next(checkpoint.state(module)._ac_generator)
+
+    def forward_hook(module: nn.Module, inputs: Tuple[Any, ...], output: Any) -> Any:
+        if checkpoint.state(module).enable_hook:
+            try:
+                next(checkpoint.state(module)._ac_generator)
+            except StopIteration:
+                pass
+            else:
+                raise RuntimeError(
+                    "Expected non-reentrant activation checkpoint generator to be exhausted, but it was not!"
+                )
+
+        #  Ensure that we no longer hold on to the generator. always_call=True helps ensure we
+        # clear this even in the case of exception in fwd pass.
+        checkpoint.state(module)._ac_generator = None
+
+    checkpoint.state(module).enable_hook = True
+    module.register_forward_pre_hook(forward_pre_hook, with_kwargs=True)
+    module.register_forward_hook(forward_hook, prepend=True, always_call=True)
+    return module

infer_4_47_1/lib/python3.10/site-packages/torch/distributed/_composable/contract.py

@@ -0,0 +1,224 @@
+# mypy: allow-untyped-defs
+import uuid
+from collections import OrderedDict
+from functools import wraps
+from typing import Callable, Dict, List, Optional, Sequence, Type, Union
+
+import torch
+import torch.nn as nn
+from torch.distributed._composable_state import _State
+from torch.distributed.utils import _get_root_modules
+
+
+def generate_state_key(string="__composable_api_state_key"):
+    return f"{string}_{str(uuid.uuid4())}"
+
+
+STATE_KEY = generate_state_key()
+REGISTRY_KEY = generate_state_key()
+
+
+# TODO: we can add additional info to RegistryItem to share across APIs. E.g.,
+# we can add args and kwargs here, and then we can detect whether fully_shard
+# is combined with reentrant activation checkpointing and error out with a clear
+# message.
+class RegistryItem:
+    pass
+
+
+def contract(state_cls: Type[_State] = _State):
+    r"""
+    Decorate a function as a composable distributed API, where the first
+    argument of the function must be an :class:`nn.Module` instance or sequence
+    of :class:`nn.Module` instances.
+
+    The decorator verifies that the decorated function does not modify
+    fully-qualified names (FQNs) for parameters, buffers, or modules. The
+    decorated function can return different module instances than the input
+    modules; the FQN invariant will be enforced following the input order.
+
+    When a function ``func`` is decorated by ``@contract()``, a
+    ``.state(module: nn.Module)`` method will be installed to the decorated
+    function. Then you can retrieve and modify the state on a module by calling
+    ``func.state(module)``.
+
+    Example::
+        >>> # xdoctest: +SKIP
+        >>> import torch.nn as nn
+        >>>
+        >>> class MyModel(nn.Module):
+        >>>     def __init__(self) -> None:
+        >>>         super().__init__()
+        >>>         self.l1 = nn.Linear(10, 10)
+        >>>         self.l2 = nn.Linear(10, 10)
+        >>>
+        >>>     def forward(self, x):
+        >>>         return self.l2(self.l1(x))
+        >>>
+        >>> @contract()
+        >>> def my_feature(module: nn.Module) -> nn.Module:
+        >>>     my_feature.state(module).some_state = "any value"
+        >>>     return module
+        >>>
+        >>> model = MyModel()
+        >>> my_feature(model.l1)
+        >>> assert my_feature.state(model.l1).some_state == "any value"
+        >>> my_feature(model.l2)
+        >>> model(torch.randn(2, 10)).sum().backward()
+    """
+
+    # wraps will make functions decorated with contract() pickleable - needed for integration with torch.package
+    @wraps(state_cls)
+    def inner(func):
+        @wraps(func)
+        def wrapper(
+            module: Union[nn.Module, Sequence[nn.Module]], *args, **kwargs
+        ) -> Optional[nn.Module]:
+            inp_module = module
+            if isinstance(module, nn.Module):
+                modules = [module]
+            else:
+                # If the user passes a sequence of modules, then we assume that
+                # we only need to insert the state object on the root modules
+                # (i.e. those without a parent) among the passed-in modules.
+                modules = _get_root_modules(list(module))
+            state = state_cls()  # shared across all modules
+            registry_item = RegistryItem()  # shared across all modules
+
+            # `func` is allowed to return different module instances than the
+            # input modules as long as FQNs are preserved following the input
+            # module order
+            all_orig_named_params: List[Dict[str, nn.Parameter]] = []
+            all_orig_named_buffers: List[Dict[str, torch.Tensor]] = []
+            all_orig_named_modules: List[Dict[str, nn.Module]] = []
+
+            for module in modules:
+                default_all_state: Dict[Callable, _State] = OrderedDict()
+                default_registry: Dict[str, RegistryItem] = OrderedDict()
+                all_state: Dict[Callable, _State] = module.__dict__.setdefault(  # type: ignore[call-overload]
+                    STATE_KEY, default_all_state
+                )
+                if not isinstance(all_state, dict):
+                    raise AssertionError(
+                        f"Distributed composable API states corrupted: {all_state}"
+                    )
+                registry: Dict[str, RegistryItem] = module.__dict__.setdefault(  # type: ignore[call-overload]
+                    REGISTRY_KEY, default_registry
+                )
+                if not isinstance(registry, dict):
+                    raise AssertionError(
+                        f"Distributed composable API registry corrupted: {registry}"
+                    )
+                if func in all_state or func.__name__ in registry:
+                    raise AssertionError(
+                        "Each distinct composable distributed API can only be applied to a "
+                        f"module once. {func.__name__} has already been applied to the "
+                        f"following module:\n{module}"
+                    )
+                all_state.setdefault(func, state)
+                registry.setdefault(func.__name__, registry_item)
+
+                all_orig_named_params.append(OrderedDict(module.named_parameters()))
+                all_orig_named_buffers.append(OrderedDict(module.named_buffers()))
+                all_orig_named_modules.append(OrderedDict(module.named_modules()))
+
+            updated = func(inp_module, *args, **kwargs)
+            if updated is None:
+                updated = inp_module
+            if isinstance(updated, nn.Module):
+                updated_modules = [updated]
+            else:
+                updated_modules = _get_root_modules(list(inp_module))
+
+            all_new_named_params: List[Dict[str, nn.Parameter]] = []
+            all_new_named_buffers: List[Dict[str, torch.Tensor]] = []
+            all_new_named_modules: List[Dict[str, nn.Module]] = []
+            for module in updated_modules:
+                all_new_named_params.append(OrderedDict(module.named_parameters()))
+                all_new_named_buffers.append(OrderedDict(module.named_buffers()))
+                all_new_named_modules.append(OrderedDict(module.named_modules()))
+
+            num_orig_modules = len(all_orig_named_modules)
+            num_new_modules = len(all_new_named_modules)
+            if num_orig_modules != num_new_modules:
+                raise AssertionError(
+                    f"{func.__name__} should return the same number of modules as input modules"
+                    f"Inputs: {num_orig_modules} modules\n"
+                    f"Outputs: {num_new_modules} modules"
+                )
+
+            def check_fqn(orig_fqns: List[str], new_fqns: List[str], check_key: str):
+                if orig_fqns == new_fqns:
+                    return
+
+                orig_fqn_set, new_fqn_set = set(orig_fqns), set(new_fqns)
+                orig_only = orig_fqn_set - new_fqn_set
+                new_only = new_fqn_set - orig_fqn_set
+                if len(orig_only) or len(new_only):
+                    raise RuntimeError(
+                        f"{check_key}"
+                        "Composable distributed API implementations cannot modify FQNs.\n"
+                        f"FQNs only in original: {orig_only}\n"
+                        f"FQNs only in new: {new_only}"
+                    )
+                else:
+                    raise RuntimeError(
+                        f"{check_key}"
+                        "Composable distributed API implementations cannot modify "
+                        "the order of FQNs.\n"
+                        f"Original FQNs: {orig_only}\n"
+                        f"New FQNs: {new_only}"
+                    )
+
+            for orig_named_params, new_named_params in zip(
+                all_orig_named_params, all_new_named_params
+            ):
+                check_fqn(
+                    list(orig_named_params.keys()),
+                    list(new_named_params.keys()),
+                    "Checking parameters: ",
+                )
+            for orig_named_buffers, new_named_buffers in zip(
+                all_orig_named_buffers, all_new_named_buffers
+            ):
+                check_fqn(
+                    list(orig_named_buffers.keys()),
+                    list(new_named_buffers.keys()),
+                    "Checking buffers: ",
+                )
+            for orig_named_modules, new_named_modules in zip(
+                all_orig_named_modules, all_new_named_modules
+            ):
+                check_fqn(
+                    list(orig_named_modules.keys()),
+                    list(new_named_modules.keys()),
+                    "Checking modules: ",
+                )
+
+            # TODO: verify that installed distributed paradigms are compatible with
+            # each other.
+
+            return updated
+
+        def get_state(module: nn.Module) -> Optional[_State]:
+            return module.__dict__.setdefault(  # type: ignore[call-overload]
+                STATE_KEY,
+                {},  # TODO(@yhcharles): this is a temporary fix, need a better way
+            ).get(
+                func
+            )  # type: ignore[call-overload]
+
+        wrapper.state = get_state  # type: ignore[attr-defined]
+
+        return wrapper
+
+    return inner
+
+
+def _get_registry(module: nn.Module) -> Optional[Dict[str, RegistryItem]]:
+    r"""
+    Get an ``OrderedDict`` of composable APIs that have been applied to the
+    ``module``, indexed by the API name. If no API has been applied, then this
+    returns ``None``.
+    """
+    return getattr(module, REGISTRY_KEY, None)

infer_4_47_1/lib/python3.10/site-packages/torch/distributed/_composable/fsdp/__init__.py

@@ -0,0 +1,2 @@
+from ._fsdp_api import CPUOffloadPolicy, MixedPrecisionPolicy, OffloadPolicy
+from .fully_shard import FSDPModule, fully_shard, register_fsdp_forward_method

infer_4_47_1/lib/python3.10/site-packages/torch/distributed/_composable/fsdp/_fsdp_api.py

@@ -0,0 +1,80 @@
+# mypy: allow-untyped-defs
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+
+
+@dataclass(frozen=True)
+class MixedPrecisionPolicy:
+    """
+    This configures FSDP's mixed precision. Unlike autocast, this applies mixed
+    precision at the module level, not op level, which means low-precision
+    activations are saved for backward and high-to-low-precision casts are
+    incurred only at module boundaries.
+
+    FSDP works well with module-level mixed precision since it keeps the
+    high-precision sharded parameters in memory anyway. In other words, FSDP
+    does not require any extra memory to keep a high-precision copy of the
+    parameters for the optimizer step.
+
+    Attributes:
+        param_dtype (Optional[torch.dtype]): This specifies the dtype for
+            the unsharded parameter and hence the dtype for forward/backward
+            computation and the parameter all-gather. If this is ``None``, then
+            the unsharded parameter uses the original dtype. The optimizer step
+            uses the sharded parameter in the original dtype. (Default:
+            ``None``)
+        reduce_dtype (Optional[torch.dtype]): This specifies the dtype for
+            gradient reduction (i.e. reduce-scatter or all-reduce). If this is
+            ``None`` but ``param_dtype`` is not ``None``, then the reduction
+            uses the compute dtype. This can be used to run gradient reduction
+            in full precision while using low precision for compute. If also
+            gradient reduction is disabled via :meth:`set_requires_gradient_sync`,
+            then FSDP will accumulate gradients using ``reduce_dtype``.
+            (Default: ``None``)
+        output_dtype (Optional[torch.dtype]): This specifies the dtype for
+            casting floating-point forward outputs. This can be used to
+            help implement cases where different modules have different mixed
+            precision policies. (Default: ``None``)
+        cast_forward_inputs (bool): This specifies whether FSDP should cast the
+            forward's floating-point input tensors to ``param_dtype`` or not.
+    """
+
+    param_dtype: Optional[torch.dtype] = None
+    reduce_dtype: Optional[torch.dtype] = None
+    output_dtype: Optional[torch.dtype] = None
+    cast_forward_inputs: bool = True
+
+    def __post_init__(self):
+        # Clamp `reduce_dtype` to `None` if no casting is required: since
+        # gradients are computed in `param_dtype`, if `reduce_dtype` matches,
+        # then we do not need extra casting
+        if self.param_dtype == self.reduce_dtype:
+            # Bypass the frozen dataclass checks
+            object.__setattr__(self, "reduce_dtype", None)
+
+
+@dataclass
+class OffloadPolicy:
+    """This base class represents the policy of no offloading."""
+
+
+@dataclass
+class CPUOffloadPolicy(OffloadPolicy):
+    """
+    This offload policy offloads parameters, gradients, and optimizer states to
+    CPU. Sharded parameters are copied host-to-device before all-gather. The
+    all-gathered parameters are freed according to ``reshard_after_forward``.
+    Sharded gradients are copied device-to-host in backward, and the optimizer
+    step runs on CPU with CPU optimizer states.
+
+    Attributes:
+        pin_memory (bool): Whether to pin sharded parameter and gradient
+            memory. Pinning memory allows H2D/D2H copying without blocking the
+            CPU and in turn, overlap with compute, but pinned memory cannot be
+            used by other processes. Set this to ``False`` if you have
+            insufficient CPU memory. (Default: ``True``)
+    """
+
+    pin_memory: bool = True

infer_4_47_1/lib/python3.10/site-packages/torch/distributed/_composable/fsdp/_fsdp_collectives.py

@@ -0,0 +1,477 @@
+# mypy: allow-untyped-decorators
+from typing import cast, List, NamedTuple, Optional, Tuple, Union
+
+import torch
+import torch._dynamo.compiled_autograd as ca
+import torch.distributed as dist
+from torch.distributed.distributed_c10d import ReduceOp
+from torch.distributed.tensor import DTensor
+
+from ._fsdp_common import (
+    _get_dim0_padded_size,
+    _raise_assert_with_print,
+    _to_dtype_if_needed,
+)
+from ._fsdp_param import FSDPParam, ShardedState
+
+
+class AllGatherResult(NamedTuple):
+    all_gather_output: torch.Tensor
+    all_gather_event: Optional[torch.cuda.Event]
+    all_gather_work: Optional[dist.distributed_c10d.Work]
+    # For each parameter, the all-gather input dtype for each input
+    param_all_gather_input_dtypes: List[List[torch.dtype]]
+    # For each parameter, the all-gather input numel for each input
+    param_all_gather_input_numels: List[List[int]]
+    # 1D flattened version of `param_all_gather_input_numels` saved to avoid
+    # CPU overhead from recomputing
+    all_gather_input_split_sizes: List[int]
+
+
+lib = torch.library.Library("fsdp", "FRAGMENT")  # noqa: TOR901
+
+lib.define(
+    """
+    all_gather_copy_in(
+        Tensor[] all_gather_inputs,
+        SymInt[] inp_split_sizes,
+        SymInt all_gather_input_numel,
+        SymInt world_size,
+        SymInt rank,
+        ScalarType dtype,
+        Device device
+    ) -> (Tensor, Tensor)
+    """
+)
+
+
+@torch.library.impl(lib, "all_gather_copy_in", "Meta")
+def all_gather_copy_in_meta(
+    all_gather_inputs: List[torch.Tensor],
+    inp_split_sizes: List[int],
+    all_gather_input_numel: int,
+    world_size: int,
+    rank: int,
+    dtype: torch.dtype,
+    device: torch.device,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    all_gather_output = torch.empty(
+        (all_gather_input_numel * world_size,), dtype=dtype, device="meta"
+    )
+    all_gather_input = all_gather_output.narrow(
+        0, all_gather_input_numel * rank, all_gather_input_numel
+    )
+    return all_gather_input, all_gather_output
+
+
+@torch.library.impl(lib, "all_gather_copy_in", "CUDA")
+@torch.library.impl(lib, "all_gather_copy_in", "CPU")
+def all_gather_copy_in_cuda(
+    all_gather_inputs: List[torch.Tensor],
+    inp_split_sizes: List[int],
+    all_gather_input_numel: int,
+    world_size: int,
+    rank: int,
+    dtype: torch.dtype,
+    device: torch.device,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    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)
+    with torch.no_grad():
+        torch._foreach_copy_(foreach_copy_dsts, all_gather_inputs)
+    return all_gather_input, all_gather_output
+
+
+lib.define(
+    "split_with_sizes_copy(Tensor all_gather_output, SymInt[] all_gather_input_split_sizes, int dim=0, *, Tensor(a!)[] out) -> ()"
+)
+
+
+@torch.library.impl(lib, "split_with_sizes_copy", "Meta")
+@torch.library.impl(lib, "split_with_sizes_copy", "CUDA")
+@torch.library.impl(lib, "split_with_sizes_copy", "CPU")
+def split_with_sizes_copy(
+    all_gather_output: torch.Tensor,
+    all_gather_input_split_sizes: List[int],
+    dim: int,
+    out: List[torch.Tensor],
+) -> None:
+    torch.split_with_sizes_copy(
+        all_gather_output, all_gather_input_split_sizes, dim=dim, out=out
+    )
+
+
+lib.define(
+    "chunk_cat(Tensor[] tensors, int dim, int num_chunks, *, Tensor(a!) out) -> ()"
+)
+
+
+@torch.library.impl(lib, "chunk_cat", "Meta")
+@torch.library.impl(lib, "chunk_cat", "CUDA")
+@torch.library.impl(lib, "chunk_cat", "CPU")
+def chunk_cat(
+    tensors: List[torch.Tensor],
+    dim: int,
+    num_chunks: int,
+    out: torch.Tensor,
+) -> None:
+    torch._chunk_cat(tensors, dim, num_chunks, out=out)
+
+
+@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()
+    with torch.cuda.stream(all_gather_copy_in_stream):
+        param_all_gather_inputs = _get_param_all_gather_inputs(fsdp_params)
+        (
+            param_all_gather_input_dtypes,
+            param_all_gather_input_numels,
+            dtype,
+        ) = _get_all_gather_input_metadatas(param_all_gather_inputs)
+        if dtype == torch.uint8:
+            all_gather_inputs = [
+                t.view(torch.uint8) for ts in param_all_gather_inputs for t in ts
+            ]
+        else:
+            all_gather_inputs = [t for ts in param_all_gather_inputs for t in ts]
+        inp_split_sizes = [t.numel() for t in all_gather_inputs]
+        all_gather_input_numel = sum(inp_split_sizes)
+        all_gather_input, all_gather_output = torch.ops.fsdp.all_gather_copy_in(
+            all_gather_inputs,
+            inp_split_sizes,
+            all_gather_input_numel,
+            world_size,
+            rank,
+            dtype,
+            device,
+        )
+        del param_all_gather_inputs
+    all_gather_stream.wait_stream(all_gather_copy_in_stream)
+    with torch.cuda.stream(all_gather_stream):
+        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,
+            param_all_gather_input_dtypes,
+            param_all_gather_input_numels,
+            inp_split_sizes,
+        )
+
+
+@torch.no_grad()
+def _get_param_all_gather_inputs(
+    fsdp_params: List[FSDPParam],
+) -> List[List[torch.Tensor]]:
+    if ca.compiled_autograd_enabled:
+        return [fsdp_param.all_gather_inputs for fsdp_param in fsdp_params]
+
+    # Intentionally try to run a fast-path that bypasses abstractions for the
+    # common FSDP case of bf16/fp32 mixed precision in order to use foreach
+    # copy for lower CPU overhead and more efficient copying in eager
+    def use_foreach_copy(fsdp_param: FSDPParam) -> bool:
+        return (
+            fsdp_param.param_dtype is not None
+            and not fsdp_param.offload_to_cpu
+            and not hasattr(fsdp_param._sharded_local_tensor, "fsdp_pre_all_gather")
+        )
+
+    param_all_gather_inputs: List[List[torch.Tensor]] = [[] for _ in fsdp_params]
+    foreach_copy_indices: List[int] = []
+    foreach_copy_inputs: List[torch.Tensor] = []
+    foreach_copy_input_numels: List[int] = []
+
+    # 1st pass: for foreach-copy parameters, get inputs and metadata for the
+    # foreach copy, and for the others, actually get their all-gather inputs
+    for i, fsdp_param in enumerate(fsdp_params):
+        if use_foreach_copy(fsdp_param):
+            foreach_copy_indices.append(i)
+            all_gather_input = (
+                fsdp_param._sharded_param_data
+                if fsdp_param.sharded_state == ShardedState.SHARDED
+                else cast(torch.Tensor, fsdp_param._sharded_post_forward_param_data)
+            )
+            foreach_copy_inputs.append(all_gather_input)
+            foreach_copy_input_numels.append(all_gather_input.numel())
+        else:
+            param_all_gather_inputs[i] = fsdp_param.all_gather_inputs
+
+    # 2nd pass: use foreach copy to compute the remaining all-gather inputs
+    if foreach_copy_inputs:
+        fsdp_param_0 = fsdp_params[foreach_copy_indices[0]]
+        param_dtype, device = fsdp_param_0.param_dtype, fsdp_param_0.device
+        flat_foreach_copy_input = torch.empty(
+            (sum(foreach_copy_input_numels),), device=device, dtype=param_dtype
+        )
+        splits = torch.split(flat_foreach_copy_input, foreach_copy_input_numels)
+        torch._foreach_copy_(splits, foreach_copy_inputs)
+        for i, split in zip(foreach_copy_indices, splits):
+            param_all_gather_inputs[i] = [split]
+
+    return param_all_gather_inputs
+
+
+@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,
+        param_all_gather_input_dtypes,
+        param_all_gather_input_numels,
+        all_gather_input_split_sizes,
+    ) = all_gather_result
+    if all_gather_event is not None:  # sync op
+        torch.cuda.current_stream().wait_event(all_gather_event)
+    if isinstance(all_gather_work, dist.distributed_c10d.Work):  # async op
+        all_gather_work.wait()
+    world_size, device = group.size(), all_gather_output.device
+    for all_gather_input_numels, all_gather_input_dtypes, fsdp_param in zip(
+        param_all_gather_input_numels, param_all_gather_input_dtypes, fsdp_params
+    ):
+        if ca.compiled_autograd_enabled:
+            fsdp_param.init_all_gather_outputs(
+                all_gather_input_numels,
+                all_gather_input_dtypes,
+                world_size,
+                device,
+                # NOTE: Under compile, make sure we always recreate all_gather_outputs
+                # per AllGather. See [Note: Invariants for torch.compile Traceable FSDP2].
+                force_recreate=True,
+            )
+        else:
+            fsdp_param.init_all_gather_outputs(
+                all_gather_input_numels, all_gather_input_dtypes, world_size, device
+            )  # no-op after 1st call
+            fsdp_param.alloc_all_gather_outputs()
+    all_gather_output = all_gather_output.view(world_size, -1)
+    gen = (t for fsdp_param in fsdp_params for t in fsdp_param.all_gather_outputs)
+    if all_gather_output.dtype == torch.uint8:
+        out = [t.view(world_size, -1).view(torch.uint8) for t in gen]
+    else:
+        out = [t.view(world_size, -1) for t in gen]
+    torch.ops.fsdp.split_with_sizes_copy(
+        all_gather_output, all_gather_input_split_sizes, dim=1, out=out
+    )
+
+
+@torch.no_grad()
+def foreach_reduce(
+    fsdp_params: List[FSDPParam],
+    unsharded_grads: List[torch.Tensor],
+    reduce_scatter_group: dist.ProcessGroup,
+    reduce_scatter_stream: torch.cuda.Stream,
+    orig_dtype: torch.dtype,
+    reduce_dtype: Optional[torch.dtype],
+    device: torch.device,
+    reduce_scatter_reduce_op: Optional[Union[dist.ReduceOp, dist.ReduceOp.RedOpType]],
+    all_reduce_group: Optional[dist.ProcessGroup],  # not `None` iff HSDP
+    all_reduce_stream: torch.cuda.Stream,
+    all_reduce_grads: bool,
+    partial_reduce_output: Optional[torch.Tensor],  # only used for HSDP
+) -> Tuple[torch.Tensor, torch.cuda.Event, torch.cuda.Event, Optional[torch.Tensor]]:
+    """
+    ``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
+    predivide_factor, postdivide_factor = _get_gradient_divide_factors(
+        reduce_scatter_group, all_reduce_group, reduce_dtype
+    )
+    world_size = reduce_scatter_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
+    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)
+    current_stream = torch.cuda.current_stream()
+    # Only after the copy-in finishes can we free the gradients
+    unsharded_grads.clear()
+    reduce_scatter_stream.wait_stream(current_stream)
+    with torch.cuda.stream(reduce_scatter_stream):
+        reduce_output = reduce_scatter_input.new_empty((reduce_scatter_output_numel,))
+        _div_if_needed(reduce_scatter_input, predivide_factor)
+        if reduce_scatter_reduce_op is None:
+            if predivide_factor is None:
+                reduce_scatter_reduce_op = ReduceOp.AVG
+            else:
+                reduce_scatter_reduce_op = ReduceOp.SUM
+        dist.reduce_scatter_tensor(
+            output=reduce_output,
+            input=reduce_scatter_input,
+            group=reduce_scatter_group,
+            op=reduce_scatter_reduce_op,
+        )
+        reduce_scatter_event = reduce_scatter_stream.record_event()
+        post_reduce_stream = reduce_scatter_stream
+        if all_reduce_group is not None:  # HSDP
+            # Accumulations must run in the reduce-scatter stream
+            if not all_reduce_grads:
+                if partial_reduce_output is not None:
+                    partial_reduce_output += reduce_output
+                else:
+                    partial_reduce_output = reduce_output
+                return (
+                    reduce_scatter_input,
+                    reduce_scatter_event,
+                    post_reduce_stream.record_event(),
+                    partial_reduce_output,
+                )
+            if partial_reduce_output is not None:
+                reduce_output += partial_reduce_output
+            post_reduce_stream = all_reduce_stream
+            all_reduce_stream.wait_stream(reduce_scatter_stream)
+            with torch.cuda.stream(all_reduce_stream):
+                dist.all_reduce(
+                    reduce_output,
+                    group=all_reduce_group,
+                    op=ReduceOp.AVG if predivide_factor is None else ReduceOp.SUM,
+                )
+    with torch.cuda.stream(post_reduce_stream):
+        _div_if_needed(reduce_output, postdivide_factor)
+        reduce_output = _to_dtype_if_needed(reduce_output, orig_dtype)
+        # View out and accumulate sharded gradients
+        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_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
+            if fsdp_param.offload_to_cpu:
+                # Only overlap the D2H copy (copying to pinned memory) if not
+                # accumulating gradients since the CPU add kernel depends on
+                # the copy result and we cannot run the add as a callback
+                non_blocking = fsdp_param.pin_memory and not to_accumulate_grad
+                # Since the GPU sharded gradient is allocated in the RS stream,
+                # we can free it here by not keeping a ref without waiting for
+                # the D2H copy since future RS-stream ops run after the copy
+                new_sharded_grad = new_sharded_grad.to(
+                    torch.device("cpu"), non_blocking=non_blocking
+                )
+                if non_blocking:
+                    # Record an event on which to block the CPU thread to
+                    # ensure that the D2H copy finishes before the optimizer
+                    fsdp_param.grad_offload_event = reduce_scatter_stream.record_event()
+            if to_accumulate_grad:
+                assert isinstance(fsdp_param.sharded_param.grad, DTensor)
+                fsdp_param.sharded_param.grad._local_tensor += new_sharded_grad
+            else:
+                new_sharded_dtensor_grad = fsdp_param.to_sharded_dtensor(
+                    new_sharded_grad
+                )
+                fsdp_param.sharded_param.grad = new_sharded_dtensor_grad
+            if not ca.compiled_autograd_enabled:
+                for hook in (
+                    getattr(fsdp_param.sharded_param, "_post_accumulate_grad_hooks", {})
+                    or {}
+                ).values():
+                    hook(fsdp_param.sharded_param)
+            padded_sharded_numel = padded_unsharded_size.numel() // world_size
+            flat_grad_offset += padded_sharded_numel
+        post_reduce_event = post_reduce_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_input, reduce_scatter_event, post_reduce_event, None
+
+
+def foreach_reduce_scatter_copy_in(
+    unsharded_grads: List[torch.Tensor],
+    reduce_scatter_input: torch.Tensor,
+    world_size: int,
+) -> None:
+    reduce_scatter_input = reduce_scatter_input.view(world_size, -1)
+    torch.ops.fsdp.chunk_cat(
+        unsharded_grads, dim=0, num_chunks=world_size, out=reduce_scatter_input
+    )
+
+
+def _get_all_gather_input_metadatas(
+    param_all_gather_inputs: List[List[torch.Tensor]],
+) -> Tuple[List[List[torch.dtype]], List[List[int]], torch.dtype]:
+    param_all_gather_input_dtypes: List[List[torch.dtype]] = []
+    param_all_gather_input_numels: List[List[int]] = []
+    all_gather_dtype = param_all_gather_inputs[0][0].dtype
+    for all_gather_inputs in param_all_gather_inputs:
+        input_dtypes: List[torch.dtype] = []
+        input_numels: List[int] = []
+        for all_gather_input in all_gather_inputs:
+            if all_gather_input.dtype != all_gather_dtype:
+                all_gather_dtype = torch.uint8
+            input_dtypes.append(all_gather_input.dtype)
+            input_numels.append(all_gather_input.numel())
+        param_all_gather_input_dtypes.append(input_dtypes)
+        param_all_gather_input_numels.append(input_numels)
+    return (
+        param_all_gather_input_dtypes,
+        param_all_gather_input_numels,
+        all_gather_dtype,
+    )
+
+
+def _get_gradient_divide_factors(
+    reduce_scatter_group: dist.ProcessGroup,
+    all_reduce_group: Optional[dist.ProcessGroup],
+    reduce_dtype: torch.dtype,
+) -> Union[Tuple[None, None], Tuple[float, float]]:
+    # For fp32/bf16, we do not need to worry about overflow/underflow, so we
+    # use NCCL's built-in division to avoid separate div kernels
+    if reduce_dtype in (torch.float32, torch.bfloat16):
+        return None, None
+    data_parallel_size = reduce_scatter_group.size()
+    if all_reduce_group is not None:
+        data_parallel_size *= all_reduce_group.size()
+    # Since fp16 has smaller dynamic range than fp32/bf16, we want to avoid
+    # overflow/underflow. For N data parallel workers, each worker computes
+    # g_i, and they collectively reduce (g_1 + ... + g_N) / N. To avoid
+    # overflow/underflow, we divide by ~sqrt(N) before/after the reduction.
+    factor: int = 1
+    while data_parallel_size % factor == 0 and data_parallel_size / factor > factor:
+        factor *= 2
+    factor = float(factor)
+    return (factor, data_parallel_size / factor)
+
+
+def _div_if_needed(tensor: torch.Tensor, div_factor: Optional[float]) -> None:
+    if div_factor is not None and div_factor > 1:
+        tensor.div_(div_factor)

infer_4_47_1/lib/python3.10/site-packages/torch/distributed/_composable/fsdp/_fsdp_common.py

@@ -0,0 +1,152 @@
+# mypy: allow-untyped-defs
+import math
+import traceback
+from dataclasses import dataclass
+from enum import auto, Enum
+from typing import Any, cast, List, Optional
+
+import torch
+import torch._dynamo.compiled_autograd as ca
+import torch.distributed as dist
+import torch.nn as nn
+from torch.distributed._composable.contract import _get_registry
+from torch.distributed.tensor import DeviceMesh, DTensor
+from torch.distributed.tensor._dtensor_spec import DTensorSpec
+
+
+@dataclass
+class DataParallelMeshInfo:
+    mesh: DeviceMesh
+    shard_mesh_dim: Optional[int] = None
+    replicate_mesh_dim: Optional[int] = None
+
+    def __post_init__(self):
+        if self.shard_mesh_dim is None and self.replicate_mesh_dim is None:
+            raise AssertionError(
+                "At least one of shard_mesh_dim and replicate_mesh_dim must not be None"
+            )
+
+
+@dataclass
+class FSDPMeshInfo(DataParallelMeshInfo):
+    def __post_init__(self):
+        super().__post_init__()
+        if self.shard_mesh_dim is None:
+            raise AssertionError("Expects non-None shard_mesh_dim")
+        self.shard_mesh_size: int = self.mesh.size(self.shard_mesh_dim)
+        self.shard_process_group = self.mesh.get_group(self.shard_mesh_dim)
+        self.shard_mesh_rank: int = self.shard_process_group.rank()
+
+
+@dataclass
+class DDPMeshInfo(DataParallelMeshInfo):
+    def __post_init__(self):
+        super().__post_init__()
+        if self.replicate_mesh_dim is None:
+            raise AssertionError("Expects non-None replicate_mesh_dim")
+        self.replicate_mesh_size: int = self.mesh.size(self.replicate_mesh_dim)
+        self.replicate_process_group = self.mesh.get_group(self.replicate_mesh_dim)
+        self.replicate_mesh_rank: int = self.replicate_process_group.rank()
+
+
+@dataclass
+class HSDPMeshInfo(FSDPMeshInfo, DDPMeshInfo):
+    def __post_init__(self):
+        # Calls `FSDPMeshInfo` -> `DDPMeshInfo` -> `DataParallelMeshInfo`
+        super().__post_init__()
+
+
+class TrainingState(Enum):
+    """Describes the training state of one FSDP state / parameter group."""
+
+    # Transition to forward starting pre-forward until post-forward
+    FORWARD = auto()
+    # Transition to pre-backward when unsharding in backward
+    PRE_BACKWARD = auto()
+    # Transition to post-backward when resharding and reducing gradients
+    POST_BACKWARD = auto()
+    # Idle before/after forward or before pre-backward/after post-backward
+    IDLE = auto()
+
+
+def _raise_assert_with_print(*args: Any, **kwargs: Any):
+    print(f"[Rank {dist.get_rank()}] ", end="")
+    print(*args, **kwargs)
+    traceback.print_stack()
+    raise AssertionError(*args, **kwargs)
+
+
+def _is_composable_with_fsdp(module: nn.Module) -> bool:
+    registry = _get_registry(module)
+    if registry is None:
+        return True
+    # Registry keys by function name
+    return "replicate" not in registry
+
+
+def _get_dim0_padded_size(tensor_size: torch.Size, dim0_factor: int) -> torch.Size:
+    padded_dim0 = math.ceil(tensor_size[0] / dim0_factor) * dim0_factor
+    return cast(torch.Size, torch.Size([padded_dim0]) + tensor_size[1:])
+
+
+def _chunk_with_empty(
+    tensor: torch.Tensor, num_chunks: int, dim: int
+) -> List[torch.Tensor]:
+    chunks = list(torch.chunk(tensor, num_chunks, dim=dim))
+    while len(chunks) < num_chunks:
+        chunks.append(chunks[0].new_empty(0))
+    return chunks
+
+
+def _get_dim0_chunked_size(
+    chunk: torch.Tensor, unchunked_size: torch.Size
+) -> torch.Size:
+    if chunk.numel() > 0:
+        return chunk.size()
+    # For 0 numel, we need to preserve trailing dims for DTensor APIs
+    return cast(torch.Size, torch.Size([0]) + unchunked_size[1:])
+
+
+def _from_local_no_grad(
+    local_tensor: torch.Tensor,
+    sharding_spec: DTensorSpec,
+) -> DTensor:
+    """
+    This method is similar to ``DTensor.from_local()`` except that in eager mode
+    it avoids some CPU overhead by avoiding default args and not being differentiable.
+    """
+
+    if not ca.compiled_autograd_enabled:
+        return DTensor(
+            # Use the local tensor directly instead of constructing a new tensor
+            # variable, e.g. with `view_as()`, since this is not differentiable
+            local_tensor,
+            sharding_spec,
+            requires_grad=local_tensor.requires_grad,
+        )
+    else:
+        return DTensor.from_local(
+            local_tensor,
+            sharding_spec.mesh,
+            sharding_spec.placements,
+            shape=sharding_spec.shape,
+            stride=sharding_spec.stride,
+        )
+
+
+def _to_dtype_if_needed(
+    tensor: torch.Tensor, dtype: Optional[torch.dtype]
+) -> torch.Tensor:
+    if dtype is not None and tensor.dtype != dtype:
+        return tensor.to(dtype)
+    return tensor
+
+
+def _cast_fp_tensor(dtype: torch.dtype, x: torch.Tensor) -> torch.Tensor:
+    if (
+        not isinstance(x, torch.Tensor)
+        or not torch.is_floating_point(x)
+        or x.dtype == dtype
+    ):
+        return x
+    return x.to(dtype)

infer_4_47_1/lib/python3.10/site-packages/torch/distributed/_composable/fsdp/_fsdp_init.py

@@ -0,0 +1,168 @@
+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.device_mesh import _get_device_handle
+from torch.distributed.tensor import DeviceMesh, DTensor, init_device_mesh
+from torch.utils._python_dispatch import is_traceable_wrapper_subclass
+
+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_modules: Tuple[nn.Module, ...]) -> List[nn.Module]:
+    modules: List[nn.Module] = []
+    root_modules_set = set(root_modules)
+    # 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 not in root_modules_set
+            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)
+
+    for root_module in root_modules:
+        dfs(root_module)
+    return modules
+
+
+def _verify_managed_param(name: str, param: nn.Parameter) -> None:
+    """
+    Verify if the parameter is accepted by fully_shard. The only restriction now
+    is that the parameter cannot be a scalar tensor (param.numel == 0) since we
+    need at least one dim to shard.
+    """
+    if len(param.shape) == 0:
+        raise ValueError(
+            "fully_shard doesn't support salar parameters. "
+            f"Change {name} to a 1D tensor with numel equal to 1."
+        )
+
+
+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 name, param in module.named_parameters(recurse=False):
+            if param not in visited_params:
+                _verify_managed_param(name, param)
+                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,
+) -> 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.
+    """
+    # Follow the logic in `nn.Module._apply`
+    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.device_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_ = tensor
+        if is_traceable_wrapper_subclass(tensor_):
+            with torch.no_grad():  # avoid autograd increasing C++ refcount by 1
+                tensor_on_device = nn.Parameter(tensor.to(device))
+            torch.utils.swap_tensors(tensor, tensor_on_device)
+        else:
+            tensor.data = tensor.to(device)

infer_4_47_1/lib/python3.10/site-packages/torch/distributed/_composable/fsdp/_fsdp_param.py

@@ -0,0 +1,754 @@
+# mypy: allow-untyped-defs
+import itertools
+from dataclasses import dataclass, field
+from enum import auto, Enum
+from typing import Any, cast, List, Optional, Sequence, Tuple
+
+import torch
+import torch._dynamo.compiled_autograd as ca
+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, Replicate, Shard
+from torch.distributed.tensor._dtensor_spec import DTensorSpec, TensorMeta
+from torch.distributed.tensor.device_mesh import _mesh_resources
+from torch.distributed.tensor.placement_types import _StridedShard, Placement
+
+from ._fsdp_api import CPUOffloadPolicy, MixedPrecisionPolicy, OffloadPolicy
+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 inputs: the ``torch.Tensor`` or ``Tensor`` s passed to all-gather,
+  derived from the sharded parameter
+- All-gather output: the ``torch.Tensor`` or ``Tensor`` s resulting from
+  all-gathering the all-gather inputs
+- 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-inputs = pre-all-gather-transform(sharded-parameter)
+- unsharded-parameter = post-all-gather-transform(all-gather-outputs)
+
+For the default ``torch.Tensor`` case, there is only one all-gather input, and
+it shares the same underlying tensor data as the sharded parameter, 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, and some may have multiple all-gather inputs/outputs (e.g.
+quantized data and scales).
+
+[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.
+"""
+
+lib = torch.library.Library("fsdp", "FRAGMENT")  # noqa: TOR901
+
+lib.define("set_(Tensor(a!) tensor, Tensor data) -> ()")
+
+
+@torch.library.impl(lib, "set_", "Meta")
+@torch.library.impl(lib, "set_", "CUDA")
+@torch.library.impl(lib, "set_", "CPU")
+def set_(tensor, data):
+    tensor.set_(data)
+
+
+"""
+[Note: Avoiding functionalization for fsdp.set_ and inductor.resize_storage_bytes_(0)]
+
+Currently we don't functionalize `fsdp.set_` op or `inductor.resize_storage_bytes_(0)` op
+(i.e. they show up as a mutation op in the middle of the AOT joint graph).
+
+Reason:
+Traceable FSDP2 compiled autograd BWD graph have the following traits:
+(1) Two inputs of the graph were aliased to each other (one from hook closed-over tensors, one from FWD saved tensors).
+(2) One of them is mutated (set_ and resize_(0) to handle the all-gathered param).
+(3) They are both subclasses.
+The combination of these traits is not supported by AOTAutograd (it's difficult to reason about subclass aliasing).
+So this doesn't work at all for Traceable FSDP2.
+
+The compromise we use is to avoid functionalization for the FSDP2 set_ and resize_(0) ops.
+This avoids the problem above, because from AOTAutograd point-of-view there are no mutations
+that functionalization needs to handle. (Although we need to be careful not to DCE those mutable ops.)
+
+We can avoid this functionalization because:
+(1) The nn.Parameter is never used before its .set_() is called in eager code (i.e. no alias of it is created),
+so it's safe to call .set_() in the middle of the graph to swap out its storage and start using the nn.Parameter downstream.
+(2) We always re-allocate the buffer for nn.Parameter to store the AllGather output and to be used in downstream user ops.
+So calling resize-to-0 in the middle of the graph to free nn.Parameter memory after use should always be okay
+(since we always allocate anew next time we need it, we strictly don't need to keep the old tensor storage around anymore).
+
+Q: But doesn't the torch.compile stack have the "functional graph" assumption in many places?
+A: Yes - this is WIP but we will try to get back to functional graph as early as possible in the lowering process.
+Specifically, we believe we can move both .set_ and .resize_(0) ops to end of graph in AOT joint graph before partitioner
+(i.e. effectively "re-functionalizing" those ops). Put it in another way, we avoid functionalization for those two ops just to
+make AOTAutograd alias analysis happy, and as soon as we are past that point, we "re-functionalize" the graph.
+This requires a custom FX pass but we believe it's not hard to write and maintain.
+
+Q: What's the importance of partitioner not saving views of nn.Parameter as FWD saved tensors?
+A: This is critical: we do want to save FWD nn.Parameter graph input (instead of its view) for BWD use,
+so that downstream ops in BWD graph uses the post-`.set_` nn.Parameter instead of any of its saved views as input.
+This is because .set_ will not update any of the nn.Parameter's views, so BWD downstream ops must use the original
+nn.Parameter in order to see the result of .set_.
+"""
+
+
+@torch.library.impl(lib, "set_", "Functionalize")
+def set__functionalize(tensor, data):
+    torch._sync(tensor)
+    torch._sync(data)
+    # AOTDispatcher needs to know if any inputs had their storages mutated.
+    # (Why? It sometimes detaches inputs before sending them into the graph,
+    #  when it sees that they do not need to have any gradients computed)
+    torch._functionalize_set_storage_changed(tensor)
+    tensor_inner = torch._from_functional_tensor(tensor)
+    data_inner = torch._from_functional_tensor(data)
+    with torch._C._ExcludeDispatchKeyGuard(
+        torch._C.DispatchKeySet(torch._C.DispatchKey.Functionalize)
+    ):
+        torch.ops.fsdp.set_.default(tensor_inner, data_inner)
+
+
+torch.fx.node.has_side_effect(torch.ops.fsdp.set_.default)
+
+
+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)
+
+
+@dataclass
+class ExtensionsData:
+    # User-defined metadata passed from pre to post-all-gather
+    all_gather_metadata: Optional[Any] = None
+    # Save the all-gather input sizes to unflatten the all-gather outputs to ND
+    all_gather_input_sizes: Sequence[torch.Size] = ()  # ND
+
+    def clear(self):
+        self.all_gather_metadata = None
+        self.all_gather_input_sizes = ()
+
+
+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
+    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
+    unsharded_accumulated_grad: Optional[torch.Tensor]  # ND
+    _sharding_spec: DTensorSpec
+    # DTensor attributes (only defined for DTensor `param`):
+    _tp_spec: DTensorSpec
+    all_gather_outputs: List[torch.Tensor]  # 1D
+    # All-gather extension attributes
+    _extensions_data: ExtensionsData
+    _unsharded_inner_tensors: List[torch.Tensor]
+
+    def __init__(
+        self,
+        param: nn.Parameter,
+        module_info: ParamModuleInfo,
+        mesh_info: FSDPMeshInfo,
+        post_forward_mesh_info: Optional[FSDPMeshInfo],
+        device: torch.device,
+        mp_policy: MixedPrecisionPolicy,
+        offload_policy: OffloadPolicy,
+    ):
+        self._module_info: ParamModuleInfo = module_info
+        self.mesh_info = mesh_info
+        self.post_forward_mesh_info = post_forward_mesh_info
+        self.device = device
+        self.offload_to_cpu: bool = isinstance(offload_policy, CPUOffloadPolicy)
+        self.pin_memory = (
+            self.offload_to_cpu and cast(CPUOffloadPolicy, offload_policy).pin_memory
+        )
+        self.grad_offload_event: Optional[torch.cuda.Event] = None
+        self._init_sharded_param(param, device)
+        if self.post_forward_mesh_info:
+            self._init_sharded_post_forward_param_metadata(param)
+        self._init_extensions()
+        self.all_gather_outputs: List[torch.Tensor] = []
+        self.unsharded_accumulated_grad = None
+        self._param_fqn: Optional[str] = None  # prefixed from root module
+        # TODO: Remove this padding logic once DTensor pads the local tensor:
+        # https://github.com/pytorch/pytorch/issues/113045
+        self._post_load_hook_handle = (
+            module_info.module.register_load_state_dict_post_hook(
+                lambda *args, **kwargs: self.reset_sharded_param()
+            )
+        )
+
+    @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
+            dp_mesh, tp_mesh = (self.mesh_info.mesh, self._tp_spec.mesh)
+            dp_global_mesh = _mesh_resources.get_root_mesh(dp_mesh)
+            tp_global_mesh = _mesh_resources.get_root_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}"
+                )
+
+            name_dims_error = "FSDP requires named DeviceMesh dims for ND parallelism"
+            assert dp_mesh.mesh_dim_names is not None, name_dims_error
+            assert tp_mesh.mesh_dim_names is not None, name_dims_error
+            submesh_names = dp_mesh.mesh_dim_names + tp_mesh.mesh_dim_names
+            self._spmd_mesh = dp_global_mesh[submesh_names]
+            if len(self._tp_spec.placements) != 1:
+                raise NotImplementedError(
+                    f"FSDP only supports 1D TP, not {self._tp_spec.placements}"
+                )
+            split_factor = self._tp_spec.num_shards_map[0]
+            assert (
+                2 <= self._spmd_mesh.ndim <= 3
+            ), f"_spmd_mesh.ndim can only be 2 or 3 but got {self._spmd_mesh.ndim}."
+            self._spmd_placements: Tuple[Placement, ...]
+            dp_shard_tp_placement = (
+                (
+                    _StridedShard(0, split_factor=split_factor)
+                    if split_factor > 1
+                    else Shard(0)
+                ),
+                self._tp_spec.placements[0],
+            )
+            if self._spmd_mesh.ndim == 2:
+                self._spmd_placements = dp_shard_tp_placement
+            else:
+                assert self.mesh_info.replicate_mesh_dim == 0
+                self._spmd_placements = (Replicate(),) + dp_shard_tp_placement
+            self._sharding_spec = DTensorSpec(
+                self._spmd_mesh,
+                self._spmd_placements,
+                tensor_meta=self._tp_spec.tensor_meta,
+            )
+            # NOTE: FSDP+TP does not support uneven sharding for now
+            # TODO: enable uneven sharding for FSDP+TP
+            if split_factor > 1:  # FSDP has strided sharding on tensor dim 0
+                num_shards = self._sharding_spec.num_shards_map[0]
+                tensor_size_dim_0 = self._sharding_spec.shape[0]
+                if tensor_size_dim_0 % num_shards != 0:
+                    raise NotImplementedError(
+                        "FSDP+TP sharding does not support uneven sharding for now: "
+                        f"tensor dim 0 has size {tensor_size_dim_0} which cannot be "
+                        f"evenly sharded into {num_shards} shards."
+                    )
+
+            param_data = cast(DTensor, param)._local_tensor
+        else:
+            self._spmd_mesh = self.mesh_info.mesh
+            if isinstance(self.mesh_info, HSDPMeshInfo):
+                self._spmd_placements = (Replicate(), Shard(0))
+            else:
+                self._spmd_placements = (Shard(0),)
+            self._sharding_spec = DTensorSpec(
+                self._spmd_mesh,
+                self._spmd_placements,
+                tensor_meta=TensorMeta(
+                    param.size(),
+                    param.stride(),
+                    param.dtype,
+                ),
+            )
+            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)
+        if self.offload_to_cpu and not padded_sharded_param.is_meta:
+            padded_sharded_param = padded_sharded_param.cpu()
+            if self.pin_memory:
+                padded_sharded_param = padded_sharded_param.pin_memory()
+        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_extensions(self) -> None:
+        inner_tensor = self._sharded_local_tensor
+        has_fsdp_pre_all_gather = hasattr(inner_tensor, "fsdp_pre_all_gather")
+        has_fsdp_post_all_gather = hasattr(inner_tensor, "fsdp_post_all_gather")
+        if has_fsdp_pre_all_gather != has_fsdp_post_all_gather:
+            raise AssertionError(
+                "Both fsdp_pre_all_gather and fsdp_post_all_gather should be defined "
+                f"if using all-gather extensions: {inner_tensor}"
+            )
+        if has_fsdp_pre_all_gather:
+            if self.padded_sharded_param_size != self._sharded_local_tensor.size():
+                raise NotImplementedError(
+                    "FSDP all-gather extensions require even sharding on dim-0.\n"
+                    f"{self._orig_size} is not divisible by FSDP world size {self.mesh_info.mesh.size()}."
+                )
+            self._extensions_data = ExtensionsData()
+        self._unsharded_inner_tensors: List[torch.Tensor] = []
+
+    def init_all_gather_outputs(
+        self,
+        all_gather_input_numels: List[int],
+        all_gather_input_dtypes: List[torch.dtype],
+        world_size: int,
+        device: torch.device,
+        force_recreate: bool = False,
+    ):
+        if not force_recreate and len(self.all_gather_outputs) > 0:
+            return  # already initialized
+        self.all_gather_outputs = [
+            torch.empty(torch.Size([numel * world_size]), dtype=dtype, device=device)
+            for numel, dtype in zip(all_gather_input_numels, all_gather_input_dtypes)
+        ]
+
+    def init_unsharded_param(self):
+        """
+        [Note: Invariants for torch.compile Traceable FSDP2]
+        1. Under compile, we always re-populate the content of `self._unsharded_param`
+           per AllGather using the slow path.
+        2. Under compile, we always recreate `self.all_gather_outputs` per AllGather.
+           This is to ensure the buffer creation is internal to the graph and
+           avoid `self.all_gather_outputs` being captured as a graph input.
+        3. Under compile, at the end of `free_unsharded_param()`, we always clean up
+           `self.all_gather_outputs` and `self._unsharded_inner_tensors`,
+           to avoid them being captured as graph output.
+
+        With these invariants, only these tensors will be inputs to the graph:
+        - Sharded parameters
+        - Placeholders for the `self._unsharded_param` nn.Parameter
+        """
+        if not ca.compiled_autograd_enabled and hasattr(
+            self, "_unsharded_param"
+        ):  # after the 1st all-gather
+            inner_tensor = self._sharded_local_tensor
+            if not hasattr(inner_tensor, "fsdp_post_all_gather"):
+                return  # already initialized
+            for tensor in self._unsharded_inner_tensors:
+                alloc_storage(tensor)
+            all_gather_outputs = self._unflatten_all_gather_outputs()
+            inner_tensor.fsdp_post_all_gather(
+                all_gather_outputs,
+                self._extensions_data.all_gather_metadata,
+                self.param_dtype or self.orig_dtype,
+                out=self._unsharded_param,
+            )
+            self._extensions_data.clear()
+            return
+        inner_tensor = self._sharded_local_tensor
+        if not ca.compiled_autograd_enabled and hasattr(
+            inner_tensor, "fsdp_post_all_gather"
+        ):
+            all_gather_outputs = self._unflatten_all_gather_outputs()
+            (
+                unsharded_tensor,
+                self._unsharded_inner_tensors,
+            ) = inner_tensor.fsdp_post_all_gather(
+                all_gather_outputs,
+                self._extensions_data.all_gather_metadata,
+                self.param_dtype or self.orig_dtype,
+            )
+            self._extensions_data.clear()
+        else:
+            # For the default path (no post-all-gather), the all-gather output
+            # gives the unsharded parameter data directly
+            assert len(self.all_gather_outputs) == 1, f"{len(self.all_gather_outputs)}"
+            unsharded_tensor = self.all_gather_outputs[0]
+        unsharded_param = torch.as_strided(
+            unsharded_tensor,
+            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)
+        if hasattr(self, "_unsharded_param"):
+            assert ca.compiled_autograd_enabled
+            with torch.no_grad(), torch.autograd._unsafe_preserve_version_counter(
+                self._unsharded_param
+            ):
+                torch.ops.fsdp.set_.default(self._unsharded_param, unsharded_param)
+        else:
+            self._unsharded_param = nn.Parameter(
+                unsharded_param, requires_grad=self.sharded_param.requires_grad
+            )
+
+    def _unflatten_all_gather_outputs(self) -> Tuple[torch.Tensor, ...]:
+        return tuple(
+            t.view(-1, *s[1:])
+            for t, s in zip(
+                self.all_gather_outputs, self._extensions_data.all_gather_input_sizes
+            )
+        )
+
+    def to_sharded(self) -> None:
+        self._setattr_on_modules(self.sharded_param)
+        self.free_unsharded_param()
+        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
+        assert len(self.all_gather_outputs) == 1
+        shard_world_size = self.post_forward_mesh_info.shard_mesh_size
+        if (numel := self.all_gather_outputs[0].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_outputs[0].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_unsharded_param()
+        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._sharding_spec,
+        )
+
+    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.
+        post_forward_sharding_spec = DTensorSpec(
+            self.post_forward_mesh_info.mesh,
+            (Replicate(), Shard(0)),
+            tensor_meta=self._sharding_spec.tensor_meta,
+        )
+        return _from_local_no_grad(tensor, post_forward_sharding_spec)
+
+    def to_accumulated_grad_if_needed(self) -> None:
+        # Access `_unsharded_param` to bypass the sharded state check since we
+        # prefer to reshard before upcasting the gradient to save memory
+        if (
+            self.reduce_dtype is None
+            or self._unsharded_param.grad is None
+            or self._unsharded_param.grad.dtype == self.reduce_dtype
+        ):
+            return
+        unsharded_grad = self._unsharded_param.grad
+        self._unsharded_param.grad = None
+        self.unsharded_accumulated_grad = unsharded_grad.to(self.reduce_dtype)
+
+    def accumulate_unsharded_grad_if_needed(self) -> None:
+        if (
+            self.unsharded_accumulated_grad is not None
+            and self.unsharded_param.grad is not None
+        ):
+            self.unsharded_accumulated_grad += self.unsharded_param.grad
+            self.unsharded_param.grad = None
+
+    def alloc_all_gather_outputs(self) -> None:
+        for tensor in self.all_gather_outputs:
+            alloc_storage(tensor)
+
+    def free_unsharded_param(self) -> None:
+        for tensor in itertools.chain(
+            self.all_gather_outputs, self._unsharded_inner_tensors
+        ):
+            free_storage(tensor)
+        if ca.compiled_autograd_enabled:
+            self.all_gather_outputs = []
+            self._unsharded_inner_tensors = []
+
+    @property
+    def all_gather_inputs(self) -> List[torch.Tensor]:  # 1D
+        self._assert_in_states(ShardedState.SHARDED, ShardedState.SHARDED_POST_FORWARD)
+        if self.sharded_state == ShardedState.SHARDED:
+            if not ca.compiled_autograd_enabled and hasattr(
+                self._sharded_local_tensor, "fsdp_pre_all_gather"
+            ):
+                sharded_local_tensor = self._sharded_local_tensor
+                if self.offload_to_cpu:
+                    sharded_local_tensor = sharded_local_tensor.to(
+                        self.device, non_blocking=True
+                    )
+                (
+                    all_gather_inputs,
+                    self._extensions_data.all_gather_metadata,
+                ) = sharded_local_tensor.fsdp_pre_all_gather(self.mesh_info.mesh)
+                self._extensions_data.all_gather_input_sizes = [
+                    t.size() for t in all_gather_inputs
+                ]
+                return [t.view(-1) for t in all_gather_inputs]
+            sharded_param_data = self._sharded_param_data
+            if self.offload_to_cpu:
+                sharded_param_data = sharded_param_data.to(
+                    self.device, non_blocking=True
+                )
+            return [_to_dtype_if_needed(sharded_param_data, self.param_dtype)]
+        elif self.sharded_state == ShardedState.SHARDED_POST_FORWARD:
+            if not ca.compiled_autograd_enabled and hasattr(
+                self._sharded_local_tensor, "fsdp_pre_all_gather"
+            ):
+                raise NotImplementedError
+            all_gather_input = _to_dtype_if_needed(
+                cast(torch.Tensor, self._sharded_post_forward_param_data),
+                self.param_dtype,
+            )
+            return [all_gather_input]
+        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)
+
+    @property
+    def unsharded_accumulated_grad_data(self) -> torch.Tensor:
+        grad = self.unsharded_accumulated_grad
+        assert grad is not None, "Expects unsharded_accumulated_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()
+            assert isinstance(grad, DTensor), f"{type(grad)}"
+            if any(pl.is_partial() for pl in grad.placements):
+                placements = [
+                    Replicate() if pl.is_partial() else pl for pl in grad.placements
+                ]
+                grad = grad.redistribute(placements=placements)
+            grad = grad._local_tensor
+        return grad
+
+    @property
+    def _sharded_local_tensor(self) -> torch.Tensor:
+        return cast(DTensor, self.sharded_param)._local_tensor
+
+    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}"
+            )
+
+    def reset_sharded_param(self):
+        # For ops like `nn.Module._apply` or `load_state_dict(assign=True)`
+        # that change the sharded parameter tensor, we may need to re-pad the
+        # sharded local tensor and re-save the reference.
+        module_info = self._module_info
+        new_param = getattr(module_info.module, module_info.param_name)
+        if new_param is not self.sharded_param:
+            if torch.__future__.get_swap_module_params_on_conversion():
+                raise AssertionError(
+                    f"Expects swap_tensors to preserve object but got {new_param} "
+                    f"instead of {self.sharded_param}"
+                )
+            self.sharded_param = new_param
+        local_tensor = new_param._local_tensor
+        if local_tensor.is_meta:
+            return
+        padded_sharded_size = self.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
+        if self.pin_memory and not local_tensor.is_pinned():
+            local_tensor = local_tensor.cpu().pin_memory()
+        self._sharded_param_data = local_tensor.view(-1)
+        assert isinstance(self.sharded_param, DTensor)  # mypy
+        self.sharded_param._local_tensor = local_tensor[: self.sharded_size[0]]
+
+    def __repr__(self):
+        return f"FSDPParam(fqn={self._param_fqn}, orig_size={self._orig_size})"
+
+
+def alloc_storage(tensor: torch.Tensor) -> None:
+    size = tensor.numel() * tensor.itemsize
+    if (storage := tensor.untyped_storage()).size() != size:
+        storage.resize_(size)
+
+
+def free_storage(tensor: torch.Tensor) -> None:
+    if (storage := tensor.untyped_storage()).size() != 0:
+        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)

infer_4_47_1/lib/python3.10/site-packages/torch/distributed/_composable/fsdp/_fsdp_param_group.py

@@ -0,0 +1,614 @@
+# mypy: allow-untyped-defs
+import contextlib
+import logging
+from typing import Any, cast, Dict, List, NamedTuple, Optional, Set, Tuple
+
+import torch
+import torch._dynamo.compiled_autograd as ca
+import torch.distributed as dist
+import torch.nn as nn
+from torch.distributed.fsdp._common_utils import _named_parameters_with_duplicates
+from torch.profiler import record_function
+from torch.utils._pytree import tree_flatten, tree_unflatten
+from torch.utils.hooks import RemovableHandle
+
+from ._fsdp_api import MixedPrecisionPolicy, OffloadPolicy
+from ._fsdp_collectives import (
+    AllGatherResult,
+    foreach_all_gather,
+    foreach_all_gather_copy_out,
+    foreach_reduce,
+)
+from ._fsdp_common import FSDPMeshInfo, HSDPMeshInfo, TrainingState
+from ._fsdp_param import FSDPParam, ParamModuleInfo, ShardedState
+
+
+logger = logging.getLogger("torch.distributed._composable.fsdp")
+
+_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 lazy_init(self):
+        if not torch.cuda.is_available():
+            raise RuntimeError("FSDP requires CUDA for streams")
+        # 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 (this is different from high-pri NCCL streams)
+        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)
+        # 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)
+        # Run the HSDP all-reduces concurrently with all-gather/reduce-scatter
+        # since collectives use different network resources and can overlap
+        # in the typical intra-node sharding / inter-node replication case
+        self.all_reduce_stream = torch.cuda.Stream()
+        # All-gather/reduce-scatter states keep references to collective
+        # tensors produced in one stream and used in another and accompanying
+        # CUDA events for synchronization
+        self.all_gather_state: Optional[AllGatherState] = None
+        self.reduce_scatter_state: Optional[ReduceScatterState] = None
+        # 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 ReduceScatterState(NamedTuple):
+    reduce_scatter_input: torch.Tensor
+    event: torch.cuda.Event  # reduce-scatter event
+
+
+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],
+        modules: Tuple[nn.Module, ...],
+        mesh_info: FSDPMeshInfo,
+        post_forward_mesh_info: Optional[FSDPMeshInfo],
+        device: torch.device,
+        mp_policy: MixedPrecisionPolicy,
+        offload_policy: OffloadPolicy,
+    ):
+        self.modules = modules  # permit ref cycle because 1:1 lifetime
+        param_module_infos = _get_param_module_infos(params, modules)
+        self.fsdp_params = [
+            FSDPParam(
+                param,
+                module_info,
+                mesh_info,
+                post_forward_mesh_info,
+                device,
+                mp_policy,
+                offload_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
+        # Only consider resetting sharded parameters once in lazy init since it
+        # can incur nontrivial overhead to reset them
+        self._reset_sharded_params: bool = False
+
+        # - 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] = []
+        # Whether to reduce gradients at all (whether for FSDP or HSDP)
+        self.reduce_grads: bool = True
+        # Whether to all-reduce gradients for HSDP; only used if
+        # `self.reduce_grads` is true, in which case setting this to false
+        # means reduce-scatter but no all-reduce
+        self.all_reduce_grads: bool = True
+        # Whether to reshard parameters after backward (only useful for
+        # gradient accumulation)
+        self.reshard_after_backward: bool = True
+        # Optional custom reduce-scatter reduce op (e.g. to divide by a
+        # factor other than the shard world size)
+        self.reduce_scatter_reduce_op: Optional[dist.ReduceOp] = None
+
+        # - 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/all-reduce view-out CUDA event that marks the end of
+        # the group's post-backward (e.g. reduce-scatter, all-reduce and div), which
+        # should be waited on at the end of backward
+        self._post_reduce_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
+
+        # Only for HSDP, if accumulating gradients without all-reduce, save the
+        # partial reduce output (only reduce-scattered but not all-reduced)
+        self._partial_reduce_output: Optional[torch.Tensor] = 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 lazy_init(self):
+        # Lazy init should be idempotent
+        # Users may change or register parameters after construction time.
+        # For example, DoRA (https://arxiv.org/abs/2402.09353) initializes linear magnitudes based on
+        # other parameters (e.g. loaded from the state dict).
+        if self.is_sharded and not self._reset_sharded_params:
+            for fsdp_param in self.fsdp_params:
+                fsdp_param.reset_sharded_param()
+            self._reset_sharded_params = True
+        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._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
+        with record_function(self._with_fqn("FSDP::all_gather")):
+            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
+        with record_function(self._with_fqn("FSDP::all_gather_copy_out")):
+            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()
+        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):
+        # Calling `unshard` before lazy init means streams are not initialized
+        if hasattr(self.comm_ctx, "all_gather_copy_in_stream"):
+            self.comm_ctx.all_gather_copy_in_stream.wait_event(event)
+        if hasattr(self.comm_ctx, "all_gather_stream"):
+            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]]:
+        if not ca.compiled_autograd_enabled:
+            logger.debug("%s", self._with_fqn("FSDP::pre_forward"))
+        with record_function(self._with_fqn("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):
+        if not ca.compiled_autograd_enabled:
+            logger.debug("%s", self._with_fqn("FSDP::post_forward"))
+        with record_function(self._with_fqn("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, default_prefetch: bool, *unused: Any):
+        if self._training_state == TrainingState.PRE_BACKWARD:
+            return
+        if not ca.compiled_autograd_enabled:
+            logger.debug("%s", self._with_fqn("FSDP::pre_backward"))
+        with record_function(self._with_fqn("FSDP::pre_backward")):
+            self._training_state = TrainingState.PRE_BACKWARD
+            self.unshard()  # no-op if prefetched
+            self.wait_for_unshard()
+            if default_prefetch and not ca.compiled_autograd_enabled:
+                self._backward_prefetch()
+
+    def post_backward(self, *unused: Any):
+        if not ca.compiled_autograd_enabled:
+            logger.debug("%s", self._with_fqn("FSDP::post_backward"))
+        self._training_state = TrainingState.POST_BACKWARD
+        with record_function(self._with_fqn("FSDP::post_backward_accumulate")):
+            for fsdp_param in self.fsdp_params:
+                fsdp_param.accumulate_unsharded_grad_if_needed()
+        with record_function(self._with_fqn("FSDP::post_backward_reshard")):
+            if not self.reduce_grads:
+                if self.reshard_after_backward:
+                    self.reshard()
+                for fsdp_param in self.fsdp_params:
+                    fsdp_param.to_accumulated_grad_if_needed()
+                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:
+                # May have an accumulated gradient of the reduce dtype if the
+                # previous backward did not reduce-scatter
+                if fsdp_param.unsharded_accumulated_grad is not None:
+                    fsdp_params_with_grad.append(fsdp_param)
+                    unsharded_grads.append(fsdp_param.unsharded_accumulated_grad_data)
+                    fsdp_param.unsharded_accumulated_grad = None
+                elif 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
+            if self.reshard_after_backward:
+                self.reshard()
+        if len(fsdp_params_with_grad) == 0:
+            return
+        with record_function(self._with_fqn("FSDP::post_backward_reduce")):
+            if self.comm_ctx.reduce_scatter_state is not None:
+                torch.cuda.current_stream().wait_event(
+                    self.comm_ctx.reduce_scatter_state.event
+                )
+                self.comm_ctx.reduce_scatter_state = None
+            (
+                reduce_scatter_input,
+                reduce_scatter_event,
+                self._post_reduce_event,
+                self._partial_reduce_output,
+            ) = foreach_reduce(
+                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.reduce_scatter_reduce_op,
+                self._all_reduce_process_group if self._is_hsdp else None,
+                self.comm_ctx.all_reduce_stream,
+                self.all_reduce_grads,
+                self._partial_reduce_output,
+            )
+            self.comm_ctx.reduce_scatter_state = ReduceScatterState(
+                reduce_scatter_input, reduce_scatter_event
+            )
+
+    def finalize_backward(self):
+        if self._post_reduce_event is not None:
+            torch.cuda.current_stream().wait_event(self._post_reduce_event)
+            self._post_reduce_event = None
+        for fsdp_param in self.fsdp_params:
+            if fsdp_param.grad_offload_event is not None:
+                fsdp_param.grad_offload_event.synchronize()
+                fsdp_param.grad_offload_event = None
+        self._post_forward_indices.clear()
+
+    def _backward_prefetch(self) -> None:
+        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
+            # Prefetch naively using the reverse post-forward order, which may
+            # have mistargeted prefetches if not all modules used in forward
+            # are used in this backward
+            target_fsdp_param_group = self.comm_ctx.post_forward_order[target_index]
+            self._prefetch_unshard(target_fsdp_param_group, "backward")
+
+    @staticmethod
+    def _prefetch_unshard(
+        target_fsdp_param_group: "FSDPParamGroup", pass_type: str
+    ) -> None:
+        if pass_type == "backward":
+            training_state = TrainingState.PRE_BACKWARD
+        elif pass_type == "forward":
+            training_state = TrainingState.FORWARD
+        else:
+            raise ValueError(f"Unknown pass type: {pass_type}")
+        target_fqn = target_fsdp_param_group._module_fqn
+        with record_function(
+            f"FSDP::{pass_type}_prefetch for {target_fqn}"
+        ), target_fsdp_param_group.use_training_state(training_state):
+            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]]:
+        # Compile relies on `root_post_backward_callback` to call each
+        # `FSDPParamGroup.post_backward`
+        if ca.compiled_autograd_enabled:
+            return args, kwargs
+        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:
+        num_pre_save_hooks = len(self._module_to_pre_save_state_dict_hook_handle)
+        num_pre_load_hooks = len(self._module_to_pre_load_state_dict_hook_handle)
+        assert (
+            num_pre_save_hooks == num_pre_load_hooks
+        ), f"Pre-save: {num_pre_save_hooks} pre-load: {num_pre_load_hooks}"
+        if num_pre_save_hooks > 0:
+            return  # already registered
+        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 _is_hsdp(self) -> bool:
+        return isinstance(self.mesh_info, HSDPMeshInfo)
+
+    @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:
+        assert isinstance(self.mesh_info, FSDPMeshInfo)
+        return self.mesh_info.shard_process_group
+
+    @property
+    def _all_reduce_process_group(self) -> dist.ProcessGroup:
+        assert isinstance(self.mesh_info, HSDPMeshInfo)
+        return self.mesh_info.replicate_process_group
+
+    def _with_fqn(self, label: str) -> str:
+        if self._module_fqn:
+            return f"{label} ({self._module_fqn})"
+        return label
+
+    def __repr__(self):
+        return f"FSDPParamGroup(fqn={self._module_fqn})"
+
+
+def _get_param_module_infos(
+    params: List[nn.Parameter], modules: Tuple[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 module in modules:
+        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

infer_4_47_1/lib/python3.10/site-packages/torch/distributed/_composable/fsdp/_fsdp_state.py

@@ -0,0 +1,383 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+import functools
+import logging
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    Optional,
+    Sequence,
+    Set,
+    Tuple,
+    TYPE_CHECKING,
+)
+
+import torch
+import torch._dynamo.compiled_autograd as ca
+import torch.nn as nn
+from torch._logging import warning_once
+from torch.autograd import Variable
+from torch.autograd.graph import _MultiHandle
+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 ._fsdp_api import MixedPrecisionPolicy
+from ._fsdp_common import _cast_fp_tensor, TrainingState
+from ._fsdp_param_group import FSDPCommContext, FSDPParamGroup
+
+
+if TYPE_CHECKING:
+    from ._fsdp_param import FSDPParam
+
+
+logger = logging.getLogger("torch.distributed._composable.fsdp")
+
+
+class FSDPStateContext:
+    """This has state shared across FSDP states."""
+
+    def __init__(self) -> None:
+        # 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
+        # Optional user-provided event recorded after optimizer for the
+        # all-gather streams to wait on in the root pre-forward
+        self.post_optim_event: Optional[torch.cuda.Event] = None
+
+
+def disable_if_config_true(func):
+    @functools.wraps(func)
+    def fsdp_hook_wrapper(*args, **kwargs):
+        if torch._dynamo.config.skip_fsdp_hooks:
+            return torch._dynamo.disable(func, recursive=True)(*args, **kwargs)
+        else:
+            return func(*args, **kwargs)
+
+    return fsdp_hook_wrapper
+
+
+class FSDPState(_State):
+    def __init__(self) -> None:
+        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._states_to_forward_prefetch: List[FSDPState] = []
+        self._states_to_backward_prefetch: List[FSDPState] = []
+        self._modules_to_run_forward: Set[nn.Module] = set()
+
+    # Define a separate init since `__init__` is called in the contract
+    def init(
+        self,
+        modules: Tuple[nn.Module, ...],
+        device: torch.device,
+        mp_policy: MixedPrecisionPolicy,
+    ) -> None:
+        for module in modules:
+            _insert_module_state(module, self)
+        self._modules = modules
+        self._device = device
+        self._mp_policy = mp_policy
+        if len(modules) == 1:
+            self._pre_forward_hook_handle = modules[0].register_forward_pre_hook(
+                self._pre_forward, prepend=True, with_kwargs=True
+            )
+            self._post_forward_hook_handle = modules[0].register_forward_hook(
+                self._post_forward, prepend=False
+            )
+        else:
+            hook_handle = _register_group_forward_hooks(
+                modules,
+                self._pre_forward,
+                self._post_forward,
+                self._modules_to_run_forward,
+            )
+            self._pre_forward_hook_handle = hook_handle
+            self._post_forward_hook_handle = hook_handle
+
+    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
+        if not ca.compiled_autograd_enabled:
+            logger.debug("FSDP::root_pre_forward")
+        self._state_ctx.iter_forward_root = self
+        with torch.profiler.record_function("FSDP::root_pre_forward"):
+            # Wait for optimizer before implicitly prefetched all-gathers
+            if (event := self._state_ctx.post_optim_event) is not None:
+                self._comm_ctx.all_gather_copy_in_stream.wait_event(event)
+                self._comm_ctx.all_gather_stream.wait_event(event)
+                self._state_ctx.post_optim_event = None
+            else:
+                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
+        if len(self._modules) > 1:
+            raise RuntimeError(
+                f"FSDP requires a single root module but got {self._modules}"
+            )
+        root_module = self._modules[0]
+        visited_states: Set[FSDPState] = set()
+        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 not in visited_states and 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)
+            visited_states.add(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.lazy_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._modules[0]
+        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
+                for module in fsdp_param_group.modules:
+                    module_to_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_fqn = module_to_fsdp_param_group[module]._module_fqn
+                if module_fqn is None:
+                    module_to_fsdp_param_group[module]._module_fqn = module_name
+                else:
+                    assert isinstance(module_fqn, str), f"{module_fqn}"
+                    module_fqn += f", {module_name}"
+                    module_to_fsdp_param_group[module]._module_fqn = module_fqn
+
+    @disable_if_config_true
+    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)
+        for fsdp_state in self._states_to_forward_prefetch:
+            if (target_param_group := fsdp_state._fsdp_param_group) is not None:
+                FSDPParamGroup._prefetch_unshard(target_param_group, "forward")
+        return args, kwargs
+
+    @disable_if_config_true
+    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, grad: torch.Tensor) -> torch.Tensor:
+        self._training_state = TrainingState.PRE_BACKWARD
+        self._register_root_post_backward_final_callback()
+        if self._fsdp_param_group:
+            default_prefetch = len(self._states_to_backward_prefetch) == 0
+            self._fsdp_param_group.pre_backward(default_prefetch)
+        for fsdp_state in self._states_to_backward_prefetch:
+            if (target_param_group := fsdp_state._fsdp_param_group) is not None:
+                FSDPParamGroup._prefetch_unshard(target_param_group, "backward")
+        return grad
+
+    def _root_post_backward_final_callback(self) -> None:
+        if not ca.compiled_autograd_enabled:
+            logger.debug("FSDP::root_post_backward")
+        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()
+                state._training_state = TrainingState.IDLE
+                if state._fsdp_param_group:
+                    state._fsdp_param_group._training_state = TrainingState.IDLE
+                if self._state_ctx.is_last_backward:
+                    state._finalize_backward()
+            if self._state_ctx.is_last_backward:
+                self._comm_ctx.post_forward_order.clear()
+                if self._comm_ctx.reduce_scatter_state is not None:
+                    torch.cuda.current_stream().wait_event(
+                        self._comm_ctx.reduce_scatter_state.event
+                    )
+                    self._comm_ctx.reduce_scatter_state = None
+            self._state_ctx.post_backward_final_callback_queued = False
+
+    def _finalize_backward(self) -> None:
+        if self._modules_to_run_forward:
+            msg = (
+                f"{len(self._modules_to_run_forward)} of the {len(self._modules)} "
+                f"modules passed to fully_shard did not run forward before backward, "
+                "which is error-prone since FSDP post-forward/pre-backward logic "
+                "will not run for these modules. We recommend passing only modules "
+                "that run forward together. Modules that did not run forward: "
+                f"{list(self._modules_to_run_forward)}"
+            )
+            warning_once(logger, msg, stacklevel=2)
+            # Clear since we want the next forward to run
+            self._modules_to_run_forward.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)
+        for t in flat_outputs:
+            if torch.is_tensor(t) and t.requires_grad:
+                t.register_hook(self._pre_backward)
+        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
+
+
+def _register_group_forward_hooks(
+    modules: Sequence[nn.Module],
+    pre_hook: Callable,
+    post_hook: Callable,
+    modules_to_run: Set[nn.Module],
+):
+    """
+    Registers group forward pre and post-hooks. The pre-hook runs upon the
+    first module pre-forward, and the post-hook runs upon the last. If at least
+    one module does not run forward, then the post-hook does not run.
+    """
+    modules_set = set(modules)
+
+    @disable_if_config_true
+    @functools.wraps(pre_hook)
+    def wrapped_pre_hook(*args: Any, **kwargs: Any):
+        if len(modules_to_run) == 0:  # first to run
+            modules_to_run.update(modules_set)
+            return pre_hook(*args, **kwargs)
+
+    @disable_if_config_true
+    def get_wrapped_post_hook(module: nn.Module):
+        @functools.wraps(post_hook)
+        def wrapped_post_hook(*args: Any, **kwargs: Any):
+            modules_to_run.discard(module)
+            if len(modules_to_run) == 0:
+                return post_hook(*args, **kwargs)
+
+        return wrapped_post_hook
+
+    pre_handles = [
+        module.register_forward_pre_hook(
+            wrapped_pre_hook, prepend=True, with_kwargs=True
+        )
+        for module in modules
+    ]
+    post_handles = [
+        module.register_forward_hook(
+            get_wrapped_post_hook(module), prepend=False, always_call=True
+        )
+        for module in modules
+    ]
+    return _MultiHandle(tuple(pre_handles + post_handles))

infer_4_47_1/lib/python3.10/site-packages/torch/distributed/_composable/fsdp/fully_shard.py

@@ -0,0 +1,446 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+import functools
+from typing import Any, cast, Dict, Iterable, List, NoReturn, Optional, Type, Union
+
+import torch
+import torch.nn as nn
+from torch.distributed._composable import contract
+from torch.distributed.tensor import DeviceMesh
+from torch.distributed.utils import _get_root_modules
+
+from ._fsdp_api import MixedPrecisionPolicy, OffloadPolicy
+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
+
+
+cls_to_fsdp_cls: Dict[Type, Type] = {}
+
+
+# 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)  # type: ignore[operator]
+def fully_shard(
+    module: Union[nn.Module, List[nn.Module]],
+    *,
+    mesh: Optional[DeviceMesh] = None,
+    reshard_after_forward: Union[bool, int] = True,
+    mp_policy: MixedPrecisionPolicy = MixedPrecisionPolicy(),
+    offload_policy: OffloadPolicy = OffloadPolicy(),
+):
+    """
+    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:
+        module (Union[nn.Module, List[nn.Module]): The module or modules to
+            shard with FSDP and group together for communication.
+        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.
+        offload_policy (OffloadPolicy): This controls the offloading policy,
+            which offers parameter/gradient/optimizer state offloading. See
+            :class:`OffloadPolicy` and its subclasses 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
+    )
+
+    arg_module = module
+    modules = (
+        (module,) if isinstance(module, nn.Module) else tuple(_get_root_modules(module))
+    )
+    state = fully_shard.state(modules[0])
+    state.init(modules, device, mp_policy)
+
+    managed_modules = _get_managed_modules(modules)
+    params, buffers = _get_managed_states(managed_modules)
+    _move_states_to_device(params, buffers, device)
+    if params:
+        state._fsdp_param_group = FSDPParamGroup(
+            params,
+            modules,
+            mesh_info,
+            post_forward_mesh_info,
+            device,
+            mp_policy,
+            offload_policy,
+        )
+
+    # For Dynamo
+    for managed_module in managed_modules:
+        managed_module._is_fsdp_managed_module = True  # type: ignore[assignment]
+        managed_module._fsdp_use_orig_params = True  # type: ignore[assignment]
+
+    # Place FSDP leftmost for highest priority in the method resolution order
+    for module in modules:
+        cls = module.__class__
+        new_cls = cls_to_fsdp_cls.get(cls, None)
+        if not new_cls:
+            dct = {"__deepcopy__": unimplemented_deepcopy}
+            new_cls = type(f"FSDP{cls.__name__}", (FSDPModule, cls), dct)
+            cls_to_fsdp_cls[cls] = new_cls
+        module.__class__ = new_cls
+    return arg_module
+
+
+def unimplemented_deepcopy(*args: Any, **kwargs: Any) -> NoReturn:
+    raise AssertionError(
+        "FSDP does not support deepcopy. Please use state dict for serialization."
+    )
+
+
+class FSDPModule:
+    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 `FSDPModule` 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 unshard(self, async_op: bool = False) -> Optional["UnshardHandle"]:
+        """
+        Unshards the module's parameters by allocating memory and all-gathering
+        the parameters. This method is *not* recursive.
+
+        Args:
+            async_op (bool): If ``True``, then returns a :class:`UnshardHandle`
+                that has a :meth:`wait` method to wait on the unshard op. If
+                ``False``, then returns ``None`` and waits on the handle inside
+                this function.
+
+        .. warning:: This method is experimental and subject to change.
+
+        .. note:: If ``async_op=True``, then the user does not have to call
+            :meth:`wait` on the returned handle if waiting on the unshard op
+            in the module's pre-forward is tolerable. FSDP will wait on the
+            pending unshard op in the pre-forward automatically.
+        """
+        state = self._get_fsdp_state()
+        fsdp_param_group = state._fsdp_param_group
+        if fsdp_param_group is not None:
+            fsdp_param_group.lazy_init()
+            fsdp_param_group.unshard(async_op=async_op)
+        handle = UnshardHandle(fsdp_param_group)
+        if async_op:
+            return handle
+        handle.wait()
+        return None
+
+    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.
+        """
+        self_module = cast(nn.Module, self)
+        modules = list(self_module.modules()) if recurse else [self_module]
+        for module in modules:
+            if isinstance(module, FSDPModule):
+                state = module._get_fsdp_state()
+                if fsdp_param_group := state._fsdp_param_group:
+                    fsdp_param_group.reduce_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
+    ) -> None:
+        """
+        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.
+        """
+        self_module = cast(nn.Module, self)
+        modules = list(self_module.modules()) if recurse else [self_module]
+        for module in modules:
+            if isinstance(module, FSDPModule):
+                state = module._get_fsdp_state()
+                if fsdp_param_group := state._fsdp_param_group:
+                    fsdp_param_group.all_reduce_grads = requires_all_reduce
+
+    def set_reshard_after_backward(
+        self, reshard_after_backward: bool, *, recurse: bool = True
+    ) -> None:
+        """
+        Sets if the module should reshard parameters after backward. This can
+        be used during gradient accumulation to trade off higher memory for
+        reduced communication.
+
+        Args:
+            reshard_after_backward (bool): Whether to reshard parameters after
+                backward.
+            recurse (bool): Whether to set for all submodules or just the
+                passed-in module.
+        """
+        self_module = cast(nn.Module, self)
+        modules = list(self_module.modules()) if recurse else [self_module]
+        for module in modules:
+            if isinstance(module, FSDPModule):
+                state = module._get_fsdp_state()
+                if fsdp_param_group := state._fsdp_param_group:
+                    fsdp_param_group.reshard_after_backward = reshard_after_backward
+
+    def set_modules_to_forward_prefetch(self, modules: List["FSDPModule"]) -> None:
+        """
+        Sets the FSDP modules for which this FSDP module should explicitly
+        prefetch all-gathers in forward. The prefetching runs after this
+        module's all-gather copy-out.
+
+        Passing a singleton list containing the next FSDP module gives the same
+        all-gather overlap behavior as the default overlap behavior, except the
+        prefetched all-gather is issued earlier from the CPU. Passing a list
+        with at least length two is required for more aggressive overlap and
+        will use more reserved memory.
+
+        Args:
+            modules (List[FSDPModule]): FSDP modules to prefetch.
+        """
+        _assert_all_fsdp_modules(modules)
+        self._get_fsdp_state()._states_to_forward_prefetch = [
+            module._get_fsdp_state() for module in modules
+        ]
+
+    def set_modules_to_backward_prefetch(self, modules: List["FSDPModule"]) -> None:
+        """
+        Sets the FSDP modules for which this FSDP module should explicitly
+        prefetch all-gathers in backward. This overrides the default backward
+        pretching implementation that prefetches the next FSDP module based on
+        the reverse post-forward order.
+
+        Passing a singleton list containing the previous FSDP module gives the
+        same all-gather overlap behavior as the default overlap behavior.
+        Passing a list with at least length two is required for more aggressive
+        overlap and will use more reserved memory.
+
+        Args:
+            modules (List[FSDPModule]): FSDP modules to prefetch.
+        """
+        _assert_all_fsdp_modules(modules)
+        self._get_fsdp_state()._states_to_backward_prefetch = [
+            module._get_fsdp_state() for module in modules
+        ]
+
+    def set_post_optim_event(self, event: torch.cuda.Event) -> None:
+        """
+        Sets a post-optimizer-step event for the root FSDP module to wait the
+        all-gather streams on.
+
+        By default, the root FSDP module waits the all-gather streams on the
+        current stream to ensure that the optimizer step has finished before
+        all-gathering. However, this may introduce false dependencies if
+        there is unrelated computation after the optimizer step. This API
+        allows the user to provide their own event to wait on. After the root
+        waits on the event, the event is discarded, so this API should be
+        called with a new event each iteration.
+
+        Args:
+            event (torch.cuda.Event): Event recorded after the optimizer step
+                to wait all-gather streams on.
+        """
+        self._get_fsdp_state()._state_ctx.post_optim_event = event
+
+    def set_reduce_scatter_divide_factor(self, factor: float) -> None:
+        """
+        Sets a custom divide factor for the reduce-scatter. This becomes a
+        custom reduce op using NCCL's PreMulSum, which allows multiplying by
+        the factor before reduction.
+
+        Args:
+            factor (float): Custom divide factor.
+        """
+        state = self._get_fsdp_state()
+        if (fsdp_param_group := state._fsdp_param_group) is not None:
+            mul_factor = 1.0 / float(factor)
+            reduce_op = torch.distributed._make_nccl_premul_sum(mul_factor)
+            fsdp_param_group.reduce_scatter_reduce_op = reduce_op
+
+    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:
+                fsdp_param.reset_sharded_param()
+        return ret
+
+
+class UnshardHandle:
+    """
+    A handle to wait on the unshard op.
+
+    Args:
+        fsdp_param_group (FSDPParamGroup, optional): FSDP parameter group to
+            unshard. This should be ``None`` iff the FSDP module does not
+            manage any parameters, meaning the unshard is a no-op.
+    """
+
+    def __init__(self, fsdp_param_group: Optional[FSDPParamGroup]):
+        self._fsdp_param_group = fsdp_param_group
+
+    def wait(self):
+        """
+        Waits on the unshard op.
+
+        This ensures that the current stream can use the unsharded parameters,
+        which are now registered to the module.
+        """
+        if self._fsdp_param_group is not None:
+            self._fsdp_param_group.wait_for_unshard()
+            # Avoid keeping a reference
+            self._fsdp_param_group = None
+
+
+def register_fsdp_forward_method(module: nn.Module, method_name: str) -> None:
+    """
+    Registers a method on ``module`` to be a forward method for FSDP.
+
+    FSDP only knows to run its pre-forward and post-forward hooks on the
+    default :meth:`nn.Module.forward` method. This function patches a user
+    specified method to run the pre/post-forward hooks before/after the method,
+    respectively. If ``module`` is not an :class:`FSDPModule`, then this is a
+    no-op.
+
+    Args:
+        module (nn.Module): Module to register the forward method on.
+        method_name (str): Name of the forward method.
+    """
+    if not isinstance(module, FSDPModule):
+        # Make no-op to allow including both when using/not using FSDP
+        return
+    if not hasattr(module, method_name):
+        raise ValueError(f"{type(module)} does not have a method {method_name}")
+    orig_method = getattr(module, method_name)
+
+    @functools.wraps(orig_method)
+    def wrapped_method(self, *args, **kwargs):
+        fsdp_state = self._get_fsdp_state()
+        args, kwargs = fsdp_state._pre_forward(self, args, kwargs)
+        out = orig_method(*args, **kwargs)
+        return fsdp_state._post_forward(self, args, out)
+
+    # Use `__get__` to make `wrapped_method` an instance method
+    setattr(
+        module,
+        method_name,
+        wrapped_method.__get__(module, type(module)),  # type:ignore[attr-defined]
+    )
+
+
+def _assert_all_fsdp_modules(modules: Iterable[Any]) -> None:
+    for module in modules:
+        if not isinstance(module, FSDPModule):
+            raise ValueError(f"Expects FSDPModule but got {type(module)}: {module}")

infer_4_47_1/lib/python3.10/site-packages/torch/distributed/_composable/fully_shard.py

@@ -0,0 +1,131 @@
+# mypy: allow-untyped-decorators
+from typing import Callable, Iterable, Optional, Union
+from typing_extensions import deprecated
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+from torch.distributed._composable.contract import contract
+from torch.distributed._composable_state import _get_module_state, _insert_module_state
+from torch.distributed.fsdp._common_utils import _FSDPState
+from torch.distributed.fsdp._dynamo_utils import _annotate_modules_for_dynamo
+from torch.distributed.fsdp._init_utils import (
+    _init_buffer_state,
+    _init_core_state,
+    _init_device_handle,
+    _init_ignored_module_states,
+    _init_param_handle_from_module,
+    _init_prefetching_state,
+    _init_process_group_state,
+    _init_runtime_state,
+    _init_state_dict_state,
+    HYBRID_SHARDING_STRATEGIES,
+)
+from torch.distributed.fsdp._runtime_utils import (
+    _register_post_forward_hook,
+    _register_pre_forward_hook,
+    _register_root_pre_forward_hook,
+)
+from torch.distributed.fsdp._state_dict_utils import _register_all_state_dict_hooks
+from torch.distributed.fsdp._wrap_utils import _auto_wrap
+from torch.distributed.fsdp.api import (
+    BackwardPrefetch,
+    CPUOffload,
+    MixedPrecision,
+    ShardingStrategy,
+)
+from torch.distributed.fsdp.wrap import _Policy
+
+
+@contract(state_cls=_FSDPState)
+@deprecated(
+    "`torch.distributed._composable.fully_shard` is being deprecated. "
+    "You can continue to use the wrapper based FSDP. "
+    "See usage in: https://github.com/pytorch/pytorch/blob/main/torch/distributed/fsdp/fully_sharded_data_parallel.py. "
+    "`torch.distributed._composable.fully_shard` will be removed after PyTorch 2.5.",
+    category=FutureWarning,
+)
+def fully_shard(
+    module: nn.Module,
+    *,
+    process_group: Optional[dist.ProcessGroup] = None,
+    policy: Optional[_Policy] = None,
+    strategy: Optional[ShardingStrategy] = None,
+    mixed_precision: Optional[MixedPrecision] = None,
+    cpu_offload: Optional[CPUOffload] = None,
+    ignored_modules: Optional[Iterable[torch.nn.Module]] = None,
+    device_id: Optional[Union[int, torch.device]] = None,
+    param_init_fn: Optional[Callable[[nn.Module], None]] = None,
+    sync_module_states: bool = False,
+    forward_prefetch: bool = False,
+    ignored_states: Union[
+        Optional[Iterable[torch.nn.Parameter]], Optional[Iterable[torch.nn.Module]]
+    ] = None,
+) -> nn.Module:
+    """Applies ``FullyShardedDataParallel`` (FSDP) semantics to ``module``."""
+    torch._C._log_api_usage_once("torch.distributed.fully_shard")
+    # Enforce the new auto wrap policy
+    if policy is not None and not isinstance(policy, _Policy):
+        raise ValueError(f"Expects a `_Policy` but got {policy}")
+    state = fully_shard.state(module)
+    state = _init_ignored_module_states(state, module, ignored_modules, ignored_states)
+    state = _init_device_handle(state, module, state._ignored_params, device_id)
+    _annotate_modules_for_dynamo(module, state._ignored_modules, True)
+    state = _init_process_group_state(state, process_group, strategy, policy)
+    if policy is not None:
+        root_kwargs = {
+            "process_group": process_group,
+            "strategy": strategy,
+            "mixed_precision": mixed_precision,
+            "cpu_offload": cpu_offload,
+            "ignored_modules": ignored_modules,
+            "device_id": device_id,
+            "param_init_fn": param_init_fn,
+            "sync_module_states": sync_module_states,
+            "forward_prefetch": forward_prefetch,
+            "ignored_states": ignored_states,
+        }
+        if strategy in HYBRID_SHARDING_STRATEGIES:
+            root_kwargs["process_group"] = (state.process_group, state._inter_node_pg)
+        _auto_wrap(
+            module,
+            policy,
+            state._ignored_modules,
+            state._ignored_params,
+            root_kwargs,
+            fully_shard,
+        )
+    state = _init_core_state(
+        state,
+        strategy or ShardingStrategy.FULL_SHARD,
+        mixed_precision,
+        cpu_offload,
+        limit_all_gathers=True,
+        use_orig_params=True,
+        backward_prefetch_limit=1,
+        forward_prefetch_limit=1,
+    )
+    state = _init_runtime_state(state)
+    state = _init_prefetching_state(
+        state, BackwardPrefetch.BACKWARD_PRE, forward_prefetch=forward_prefetch
+    )
+    state = _init_buffer_state(state, module)
+    state = _init_param_handle_from_module(
+        state, module, device_id, param_init_fn, sync_module_states
+    )
+    state = _init_state_dict_state(state)
+    _register_all_state_dict_hooks(state)
+    _register_pre_forward_hook(state, module)
+    _register_post_forward_hook(state, module)
+    _register_root_pre_forward_hook(state, module)  # prepend last
+    # Always insert the state for the passed-in module even if it has no
+    # managed parameters, in which case it has no handles and does not appear
+    # in `_fully_sharded_module_to_handles`
+    _insert_module_state(module, state)
+    for submodule in module.modules():
+        if (
+            submodule in state._fully_sharded_module_to_handle
+            and _get_module_state(submodule) is None
+        ):
+            _insert_module_state(submodule, state)
+    return module

infer_4_47_1/lib/python3.10/site-packages/torch/distributed/_composable/replicate.py

@@ -0,0 +1,256 @@
+# mypy: allow-untyped-decorators
+# mypy: allow-untyped-defs
+import weakref
+from typing import Any, cast, Dict, Iterable, List, NoReturn, Optional, Set, Tuple
+
+import torch
+import torch.nn as nn
+from torch.distributed._composable_state import _State
+from torch.nn.parallel import DistributedDataParallel
+
+from .contract import _get_registry, contract
+
+
+_ROOT_MODULE_PREFIX = ""
+
+
+class _ReplicateState(_State):
+    def __init__(self) -> None:
+        super().__init__()
+        self.module: nn.Module = nn.ParameterList()
+        self.has_initialized: bool = False
+        self._param_list: nn.ParameterList = nn.ParameterList()
+        # TODO(@fegin): this variable is originally create for testing, we
+        # should remove this if possible.
+        self._orig_module = self.module
+        self._param_names: List[str] = []
+        self._no_sync: bool = False
+        self._init_args: Optional[Tuple[Any, ...]] = None
+        self._init_kwargs: Dict[str, Any] = {}
+        self._comm_hook_args: List[Any] = []
+
+    def _collect_params(
+        self,
+        module: nn.Module,
+        ignored_modules: Set[nn.Module],
+        ignored_params: Set[nn.Parameter],
+        prefix: str = _ROOT_MODULE_PREFIX,
+    ) -> None:
+        # skip if managed by fully_sharded API
+        if _is_fully_sharded(module):
+            return
+
+        # if a module is ignored, all descendants of the module are ignored.
+        if module in ignored_modules:
+            return
+
+        recurse_prefix = (
+            f"{prefix}." if prefix != _ROOT_MODULE_PREFIX else _ROOT_MODULE_PREFIX
+        )
+
+        for n, p in module.named_parameters(recurse=False):
+            if p not in ignored_params:
+                self._param_list.append(p)
+                self._param_names.append(f"{recurse_prefix}{n}")
+
+        for name, child_module in module.named_children():
+            self._collect_params(
+                child_module,
+                ignored_modules,
+                ignored_params,
+                prefix=f"{recurse_prefix}{name}",
+            )
+
+    def lazy_init(self) -> None:
+        @torch._disable_dynamo(recursive=True)
+        def _lazy_init():
+            assert self._init_args is not None
+            self.init(*self._init_args, **self._init_kwargs)
+            self.register_comm_hook()
+            self._init_args = ()
+            self._init_kwargs = {}
+
+        _lazy_init()
+
+    def init(
+        self,
+        module: nn.Module,
+        ignored_modules: Set[nn.Module],
+        **kwargs,
+    ) -> None:
+        if self.has_initialized:
+            return
+
+        self.has_initialized = True
+
+        device_mesh = kwargs.get("device_mesh", None)
+        self.module = module
+        ignored_params = {p for m in ignored_modules for p in m.parameters()}
+        for submodule in module.modules():
+            if _is_fully_sharded(submodule):
+                ignored_params.update(submodule.parameters())
+        from torch.distributed.tensor.parallel.ddp import _localize_dtensor
+
+        _localize_dtensor(module, ignored_params=ignored_params)
+        self._collect_params(module, ignored_modules, ignored_params)
+
+        if "device_id" in kwargs:
+            # replicate() supports a small usability enhancement where
+            # user can pass in device_id as a Union[int, torch.device] even for
+            # CPU devices so users don't have to change code for CPU/GPU runs.
+            # We derive the right device_ids to feed into DDP to support this.
+            if kwargs["device_id"] is not None:
+                device_id = kwargs["device_id"]
+                # Convert to device_ids that DDP expects.
+                if isinstance(device_id, torch.device) and device_id.type == "cpu":
+                    # CPU modules receive device_ids None
+                    kwargs["device_ids"] = None
+                else:
+                    # GPU modules expect device_ids=[cuda_device]
+                    kwargs["device_ids"] = [device_id]
+            else:
+                kwargs["device_ids"] = None
+            kwargs.pop("device_id")
+
+        self._ddp = DistributedDataParallel(self._param_list, **kwargs)
+        # Weakref to the DDP instance is currently only used for testing.
+        replicate.state(self.module)._ddp_weakref = weakref.ref(self._ddp)
+
+    def register_comm_hook(self) -> None:
+        for comm_args, comm_kwargs in self._comm_hook_args:
+            self._ddp.register_comm_hook(*comm_args, **comm_kwargs)
+        self._comm_hook_args.clear()
+
+    def record_init_args(self, *args, **kwargs) -> None:
+        self._init_args = args
+        self._init_kwargs = kwargs
+
+    def forward_pre_hook(
+        self, module: nn.Module, args: Tuple[Any, ...], kwargs: Dict[str, Any]
+    ) -> Any:
+        if self._init_args or self._init_kwargs:
+            self.lazy_init()
+        self._ddp.require_backward_grad_sync = not self._no_sync
+        return self._ddp._pre_forward(*args, **kwargs)
+
+    def forward_post_hook(
+        self,
+        module: nn.Module,
+        input: Tuple[torch.Tensor],
+        output: torch.Tensor,
+    ) -> torch.Tensor:
+        return self._ddp._post_forward(output)
+
+
+def unimplemented_deepcopy(*args: Any, **kwargs: Any) -> NoReturn:
+    raise AssertionError(
+        "DDP does not support deepcopy. Please use state dict for serialization."
+    )
+
+
+# Follow the same pattern as FSDP/fully_shard
+class DDP:
+    def __new__(cls, *args, **kwargs):
+        """
+        Override ``__new__`` to remove the DDP class and directly construct
+        the original class for cases like indexing into a container module.
+        """
+        # Use index 2 since 0 is the dynamically constructed `DDP<...>` class
+        # and index 1 is the `DDP` class itself
+        orig_cls = cls.__mro__[2]
+        return orig_cls.__new__(orig_cls, *args, **kwargs)
+
+    def set_requires_gradient_sync(self, requires_gradient_sync: bool) -> None:
+        """
+        Sets if the module should sync gradients. This can be used to implement
+        gradient accumulation without communication.
+
+        Args:
+            requires_gradient_sync (bool): Whether to reduce gradients for the
+                module's parameters.
+        """
+        replicate.state(self)._no_sync = not requires_gradient_sync
+
+    def register_comm_hook(self, *args, **kwargs) -> None:
+        replicate.state(self)._comm_hook_args.append((args, kwargs))
+
+
+@contract(state_cls=_ReplicateState)
+def replicate(
+    module: nn.Module,
+    ignored_modules: Optional[Iterable[torch.nn.Module]] = None,
+    **kwargs,
+) -> nn.Module:
+    r"""Replicates a module
+
+    Args:
+        module (torch.nn.Module): module to replicate
+
+    Example::
+        >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d)
+        >>> module = nn.Linear(3, 3)
+        >>> replicate(module)
+    """
+    torch._C._log_api_usage_once("torch.distributed.replicate")
+
+    # TODO(fegin): using kwargs is not a good idea if we would like to make
+    # replicate a formal API to replace DDP.
+    if "device_id" in kwargs:
+        if not isinstance(kwargs["device_id"], (int, torch.device)):
+            raise RuntimeError(
+                "Expected device_id to be int or torch.device, "
+                f"but got {type(kwargs['device_id'])}"
+            )
+
+    if _is_fully_sharded(module):
+        raise RuntimeError(
+            "Cannot apply `replicate()` on a Module already managed by `fully_shard`"
+        )
+
+    if ignored_modules is None:
+        ignored_modules = {}
+    else:
+        ignored_modules = set(ignored_modules)
+
+    state = cast(_ReplicateState, replicate.state(module))
+    module.register_forward_pre_hook(state.forward_pre_hook, with_kwargs=True)
+    device_mesh = kwargs.get("device_mesh", None)
+    if device_mesh is not None:
+        from torch.distributed.device_mesh import _mesh_resources
+
+        root_mesh = _mesh_resources.get_root_mesh(device_mesh)
+        # if a root mesh is not the same as device_mesh,
+        # meaning the device_mesh is sliced out from the root mesh.
+        if root_mesh != device_mesh:
+            # TODO: This is a temporary work around to enable DDP + TP.
+            # We should do the logic in DDP so that the 2D implementation is
+            # sound and the state_dict works out of the box.
+            #
+            # This won't conflict with what is done in DDP class as the module
+            # replicate is going to pass is NOT the original module.
+            from torch.distributed.tensor.parallel.ddp import (
+                _localize_dtensor,
+                _reconstruct_dtensor,
+            )
+
+            module.register_forward_pre_hook(_reconstruct_dtensor)
+            module.register_forward_hook(_localize_dtensor)
+
+    module.register_forward_hook(state.forward_post_hook)  # type: ignore[arg-type]
+
+    state.record_init_args(module, ignored_modules, **kwargs)
+
+    # Place DDP leftmost for highest priority in the method resolution order
+    cls = module.__class__
+    dct = {"__deepcopy__": unimplemented_deepcopy}
+    new_cls = type(f"DDP{cls.__name__}", (DDP, cls), dct)
+    module.__class__ = new_cls
+    return module
+
+
+def _is_fully_sharded(module: nn.Module) -> bool:
+    r"""Check if module is marked with fully_shard."""
+    registry = _get_registry(module)
+    if registry is None:
+        return False
+    return "fully_shard" in registry

infer_4_47_1/lib/python3.10/site-packages/torch/distributed/_tools/mem_tracker.py

@@ -0,0 +1,943 @@
+import math
+import os
+import re
+import warnings
+from copy import deepcopy
+from enum import auto, Enum
+from functools import partial, wraps
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    List,
+    Optional,
+    Set,
+    Tuple,
+    Type,
+    TYPE_CHECKING,
+    Union,
+)
+from typing_extensions import Self
+
+import torch
+from torch import nn, optim
+from torch.distributed._tools.mod_tracker import ModTracker
+from torch.optim.optimizer import (
+    register_optimizer_step_post_hook,
+    register_optimizer_step_pre_hook,
+)
+from torch.utils._python_dispatch import (
+    is_traceable_wrapper_subclass,
+    TorchDispatchMode,
+)
+from torch.utils._pytree import tree_flatten, tree_map_only
+from torch.utils.weak import WeakIdKeyDictionary, weakref
+
+
+if TYPE_CHECKING:
+    from torch.utils.hooks import RemovableHandle
+
+# This value is hard-coded here:
+# https://github.com/pytorch/pytorch/blob/5fba5d83f0703ff8077ab65448a998e9ad6598fd/c10/cuda/CUDACachingAllocator.cpp#L117
+_PYTORCH_MIN_ALLOCATE = (
+    2**9 if int(os.environ.get("PYTORCH_NO_CUDA_MEMORY_CACHING", 0)) == 0 else 1
+)
+_TOTAL_KEY = "Total"
+
+__all__ = ["MemTracker"]
+
+
+class _RefType(str, Enum):
+    """Base Class for defining memory reference types, categorizing tensors based on their usage within a model."""
+
+
+class _State(str, Enum):
+    """Base Class for defining module state to capture snapshots ."""
+
+
+class _MemRefType(_RefType):
+    """
+    An enum to define memory reference types, categorizing tensors based on their usage within a model.
+
+        - PARAM: Tensors registered as nn.Parameter within modules.
+        - BUFFER: Tensors registered as nn.Buffer within modules.
+        - GRAD: Gradients associated with parameters.
+        - ACT: Tensors produced during the forward pass and recomputation in activation checkpointing.
+        - TMP: Temporary memory used during the backward pass, including gradients of activations.
+        - OPT: Tensors holding optimizer states.
+        - OTH: Tensors registered via `track_external` that do not fit the above categories.
+    """
+
+    PARAM = "Parameter"
+    BUFFER = "Buffer"
+    GRAD = "Gradient"
+    ACT = "Activation"
+    TEMP = "Temp"
+    OPT = "Optstate"
+    OTH = "Other"
+
+
+class _ModState(_State):
+    """
+    An enum to define the state of a module.
+
+        - PRE_FW: The module is about to run the forward pass.
+        - POST_FW: The module has finished running the forward pass.
+        - PEAK_FW: The module has reached the peak memory usage during the forward pass.
+        - PRE_BW: The module is about to run the backward pass.
+        - PRE_FW_AC: The module is about to run the forward pass with activation checkpointing.
+        - POST_FW_AC: The module has finished running the forward pass with activation checkpointing.
+        - POST_BW: The module has finished running the backward pass.
+        - PEAK_BW: The module has reached the peak memory usage during the backward pass.
+    """
+
+    PRE_FW = "Pre-Forward"
+    POST_FW = "Post-Forward"
+    PEAK_FW = "Peak-Forward"
+    PRE_BW = "Pre-Backward"
+    PRE_FW_AC = "Pre-Forward-AC"
+    POST_FW_AC = "Post-Forward-AC"
+    POST_BW = "Post-Backward"
+    PEAK_BW = "Peak-Backward"
+
+
+class _ModMemStats:
+    """
+    A class to store the memory statistics of a module.
+
+    Args:
+        mod_fqn (str): The fully qualified name of the module.
+    Attributes:
+        mod_fqn (str): The fully qualified name of the module.
+        parameter_mem (int): The memory usage of the parameters of the module.
+        buffer_mem (int): The memory usage of the buffers of the module.
+        input_mem (int): The memory usage of the inputs to the module.
+        output_mem (int): The memory usage of the outputs from the module.
+        snapshots (Dict[_ModState, Dict[torch.device, Dict[str, int]]]): A dictionary of memory snapshots
+        of the module at different states defined by ``_ModState``.
+    Note:
+        The memory snapshot is stored as a dictionary - Dict[torch.device, Dict[str, int]], where each key is a device,
+         and each value is another dictionary with keys as memory reference types defined by `_MemRefType` and
+         values as the memory consumed in bytes.
+    """
+
+    def __init__(self, mod_fqn: str):
+        self.mod_fqn = mod_fqn
+        self.parameter_mem: int
+        self.buffer_mem: int
+        self.input_mem: int
+        self.output_mem: int
+        self.local_peak: Dict[torch.device, int] = {}
+        self.snapshots: Dict[_ModState, List[Dict[torch.device, Dict[str, int]]]] = {}
+
+
+class _WeakRefInfo:
+    """
+    Manages memory statistics and device attributes for tensor storages.
+    """
+
+    def __init__(
+        self, size: int, element_size: int, device: torch.device, reftype: _RefType
+    ) -> None:
+        """
+        Initializes the ``_WeakRefInfo`` object with tensor storage properties.
+
+        Args:
+            size (int): The number of elements in the tensor storage.
+            element_size (int): The size of each element in the tensor storage.
+            device (torch.device): The device on which the tensor is allocated.
+            reftype (_RefType): The reference type of the tensor.
+        """
+        self.size = size
+        self.element_size = element_size
+        self.reftype = reftype
+        self.device = device
+        self.mem_consumed = self._calculate_mem_consumed()
+
+    def _calculate_mem_consumed(self) -> int:
+        """
+        Calculates the memory consumed by the tensor storage, considering device-specific allocation rules.
+
+        Returns:
+            int: The memory consumed in bytes.
+        """
+        mem = self.size * self.element_size
+        if self.device.type == "cuda":
+            return math.ceil((mem) / _PYTORCH_MIN_ALLOCATE) * _PYTORCH_MIN_ALLOCATE
+        return mem
+
+    def update_mem_consumed(self, st: torch.UntypedStorage) -> int:
+        """
+        Updates and returns the memory consumed if the storage size has changed.
+
+        Args:
+            st (torch.UntypedStorage): The tensor storage to check for size updates.
+
+        Returns:
+            int: The updated memory consumed in bytes.
+        """
+        if st.size() != self.size:
+            self.size = st.size()
+            self.mem_consumed = self._calculate_mem_consumed()
+        return self.mem_consumed
+
+    @staticmethod
+    def get_untyped_storages(t: torch.Tensor) -> Set[torch.UntypedStorage]:
+        """
+        Recursively extracts untyped storages from a tensor or its subclasses.
+
+        Args:
+            t (torch.Tensor): The tensor to extract storages from.
+
+        Returns:
+            Set[torch.UntypedStorage]: A set of untyped storages.
+        """
+        unflattened_tensors = [t]
+        flattened_tensor_storages = set()
+        while len(unflattened_tensors) > 0:
+            obj = unflattened_tensors.pop()
+            if is_traceable_wrapper_subclass(obj):
+                attrs, _ = obj.__tensor_flatten__()  # type: ignore[attr-defined]
+                unflattened_tensors.extend([getattr(obj, attr) for attr in attrs])
+            else:
+                if not hasattr(obj, "untyped_storage"):
+                    warnings.warn(
+                        f"Expected a tensor or a traceable wrapper-subclass of tensor, but got {type(obj)}",
+                        category=UserWarning,
+                        stacklevel=2,
+                    )
+                else:
+                    flattened_tensor_storages.add(obj.untyped_storage())
+        return flattened_tensor_storages
+
+    @classmethod
+    def create_winfo(
+        cls,
+        st: torch.UntypedStorage,
+        device: torch.device,
+        reftype: _RefType,
+        callback: Optional[Callable[[Self, weakref.ref], Any]] = None,
+    ) -> Tuple[Self, weakref.ref]:
+        """
+        Creates a new ``_WeakRefInfo`` instance and a weak reference to a ``torch.UntypedStorage`` object,
+        optionally attaching a callback to the weak reference.
+
+        Args:
+            st (torch.UntypedStorage): The storage object for which to create the weak reference info.
+            device (torch.device): The device associated with the storage object.
+            reftype (_RefType): The type of reference, used to categorize the storage.
+            callback (Optional[Callable[[Self, weakref.ref]]]): A callback function that is called when
+                the storage object is about to be finalized (garbage collected). The callback function
+                should accept two arguments: the ``_WeakRefInfo`` instance and the weak reference to the storage.
+        Returns:
+            Tuple[Self, weakref.ref]: A tuple containing the newly created ``_WeakRefInfo`` instance and the
+            weak reference to the storage object. The weak reference may have an attached callback if provided.
+        """
+
+        winfo = cls(st.size(), st.element_size(), device, reftype)
+        w_st = weakref.ref(st, partial(callback, winfo) if callback else None)
+        return winfo, w_st
+
+
+def _get_mem_divisor(units: str) -> int:
+    unit_dict = {"B": 1, "KiB": 2**10, "MiB": 2**20, "GiB": 2**30}
+    if units in unit_dict:
+        return unit_dict[units]
+    else:
+        raise ValueError(
+            f"Unsupported unit: {units}. Supported units are: {', '.join(unit_dict.keys())}"
+        )
+
+
+def _rounding_fn(value: int, divisor: int, precision: int) -> Union[float, int]:
+    return value if divisor == 1 else round(value / divisor, precision)
+
+
+def _print_snapshot(snapshot: Dict[torch.device, Dict[str, int]], units: str) -> None:
+    if len(snapshot) == 0:
+        print("No memory tracked.")
+        return
+    divisor = _get_mem_divisor(units)
+    for dev, dev_snap in snapshot.items():
+        if _rounding_fn(dev_snap[_TOTAL_KEY], divisor, 2) <= 0:
+            continue
+        print(
+            f"Device: {dev}",
+            *(
+                f"\t{k}: {_rounding_fn(v, divisor, 2)} {units}"
+                for k, v in dev_snap.items()
+            ),
+            sep="\n",
+        )
+
+
+def _print_snapshot_tabular(
+    snapshot: Dict[torch.device, Dict[str, int]], units: str
+) -> None:
+    if len(snapshot) == 0:
+        print("No memory tracked.")
+        return
+    try:
+        from tabulate import tabulate
+    except ImportError as err:
+        raise ImportError(
+            "Please install tabulate to use the tabulate option."
+        ) from err
+    divisor = _get_mem_divisor(units)
+    table_data = []
+    key_list = list(next(iter(snapshot.values())).keys())
+    headers = ["Device"] + [f"{key}" for key in key_list]
+
+    for dev, dev_snap in snapshot.items():
+        if _rounding_fn(dev_snap[_TOTAL_KEY], divisor, 2) <= 0:
+            continue
+        row = [str(dev)]
+        row.extend(f"{_rounding_fn(v, divisor, 2)} {units}" for v in dev_snap.values())
+        table_data.append(row)
+    print(tabulate(table_data, headers=headers, tablefmt="rst"))
+
+
+def _print_state_snapshots(
+    snapshots: Dict[_State, List[Dict[torch.device, Dict[str, int]]]], units: str
+) -> None:
+    for state, snapshot_list in snapshots.items():
+        print(f"{state}")
+        for i, snapshot in enumerate(snapshot_list):
+            print(f"# {i + 1}:")
+            _print_snapshot(snapshot, units)
+    print()
+
+
+def _print_state_snapshots_tabular(
+    snapshots: Dict[_State, List[Dict[torch.device, Dict[str, int]]]], units: str
+) -> None:
+    try:
+        from tabulate import tabulate
+    except ImportError as err:
+        raise ImportError(
+            "Please install tabulate to use the tabulate option."
+        ) from err
+
+    table_data = []
+    last_state_call = None
+    divisor = _get_mem_divisor(units)
+    for state, snapshot_list in snapshots.items():
+        for i, snapshot in enumerate(snapshot_list):
+            state_call = f"{state} # {i + 1}"
+            for dev, dev_snap in snapshot.items():
+                if _rounding_fn(dev_snap[_TOTAL_KEY], divisor, 2) <= 0:
+                    continue
+                row = {
+                    "State & Call": (
+                        state_call if state_call != last_state_call else ""
+                    ),
+                    "Device": str(dev),
+                }
+                last_state_call = state_call
+                for k, v in dev_snap.items():
+                    row[f"{k}"] = f"{_rounding_fn(v, divisor, 2)} {units}"
+                table_data.append(row)
+    print(tabulate(table_data, headers="keys", tablefmt="rst"))
+
+
+class _UpdateType(Enum):
+    # These are used for tracking updates to the continuouly maintained memory snapshot.
+    # ADD - When a new tensor storage is tracked
+    # DEL - When a tensor storage is about to be finalized (garbage collected).
+    # REF - When a tensor reference is updated, for instance, the gradients are marked as
+    #       generic backward reference types until the grad_hook categorizes them as gradients.
+    # SIZE - When a tensor's storage is resized.
+    ADD = auto()
+    DEL = auto()
+    REF = auto()
+    SIZE = auto()
+
+
+class MemTracker(TorchDispatchMode):
+    """
+    A TorchDispatchMode to track, categorize and attribute the tensor memory created or accessed within its context.
+
+    It categorizes the tracked tensors as parameters, buffers, activations, gradients, temporary memory and optimizer states
+    as defined by ``_MemRefType`` within its context. It captures memory `snapshots` for the modules, called within its context,
+    at various states defined by ``_ModState``.
+
+    Attributes:
+        memory_tracking: A weakref key dictionary to store the memory statistics of each module. Each key
+        is a reference to a module, and each value is a ``_ModMemStats`` object that stores the memory
+        statistics of the module.
+
+    Note:
+        The MemTracker should be used as a context manager. The modules, optimizers, and any other tensors created within
+        the context of MemTracker will be tracked by default. Any tensors or stateful objects such as modules, optimizers etc.
+        that need to be tracked but are created outside the MemTracker should be registered using the `track_external` method.
+        The `track_external` method should be called before the MemTracker is used. Any tensors created outside the ``MemTracker``
+        and not supplied to the `track_external` method will not be tracked by the ``MemTracker``.
+
+    Example usage:
+
+        .. code-block:: python
+
+            module = ...
+            optimizer = ...
+            inp = ...
+            mem_tracker = MemTracker()
+            mem_tracker.track_external(module, optimizer, inp)
+            with mem_tracker as mt:
+                loss = module(inp)
+                print("After Forward:")
+                mt.display_snapshot("current")
+                loss.backward()
+                optimizer.step()
+                optimizer.zero_grad()
+            mt.display_snapshot("peak")
+            mt.display_modulewise_snapshots(depth = 3, units = "MiB")
+
+    Known Limitations:
+        - The ``MemTracker`` does not track memory for tensors that bypass the ``TorchDispatchMode`` ex. under ``no_dispatch``.
+        - Resizing tensor storages directly by using non-Tensor methods other than using ``torch.Untyped_Storage.resize_``
+          is not tracked. File a Github issue if you have use-cases for this.
+        - If the tensors are not traceable or wrappable subclasses of ``torch.Tensor``, then the tracker does not know how to
+            track their storages. File a Github issue if you have use-cases for this.
+        - During AC in the backward pass there might be misattribution between activation and temp memory, but the peak memory
+          will be tracked accurately. This will be fixed in the next update by hooking intricately with ``torch.uitls.checkpoint``.
+    """
+
+    def __init__(self) -> None:
+        self.memory_tracking = WeakIdKeyDictionary()
+        self._curr_mem_snap: Dict[torch.device, Dict[str, int]] = {}
+        self._peak_mem: Dict[torch.device, int] = {}
+        self._peak_mem_snap: Dict[torch.device, Dict[str, int]] = {}
+        self._param_to_grad_hook_handles = WeakIdKeyDictionary()
+        self._optimizer_hook_handles: Optional[
+            Tuple[RemovableHandle, RemovableHandle]
+        ] = None
+        # Dictionary to store the ``_WeakRefInfo`` instances corresponding to each tensor's storage.
+        self._WINFO = WeakIdKeyDictionary()
+        self._mod_tracker = ModTracker()
+        # This is a general memory tracker which can be used with any ``_RefType`` subclass
+        self._ref_class: Type[_RefType] = _MemRefType
+        # Flags to track if we are in the AC region or optimizer step region
+        self._in_opt: bool = False
+        self._in_ac: bool = False
+        # Weak references to the topmost AC module currently active
+        self._ac_mod: Optional[weakref.ref] = None
+        self._orig_resize = torch.UntypedStorage.resize_
+
+    def _update_snap(
+        self,
+        u_type: _UpdateType,
+        winfo: _WeakRefInfo,
+        old_mem_consumed: Optional[int] = None,
+        old_reftype: Optional[_RefType] = None,
+    ) -> None:
+        # Initialize a flag to track if the total memory might drop to zero after updates.
+        maybe_zero = False
+        # Ensure the device entry exists in the current memory snapshot, initializing if necessary.
+        dev_snap = self._curr_mem_snap.setdefault(
+            winfo.device, dict.fromkeys(self._ref_class, 0)
+        )
+        dev_snap.setdefault(_TOTAL_KEY, 0)
+        # Handle different types of updates based on the update type (`u_type`).
+        if u_type == _UpdateType.ADD:
+            # Increase the memory consumed for the specific reference type and update the total.
+            dev_snap[winfo.reftype] += winfo.mem_consumed
+            dev_snap[_TOTAL_KEY] += winfo.mem_consumed
+        elif u_type == _UpdateType.DEL:
+            # Decrease the memory consumed for the specific reference type and reduce the total.
+            dev_snap[winfo.reftype] -= winfo.mem_consumed
+            dev_snap[_TOTAL_KEY] -= winfo.mem_consumed
+            maybe_zero = True
+        elif u_type == _UpdateType.REF:
+            assert old_reftype is not None
+            # Adjust memory consumption between two reference types within the same device.
+            dev_snap[old_reftype] -= winfo.mem_consumed
+            dev_snap[winfo.reftype] += winfo.mem_consumed
+        elif u_type == _UpdateType.SIZE:
+            assert old_mem_consumed is not None
+            # Adjust the memory consumed for a reference type due to a change in size.
+            change = winfo.mem_consumed - old_mem_consumed
+            dev_snap[winfo.reftype] += change
+            dev_snap[_TOTAL_KEY] += change
+            maybe_zero = True
+        else:
+            raise ValueError(f"Invalid update type: {u_type}")
+        # Check if the total memory for the device has dropped to zero.
+        if maybe_zero:
+            if self._curr_mem_snap[winfo.device][_TOTAL_KEY] == 0:
+                # Remove the device entry from the memory snapshot if the total memory is zero.
+                del self._curr_mem_snap[winfo.device]
+
+    def _update_and_maybe_create_winfos(
+        self,
+        t: torch.Tensor,
+        reftype: _RefType,
+        update_existing: bool = False,
+    ) -> Set[_WeakRefInfo]:
+        sts = _WeakRefInfo.get_untyped_storages(t)
+        winfos = set()
+        for st in sts:
+            # Attempt to retrieve existing ``_WeakRefInfo`` and its weak reference from the tracking dictionary.
+            winfo, _ = self._WINFO.get(st, (None, None))
+            if winfo is not None:
+                # If ``_WeakRefInfo`` exists, check if the reference type needs to be updated.
+                old_reftype = winfo.reftype
+                if old_reftype != reftype:
+                    # Update the reference type and apply changes via ``_update_snap``.
+                    winfo.reftype = reftype
+                    self._update_snap(_UpdateType.REF, winfo, old_reftype=old_reftype)
+                winfos.add(winfo)
+            elif update_existing:
+                # If no existing ``_WeakRefInfo`` is found and update_existing is True, raise an error.
+                raise KeyError("No existing winfo found")
+            else:
+                # If no existing _WeakRefInfo is found and update_existing is False, create a new ``_WeakRefInfo``.
+                winfo, w_st = _WeakRefInfo.create_winfo(
+                    st, t.device, reftype, self._delete_callback
+                )
+                # Store the new ``_WeakRefInfo`` and its weak reference in the tracking dictionary.
+                self._WINFO[st] = (winfo, w_st)
+                # Update the snapshot for the newly added ``_WeakRefInfo``.
+                if winfo.mem_consumed > 0:
+                    self._update_snap(_UpdateType.ADD, winfo)
+                winfos.add(winfo)
+        return winfos
+
+    def _delete_callback(self, winfo: _WeakRefInfo, w_st: weakref.ref) -> None:
+        # Callback to be called when the storage object corresponding to the  ``_WeakRefInfo``
+        # instance is about to be finalized.
+        if winfo.mem_consumed > 0:
+            self._update_snap(_UpdateType.DEL, winfo)
+
+    def _track_resize(self) -> None:
+        # Need to monkey-patch this because ``torch.UntypedStorage.resize_`` is not captured
+        # by ``TorchDispatchMode``.
+        @wraps(self._orig_resize)
+        def resize_(st: torch.UntypedStorage, size: int) -> None:
+            self._orig_resize(st, size)
+            winfo, _ = self._WINFO.get(st, (None, None))
+            if winfo is not None and winfo.size != st.size():
+                old_mem_consumed = winfo.mem_consumed
+                winfo.update_mem_consumed(st)
+                self._update_snap(
+                    _UpdateType.SIZE, winfo, old_mem_consumed=old_mem_consumed
+                )
+
+        torch.UntypedStorage.resize_ = resize_  # type: ignore[method-assign, assignment]
+
+    def _restore_resize(self) -> None:
+        torch.UntypedStorage.resize_ = self._orig_resize  # type: ignore[method-assign]
+
+    def _update_peak_stats(self, peak_state: _State) -> None:
+        # We first capture the current memory snapshot of the current tracker state then,
+        # We step through each of the modules we have tracked so far in ``memory_tracking``
+        #  and check if it is currently active by querying ``_mod_tracker.parents``
+        # If it is active, we update the per device peak memory usage for the module
+        #  corresponding to the ``_State`` which can be ``PEAK_FW`` or ``PEAK_BW``.
+        curr_snap = self._curr_mem_snap
+
+        for mod_stats in self.memory_tracking.values():
+            if mod_stats.mod_fqn in self._mod_tracker.parents:
+                if peak_state in mod_stats.snapshots:
+                    for dev, dev_snap in curr_snap.items():
+                        if mod_stats.local_peak.get(dev, 0) < dev_snap[_TOTAL_KEY]:
+                            mod_stats.local_peak[dev] = dev_snap[_TOTAL_KEY]
+                            mod_stats.snapshots[peak_state][-1][dev] = deepcopy(
+                                dev_snap
+                            )
+
+        for dev, dev_snap in curr_snap.items():
+            if self._peak_mem.get(dev, 0) < dev_snap[_TOTAL_KEY]:
+                self._peak_mem[dev] = dev_snap[_TOTAL_KEY]
+                self._peak_mem_snap[dev] = deepcopy(dev_snap)
+
+    def _track(self, reftype: _RefType, t: torch.Tensor) -> None:
+        # Get the storages of the tensor and check if we have already tracked them.
+        # If yes, then check if the storage size has changed and update the current snapshot.
+        # Else create a new ``_WeakRefInfo`` instance and add it to the dictionary.
+        sts = _WeakRefInfo.get_untyped_storages(t)
+        for st in sts:
+            winfo, _ = self._WINFO.get(st, (None, None))
+            if winfo is not None:
+                if winfo.size != st.size():
+                    old_mem_consumed = winfo.mem_consumed
+                    winfo.update_mem_consumed(st)
+                    self._update_snap(
+                        _UpdateType.SIZE, winfo, old_mem_consumed=old_mem_consumed
+                    )
+                return
+            else:
+                winfo, w_st = _WeakRefInfo.create_winfo(
+                    st, t.device, reftype, self._delete_callback
+                )
+                self._WINFO[st] = (winfo, w_st)
+                # Update the current snapshot for the newly added ``_WeakRefInfo``.
+                if winfo.mem_consumed > 0:
+                    self._update_snap(_UpdateType.ADD, winfo)
+
+    def get_tracker_snapshot(
+        self, type: str = "current"
+    ) -> Dict[torch.device, Dict[str, int]]:
+        """
+        Capture a snapshot of the memory usage breakdown per device, based on the specified type.
+
+        Args:
+            type (str): The type of snapshot to capture. Can be "current" for the current memory usage or "peak" for the
+                        peak memory usage. Defaults to "current".
+        Returns:
+            Dict[torch.device, Dict[str, int]]: A dictionary where each key is a torch.device, and each value is another
+                                                dictionary. This inner dictionary has keys representing memory reference
+                                                types as defined in ``_MemRefType`` and values representing the amount of
+                                                memory consumed in bytes.
+        Raises:
+            ValueError: If an invalid type is specified.
+        """
+        if type == "current":
+            return deepcopy(self._curr_mem_snap)
+        elif type == "peak":
+            return deepcopy(self._peak_mem_snap)
+        else:
+            raise ValueError(f"Invalid type {type}")
+
+    def _track_module_params_and_buffers(
+        self, module: nn.Module, install_grad_hooks: bool = True
+    ) -> Tuple[int, int]:
+        # Track the parameters and buffers of the module if not already tracked.
+        # If the parameters have gradients, track the gradients as well.
+        # If install_grad_hooks is True, install a gradient hook on the parameters
+        #  to track the gradients, if it has not already been installed.
+        # Return the total memory consumed by the parameters and buffers.
+        def _grad_hook(grad: torch.Tensor) -> None:
+            self._update_and_maybe_create_winfos(
+                grad,
+                _MemRefType.GRAD,
+            )
+
+        param_memory = 0
+        for param in module.parameters():
+            winfos = self._update_and_maybe_create_winfos(
+                param,
+                _MemRefType.PARAM,
+            )
+            param_memory += sum(winfo.mem_consumed for winfo in winfos)
+            if param.grad is not None:
+                self._update_and_maybe_create_winfos(
+                    param.grad,
+                    _MemRefType.GRAD,
+                )
+            if (
+                self._param_to_grad_hook_handles.get(param, None) is None
+                and install_grad_hooks
+            ):
+                grad_hook_handle = param.register_hook(_grad_hook)
+                post_acc_grad_hook_handle = param.register_post_accumulate_grad_hook(
+                    lambda p: (_grad_hook(p.grad))
+                )
+                self._param_to_grad_hook_handles[param] = (
+                    grad_hook_handle,
+                    post_acc_grad_hook_handle,
+                )
+        buffer_memory = 0
+        for buffer in module.buffers():
+            winfos = self._update_and_maybe_create_winfos(
+                buffer,
+                _MemRefType.BUFFER,
+            )
+            buffer_memory += sum(winfo.mem_consumed for winfo in winfos)
+        return (param_memory, buffer_memory)
+
+    def _track_inputs_or_outputs(self, args: Any) -> int:
+        # Calculate the memory consumed by the inputs or outputs of the module.
+        input_or_output_memory = 0
+
+        def add_inps_or_outs(t: torch.Tensor) -> None:
+            nonlocal input_or_output_memory
+            sts = _WeakRefInfo.get_untyped_storages(t)
+            for st in sts:
+                winfo, _ = self._WINFO.get(st, (None, None))
+                if winfo is not None:
+                    input_or_output_memory += winfo.mem_consumed
+
+        tree_map_only(torch.Tensor, add_inps_or_outs, args)
+        return input_or_output_memory
+
+    def _pre_fw_hook(self, module: nn.Module, inputs: Any) -> None:
+        # This is installed as a pre-fwd user hook with ``ModTracker.`` Based on the following cases we
+        # set the state and capture the memory snapshot for the module.
+        # Case 1: If the module is not in the ``memory_tracking`` dictionary, we track the parameters, buffers,
+        #         input and output memory of the module. Create a new ``_ModMemStats`` instance for the module
+        #         and add it to the ``memory_tracking`` dictionary.
+        # Case 2: If the module is already in the ``memory_tracking`` dictionary and we are in backward, this means
+        #         we are in the AC region. We check if this is the top most module in the AC region. If it is,
+        #         we store a weak reference and set the flag ``_in_ac`` to True.
+        # Case 3: If the module is already in the ``memory_tracking`` dictionary and we are in forward, this means
+        #         this module is called for the second time. If it is a root module, that means we are in the next
+        #         iteration and we error out. If it is not a root module, that means it's a submodule that is being
+        #         used multiple times in the same iteration, which we allow and track.
+        # For Case 1 and 3, we also initialiaze the ``local_peak`` and ``PEAK_FW`` snapshot for the module.
+        mod_name = self._mod_tracker.get_known_fqn(module)
+        assert mod_name is not None
+        if module not in self.memory_tracking:
+            mod_stats = _ModMemStats(mod_name)
+            param_mem, buffer_mem = self._track_module_params_and_buffers(
+                module, install_grad_hooks=True
+            )
+            input_mem = self._track_inputs_or_outputs(inputs)
+            mod_stats.parameter_mem = param_mem
+            mod_stats.buffer_mem = buffer_mem
+            mod_stats.input_mem = input_mem
+            self.memory_tracking[module] = mod_stats
+            state = _ModState.PRE_FW
+
+        elif self._mod_tracker.is_bw:
+            mod_stats = self.memory_tracking[module]
+            state = _ModState.PRE_FW_AC
+            if self._ac_mod is None:
+                self._ac_mod = weakref.ref(module)
+                self._in_ac = True
+        else:
+            parents = set(self._mod_tracker.parents) - {mod_name}
+            if len(parents) == 1 and "Global" in parents:
+                raise NotImplementedError(
+                    "MemTracker does not support memory tracking for multiple iterative calls."
+                    " Either use ``reset_mod_stats`` to clear module memory stats for the previous iteration"
+                    " or file a github issue if you need this feature."
+                )
+            mod_stats = self.memory_tracking[module]
+            state = _ModState.PRE_FW
+            input_mem = self._track_inputs_or_outputs(inputs)
+            mod_stats.input_mem = input_mem
+
+        mem_snapshot = self.get_tracker_snapshot()
+        if state == _ModState.PRE_FW:
+            mod_stats.local_peak = {
+                dev: dev_snap[_TOTAL_KEY] for dev, dev_snap in mem_snapshot.items()
+            }
+            mod_stats.snapshots.setdefault(_ModState.PEAK_FW, []).append(mem_snapshot)
+        mod_stats.snapshots.setdefault(state, []).append(deepcopy(mem_snapshot))
+
+    def _post_fw_hook(self, module: nn.Module, inputs: Any, outputs: Any) -> None:
+        # This is installed as a post-fwd user hook with ``ModTracker``. Based on the following cases we
+        # set the state and capture the memory snapshot for the module.
+        # Case 1: This is called in backward, which means we are in the AC region. If this is the top most module
+        #         in the AC region, we set the flag ``_in_ac`` to False.
+        # Case 2: This is called in forward so we calculate the output memory
+        #         of the module and update its mod_stats.
+        mod_stats = self.memory_tracking[module]
+        if self._mod_tracker.is_bw:
+            state = _ModState.POST_FW_AC
+            if self._ac_mod is not None and self._ac_mod() is module:
+                self._ac_mod = None
+                self._in_ac = False
+        else:
+            state = _ModState.POST_FW
+            output_mem = self._track_inputs_or_outputs(outputs)
+            mod_stats.output_mem = output_mem
+        mod_stats.snapshots.setdefault(state, []).append(self.get_tracker_snapshot())
+
+    def _pre_bw_hook(self, module: nn.Module, args: Any) -> None:
+        # This is installed as a pre-bwd user hook with ``ModTracker``. We set the state and capture the
+        # snapshot for the module. We also initialize the ``local_peak`` and ``PEAK_BW`` snapshot for it.
+        # If the module is None, we skip the hook.
+        # This can happen since this installed inside a multi-grad hook on the module's output tensors
+        # and the module itself may not be alive during backward.
+        if module is None:
+            warnings.warn("Module is None. Skipping PRE_BW hook.", stacklevel=2)
+            return
+        mod_stats = self.memory_tracking[module]
+        mem_snapshot = self.get_tracker_snapshot()
+        mod_stats.local_peak = {
+            dev: dev_snap[_TOTAL_KEY] for dev, dev_snap in mem_snapshot.items()
+        }
+        mod_stats.snapshots.setdefault(_ModState.PEAK_BW, []).append(mem_snapshot)
+        mod_stats.snapshots.setdefault(_ModState.PRE_BW, []).append(
+            deepcopy(mem_snapshot)
+        )
+
+    def _post_bw_hook(self, module: nn.Module, args: Any) -> None:
+        # This is installed as a post-bwd user hook with ``ModTracker``. We set the state and capture the
+        # snapshot for the module if it is not None.
+        # This can happen since this installed inside a multi-grad hook on the module's input tensors
+        # and the module itself may not be alive during backward.
+        if module is None:
+            warnings.warn("Module is None. Skipping POST_BW hook.", stacklevel=2)
+            return
+        mod_stats = self.memory_tracking[module]
+        mod_stats.snapshots.setdefault(_ModState.POST_BW, []).append(
+            self.get_tracker_snapshot()
+        )
+
+    def _track_optimizer_states(
+        self, reftype: _RefType, optimizer: optim.Optimizer
+    ) -> None:
+        for states in optimizer.state.values():
+            for val in states.values():
+                if isinstance(val, torch.Tensor):
+                    self._update_and_maybe_create_winfos(
+                        val,
+                        reftype,
+                    )
+
+    def _register_global_optimizer_hook(self) -> None:
+        # Register a hook on the optimizer step to track the optimizer states.
+        # The pre-hook is to set the flag ``_in_opt`` to True. The post-hook unsets the flag,
+        # and also tracks any optimizer states that are created during the optimizer step.
+        def _opt_step_pre_hook(
+            optimizer: optim.Optimizer, args: Any, kwargs: Any
+        ) -> None:
+            self._in_opt = True
+
+        def _opt_step_post_hook(
+            optimizer: optim.Optimizer, args: Any, kwargs: Any
+        ) -> None:
+            self._track_optimizer_states(_MemRefType.OPT, optimizer)
+            self._in_opt = False
+
+        self._optimizer_hook_handles = (
+            register_optimizer_step_pre_hook(_opt_step_pre_hook),
+            register_optimizer_step_post_hook(_opt_step_post_hook),
+        )
+
+    def _deregister_param_and_optimizer_hooks(self) -> None:
+        for (
+            grad_hook_handle,
+            post_acc_grad_hook_handle,
+        ) in self._param_to_grad_hook_handles.values():
+            grad_hook_handle.remove()
+            post_acc_grad_hook_handle.remove()
+        self._param_to_grad_hook_handles.clear()
+
+        if self._optimizer_hook_handles is not None:
+            for handle in self._optimizer_hook_handles:
+                handle.remove()
+            self._optimizer_hook_handles = None
+
+    def track_external(
+        self, *external: Union[nn.Module, optim.Optimizer, torch.Tensor]
+    ) -> None:
+        """
+        Track tensors and stateful objects like modules, optimizers etc. that are created outside the MemTracker.
+
+        This method should be called before the ``MemTracker`` is used. Any tensors that are not module parameters, buffers,
+        gradients activations, or optimizer states will be categorized as ``Other``. If you want them categorized with a
+        custom name, please file a GitHub issue. Any tensors created outside the MemTracker and not supplied to this
+        method will not be be tracked by ``MemTracker``.
+
+        Args:
+            *external (Union[nn.Module, optim.Optimizer, torch.Tensor]): The external modules, optimizers, and
+                                                                         tensors to be tracked.
+        """
+        flat_external, _ = tree_flatten(external)
+        for obj in flat_external:
+            if isinstance(obj, torch.Tensor):
+                self._update_and_maybe_create_winfos(
+                    obj,
+                    _MemRefType.OTH,
+                )
+            elif isinstance(obj, torch.nn.Module):
+                self._track_module_params_and_buffers(obj, install_grad_hooks=False)
+            elif isinstance(obj, optim.Optimizer):
+                self._track_optimizer_states(_MemRefType.OPT, obj)
+            else:
+                raise TypeError(
+                    f"Object of type {type(obj)} is not supported for tracking. "
+                    f"Only stateful objects like modules, optimizers, and tensors are supported."
+                )
+
+    def display_snapshot(
+        self, type: str = "current", units: str = "B", tabulate: bool = False
+    ) -> None:
+        """
+        Display the memory usage breakdown snapshot of the tracker based on the specified type and units.
+
+        Keyword args:
+            type (str): The type of snapshot to display. Can be "current" for the current memory usage or "peak" for the
+                        peak memory usage. Defaults to "current".
+            units (str): The units to use for displaying memory usage. Defaults to "B". Supports ["B", "KiB", "MiB", "GiB"].
+            tabulate (bool): Whether to display the snapshot in a tabular format. Defaults to False.
+        """
+        snapshot = self.get_tracker_snapshot(type)
+        if tabulate:
+            _print_snapshot_tabular(snapshot, units)
+        else:
+            _print_snapshot(snapshot, units)
+
+    def display_modulewise_snapshots(
+        self, depth: int = 2, units: str = "B", tabulate: bool = False
+    ) -> None:
+        """
+        Print per device memory breakdown snapshot for each module called within MemTracker.
+
+        Snapshots are displayed for the states defined by ``_ModState``.
+        The module hierarchy is displayed up to the specified depth.
+
+        Keyword Args:
+            depth (int, optional): The depth of the module hierarchy to display. Defaults to 2.
+            units (str, optional): The units to use for memory tracking. Defaults to "B". Supports ["B", "KiB", "MiB", "GiB"].
+            tabulate (bool, optional): Whether to display the snapshot in a tabular format. Defaults to False.
+        """
+
+        def natural_sort_key(s: str) -> List[Union[int, str]]:
+            return [
+                int(text) if text.isdigit() else text.lower()
+                for text in re.split("([0-9]+)", s)
+            ]
+
+        for mod_stats in sorted(
+            self.memory_tracking.values(),
+            key=lambda m_stats: natural_sort_key(m_stats.mod_fqn),
+        ):
+            mod_fqn = mod_stats.mod_fqn
+            mod_depth = mod_fqn.count(".") + 1
+            if mod_depth > depth:
+                continue
+            print(f"Module:  {mod_fqn}")
+            if tabulate:
+                _print_state_snapshots_tabular(mod_stats.snapshots, units)
+            else:
+                _print_state_snapshots(mod_stats.snapshots, units)
+
+    def reset_mod_stats(self) -> None:
+        """
+        Reset all the module memory stats. Clears ``memory_tracking`` dictionary.
+        """
+        self.memory_tracking.clear()
+
+    def __enter__(self) -> "MemTracker":
+        self._register_global_optimizer_hook()
+        self._mod_tracker.register_user_hooks(
+            self._pre_fw_hook,
+            self._post_fw_hook,
+            self._pre_bw_hook,
+            self._post_bw_hook,
+        )
+        self._track_resize()
+        self._peak_mem_snap = self.get_tracker_snapshot()
+        self._peak_mem = {
+            dev: dev_snap[_TOTAL_KEY] for dev, dev_snap in self._peak_mem_snap.items()
+        }
+        self._mod_tracker.__enter__()
+        super().__enter__()
+        return self
+
+    def __exit__(self, *args: Any) -> None:
+        self._deregister_param_and_optimizer_hooks()
+        self._mod_tracker.clear_user_hooks()
+        self._restore_resize()
+        super().__exit__(*args)
+        self._mod_tracker.__exit__(*args)
+
+    def __torch_dispatch__(self, func, types, args=(), kwargs=None):  # type: ignore[no-untyped-def]
+        res = func(*args, **kwargs or {})
+        # If we are tracking an optimizer state, we use the optimizer reference type.
+        # If we are in backward region and not in AC region, we use the backward reference type.
+        # Else we use the forward reference type.
+        if self._in_opt:
+            reftype = _MemRefType.OPT
+        elif self._mod_tracker.is_bw and not self._in_ac:
+            reftype = _MemRefType.TEMP
+        else:
+            reftype = _MemRefType.ACT
+        tree_map_only(torch.Tensor, partial(self._track, reftype), res)
+        peak_state = _ModState.PEAK_BW if self._mod_tracker.is_bw else _ModState.PEAK_FW
+        self._update_peak_stats(peak_state)
+        return res

infer_4_47_1/lib/python3.10/site-packages/torch/distributed/fsdp/_optim_utils.py

@@ -0,0 +1,2091 @@
+# mypy: allow-untyped-defs
+import copy
+import functools
+import logging
+import warnings
+from contextlib import ExitStack
+from dataclasses import dataclass, field
+from typing import (
+    Any,
+    cast,
+    Dict,
+    Iterable,
+    Iterator,
+    List,
+    NamedTuple,
+    no_type_check,
+    Optional,
+    Sequence,
+    Set,
+    Tuple,
+    TYPE_CHECKING,
+    Union,
+)
+
+import torch
+import torch.distributed as dist
+import torch.distributed.fsdp._traversal_utils as traversal_utils
+import torch.nn as nn
+from torch.distributed._state_dict_utils import _gather_state_dict
+from torch.distributed.distributed_c10d import _get_pg_default_device
+from torch.distributed.fsdp._common_utils import (
+    _apply_to_modules,
+    _FSDPState,
+    _get_module_fsdp_state_if_fully_sharded_module,
+    _get_param_to_fqns,
+    _module_handle,
+    _named_parameters_with_duplicates,
+    clean_tensor_name,
+)
+from torch.distributed.fsdp._debug_utils import SimpleProfiler
+from torch.distributed.fsdp._flat_param import FlatParameter, FlatParamHandle
+from torch.distributed.fsdp._fsdp_extensions import (
+    _ext_chunk_dtensor,
+    _ext_chunk_tensor,
+)
+from torch.distributed.fsdp._runtime_utils import (
+    _lazy_init,
+    _reset_flat_param_grad_info_if_needed,
+)
+from torch.distributed.fsdp.api import (
+    ShardingStrategy,
+    StateDictSettings,
+    StateDictType,
+)
+from torch.distributed.tensor import DTensor, Replicate
+from torch.utils._pytree import tree_map_only
+
+
+if TYPE_CHECKING:
+    from torch.distributed._shard.sharded_tensor import ShardedTensor
+
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class FSDPParamInfo:
+    state: _FSDPState
+    handle: FlatParamHandle
+    param_indices: Dict[str, int]
+    param_requires_grad: List[bool]
+
+
+def sorted_items(dictionary: Dict[str, Any]) -> Iterator[Tuple[str, Any]]:
+    keys = sorted(dictionary.keys())
+    for k in keys:
+        yield k, dictionary[k]
+
+
+@dataclass
+class _ConsolidatedOptimState:
+    """
+    This holds the consolidated optimizer state on the target rank. Positive-
+    dimension tensor state is communicated across ranks, while zero-dimension
+    tensor state and non-tensor state is taken directly from the target rank.
+
+    PyTorch version 1.12 moved to using zero-dimension tensors for scalar
+    values, but user implemented optimizers may still use float (i.e. a
+    non-tensor). Thus, we support both and handle them identically.
+
+    Attributes:
+        tensor_state (Dict[str, torch.Tensor]): Mapping from positive-dimension
+            tensor state name to the unsharded flat tensor representing the
+            state.
+        zero_dim_tensor_state (Dict[str, torch.Tensor]): Mapping from zero-
+            dimension tensor state name to its value.
+        non_tensor_state (Dict[str, Any]): Mapping from non-tensor state
+            name to its value.
+    """
+
+    tensor_state: Dict[str, torch.Tensor] = field(default_factory=dict)
+    zero_dim_tensor_state: Dict[str, torch.Tensor] = field(default_factory=dict)
+    non_tensor_state: Dict[str, Any] = field(default_factory=dict)
+
+
+class _PosDimTensorInfo(NamedTuple):
+    """
+    Meatadata for positive-dimension tensors used internally for
+    :meth:`scatter_full_optim_state_dict`.
+
+    Attributes:
+        shape (torch.Size): Sharded tensor shape (which is equal to the
+            unsharded tensor shape if the tensor is optimizer state for a
+            non-FSDP parameter and is hence not sharded).
+        dtype (torch.dtype): Data type of the tensor.
+    """
+
+    shape: torch.Size
+    dtype: torch.dtype
+
+
+class _OptimStateKey(NamedTuple):
+    """
+    This represents an optimizer state key that may be used commonly across
+    ranks. It is based on the unflattened parameter names rather than parameter
+    IDs to make it independent of each rank's own optimizer construction.
+    """
+
+    unflat_param_names: Tuple[str, ...]
+    is_fsdp_managed: bool
+
+
+def _unflatten_optim_state(
+    fsdp_param_info: FSDPParamInfo,
+    flat_param_state: Dict[str, Any],
+    to_save: bool,
+    shard_state: bool,
+    cpu_offload: bool,
+) -> List[Dict[str, Any]]:
+    """
+    Unflattens the optimizer state, consisting of the "state" part and the
+    "param_groups" part. Unflattening the "state" part involves consolidating
+    the state on the target rank and remapping from flattened to unflattened
+    parameter IDs, and the "param_groups" part only involves remapping from
+    flattened to unflattened parameter IDs.
+
+    Args:
+        fsdp_param_info (FSDPParamInfo): The FSDP state, the handle, and a
+            mapping from FQN to original parameter index.
+        flat_param_state (Dict[str, Any]): Entry for the flat parameter in the
+            "state" part of the optimizer state dict.
+        to_save (bool): Whether to save the state on this rank.
+
+    Returns:
+        List[Dict[str, Any]]: A :class:`list` holding the entries in the
+        "state" part of the optimizer state dict corresponding to the
+        unflattened parameters comprising the flat parameter if on the target
+        rank or an empty :class:`list` otherwise. The final optimizer state
+        dict will need to map these entries using the proper unflattened
+        parameter IDs.
+    """
+    assert (
+        not shard_state or to_save
+    ), "If ``shard_state`` is True, ``to_save`` has to be True."
+    consolidated_state = _communicate_optim_state(
+        fsdp_param_info,
+        flat_param_state,
+    )
+    if to_save:
+        unflat_param_state = _unflatten_communicated_optim_state(
+            fsdp_param_info,
+            consolidated_state,
+            shard_state,
+        )
+        for optim_state in unflat_param_state:
+            # We can't use .items() below cuz we'd run into a concurrent modification error
+            if cpu_offload:
+                for key in list(optim_state.keys()):
+                    state = optim_state[key]
+                    if not isinstance(state, torch.Tensor):
+                        continue
+                    optim_state[key] = state.cpu()
+        return unflat_param_state
+    else:
+        return []
+
+
+def _is_zero_dim_tensor(x: Any) -> bool:
+    return torch.is_tensor(x) and x.dim() == 0
+
+
+def _communicate_optim_state(
+    fsdp_param_info: FSDPParamInfo,
+    flat_param_state: Dict[str, Any],
+) -> _ConsolidatedOptimState:
+    """
+    Communicates the optimizer state for a flat parameter across ranks. All
+    ranks will hold the entire non-sharded optimizer state on GPU.
+
+    If ``N`` is the number of tensor optimizer states in the optimizer state
+    dict, then the communication complexity is 0 if ``N = 0`` and ``N + 1``
+    otherwise (where the plus 1 comes from all-gathering the padding per rank).
+
+    Args:
+        fsdp_param_info (FSDPParamInfo): The FSDP state, the handle, and a
+            mapping from FQN to original parameter index.
+        flat_param_state (Dict[str, Any]): The entry in the "state" part of the
+            optimizer state dict corresponding to the flat parameter.
+
+    Returns:
+        ConsolidatedOptimState: Consolidated optimizer state for the target
+        flat parameter.
+    """
+    fsdp_state = fsdp_param_info.state
+    flat_param = fsdp_param_info.handle.flat_param
+    state = _ConsolidatedOptimState()
+    tensor_state, zero_dim_tensor_state, non_tensor_state = (
+        state.tensor_state,
+        state.zero_dim_tensor_state,
+        state.non_tensor_state,
+    )
+
+    for state_name, value in sorted_items(flat_param_state):
+        # Positive-dimension tensor state: communicate across ranks
+        if torch.is_tensor(value) and value.dim() > 0:
+            # If the parameter is not sharded, then neither is the
+            # positive-dimension tensor state, so no need to communicate it --
+            # we take the target rank's value
+            if (
+                fsdp_state.world_size == 1
+                or fsdp_state.sharding_strategy == ShardingStrategy.NO_SHARD
+            ):
+                tensor_state[state_name] = value
+                continue
+            assert (
+                fsdp_state.compute_device is not None
+            ), "compute_device has not been initialized"
+            if value.device.type != fsdp_state.compute_device.type:
+                value = value.to(fsdp_state.compute_device)
+            # Assume that positive-dimension tensor optimizer state
+            # has the same shape as the sharded flat parameter
+            buffer_size = flat_param._full_param_padded.size()  # type: ignore[attr-defined]
+            tensor_buffer = value.new_zeros(*buffer_size)
+            dist.all_gather_into_tensor(
+                tensor_buffer, value, group=fsdp_state.process_group
+            )
+            fsdp_state._device_handle.synchronize()
+            unpadded_numel = cast(
+                nn.Parameter, flat_param._unpadded_unsharded_size
+            ).numel()
+            tensor_state[state_name] = tensor_buffer[:unpadded_numel]
+        # Zero-dimension tensor state and non-tensor state: take this rank's
+        # value directly
+        else:
+            if _is_zero_dim_tensor(value):
+                zero_dim_tensor_state[state_name] = value.detach().clone()
+            else:
+                non_tensor_state[state_name] = value
+    return state
+
+
+def _unflatten_communicated_optim_state(
+    fsdp_param_info: FSDPParamInfo,
+    state: _ConsolidatedOptimState,
+    shard_state: bool,
+) -> List[Dict[str, Any]]:
+    """
+    Unflattens the communicated optimizer state (given by ``tensor_state``,
+    ``non_tensor_state``, and ``zero_dim_tensor_state``) for a single flat
+    parameter. This should only be called on the target rank.
+
+    Args:
+        fsdp_param_info (FSDPParamInfo): The FSDP state, the handle, and a
+            mapping from FQN to original parameter index.
+        state (_ConsolidatedOptimState): Consolidated optimizer state.
+
+    Returns:
+        List[Dict[str, Any]]: A :class:`list` holding the entries in the
+        "state" part of the optimizer state dict corresponding to the
+        unflattened parameters comprising the flat parameter. The final
+        optimizer state dict will need to map these entries using the proper
+        unflattened parameter IDs.
+    """
+    fsdp_state = fsdp_param_info.state
+    handle = fsdp_param_info.handle
+    flat_param = handle.flat_param
+    unflat_param_state: List[Dict[str, Any]] = []
+    flat_param_views: Dict[str, Iterator] = {}
+    num_unflat_params = flat_param._num_params
+    tensor_state, zero_dim_tensor_state, non_tensor_state = (
+        state.tensor_state,
+        state.zero_dim_tensor_state,
+        state.non_tensor_state,
+    )
+
+    for _ in range(num_unflat_params):
+        unflat_state_param = {}
+        # Add positive-dimension tensor state: unflatten with views
+        for state_name, flat_tensor in sorted_items(tensor_state):
+            views_generated = state_name in flat_param_views
+            if not views_generated:
+                views = handle._get_unflat_views(flat_tensor)
+                flat_param_views[state_name] = views
+            else:
+                views = flat_param_views[state_name]
+            optim_state: Union[torch.Tensor, ShardedTensor, DTensor] = next(views)
+            if shard_state:
+                osd_config = fsdp_state._optim_state_dict_config
+                if getattr(osd_config, "_use_dtensor", False):
+                    assert fsdp_state._device_mesh is not None
+                    optim_state = _ext_chunk_dtensor(
+                        optim_state,
+                        fsdp_state.rank,
+                        fsdp_state._device_mesh,
+                        fsdp_state._fsdp_extension,
+                    )
+                else:
+                    assert fsdp_state.process_group is not None
+                    optim_state = _ext_chunk_tensor(
+                        optim_state,
+                        fsdp_state.rank,
+                        fsdp_state.world_size,
+                        fsdp_state._device_handle.device_count(),
+                        fsdp_state.process_group,
+                        fsdp_state._fsdp_extension,
+                    )
+            unflat_state_param[state_name] = optim_state
+
+        # Add zero-dimension tensor state: take the target rank's value
+        for state_name, zero_dim_tensor in sorted_items(zero_dim_tensor_state):
+            unflat_state_param[state_name] = zero_dim_tensor
+        # Add non-tensor state: take the target rank's value
+        for state_name, non_tensor in sorted_items(non_tensor_state):
+            unflat_state_param[state_name] = non_tensor
+        unflat_param_state.append(unflat_state_param)
+    return unflat_param_state
+
+
+def _broadcast_processed_state(
+    fsdp_state: _FSDPState,
+    optim_state: Dict[str, Any],
+    group: Optional[dist.ProcessGroup],
+) -> Dict[str, Any]:
+    objects: List[Any] = [None]
+    if dist.get_rank(group) == 0:
+        objects[0] = tree_map_only(
+            torch.Tensor,
+            lambda v: v.cpu() if v.dim() == 0 else _PosDimTensorInfo(v.shape, v.dtype),  # type: ignore[union-attr]
+            optim_state,
+        )
+    dist.broadcast_object_list(objects, src=0, group=group)
+    if dist.get_rank(group) == 0:
+        return optim_state
+    else:
+        return objects[0]
+
+
+def _broadcast_state(
+    fsdp_state: _FSDPState, state: Any, group: Optional[dist.ProcessGroup]
+) -> Any:
+    if dist.get_rank(group) == 0:
+        if not isinstance(state, torch.Tensor) or state.dim() == 0:
+            return state
+        tensor = state.to(fsdp_state.compute_device)
+    else:
+        if isinstance(state, torch.Tensor):
+            assert state.dim() == 0, (
+                "For non-zero ranks, a tensor state should have zero dimension, "
+                "but got the state with shape {state.shape()}."
+            )
+            return state
+        elif not isinstance(state, _PosDimTensorInfo):
+            return state
+        tensor = torch.zeros(
+            state.shape, dtype=state.dtype, device=fsdp_state.compute_device
+        )
+    dist.broadcast(tensor, src=0, group=group)
+    return tensor
+
+
+def _shard_orig_param_state(
+    fsdp_param_info: FSDPParamInfo,
+    fqn: str,
+    optim_state: Dict[str, Any],
+) -> Dict[str, Any]:
+    """
+    Shard the optimizer state for the original parameter with the name ``fqn``.
+    This API should only be used when ``use_orig_params`` is True.
+    """
+    if not optim_state:
+        return {}
+    fsdp_state = fsdp_param_info.state
+    flat_param = fsdp_param_info.handle.flat_param
+    param_idx = fsdp_param_info.param_indices[fqn]
+    shard_param_info = flat_param._shard_param_infos[param_idx]  # type: ignore[attr-defined]
+    optim_state = _gather_state_dict(
+        optim_state, pg=fsdp_state.process_group, device=fsdp_state.compute_device
+    )
+    if not shard_param_info.in_shard:
+        return {}
+    # Flatten and shard the state.
+    new_optim_state: Dict[str, Any] = {}
+    intra_param_start_idx = shard_param_info.intra_param_start_idx
+    intra_param_end_idx = shard_param_info.intra_param_end_idx
+    for state_name, value in optim_state.items():
+        if (
+            torch.is_tensor(value)
+            and value.dim() > 0
+            and fsdp_state.sharding_strategy != ShardingStrategy.NO_SHARD
+        ):
+            value = value.flatten()[intra_param_start_idx : intra_param_end_idx + 1].clone()  # type: ignore[operator]
+        new_optim_state[state_name] = value
+    return new_optim_state
+
+
+def _flatten_optim_state_dict(
+    optim_state_dict: Dict[str, Any],
+    model: nn.Module,
+    use_orig_params: bool = False,
+    optim: Optional[torch.optim.Optimizer] = None,
+    rank0_only: bool = False,
+    group: Optional[dist.ProcessGroup] = None,
+) -> Dict[str, Any]:
+    """
+    Flattens the full optimizer state dict, still keying by unflattened parameter
+    names.
+
+    If ``use_orig_params`` is True, each rank will have all FSDP-managed
+    parameters but some of these parameters may be empty due to the sharding.
+    For a regular optim.Optimizer, states for those empty parameters will
+    not be initialized. So, when aggregating the FQNs across ranks, no assert
+    will be raised on a rank even if it does not have all the states -- it is
+    valid and FSDP know how to aggregate them. However, FSDP has to ignore
+    handling those parameters that are not managed by FSDP and do not exist on
+    the local rank -- it is managed by other parallelism and FSDP does not
+    know ho to handle/aggregate them.
+
+    Note that ``_flatten_tensor_optim_state`` does not need ``optim`` to
+    flatten/shard the state. However, NamedOptimizer and KeyedOptimizer require
+    all the states even if the corresponding parameters are empty. To this end,
+    ``optim`` will be used to to get the initial state of the empty parameters.
+    ``optim`` should only be non-None if the ``optim` is KeyedOptimizer or
+    NamedOptimizer.
+
+    Returns:
+        Dict[str, Any]: The flattened optimizer state dict.
+    """
+    SimpleProfiler.reset()
+
+    unflat_osd = optim_state_dict
+    if "state" not in unflat_osd and not rank0_only:
+        raise ValueError(
+            '`optim_state_dict` must have the keys "state"'
+            "to be a valid optimizer state dict"
+        )
+    param_to_fqns = _get_param_to_fqns(model)
+    fqn_to_fsdp_param_info = _get_fqn_to_fsdp_param_info(model)
+    fsdp_state = next(iter(fqn_to_fsdp_param_info.values())).state
+
+    # Broadcast unflat_osd without non-scalar tensor if rank0_only is True.
+    if rank0_only:
+        unflat_osd = _broadcast_processed_state(fsdp_state, unflat_osd, group=group)
+
+    # Construct the "state" part
+    flat_osd_state: Dict[Union[_OptimStateKey, str], Any] = {}
+    unflat_osd_state = unflat_osd["state"]
+    all_state_keys = set(unflat_osd_state.keys())
+
+    for param, fqns in param_to_fqns.items():
+        fqn = fqns[0]
+        if fqn not in unflat_osd_state:
+            continue
+        all_state_keys.difference_update(fqns)
+
+        if rank0_only:
+            for fqn in fqns:
+                if not unflat_osd_state[fqn]:
+                    continue
+                for state_name in unflat_osd_state[fqn].keys():
+                    unflat_osd_state[fqn][state_name] = _broadcast_state(
+                        fsdp_state, unflat_osd_state[fqn][state_name], group=group
+                    )
+            fqn = fqns[0]
+        if fqn in fqn_to_fsdp_param_info:
+            fsdp_param_info = fqn_to_fsdp_param_info[fqn]
+            if use_orig_params:
+                with SimpleProfiler.profile(SimpleProfiler.Type.RESHARDING):
+                    flat_state = _shard_orig_param_state(
+                        fsdp_param_info,
+                        fqn,
+                        unflat_osd_state[fqn],
+                    )
+            else:
+                flat_state = _flatten_optim_state(
+                    fsdp_param_info,
+                    unflat_osd_state,
+                    fqns,
+                )
+            key = _OptimStateKey(tuple(fqns), True)
+            # Only include non-empty states since as expected by
+            # `torch.optim.Optimizer` s unless the optimizer is KeyedOptimizer
+            # or NamedOptimizer.
+            if flat_state:
+                flat_osd_state[key] = flat_state
+            elif use_orig_params:
+                assert (
+                    len(fqns) == 1
+                ), f"use_orig_params is True but there are multiple FQNs, {fqns}."
+                if optim is not None:  # NamedOptimizer or KeyedOptimizer case.
+                    state = optim.state.get(param, None)  # type: ignore[call-overload]
+                    if state is not None:
+                        flat_osd_state[key] = copy.deepcopy(state)
+                    else:
+                        warnings.warn(
+                            f"optim_state[{key}] is not on rank{fsdp_state.rank}."
+                        )
+
+            else:
+                raise RuntimeError(
+                    f"The state of {key} is empty. This should happen when "
+                    "use_orig_params=True."
+                )
+        else:  # do not flatten non-FSDP parameters' states
+            assert len(fqns) == 1
+            key = _OptimStateKey(tuple(fqns), False)
+            flat_osd_state[key] = copy.copy(unflat_osd_state[fqn])
+
+        if rank0_only:
+            for fqn in fqns:
+                if not unflat_osd_state[fqn]:
+                    continue
+                for state_name, param_state in list(unflat_osd_state[fqn].items()):
+                    if fsdp_state.rank > 0:
+                        # Deference the tensor so that PyTorch can collect the memory.
+                        del unflat_osd_state[fqn][state_name]
+                    else:
+                        # Move the tensor in the original osd back to CPU to make the
+                        # original osd unaffected.
+                        unflat_osd_state[fqn][state_name] = unflat_osd_state[fqn][
+                            state_name
+                        ].cpu()
+
+    # Handle user-defined state, states that are not associated with parameters.
+    for key in all_state_keys:
+        user_state = unflat_osd_state[key]
+        if isinstance(user_state, torch.Tensor) and rank0_only and use_orig_params:
+            user_state = _broadcast_state(fsdp_state, user_state, group=group)
+        flat_osd_state[key] = copy.copy(user_state)
+
+    SimpleProfiler.dump_and_reset("FSDP _flatten_optim_state_dict() profiling: ")
+    # Construct the "param_groups" part -- copy as is since it will be
+    # rekeyed later according to the target rank's optimizer
+    # Only copy param_groups if it exists in unflat_osd
+    if "param_groups" in unflat_osd:
+        flat_osd_param_groups = copy.deepcopy(unflat_osd["param_groups"])
+        return {"state": flat_osd_state, "param_groups": flat_osd_param_groups}
+    else:
+        return {"state": flat_osd_state}
+
+
+def _flatten_optim_state(
+    fsdp_param_info: FSDPParamInfo,
+    unflat_osd_state: Dict[str, Dict[str, Any]],
+    unflat_param_names: List[str],
+) -> Dict[str, Any]:
+    """
+    Flattens the optimizer state in ``full_optim_state_dict`` for a single
+    flat parameter in ``fsdp_param_info`` corresponding to the unflattened
+    parameter names in ``unflat_param_names``.
+
+    Args:
+        fsdp_param_info (FSDPParamInfo): The FSDP state, the handle, and a
+            mapping from FQN to original parameter index.
+        unflat_osd_state (Dict[str, Dict[str, Any]]): The "state" part of the
+            optimizer state dict corresponding to the unflattened parameters.
+        unflat_param_names (List[str]): A :class:`list` of unflattened
+            parameter names corresponding to the flat parameter ``flat_param``.
+
+    Returns:
+        Dict[str, Any]: A :class:`dict` mapping state names to their values for
+        a particular flat parameter. The sharded optimizer state dict's "state"
+        part will map a key to this returned value.
+    """
+    fsdp_state = fsdp_param_info.state
+    handle = fsdp_param_info.handle
+    flat_param = handle.flat_param
+    num_unflat_params = len(unflat_param_names)
+    assert num_unflat_params > 0, (
+        "Expects at least one unflattened parameter corresponding to the "
+        "flat parameter"
+    )
+    unflat_param_shapes = flat_param._shapes
+    num_unflat_param_shapes = len(unflat_param_shapes)
+    assert (
+        num_unflat_params == num_unflat_param_shapes
+    ), f"Expects {num_unflat_params} shapes but got {num_unflat_param_shapes}"
+
+    # Check if these unflattened parameters have any optimizer state
+    has_state = [
+        bool(unflat_param_name in unflat_osd_state)
+        for unflat_param_name in unflat_param_names
+    ]
+    # If none of the unflattened parameters comprising this flat parameter have
+    # any state, then we do not want an entry in the optimizer state dict
+    if not any(has_state):
+        return {}  # no need to flatten any state
+    # There may still be some unflattened parameters with state and some
+    # without
+    unflat_param_states = [
+        _gather_state_dict(
+            unflat_osd_state[unflat_param_name],
+            pg=fsdp_state.process_group,
+            device=fsdp_state.compute_device,
+        )
+        if unflat_param_name in unflat_osd_state
+        else None
+        for unflat_param_name in unflat_param_names
+    ]
+    # Check that the unflattened parameters have the same state names
+    state_names = None
+    for unflat_param_state in unflat_param_states:
+        if unflat_param_state is None:
+            continue
+        if state_names is None:
+            state_names = set(unflat_param_state.keys())
+        else:
+            if state_names != set(unflat_param_state.keys()):
+                raise ValueError(
+                    "Differing optimizer state names for the unflattened "
+                    f"parameters: {unflat_param_names}"
+                )
+    assert state_names is not None
+
+    # Flatten the state
+    flat_state: Dict[str, Any] = {}
+    for state_name in state_names:
+        state_values = [
+            unflat_param_state[state_name] if unflat_param_state is not None else None
+            for unflat_param_state in unflat_param_states
+        ]
+        non_none_state_values = [v for v in state_values if v is not None]
+        # If all ranks have None, this is a None value
+        if not non_none_state_values:
+            flat_state[state_name] = None
+            continue
+        are_pos_dim_tensors = are_zero_dim_tensors = are_non_tensors = True
+        for v in non_none_state_values:
+            are_pos_dim_tensors &= torch.is_tensor(v) and v.dim() > 0
+            are_zero_dim_tensors &= _is_zero_dim_tensor(v)
+            are_non_tensors &= not torch.is_tensor(v)
+        types = {type(v) for v in non_none_state_values}
+        if len(types) != 1 or not (
+            are_pos_dim_tensors or are_zero_dim_tensors or are_non_tensors
+        ):
+            raise ValueError(
+                f"Differing optimizer state types for state {state_name}, "
+                f"values {non_none_state_values}, and unflattened parameter "
+                f"names {unflat_param_names}"
+            )
+        if are_pos_dim_tensors:
+            flat_tensor = _flatten_tensor_optim_state(
+                state_name,
+                state_values,
+                unflat_param_names,
+                unflat_param_shapes,
+                handle,
+            )
+            # Shard the flattened tensor immediately to minimize max memory
+            # usage
+            if (
+                fsdp_state.world_size != 1
+                and fsdp_state.sharding_strategy != ShardingStrategy.NO_SHARD
+            ):
+                sharded_flat_tensor, _ = FlatParamHandle._get_shard(
+                    flat_tensor,
+                    fsdp_state.rank,
+                    fsdp_state.world_size,
+                )
+            else:
+                sharded_flat_tensor = flat_tensor
+            flat_state[state_name] = sharded_flat_tensor
+        elif are_zero_dim_tensors:
+            flat_state[state_name] = _flatten_zero_dim_tensor_optim_state(
+                state_name,
+                state_values,
+                unflat_param_names,
+            )
+        else:
+            assert are_non_tensors
+            flat_state[state_name] = _flatten_non_tensor_optim_state(
+                state_name,
+                state_values,
+                unflat_param_names,
+            )
+
+    return flat_state
+
+
+def _flatten_tensor_optim_state(
+    state_name: str,
+    pos_dim_tensors: List[torch.Tensor],
+    unflat_param_names: List[str],
+    unflat_param_shapes: Sequence[torch.Size],
+    handle: FlatParamHandle,
+) -> torch.Tensor:
+    """
+    Flattens the positive-dimension tensor optimizer state given by the values
+    ``tensors`` for the state ``state_name`` for a single flat parameter
+    from ``handle`` corresponding to the unflattened parameter names
+    ``unflat_param_names`` and unflatted parameter shapes
+    ``unflat_param_shapes``. This flattens each unflattened parameter's tensor
+    state into one tensor.
+
+    NOTE: We use zero tensors for any unflattened parameters without state
+    since some value is required to fill those entries. This assumes that the
+    zero tensor is mathematically equivalent to having no state, which is true
+    for Adam's "exp_avg" and "exp_avg_sq" but may not be true for all
+    optimizers.
+
+    Args:
+        state_name (str): Optimizer state name.
+        pos_dim_tensors (List[torch.Tensor]): Positive-dimension tensor
+            optimizer state values for the unflattened parameters corresponding
+            to the single flat parameter.
+        unflat_param_names (List[str]): A :class:`list` of unflattened
+            parameter names corresponding to the single flat parameter.
+        unflat_param_shapes (List[torch.Size]): Unflattened parameter shapes
+            corresponding to the single flat parameter.
+        handle (FlatParamHandle): The flat parameter's handle.
+
+    Returns:
+        torch.Tensor: A flat tensor containing the optimizer state
+        corresponding to ``state_name`` constructed by concatenating the
+        unflattened parameter tensor states in ``pos_dim_tensors`` (using zero
+        tensors for any unflattened parameters without the state).
+    """
+    flat_param = handle.flat_param
+    non_none_tensors = [t for t in pos_dim_tensors if t is not None]
+    # Check that all are tensors with the same dtype
+    dtypes = {t.dtype for t in non_none_tensors}
+    if len(dtypes) != 1:
+        raise ValueError(
+            "All unflattened parameters comprising a single flat "
+            "parameter must have positive-dimension tensor state with the "
+            f"same dtype but got dtypes {dtypes} for state {state_name} and "
+            f"unflattened parameter names {unflat_param_names}"
+        )
+    dtype = next(iter(dtypes))
+    # Check that each tensor state matches its parameter's shape
+    for tensor, shape in zip(pos_dim_tensors, unflat_param_shapes):
+        if tensor is None and len(shape) == 0:
+            raise ValueError("Flattening a zero-dimension parameter is not supported")
+        elif tensor is not None and tensor.shape != shape:
+            raise ValueError(
+                "Tensor optimizer state does not have same shape as its "
+                f"parameter: {tensor.shape} {shape}"
+            )
+    # Flatten the tensor states: we do not need to add any right-hand-side
+    # padding since the flat optimizer state tensor is sharded via
+    # `_get_shard()`, which pads the shard as needed (just like for the flat
+    # parameter)
+    cpu_device = torch.device("cpu")
+    tensors_to_flatten = [
+        torch.flatten(state_value.to(cpu_device))
+        if state_value is not None
+        else torch.flatten(
+            torch.zeros(
+                size=shape,
+                dtype=dtype,
+                device=cpu_device,
+            )
+        )
+        for state_value, shape in zip(pos_dim_tensors, unflat_param_shapes)
+    ]
+    flat_tensor = handle.flatten_tensors(tensors_to_flatten, handle._aligned_numel)
+    flat_param_shape = flat_param._unpadded_unsharded_size  # type: ignore[attr-defined]
+    assert flat_tensor.shape == flat_param_shape, (
+        f"tensor optim state: {flat_tensor.shape} "
+        f"flat parameter: {flat_param_shape}"
+    )
+    return flat_tensor
+
+
+def _flatten_zero_dim_tensor_optim_state(
+    state_name: str,
+    zero_dim_tensors: List[torch.Tensor],
+    unflat_param_names: List[str],
+) -> torch.Tensor:
+    """
+    Flattens the zero-dimension tensor optimizer state given by the values
+    ``zero_dim_tensors`` for the state ``state_name`` for a single flat
+    parameter corresponding to the unflattened parameter names
+    ``unflat_param_names`` by enforcing that all tensors are the same and using
+    that common value.
+
+    NOTE: The requirement that the tensors are the same across all unflattened
+    parameters comprising the flat parameter is needed to maintain the
+    invariant that FSDP performs the same computation as its non-sharded
+    equivalent. This means that none of the unflattened parameters can be
+    missing this state since imposing a value may differ from having no value.
+    For example, for Adam's "step", no value means maximum bias correction,
+    while having some positive value means less bias correction.
+
+    Args:
+        state_name (str): Optimizer state name.
+        zero_dim_tensors (List[torch.Tensor]): Zero-dimension optimizer state
+            for the unflattened parameters corresponding to the single
+            flat parameter.
+        unflat_param_names (List[str]): A :class:`list` of unflattened
+            parameter names corresponding to the single flat parameter.
+
+    Returns:
+        torch.Tensor: A zero-dimensional tensor giving the value of the state
+        ``state_name`` for all unflattened parameters corresponding to the
+        names ``unflat_param_names``.
+    """
+    non_none_tensors = [t for t in zero_dim_tensors if t is not None]
+    # Enforce that all have the same value and dtype
+    values_set = {t.item() if t is not None else None for t in zero_dim_tensors}
+    dtypes = {t.dtype if t is not None else None for t in zero_dim_tensors}
+    if (
+        len(non_none_tensors) != len(zero_dim_tensors)
+        or len(values_set) != 1
+        or len(dtypes) != 1
+    ):
+        raise ValueError(
+            "All unflattened parameters comprising a single flat "
+            "parameter must have scalar state with the same value and dtype "
+            f"but got values {values_set} and dtypes {dtypes} for state "
+            f"{state_name} and unflattened parameter names "
+            f"{unflat_param_names}"
+        )
+    value = next(iter(values_set))
+    dtype = next(iter(dtypes))
+    return torch.tensor(value, dtype=dtype, device=torch.device("cpu"))
+
+
+def _flatten_non_tensor_optim_state(
+    state_name: str,
+    non_tensors: List[Any],
+    unflat_param_names: List[str],
+) -> Any:
+    """
+    Flattens the non-tensor optimizer state given by the values ``non_tensors``
+    for the state ``state_name`` for a single flat parameter corresponding
+    to the unflattened parameter names ``unflat_param_names`` by enforcing that
+    all values are the same and using that common value.
+
+    See the note in :func:`_flatten_zero_dim_tensor_optim_state`.
+
+    Args:
+        state_name (str): Optimizer state name.
+        non_tensors (List[Any]): Non-tensor optimizer state for the unflattened
+            parameters corresponding to the single flat parameter.
+        unflat_param_names (List[str]): A :class:`list` of unflattened
+            parameter names corresponding to the single flat parameter.
+
+    Returns:
+        Any: A non-tensor giving the value of the state ``state_name`` for all
+        unflattened parameters corresponding to the names
+        ``unflat_param_names``.
+    """
+    non_none_non_tensors = [nt for nt in non_tensors if nt is not None]
+    # Enforce that all have the same value (same type already checked)
+    non_tensor_set = set(non_tensors)
+    if len(non_none_non_tensors) != len(non_tensors) or len(non_tensor_set) != 1:
+        raise ValueError(
+            "All unflattened parameters comprising a single flat "
+            "parameter must have scalar state with the same value and dtype "
+            f"but got values {non_tensor_set} for state {state_name} and  "
+            f"unflattened parameter names {unflat_param_names}"
+        )
+    non_tensor = next(iter(non_tensor_set))
+    return non_tensor
+
+
+def _rekey_sharded_optim_state_dict(
+    sharded_osd: Dict[str, Any],
+    model: nn.Module,
+    optim: torch.optim.Optimizer,
+    optim_input: Optional[
+        Union[
+            List[Dict[str, Any]],
+            Iterable[nn.Parameter],
+        ]
+    ],
+    using_optim_input: bool,
+    is_named_optimizer: bool = False,
+) -> Dict[str, Any]:
+    """
+    Rekeys the optimizer state dict from unflattened parameter names to flat
+    parameter IDs according to the calling rank's ``optim``, which may be
+    different across ranks. In particular, the unflattened parameter names are
+    represented as :class:`_OptimStateKey` s.
+    """
+    param_to_fqns = _get_param_to_fqns(model)
+    flat_param_to_fqn = _get_flat_param_to_fqn(model)
+    param_to_param_key: Dict[nn.Parameter, Union[int, str]] = cast(
+        Dict[nn.Parameter, Union[int, str]],
+        (
+            _get_param_to_param_id_from_optim_input(model, optim_input)
+            if using_optim_input
+            else _get_param_to_param_key(
+                optim, model, is_named_optimizer, param_to_fqns, flat_param_to_fqn
+            )
+        ),
+    )
+    # All parameter keys in `param_to_param_key` should be in
+    # `param_to_fqns` -- strict inequality follows when not all parameters are
+    # passed to the optimizer
+    assert len(param_to_param_key) <= len(param_to_fqns)
+
+    unflat_param_names_to_flat_param_key: Dict[
+        Tuple[str, ...], Union[int, str]
+    ] = {}  # for "state"
+    unflat_param_name_to_flat_param_key: Dict[
+        str, Union[int, str]
+    ] = {}  # for "param_groups"
+    for param, unflat_param_names in param_to_fqns.items():
+        if param not in param_to_param_key:
+            # This parameter was not passed to the optimizer
+            continue
+        flat_param_key = param_to_param_key[param]
+        unflat_param_names_to_flat_param_key[tuple(unflat_param_names)] = flat_param_key
+        for unflat_param_name in unflat_param_names:
+            unflat_param_name_to_flat_param_key[unflat_param_name] = flat_param_key
+
+    sharded_osd_state = sharded_osd["state"]
+    rekeyed_osd_state: Dict[Union[str, int], Any] = {}
+    for key, param_state in sharded_osd_state.items():
+        if isinstance(key, str):
+            rekeyed_osd_state[key] = param_state
+            continue
+        flat_param_key = unflat_param_names_to_flat_param_key.get(
+            key.unflat_param_names, key.unflat_param_names
+        )
+        rekeyed_osd_state[flat_param_key] = param_state
+
+    # Only process param_groups if it exists in sharded_osd
+    if "param_groups" in sharded_osd:
+        rekeyed_osd_param_groups: List[Dict[str, Any]] = []
+        for unflat_param_group in sharded_osd["param_groups"]:
+            flat_param_group = copy.deepcopy(unflat_param_group)
+            flat_param_keys = sorted(
+                {
+                    unflat_param_name_to_flat_param_key[unflat_param_name]
+                    for unflat_param_name in unflat_param_group["params"]
+                }
+            )
+            flat_param_group["params"] = flat_param_keys
+            rekeyed_osd_param_groups.append(flat_param_group)
+        return {"state": rekeyed_osd_state, "param_groups": rekeyed_osd_param_groups}
+    else:
+        return {"state": rekeyed_osd_state}
+
+
+def _get_param_id_to_param_from_optim_input(
+    model: nn.Module,
+    optim_input: Optional[
+        Union[
+            List[Dict[str, Any]],
+            Iterable[nn.Parameter],
+        ]
+    ] = None,
+) -> Dict[int, nn.Parameter]:
+    """
+    Constructs a mapping from parameter IDs to parameters. This may be used
+    both for models with ``FlatParameter`` s and without.
+
+    NOTE: This method is only preserved for backward compatibility. The method
+    :meth:`_get_param_key_to_param` is the preferred code path that does not
+    rely on ``optim_input``.
+
+    NOTE: We critically assume that, whether the optimizer input is a list of
+    parameters or a list of parameter groups, :class:`torch.optim.Optimizer`
+    enumerates the parameter IDs in order. In other words, for a parameter list
+    input, the parameter IDs should be in that list order, and for a parameter
+    groups input, the parameter IDs should be in order within each parameter
+    group and in order across parameter groups.
+
+    Args:
+        model (nn.Module): Model whose parameters are passed into the
+            optimizer.
+        optim_input (Optional[Union[List[Dict[str, Any]],
+        Iterable[nn.Parameter]]]): Input passed into the optimizer
+            representing either a :class:`list` of parameter groups or an
+            iterable of parameters; if ``None``, then this method assumes the
+            input was ``model.parameters()``. (Default: ``None``)
+
+    Returns:
+        List[nn.Parameter]: Mapping from parameter IDs to parameters,
+        where the parameter ID is implicitly the index in the :class:`list`.
+    """
+    # Assume the standard case of passing `model.parameters()` to the optimizer
+    # if `optim_input` is not specified
+    if optim_input is None:
+        return dict(enumerate(model.parameters()))
+    try:
+        params = cast(List[nn.Parameter], list(optim_input))
+    except TypeError as e:
+        raise TypeError(
+            "Optimizer input should be an iterable of Tensors or dicts, "
+            f"but got {optim_input}"
+        ) from e
+    if len(params) == 0:
+        raise ValueError("Optimizer input should not be empty")
+
+    # Check if the optimizer input represents tensors or parameter groups
+    all_tensors = True
+    all_dicts = True
+    for param in params:
+        all_tensors &= isinstance(param, torch.Tensor)
+        all_dicts &= isinstance(param, dict)
+    if not all_tensors and not all_dicts:
+        raise TypeError("Optimizer input should be an iterable of Tensors or dicts")
+    if all_tensors:
+        return dict(enumerate(params))
+    assert all_dicts
+    param_id_to_param: List[nn.Parameter] = []
+    for param_group in params:
+        has_params_key = "params" in param_group  # type: ignore[operator]
+        assert has_params_key, (
+            'A parameter group should map "params" to a list of the '
+            "parameters in the group"
+        )
+        # Implicitly map `flat_param_id` (current length of the list) to
+        # `param`
+        param_id_to_param.extend(param_group["params"])  # type: ignore[index]
+    return dict(enumerate(param_id_to_param))
+
+
+def _get_flat_param_to_fqn(model: torch.nn.Module) -> Dict[FlatParameter, str]:
+    """
+    Constructs a mapping from ``FlatParameter`` to a cleaned (devoid of prefixes
+    from wrappers) fully qualified name (FQN). Note that this FQN is "non-canonical"
+    because ``FlatParameter``  s do not come from the original module but are
+    registered only after FSDP has been applied. This function returns the FSDP-given
+    name for the ``FlatParameter`` (usually module._flat_param) as opposed to the
+    canonical FQNs returned for ``FlatParameter`` s in ``_common_utils._get_param_to_fqns(...)``).
+
+    Consequently, this function will only return a non-empty mapping if FSDP was
+    applied with ``use_orig_params=False`` as, otherwise, the original parameters
+    are used within the module and there would be no ``FlatParameter`` s in the module.
+
+    """
+
+    def module_fn(module, prefix, tree_level, flat_param_to_fqn):
+        for param_name, param in _named_parameters_with_duplicates(
+            module, recurse=False
+        ):
+            if not isinstance(param, FlatParameter):
+                continue
+            fqn = clean_tensor_name(prefix + param_name)
+            flat_param_to_fqn[param] = fqn
+
+    def return_fn(flat_param_to_fqn):
+        return flat_param_to_fqn
+
+    flat_param_to_fqn_ret: Dict[FlatParameter, str] = {}
+    return _apply_to_modules(
+        model,
+        module_fn,
+        return_fn,
+        [fqn for fqn, _ in _named_parameters_with_duplicates(model)],
+        flat_param_to_fqn_ret,
+    )
+
+
+def _get_param_key_to_param(
+    optim: torch.optim.Optimizer,
+    model: Optional[nn.Module] = None,
+    is_named_optimizer: bool = False,
+    param_to_fqns: Optional[Dict[nn.Parameter, List[str]]] = None,
+    flat_param_to_fqn: Optional[Dict[FlatParameter, str]] = None,
+) -> Dict[Union[int, str], nn.Parameter]:
+    """
+    Constructs a mapping from parameter keys to parameters. For the regular
+    optimizers, the keys are parameter IDs. For NamedOptimizer, the keys
+    are FQNs. This API may be used both for models with ``FlatParameter`` s and
+    without.
+    """
+    clean_fqn_to_curr_fqn: Dict[str, str] = {}
+    if is_named_optimizer:
+        assert (
+            param_to_fqns is not None and flat_param_to_fqn is not None
+        ), "The optimizer is a NamedOptimizer, `param_to_fqns` must not be None."
+        assert model is not None
+        for key, _ in _named_parameters_with_duplicates(model):
+            clean_fqn_to_curr_fqn[clean_tensor_name(key)] = key
+
+    param_key_to_param: Dict[Union[str, int], nn.Parameter] = {}
+    pid = 0
+    for param_group in optim.param_groups:
+        if is_named_optimizer:
+            for param in param_group["params"]:
+                assert flat_param_to_fqn is not None
+                if param in flat_param_to_fqn:
+                    # FlatParameter case
+                    key = flat_param_to_fqn[param]
+                else:
+                    assert param_to_fqns is not None
+                    # use_orig_params case
+                    assert len(param_to_fqns[param]) == 1
+                    key = param_to_fqns[param][0]
+                try:
+                    key = clean_fqn_to_curr_fqn[key]
+                except KeyError as e:
+                    raise KeyError(
+                        f"Can't find {key} from {list(clean_fqn_to_curr_fqn.keys())}."
+                    ) from e
+                param_key_to_param[key] = param
+        else:
+            for param in param_group["params"]:
+                param_key_to_param[pid] = param
+                pid += 1
+
+    return param_key_to_param
+
+
+def _get_param_to_param_key(
+    optim: torch.optim.Optimizer,
+    model: Optional[nn.Module] = None,
+    is_named_optimizer: bool = False,
+    param_to_fqns: Optional[Dict[nn.Parameter, List[str]]] = None,
+    flat_param_to_fqn: Optional[Dict[FlatParameter, str]] = None,
+) -> Dict[nn.Parameter, Union[int, str]]:
+    """
+    Constructs the inverse mapping of :func:`_get_param_key_to_param`. This API
+    only supports the case where `optim` is a regular optimizer, not NamedOptimizer.
+    So the parameter keys will be parameter ids.
+    """
+    param_id_to_param = _get_param_key_to_param(
+        optim, model, is_named_optimizer, param_to_fqns, flat_param_to_fqn
+    )
+    return {param: param_id for param_id, param in param_id_to_param.items()}
+
+
+def _get_param_to_param_id_from_optim_input(
+    model: nn.Module,
+    optim_input: Optional[
+        Union[
+            List[Dict[str, Any]],
+            Iterable[nn.Parameter],
+        ]
+    ] = None,
+) -> Dict[nn.Parameter, int]:
+    """Constructs the inverse mapping of :func:`_get_param_id_to_param_from_optim_input`."""
+    param_id_to_param = _get_param_id_to_param_from_optim_input(model, optim_input)
+    return {param: param_id for param_id, param in param_id_to_param.items()}
+
+
+def _check_missing_keys_on_rank(
+    r0_optim_state_keys: List[_OptimStateKey],
+    optim_state_key_to_param_key: Dict[_OptimStateKey, Union[str, int]],
+    param_key_to_param: Dict[Union[str, int], nn.Parameter],
+    group: Optional[dist.ProcessGroup],
+) -> None:
+    # Ensure that all ranks have at least the optimizer states needed by
+    # rank 0's optimizer
+    missing_keys: List[_OptimStateKey] = []
+    for r0_optim_state_key in r0_optim_state_keys:
+        if r0_optim_state_key not in optim_state_key_to_param_key:
+            # A parameter from rank 0's optimizer does not exist for this
+            # rank's optimizer
+            missing_keys.append(r0_optim_state_key)
+            continue
+        param_key = optim_state_key_to_param_key[r0_optim_state_key]
+        if isinstance(param_key, int):
+            assert param_key >= 0 and param_key < len(
+                param_key_to_param
+            ), "Check the `param_key_to_param` construction"
+    # We cannot use FSDPState.compute_device as this API is a global view.
+    device = _get_pg_default_device(group)
+    num_missing = torch.tensor([len(missing_keys)], dtype=torch.int32, device=device)
+    dist.all_reduce(num_missing, group=group)
+    if num_missing.item() > 0:
+        obj_list = [None for _ in range(dist.get_world_size(group))]
+        dist.all_gather_object(obj_list, missing_keys, group=group)
+        error_msg = (
+            "FSDP currently requires each rank to have at least the "
+            "optimizer states needed by rank 0's optimizer but some ranks "
+            "are missing some of those states"
+        )
+        for rank, keys in enumerate(obj_list):
+            keys = cast(List[_OptimStateKey], keys)
+            if len(keys) > 0:
+                error_msg += (
+                    f"\nRank {rank} is missing states for the parameters: "
+                    f"{[key.unflat_param_names for key in keys]}"
+                )
+        raise RuntimeError(error_msg)
+
+
+def _map_param_key_to_optim_keys(
+    optim_state_dict: Dict[str, Any],
+    group: Optional[dist.ProcessGroup],
+    param_key_to_param: Dict[Union[int, str], nn.Parameter],
+    param_to_fqns: Dict[nn.Parameter, List[str]],
+    fqn_to_fsdp_param_info: Dict[str, FSDPParamInfo],
+    merge_keys: bool = False,
+) -> Tuple[List[_OptimStateKey], Dict[_OptimStateKey, Union[int, str]]]:
+    """
+    Construct the local mapping between the ``_OptimStateKey`` and parameter keys
+    and all the ``_OptimStateKey`` across ranks. If ``merge_keys`` is False, rank0
+    must contain all the ``_OptimStateKey``, an exception will be raised otherwise.
+    Note that ``merge_keys`` should equal to ``use_orig_params``.
+    """
+    rank = dist.get_rank(group)
+    optim_state_key_to_param_key: Dict[_OptimStateKey, Union[int, str]] = {}  # local
+    all_optim_state_keys: List[_OptimStateKey] = []
+
+    for param_key, param in param_key_to_param.items():
+        # Do not include parameters without state to avoid empty mappings
+        # just like in normal `torch.optim.Optimizer.state_dict()`
+        if param_key not in optim_state_dict["state"]:
+            continue
+        fqns = param_to_fqns[param]
+        is_fsdp_managed = isinstance(param, FlatParameter)
+        if is_fsdp_managed:
+            assert fqns[0] in fqn_to_fsdp_param_info, (
+                fqns[0],
+                list(fqn_to_fsdp_param_info.keys()),
+            )
+        is_fsdp_managed = fqns[0] in fqn_to_fsdp_param_info
+        optim_state_key = _OptimStateKey(
+            unflat_param_names=tuple(fqns),
+            is_fsdp_managed=is_fsdp_managed,
+        )
+        if rank == 0 or merge_keys:
+            all_optim_state_keys.append(optim_state_key)
+        optim_state_key_to_param_key[optim_state_key] = param_key
+
+    if merge_keys:
+        all_keys: List[List[_OptimStateKey]] = [
+            [] for _ in range(dist.get_world_size(group))
+        ]
+        dist.all_gather_object(all_keys, all_optim_state_keys, group=group)
+        merge_all_optim_state_keys = [
+            key for local_keys in all_keys for key in local_keys
+        ]
+        all_optim_state_keys = sorted(set(merge_all_optim_state_keys))
+    else:
+        key_obj_list: List[Optional[List[_OptimStateKey]]] = (
+            [all_optim_state_keys] if rank == 0 else [None]
+        )
+        dist.broadcast_object_list(key_obj_list, src=0, group=group)
+        assert key_obj_list[0] is not None
+        all_optim_state_keys = key_obj_list[0]
+        _check_missing_keys_on_rank(
+            all_optim_state_keys,
+            optim_state_key_to_param_key,
+            param_key_to_param,
+            group,
+        )
+
+    return all_optim_state_keys, optim_state_key_to_param_key
+
+
+def _unflatten_param_groups(
+    state_dict: Dict[str, Any],
+    param_key_to_param: Dict[Union[int, str], nn.Parameter],
+    param_to_fqns: Dict[nn.Parameter, List[str]],
+) -> List[Dict[str, Any]]:
+    param_groups: List[Dict[str, Any]] = []
+    for flat_param_group in state_dict["param_groups"]:
+        unflat_param_group = copy.deepcopy(flat_param_group)
+        param_group_params = [
+            param_key_to_param[flat_param_key]
+            for flat_param_key in flat_param_group["params"]
+        ]
+        nested_unflat_param_names = [
+            param_to_fqns[param] for param in param_group_params
+        ]
+        unflat_param_group["params"] = [
+            unflat_param_name
+            for unflat_param_names in nested_unflat_param_names
+            for unflat_param_name in unflat_param_names
+        ]  # flatten the list of lists
+        param_groups.append(unflat_param_group)
+    return param_groups
+
+
+def _is_named_optimizer(optim_state_dict: Dict[str, Any]) -> bool:
+    """
+    Returns whether the state_dict is from a NamedOptimizer.
+    This function checks that the keys in the state_dict['state'] are strings
+    (which usually are FQNs) versus integers (which usually refer to param_ids
+    from a vanilla torch.optim.Optimizer).
+    """
+    state = optim_state_dict.get("state", None)
+    if not state:
+        # If we cannot find a state, assume it is not NamedOptimizer as
+        # NamedOptimizer has eager initialization.
+        return False
+    try:
+        key = next(iter(state.keys()))
+    except Exception as e:
+        raise Exception(optim_state_dict) from e  # noqa: TRY002
+    return isinstance(key, str)
+
+
+@dataclass
+class StateInfo:
+    # The key of these dictionaries are the state name, e.g., `exp_avg`.
+    tensors: Dict[str, _PosDimTensorInfo]
+    scalar_tensors: Dict[str, torch.Tensor]
+    non_tensors: Dict[str, Any]
+
+
+def _allgather_state_info(
+    fsdp_state: _FSDPState,
+    input_states: Dict[str, Any],
+) -> List[Dict[str, StateInfo]]:
+    """
+    Given the ``input_states``, allgather StateInfo for each state. The function
+    uses all_gather_object to gather StateInfo so no GPU tensors are sent.
+    """
+
+    processed_state_dict: Dict[str, StateInfo] = {}
+    gathered_state_info: List[Dict[str, StateInfo]] = [
+        {} for _ in range(fsdp_state.world_size)
+    ]
+
+    for fqn, optim_state in input_states.items():
+        # Allgather the scalar tensor state, non-tensor states and tensors metadata.
+        processed_state = StateInfo({}, {}, {})
+        for state_name, value in sorted_items(optim_state):
+            if torch.is_tensor(value):
+                if value.dim() == 0:
+                    # Ensure that `step` is on CPU.
+                    processed_state.scalar_tensors[state_name] = value.cpu()
+                else:
+                    processed_state.tensors[state_name] = _PosDimTensorInfo(
+                        value.shape, value.dtype
+                    )
+            else:
+                processed_state.non_tensors[state_name] = value
+        processed_state_dict[fqn] = processed_state
+    dist.all_gather_object(
+        gathered_state_info,
+        processed_state_dict,
+        group=fsdp_state.process_group,
+    )
+    return gathered_state_info
+
+
+def _convert_all_state_info(
+    fsdp_param_info: FSDPParamInfo,
+    gathered_state_info: List[Dict[str, StateInfo]],
+    input_states: Dict[str, Any],
+    output_states: Dict[str, Dict[str, Any]],
+) -> Tuple[Optional[torch.dtype], Dict[str, List[Optional[torch.Tensor]]]]:
+    """
+    Given the ``gathered_state_info`` and ``input_states``, the API converted
+    the StateInfo into the original state if the state is not a non-scalar
+    tensor. For a multi-dimensional tensor, the local state will be stored in
+    ``state_buffer`` in a correct order for later allgather purpose.
+    """
+
+    state_buffers: Dict[str, List[Optional[torch.Tensor]]] = {}
+
+    for fqn, gathered_state in output_states.items():
+        state_info = [s[fqn] for s in gathered_state_info]
+        all_tensor_states = sorted(
+            {n for state in state_info for n in state.tensors.keys()}
+        )
+        empty_ranks: Set[int] = set()
+        dtype: Optional[torch.dtype] = None
+        # First check all the non-scalar states and get the information of
+        # states on each rank.
+        for state_name in all_tensor_states:
+            numels = []
+            _empty_ranks: Set[int] = set()
+            for rank, object_state in enumerate(state_info):
+                numels.append(0)
+                info = object_state.tensors.get(state_name, None)
+                if info is not None:
+                    numels[-1] = info.shape.numel()
+                    if not dtype:
+                        dtype = info.dtype
+                    else:
+                        assert dtype == info.dtype
+                if numels[-1] == 0:
+                    _empty_ranks.add(rank)
+
+            assert not empty_ranks or empty_ranks == _empty_ranks
+            empty_ranks = _empty_ranks
+            if state_name not in state_buffers:
+                state_buffers[state_name] = [
+                    None for _ in fsdp_param_info.param_indices
+                ]
+            local_state = input_states[fqn].get(state_name, None)
+            # N.B. We need to move the state to compute_device. The reason is
+            # not yet clear and we need to figure out why the state may be on a
+            # different device.
+            if local_state is not None:
+                local_state = local_state.to(fsdp_param_info.state.compute_device)
+            state_buffers[state_name][fsdp_param_info.param_indices[fqn]] = local_state
+
+        # Restoring the scalar and non-tensor states. If the corresponding
+        # non-scalar states do not exist on the rank, we also skip the scalar
+        # non-tensor states on that rank.
+        for rank, object_state in enumerate(state_info):
+            if rank in empty_ranks:
+                continue
+            for name, non_tensor_value in object_state.non_tensors.items():
+                curr_non_tensor_value = gathered_state.get(name, None)
+                assert (
+                    curr_non_tensor_value is None
+                    or curr_non_tensor_value == non_tensor_value
+                ), (
+                    f"Rank {rank} has different values for {name}: {non_tensor_value}."
+                    + f" Other ranks: {curr_non_tensor_value}"
+                )
+                gathered_state[name] = non_tensor_value
+
+            for name, scalar_tensor_value in object_state.scalar_tensors.items():
+                curr_scalar_tensor_value = gathered_state.get(name, None)
+                assert curr_scalar_tensor_value is None or torch.equal(
+                    scalar_tensor_value, curr_scalar_tensor_value
+                ), (
+                    f"Rank {rank} has different values for {name}: {scalar_tensor_value}."
+                    + f" Other ranks: {curr_scalar_tensor_value}"
+                )
+                gathered_state[name] = scalar_tensor_value
+
+    return dtype, state_buffers  # type: ignore[possibly-undefined]
+
+
+def _unflatten_orig_param_states(
+    fsdp_param_info: FSDPParamInfo,
+    output_states: Dict[str, Dict[str, Any]],
+    state_name: str,
+    shard_state: bool,
+    to_save: bool,
+    cpu_offload: bool,
+) -> None:
+    """
+    Given a output state dict, ``output_states``, which the keys are FQNs to the
+    original parameters (not FlatParameters nor parmeter ID), and the values
+    are gathered states, unflatten the states to the original dimensions.
+
+    This function performs the unflattening process in-place.
+    """
+    if not to_save:
+        return
+    flat_param = fsdp_param_info.handle.flat_param
+    fsdp_state = fsdp_param_info.state
+    for fqn, gathered_state in output_states.items():
+        value = gathered_state[state_name]
+        param_idx = fsdp_param_info.param_indices[fqn]
+
+        # TODO: This solution is not general and only apply to PTD TP solution.
+        if isinstance(value, DTensor):
+            placement = value.placements[0]
+            # If gathered state is a DTensor and its TP placement is not Replicate(), we need to
+            # gather the tensor on its TP dimension before chunking them into DTensor again.
+            if placement != Replicate():
+                placement_dim = placement.dim  # type: ignore[attr-defined]
+                value_local = value.redistribute(placements=(Replicate(),))
+                reshape_size = list(flat_param._shapes[param_idx])
+                reshape_size[placement_dim] *= value.device_mesh.size(0)
+                reshape_size = torch.Size(reshape_size)
+                value = value.reshape(reshape_size)
+            # If gathered state is a replicate DTensor, we directly reshape it.
+            else:
+                value = value.reshape(flat_param._shapes[param_idx])
+        else:
+            # If gathered state is a tensor, we directly reshape it into unflatten state.
+            value = value.reshape(flat_param._shapes[param_idx])
+
+        if shard_state:
+            osd_config = fsdp_state._optim_state_dict_config
+            if getattr(osd_config, "_use_dtensor", False):
+                assert fsdp_state._device_mesh is not None
+                value = _ext_chunk_dtensor(
+                    value,
+                    fsdp_state.rank,
+                    fsdp_state._device_mesh,
+                    fsdp_state._fsdp_extension,
+                )
+            else:
+                assert fsdp_state.process_group is not None
+                value = _ext_chunk_tensor(
+                    value,
+                    fsdp_state.rank,
+                    fsdp_state.world_size,
+                    fsdp_state._device_handle.device_count(),
+                    fsdp_state.process_group,
+                    fsdp_state._fsdp_extension,
+                )
+        elif not cpu_offload:
+            with SimpleProfiler.profile("clone"):
+                value = value.detach().clone()
+
+        if cpu_offload:
+            with SimpleProfiler.profile(SimpleProfiler.Type.D2H):
+                value = value.cpu()
+        gathered_state[state_name] = value
+
+
+def _allgather_orig_param_states(
+    fsdp_param_info: FSDPParamInfo,
+    gathered_state_info: List[Dict[str, StateInfo]],
+    input_states: Dict[str, Any],
+    shard_state: bool,
+    to_save: bool,
+    cpu_offload: bool,
+) -> Dict[str, Dict[str, Any]]:
+    """
+    Given the ``gathered_state_info`` and ``input_states``, the API allgathers
+    all tensor states and restore non-tensor states from ``gathered_state_info``.
+    """
+    fsdp_state = fsdp_param_info.state
+    if fsdp_state.rank == 0 and dist.get_debug_level() == dist.DebugLevel.DETAIL:
+        logger.info(
+            "Memory Summary before calling to _allgather_orig_param_states %s",
+            fsdp_state._device_handle.memory_summary(),
+        )
+
+    output_states: Dict[str, Dict[str, Any]] = {fqn: {} for fqn in input_states.keys()}
+
+    dtype, state_buffers = _convert_all_state_info(
+        fsdp_param_info, gathered_state_info, input_states, output_states
+    )
+
+    if len(state_buffers) == 0:
+        return output_states
+
+    has_state_params: List[bool] = [
+        True if fqn in output_states else False
+        for fqn, idx in fsdp_param_info.param_indices.items()
+    ]
+
+    # Loop through the ``state_buffers`` and construct the flattened, concatenated,
+    # sharded states. The size of the constructed state will be the same size as
+    # flat_param (also sharded).
+    # Then we perform an allgather_into_tensor to get the full flat_param state.
+    # The full flat_param state is the result of concatenation of multiple states
+    # the order of of flat_param._fqns.
+    # The final step is to split the flat_param state into original param states
+    # and return the result.
+    flat_param = fsdp_param_info.handle.flat_param
+    empty_func = functools.partial(
+        torch.empty, dtype=dtype, device=fsdp_state.compute_device
+    )
+    gathered_tensor = empty_func(flat_param._padded_unsharded_size)
+    # Synchronize can be slow but this will be easier for us to debug.
+    fsdp_state._device_handle.synchronize()
+    for state_name, buffers in state_buffers.items():
+        local_buffers: List[torch.Tensor] = []
+        begin = fsdp_state.rank * flat_param._sharded_size.numel()
+        # End is inclusive.
+        end = begin + flat_param._sharded_size.numel() - 1
+        # param_idx corresponds to the parameter index in the FlatParameter.
+        mem_offset, param_idx = 0, 0
+        for numel, is_padding in zip(
+            flat_param._numels_with_padding, flat_param._is_padding_mask
+        ):
+            frozen_and_no_state = not is_padding and (
+                not fsdp_param_info.param_requires_grad[param_idx]
+                and not has_state_params[param_idx]
+            )
+
+            if is_padding or frozen_and_no_state:
+                # This memory range is a padding or the param is frozen and does
+                # not require gradient. For the later case, we treat it as a
+                # padding and add empty values to the local_buffers.
+
+                padding_begin, padding_end = mem_offset, mem_offset + numel - 1
+                if padding_begin <= begin <= padding_end:
+                    # The range is an align padding before the first parameter in
+                    # the shard. The shard includes parts of this align padding.
+                    padding_len = (
+                        padding_end - begin + 1
+                        if end >= padding_end
+                        else end - begin + 1
+                    )
+                elif padding_begin <= end <= padding_end:
+                    # The range is an align padding after the last parameter in
+                    # the shard. The shard includes parts of this align padding.
+                    padding_len = (
+                        end - padding_begin + 1
+                        if begin <= padding_begin
+                        else end - begin + 1
+                    )
+                elif begin < padding_begin <= padding_end < end:
+                    # The range is an align padding that is completely in the
+                    # shard.
+                    padding_len = numel
+                else:
+                    padding_len = 0
+                if padding_len:
+                    local_buffers.append(empty_func(padding_len))
+
+            if not is_padding:
+                # This memory range is a parameter in FlatParameter. So there
+                # should be an corresponding state in the optimizer unless the
+                # parameter is frozen, which we treat it as a padding above.
+
+                # We need to check if this rank owns the buffer. If this is None:
+                # 1.) the rank does not own any part of the original parameter.
+                #     As a result, there is no corresponding optimizer state on
+                #     the rank as well.
+                # 2.) the parameter is frozen AND no optimizer state for the
+                #     parameter. If a parameter is frozen, there can still be
+                #     optimizer state if the parameter is not frozen in the
+                #     previous steps.
+                if buffers[param_idx] is not None:
+                    local_buffers.append(cast(torch.Tensor, buffers[param_idx]))
+                param_idx += 1
+
+            mem_offset += numel
+
+        shard_numel_padded = flat_param._sharded_size.numel() - (
+            sum(t.numel() for t in local_buffers)
+        )
+
+        assert flat_param._shard_numel_padded == shard_numel_padded, (
+            "Manually calculated _sharded_numel_padded is incorrect. "
+            f"_shard_numel_padded={flat_param._shard_numel_padded}, "
+            f"shard_numel_padded={shard_numel_padded}, "
+            f"_sharded_size.numel={flat_param._sharded_size.numel()}, "
+            f"_numels_with_padding={flat_param._numels_with_padding}, "
+            f"begin={begin}, end={end},"
+        )
+        if shard_numel_padded > 0:
+            # Add right-handed padding.
+            local_buffers.append(empty_func(shard_numel_padded))
+        local_shard = torch.cat(local_buffers)
+        assert local_shard.numel() * fsdp_state.world_size == gathered_tensor.numel(), (
+            "The size of local shard times the world size should equal to the "
+            "gathered tensor size. The inconsistency may be from a bug of "
+            "FlatParameter's metadata or the reconstruction logic in optimizer "
+            "state dict."
+        )
+        fsdp_state._device_handle.synchronize()
+        with SimpleProfiler.profile(SimpleProfiler.Type.ALLGATHER):
+            dist.all_gather_into_tensor(
+                gathered_tensor, local_shard, group=fsdp_state.process_group
+            )
+            # Synchronize can be slow but this will be easier for us to debug.
+            fsdp_state._device_handle.synchronize()
+
+        unpadded_tensor = gathered_tensor[: flat_param._unpadded_unsharded_size.numel()]
+        flat_param_handle = fsdp_param_info.handle
+        orig_states = flat_param_handle._get_unflat_views_aligned(unpadded_tensor)
+        assert len(orig_states) == len(fsdp_param_info.param_indices), (
+            "The number of parameters from FlatParameter is not consistent to "
+            "the number of states used by optimizer state dict reconstruction "
+            "logic."
+        )
+        for fqn, idx in fsdp_param_info.param_indices.items():
+            if fsdp_param_info.param_requires_grad[idx] or fqn in output_states:
+                output_states[fqn][state_name] = orig_states[idx]
+
+        _unflatten_orig_param_states(
+            fsdp_param_info,
+            output_states,
+            state_name,
+            shard_state,
+            to_save,
+            cpu_offload,
+        )
+
+    del gathered_tensor
+    return output_states
+
+
+def _gather_all_orig_param_state(
+    fsdp_param_info: FSDPParamInfo,
+    input_states: Dict[str, Any],
+    shard_state: bool,
+    to_save: bool,
+    cpu_offload: bool,
+) -> Dict[str, Any]:
+    """
+    Given a optimizer state dict, ``input_states``, which the keys are FQNs to the
+    original parameters (not FlatParameters nor parmeter ID), gather all the
+    states and unflatten them to the original dimensions. Note that all the
+    params referred by the ``input_states`` must be managed by FSDP.
+    """
+    fsdp_state = fsdp_param_info.state
+    if (
+        fsdp_state.world_size == 1
+        or fsdp_state.sharding_strategy == ShardingStrategy.NO_SHARD
+    ):
+        return input_states if to_save else {}
+
+    with SimpleProfiler.profile(SimpleProfiler.Type.RESHARDING):
+        with SimpleProfiler.profile(SimpleProfiler.Type.ALLGATHER_OBJ):
+            gathered_state_info = _allgather_state_info(fsdp_state, input_states)
+        output_states = _allgather_orig_param_states(
+            fsdp_param_info,
+            gathered_state_info,
+            input_states,
+            shard_state,
+            to_save,
+            cpu_offload,
+        )
+    if to_save:
+        for key, idx in fsdp_param_info.param_indices.items():
+            if key in output_states:
+                continue
+            if not fsdp_param_info.param_requires_grad[idx]:
+                continue
+
+            raise RuntimeError(
+                f"{key} is not in the output state. "
+                "The FSDPParamInfo has the param keys "
+                f"{sorted(fsdp_param_info.param_indices.keys())} while "
+                "the output_states has the param keys "
+                f"{sorted(output_states.keys())}."
+            )
+        return output_states
+    else:
+        return {}
+
+
+def _convert_state_with_orig_params(
+    all_optim_state_keys: List[_OptimStateKey],
+    optim_state_key_to_param_key: Dict[_OptimStateKey, Union[int, str]],
+    fqn_to_fsdp_param_info: Dict[str, FSDPParamInfo],
+    optim_state_dict: Dict[Union[str, int], Any],
+    to_save: bool,
+    shard_state: bool,
+    cpu_offload: bool = True,
+) -> Dict[str, Any]:
+    fsdp_osd_state: Dict[str, Any] = {}
+    # This variable is used to deduplicate the FSDPParamInfo as one FSDPParamInfo
+    # usually corresponds to multiple parameters. We could not use FSDPParamInfo
+    # as the key because FSDPParamInfo is not hashable. As a result, we fall back
+    # to `id(FSDPParamInfo)`, which the type is an integer.
+    all_states: Dict[int, Dict[str, Any]] = {}
+    # Iterate in rank 0's flat parameter ID order to ensure aligned all-gathers
+    # across ranks
+    for optim_state_key in all_optim_state_keys:
+        param_key: Union[str, int, None] = optim_state_key_to_param_key.get(
+            optim_state_key, None
+        )
+
+        if param_key is None and not optim_state_key.is_fsdp_managed:
+            continue
+
+        if optim_state_key.is_fsdp_managed:
+            fqn = optim_state_key.unflat_param_names[0]
+            fsdp_param_info = fqn_to_fsdp_param_info.get(fqn, None)
+            if fsdp_param_info is None:
+                # This can happen if the not all FSDP instances have all the
+                # parameters. This can happen with FSDP + some MPMD style
+                # parallelism.
+
+                # TODO: it is unclear if we need to do the same check with
+                # non-FSDP managed keys.
+                continue
+            state = {} if param_key is None else optim_state_dict[param_key]
+            if id(fsdp_param_info) not in all_states:
+                all_states[id(fsdp_param_info)] = {}
+            all_states[id(fsdp_param_info)][fqn] = state
+
+        elif to_save:
+            assert len(optim_state_key.unflat_param_names) == 1
+            unflat_param_name = optim_state_key.unflat_param_names[0]
+            with SimpleProfiler.profile("none_fsdp_managed_copy"):
+                param_key = cast(Union[str, int], param_key)
+                fsdp_osd_state[unflat_param_name] = copy.copy(
+                    optim_state_dict[param_key]
+                )
+                if cpu_offload:
+                    for state_name, value in sorted_items(
+                        fsdp_osd_state[unflat_param_name]
+                    ):
+                        if not torch.is_tensor(value):
+                            continue
+                        fsdp_osd_state[unflat_param_name][state_name] = value.cpu()
+
+    # Instead of gathering the state of each parameter individually, we perform
+    # the gathering  all at once to speed up the process.
+    for _all_states in all_states.values():
+        fqn = next(iter(_all_states.keys()))
+        fsdp_param_info = fqn_to_fsdp_param_info[fqn]
+        assert len(fsdp_param_info.param_requires_grad) > 0, (
+            "With use_orig_params, FSDPParamInfo should have requires_grad "
+            "information. However, the length is zero."
+        )
+        for key, idx in fsdp_param_info.param_indices.items():
+            if key in _all_states:
+                continue
+            if not fsdp_param_info.param_requires_grad[idx]:
+                continue
+            raise RuntimeError(
+                f"{key} is not in the optimizer state. "
+                "The FSDPParamInfo has the param keys "
+                f"{sorted(fsdp_param_info.param_indices.keys())} while "
+                "the optimizer has the param keys "
+                f"{sorted(_all_states.keys())}."
+            )
+        fsdp_osd_state.update(
+            _gather_all_orig_param_state(
+                fsdp_param_info,
+                _all_states,
+                shard_state,
+                to_save,
+                cpu_offload,
+            )
+        )
+
+    return fsdp_osd_state
+
+
+def _convert_state_with_flat_params(
+    all_optim_state_keys: List[_OptimStateKey],
+    optim_state_key_to_param_key: Dict[_OptimStateKey, Union[int, str]],
+    fqn_to_fsdp_param_info: Dict[str, FSDPParamInfo],
+    optim_state_dict: Dict[Union[str, int], Any],
+    to_save: bool,
+    shard_state: bool,
+    cpu_offload: bool = True,
+) -> Dict[str, Any]:
+    fsdp_osd_state: Dict[str, Any] = {}
+    # Iterate in rank 0's flat parameter ID order to ensure aligned all-gathers
+    # across ranks
+    for optim_state_key in all_optim_state_keys:
+        param_key: Union[str, int, None] = optim_state_key_to_param_key.get(
+            optim_state_key, None
+        )
+
+        assert param_key is not None, (
+            "If use_orig_params is False, we must be able to find the "
+            f"corresponding param id. {optim_state_key} {param_key}"
+        )
+
+        if optim_state_key.is_fsdp_managed:
+            # If there are multiple unflat_param_names (not use_orig_params),
+            # they share the same FSDPParamInfo. So the first unflat_param_name
+            # is sufficient to fetch the FSDPParamInfo.
+            fqn = optim_state_key.unflat_param_names[0]
+            fsdp_param_info = fqn_to_fsdp_param_info[fqn]
+            unflat_state = _unflatten_optim_state(
+                fsdp_param_info,
+                optim_state_dict[param_key],
+                to_save,
+                shard_state,
+                cpu_offload,
+            )
+            if to_save:
+                assert len(unflat_state) == len(optim_state_key.unflat_param_names)
+                for unflat_param_name, unflat_param_state in zip(
+                    optim_state_key.unflat_param_names,
+                    unflat_state,
+                ):
+                    fsdp_osd_state[unflat_param_name] = unflat_param_state
+        elif to_save:
+            assert len(optim_state_key.unflat_param_names) == 1
+            unflat_param_name = optim_state_key.unflat_param_names[0]
+            fsdp_osd_state[unflat_param_name] = copy.copy(optim_state_dict[param_key])
+            if cpu_offload:
+                for state_name, value in sorted_items(
+                    fsdp_osd_state[unflat_param_name]
+                ):
+                    if not torch.is_tensor(value):
+                        continue
+                    fsdp_osd_state[unflat_param_name][state_name] = value.cpu()
+
+    return fsdp_osd_state
+
+
+@torch.no_grad()
+def _optim_state_dict(
+    model: nn.Module,
+    optim: torch.optim.Optimizer,
+    optim_state_dict: Dict[str, Any],
+    optim_input: Optional[
+        Union[
+            List[Dict[str, Any]],
+            Iterable[nn.Parameter],
+        ]
+    ],
+    rank0_only: bool,
+    shard_state: bool,
+    group: Optional[dist.ProcessGroup],
+    using_optim_input: bool,
+    use_orig_params: bool = False,
+    cpu_offload: bool = True,
+) -> Dict[str, Any]:
+    """
+    Consolidates the optimizer state and returns it as a :class:`dict`
+    following the convention of :meth:`torch.optim.Optimizer.state_dict`,
+    i.e. with keys ``"state"`` and ``"param_groups"``.
+    The flat parameters in ``FSDP`` modules contained in ``model`` are mapped
+    back to their unflattened parameters.
+
+    Parameter keys are not well-defined. For a regular optimizer, the optimizer
+    state_dict contains a mapping from parameter IDs to parameter states.
+    Parameter IDs are the order of parameters in ``optim.param_groups()`` across
+    all the groups. This API also allows user to pass ``optim_input`` for the
+    mapping between parameters and parameter IDs. Using ``optim_input`` is being
+    deprecated.
+
+    If the optimizer is a ``NamedOptimizer``, the optimizer state_dict does not
+    contain parameter IDs mapping but a mapping from parameter FQNs to parameter
+    states. This API finds the mapping from FQNs to parameters if the optimizer
+    is a ``NamedOptimizer``.
+
+    If ``use_orig_params`` is True, each rank will have all FSDP-managed
+    parameters but some of these parameters may be empty due to the sharding.
+    For a regular optim.Optimizer, states for those empty parameters will
+    not be initialized. So, when aggregating the FQNs across ranks, no assert
+    will be raised on a rank even if it does not have all the states -- it is
+    valid and FSDP knows how to aggregate them. However, FSDP has to ignore
+    handling those parameters that are not managed by FSDP and do not exist on
+    the local rank -- those are managed by other parallelisms and FSDP does not
+    know how to handle/aggregate them.
+
+    Args:
+        model (nn.Module): Root module (which may or may not be a
+            :class:`FullyShardedDataParallel` instance) whose parameters
+            were passed into the optimizer ``optim``.
+        optim (torch.optim.Optimizer): Optimizer for ``model`` 's
+            parameters.
+        rank0_only (bool): If ``True``, saves the populated :class:`dict`
+            only on rank 0; if ``False``, saves it on all ranks. (Default:
+            ``True``)
+        shard_state (bool): If ``True``, shard and distribute all
+            non-zero-dimension states.
+
+    Returns:
+        Dict[str, Any]: A :class:`dict` containing the optimizer state for
+        ``model`` 's original unflattened parameters and including keys
+        "state" and "param_groups" following the convention of
+        :meth:`torch.optim.Optimizer.state_dict`. If ``rank0_only=False``,
+        then nonzero ranks return an empty :class:`dict`.
+    """
+    SimpleProfiler.reset()
+    cm = ExitStack()
+    cm.enter_context(SimpleProfiler.profile(SimpleProfiler.Type.ALL))
+    _reset_flat_param_grad_info_if_needed(traversal_utils._get_fsdp_handles(model))
+    to_save = not rank0_only or dist.get_rank(group) == 0 or shard_state
+
+    with SimpleProfiler.profile("preprocessing"):
+        param_to_fqns = _get_param_to_fqns(model)
+        flat_param_to_fqn = _get_flat_param_to_fqn(model)
+        is_named_optimizer = _is_named_optimizer(optim_state_dict)
+
+        param_key_to_param = cast(
+            Dict[Union[int, str], nn.Parameter],
+            (
+                _get_param_id_to_param_from_optim_input(model, optim_input)
+                if using_optim_input
+                else _get_param_key_to_param(
+                    optim, model, is_named_optimizer, param_to_fqns, flat_param_to_fqn
+                )
+            ),
+        )
+        fqn_to_fsdp_param_info = _get_fqn_to_fsdp_param_info(model)
+
+    with SimpleProfiler.profile("preprocessing_with_comm"):
+        (
+            all_optim_state_keys,
+            optim_state_key_to_param_key,
+        ) = _map_param_key_to_optim_keys(
+            optim_state_dict,
+            group,
+            param_key_to_param,
+            param_to_fqns,
+            fqn_to_fsdp_param_info,
+            merge_keys=use_orig_params,
+        )
+
+    with SimpleProfiler.profile("state_converting"):
+        convert_fn = (
+            _convert_state_with_orig_params
+            if use_orig_params
+            else _convert_state_with_flat_params
+        )
+        fsdp_osd_state = convert_fn(
+            all_optim_state_keys,
+            optim_state_key_to_param_key,
+            fqn_to_fsdp_param_info,
+            optim_state_dict["state"],
+            to_save,
+            shard_state,
+            cpu_offload,
+        )
+
+    # At this point, communication is complete and ranks can return early if nothing
+    # will be saved on that rank.
+    if not to_save:
+        return {}
+
+    fsdp_osd: Dict[str, Any] = {"state": fsdp_osd_state}
+
+    flat_param_fqns = set(flat_param_to_fqn.values())
+    for key, value in optim_state_dict["state"].items():
+        if key in fsdp_osd_state:
+            continue
+        if key in flat_param_fqns:
+            continue
+        if key in param_key_to_param:
+            continue
+        # This key is not recognized by FSDP. It may be a user-defined state
+        # or some parameters state that FSDP is unable to map from
+        # ``optim.param_groups``.
+        warnings.warn(
+            f"Found a optim state, {key}, that FSDP cannot process. FSDP "
+            "will directly copy everything to the returned state_dict. In "
+            "most cases, this is a user-defined state that is not "
+            "associated with any particular parameter. Another possible "
+            "case is this state is managed by TorchRec. Otherwise, there may "
+            " be a mismatched assumption of optim_state_dict of this mode."
+        )
+        fsdp_osd_state[key] = value
+
+    if "param_groups" in optim_state_dict:
+        fsdp_osd["param_groups"] = _unflatten_param_groups(
+            optim_state_dict, param_key_to_param, param_to_fqns
+        )
+
+    cm.close()
+    SimpleProfiler.dump_and_reset("FSDP _optim_state_dict() profiling: ")
+
+    return fsdp_osd
+
+
+def _get_fqn_to_fsdp_param_info(model: nn.Module) -> Dict[str, FSDPParamInfo]:
+    """
+    Construct the mapping from a param's fqn to its corresponding ``FSDPParamInfo``
+    if the param is managed by FSDP. Shared parameters, or original parameters that
+    are shared across multiple nn.Modules, are required to belong to one and only
+    one FSDP instance and thus correspond to one ``FlatParameter``. Within the one
+    ``FlatParameter``, ``FlatParameter._fqns`` only stores the first FQN of a shared
+    parameter. Thus, the keys in the mapping are guaranteed to map to unique parameters.
+    """
+
+    def module_fn(module, prefix, tree_level, fqn_to_param_info):
+        fsdp_state = _get_module_fsdp_state_if_fully_sharded_module(module)
+        if fsdp_state is None:
+            return
+        _lazy_init(fsdp_state, module)
+        handle = _module_handle(fsdp_state, module)
+        if not handle:
+            return
+        flat_param = handle.flat_param
+        fsdp_param_info = FSDPParamInfo(fsdp_state, handle, {}, [])
+        # NOTE: `idx` indexes into the data structures *without* padding
+        # elements
+        for idx, local_fqn in enumerate(flat_param._fqns):
+            fqn = clean_tensor_name(prefix + local_fqn)
+            if fqn in fqn_to_param_info:
+                assert fqn_to_param_info[fqn].handle.flat_param is flat_param, fqn
+            fqn_to_param_info[fqn] = fsdp_param_info
+            fsdp_param_info.param_indices[fqn] = idx
+            if flat_param._params is not None:
+                fsdp_param_info.param_requires_grad.append(
+                    flat_param._params[idx].requires_grad
+                )
+
+    def return_fn(fqn_to_param_info):
+        return fqn_to_param_info
+
+    fqn_to_param_info: Dict[str, FSDPParamInfo] = {}
+    # FlatParameter._fqns stores the local fqn, starting from the root of the
+    # FSDP. Using _apply_to_modules() with model (may not be the FSDP root
+    # module) allows us to construct the global fqn.
+    return _apply_to_modules(
+        model,
+        module_fn,
+        return_fn,
+        [fqn for fqn, _ in _named_parameters_with_duplicates(model)],
+        fqn_to_param_info,
+    )
+
+
+@no_type_check
+def _set_optim_use_dtensor(
+    fsdp_state: _FSDPState,
+    state_dict_settings: StateDictSettings,
+) -> None:
+    # If device_mesh is passed in when initalizing FSDP, we automatically turn the
+    # _use_dtensor flag to be true for ShardedOptimStateDictConfig() if state_dict_type
+    # has to be set to SHARDED_STATE_DICT.
+    if getattr(fsdp_state, "_device_mesh", None):
+        state_dict_type = state_dict_settings.state_dict_type
+        if state_dict_type == StateDictType.LOCAL_STATE_DICT:
+            raise RuntimeError(
+                "Found state_dict_type LOCAL_STATE_DICT.",
+                "DeviceMesh is not compatible with LOCAL_STATE_DICT.",
+                "Please set state_dict_type to SHARDED_STATE_DICT to get DTensor state_dict.",
+            )
+        else:
+            state_dict_settings.optim_state_dict_config._use_dtensor = True

infer_4_47_1/lib/python3.10/site-packages/torch/distributed/fsdp/_runtime_utils.py

@@ -0,0 +1,1638 @@
+# mypy: allow-untyped-defs
+import functools
+import logging
+from enum import auto, Enum
+from typing import Any, Callable, Dict, List, no_type_check, Optional, Set, Tuple
+
+import torch
+import torch.distributed as dist
+import torch.distributed.fsdp._traversal_utils as traversal_utils
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+from torch.autograd.graph import register_multi_grad_hook
+from torch.distributed.algorithms._comm_hooks import LOW_PRECISION_HOOKS
+from torch.distributed.fsdp._common_utils import (
+    _assert_in_training_states,
+    _FSDPState,
+    _get_module_fsdp_state,
+    _is_composable,
+    _log_post_backward_hook,
+    _no_dispatch_record_stream,
+    clean_tensor_name,
+    TrainingState,
+)
+from torch.distributed.fsdp._flat_param import (
+    FlatParameter,
+    FlatParamHandle,
+    HandleShardingStrategy,
+    HandleTrainingState,
+    RESHARD_AFTER_FORWARD_HANDLE_STRATEGIES,
+)
+from torch.distributed.fsdp._init_utils import HYBRID_SHARDING_STRATEGIES
+from torch.distributed.fsdp.api import BackwardPrefetch
+from torch.distributed.utils import (
+    _apply_to_tensors,
+    _cast_forward_inputs,
+    _p_assert,
+    _to_kwargs,
+)
+from torch.utils import _pytree as pytree
+
+
+logger = logging.getLogger(__name__)
+
+# Do not include "process_group" to enable hybrid shard and MoE cases
+HOMOGENEOUS_ATTR_NAMES = (
+    "_use_orig_params",
+    "limit_all_gathers",
+    "_use_full_prec_in_eval",
+)
+
+
+class _PrefetchMode(Enum):
+    BACKWARD = auto()
+    FORWARD = auto()
+
+
+def _get_fsdp_root_states_with_modules(
+    module: nn.Module,
+) -> Tuple[List[_FSDPState], List[nn.Module]]:
+    """
+    Returns a tuple containing:
+    1. A list of the root ``_FSDPState`` instances in the module tree rooted at
+    ``module`` without any duplicates and following the ``module.modules()``
+    traversal order (which is assumed to be depth-first).
+    2. A corresponding list of the root modules owning the states in the first
+    list.
+
+    This is similar to :func:`_get_fsdp_states_with_modules` except that we
+    must call :func:`_is_fsdp_root` to force a lazy initialization to determine
+    the FSDP root in case lazy initialization has not yet happened.
+    """
+    fsdp_root_states: List[_FSDPState] = []
+    fsdp_root_modules: List[nn.Module] = []
+    visited_fsdp_states: Set[_FSDPState] = set()
+    # NOTE: This function assumes that `module.modules()` proceeds top-down.
+    for submodule in module.modules():
+        optional_state = _get_module_fsdp_state(submodule)
+        if (
+            optional_state is not None
+            and optional_state not in visited_fsdp_states
+            and _is_fsdp_root(optional_state, submodule)
+        ):
+            visited_fsdp_states.add(optional_state)
+            fsdp_root_states.append(optional_state)
+            fsdp_root_modules.append(submodule)
+    return fsdp_root_states, fsdp_root_modules
+
+
+def _get_fsdp_root_states(module: nn.Module) -> List[_FSDPState]:
+    """See :func:`_get_fsdp_root_states_with_modules`."""
+    fsdp_root_states, _ = _get_fsdp_root_states_with_modules(module)
+    return fsdp_root_states
+
+
+def _is_fsdp_root(state: _FSDPState, module: nn.Module) -> bool:
+    """
+    Returns if ``state`` corresponds to that of an FSDP root.
+
+    For the wrapper code path, ``state`` and ``module`` should be the same. For
+    the non-wrapper code path, ``state`` should be ``module`` 's state.
+    """
+    # Force a lazy initialization to determine the FSDP root
+    _lazy_init(state, module)
+    assert state._is_root is not None  # mypy
+    return state._is_root
+
+
+@no_type_check
+def _lazy_init(
+    state: _FSDPState,
+    root_module: nn.Module,
+) -> _FSDPState:
+    """
+    Performs initialization lazily, typically right before the first forward
+    pass. The laziness is needed to ensure that the parameter device/dtype and
+    the FSDP hierarchy have finalized. This method's actual logic only runs on
+    the root FSDP instance, which performs initialization for all non-root FSDP
+    instances to avoid partial initialization.
+
+    For the non-composable code path, ``state`` and ``root_module`` should be
+    the same, namely the FSDP instance itself.
+    """
+    if state._is_root is not None:
+        return  # no-op: already lazily initialized
+    if not state._device_handle.is_available():
+        # Allow the FSDP constructor to run even without CUDA but check this
+        # once we start real execution
+        raise RuntimeError("FSDP does not support CPU only execution")
+    # The following logic is only run on the root FSDP instance since it will
+    # set `_is_root=False` for the non-root instances
+    state._is_root = True
+    _assert_in_training_states(state, [TrainingState.IDLE])
+    _check_flat_params_on_expected_device(state, root_module)
+    state._all_fsdp_states = traversal_utils._get_fsdp_states(root_module)
+    _init_streams(state)
+    buffers, buffer_dtypes = _get_buffers_and_dtypes_for_computation(state, root_module)
+    _cast_buffers_to_dtype_and_device(buffers, buffer_dtypes, state.compute_device)
+    state._exec_order_data.init(state, root_module, state.process_group)
+    _share_state_and_init_handle_attrs(state, root_module)
+    return state
+
+
+def _check_flat_params_on_expected_device(state: _FSDPState, module: nn.Module):
+    """
+    Checks that all ``FlatParameter``s in ``module`` 's tree managed by
+    ``state`` are on the expected device for *lazy initialization*.
+    """
+    cpu_device = torch.device("cpu")
+    for handle in traversal_utils._get_fsdp_handles(module):
+        if (
+            not handle._offload_params
+            and handle.flat_param.device != state.compute_device
+        ):
+            raise RuntimeError(
+                "An FSDP-managed module unexpectedly has parameters on "
+                f"{handle.flat_param.device}. Make sure to move the module to "
+                f"{state.compute_device} before training."
+            )
+        elif handle._offload_params and handle.flat_param.device != cpu_device:
+            raise RuntimeError(
+                "An FSDP-managed module with parameter CPU offloading enabled "
+                f"has parameters on {handle.flat_param.device}. Make sure to "
+                f"not move the module from CPU when offloading parameters."
+            )
+
+
+@no_type_check
+def _share_state_and_init_handle_attrs(
+    root_state: _FSDPState,
+    root_module: nn.Module,
+) -> None:
+    """
+    Shares data structure state from the ``root_state`` to all FSDP states in
+    ``root_module`` 's module tree, and initializes handle attributes. These
+    are done together to require a single loop over the states.
+    """
+    handle = root_state._handle
+    if handle:
+        handle.init_flat_param_attributes()
+    attr_name_to_values: Dict[str, Set[Any]] = {}
+    for attr_name in HOMOGENEOUS_ATTR_NAMES:
+        attr_name_to_values[attr_name] = set()
+    root_state._all_handles = root_state._exec_order_data.all_handles  # share reference
+    # Update _has_optim_in_backward for each handle.
+    for handle in root_state._all_handles:
+        flat_param = handle.flat_param
+        if hasattr(flat_param, "_in_backward_optimizers"):
+            raise RuntimeError(
+                "FSDP optimizer in backward only supported with use_orig_params=True!"
+            )
+        handle._has_optim_in_backward = flat_param._params is not None and any(
+            hasattr(param, "_in_backward_optimizers") for param in flat_param._params
+        )
+        if handle._has_optim_in_backward:
+            torch._C._log_api_usage_once("fsdp.optimizer_in_backward")
+    for fsdp_state in root_state._all_fsdp_states:
+        for attr_name in HOMOGENEOUS_ATTR_NAMES:
+            _p_assert(
+                hasattr(fsdp_state, attr_name),
+                f"FSDP state missing attribute {attr_name}",
+            )
+            attr_name_to_values[attr_name].add(getattr(fsdp_state, attr_name))
+        if fsdp_state is root_state:
+            continue
+        # Relax the assert for non-root FSDP instances in case the nested
+        # initialized module is wrapped again in FSDP later (e.g. after
+        # training to run inference)
+        _p_assert(
+            fsdp_state._is_root is None or not fsdp_state._is_root,
+            "Non-root FSDP instance's `_is_root` should not have been "
+            "set yet or should have been set to `False`",
+        )
+        fsdp_state._is_root = False
+        fsdp_state._unshard_stream = root_state._unshard_stream
+        fsdp_state._post_backward_stream = root_state._post_backward_stream
+        fsdp_state._pre_unshard_stream = root_state._pre_unshard_stream
+        fsdp_state._all_reduce_stream = root_state._all_reduce_stream
+        fsdp_state._default_stream = root_state._default_stream
+        fsdp_state._exec_order_data = root_state._exec_order_data
+        fsdp_state._free_event_queue = root_state._free_event_queue
+        if fsdp_state._fsdp_extension is not None:
+            fsdp_state._fsdp_extension.compute_stream = root_state._default_stream
+        handle = fsdp_state._handle
+        if handle:
+            handle.init_flat_param_attributes()
+    for attr_name, attr_values in attr_name_to_values.items():
+        if len(attr_values) != 1:
+            raise ValueError(
+                f"Expects one homogeneous value for {attr_name} but got {attr_values}"
+            )
+
+
+@no_type_check
+def _init_streams(
+    state: _FSDPState,
+) -> None:
+    """
+    Initializes CUDA streams for overlapping communication, computation, and
+    data transfers. The streams should be shared across FSDP instances.
+    """
+    assert state._is_root
+    assert state._device_handle.is_available()
+    uses_hybrid_sharding = any(
+        fsdp_state.sharding_strategy in HYBRID_SHARDING_STRATEGIES
+        for fsdp_state in state._all_fsdp_states
+    )
+    # Prioritize all-gathers/reduce-scatters over async all-reduce for HSDP and
+    # preserve the default priority of 0 otherwise
+    high_priority = -1 if state.limit_all_gathers and uses_hybrid_sharding else 0
+    # Default stream for computation
+    state._default_stream = state._device_handle.current_stream()
+    if state._fsdp_extension is not None:
+        # set the compute stream to the FSDP extension
+        state._fsdp_extension.compute_stream = state._default_stream
+
+    # Stream for unshard logic, including allocating the all-gather destination
+    # tensors and the all-gathers themselves
+    state._unshard_stream = state._device_handle.Stream(priority=high_priority)
+    # Stream for overlapping gradient reduction with the backward pass gradient
+    # computation
+    state._post_backward_stream = state._device_handle.Stream(priority=high_priority)
+    # Stream for pre-unshard logic, namely allocations and writes for CPU
+    # offloading (H2D copy) and mixed precision (low precision cast)
+    state._pre_unshard_stream = state._device_handle.Stream(priority=high_priority)
+    # Stream to run HSDP's all-reduce as async (if using HSDP)
+    state._all_reduce_stream = (
+        state._device_handle.Stream() if uses_hybrid_sharding else state._default_stream
+    )
+
+
+@no_type_check
+def _unshard(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+    unshard_stream: torch.Stream,
+    pre_unshard_stream: torch.Stream,
+) -> None:
+    """
+    Unshards the handles in ``handles``. If the handles are in
+    :meth:`summon_full_params` and are using mixed precision, then they are
+    forced to full precision.
+
+    Postcondition: handle's ``FlatParameter`` 's data is the padded
+    unsharded flat parameter on the compute device.
+    """
+    if not handle:
+        return
+    with state._device_handle.stream(pre_unshard_stream):
+        ran_pre_unshard = handle.pre_unshard()
+    if ran_pre_unshard:
+        unshard_stream.wait_stream(pre_unshard_stream)
+    if state.limit_all_gathers:
+        event = state._free_event_queue.dequeue_if_needed()
+        if event:
+            with torch.profiler.record_function(
+                "FullyShardedDataParallel.rate_limiter"
+            ):
+                event.synchronize()
+    with state._device_handle.stream(unshard_stream):
+        handle.unshard()
+        handle.post_unshard()
+
+
+@no_type_check
+def _reshard(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+    free_unsharded_flat_param: bool,
+):
+    """
+    Reshards the handle. ``free_unsharded_flat_param`` indicates whether to
+    free the handle's padded unsharded flat parameter.
+    """
+    handle.reshard(free_unsharded_flat_param)
+    if state.limit_all_gathers and free_unsharded_flat_param:
+        if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+            # We don't run a even queue for freeing under torch compile atm
+            # But maybe we need to? TODO(voz): Look into this
+            free_event = state._device_handle.Event()
+            free_event.record()
+            state._free_event_queue.enqueue(free_event)
+    handle.post_reshard()
+    # Flat parameter freed or not, we always have to "unshard" the parameter
+    # upon next access to get its shape correct.
+    handle._prefetched = False
+
+
+def _unshard_grads(
+    handle: Optional[FlatParamHandle],
+) -> None:
+    if handle:
+        handle.unshard_grad()
+
+
+def _reshard_grads(
+    handle: Optional[FlatParamHandle],
+) -> None:
+    if handle:
+        handle.reshard_grad()
+
+
+@no_type_check
+def _pre_forward(
+    state: _FSDPState,
+    handle: Optional[FlatParamHandle],
+    unshard_fn: Callable,
+    module: nn.Module,
+    args: Tuple[Any, ...],
+    kwargs: Dict[str, Any],
+) -> Tuple[Tuple[Any, ...], Dict[str, Any]]:
+    """
+    Runs the pre-forward logic. This includes an opportunity to unshard
+    currently sharded parameters such as those for the current forward and
+    registering post-backward hooks for these current parameters. This function
+    also converts forward ``args`` and ``kwargs`` to the given precision.
+
+    Args:
+        handles (List[FlatParamHandle]): Handles giving the parameters used in
+            the current forward.
+        unshard_fn (Optional[Callable]): A callable to unshard any currently
+            sharded parameters or ``None`` to not do any unsharding.
+        module (nn.Module): Module whose forward this method runs right before;
+            expected by the hook signature.
+        args (Tuple[Any, ...]): Module forward ``args``.
+        kwargs (Dict[str, Any]): Module forward ``kwargs``.
+    """
+    with torch.profiler.record_function("FullyShardedDataParallel._pre_forward"):
+        # For `fully_shard` + `checkpoint`, skip pre-forward logic in the
+        # recomputed forward
+        if handle and handle._training_state == HandleTrainingState.BACKWARD_PRE:
+            # For both checkpoint implementations, we do not need to re-cast
+            # inputs here since they will be checkpointed in the low precision
+            # either by AC or normally by autograd as long as the AC region is
+            # nested within FSDP
+            return args, kwargs
+        state.training_state = TrainingState.FORWARD_BACKWARD
+        state._exec_order_data.record_pre_forward(handle, module.training)
+        if handle:
+            handle._training_state = HandleTrainingState.FORWARD
+        if unshard_fn is not None:
+            unshard_fn(state, handle)
+        # Register post-backward hooks to reshard the parameters and reduce-scatter
+        # their gradients. They must be re-registered every forward pass in case
+        # the `grad_fn` is mutated.
+        _register_post_backward_hook(state, handle)
+        # We have to reallocate the _cpu_grad if optimizer overlap
+        # set the grad to None in the backward pass.
+        if handle and handle._offload_params and handle.flat_param._cpu_grad is None:
+            handle.flat_param._cpu_grad = torch.zeros_like(
+                handle.flat_param._local_shard, device=torch.device("cpu")
+            ).pin_memory(device=state.compute_device)
+
+        should_cast_forward_inputs = (
+            state._handle and not state._handle._force_full_precision
+        )
+
+        if should_cast_forward_inputs and state.mixed_precision.cast_forward_inputs:
+            # Recursively convert args and kwargs to specified precision.
+            input_dtype: Optional[torch.dtype] = state.mixed_precision.param_dtype
+            args, kwargs = _cast_forward_inputs(input_dtype, *args, **kwargs)
+        _register_post_backward_reshard_only_hook(state, handle, args, kwargs)
+        return args, kwargs
+
+
+@no_type_check
+def _pre_forward_unshard(
+    state: _FSDPState,
+    handle: Optional[FlatParamHandle],
+) -> None:
+    """Unshards parameters in the pre-forward."""
+    if not handle:
+        return
+    # If the handles have been prefetched, then there is no need to call
+    # `_unshard()` again
+    if not handle._prefetched:
+        _unshard(state, handle, state._unshard_stream, state._pre_unshard_stream)
+    handle._needs_pre_forward_unshard = False
+    # Don't wait during trace
+    if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+        current_stream = state._device_handle.current_stream()
+        if state._unshard_event is not None:
+            current_stream.wait_event(state._unshard_event)
+            state._unshard_event = None
+        else:
+            current_stream.wait_stream(state._unshard_stream)
+    with torch.profiler.record_function(
+        "FullyShardedDataParallel._pre_forward_prefetch"
+    ):
+        _prefetch_handle(state, handle, _PrefetchMode.FORWARD)
+
+
+@no_type_check
+def _post_forward(
+    state: _FSDPState,
+    handle: Optional[FlatParamHandle],
+    reshard_fn: Callable,
+    module: nn.Module,
+    input: Any,
+    output: Any,
+) -> Any:
+    """
+    Runs the post-forward logic. This includes an opportunity to reshard
+    currently unsharded parameters such as those used in the current forward
+    and registering pre-backward hooks on the forward outputs.
+
+    Args:
+        handles (List[FlatParamHandle]): Handles giving the parameters used in
+            the current forward.
+        reshard_fn (Optional[Callable]): A callable to reshard any currently
+            unsharded parameters (e.g. from the current forward) or ``None`` to
+            not do any resharding.
+        module (nn.Module): Module whose forward just ran, which should be a
+            fully sharded module (see [Note: Fully Sharded Module]); expected
+            by the hook signature.
+        input (Any): Unused; expected by the hook signature.
+        output (Any): Forward pass output; pre-backward hooks are registered on
+            the tensors that require gradients in this output.
+
+    Postcondition: Each ``FlatParameter`` 's data points to the sharded flat
+    parameter.
+    """
+    with torch.profiler.record_function("FullyShardedDataParallel._post_forward"):
+        # For `fully_shard` + `checkpoint`, skip post-forward logic in the
+        # recomputed forward
+        if handle and handle._training_state == HandleTrainingState.BACKWARD_PRE:
+            return output
+
+        state._exec_order_data.record_post_forward(handle)
+        if reshard_fn is not None:
+            reshard_fn(state, handle)
+        # Register pre-backward hooks to unshard the flat parameters for the
+        # gradient computation (if needed)
+        output = _register_pre_backward_hooks(state, module, output, handle)
+        state.training_state = TrainingState.IDLE
+        if handle:
+            handle._training_state = HandleTrainingState.IDLE
+        return output
+
+
+@no_type_check
+def _post_forward_reshard(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+) -> None:
+    """Reshards parameters in the post-forward."""
+    if not handle:
+        return
+    # Do not free the root's parameters in the post-forward for `FULL_SHARD`
+    # with the intention that they are immediately used for backward
+    # computation (though this may not be true)
+    free_unsharded_flat_param = (
+        not state._is_root
+        and handle._sharding_strategy in RESHARD_AFTER_FORWARD_HANDLE_STRATEGIES
+    )
+    _reshard(state, handle, free_unsharded_flat_param)
+
+
+@no_type_check
+def _root_pre_forward(
+    state: _FSDPState,
+    module: nn.Module,
+    args,
+    kwargs,
+) -> None:
+    """
+    Runs pre-forward logic specific to the root FSDP instance, which should run
+    before any individual module's pre-forward. This starts with an attempt at
+    lazy initialization (which only runs non-vacuously once). Otherwise, if
+    this is called on a non-root FSDP instance, then it returns directly.
+
+    Args:
+        module (nn.Module): Module for which this logic tries to run. It may or
+            may not be the root. If not, then this method does not do anything.
+    """
+    with torch.profiler.record_function("FullyShardedDataParallel._root_pre_forward"):
+        _lazy_init(state, module)
+        _p_assert(state._is_root is not None, "Expects a root FSDP to have been set")
+        if not state._is_root:
+            # Always cast forward inputs in the root of this local FSDP unit for mixed
+            # precision, as this is where mixed precision could be configed.
+            # This is more useful for auto wrapping that is recommended in composable path.
+            # For manual wrapping, cast forward inputs on each local FSDP unit root will
+            # increase some overhead, so not turned on for model wrapper path right now where
+            # manual wrapping is more broadly used.
+            if _is_composable(state):
+                return _root_cast_forward_input(state, module, args, kwargs)
+            return args, kwargs
+
+        # We cast buffers back to full precision if we're forcing full precision. Disjointly, we check if buffers
+        # are in full precision and if we should cast them back to lower precision, which happens when
+        # exiting eval() mode.
+        handle = state._handle
+        if handle:
+            should_cast_buffers_to_full_prec = handle._force_full_precision
+        else:
+            should_cast_buffers_to_full_prec = True
+
+        if should_cast_buffers_to_full_prec:
+            _cast_buffers_to_dtype_and_device(
+                buffers=dict(module.named_buffers()).values(),
+                buffer_dtypes=list(state._buffer_name_to_orig_dtype.values()),
+                device=state.compute_device,
+            )
+            # This flag is only set when we cast buffers to full precision, to avoid the
+            # CPU overhead that can stem from retrieving all buffers and their types in the
+            # following else branch.
+            state._needs_buffer_dtype_restore_check = True
+        elif getattr(state, "_needs_buffer_dtype_restore_check", False):
+            # Check if buffers are in full precision and we need to cast them
+            # back down.
+            (
+                buffers,
+                buffer_dtypes_for_computation,
+            ) = _get_buffers_and_dtypes_for_computation(state, module)
+            if len(buffers) > 0 and len(buffer_dtypes_for_computation) > 0:
+                if any(
+                    buffer.dtype != buffer_dtype_for_computation
+                    for buffer, buffer_dtype_for_computation in zip(
+                        buffers, buffer_dtypes_for_computation
+                    )
+                ):
+                    # Assume we have to cast everything if there is one mismatch
+                    _cast_buffers_to_dtype_and_device(
+                        buffers, buffer_dtypes_for_computation, state.compute_device
+                    )
+            # We don't have to check this again until we cast buffers to full precision again.
+            state._needs_buffer_dtype_restore_check = False
+
+        if state.forward_prefetch:
+            handles = []
+            for fsdp_state in state._all_fsdp_states:
+                if fsdp_state._handle:
+                    handles.append(fsdp_state._handle)
+            for handle in handles:
+                handle._needs_pre_forward_unshard = True
+                handle._prefetched = False
+        _wait_for_computation_stream(
+            state._device_handle.current_stream(),
+            state._unshard_stream,
+            state._pre_unshard_stream,
+        )
+        _reset_flat_param_grad_info_if_needed(state._all_handles)
+
+        # Prepares the forward inputs by moving them to ``compute_device``
+        # TODO: Do not use the side stream for tensor copies for now; investigate
+        # the perf with/without it.
+        with torch.profiler.record_function("FullyShardedDataParallel._to_kwargs"):
+            args_tuple, kwargs_tuple = _to_kwargs(
+                args, kwargs, state.compute_device, False
+            )
+        args = args_tuple[0]
+        kwargs = kwargs_tuple[0]
+
+        return _root_cast_forward_input(state, module, args, kwargs)
+
+
+@no_type_check
+def _root_cast_forward_input(
+    state: _FSDPState, module: torch.nn.Module, args, kwargs
+) -> Tuple[Any, Any]:
+    if state._handle:
+        force_full_precision = not state._handle._force_full_precision
+    else:
+        force_full_precision = True
+
+    should_cast_forward_inputs = (
+        (module.training or not state._use_full_prec_in_eval) and force_full_precision
+    ) and state.mixed_precision.cast_root_forward_inputs
+
+    if should_cast_forward_inputs:
+        input_dtype: Optional[torch.dtype] = state.mixed_precision.param_dtype
+        args, kwargs = _cast_forward_inputs(input_dtype, *args, **kwargs)
+
+    return args, kwargs
+
+
+@no_type_check
+def _pre_backward_hook(
+    state: _FSDPState,
+    module: nn.Module,
+    handle: FlatParamHandle,
+    grad,
+    *unused: Any,
+) -> Any:
+    """
+    Prepares ``_handle`` 's ``FlatParameter`` s for gradient computation.
+
+    Args:
+        module (nn.Module): Fully sharded module (see [Note: Fully Sharded
+            Module]).
+    """
+    # Only run the pre-backward hook once per group of handles involved in the
+    # same module forward computation
+    if (
+        handle
+        and hasattr(handle, "_ran_pre_backward_hook")
+        and handle._ran_pre_backward_hook
+    ):
+        return grad
+
+    with torch.profiler.record_function("FullyShardedDataParallel._pre_backward_hook"):
+        # Queue the post-backward callback once for the root FSDP instance to
+        # attach it to the outermost backward graph task so that it is called
+        # after all backward calls complete
+        if state._is_root and not state._post_backward_callback_queued:
+            _register_post_backward_final_callback(state, module)
+            _reset_flat_param_grad_info_if_needed(state._all_handles)
+        elif handle:
+            allowed_states = [TrainingState.IDLE]
+            if _is_composable(state):
+                allowed_states.append(TrainingState.FORWARD_BACKWARD)
+            _assert_in_training_states(state, allowed_states)
+        state.training_state = TrainingState.FORWARD_BACKWARD
+        # Queueing the post-backward callback is the only logic that is not
+        # per-handle in the pre-backward hook, so we can return early here if
+        # there are no handles.
+        if not handle:
+            return grad
+        handle._training_state = HandleTrainingState.BACKWARD_PRE
+
+        if handle._needs_pre_backward_unshard:
+            # If the handles have been prefetched, then there is no need to
+            # call `_unshard()` again
+            if not handle._prefetched:
+                _unshard(
+                    state,
+                    handle,
+                    state._unshard_stream,
+                    state._pre_unshard_stream,
+                )
+            # Don't wait during trace
+            if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+                state._device_handle.current_stream().wait_stream(state._unshard_stream)
+
+        # Set this to `False` to ensure that a mistargeted prefetch does not
+        # actually unshard these handles
+        handle._needs_pre_backward_unshard = False
+        with torch.profiler.record_function(
+            "FullyShardedDataParallel._pre_backward_prefetch"
+        ):
+            _prefetch_handle(state, handle, _PrefetchMode.BACKWARD)
+        handle.prepare_gradient_for_backward()
+        handle._ran_pre_backward_hook = True
+        return grad
+
+
+@no_type_check
+@torch.no_grad()
+def _post_backward_hook(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+    flat_param,
+    *unused: Any,
+):
+    """
+    Reduce-scatters the gradient of ``handle`` 's ``FlatParameter``.
+
+    Precondition: The ``FlatParameter`` 's ``.grad`` attribute contains the
+    unsharded gradient for the local batch.
+
+    Postcondition:
+    - If using ``NO_SHARD``, then the ``.grad`` attribute is the reduced
+    unsharded gradient.
+    - Otherwise, the ``_saved_grad_shard`` attribute is the reduced sharded
+    gradient (accumulating with any existing gradient).
+    """
+    _log_post_backward_hook(state, handle, logger)
+    flat_param = handle.flat_param
+    flat_param._post_backward_called = True
+    with torch.autograd.profiler.record_function(
+        "FullyShardedDataParallel._post_backward_hook"
+    ):
+        _assert_in_training_states(state, [TrainingState.FORWARD_BACKWARD])
+        # For multiple applications of reentrant AC across submodules sharing
+        # the same `FlatParameter`, the post-backward hook may run multiple
+        # times in one backward, in which case we permit the state to already
+        # be in `BACKWARD_POST`.
+        _p_assert(
+            handle._training_state
+            in (HandleTrainingState.BACKWARD_PRE, HandleTrainingState.BACKWARD_POST),
+            f"Expects `BACKWARD_PRE` or `BACKWARD_POST` state but got {handle._training_state}",
+        )
+        handle._training_state = HandleTrainingState.BACKWARD_POST
+
+        if flat_param.grad is None:
+            return
+        if flat_param.grad.requires_grad:
+            raise RuntimeError("FSDP does not support gradients of gradients")
+
+        _post_backward_reshard(state, handle)
+        if not state._sync_gradients:
+            if handle._use_orig_params:
+                handle._use_unsharded_grad_views()
+            return
+
+        # Wait for all ops in the current stream (e.g. gradient computation) to
+        # finish before reduce-scattering the gradient
+        if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+            state._post_backward_stream.wait_stream(
+                state._device_handle.current_stream()
+            )
+
+        with state._device_handle.stream(state._post_backward_stream):
+            autograd_computed_grad = flat_param.grad.data
+            if (
+                not _low_precision_hook_enabled(state)
+                and flat_param.grad.dtype != handle._reduce_dtype
+                # If we are forcing full precision but communicating grads
+                # (i.e. model.eval() + full precision in eval was configured), don't downcast gradient.
+                and not handle._force_full_precision
+            ):
+                flat_param.grad.data = flat_param.grad.to(handle._reduce_dtype)
+            if handle.uses_sharded_strategy:
+                _reduce_grad(state, handle)
+            else:
+                _reduce_grad_no_shard(state, handle)
+            # Since the unsharded gradient is produced in the computation
+            # stream and consumed in the post-backward stream, inform the
+            # caching allocator (before it goes out of scope)
+            _no_dispatch_record_stream(
+                autograd_computed_grad, state._post_backward_stream
+            )
+
+
+def _post_backward_reshard_only_hook(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+    *unused: Any,
+) -> None:
+    with torch.profiler.record_function(
+        "FullyShardedDataParallel._post_backward_hook_reshard_only"
+    ):
+        # `_pre_backward_hook` may not get executed
+        # if forward output does not require grad
+        # overwrite IDLE state for post-backward prefetching
+        state.training_state = TrainingState.FORWARD_BACKWARD
+        handle._training_state = HandleTrainingState.BACKWARD_POST
+        _post_backward_reshard(state, handle)
+
+
+def _post_backward_reshard(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+    *unused: Any,
+) -> None:
+    free_unsharded_flat_param = _should_free_in_backward(state, handle)
+    _reshard(state, handle, free_unsharded_flat_param)
+
+    # TODO: Post-backward prefetching does not support the multiple handles
+    # per module case since the post-backward hook runs per handle, not per
+    # group of handles.
+    with torch.profiler.record_function(
+        "FullyShardedDataParallel._post_backward_prefetch"
+    ):
+        _prefetch_handle(state, handle, _PrefetchMode.BACKWARD)
+
+
+@no_type_check
+def _should_free_in_backward(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+) -> bool:
+    """
+    Returns whether FSDP should free the unsharded flat parameter in the
+    post-backward or not.
+    """
+    if not handle.uses_sharded_strategy:
+        return False
+    # If not syncing gradients, then we do not free for strategies that do not
+    # reshard after forward as a *heuristic* to tradeoff higher memory for
+    # higher throughput.
+    return (
+        state._sync_gradients
+        or handle._sharding_strategy in RESHARD_AFTER_FORWARD_HANDLE_STRATEGIES
+    )
+
+
+@no_type_check
+def _reduce_grad(state: _FSDPState, handle: FlatParamHandle) -> None:
+    """
+    For sharded strategies, this runs gradient reduction, sharded gradient
+    accumulation if needed, and the post-reduction callback.
+    """
+    flat_param = handle.flat_param
+    uses_hybrid_sharded_strategy = handle._sharding_strategy in (
+        HandleShardingStrategy.HYBRID_SHARD,
+        HandleShardingStrategy._HYBRID_SHARD_ZERO2,
+    )
+    # We clear `.grad` to permit multiple backwards. This avoids a race where
+    # the second backward pass computation precedes ahead of the first backward
+    # pass reduction, which is possible since the reduction is issued in a
+    # separate stream and is async and would result in reducing the wrong
+    # gradient.
+    unsharded_grad = flat_param.grad.data
+    flat_param.grad = None
+    padded_unsharded_grad, new_sharded_grad = _get_reduce_scatter_tensors(
+        state, unsharded_grad
+    )
+    if state._comm_hook is None:  # default path
+        _div_if_needed(padded_unsharded_grad, state._gradient_predivide_factor)
+        pg = (
+            handle._fake_process_group
+            if handle._use_fake_reduce
+            else state.process_group
+        )
+        dist.reduce_scatter_tensor(
+            new_sharded_grad,
+            padded_unsharded_grad,
+            group=pg,
+        )
+        if uses_hybrid_sharded_strategy:
+            # Don't wait during trace
+            if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+                state._all_reduce_stream.wait_stream(state._post_backward_stream)
+            with state._device_handle.stream(state._all_reduce_stream):
+                # Since the new sharded gradient is produced in the post-
+                # backward stream and consumed in the all-reduce stream,
+                # inform the caching allocator
+                _no_dispatch_record_stream(new_sharded_grad, state._all_reduce_stream)
+                dist.all_reduce(new_sharded_grad, group=state._inter_node_pg)
+                _div_if_needed(new_sharded_grad, state._gradient_postdivide_factor)
+                grad_to_offload = _accumulate_sharded_grad(
+                    state, handle, new_sharded_grad
+                )
+                _post_reduce_grad_callback(state, handle, grad_to_offload)
+                return
+        _div_if_needed(new_sharded_grad, state._gradient_postdivide_factor)
+    else:
+        state._comm_hook(
+            state._comm_hook_state, padded_unsharded_grad, new_sharded_grad
+        )
+        # NOTE: HSDP variants do not support communication hook.
+    grad_to_offload = _accumulate_sharded_grad(state, handle, new_sharded_grad)
+    _post_reduce_grad_callback(state, handle, grad_to_offload)
+
+
+@no_type_check
+def _get_reduce_scatter_tensors(
+    state: _FSDPState, unsharded_grad: torch.Tensor
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Returns the input and output tensors to reduce-scatter, respectively.
+    """
+    chunks = list(unsharded_grad.chunk(state.world_size))
+    numel_to_pad = state.world_size * chunks[0].numel() - unsharded_grad.numel()
+    padded_unsharded_grad = (
+        F.pad(unsharded_grad, [0, numel_to_pad]) if numel_to_pad > 0 else unsharded_grad
+    )
+    new_sharded_grad = torch.empty_like(chunks[0])  # padded
+    return padded_unsharded_grad, new_sharded_grad
+
+
+@no_type_check
+def _accumulate_sharded_grad(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+    sharded_grad: torch.Tensor,
+) -> torch.Tensor:
+    """
+    Accumulates the reduce-scattered sharded gradient with any existing sharded
+    gradient if needed, returning the gradient to offload (if CPU offloading is
+    enabled).
+    """
+    flat_param = handle.flat_param
+    _cast_grad_to_param_dtype(state, sharded_grad, flat_param)
+    # Save the sharded gradient in `_saved_grad_shard` to support gradient
+    # accumulation -- for multiple backwards, the gradient reductions may
+    # happen in arbitrary order
+    accumulate_grad = hasattr(flat_param, "_saved_grad_shard")
+    if accumulate_grad:
+        _check_grad_to_accumulate(sharded_grad, flat_param._saved_grad_shard)
+        flat_param._saved_grad_shard += sharded_grad
+    else:
+        flat_param._saved_grad_shard = sharded_grad
+    grad_to_offload = flat_param._saved_grad_shard
+    return grad_to_offload
+
+
+@no_type_check
+def _reduce_grad_no_shard(state: _FSDPState, handle: FlatParamHandle) -> None:
+    """
+    For no-shard, this runs gradient reduction (which directly covers any
+    gradient accumulation implicitly) and the post-reduction callback.
+    """
+    flat_param = handle.flat_param
+    if state._comm_hook is None:  # default path
+        _div_if_needed(flat_param.grad, state._gradient_predivide_factor)
+        dist.all_reduce(flat_param.grad, group=state.process_group)
+        _div_if_needed(flat_param.grad, state._gradient_postdivide_factor)
+    else:
+        state._comm_hook(state._comm_hook_state, flat_param.grad)
+    # For `NO_SHARD`, we can keep the low precision gradients by simply
+    # omitting the cast altogether
+    if not handle._keep_low_precision_grads:
+        _cast_grad_to_param_dtype(state, flat_param.grad, flat_param)
+    grad_to_offload = flat_param.grad.data
+    _post_reduce_grad_callback(state, handle, grad_to_offload)
+
+
+@no_type_check
+def _post_reduce_grad_callback(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+    # Additional arguments needed for the callback logic
+    grad_to_offload: torch.Tensor,
+):
+    """
+    This callback captures any logic to run after the gradient reduction
+    finishes. Currently, this offloads the gradient to CPU if CPU offloading is
+    enabled and uses sharded gradient views if ``use_orig_params=True``.
+    """
+    _offload_grad(state, handle, grad_to_offload)
+    _post_backward_use_sharded_grad_views(handle)
+
+
+@no_type_check
+def _offload_grad(
+    state: _FSDPState,
+    handle: FlatParamHandle,
+    grad_to_offload: torch.Tensor,
+):
+    if not handle._offload_params:
+        return
+    # Offload the gradient to CPU to ensure parameters and gradients are on the
+    # same device as required by the optimizer
+    # TODO: Investigate why `NO_SHARD` breaks correctness when using
+    # `non_blocking=True` here.
+    # TODO (rohan-varma): When CPU offload and optimizer overlap,
+    # non_blocking=True won't work since the copy may have not finished before
+    # the optimizer step executes on CPU. If we want to use non-blocking=True
+    # here, we'll have to synchronize before using result on CPU.
+    non_blocking = handle.uses_sharded_strategy and not handle._has_optim_in_backward
+    handle.flat_param._cpu_grad.copy_(
+        grad_to_offload.detach(), non_blocking=non_blocking
+    )  # synchronized in the post-backward callback
+    # Since the gradient being offloaded may have been produced in the
+    # computation stream and is being consumed here in the post-backward
+    # stream, inform the caching allocator
+    _no_dispatch_record_stream(grad_to_offload.data, state._post_backward_stream)
+
+
+@no_type_check
+def _post_backward_use_sharded_grad_views(handle: FlatParamHandle):
+    if not handle._use_orig_params:
+        return
+    # Since the handle's `FlatParameter` completed its gradient computation, we
+    # should reset the gradient noneness mask
+    handle._reset_is_grad_none()
+    # Delay using sharded gradient views until after the reduce-scatter instead
+    # of immediately after resharding
+    handle._use_sharded_grad_views()
+    if handle._has_optim_in_backward:
+        handle.prepare_gradient_for_optim()
+        for orig_param in handle.flat_param._params:
+            # Check for `None` gradient to filter parameters not in the rank
+            if orig_param.grad is not None and hasattr(
+                orig_param, "_in_backward_optimizers"
+            ):
+                # TODO (rohan-varma): For CPU offload, this unfortunately
+                # operates on CPU because the parameters and gradients have
+                # already been offloaded. We should run this on GPU after
+                # refactoring.
+                for optim in orig_param._in_backward_optimizers:
+                    optim.step()
+
+                optim.zero_grad(set_to_none=True)
+        handle._reset_flat_param_grad_info_if_needed()
+        if handle._offload_params:
+            handle.flat_param._cpu_grad = None
+
+
+def _div_if_needed(tensor: torch.Tensor, div_factor: float) -> None:
+    if div_factor > 1:
+        tensor.div_(div_factor)
+
+
+@no_type_check
+def _cast_grad_to_param_dtype(
+    state: _FSDPState,
+    sharded_grad: torch.Tensor,
+    param: FlatParameter,
+):
+    """
+    Casts ``sharded_grad`` back to the full parameter dtype so that the
+    optimizer step runs with that dtype. This performs an actual cast if
+    1. parameters were in reduced precision during the forward since then
+    gradients would be in that reduced precision, or
+    2. parameters were not in reduced precision but gradients were in
+    reduced precision for communication.
+    However, if a low precision communication hook is registered, then this
+    dtype cast happens in the hook instead.
+    """
+    _assert_in_training_states(state, [TrainingState.FORWARD_BACKWARD])
+    if not _low_precision_hook_enabled(state) and sharded_grad.dtype != param.dtype:
+        low_prec_grad_data = sharded_grad.data
+        sharded_grad.data = sharded_grad.data.to(dtype=param.dtype)
+        # Since for `NO_SHARD`, the gradient is produced in the computation
+        # stream and consumed here in the post-backward stream, inform the
+        # caching allocator; for the sharded strategies, the gradient is
+        # produced in the post-backward stream, so this `record_stream()`
+        # should be a no-op
+        _no_dispatch_record_stream(
+            low_prec_grad_data, state._device_handle.current_stream()
+        )
+
+
+def _check_grad_to_accumulate(
+    new_sharded_grad: torch.Tensor,
+    accumulated_grad: torch.Tensor,
+) -> None:
+    _p_assert(
+        accumulated_grad.shape == new_sharded_grad.shape,
+        "Shape mismatch when accumulating gradients: "
+        f"existing gradient shape={accumulated_grad.shape} "
+        f"new gradient shape={new_sharded_grad.shape}",
+    )
+    _p_assert(
+        accumulated_grad.device == new_sharded_grad.device,
+        "Device mismatch when accumulating gradients: "
+        f"existing gradient device={accumulated_grad.device} "
+        f"new gradient device={new_sharded_grad.device}",
+    )
+
+
+@no_type_check
+def _low_precision_hook_enabled(state: _FSDPState) -> bool:
+    return state._comm_hook in LOW_PRECISION_HOOKS
+
+
+@no_type_check
+@torch.no_grad()
+def _post_backward_final_callback(
+    state: _FSDPState,
+    module: nn.Module,
+):
+    """
+    This waits for the post-backward to finish and performs some final cleanup.
+    This runs at the end of the entire backward pass and should only be called
+    on the root FSDP instance.
+    """
+    _p_assert(
+        state._is_root,
+        "The post-backward callback should only be called on the root FSDP instance",
+    )
+    root_state = state
+
+    if root_state._sync_gradients:
+        current_stream = state._device_handle.current_stream()
+        # TODO (rohan-varma): this also waits for the overlapped optimizer step to finish
+        # since it currently runs in the post-backward stream. That can be
+        # pushed to the next forward if run in a different stream
+        current_stream.wait_stream(root_state._post_backward_stream)
+        if root_state._all_reduce_stream is not current_stream:  # uses HSDP
+            current_stream.wait_stream(root_state._all_reduce_stream)
+        if root_state.cpu_offload.offload_params:
+            # Wait for non-blocking GPU -> CPU sharded gradient copies from the
+            # post-backward hooks to finish explicitly since CPU gradients do
+            # not automatically synchronize with the GPU
+            state._device_handle.current_stream().synchronize()
+    root_state._exec_order_data.next_iter()
+
+    for fsdp_state in state._all_fsdp_states:
+        _catch_all_reshard(fsdp_state)
+        _finalize_params(fsdp_state)
+        fsdp_state.training_state = TrainingState.IDLE
+        handle = fsdp_state._handle
+        if handle:
+            handle._ran_pre_backward_hook = False
+            handle._needs_pre_backward_unshard = False
+            handle._post_forward_index = None
+            handle._training_state = HandleTrainingState.IDLE
+            handle._prefetched = False
+    # Reset for cases like one forward and multiple backwards
+    root_state._post_backward_callback_queued = False
+
+
+@no_type_check
+def _catch_all_reshard(
+    state: _FSDPState,
+) -> None:
+    """
+    Reshards the parameters that may not have been resharded in the
+    post-backward hook. This can happen when a module's output is used in the
+    forward pass, meaning that its pre-backward hook runs (unsharding the
+    parameter), but the post-backward hook does not run because the output was
+    not jused in the loss computation corresponding to this backward pass.
+    """
+    # Wrap with a try-except to provide a more informative traceback if an
+    # error is raised
+    try:
+        if state._handle:
+            # TODO: This already-resharded check is brittle:
+            # https://github.com/pytorch/pytorch/issues/83956
+            already_resharded = (
+                state._handle.flat_param.data_ptr()
+                == state._handle.flat_param._local_shard.data_ptr()
+                # If FSDP skipped using sharded views, then the flat parameter
+                # still points to the sharded data, so we need to reshard to
+                # use sharded views
+                and not state._handle._skipped_use_sharded_views
+            )
+            if already_resharded:
+                return
+            free_unsharded_flat_param = _should_free_in_backward(state, state._handle)
+            _reshard(state, state._handle, free_unsharded_flat_param)
+    except Exception as e:
+        _p_assert(
+            False,
+            f"Got exception in the catch-all reshard for {state}: {str(e)}",
+            raise_assertion_error=False,
+        )
+        raise e
+
+
+@no_type_check
+def _finalize_params(
+    state: _FSDPState,
+) -> None:
+    """Finalizes the parameters before the next iteration."""
+    handle = state._handle
+    if not handle:
+        return
+    flat_param = handle.flat_param
+    if torch.distributed._functional_collectives.is_torchdynamo_compiling():
+        if hasattr(flat_param, "_post_backward_hook_handle"):
+            pbhs_handle = flat_param._post_backward_hook_handle
+            pbhs_handle.remove()
+            del flat_param._post_backward_hook_handle
+    else:
+        if hasattr(flat_param, "_post_backward_hook_state"):
+            post_backward_hook_state_len = len(flat_param._post_backward_hook_state)
+            expected_post_backward_hook_state_len = int(flat_param.requires_grad) + 1
+            _p_assert(
+                post_backward_hook_state_len == expected_post_backward_hook_state_len,
+                f"Invalid: ``_post_backward_hook_state``: {flat_param._post_backward_hook_state}",
+            )
+            flat_param._post_backward_hook_state[-1].remove()
+            delattr(flat_param, "_post_backward_hook_state")
+    if flat_param.requires_grad:
+        if not state._sync_gradients:
+            # Preserve the gradient accumulation state if not synchronizing
+            # gradients: `.grad` remains the unsharded gradient  from prior
+            # `no_sync()` iterations, and `_saved_grad_shard` remains the
+            # sharded gradient from the last synchronized iteration
+            return
+        if not handle._has_optim_in_backward:
+            handle.prepare_gradient_for_optim()
+        _p_assert(
+            hasattr(flat_param, "_post_backward_called"),
+            "Expects `_post_backward_called` to be set on the `FlatParameter`",
+        )
+        flat_param._post_backward_called = False
+
+
+@no_type_check
+def _prefetch_handle(
+    state: _FSDPState,
+    current_handle: Optional[FlatParamHandle],
+    prefetch_mode: _PrefetchMode,
+) -> None:
+    """
+    Prefetches the next handles if needed (without synchronization). An empty
+    handles key cannot prefetch.
+    """
+    if not current_handle:
+        return
+    handle = _get_handle_to_prefetch(state, current_handle)
+    if not handle:
+        return
+    # Temporarily emulate the training state while calling `_unshard` to
+    # ensure the correct `as_params` for `_use_unsharded_views()`
+    prev_training_state = handle._training_state
+    if prefetch_mode == _PrefetchMode.BACKWARD:
+        handle._training_state = HandleTrainingState.BACKWARD_PRE
+    elif prefetch_mode == _PrefetchMode.FORWARD:
+        handle._training_state = HandleTrainingState.FORWARD
+    else:
+        raise ValueError(f"Invalid prefetch mode on rank {state.rank}: {prefetch_mode}")
+    # Prefetch the next set of handles without synchronizing to allow
+    # the sync to happen as late as possible to maximize overlap
+    _unshard(state, handle, state._unshard_stream, state._pre_unshard_stream)
+    handle._training_state = prev_training_state
+    handle._prefetched = True
+
+
+@no_type_check
+def _get_handle_to_prefetch(
+    state: _FSDPState,
+    current_handle: FlatParamHandle,
+) -> FlatParamHandle:
+    """
+    Returns a :class:`list` of the handles keys to prefetch for the next
+    module(s), where ``current_handle`` represents the current module.
+
+    "Prefetching" refers to running the unshard logic early (without
+    synchronization), and the "next" modules depend on the recorded execution
+    order and the current training state.
+    """
+    training_state = _get_training_state(current_handle)
+    valid_training_states = (
+        HandleTrainingState.BACKWARD_PRE,
+        HandleTrainingState.BACKWARD_POST,
+        HandleTrainingState.FORWARD,
+    )
+    _p_assert(
+        training_state in valid_training_states,
+        f"Prefetching is only supported in {valid_training_states} but "
+        f"currently in {training_state}",
+    )
+    eod = state._exec_order_data
+    target_handle: Optional[FlatParamHandle] = None
+    if (
+        training_state == HandleTrainingState.BACKWARD_PRE
+        and state.backward_prefetch == BackwardPrefetch.BACKWARD_PRE
+    ) or (
+        training_state == HandleTrainingState.BACKWARD_POST
+        and state.backward_prefetch == BackwardPrefetch.BACKWARD_POST
+    ):
+        target_handle_candidate = eod.get_handle_to_backward_prefetch(current_handle)
+        if (
+            target_handle_candidate
+            and target_handle_candidate._needs_pre_backward_unshard
+            and not target_handle_candidate._prefetched
+        ):
+            target_handle = target_handle_candidate
+        else:
+            target_handle = None
+    elif training_state == HandleTrainingState.FORWARD and state.forward_prefetch:
+        target_handle_candidate = eod.get_handle_to_forward_prefetch(current_handle)
+        if (
+            target_handle_candidate
+            and target_handle_candidate._needs_pre_forward_unshard
+            and not target_handle_candidate._prefetched
+        ):
+            target_handle = target_handle_candidate
+        else:
+            target_handle = None
+
+    return target_handle
+
+
+def _get_training_state(
+    handle: FlatParamHandle,
+) -> HandleTrainingState:
+    """Returns the training state of the handles in ``handle``."""
+    _p_assert(handle, "Expects a non-empty handle")
+    return handle._training_state
+
+
+@no_type_check
+def _register_pre_forward_hook(
+    state: _FSDPState,
+    module: nn.Module,
+) -> None:
+    """
+    Registers a pre-forward hook on ``module``.
+    """
+    for forward_handle in state._pre_forward_handles:
+        forward_handle.remove()
+    state._pre_forward_handles.clear()
+    module_param_handle = state._fully_sharded_module_to_handle.get(module, None)
+    hook = functools.partial(
+        _pre_forward, state, module_param_handle, _pre_forward_unshard
+    )
+    state._pre_forward_handles.append(
+        module.register_forward_pre_hook(hook, prepend=True, with_kwargs=True)
+    )
+
+
+@no_type_check
+def _register_post_forward_hook(
+    state: _FSDPState,
+    module: nn.Module,
+) -> None:
+    """
+    Registers a post-forward hook on ``module``. Even if the module has no
+    handles, we should register the hook since it will register the module's
+    pre-backward hook.
+    """
+    for forward_handle in state._post_forward_handles:
+        forward_handle.remove()
+    state._post_forward_handles.clear()
+    module_param_handle = state._fully_sharded_module_to_handle.get(module, None)
+    hook = functools.partial(
+        _post_forward,
+        state,
+        module_param_handle,
+        _post_forward_reshard,
+    )
+    state._post_forward_handles.append(module.register_forward_hook(hook))
+
+
+@no_type_check
+def _register_root_pre_forward_hook(
+    state: _FSDPState,
+    module: nn.Module,
+):
+    """
+    Registers root pre-forward hook on ``module``, which should be the local
+    FSDP root.
+
+    NOTE: For the current composable FSDP design, we have each application of
+    ``fully_shard()`` to a module to indicate that that module is the local
+    FSDP root. We may remove this assumption in the future, in which case we
+    will need to register this root pre-forward hook on any candidate module
+    that may be the local FSDP root.
+    """
+    for forward_handle in state._root_pre_forward_handles:
+        forward_handle.remove()
+    state._root_pre_forward_handles.clear()
+    hook = functools.partial(_root_pre_forward, state)
+    state._root_pre_forward_handles.append(
+        module.register_forward_pre_hook(hook, prepend=True, with_kwargs=True)
+    )
+
+
+@no_type_check
+def _register_pre_backward_hooks(
+    state: _FSDPState,
+    module: nn.Module,
+    outputs: Any,
+    handle: FlatParamHandle,
+) -> None:
+    """
+    Registers pre-backward hooks on the tensors that require gradients in the
+    forward pass outputs ``outputs``, which were computed using the
+    ``FlatParameter`` s of ``handles``.
+
+    Args:
+        module (nn.Module): Fully sharded module (see [Note: Fully Sharded
+            Module]).
+
+    Returns:
+        Forward pass outputs with pre-backward hooks registered to tensors that
+        require gradients.
+    """
+    # If there is no gradient computation, then there is no need for
+    # pre-backward logic
+    if not torch.is_grad_enabled():
+        return outputs
+    if state._is_root:
+        state._post_backward_callback_queued = False  # only defined on the root
+
+    if handle:
+        handle._needs_pre_backward_unshard = False
+        # Since these handles' `FlatParameter`s participated in a forward, we
+        # conservatively assume that they will be used in the backward
+        handle._ran_pre_backward_hook = False
+
+    def _register_hook(t: torch.Tensor) -> torch.Tensor:
+        if t.requires_grad:
+            t.register_hook(
+                torch.utils.hooks.unserializable_hook(
+                    functools.partial(_pre_backward_hook, state, module, handle)
+                )
+            )
+            if handle:
+                handle._needs_pre_backward_unshard = True
+        return t
+
+    return _apply_to_tensors(_register_hook, outputs)
+
+
+def _register_post_backward_hook(
+    state: _FSDPState,
+    handle: Optional[FlatParamHandle],
+) -> None:
+    """
+    Registers post-backward hooks on the ``FlatParameter`` s'
+    ``AccumulateGrad`` objects to reshard and to reduce-scatter gradients.
+
+    The ``AccumulateGrad`` object represents the last function that finalizes
+    the ``FlatParameter`` 's gradient, so it only runs after its entire
+    gradient computation has finished.
+
+    We register the post-backward hook only once in the *first* forward that a
+    ``FlatParameter`` participates in. This relies on the ``AccumulateGrad``
+    object being preserved through multiple forwards.
+
+    NOTE: We follow this heuristic to prefer the *first* forward to target the
+    parameter mixed precision case, where there are *separate*
+    ``AccumulateGrad`` objects across the different forwards. (Without
+    parameter mixed precision, the ``AccumulateGrad`` objects are the same.) If
+    we instead prefer the *last* forward, then the hook runs early.
+    """
+    # If there is no gradient computation, then there is no need for
+    # post-backward logic
+    if not torch.is_grad_enabled():
+        return
+    if not handle:
+        return
+    flat_param = handle.flat_param
+
+    if torch.distributed._functional_collectives.is_torchdynamo_compiling():
+        already_registered = hasattr(flat_param, "_post_backward_hook_handle")
+        if already_registered or not flat_param.requires_grad:
+            return
+        hook = functools.partial(_post_backward_hook, state, handle)
+        hook_handle = flat_param.register_post_accumulate_grad_hook(hook)
+        flat_param._post_backward_hook_handle = hook_handle  # type: ignore[attr-defined]
+    else:
+        already_registered = hasattr(flat_param, "_post_backward_hook_state")
+        if already_registered or not flat_param.requires_grad:
+            return
+        # Get the `AccumulateGrad` object
+        temp_flat_param = flat_param.expand_as(flat_param)
+        _p_assert(
+            temp_flat_param.grad_fn is not None,
+            "The `grad_fn` is needed to access the `AccumulateGrad` and "
+            "register the post-backward hook",
+        )
+        acc_grad = temp_flat_param.grad_fn.next_functions[0][0]  # type: ignore[union-attr]
+        assert acc_grad is not None
+        hook_handle = acc_grad.register_hook(
+            functools.partial(_post_backward_hook, state, handle)
+        )
+        flat_param._post_backward_hook_state = (acc_grad, hook_handle)  # type: ignore[attr-defined]
+
+
+def _register_post_backward_reshard_only_hook(
+    state: _FSDPState,
+    handle: Optional[FlatParamHandle],
+    args: Tuple[Any, ...],
+    kwargs: Dict[str, Any],
+) -> None:
+    """
+    Registers post-backward hooks to reshard flat parameters that do not
+    require gradient. We register these using multi-post-grad hooks on the
+    input activations to ensure that all gradients that may depend on the
+    parameters have been computed before resharding.
+    """
+    # If there is no gradient computation, then there is no need for
+    # post-backward logic
+    if not torch.is_grad_enabled():
+        return
+    # Construct `inp_tensors` lazily to avoid CPU overhead in typical case
+    # where each flat parameter requires gradient
+    inp_tensors: Optional[List[torch.Tensor]] = None
+    if not handle:
+        return
+    flat_param = handle.flat_param
+
+    if torch.distributed._functional_collectives.is_torchdynamo_compiling():
+        already_registered = hasattr(flat_param, "_post_backward_hook_handle")
+    else:
+        already_registered = hasattr(flat_param, "_post_backward_hook_state")
+
+    if already_registered or flat_param.requires_grad:
+        return
+    if inp_tensors is None:
+        args_flat = pytree.arg_tree_leaves(*args, **kwargs)
+        inp_tensors = [
+            obj for obj in args_flat if torch.is_tensor(obj) and obj.requires_grad
+        ]
+    assert inp_tensors is not None  # mypy
+    hook_handle = register_multi_grad_hook(
+        inp_tensors, functools.partial(_post_backward_reshard_only_hook, state, handle)
+    )
+    if torch.distributed._functional_collectives.is_torchdynamo_compiling():
+        flat_param._post_backward_hook_handle = hook_handle  # type: ignore[attr-defined, assignment]
+    else:
+        flat_param._post_backward_hook_state = (hook_handle,)  # type: ignore[attr-defined, assignment]
+
+
+@no_type_check
+def _register_post_backward_final_callback(
+    state: _FSDPState, module: nn.Module
+) -> None:
+    """
+    Registers the post-backward final callback that runs at the end of the
+    backward pass. This should be called from the root FSDP instance at the
+    beginning of the pre-backward.
+    """
+    _p_assert(
+        state._is_root,
+        "Only the root FSDP instance should register the post-backward callback",
+    )
+    if state._post_backward_callback_queued:
+        return
+    _assert_in_training_states(state, [TrainingState.IDLE])
+    # Trace does not need this callback
+    if not torch.distributed._functional_collectives.is_torchdynamo_compiling():
+        state._post_backward_callback_queued = True
+        Variable._execution_engine.queue_callback(
+            functools.partial(_post_backward_final_callback, state, module)
+        )
+
+
+def _wait_for_computation_stream(
+    computation_stream: torch.Stream,
+    unshard_stream: torch.Stream,
+    pre_unshard_stream: torch.Stream,
+):
+    """
+    Has the unshard and pre-unshard streams wait for the computation stream.
+    For example, this should be called in the FSDP root's pre-forward to
+    respect optimizer step computation.
+    """
+    # Tracing does not need to wait
+    if torch.distributed._functional_collectives.is_torchdynamo_compiling():
+        return
+    unshard_stream.wait_stream(computation_stream)  # type: ignore[attr-defined]
+    # Having the pre-all-gather stream wait for the current stream even if we
+    # do not leverage the pre-all-gather stream is tolerable since this only
+    # runs once per iteration
+    pre_unshard_stream.wait_stream(computation_stream)  # type: ignore[attr-defined]
+
+
+def _reset_flat_param_grad_info_if_needed(
+    handles: List[FlatParamHandle],
+):
+    """
+    Clears the original parameters' gradients if needed. This method's CPU
+    overhead is minimal, so we may call it throughout FSDP methods, which serve
+    as callsites to free the gradient memory earlier.
+    """
+    if not isinstance(handles, list):
+        handles = [handles]
+    for handle in handles:
+        if handle._use_orig_params:
+            handle._reset_flat_param_grad_info_if_needed()
+
+
+@no_type_check
+def _get_buffers_and_dtypes_for_computation(
+    state: _FSDPState,
+    root_module: nn.Module,
+) -> Tuple[List[torch.Tensor], List[Optional[torch.dtype]]]:
+    """
+    Returns all buffers in the module tree rooted at ``root_module`` and a
+    corresponding list of the buffer dtypes for computation. Each buffer dtype
+    is either ``None`` if buffer mixed precision is not enabled or the buffer
+    low precision dtype otherwise.
+    """
+    _p_assert(state._is_root, "Expects the root to cast buffers")
+    buffers: List[torch.Tensor] = []
+    buffer_dtypes: List[Optional[torch.dtype]] = []
+    visited_buffers: Set[torch.Tensor] = set()
+    # Traverse the FSDP states bottom-up so that we prefer the owning FSDP
+    # instance's mixed precision setting for each buffer
+    fsdp_states, fsdp_modules = traversal_utils._get_fsdp_states_with_modules(
+        root_module
+    )
+    for fsdp_state, fsdp_module in zip(reversed(fsdp_states), reversed(fsdp_modules)):
+        for buffer_name, buffer in fsdp_module.named_buffers():
+            if buffer in visited_buffers:
+                continue
+            visited_buffers.add(buffer)
+            if clean_tensor_name(buffer_name) in fsdp_state._ignored_buffer_names:
+                continue
+            buffers.append(buffer)
+            buffer_dtypes.append(fsdp_state.mixed_precision.buffer_dtype)
+    assert len(buffers) == len(buffer_dtypes), f"{len(buffers)} {len(buffer_dtypes)}"
+    return buffers, buffer_dtypes
+
+
+@no_type_check
+def _get_orig_buffer_dtypes(
+    state: _FSDPState,
+    buffer_names: List[str],
+) -> List[torch.dtype]:
+    """
+    Returns the original buffer types of the given buffer names.
+    """
+    buffer_dtypes: List[torch.dtype] = []
+    for buffer_name in buffer_names:
+        _p_assert(
+            buffer_name in state._buffer_name_to_orig_dtype,
+            f"{buffer_name} is missing from pre-computed dict on rank "
+            f"{state.rank}, which only has keys "
+            f"{state._buffer_name_to_orig_dtype.keys()}",
+        )
+        buffer_dtypes.append(state._buffer_name_to_orig_dtype[buffer_name])
+    return buffer_dtypes
+
+
+def _cast_buffers_to_dtype_and_device(
+    buffers: List[torch.Tensor],
+    buffer_dtypes: List[Optional[torch.dtype]],
+    device: torch.device,
+) -> None:
+    """
+    Casts ``buffers`` to the dtypes given by ``buffer_dtypes`` and moves them
+    to ``device``. If an element in ``buffer_dtypes`` is ``None``, then the
+    corresponding buffer is only moved to ``device``.
+    """
+    _p_assert(
+        buffer_dtypes is None or len(buffers) == len(buffer_dtypes),
+        f"Expects `buffers` and `buffer_dtypes` to have the same length if "
+        f"`buffer_dtypes` is specified but got {len(buffers)} and "
+        f"{len(buffer_dtypes)}",
+    )
+    for buffer, buffer_dtype in zip(buffers, buffer_dtypes):
+        if not torch.is_floating_point(buffer) or buffer_dtype is None:
+            buffer.data = buffer.to(device=device)
+        else:
+            buffer.data = buffer.to(device=device, dtype=buffer_dtype)

infer_4_47_1/lib/python3.10/site-packages/torch/distributed/fsdp/_shard_utils.py

@@ -0,0 +1,137 @@
+# mypy: allow-untyped-defs
+import copy
+import itertools
+import math
+from typing import Optional
+
+import torch
+import torch.distributed as dist
+from torch._utils import _get_device_module
+from torch.distributed import distributed_c10d
+from torch.distributed._shard.sharded_tensor import (
+    Shard,
+    ShardedTensor,
+    ShardedTensorMetadata,
+    TensorProperties,
+)
+from torch.distributed._shard.sharding_spec import ShardMetadata
+from torch.distributed.tensor import DeviceMesh, DTensor, Replicate, Shard as DShard
+
+
+def _get_remote_device_str(rank, device_type, num_devices_per_node):
+    if device_type.lower() == "cpu":
+        return f"rank:{rank}/{device_type}"
+    elif device_type.lower() == "hpu":
+        return f"rank:{rank}/{device_type}:{_get_device_module(device_type).current_device()}"
+    else:
+        return f"rank:{rank}/{device_type}:{rank % num_devices_per_node}"
+
+
+def _create_chunk_sharded_tensor(
+    tensor: torch.Tensor,
+    rank: int,
+    world_size: int,
+    num_devices_per_node: int,
+    pg: dist.ProcessGroup,
+    device: Optional[torch.device] = None,
+) -> ShardedTensor:
+    """
+    Shard a tensor to chunks along the first dimension. The local rank will gets its
+    corresponding chunk as the local shard to create a ShardedTensor.
+    """
+    chunks = tensor.chunk(world_size, dim=0)
+    if len(chunks) > rank:
+        local_shard = chunks[rank].clone()
+        offsets = [0 for _ in tensor.size()]
+        offsets[0] = math.ceil(tensor.size()[0] / world_size) * rank
+        local_shards = [Shard.from_tensor_and_offsets(local_shard, offsets, rank)]
+    else:
+        local_shards = []
+
+    # Create a ShardedTensor without invoking communication.
+    chunk_sizes = [list(chunk.size()) for chunk in chunks]
+    dim0_offsets = [0] + list(
+        itertools.accumulate([chunk_size[0] for chunk_size in chunk_sizes])
+    )[:-1]
+    offsets = [0] * (len(chunk_sizes[0]) - 1)
+    chunk_offsets = [[d0] + offsets for d0 in dim0_offsets]
+    device_type = (
+        distributed_c10d._get_pg_default_device(pg).type
+        if device is None
+        else device.type
+    )
+    placements = [
+        _get_remote_device_str(
+            dist.get_global_rank(pg, r),
+            device_type,
+            num_devices_per_node,
+        )
+        for r in range(len(chunk_sizes))
+    ]
+    assert len(chunk_sizes) == len(chunk_offsets) == len(placements)
+    shard_metadata = [
+        ShardMetadata(offset, size, placement)
+        for offset, size, placement in zip(chunk_offsets, chunk_sizes, placements)
+    ]
+    sharded_tensor_metadata = ShardedTensorMetadata(
+        shards_metadata=shard_metadata,
+        size=tensor.size(),
+        tensor_properties=TensorProperties(
+            dtype=tensor.dtype,
+            layout=tensor.layout,
+            requires_grad=False,
+            memory_format=torch.contiguous_format,
+            pin_memory=tensor.is_pinned(),
+        ),
+    )
+    return ShardedTensor._init_from_local_shards_and_global_metadata(
+        local_shards, sharded_tensor_metadata=sharded_tensor_metadata, process_group=pg
+    )
+
+
+def _create_chunk_dtensor(
+    tensor: torch.Tensor,
+    rank: int,
+    device_mesh: DeviceMesh,
+) -> DTensor:
+    """
+    Shard a tensor to chunks along the first dimension. The local rank will gets its
+    corresponding chunk as the local tensor to create a DTensor.
+    """
+    # We need to explicitly call .detach() to return a new tensor detached from the current graph.
+    tensor = tensor.clone().detach()
+
+    # FSDP placements: [Shard(0)]
+    # HSDP placements: [Replicate(), Shard(0)]
+    replicate_placements = [Replicate() for _ in range(device_mesh.ndim)]
+    shard_placements = [Replicate() for _ in range(device_mesh.ndim)]
+    shard_placements[-1] = DShard(0)  # type: ignore[call-overload]
+
+    return DTensor.from_local(
+        tensor, device_mesh, replicate_placements, run_check=False
+    ).redistribute(
+        placements=shard_placements,
+    )
+
+
+def _all_gather_dtensor(
+    tensor: DTensor,
+    root_mesh: Optional[DeviceMesh],
+) -> torch.Tensor:
+    """
+    All gather a DTensor in its sharded dimension and return the local tensor.
+    """
+    assert (
+        root_mesh == tensor.device_mesh
+    ), "The device mesh of a tensor should be a root mesh."
+
+    placements = list(copy.deepcopy(tensor.placements))
+    # FSDP placements: [Shard(0)] -> [Replicate()]
+    # HSDP placements: [Replicate(), Shard(0)] -> [Replicate(), Replicate()]
+    placements[-1] = Replicate()
+    tensor = tensor.redistribute(
+        device_mesh=tensor.device_mesh,
+        placements=placements,
+    )
+
+    return tensor.to_local()

infer_4_47_1/lib/python3.10/site-packages/torch/distributed/fsdp/_state_dict_utils.py

@@ -0,0 +1,924 @@
+# mypy: allow-untyped-defs
+import contextlib
+import logging
+import math
+import warnings
+from typing import (
+    Any,
+    Callable,
+    cast,
+    Dict,
+    Generator,
+    Iterator,
+    List,
+    no_type_check,
+    Tuple,
+)
+
+import torch
+import torch.distributed as dist
+import torch.distributed.algorithms._checkpoint.checkpoint_wrapper as checkpoint_wrapper
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.distributed._shard.sharded_tensor import (
+    init_from_local_shards,
+    Shard,
+    ShardedTensor,
+)
+from torch.distributed.device_mesh import _mesh_resources
+from torch.distributed.fsdp._common_utils import (
+    _FSDPState,
+    _get_module_fsdp_state_if_fully_sharded_module,
+    _has_fsdp_params,
+    _is_composable,
+    _module_handle,
+    clean_tensor_name,
+    FSDP_PREFIX,
+    FSDP_WRAPPED_MODULE,
+)
+from torch.distributed.fsdp._debug_utils import SimpleProfiler
+from torch.distributed.fsdp._runtime_utils import (
+    _cast_buffers_to_dtype_and_device,
+    _get_orig_buffer_dtypes,
+    _lazy_init,
+    _reset_flat_param_grad_info_if_needed,
+)
+from torch.distributed.fsdp.api import (
+    FullStateDictConfig,
+    ShardingStrategy,
+    StateDictType,
+)
+from torch.distributed.tensor import DTensor
+from torch.distributed.utils import _replace_by_prefix
+
+from ._fsdp_extensions import (
+    _ext_all_gather_dtensor,
+    _ext_chunk_dtensor,
+    _ext_chunk_tensor,
+    _ext_post_unflatten_transform,
+    _ext_pre_load_state_dict_transform,
+)
+from ._unshard_param_utils import _unshard_fsdp_state_params, FLAT_PARAM
+
+
+logger = logging.getLogger(__name__)
+
+
+def _should_unshard_params(fsdp_state: _FSDPState) -> bool:
+    return not (
+        fsdp_state.sharding_strategy == ShardingStrategy.NO_SHARD
+        and (_is_composable(fsdp_state) or fsdp_state._use_orig_params)
+    )
+
+
+def _convert_to_wrapped_module_name(module_name: str) -> str:
+    module_name = module_name.replace(f"{FSDP_PREFIX}", "")
+    module_name = module_name.replace(f"{FSDP_WRAPPED_MODULE}", "")
+    if module_name:
+        module_name = f"{module_name}."
+    # `CheckpointWrapper` adds a prefix that has to be removed as well.
+    module_name = module_name.replace(checkpoint_wrapper._CHECKPOINT_PREFIX, "")
+    return module_name
+
+
+def _param_name_infos(
+    module: nn.Module, fsdp_state: _FSDPState
+) -> Iterator[Tuple[str, str, str]]:
+    if not _has_fsdp_params(fsdp_state, module):
+        return
+    for param_name, module_name in _module_handle(
+        fsdp_state, module
+    ).param_module_names():
+        module_name = _convert_to_wrapped_module_name(module_name)
+        fqn = f"{module_name}{param_name}"
+        yield fqn, param_name, module_name
+
+
+def _shared_param_name_infos(
+    module: nn.Module, fsdp_state
+) -> Iterator[Tuple[str, str, str]]:
+    for param_name, module_name in _module_handle(
+        fsdp_state, module
+    ).shared_param_module_names():
+        module_name = _convert_to_wrapped_module_name(module_name)
+        fqn = f"{module_name}{param_name}"
+        yield fqn, param_name, module_name
+
+
+@no_type_check
+def _enter_unshard_params_ctx(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    writeback: bool = False,
+    rank0_only: bool = False,
+    offload_to_cpu: bool = False,
+    with_grads: bool = False,
+) -> None:
+    """
+    state_dict hooks cannot use the pure context call as the checkpoint flow
+    requires to enter the context in the pre-hook but leave the context in the
+    post-hook. This API enters the context of ``_unshard_fsdp_state_params``.
+    """
+    assert module not in fsdp_state._unshard_params_ctx, (
+        "Entering the ``_unshard_fsdp_state_params`` context but _unshard_params_ctx[module] "
+        "is not None."
+    )
+    fsdp_state._unshard_params_ctx[module] = _unshard_fsdp_state_params(
+        module,
+        fsdp_state,
+        writeback=writeback,
+        rank0_only=rank0_only,
+        offload_to_cpu=offload_to_cpu,
+        with_grads=with_grads,
+    )
+    fsdp_state._unshard_params_ctx[module].__enter__()
+
+
+@no_type_check
+def _exit_unshard_params_ctx(module: nn.Module, fsdp_state: _FSDPState) -> None:
+    """A helper function to exit ``_unshard_fsdp_state_params`` context."""
+    fsdp_state._unshard_params_ctx[module].__exit__(None, None, None)
+    fsdp_state._unshard_params_ctx.pop(module)
+
+
+def _common_pre_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+) -> None:
+    """Performs the pre-state_dict tasks shared by all state_dict types."""
+    if fsdp_state._device_handle.is_available():
+        fsdp_state._device_handle.synchronize()
+    # TODO: need to check if this is always correct for composable FSDP.
+    _lazy_init(fsdp_state, module)
+    if fsdp_state._is_root:
+        _reset_flat_param_grad_info_if_needed(fsdp_state._all_handles)
+
+
+def _common_unshard_pre_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    offload_to_cpu: bool,
+    rank0_only: bool,
+) -> None:
+    """
+    Performs the pre-state_dict tasks shared by all state_dict types that require
+    ``_unshard_fsdp_state_params()``. FULL_STATE_DICT and SHARDED_STATE_DICT use this hook.
+    """
+    # For composable `fully_shard`, it does not need to unshard parameters for `NO_SHARD` cases.
+    if not _should_unshard_params(fsdp_state):
+        return
+    _enter_unshard_params_ctx(
+        module,
+        fsdp_state,
+        writeback=False,
+        offload_to_cpu=offload_to_cpu,
+        rank0_only=rank0_only,
+    )
+
+
+@no_type_check
+def _common_unshard_post_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    state_dict: Dict[str, Any],
+    prefix: str,
+    param_hook: Callable,
+) -> Dict[str, Any]:
+    """
+    The post-state_dict flow that shared by all state_dict types that require
+    ``_unshard_fsdp_state_params()``. FULL_STATE_DICT and SHARDED_STATE_DICT use this
+    hook.
+    """
+    _replace_by_prefix(state_dict, prefix + f"{FSDP_PREFIX}", prefix)
+    # Return early for trivial cases
+    if not state_dict or not _has_fsdp_params(fsdp_state, module):
+        if _should_unshard_params(fsdp_state):
+            _exit_unshard_params_ctx(module, fsdp_state)
+        return state_dict
+
+    # If a rank does not have unsharded parameters(when `rank0_only=True`
+    # and `rank != 0`), then the rank only needed to participate in the
+    # all-gather and does not need to save the # state dict. We simply check
+    # rank0_only to ensure this issue.
+    rank0_only = (
+        fsdp_state._state_dict_type == StateDictType.FULL_STATE_DICT
+        and cast(FullStateDictConfig, fsdp_state._state_dict_config).rank0_only
+    )
+    # no_fsdp_return means the state_dict returned by this rank should contain
+    # only non-FSDP controlled parameters and buffers.
+    no_fsdp_return = rank0_only and fsdp_state.rank != 0
+    if no_fsdp_return and not fsdp_state._use_orig_params:
+        for clean_key in fsdp_state._buffer_names:
+            # This is a hack to support activation checkpoint.
+            clean_key = clean_key.replace(
+                f"{checkpoint_wrapper._CHECKPOINT_PREFIX}.", ""
+            )
+            state_dict.pop(f"{prefix}{clean_key}", None)
+        # Non-zero ranks have flat_param key when rank0_only=True, because rank0_only=True is
+        # passed in to unshard context, but nonzero ranks reshard early, causing this flat_param
+        # to appear in state_dict.
+        state_dict.pop(f"{prefix}{FLAT_PARAM}")
+        _exit_unshard_params_ctx(module, fsdp_state)
+        return state_dict
+
+    # Loop only the parameters saved in this instance's wrapped module to
+    # avoid processing buffers.
+    for fqn, param_name, module_name in _param_name_infos(module, fsdp_state):
+        fqn = f"{prefix}{fqn}"
+        if no_fsdp_return:
+            state_dict.pop(fqn)
+            continue
+        assert fqn in state_dict, (
+            f"FSDP assumes {fqn} is in the state_dict but the state_dict only "
+            f"has {state_dict.keys()}. "
+            f"prefix={prefix}, module_name={module_name}, "
+            f"param_name={param_name} rank={fsdp_state.rank}."
+        )
+
+        param_hook(state_dict, prefix, fqn)
+
+    if _should_unshard_params(fsdp_state):
+        _exit_unshard_params_ctx(module, fsdp_state)
+
+    cpu_device = torch.device("cpu")
+    buffer_clean_fqns = []
+    buffers = []
+    for clean_key in fsdp_state._buffer_names:
+        # This is a hack to support activation checkpoint.
+        clean_key = clean_tensor_name(clean_key)
+        fqn = f"{prefix}{clean_key}"
+        if fqn not in state_dict:
+            # A buffer can be registered as non-persistent.
+            continue
+        if no_fsdp_return:
+            state_dict.pop(fqn)
+        else:
+            buffer = state_dict[fqn]
+            if (
+                fsdp_state._state_dict_config.offload_to_cpu
+                and buffer.device != cpu_device
+            ):
+                state_dict[fqn] = buffer.to(cpu_device)
+            # skip upcasting for ignored buffers
+            if clean_key not in fsdp_state._ignored_buffer_names:
+                buffer_clean_fqns.append(clean_key)
+                buffers.append(state_dict[fqn])
+
+    if buffers:
+        mixed_precision_enabled_for_buffers = (
+            fsdp_state._mixed_precision_enabled_for_buffers()
+            if not _is_composable(fsdp_state)
+            else (fsdp_state.mixed_precision.buffer_dtype is not None)
+        )
+        if mixed_precision_enabled_for_buffers:
+            buffer_dtypes = _get_orig_buffer_dtypes(fsdp_state, buffer_clean_fqns)
+            _cast_buffers_to_dtype_and_device(
+                buffers, buffer_dtypes, fsdp_state.compute_device
+            )
+            for buffer, clean_fqn in zip(buffers, buffer_clean_fqns):
+                fqn = f"{prefix}{clean_fqn}"
+                logger.info("FSDP is casting the dtype of %s to %s", fqn, buffer.dtype)
+                state_dict[fqn] = buffer.clone()
+    return state_dict
+
+
+@no_type_check
+def _full_pre_state_dict_hook(
+    fsdp_state: _FSDPState,
+    module: nn.Module,
+    *args,
+    **kwargs,
+) -> None:
+    """
+    Hook that runs before model.state_dict() is called. pre-state_dict hook is
+    not actually supported by ``nn.Module``. As a result, this API is called
+    from ``_full_post_state_dict_hook()`` to simulate the case. Once pre-state_dict
+    is supported in ``nn.Module``, this hook will be registered as a hook in
+    ``nn.Module``.
+    """
+    if getattr(fsdp_state, "_device_mesh", False):
+        root_mesh = _mesh_resources.get_root_mesh(fsdp_state._device_mesh)
+
+    _common_pre_state_dict_hook(module, fsdp_state)
+    _common_unshard_pre_state_dict_hook(
+        module,
+        fsdp_state,
+        offload_to_cpu=fsdp_state._state_dict_config.offload_to_cpu,
+        rank0_only=cast(FullStateDictConfig, fsdp_state._state_dict_config).rank0_only,
+    )
+
+
+@no_type_check
+def _full_post_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    state_dict: Dict[str, Any],
+    prefix: str,
+) -> Dict[str, Any]:
+    """
+    Hook that runs after model.state_dict() is called before returning result to
+    user. For FSDP, we may have to clone the tensors in state_dict as params go
+    back to sharded version after _unshard_fsdp_state_params ends, and also remove
+    the ``FSDP_WRAPPED_MODULE`` prefix.
+    """
+
+    def param_hook(
+        state_dict: Dict[str, Any],
+        prefix: str,
+        fqn: str,
+    ) -> None:
+        clean_key = fqn
+        clean_prefix = clean_tensor_name(prefix)
+        # Strip prefix out of key if needed as buffer names and param names
+        # do not have prefix considered as they are not computed in `state_dict`
+        # call.
+        if clean_key.startswith(clean_prefix):
+            clean_key = clean_key[len(clean_prefix) :]
+
+        # Clone parameters before exiting the `_unshard_fsdp_state_params()` context.
+        if not getattr(state_dict[fqn], "_has_been_cloned", False):
+            try:
+                state_dict[fqn] = state_dict[fqn].clone().detach()
+                state_dict[fqn]._has_been_cloned = True  # type: ignore[attr-defined]
+            except BaseException as e:
+                warnings.warn(
+                    f"Failed to clone() tensor with name {fqn} on rank {fsdp_state.rank}. "
+                    "This may mean that this state_dict entry could point to invalid "
+                    "memory regions after returning from state_dict() call if this "
+                    "parameter is managed by FSDP. Please check clone "
+                    f"implementation of {fqn}. Error: {str(e)}"
+                )
+
+    return _common_unshard_post_state_dict_hook(
+        module, fsdp_state, state_dict, prefix, param_hook
+    )
+
+
+def _full_pre_load_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    state_dict: Dict[str, Any],
+    prefix: str,
+) -> None:
+    _lazy_init(fsdp_state, module)
+    if _should_unshard_params(fsdp_state):
+        with SimpleProfiler.profile("_enter_unshard_params_ctx"):
+            _enter_unshard_params_ctx(module, fsdp_state, writeback=True)
+    # Add FSDP_PREFIX only for wrapper-based FSDP.
+    if not _is_composable(fsdp_state):
+        _replace_by_prefix(state_dict, prefix, prefix + f"{FSDP_PREFIX}")
+
+
+def _full_post_load_state_dict_hook(
+    module: nn.Module, fsdp_state: _FSDPState, *args, **kwargs
+) -> None:
+    if _should_unshard_params(fsdp_state):
+        with SimpleProfiler.profile("_exit_unshard_params_ctx"):
+            _exit_unshard_params_ctx(module, fsdp_state)
+
+
+def _local_pre_state_dict_hook(
+    fsdp_state: _FSDPState,
+    module: nn.Module,
+    *args,
+    **kwargs,
+) -> None:
+    """
+    Hook that runs before model.state_dict() is called. Right now, pre-state_dict
+    hook is not supported by the PyTorch core. So this API is called from
+    `_local_post_state_dict_hook()` to simulate the case.
+    """
+    if (
+        _has_fsdp_params(fsdp_state, module)
+        and not _module_handle(fsdp_state, module).uses_sharded_strategy
+    ):
+        raise RuntimeError(
+            "``local_state_dict`` can only be used when parameters are flatten "
+            "and sharded."
+        )
+    _common_pre_state_dict_hook(module, fsdp_state)
+
+
+@no_type_check
+def _local_post_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    state_dict: Dict[str, Any],
+    prefix: str,
+) -> Dict[str, Any]:
+    """
+    This hook create a ShardedTensor from the local flat_param and replace
+    the state_dict[f"{prefix}{FLAT_PARAM}] with the ShardedTensor. No copy
+    will happen. The underlying storage is the same.
+    """
+
+    _replace_by_prefix(state_dict, f"{prefix}{FSDP_PREFIX}", prefix)
+    if not _has_fsdp_params(fsdp_state, module):
+        return state_dict
+
+    # state_dict[f"{prefix}{FLAT_PARAM}"] exists and has the same tensor
+    # value as the flat_param but it is a pure Tensor because
+    # nn.Module.state_dict() will detach the parameter. Therefore, we need
+    # to get flat_param to get the metadata.
+    assert _module_handle(fsdp_state, module), "Should have returned early"
+    flat_param = _module_handle(fsdp_state, module).flat_param
+    # Constructs a ShardedTensor from the flat_param "without" padding.
+    # Removing the padding allows users to change the number of ranks
+    # when loading the local_state_dict.
+    full_numel = flat_param._unpadded_unsharded_size.numel()  # type: ignore[attr-defined]
+    shard_offset = flat_param.numel() * fsdp_state.rank
+    valid_data_size = flat_param.numel() - flat_param._shard_numel_padded
+    if valid_data_size > 0:
+        # If FlatParameter is returned, FlatParameter._local_shard cause a
+        # pickling issue (can be torch.save but not torch.load). Since there
+        # is no benefit for state_dict to return the actual FlatParameter class,
+        # a view (which is a tensor) of the FlatParameter will be returned.
+        flat_param = flat_param[:valid_data_size].view(valid_data_size)
+        local_shards = [
+            Shard.from_tensor_and_offsets(flat_param, [shard_offset], fsdp_state.rank)
+        ]
+    else:
+        local_shards = []
+    sharded_tensor = init_from_local_shards(
+        local_shards, full_numel, process_group=fsdp_state.process_group
+    )  # type: ignore[assignment]
+    # TODO: Add DTensor state_dict support for LOCAL_STATE_DICT.
+    if fsdp_state._state_dict_config.offload_to_cpu:
+        sharded_tensor = sharded_tensor.cpu()
+    state_dict[f"{prefix}{FLAT_PARAM}"] = sharded_tensor
+    return state_dict
+
+
+def _local_post_load_state_dict_hook(
+    module: nn.Module, fsdp_state: _FSDPState, *args, **kwargs
+) -> None:
+    pass
+
+
+def _local_pre_load_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    state_dict: Dict[str, Any],
+    prefix: str,
+) -> None:
+    """
+    This hook finds the local flat_param for this FSDP module from the
+    state_dict. The flat_param should be a ShardedTensor. This hook converts
+    the ShardedTensor to a tensor. No copy happen unless padding is required.
+    """
+    _lazy_init(fsdp_state, module)
+    _replace_by_prefix(state_dict, prefix, f"{prefix}{FSDP_PREFIX}")
+    fqn = f"{prefix}{FSDP_PREFIX}{FLAT_PARAM}"
+    if fqn not in state_dict:
+        assert not _has_fsdp_params(fsdp_state, module), (
+            "No `FlatParameter` in `state_dict` for this FSDP instance "
+            "but it has parameters"
+        )
+        return
+    load_tensor = state_dict[fqn]
+    assert isinstance(
+        load_tensor, ShardedTensor
+    ), "Tensors in local_state_dict should be ShardedTensor."
+
+    # Convert the ShardedTensor to a Tensor.
+    flat_param = _module_handle(fsdp_state, module).flat_param
+    assert flat_param is not None
+    valid_data_size = flat_param.numel() - flat_param._shard_numel_padded
+    shards = load_tensor.local_shards()
+    if valid_data_size > 0:
+        assert len(shards), "load_local_state_dict assume one shard per ShardedTensor."
+        load_tensor = shards[0].tensor
+
+        # Get the metadata of the flat_param to decide whether to pad the loaded
+        # tensor.
+        if flat_param._shard_numel_padded > 0:
+            assert load_tensor.numel() < flat_param.numel(), (
+                f"Local shard size = {flat_param.numel()} and the tensor in "
+                f"the state_dict is {load_tensor.numel()}."
+            )
+            load_tensor = F.pad(load_tensor, [0, flat_param._shard_numel_padded])
+    else:
+        load_tensor = flat_param
+    # TODO: Add DTensor state_dict support for LOCAL_STATE_DICT.
+    state_dict[fqn] = load_tensor
+
+
+def _sharded_pre_state_dict_hook(
+    fsdp_state: _FSDPState,
+    module: nn.Module,
+    *args,
+    **kwargs,
+) -> None:
+    """
+    Hook that runs before model.state_dict() is called. Check
+    ``_full_pre_load_state_dict_hook`` for the detail.
+    """
+    if (
+        _has_fsdp_params(fsdp_state, module)
+        and not _module_handle(fsdp_state, module).uses_sharded_strategy
+    ):
+        raise RuntimeError(
+            "``sharded_state_dict`` can only be used when parameters are flatten "
+            "and sharded."
+        )
+    _common_pre_state_dict_hook(module, fsdp_state)
+    # Setting offload_to_cpu here does not work even if offload_to_cpu is True.
+    # We have to create ShardedTensor first then move it to CPU.
+    _common_unshard_pre_state_dict_hook(
+        module,
+        fsdp_state,
+        offload_to_cpu=False,
+        rank0_only=False,
+    )
+
+
+@no_type_check
+def _sharded_post_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    state_dict: Dict[str, Any],
+    prefix: str,
+) -> Dict[str, Any]:
+    """
+    The hook replaces the unflattened, unsharded parameter in the state_dict
+    with a unflattened, sharded parameter (a ShardedTensor).
+    """
+
+    def param_hook(state_dict: Dict[str, Any], prefix: str, fqn: str):
+        param = state_dict[fqn]
+        if not fsdp_state._state_dict_config._use_dtensor:
+            sharded_tensor = _ext_chunk_tensor(
+                tensor=param,
+                rank=fsdp_state.rank,
+                world_size=fsdp_state.world_size,
+                num_devices_per_node=fsdp_state._device_handle.device_count(),
+                pg=fsdp_state.process_group,
+                fsdp_extension=fsdp_state._fsdp_extension,
+            )
+        else:
+            sharded_tensor = _ext_chunk_dtensor(
+                tensor=param,
+                rank=fsdp_state.rank,
+                device_mesh=fsdp_state._device_mesh,
+                fsdp_extension=fsdp_state._fsdp_extension,
+            )
+        if fsdp_state._state_dict_config.offload_to_cpu:
+            sharded_tensor = sharded_tensor.cpu()
+        state_dict[fqn] = sharded_tensor
+
+    return _common_unshard_post_state_dict_hook(
+        module, fsdp_state, state_dict, prefix, param_hook
+    )
+
+
+@no_type_check
+def _sharded_post_load_state_dict_hook(
+    module: nn.Module, fsdp_state: _FSDPState, *args, **kwargs
+) -> None:
+    if _has_fsdp_params(fsdp_state, module):
+        with SimpleProfiler.profile("_exit_unshard_params_ctx"):
+            _exit_unshard_params_ctx(module, fsdp_state)
+
+
+@no_type_check
+def _sharded_pre_load_state_dict_hook(
+    module: nn.Module,
+    fsdp_state: _FSDPState,
+    state_dict: Dict[str, Any],
+    prefix: str,
+) -> None:
+    """
+    The hook combines the unflattened, sharded parameters (ShardedTensor) to
+    a new FlatParameter and shards the new FlatParameter to the local chunk.
+    """
+    _lazy_init(fsdp_state, module)
+    if not _is_composable(fsdp_state):
+        _replace_by_prefix(state_dict, prefix, prefix + f"{FSDP_PREFIX}")
+    if not _has_fsdp_params(fsdp_state, module):
+        return
+
+    handle = _module_handle(fsdp_state, module)
+    if not handle.uses_sharded_strategy:
+        raise RuntimeError(
+            "load_sharded_state_dict can only be called when parameters "
+            "are flattened and sharded."
+        )
+    fqn_to_param_ext = dict(
+        zip(handle.flat_param._fqns, handle.flat_param._param_extensions)
+    )
+
+    for fqn, _, _ in _param_name_infos(module, fsdp_state):
+        if not _is_composable(fsdp_state):
+            fqn_from_global_root = f"{prefix}{FSDP_PREFIX}{fqn}"
+        else:
+            fqn_from_global_root = f"{prefix}{fqn}"
+        try:
+            param = state_dict.pop(fqn_from_global_root)
+        except KeyError:
+            logger.warning(
+                f"Did not find param with FQN {fqn_from_global_root}, skipping it. "  # noqa: G004
+                "The weight will not be filled if you expect it to be."
+            )
+            continue  # TODO: Improve unittesting for state_dict finetuning
+            # cases: https://github.com/pytorch/pytorch/issues/109134
+
+        if not fsdp_state._state_dict_config._use_dtensor:
+            # All-gather the param (ShardedTensor)
+            param, shards = _ext_pre_load_state_dict_transform(
+                param, fsdp_state._fsdp_extension
+            )
+
+            assert len(shards) < 2, (
+                "Expects 0 or 1 shard per rank "
+                f"but got {len(shards)} shards on rank {fsdp_state.rank}."
+            )
+            param_numel = param.size().numel()
+            dim_0_size = param.size()[0]
+            chunk_size = (
+                math.ceil(dim_0_size / fsdp_state.world_size)
+                * param_numel
+                // dim_0_size
+            )
+            if len(shards) == 1:
+                local_tensor = shards[0].tensor.flatten()
+                with SimpleProfiler.profile(SimpleProfiler.Type.H2D):
+                    local_tensor = local_tensor.to(fsdp_state.compute_device)
+                num_padding = chunk_size - local_tensor.numel()
+                if num_padding > 0:
+                    local_tensor = F.pad(local_tensor, [0, num_padding])
+            else:
+                local_tensor = torch.zeros(
+                    chunk_size, dtype=param.dtype, device=fsdp_state.compute_device
+                )
+            tensor = torch.empty(
+                chunk_size * fsdp_state.world_size,
+                dtype=local_tensor.dtype,
+                device=fsdp_state.compute_device,
+            )
+            with SimpleProfiler.profile(SimpleProfiler.Type.ALLGATHER):
+                dist.all_gather_into_tensor(
+                    tensor, local_tensor, group=fsdp_state.process_group
+                )
+            tensor = tensor.narrow(0, 0, param_numel).reshape(param.size())
+            state_dict[fqn_from_global_root] = tensor
+        else:
+            if param.device != fsdp_state._device_mesh.device_type:
+                param = param.to(fsdp_state._device_mesh.device_type)
+
+            root_mesh = _mesh_resources.get_root_mesh(fsdp_state._device_mesh)
+            local_tensor = _ext_all_gather_dtensor(
+                param, root_mesh, fsdp_state._fsdp_extension
+            )
+
+            if fqn_to_param_ext.get(fqn) is not None:
+                ext = fqn_to_param_ext[fqn]
+                local_tensor = _ext_post_unflatten_transform(
+                    local_tensor, ext, fsdp_state._fsdp_extension
+                )
+            state_dict[fqn_from_global_root] = local_tensor
+
+    with SimpleProfiler.profile("_enter_unshard_params_ctx"):
+        _enter_unshard_params_ctx(module, fsdp_state, writeback=True)
+
+
+@contextlib.contextmanager
+def _replace_with_full_state_dict_type(fsdp_state: _FSDPState) -> Generator:
+    old_state_dict_config = fsdp_state._state_dict_config
+    old_state_dict_type = fsdp_state._state_dict_type
+    fsdp_state._state_dict_config = FullStateDictConfig()
+    fsdp_state._state_dict_type = StateDictType.FULL_STATE_DICT
+    yield
+    fsdp_state._state_dict_config = old_state_dict_config
+    fsdp_state._state_dict_type = old_state_dict_type
+
+
+@no_type_check
+@torch.no_grad()
+def _post_state_dict_hook(
+    module: nn.Module,
+    state_dict: Dict[str, Any],
+    prefix: str,
+    *args: Any,
+) -> Dict[str, Any]:
+    """
+    _post_state_dict_hook() is called after the state_dict() of this
+    FSDP module is executed. ``fsdp_state._state_dict_type`` is used to decide
+    what postprocessing will be done.
+    """
+    fsdp_state = _get_module_fsdp_state_if_fully_sharded_module(module)
+    if fsdp_state.sharding_strategy == ShardingStrategy.NO_SHARD:
+        context = _replace_with_full_state_dict_type(fsdp_state)
+        warnings.warn(
+            "When using ``NO_SHARD`` for ``ShardingStrategy``, full_state_dict will"
+            "be returned."
+        )
+    else:
+        context = contextlib.nullcontext()
+
+    with context:
+        _post_state_dict_hook_fn = {
+            StateDictType.FULL_STATE_DICT: _full_post_state_dict_hook,
+            StateDictType.LOCAL_STATE_DICT: _local_post_state_dict_hook,
+            StateDictType.SHARDED_STATE_DICT: _sharded_post_state_dict_hook,
+        }
+        processed_state_dict = _post_state_dict_hook_fn[fsdp_state._state_dict_type](
+            module, fsdp_state, state_dict, prefix
+        )
+
+    if fsdp_state._is_root:
+        logger.info("FSDP finished processing state_dict(), prefix=%s", prefix)
+        for key, tensor in sorted(processed_state_dict.items()):
+            if key.startswith(prefix) and isinstance(tensor, torch.Tensor):
+                local_shape = tensor.shape
+                if isinstance(tensor, ShardedTensor):
+                    local_shape = None
+                    shards = tensor.local_shards()
+                    if shards:
+                        local_shape = shards[0].tensor.shape
+                elif isinstance(tensor, DTensor):
+                    local_shape = tensor.to_local().shape
+                logger.info(
+                    "FQN=%s: type=%s, shape=%s, local_shape=%s, dtype=%s, device=%s",
+                    key,
+                    type(tensor),
+                    tensor.shape,
+                    local_shape,
+                    tensor.dtype,
+                    tensor.device,
+                )
+
+    return processed_state_dict
+
+
+@no_type_check
+@torch.no_grad()
+def _pre_state_dict_hook(
+    module: nn.Module,
+    *args,
+    **kwargs,
+) -> None:
+    """
+    This is called before the core state dict saving logic of ``module``.
+    ``fsdp_state._state_dict_type`` is used to decide what postprocessing will
+    be done.
+    """
+    fsdp_state = _get_module_fsdp_state_if_fully_sharded_module(module)
+    if fsdp_state.sharding_strategy == ShardingStrategy.NO_SHARD:
+        context = _replace_with_full_state_dict_type(fsdp_state)
+        warnings.warn(
+            "When using ``NO_SHARD`` for ``ShardingStrategy``, full_state_dict will"
+            "be returned."
+        )
+    else:
+        _set_use_dtensor(fsdp_state)
+        context = contextlib.nullcontext()
+
+    with context:
+        _pre_state_dict_hook_fn = {
+            StateDictType.FULL_STATE_DICT: _full_pre_state_dict_hook,
+            StateDictType.LOCAL_STATE_DICT: _local_pre_state_dict_hook,
+            StateDictType.SHARDED_STATE_DICT: _sharded_pre_state_dict_hook,
+        }
+        _pre_state_dict_hook_fn[fsdp_state._state_dict_type](
+            fsdp_state,
+            module,
+            *args,
+            **kwargs,
+        )
+
+
+@no_type_check
+def _set_use_dtensor(fsdp_state: _FSDPState) -> None:
+    # If device_mesh is passed in when initalizing FSDP, we automatically turn the
+    # _use_dtensor flag to be true for ShardedStateDictConfig().
+    if getattr(fsdp_state, "_device_mesh", None):
+        state_dict_type = fsdp_state._state_dict_type
+        if state_dict_type == StateDictType.LOCAL_STATE_DICT:
+            raise RuntimeError(
+                "Found state_dict_type LOCAL_STATE_DICT",
+                "DeviceMesh is not compatible with LOCAL_STATE_DICT.",
+                "Please set state_dict_type to SHARDED_STATE_DICT to get DTensor state_dict.",
+            )
+        else:
+            fsdp_state._state_dict_config._use_dtensor = True
+
+
+@no_type_check
+@torch.no_grad()
+def _pre_load_state_dict_hook(
+    module: nn.Module,
+    state_dict: Dict[str, Any],
+    prefix: str,
+    *args: Any,
+) -> None:
+    """
+    This is called before ``module._load_from_state_dict()``.
+    ``fsdp_state._state_dict_type`` is used to decide what preprocessing will
+    be done.
+    """
+    fsdp_state = _get_module_fsdp_state_if_fully_sharded_module(module)
+    if fsdp_state.sharding_strategy == ShardingStrategy.NO_SHARD:
+        context = _replace_with_full_state_dict_type(fsdp_state)
+        warnings.warn(
+            "When using ``NO_SHARD`` for ``ShardingStrategy``, full_state_dict will"
+            "be returned."
+        )
+    else:
+        _set_use_dtensor(fsdp_state)
+        context = contextlib.nullcontext()
+
+    _lazy_init(fsdp_state, module)
+    if fsdp_state._is_root:
+        SimpleProfiler.reset()
+
+    with context:
+        _pre_load_state_dict_hook_fn = {
+            StateDictType.FULL_STATE_DICT: _full_pre_load_state_dict_hook,
+            StateDictType.LOCAL_STATE_DICT: _local_pre_load_state_dict_hook,
+            StateDictType.SHARDED_STATE_DICT: _sharded_pre_load_state_dict_hook,
+        }
+        # Code that is common for all state_dict impls
+        if fsdp_state._device_handle.is_available():
+            fsdp_state._device_handle.synchronize()
+        # Dispatch into state_dict specific implementation of pre-hook.
+        _pre_load_state_dict_hook_fn[fsdp_state._state_dict_type](
+            module, fsdp_state, state_dict, prefix
+        )
+
+
+@no_type_check
+@torch.no_grad()
+def _post_load_state_dict_hook(
+    module: nn.Module,
+    incompatible_keys: Tuple[List[str], List[str]],
+    *args: Any,
+) -> None:
+    fsdp_state = _get_module_fsdp_state_if_fully_sharded_module(module)
+    if fsdp_state.sharding_strategy == ShardingStrategy.NO_SHARD:
+        context = _replace_with_full_state_dict_type(fsdp_state)
+        warnings.warn(
+            "When using ``NO_SHARD`` for ``ShardingStrategy``, full_state_dict will"
+            "be returned."
+        )
+    else:
+        context = contextlib.nullcontext()
+
+    with context:
+        _post_load_state_dict_hook_fn = {
+            StateDictType.FULL_STATE_DICT: _full_post_load_state_dict_hook,
+            StateDictType.LOCAL_STATE_DICT: _local_post_load_state_dict_hook,
+            StateDictType.SHARDED_STATE_DICT: _sharded_post_load_state_dict_hook,
+        }
+        # Code that is common for all state_dict impls
+        # Dispatch into state_dict type specific implementation of post-hook for
+        # loading state_dict.
+        _post_load_state_dict_hook_fn[fsdp_state._state_dict_type](module, fsdp_state)
+
+    # When reporting incompatible keys, trim FSDP prefixes.
+    missing_keys = incompatible_keys[0]
+    unexpected_keys = incompatible_keys[1]
+    for i in range(len(missing_keys)):
+        missing_keys[i] = clean_tensor_name(missing_keys[i])
+
+    for i in range(len(unexpected_keys)):
+        unexpected_keys[i] = clean_tensor_name(unexpected_keys[i])
+
+    if fsdp_state._is_root:
+        SimpleProfiler.dump_and_reset("FSDP model load_state_dict profiling: ")
+
+
+def _register_all_state_dict_hooks(state: _FSDPState):
+    """
+    Registers pre-save, post-save, pre-load, and post-load state dict hooks.
+    """
+    for hook_registration_fn_str, hook, hook_registration_fn_kwargs in (
+        ("register_state_dict_pre_hook", _pre_state_dict_hook, {}),
+        ("_register_state_dict_hook", _post_state_dict_hook, {}),
+        (
+            "_register_load_state_dict_pre_hook",
+            _pre_load_state_dict_hook,
+            {"with_module": True},
+        ),
+        ("register_load_state_dict_post_hook", _post_load_state_dict_hook, {}),
+    ):
+        _register_state_dict_hooks_base(
+            state, hook_registration_fn_str, hook, hook_registration_fn_kwargs
+        )
+
+
+@no_type_check
+def _register_state_dict_hooks_base(
+    state: _FSDPState,
+    hook_registration_fn_name: str,
+    hook: Callable,
+    hook_registration_fn_kwargs: Dict[str, Any],
+) -> None:
+    """Registers ``hook`` using ``hook_registration_fn``."""
+    if not _is_composable(state):
+        getattr(state, hook_registration_fn_name)(hook, **hook_registration_fn_kwargs)
+    else:
+        handle = state._handle
+        if handle:
+            getattr(handle._fully_sharded_module, hook_registration_fn_name)(
+                hook, **hook_registration_fn_kwargs
+            )

infer_4_47_1/lib/python3.10/site-packages/torch/distributed/fsdp/_trace_utils.py

@@ -0,0 +1,238 @@
+# mypy: allow-untyped-defs
+import functools
+from contextlib import contextmanager
+from dataclasses import dataclass, field
+from typing import Any, Callable, Dict, List, NamedTuple, Optional, Set, Tuple
+
+import torch
+import torch.nn as nn
+
+
+@dataclass
+class TracingConfig:
+    """
+    This represents a symbolic tracing configuration.
+
+    Args:
+        tracer (torch.fx.Tracer): An instance of :class:`torch.fx.Tracer` to
+            use for symbolic tracing. The default value is the native
+            :class:`torch.fx.Tracer` constructed with default arguments.
+            However, the user may want to pass a different value such as the
+            ``HFTracer`` for models in the HuggingFace Transformers_ library.
+            .. _Transformers: https://huggingface.co/docs/transformers/index
+        concrete_args (Optional[Dict[str, Any]]): Concrete arguments that
+            should not be treated as ``torch.fx.Proxy`` when tracing the
+            module ``forward()``. Passing ``concrete_args`` allows partially
+            specializing the forward, e.g. to remove control flow or data
+            structures. This ``concrete_args`` here is the same argument used
+            in :meth:`~torch.fx.Tracer.trace`.
+    """
+
+    tracer: torch.fx.Tracer = field(default_factory=torch.fx.Tracer)
+    concrete_args: Optional[Dict[str, Any]] = None
+
+
+class _ParamUsageInfo(NamedTuple):
+    """
+    This is used for ``_ExecutionInfo.module_to_param_usage_infos`` to record
+    execution information. The ``dict`` maps modules to a list of these
+    ``_ParamUsageInfo`` instances, where each instance represents a group of
+    parameters used together.
+
+    Specifically, for each module key in the ``dict``, each instance of this
+    class represents either:
+    (1) the module and some sublist of its ``named_parameters()`` used
+    together in execution (see ``_patched_create_proxy()``), or
+    (2) a submodule and all of ``submodule.named_parameters()`` (see
+    ``_patched_call_module()``).
+
+    Type (1) corresponds to directly using parameters in ops without calling
+    ``forward()``, and type (2) corresponds to calling ``forward()``. The
+    mapped-to lists in the ``dict`` follow the execution order.
+    """
+
+    module: nn.Module
+    named_params: List[Tuple[str, nn.Parameter]]
+
+
+class _ExecutionInfo:
+    """
+    This represents the execution order information from the forward pass.
+
+    Attributes:
+        curr_module (nn.Module): Current module being traced.
+        module_forward_order (List[nn.Module]): The modules in (pre-)forward
+            order, i.e. the order in which their ``forward()`` methods are
+            called. Each call to a module's ``forward()`` corresponds to one
+            element in the list.
+        module_to_param_usage_infos (Dict[nn.Module, List[_ParamUsageInfo]]):
+            Maps a module to a list of module execution infos. See
+            :class:`_ParamUsageInfo` for details.
+        param_forward_order (List[nn.Parameter]): The parameters in forward
+            execution order, where only a parameter's first participation is
+            included.
+        visited_params (Set[nn.Parameter]): The parameters visited so far
+            during the trace. This is only used during tracing for fast
+            membership check. Invariant: The parameters in
+            ``param_forward_order`` are exactly those in ``visited_params``.
+    """
+
+    def __init__(self, root_module: nn.Module) -> None:
+        self.curr_module: nn.Module = root_module
+        self.module_forward_order: List[nn.Module] = [root_module]
+        self.module_to_param_usage_infos: Dict[nn.Module, List[_ParamUsageInfo]] = {
+            root_module: []
+        }
+        self.param_forward_order: List[nn.Parameter] = []
+        self.visited_params: Set[nn.Parameter] = set()
+
+
+class _ExecOrderTracer:
+    def __init__(self) -> None:
+        self.exec_info: Optional[_ExecutionInfo] = None
+
+    @contextmanager
+    def patch_tracer(self, tracer: torch.fx.Tracer, root_module: nn.Module):
+        self.exec_info = _ExecutionInfo(root_module)
+        orig_call_module = tracer.call_module
+        orig_create_proxy = tracer.create_proxy
+        tracer.call_module = functools.partial(  # type: ignore[method-assign]
+            self._patched_call_module, orig_call_module, self.exec_info
+        )
+        fqn_to_param = dict(root_module.named_parameters())
+        tracer.create_proxy = functools.partial(  # type: ignore[method-assign]
+            self._patched_create_proxy,
+            orig_create_proxy,
+            self.exec_info,
+            fqn_to_param,
+        )
+        try:
+            yield
+        finally:
+            tracer.call_module = orig_call_module  # type: ignore[method-assign]
+            tracer.create_proxy = orig_create_proxy  # type: ignore[method-assign]
+
+    def _patched_call_module(
+        self,
+        call_module: Callable,
+        exec_info: _ExecutionInfo,
+        # Below are the expected arguments to `call_module()`
+        module: nn.Module,
+        forward: Callable,
+        args: Tuple[Any, ...],
+        kwargs: Dict[str, Any],
+    ) -> Any:
+        """
+        Overrides ``call_module`` to save execution information to
+        ``exec_info``. Note that ``call_module`` is called during symbolic
+        tracing for each non-root module.
+
+        Args:
+            call_module (Callable): Original ``call_module`` to override.
+            exec_info (_ExecutionInfo): Used to record execution information.
+            module (nn.Module): Module corresponding to this ``call_module``.
+            forward (Callable): ``forward()`` method of ``module`` to be called
+                for this ``call_module``.
+            args (Tuple[Any, ...]): Positional arguments for ``forward``.
+            kwargs (Dict[str, Any]): Keyword arguments for ``forward``.
+
+        Returns:
+            Same return value as ``call_module``.
+        """
+        exec_info.module_forward_order.append(module)
+        named_params = list(module.named_parameters())
+        curr_module = exec_info.curr_module
+        if named_params:
+            assert (
+                curr_module in exec_info.module_to_param_usage_infos
+            ), "The current module should have already been processed by a patched `call_module`"
+            exec_info.module_to_param_usage_infos[exec_info.curr_module].append(
+                _ParamUsageInfo(module, named_params)
+            )
+        prev_curr_module = curr_module
+        exec_info.curr_module = module
+        exec_info.module_to_param_usage_infos[module] = []
+        output = call_module(module, forward, args, kwargs)
+        exec_info.curr_module = prev_curr_module
+        return output
+
+    def _patched_create_proxy(
+        self,
+        create_proxy: Callable,
+        exec_info: _ExecutionInfo,
+        fqn_to_param: Dict[str, nn.Parameter],
+        # Below are the expected arguments to `create_proxy()`
+        kind: str,
+        target: torch.fx.node.Target,
+        args: Tuple[Any, ...],
+        kwargs: Dict[str, Any],
+        name: Optional[str] = None,
+        type_expr: Optional[Any] = None,
+        proxy_factory_fn: Optional[Callable[[torch.fx.Node], torch.fx.Proxy]] = None,
+    ) -> torch.fx.Proxy:
+        """
+        Overrides ``create_proxy`` to save execution information to
+        ``exec_info``. Note that ``create_proxy`` is called during symbolic
+        tracing for each leaf function/method/module.
+
+        Args:
+            create_proxy (Callable): Original ``create_proxy`` to override.
+            exec_info (_ExecutionInfo): Used to record execution information.
+            fqn_to_param (Dict[str, nn.Parameter]): ``dict`` version of the
+                root module's ``named_parameters()`` with FQN as key and
+                parameter as value.
+            kind (str): Kind of the target method ('call_function',
+                'call_method', 'get_attr', 'call_module', 'placeholder', or
+                'output'). See :class:`torch.fx.Graph` for details. This is
+                passed to ``create_proxy``.
+            target (torch.fx.node.Target): Contains the string name of the
+                function/method/module. This is passed to ``create_proxy``.
+            args (Tuple[Any, ...]): Positional arguments for the function/
+                method/module. This is passed to ``create_proxy``.
+            kwargs (Dict[str, Any]): Keyword arguments for the function/method/
+                module. This is passed to ``create_proxy``
+            name (Optional[str]): An optional string name for the ``Node``
+                created in ``create_proxy``. This is passed to
+                ``create_proxy``.
+            type_expr (Optional[Any]): An optional type annotation representing
+                the Python type that the output of the node has. This is passed
+                to ``create_proxy``.
+            proxy_factory_fn (Callable[[torch.fx.Node], torch.fx.Proxy]):
+                An alternative proxy constructor used in ``create_proxy``. This
+                is passed to ``create_proxy``.
+
+        Returns:
+            torch.fx.Proxy: Created ``Node`` wrapped in a ``Proxy`` object.
+        """
+        proxy = create_proxy(
+            kind, target, args, kwargs, name, type_expr, proxy_factory_fn
+        )
+        curr_module = exec_info.curr_module
+        if kind in ("call_function", "call_method"):
+            if args is not None:
+                named_params: List[Tuple[str, nn.Parameter]] = []
+                for arg in args:
+                    if (
+                        isinstance(arg, torch.fx.Proxy)
+                        and arg.node.target in fqn_to_param
+                    ):
+                        param = fqn_to_param[arg.node.target]  # type: ignore[index]
+                        named_params.append((arg.node.target, param))  # type: ignore[arg-type]
+                        if param not in exec_info.visited_params:
+                            exec_info.visited_params.add(param)
+                            exec_info.param_forward_order.append(param)
+                if named_params:
+                    exec_info.module_to_param_usage_infos[curr_module].append(
+                        _ParamUsageInfo(curr_module, named_params)
+                    )
+        elif kind == "call_module":
+            named_params = list(curr_module.named_parameters())
+            if named_params:
+                exec_info.module_to_param_usage_infos[curr_module].append(
+                    _ParamUsageInfo(curr_module, named_params)
+                )
+            for _, param in named_params:
+                if param not in exec_info.visited_params:
+                    exec_info.visited_params.add(param)
+                    exec_info.param_forward_order.append(param)
+        return proxy

infer_4_47_1/lib/python3.10/site-packages/torch/distributed/fsdp/_traversal_utils.py

@@ -0,0 +1,113 @@
+"""
+NOTE: This file must be imported like
+``import torch.distributed.fsdp._traversal_utils`` and not like
+``from torch.distirbuted.fsdp._traversal_utils import ...`` to avoid circular
+imports. For brevity, we may import the file as ``traversal_utils``.
+"""
+
+import collections
+from typing import Deque, List, Set, Tuple
+
+import torch.nn as nn
+from torch.distributed._composable.contract import _get_registry
+from torch.distributed.fsdp._common_utils import _FSDPState, _get_module_fsdp_state
+
+
+"""
+[Note: FSDP State Traversal]
+For the wrapper code path, ``_FSDPState`` is the ``FullyShardedDataParallel``
+module wrapping a fully sharded module, and for the non-wrapper code path,
+``_FSDPState`` is an object that gets embedded on a fully sharded module.
+See [Note: Fully Sharded Module] for the definition.
+
+There are three common traversal idioms: Given a root module,
+- ``_get_fsdp_states()`` returns all ``_FSDPState`` s in the tree.
+- ``get_fsdp_root_states()`` returns all local root ``_FSDPState`` s in the
+tree (i.e. those with ``_is_root == True``).
+- ``_get_fsdp_handles()``returns all ``FlatParamHandle`` s in the tree.
+
+All of these methods must take in the root module (i.e. an ``nn.Module``) and
+not a general ``_FSDPState`` because ``_FSDPState`` does not support a graph
+traversal, whereas ``nn.Module`` has ``nn.Module.modules()`` for traversal.
+"""
+
+
+def _composable(module: nn.Module) -> bool:
+    """
+    Returns if ``module`` can compose with ``fully_shard``.
+    """
+    # TODO: Add any other composable APIs that are mutually exclusive.
+    registry = _get_registry(module)
+    if registry is None:
+        return True
+    return "replicate" not in registry
+
+
+# TODO (awgu): We may be able to remove this function if we retired the
+# `use_orig_params=False` code path since so far we only need the module for
+# `FlatParameter` registration, which is not needed for `use_orig_params=True`.
+def _get_fsdp_states_with_modules(
+    module: nn.Module,
+) -> Tuple[List[_FSDPState], List[nn.Module]]:
+    """
+    Returns a tuple containing:
+    1. A list of the ``_FSDPState`` instances in the module tree rooted at
+    ``module`` without any duplicates and following the ``module.modules()``
+    traversal order (which is assumed to be depth-first).
+    2. A corresponding list of the modules owning the states in the first list.
+
+    For the wrapper code path, both returned lists are the same, each
+    containing all ``FullyShardedDataParallel`` instances. For the composable
+    code path, this returns a list of all composable state instances and a list
+    of the corresponding fully sharded modules. See [Note: Fully Sharded
+    Module].
+
+    NOTE: The traversal does not proceed into any module annotated by an
+    incompatible API (e.g. ``replicate``).
+    """
+    fsdp_states: List[_FSDPState] = []
+    fsdp_modules: List[nn.Module] = []
+    # Track the visited FSDP states since multiple modules may share the same
+    # one and we want to return a de-duplicated list
+    visited_fsdp_states: Set[_FSDPState] = set()
+    # Track the visited modules in case of shared modules, which implies the
+    # module graph is no longer a tree
+    visited_modules: Set[nn.Module] = set()
+
+    # Perform depth-first search from `module` to ensure that we do not
+    # traverse into an incompatible API's subtree (use DFS instead of BFS to
+    # match `.modules()` order)
+    deque: Deque[nn.Module] = collections.deque([module])
+    while deque:
+        submodule = deque.popleft()
+        visited_modules.add(submodule)
+        if not _composable(submodule):
+            continue
+        for child_module in reversed(list(submodule.children())):
+            if child_module not in visited_modules:
+                deque.appendleft(child_module)
+        optional_state = _get_module_fsdp_state(submodule)
+        if optional_state is not None and optional_state not in visited_fsdp_states:
+            visited_fsdp_states.add(optional_state)
+            fsdp_states.append(optional_state)
+            fsdp_modules.append(submodule)
+    return fsdp_states, fsdp_modules
+
+
+def _get_fsdp_states(module: nn.Module) -> List[_FSDPState]:
+    """See :func:`_get_fsdp_states_with_modules`."""
+    fsdp_states, _ = _get_fsdp_states_with_modules(module)
+    return fsdp_states
+
+
+def _get_fsdp_handles(module: nn.Module) -> List:
+    """
+    Returns all ``FlatParamHandle`` s in the module tree rooted at ``module``
+    following the rules in :func:`_get_fsdp_state`.
+    """
+    handles = [
+        fsdp_state._handle
+        for fsdp_state in _get_fsdp_states(module)
+        if fsdp_state._handle is not None
+    ]
+    return handles

infer_4_47_1/lib/python3.10/site-packages/torch/distributed/fsdp/_unshard_param_utils.py

@@ -0,0 +1,336 @@
+# mypy: allow-untyped-defs
+import contextlib
+import warnings
+from typing import cast, Generator
+
+import torch
+import torch.distributed.fsdp._traversal_utils as traversal_utils
+import torch.nn as nn
+from torch.distributed.fsdp._common_utils import (
+    _FSDPState,
+    _get_module_fsdp_state,
+    _has_fsdp_params,
+    _module_handle,
+    HandleTrainingState,
+    TrainingState,
+)
+from torch.distributed.fsdp._runtime_utils import (
+    _lazy_init,
+    _reset_flat_param_grad_info_if_needed,
+    _reshard,
+    _reshard_grads,
+    _unshard,
+    _unshard_grads,
+)
+from torch.distributed.utils import _p_assert
+
+from ._flat_param import FlatParamHandle
+
+
+FLAT_PARAM = "_flat_param"
+
+
+@torch.no_grad()
+def _writeback_to_local_shard(
+    handle: FlatParamHandle,
+    writeback_grad: bool,
+):
+    """
+    For the handle, writes back the this rank's shard of the unsharded
+    flattened parameter to the sharded flattened parameter. If
+    ``writeback_grad=True``, then writes back to the sharded gradient as
+    well.
+
+    Precondition: The handle's ``FlatParameter`` 's data points to the
+    padded unsharded flattened parameter.
+    """
+
+    def _get_shard(flat_param_or_grad: torch.Tensor) -> torch.Tensor:
+        if handle.uses_sharded_strategy:
+            # For sharded strategies, get the *unpadded* shard instead of
+            # the *padded* shard to persist user changes to the padding
+            # (though FSDP does not explicitly support this)
+            shard, _ = FlatParamHandle._get_unpadded_shard(
+                flat_param_or_grad,
+                handle.rank,
+                handle.world_size,
+            )
+            return shard
+        # For `NO_SHARD`, the `flat_param` or its gradient may be modified,
+        # so we write it back directly
+        return flat_param_or_grad
+
+    param_shard = _get_shard(handle.flat_param)
+    handle.flat_param._local_shard[: param_shard.numel()].copy_(param_shard)  # type: ignore[attr-defined]
+    if writeback_grad:
+        existing_grad = handle.sharded_grad
+        if existing_grad is not None:
+            assert handle.flat_param.grad is not None
+            grad_shard = _get_shard(handle.flat_param.grad)
+            existing_grad[: grad_shard.numel()].copy_(grad_shard)
+
+
+def _deregister_flat_param(state: _FSDPState, module: nn.Module) -> None:
+    """
+    De-registers the flattened parameter from the wrapped module, hiding it
+    from ``nn.Module`` methods.
+
+    We do not use ``del`` because we want ``FLAT_PARAM`` to always be an
+    attribute but dynamically change whether it is visible to ``nn.Module``
+    methods.
+    """
+    if _has_fsdp_params(state, module):
+        # TODO: figure out the case for the composable APIs.
+        cast(nn.Module, module.module)._parameters.pop(FLAT_PARAM, None)
+
+
+def _register_flat_param(state: _FSDPState, module: nn.Module) -> None:
+    """
+    Registers the flattened parameter to the wrapped module, making it
+    visible to ``nn.Module`` methods.
+
+    We do not use :meth:`nn.Module.register_parameter` because we want
+    ``FLAT_PARAM`` to always be an attribute but dynamically change whether
+    it is visible to ``nn.Module`` methods.
+    """
+    handle = _module_handle(state, module)
+    if _has_fsdp_params(state, module):
+        # TODO: figure out the case for the composable APIs.
+        cast(nn.Module, module.module)._parameters[FLAT_PARAM] = handle.flat_param
+
+
+@contextlib.contextmanager
+def _unflatten_as_params(state: _FSDPState, module: nn.Module) -> Generator:
+    """
+    Assumes that the flattened parameter is unsharded. When in the context,
+    de-registers the flattened parameter and unflattens the original
+    parameters as ``nn.Parameter`` views into the flattened parameter.
+    After the context, re-registers the flattened parameter and restores
+    the original parameters as ``Tensor`` views into the flattened
+    parameter.
+    """
+    handle = _module_handle(state, module)
+    if not handle:
+        yield
+    else:
+        _deregister_flat_param(state, module)
+        try:
+            with handle.unflatten_as_params():
+                yield
+        finally:
+            if not handle._use_orig_params:
+                _register_flat_param(state, module)
+
+
+def _validate_unshard_params_args(
+    state: _FSDPState,
+    writeback: bool,
+    rank0_only: bool,
+    offload_to_cpu: bool,
+    with_grads: bool,
+) -> None:
+    if with_grads and (offload_to_cpu or not state._use_orig_params):
+        raise NotImplementedError(
+            f"with_grads={with_grads}, "
+            f"use_orig_params={state._use_orig_params}, "
+            f"offload_to_cpu={offload_to_cpu} "
+            f"is not supported yet"
+        )
+    if offload_to_cpu and state._handle and (not state._handle.uses_sharded_strategy):
+        raise NotImplementedError(
+            "offload_to_cpu=True and NO_SHARD is not supported yet"
+        )
+    if writeback and rank0_only:
+        # TODO: Rank 0 can broadcast the `FlatParameter` to allow all ranks to
+        # persist the changes.
+        raise NotImplementedError(
+            "writeback=True and rank0_only=True is not supported yet"
+        )
+    if offload_to_cpu and not rank0_only:
+        warnings.warn(
+            "offload_to_cpu=True and rank0_only=False may result in the"
+            "unsharded parameters being redundantly copied to CPU memory for "
+            "GPUs sharing the same CPU memory, which risks CPU OOM. We "
+            "recommend using offload_to_cpu=True with rank0_only=True."
+        )
+
+
+@contextlib.contextmanager
+def _unshard_fsdp_state_params(
+    module: nn.Module,
+    state: _FSDPState,
+    writeback: bool,
+    rank0_only: bool,
+    offload_to_cpu: bool,
+    with_grads: bool,
+):
+    """
+    This unshards the parameters for a single FSDP state ``state`` that
+    corresponds to ``module``.
+    """
+    _validate_unshard_params_args(
+        state, writeback, rank0_only, offload_to_cpu, with_grads
+    )
+    state._device_handle.synchronize()
+    # If handles are shared by other module(s), the handle may be already unsharded.
+    maybe_handle = _module_handle(state, module)
+    handle = None
+    if (
+        maybe_handle
+        and maybe_handle._training_state != HandleTrainingState.SUMMON_FULL_PARAMS
+    ):
+        handle = maybe_handle
+    if not handle:
+        yield
+        return
+
+    assert (
+        handle._training_state == HandleTrainingState.IDLE
+    ), f"Expects the handle training to be IDLE but got {handle._training_state}"
+
+    handle._training_state = HandleTrainingState.SUMMON_FULL_PARAMS
+
+    _reset_flat_param_grad_info_if_needed(handle)
+    free_unsharded_flat_param = handle.needs_unshard()
+    # No need to call `wait_stream()` since we unshard in the computation
+    # stream directly
+    computation_stream = state._device_handle.current_stream()
+    _unshard(state, handle, computation_stream, computation_stream)
+    if with_grads:
+        _unshard_grads(handle)
+
+    if rank0_only and state.rank != 0:
+        # Free the unsharded flattened parameter early
+        _reshard(state, handle, free_unsharded_flat_param)
+        if with_grads:
+            _reshard_grads(handle)
+        try:
+            yield
+        finally:
+            handle._training_state = HandleTrainingState.IDLE
+    else:
+        # Unflatten the unsharded flattened parameters
+        with contextlib.ExitStack() as stack:
+            # Invariant: rank == 0 or !rank0_only
+            if offload_to_cpu and handle.uses_sharded_strategy:
+                stack.enter_context(handle.to_cpu())
+                # NOTE: Since PyTorch enforces that a parameter and its
+                # gradients need to match metadata (e.g. device), we must
+                # move gradients to CPU *after* we move parameters.
+            # NOTE: This assumes 1 `FlatParameter`
+            if not state._use_orig_params:
+                stack.enter_context(_unflatten_as_params(state, module))
+            try:
+                yield
+            finally:
+                stack.close()
+                if writeback:
+                    _writeback_to_local_shard(handle, with_grads)
+                _reshard(state, handle, free_unsharded_flat_param)
+                if with_grads:
+                    _reshard_grads(handle)
+                handle._training_state = HandleTrainingState.IDLE
+
+
+@contextlib.contextmanager
+def _unshard_params_for_summon(
+    module: nn.Module,
+    state: _FSDPState,
+    writeback: bool,
+    rank0_only: bool,
+    offload_to_cpu: bool,
+    with_grads: bool,
+):
+    _validate_unshard_params_args(
+        state, writeback, rank0_only, offload_to_cpu, with_grads
+    )
+    _lazy_init(state, module)
+    if state.training_state == TrainingState.FORWARD_BACKWARD:
+        raise AssertionError(
+            "Cannot manually unshard parameters during forward/backward"
+        )
+    elif state.training_state == TrainingState.SUMMON_FULL_PARAMS:
+        raise AssertionError(
+            "Cannot manually unshard parameters when already unsharding parameters"
+        )
+    with _unshard_fsdp_state_params(
+        module=module,
+        state=state,
+        writeback=writeback,
+        rank0_only=rank0_only,
+        offload_to_cpu=offload_to_cpu,
+        with_grads=with_grads,
+    ):
+        try:
+            state.training_state = TrainingState.SUMMON_FULL_PARAMS
+            yield
+        finally:
+            state.training_state = TrainingState.IDLE
+
+
+@contextlib.contextmanager
+def _unshard_params(
+    module: nn.Module,
+    recurse: bool,
+    writeback: bool,
+    rank0_only: bool,
+    offload_to_cpu: bool,
+    with_grads: bool,
+):
+    """
+    This unshards FSDP-managed parameters for all modules with FSDP applied in
+    the module tree rooted at ``module``.
+    """
+    if not recurse:
+        optional_state = _get_module_fsdp_state(module)
+        if optional_state is None:
+            with contextlib.nullcontext():
+                yield
+            return
+        states_and_modules = ([optional_state], [module])
+    else:
+        states_and_modules = traversal_utils._get_fsdp_states_with_modules(module)
+    with contextlib.ExitStack() as stack:
+        for state, module in zip(*states_and_modules):
+            stack.enter_context(
+                _unshard_params_for_summon(
+                    module=module,
+                    state=state,
+                    writeback=writeback,
+                    rank0_only=rank0_only,
+                    offload_to_cpu=offload_to_cpu,
+                    with_grads=with_grads,
+                )
+            )
+        yield
+
+
+def _deregister_orig_params(state: _FSDPState, module: nn.Module) -> None:
+    """
+    Deregisters the original parameters; registers the ``FlatParameter``.
+    """
+    handle = _module_handle(state, module)
+    if not handle:
+        return
+    _p_assert(
+        handle._use_orig_params,
+        f"Inconsistent `_use_orig_params` -- FSDP: {state._use_orig_params} "
+        f"handle: {handle._use_orig_params}",
+    )
+    handle._deregister_orig_params()
+    _register_flat_param(state, module)
+
+
+def _register_orig_params(state: _FSDPState, module: nn.Module) -> None:
+    """
+    Deregisters the ``FlatParameter``; registers the original parameters.
+    """
+    handle = _module_handle(state, module)
+    if not handle:
+        return
+    _deregister_flat_param(state, module)
+    if handle.is_sharded(handle.flat_param):
+        handle._use_sharded_views()
+        handle._use_sharded_grad_views()
+    else:
+        handle._use_unsharded_views(as_params=True)

infer_4_47_1/lib/python3.10/site-packages/torch/distributed/fsdp/api.py

@@ -0,0 +1,410 @@
+"""
+This file includes public APIs for FSDP such as the classes used for the
+constructor arguments.
+"""
+
+from dataclasses import dataclass
+from enum import auto, Enum
+from typing import Optional, Sequence, Type
+
+import torch
+from torch.nn.modules.batchnorm import _BatchNorm
+
+
+__all__ = [
+    "ShardingStrategy",
+    "BackwardPrefetch",
+    "MixedPrecision",
+    "CPUOffload",
+    "StateDictType",
+    "StateDictConfig",
+    "FullStateDictConfig",
+    "LocalStateDictConfig",
+    "ShardedStateDictConfig",
+    "OptimStateDictConfig",
+    "FullOptimStateDictConfig",
+    "LocalOptimStateDictConfig",
+    "ShardedOptimStateDictConfig",
+    "StateDictSettings",
+]
+
+
+class ShardingStrategy(Enum):
+    """
+    This specifies the sharding strategy to be used for distributed training by
+    :class:`FullyShardedDataParallel`.
+
+    - ``FULL_SHARD``: Parameters, gradients, and optimizer states are sharded.
+      For the parameters, this strategy unshards (via all-gather) before the
+      forward, reshards after the forward, unshards before the backward
+      computation, and reshards after the backward computation. For gradients,
+      it synchronizes and shards them (via reduce-scatter) after the backward
+      computation. The sharded optimizer states are updated locally per rank.
+    - ``SHARD_GRAD_OP``: Gradients and optimizer states are sharded during
+      computation, and additionally, parameters are sharded outside
+      computation. For the parameters, this strategy unshards before the
+      forward, does not reshard them after the forward, and only reshards them
+      after the backward computation. The sharded optimizer states are updated
+      locally per rank. Inside ``no_sync()``, the parameters are not resharded
+      after the backward computation.
+    - ``NO_SHARD``: Parameters, gradients, and optimizer states are not sharded
+      but instead replicated across ranks similar to PyTorch's
+      :class:`DistributedDataParallel` API. For gradients, this strategy
+      synchronizes them (via all-reduce) after the backward computation. The
+      unsharded optimizer states are updated locally per rank.
+    - ``HYBRID_SHARD``: Apply ``FULL_SHARD`` within a node, and replicate parameters across
+      nodes. This results in reduced communication volume as expensive all-gathers and
+      reduce-scatters are only done within a node, which can be more performant for medium
+      -sized models.
+    - ``_HYBRID_SHARD_ZERO2``: Apply ``SHARD_GRAD_OP`` within a node, and replicate parameters across
+      nodes. This is like ``HYBRID_SHARD``, except this may provide even higher throughput
+      since the unsharded parameters are not freed after the forward pass, saving the
+      all-gathers in the pre-backward.
+    """
+
+    FULL_SHARD = auto()
+    SHARD_GRAD_OP = auto()
+    NO_SHARD = auto()
+    HYBRID_SHARD = auto()
+    _HYBRID_SHARD_ZERO2 = auto()
+
+
+class BackwardPrefetch(Enum):
+    """
+    This configures explicit backward prefetching, which improves throughput by
+    enabling communication and computation overlap in the backward pass at the
+    cost of slightly increased memory usage.
+
+    - ``BACKWARD_PRE``: This enables the most overlap but increases memory
+      usage the most. This prefetches the next set of parameters *before* the
+      current set of parameters' gradient computation. This overlaps the *next
+      all-gather* and the *current gradient computation*, and at the peak, it
+      holds the current set of parameters, next set of parameters, and current
+      set of gradients in memory.
+    - ``BACKWARD_POST``: This enables less overlap but requires less memory
+      usage. This prefetches the next set of parameters *after* the current
+      set of parameters' gradient computation. This overlaps the *current
+      reduce-scatter* and the *next gradient computation*, and it frees the
+      current set of parameters before allocating memory for the next set of
+      parameters, only holding the next set of parameters and current set of
+      gradients in memory at the peak.
+    - FSDP's ``backward_prefetch`` argument accepts ``None``, which disables
+      the backward prefetching altogether. This has no overlap and does not
+      increase memory usage. In general, we do not recommend this setting since
+      it may degrade throughput significantly.
+
+    For more technical context: For a single process group using NCCL backend,
+    any collectives, even if issued from different streams, contend for the
+    same per-device NCCL stream, which implies that the relative order in which
+    the collectives are issued matters for overlapping. The two backward
+    prefetching values correspond to different issue orders.
+    """
+
+    # NOTE: For both modes, the ordering that defines "current" and "next" is
+    # not always exact in the current implementation. A mistargeted prefetch
+    # simply means that the parameter memory is allocated earlier than needed,
+    # possibly increasing peak memory usage, but does not affect correctness.
+    BACKWARD_PRE = auto()
+    BACKWARD_POST = auto()
+
+
+@dataclass
+class MixedPrecision:
+    """
+    This configures FSDP-native mixed precision training.
+
+    Attributes:
+        param_dtype (Optional[torch.dtype]): This specifies the dtype for model
+            parameters during forward and backward and thus the dtype for
+            forward and backward computation. Outside forward and backward, the
+            *sharded* parameters are kept in full precision (e.g. for the
+            optimizer step), and for model checkpointing, the parameters are
+            always saved in full precision. (Default: ``None``)
+        reduce_dtype (Optional[torch.dtype]): This specifies the dtype for
+            gradient reduction (i.e. reduce-scatter or all-reduce). If this is
+            ``None`` but ``param_dtype`` is not ``None``, then this takes on
+            the ``param_dtype`` value, still running gradient reduction in low
+            precision. This is permitted to differ from ``param_dtype``, e.g.
+            to force gradient reduction to run in full precision. (Default:
+            ``None``)
+        buffer_dtype (Optional[torch.dtype]): This specifies the dtype for
+            buffers. FSDP does not shard buffers. Rather, FSDP casts them to
+            ``buffer_dtype`` in the first forward pass and keeps them in that
+            dtype thereafter. For model checkpointing, the buffers are saved
+            in full precision except for ``LOCAL_STATE_DICT``. (Default:
+            ``None``)
+        keep_low_precision_grads (bool): If ``False``, then FSDP upcasts
+            gradients to full precision after the backward pass in preparation
+            for the optimizer step. If ``True``, then FSDP keeps the gradients
+            in the dtype used for gradient reduction, which can save memory if
+            using a custom optimizer that supports running in low precision.
+            (Default: ``False``)
+        cast_forward_inputs (bool): If ``True``, then this FSDP module casts
+            its forward args and kwargs to ``param_dtype``. This is to ensure
+            that parameter and input dtypes match for forward computation, as
+            required by many ops. This may need to be set to ``True`` when only
+            applying mixed precision to some but not all FSDP modules, in which
+            case a mixed-precision FSDP submodule needs to recast its inputs.
+            (Default: ``False``)
+        cast_root_forward_inputs (bool): If ``True``, then the root FSDP module
+            casts its forward args and kwargs to ``param_dtype``, overriding
+            the value of ``cast_forward_inputs``. For non-root FSDP modules,
+            this does not do anything. (Default: ``True``)
+        _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies
+            module classes to ignore for mixed precision when using an
+            ``auto_wrap_policy``: Modules of these classes will have FSDP
+            applied to them separately with mixed precision disabled (meaning
+            that the final FSDP construction would deviate from the specified
+            policy). If ``auto_wrap_policy`` is not specified, then this does
+            not do anything. This API is experimental and subject to change.
+            (Default: ``(_BatchNorm,)``)
+
+    .. note:: This API is experimental and subject to change.
+
+    .. note:: Only floating point tensors are cast to their specified dtypes.
+
+    .. note:: In ``summon_full_params``, parameters are forced to full
+        precision, but buffers are not.
+
+    .. note:: Layer norm and batch norm accumulate in ``float32`` even when
+        their inputs are in a low precision like ``float16`` or ``bfloat16``.
+        Disabling FSDP's mixed precision for those norm modules only means that
+        the affine parameters are kept in ``float32``. However, this incurs
+        separate all-gathers and reduce-scatters for those norm modules, which
+        may be inefficient, so if the workload permits, the user should prefer
+        to still apply mixed precision to those modules.
+
+    .. note:: By default, if the user passes a model with any ``_BatchNorm``
+        modules and specifies an ``auto_wrap_policy``, then the batch norm
+        modules will have FSDP applied to them separately with mixed precision
+        disabled. See the ``_module_classes_to_ignore`` argument.
+
+    .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and
+        ``cast_forward_inputs=False`` by default. For the root FSDP instance,
+        its ``cast_root_forward_inputs`` takes precedence over its
+        ``cast_forward_inputs``. For non-root FSDP instances, their
+        ``cast_root_forward_inputs`` values are ignored. The default setting is
+        sufficient for the typical case where each FSDP instance has the same
+        ``MixedPrecision`` configuration and only needs to cast inputs to the
+        ``param_dtype`` at the beginning of the model's forward pass.
+
+    .. note:: For nested FSDP instances with different ``MixedPrecision``
+        configurations, we recommend setting individual ``cast_forward_inputs``
+        values to configure casting inputs or not before each instance's
+        forward. In such a case, since the casts happen before each FSDP
+        instance's forward, a parent FSDP instance should have its non-FSDP
+        submodules run before its FSDP submodules to avoid the activation dtype
+        being changed due to a different ``MixedPrecision`` configuration.
+
+        Example::
+
+            >>> # xdoctest: +SKIP("undefined variables")
+            >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3))
+            >>> model[1] = FSDP(
+            >>>     model[1],
+            >>>     mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True),
+            >>> )
+            >>> model = FSDP(
+            >>>     model,
+            >>>     mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True),
+            >>> )
+
+        The above shows a working example. On the other hand, if ``model[1]``
+        were replaced with ``model[0]``, meaning that the submodule using
+        different ``MixedPrecision`` ran its forward first, then ``model[1]``
+        would incorrectly see ``float16`` activations instead of ``bfloat16``
+        ones.
+
+    """
+
+    param_dtype: Optional[torch.dtype] = None
+    reduce_dtype: Optional[torch.dtype] = None
+    buffer_dtype: Optional[torch.dtype] = None
+    keep_low_precision_grads: bool = False
+    cast_forward_inputs: bool = False
+    cast_root_forward_inputs: bool = True
+    _module_classes_to_ignore: Sequence[Type[torch.nn.Module]] = (_BatchNorm,)
+
+
+@dataclass
+class CPUOffload:
+    """
+    This configures CPU offloading.
+
+    Attributes:
+        offload_params (bool): This specifies whether to offload parameters to
+            CPU when not involved in computation. If ``True``, then this
+            offloads gradients to CPU as well, meaning that the optimizer step
+            runs on CPU.
+    """
+
+    offload_params: bool = False
+
+
+class StateDictType(Enum):
+    """
+    This enum indicates that which type of ``state_dict`` the FSDP module is
+    currently processing (returning or loading).
+    The default value is FULL_STATE_DICT to comply the PyTorch convention.
+    ..note::
+        FSDP currently supports three types of ``state_dict``:
+            1. ``state_dict/load_state_dict`: this pair of APIs return and load
+               the non-sharded, unflattened parameters. The semantics is the
+               same as using DDP.
+            2. ``_local_state_dict/_load_local_state_dict``: this pair of APIs return
+               and load local sharded, flattened parameters. The values returned
+               by ``_local_state_dict`` can be directly used by FSDP and is only
+               meaningful to FSDP (because parameters are flattened). Note that
+               these APIs are meant for use via the :func:`state_dict_type`
+               context manager as follows:
+                   >>> # xdoctest: +SKIP("undefined variables")
+                   >>> with fsdp.state_dict_type(StateDictType.LOCAL_STATE_DICT):
+                   ...     state = fsdp.state_dict()  # loads local state dict
+            3. ``_sharded_state_dict/_load_sharded_state_dict``: this pair of APIs
+               return and load sharded, unflattened parameters. The ``state_dict``
+               return by ``sharded_state_dict`` can be used by all other parallel
+               schemes (resharding may be required).
+    """
+
+    FULL_STATE_DICT = auto()
+    LOCAL_STATE_DICT = auto()
+    SHARDED_STATE_DICT = auto()
+
+
+@dataclass
+class StateDictConfig:
+    """
+    ``StateDictConfig`` is the base class for all ``state_dict`` configuration
+    classes. Users should instantiate a child class (e.g.
+    ``FullStateDictConfig``) in order to configure settings for the
+    corresponding ``state_dict`` type supported by FSDP.
+
+    Attributes:
+        offload_to_cpu (bool): If ``True``, then FSDP offloads the state dict
+            values to CPU, and if ``False``, then FSDP keeps them on GPU.
+            (Default: ``False``)
+    """
+
+    offload_to_cpu: bool = False
+
+
+@dataclass
+class FullStateDictConfig(StateDictConfig):
+    """
+    ``FullStateDictConfig`` is a config class meant to be used with
+    ``StateDictType.FULL_STATE_DICT``. We recommend enabling both
+    ``offload_to_cpu=True`` and ``rank0_only=True`` when saving full state
+    dicts to save GPU memory and CPU memory, respectively. This config class
+    is meant to be used via the :func:`state_dict_type` context manager as
+    follows:
+
+        >>> # xdoctest: +SKIP("undefined variables")
+        >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
+        >>> fsdp = FSDP(model, auto_wrap_policy=...)
+        >>> cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
+        >>> with FSDP.state_dict_type(fsdp, StateDictType.FULL_STATE_DICT, cfg):
+        >>>     state = fsdp.state_dict()
+        >>>     # `state` will be empty on non rank 0 and contain CPU tensors on rank 0.
+        >>> # To reload checkpoint for inference, finetuning, transfer learning, etc:
+        >>> model = model_fn() # Initialize model in preparation for wrapping with FSDP
+        >>> if dist.get_rank() == 0:
+        >>>     # Load checkpoint only on rank 0 to avoid memory redundancy
+        >>>     state_dict = torch.load("my_checkpoint.pt")
+        >>>     model.load_state_dict(state_dict)
+        >>> # All ranks initialize FSDP module as usual. `sync_module_states` argument
+        >>> # communicates loaded checkpoint states from rank 0 to rest of the world.
+        >>> fsdp = FSDP(model, device_id=torch.cuda.current_device(), auto_wrap_policy=..., sync_module_states=True)
+        >>> # After this point, all ranks have FSDP model with loaded checkpoint.
+
+    Attributes:
+        rank0_only (bool): If ``True``, then only rank 0 saves the full state
+            dict, and nonzero ranks save an empty dict. If ``False``, then all
+            ranks save the full state dict. (Default: ``False``)
+    """
+
+    rank0_only: bool = False
+
+
+@dataclass
+class LocalStateDictConfig(StateDictConfig):
+    pass
+
+
+@dataclass
+class ShardedStateDictConfig(StateDictConfig):
+    """
+    ``ShardedStateDictConfig`` is a config class meant to be used with
+    ``StateDictType.SHARDED_STATE_DICT``.
+
+    Attributes:
+        _use_dtensor (bool): If ``True``, then FSDP saves the state dict values
+            as ``DTensor``, and if ``False``, then FSDP saves them as
+            ``ShardedTensor``. (Default: ``False``)
+
+    .. warning:: ``_use_dtensor`` is a private field of :class:`ShardedStateDictConfig`
+      and it is used by FSDP to determine the type of state dict values. Users should not
+      manually modify ``_use_dtensor``.
+    """
+
+    _use_dtensor: bool = False
+
+
+@dataclass
+class OptimStateDictConfig:
+    """
+    ``OptimStateDictConfig`` is the base class for all ``optim_state_dict``
+    configuration classes.  Users should instantiate a child class (e.g.
+    ``FullOptimStateDictConfig``) in order to configure settings for the
+    corresponding ``optim_state_dict`` type supported by FSDP.
+
+    Attributes:
+        offload_to_cpu (bool): If ``True``, then FSDP offloads the state dict's
+            tensor values to CPU, and if ``False``, then FSDP keeps them on the
+            original device (which is GPU unless parameter CPU offloading is
+            enabled). (Default: ``True``)
+    """
+
+    offload_to_cpu: bool = True
+
+
+@dataclass
+class FullOptimStateDictConfig(OptimStateDictConfig):
+    """
+    Attributes:
+        rank0_only (bool): If ``True``, then only rank 0 saves the full state
+            dict, and nonzero ranks save an empty dict. If ``False``, then all
+            ranks save the full state dict. (Default: ``False``)
+    """
+
+    rank0_only: bool = False
+
+
+@dataclass
+class LocalOptimStateDictConfig(OptimStateDictConfig):
+    offload_to_cpu: bool = False
+
+
+@dataclass
+class ShardedOptimStateDictConfig(OptimStateDictConfig):
+    """
+    ``ShardedOptimStateDictConfig`` is a config class meant to be used with
+    ``StateDictType.SHARDED_STATE_DICT``.
+
+    Attributes:
+        _use_dtensor (bool): If ``True``, then FSDP saves the state dict values
+            as ``DTensor``, and if ``False``, then FSDP saves them as
+            ``ShardedTensor``. (Default: ``False``)
+
+    .. warning:: ``_use_dtensor`` is a private field of :class:`ShardedOptimStateDictConfig`
+      and it is used by FSDP to determine the type of state dict values. Users should not
+      manually modify ``_use_dtensor``.
+    """
+
+    _use_dtensor: bool = False
+
+
+@dataclass
+class StateDictSettings:
+    state_dict_type: StateDictType
+    state_dict_config: StateDictConfig
+    optim_state_dict_config: OptimStateDictConfig

infer_4_47_1/lib/python3.10/site-packages/torch/distributed/fsdp/sharded_grad_scaler.py

@@ -0,0 +1,396 @@
+# mypy: allow-untyped-defs
+import logging
+from collections import abc, defaultdict
+from typing import Any, Dict, Iterable, List, Optional, overload, Sequence, Tuple, Union
+
+import torch
+import torch.distributed as dist
+from torch.amp.grad_scaler import _MultiDeviceReplicator, GradScaler, OptState
+from torch.distributed.distributed_c10d import ProcessGroup
+
+
+logger = logging.getLogger(__name__)
+
+
+def _refresh_per_optimizer_state() -> Dict[str, Any]:
+    return {"stage": OptState.READY, "found_inf_per_device": {}}
+
+
+def _is_supported_device(tensor: torch.Tensor) -> bool:
+    return tensor.is_cuda or tensor.device.type in (
+        "xla",
+        "cpu",
+        "hpu",
+        "mtia",
+        torch._C._get_privateuse1_backend_name(),
+    )
+
+
+class _GeneralMultiDeviceReplicator(_MultiDeviceReplicator):
+    """
+    Lazily serves tensor to request device. This class extends
+    _MultiDeviceReplicator to allow support for "cpu" as a device.
+    """
+
+    def __init__(self, master_tensor: torch.Tensor) -> None:
+        assert _is_supported_device(master_tensor)
+        self.master = master_tensor
+        self._per_device_tensors: Dict[torch.device, torch.Tensor] = {}
+
+
+class ShardedGradScaler(GradScaler):
+    """
+    ShardedGradScaler helps perform gradient scaling in a shard aware manner. It extends
+    functionality from GradScaler:
+    * Supports Pytorch DDP and FSDP implementations
+    * Support CPU offloaded tensors (as used in fully sharded data parallel[FSDP])
+    * Supports the custom Mixed Precision loss dtype (fp16, bf16) that FSDP returns
+    * Sync inf/nan for scaled gradient tensors on any torch.device (where tensors are placed) across
+    nodes
+
+    Example::
+
+        # Creates a ShardedGradScaler once at the beginning of training.
+        scaler = ShardedGradScaler()
+
+        for epoch in epochs:
+            for input, target in data:
+                optimizer.zero_grad()
+                output = model(input)
+                loss = loss_fn(output, target)
+
+                # Scales loss.  Calls backward() on scaled loss to create scaled gradients.
+                scaler.scale(loss).backward()
+
+                # scaler.step() first unscales gradients of the optimizer's params.
+                # If gradients don't contain infs/NaNs, optimizer.step() is then called,
+                # otherwise, optimizer.step() is skipped.
+                scaler.step(optimizer)
+
+                # Updates the scale for next iteration.
+                scaler.update()
+
+    See :class:`GradScaler` for explanation of scaling/unscaling and more use cases.
+
+    Args:
+        init_scale (float, optional, default=2.**16):  Initial scale factor.
+        growth_factor (float, optional, default=2.0):  Factor by which the scale is multiplied during
+            :meth:`update` if no inf/NaN gradients occur for ``growth_interval`` consecutive iterations.
+        backoff_factor (float, optional, default=0.5):  Factor by which the scale is multiplied during
+            :meth:`update` if inf/NaN gradients occur in an iteration.
+        growth_interval (int, optional, default=2000):  Number of consecutive iterations without inf/NaN gradients
+            that must occur for the scale to be multiplied by ``growth_factor``.
+        enabled (bool, optional):  If ``False``, disables gradient scaling. :meth:`step` simply
+            invokes the underlying ``optimizer.step()``, and other methods become no-ops.
+            Default: ``True``
+        process_group (ProcessGroup, optional, default=torch.distributed.group.WORLD):
+            process group for sharding
+    """
+
+    def __init__(
+        self,
+        device: str = "cuda",
+        init_scale: float = 2.0**16,
+        backoff_factor: float = 0.5,
+        growth_factor: float = 2.0,
+        growth_interval: int = 2000,
+        enabled: bool = True,
+        process_group: Optional[ProcessGroup] = dist.group.WORLD,
+    ) -> None:
+        super().__init__(
+            device,
+            init_scale=init_scale,
+            backoff_factor=backoff_factor,
+            growth_factor=growth_factor,
+            growth_interval=growth_interval,
+            enabled=enabled,
+        )
+        if self._enabled:
+            self.process_group = process_group
+            self._per_optimizer_states = defaultdict(_refresh_per_optimizer_state)
+
+    @overload
+    def scale(self, outputs: torch.Tensor) -> torch.Tensor:
+        ...
+
+    @overload
+    def scale(self, outputs: List[torch.Tensor]) -> List[torch.Tensor]:
+        ...
+
+    @overload
+    def scale(self, outputs: Tuple[torch.Tensor, ...]) -> Tuple[torch.Tensor, ...]:
+        ...
+
+    @overload
+    def scale(self, outputs: Iterable[torch.Tensor]) -> Iterable[torch.Tensor]:
+        ...
+
+    def scale(
+        self, outputs: Union[torch.Tensor, Iterable[torch.Tensor]]
+    ) -> Union[torch.Tensor, Iterable[torch.Tensor]]:
+        if not self._enabled:
+            return outputs
+
+        if isinstance(outputs, torch.Tensor):
+            assert _is_supported_device(outputs)
+            if self._scale is None:
+                self._lazy_init_scale_growth_tracker(outputs.device)
+            assert self._scale is not None
+            scaled_output = outputs * self._scale.to(
+                device=outputs.device, non_blocking=True
+            )
+            # Here we ensure the return dtype is the same as the outputs dtype.
+            # For the FSDP + Mixed Precision use case, the loss output is in the Mixed Precision
+            # format (fp16, bf16) and so the scaled loss should be of the same dtype.
+            return scaled_output.type(outputs.dtype)
+
+        stash: List[_GeneralMultiDeviceReplicator] = []
+
+        def apply_scale(val: Union[torch.Tensor, Iterable[torch.Tensor]]):
+            if isinstance(val, torch.Tensor):
+                assert _is_supported_device(val)
+                if len(stash) == 0:
+                    if self._scale is None:
+                        self._lazy_init_scale_growth_tracker(val.device)
+                    assert self._scale is not None
+                    stash.append(_GeneralMultiDeviceReplicator(self._scale))
+                scaled_val = val * stash[0].get(val.device)
+                # Here we ensure the return dtype is the same as the outputs dtype.
+                # For the FSDP + Mixed Precision use case, the loss output is in the Mixed Precision
+                # format (fp16, bf16) and so the scaled loss should be of the same dtype.
+                return scaled_val.type(val.dtype)
+            if isinstance(val, abc.Iterable):
+                iterator = map(apply_scale, val)
+                if isinstance(val, (list, tuple)):
+                    return type(val)(iterator)
+                return iterator
+            raise ValueError("outputs must be a Tensor or an iterable of Tensors")
+
+        return apply_scale(outputs)
+
+    def _foreach_non_finite_check_and_unscale_cpu_(
+        self,
+        grads: Sequence[torch.Tensor],
+        found_inf: torch.Tensor,
+        inv_scale: torch.Tensor,
+    ) -> None:
+        if len(grads) == 0:
+            return
+        assert inv_scale.numel() == 1, "inv_scale must be a 1-element tensor."
+        assert found_inf.numel() == 1, "found_inf must be a 1-element tensor."
+
+        for grad in grads:
+            if grad.device.type != "cpu":
+                logger.error(
+                    "tensor device is %s but was expected to be ``cpu``",
+                    grad.device,
+                )
+                raise ValueError(
+                    "Gradients were found on a non-CPU device when"
+                    " expected to be on CPU."
+                )
+            if (
+                torch.isinf(grad).any().item() is True
+                or torch.isnan(grad).any().item() is True
+            ):
+                found_inf.data = torch.tensor([1.0])
+                break
+            else:
+                grad.data *= inv_scale.item()
+
+    def _unscale_grads_(
+        self,
+        optimizer: torch.optim.Optimizer,
+        inv_scale: torch.Tensor,
+        found_inf: torch.Tensor,
+        allow_fp16: bool = True,
+    ) -> Dict[torch.device, torch.Tensor]:
+        per_device_inv_scale = _GeneralMultiDeviceReplicator(inv_scale)
+        per_device_found_inf = _GeneralMultiDeviceReplicator(found_inf)
+
+        # To set up _amp_foreach_non_finite_check_and_unscale_, split grads by device and dtype.
+        # There could be thousands of grads, so we'd like to iterate through them just once.
+        # However, we don't know their devices or dtypes in advance.
+
+        # https://stackoverflow.com/questions/5029934/defaultdict-of-defaultdict
+        # Google says mypy struggles with defaultdicts type annotations.
+        per_device_and_dtype_grads = defaultdict(lambda: defaultdict(list))  # type: ignore[var-annotated]
+        with torch.no_grad():
+            for group in optimizer.param_groups:
+                for param in group["params"]:
+                    if param.grad is None:
+                        continue
+                    if (not allow_fp16) and param.grad.dtype == torch.float16:
+                        raise ValueError("Attempting to unscale FP16 gradients.")
+                    if param.grad.is_sparse:
+                        # is_coalesced() == False means the sparse grad has values with duplicate indices.
+                        # coalesce() deduplicates indices and adds all values that have the same index.
+                        # For scaled fp16 values, there's a good chance coalescing will cause overflow,
+                        # so we should check the coalesced _values().
+                        if param.grad.dtype is torch.float16:
+                            # coalesce is not supported in torch.float16
+                            param_grad_fp32 = param.grad.type(torch.float32).coalesce()
+                            param.grad = param_grad_fp32.type(torch.float16)
+                        to_unscale = param.grad._values()
+                    else:
+                        to_unscale = param.grad
+
+                    per_device_and_dtype_grads[to_unscale.device][
+                        to_unscale.dtype
+                    ].append(to_unscale)
+
+            for device, per_dtype_grads in per_device_and_dtype_grads.items():
+                for grads in per_dtype_grads.values():
+                    if grads[0].device.type == "cpu":
+                        self._foreach_non_finite_check_and_unscale_cpu_(
+                            grads,
+                            per_device_found_inf.get(device),
+                            per_device_inv_scale.get(device),
+                        )
+                    else:
+                        torch._amp_foreach_non_finite_check_and_unscale_(
+                            grads,
+                            per_device_found_inf.get(device),
+                            per_device_inv_scale.get(device),
+                        )
+        # There exist contexts (e.g. w/ `use_orig_params=True`) wherein some
+        # ranks may have no (non-zero sized) parameter shards, necessitating the
+        # initialization of `per_device_found_inf._per_device_tensors` here
+        if not per_device_found_inf._per_device_tensors:
+            assert self._scale is not None
+            per_device_found_inf.get(self._scale.device)
+        return per_device_found_inf._per_device_tensors
+
+    def unscale_(self, optimizer: torch.optim.Optimizer) -> None:
+        if not self._enabled:
+            return
+
+        self._check_scale_growth_tracker("unscale_")
+
+        optimizer_state = self._per_optimizer_states[id(optimizer)]
+
+        if optimizer_state["stage"] is OptState.UNSCALED:
+            raise RuntimeError(
+                "unscale_() has already been called on this optimizer since the last update()."
+            )
+        elif optimizer_state["stage"] is OptState.STEPPED:
+            raise RuntimeError("unscale_() is being called after step().")
+
+        # FP32 division can be imprecise for certain compile options, so we carry out the reciprocal in FP64.
+        assert self._scale is not None
+        inv_scale = self._scale.double().reciprocal().float()
+        found_inf = torch.full(
+            (1,), 0.0, dtype=torch.float32, device=self._scale.device
+        )
+
+        optimizer_state["found_inf_per_device"] = self._unscale_grads_(
+            optimizer, inv_scale, found_inf, True
+        )
+        optimizer_state["stage"] = OptState.UNSCALED
+
+        # Synchronize the detected inf across the ranks
+        optimizer_state = self._per_optimizer_states[id(optimizer)]
+        works = []
+        found_inf_on_cpus = []
+        found_inf_on_devices = []
+
+        for found_inf in optimizer_state["found_inf_per_device"].values():
+            if self._device != "cpu" and found_inf.device.type == "cpu":
+                found_inf_on_cpus.append(found_inf)
+                found_inf_on_device = found_inf.to(self._device)
+                found_inf_on_devices.append(found_inf_on_device)
+                works.append(
+                    dist.all_reduce(
+                        found_inf_on_device, async_op=True, group=self.process_group
+                    )
+                )
+            else:
+                works.append(
+                    dist.all_reduce(found_inf, async_op=True, group=self.process_group)
+                )
+        for work in works:
+            work.wait()
+        if found_inf_on_cpus:
+            torch._foreach_copy_(found_inf_on_cpus, found_inf_on_devices)
+
+    def _amp_update_scale_cpu_(self, found_inf: torch.Tensor) -> None:
+        """
+        If found_inf is 1.0 (True), then scale is multiplied by backoff_factor and growth_tracker is set to zero.
+        Otherwise, scale is multiplied by the growth factor when the growth interval is reached.
+        """
+        assert self._scale is not None and self._growth_tracker is not None
+
+        if found_inf.item() >= 1.0:
+            self._scale *= self._backoff_factor
+            self._growth_tracker.fill_(0)
+        else:
+            successful = self._growth_tracker + 1
+            if successful == self._growth_interval:
+                self._scale *= self._growth_factor
+                self._growth_tracker.fill_(0)
+            else:
+                self._growth_tracker = successful
+
+    def update(self, new_scale: Optional[Union[float, torch.Tensor]] = None) -> None:
+        """
+        Updates the scale factor.
+        If any optimizer steps were skipped the scale is multiplied by ``backoff_factor``
+        to reduce it. If ``growth_interval`` unskipped iterations occurred consecutively,
+        the scale is multiplied by ``growth_factor`` to increase it.
+        Passing ``new_scale`` sets the new scale value manually. (``new_scale`` is not
+        used directly, it's used to fill GradScaler's internal scale tensor. So if
+        ``new_scale`` was a tensor, later in-place changes to that tensor will not further
+        affect the scale GradScaler uses internally.)
+        Args:
+            new_scale (float or :class:`torch.Tensor`, optional, default=None):  New scale factor.
+        .. warning::
+            :meth:`update` should only be called at the end of the iteration, after ``scaler.step(optimizer)`` has
+            been invoked for all optimizers used this iteration.
+        """
+
+        if not self._enabled:
+            return
+
+        _scale, _growth_tracker = self._check_scale_growth_tracker("update")  # type: ignore[var-annotated]
+
+        if new_scale is not None:
+            # Accept a new user-defined scale.
+            if isinstance(new_scale, float):
+                self._scale.fill_(new_scale)  # type: ignore[union-attr]
+            else:
+                reason = "new_scale should be a float or a 1-element torch.cuda.FloatTensor or \
+                    torch.FloatTensor with requires_grad=False."
+                assert new_scale.device.type == self._device, reason
+                assert new_scale.numel() == 1, reason
+                assert new_scale.requires_grad is False, reason
+                self._scale.copy_(new_scale)  # type: ignore[union-attr]
+        else:
+            # Consume shared inf/nan data collected from optimizers to update the scale.
+            # If all found_inf tensors are on the same device as self._scale, this operation is asynchronous.
+            found_infs = [
+                found_inf.to(device=_scale.device, non_blocking=True)
+                for state in self._per_optimizer_states.values()
+                for found_inf in state["found_inf_per_device"].values()
+            ]
+
+            assert len(found_infs) > 0, "No inf checks were recorded prior to update."
+
+            found_inf_combined = found_infs[0]
+            if len(found_infs) > 1:
+                for i in range(1, len(found_infs)):
+                    found_inf_combined += found_infs[i]
+
+            if _scale.device.type == "cpu":
+                self._amp_update_scale_cpu_(found_inf_combined)
+            else:
+                torch._amp_update_scale_(
+                    self._scale,  # type: ignore[arg-type]
+                    self._growth_tracker,  # type: ignore[arg-type]
+                    found_inf_combined,
+                    self._growth_factor,  # type: ignore[arg-type]
+                    self._backoff_factor,  # type: ignore[arg-type]
+                    self._growth_interval,  # type: ignore[arg-type]
+                )
+
+        # To prepare for next iteration, clear the data collected from optimizers this iteration.
+        self._per_optimizer_states = defaultdict(_refresh_per_optimizer_state)

infer_4_47_1/lib/python3.10/site-packages/torch/distributed/fsdp/wrap.py

@@ -0,0 +1,608 @@
+# mypy: allow-untyped-defs
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the BSD license found in the
+# LICENSE file in the root directory of this source tree.
+
+import contextlib
+import copy
+from abc import ABC, abstractmethod
+from typing import (
+    Any,
+    Callable,
+    cast,
+    Dict,
+    Generator,
+    Iterable,
+    Optional,
+    Sequence,
+    Set,
+    Tuple,
+    Type,
+    Union,
+)
+
+import torch.nn as nn
+
+
+__all__ = [
+    "always_wrap_policy",
+    "lambda_auto_wrap_policy",
+    "transformer_auto_wrap_policy",
+    "size_based_auto_wrap_policy",
+    "enable_wrap",
+    "wrap",
+    "CustomPolicy",
+    "ModuleWrapPolicy",
+]
+
+
+# NOTE: We intentionally keep this function simple and isolate the complexity
+# to `fn` to enable using this function generically. We may move this to a
+# non-FSDP-specific folder and/or make it public in the future.
+def _post_order_apply(
+    root_module: nn.Module,
+    fn: Callable[[nn.Module], Optional[nn.Module]],
+):
+    """
+    This applies ``fn`` to every module in the module tree of ``root_module``
+    following a post-order traversal. If ``fn`` returns an :class:`nn.Module`,
+    then this replaces the original module with the newly returned one in the
+    tree. Otherwise, ``fn`` should return ``None``, in which case the module is
+    not changed.
+    """
+    # Track visited modules to avoid visiting shared modules multiple times
+    visited_modules: Set[nn.Module] = {root_module}
+
+    def _post_order_apply_inner(
+        module: nn.Module,
+        module_name: str,
+        parent_module: Optional[nn.Module],
+    ):
+        for child_module_name, child_module in module.named_children():
+            if child_module not in visited_modules:
+                visited_modules.add(child_module)
+                _post_order_apply_inner(child_module, child_module_name, module)
+        optional_module = fn(module)
+        if optional_module is not None:
+            assert isinstance(parent_module, nn.Module), (
+                "Non-root modules should have their parent module set but got "
+                f"{parent_module} for {module}"
+            )
+            assert module_name, (
+                "Non-root modules should have their module name set but got "
+                f"an empty module name for {module}"
+            )
+            assert isinstance(
+                optional_module, nn.Module
+            ), f"fn should return None or an nn.Module but got {optional_module}"
+            setattr(parent_module, module_name, optional_module)
+
+    _post_order_apply_inner(root_module, "", None)
+
+
+def _construct_wrap_fn(
+    root_module: nn.Module,
+    target_module_to_kwargs: Dict[nn.Module, Dict[str, Any]],
+    fsdp_fn: Callable,
+) -> Callable[[nn.Module], Optional[nn.Module]]:
+    """
+    This constructs the "wrap" function to pass to :func:`_post_order_apply`
+    based on ``target_module_to_kwargs``, which should be constructed from the
+    wrapping policy.
+    """
+
+    def fn(module: nn.Module) -> Optional[nn.Module]:
+        # Explicitly avoid wrapping the root module since for FSDP, it is
+        # handled by the caller
+        if module in target_module_to_kwargs and module is not root_module:
+            kwargs = target_module_to_kwargs[module]
+            return fsdp_fn(module, **kwargs)
+        return None
+
+    return fn
+
+
+def _run_mixed_precision_override_policy(
+    root_module: nn.Module,
+    module_classes: Iterable[Type[nn.Module]],
+    ignored_modules: Set[nn.Module],
+    root_kwargs: Dict[str, Any],
+    target_module_to_kwargs: Dict[nn.Module, Dict[str, Any]],
+):
+    module_classes_tuple = tuple(set(module_classes))
+    for module in root_module.modules():
+        if module in ignored_modules:
+            continue
+        elif isinstance(module, module_classes_tuple):
+            # This policy overrides any existing policy
+            if module not in target_module_to_kwargs:
+                # Only inherit from the root kwargs if not already specified
+                target_module_to_kwargs[module] = root_kwargs
+            target_module_to_kwargs[module]["mixed_precision"] = None
+    return target_module_to_kwargs
+
+
+def always_wrap_policy(*args, **kwargs) -> bool:
+    """
+    A simple recursive wrap policy that always returns ``True``. This means
+    that every submodule is wrapped by the wrapper class in
+    :func:`_recursive_wrap`.
+    """
+    return True
+
+
+class _Policy(ABC):
+    """
+    This defines an abstract base class that represents a policy for applying
+    a module-level API.
+    """
+
+    @abstractmethod
+    def _run_policy(
+        self,
+        root_module: nn.Module,
+        ignored_modules: Set[nn.Module],
+        root_kwargs: Dict[str, Any],
+    ) -> Dict[nn.Module, Dict[str, Any]]:
+        """
+        This should return a dict ``target_module_to_kwargs`` that maps from
+        each target module to wrap to its kwargs.
+        """
+        ...
+
+
+def _module_wrap_policy(
+    module: nn.Module,
+    recurse: bool,
+    nonwrapped_numel: int,
+    module_classes: Set[Type[nn.Module]],
+) -> bool:
+    """
+    This auto wrap policy wraps every module that is an instance of any type in
+    ``module_classes`` as its own FSDP instance. The root module given by
+    ``module`` is always wrapped as an FSDP instance regardless. Since the
+    wrapping proceeds bottom up, each FSDP instance manages the parameters in
+    its subtree excluding any already managed by a child FSDP instance.
+
+    Args:
+        module (nn.Module): Current module being considered.
+        recurse (bool): If ``False``, then this function must decide whether
+            ``module`` should be wrapped as an FSDP instance or not. If
+            ``True``, then the function is still recursing down the module
+            tree as a part of the DFS.
+        nonwrapped_numel (int): Parameter numel not yet wrapped.
+        module_classes (Set[Type[nn.Module]]): Set of module classes that are
+            wrapped as FSDP instances.
+
+    Returns:
+        ``True`` if ``recurse=True``, and whether ``module`` should be wrapped
+        if ``recurse=False``.
+    """
+    if recurse:
+        return True  # always recurse
+    return isinstance(module, tuple(module_classes))
+
+
+class ModuleWrapPolicy(_Policy):
+    """
+    This policy applies to every module of the specified module classes,
+    passing in the kwargs given to the root.
+    """
+
+    def __init__(self, module_classes: Iterable[Type[nn.Module]]):
+        module_classes_set = set(module_classes)
+        self._module_classes = module_classes_set
+        self._module_classes_str = str(module_classes_set)
+
+    def _run_policy(
+        self,
+        root_module: nn.Module,
+        ignored_modules: Set[nn.Module],
+        root_kwargs: Dict[str, Any],
+    ) -> Dict[nn.Module, Dict[str, Any]]:
+        module_classes = tuple(self._module_classes)
+        target_module_to_kwargs: Dict[nn.Module, Dict[str, Any]] = {}
+        for module in root_module.modules():
+            if module in ignored_modules:
+                continue
+            elif isinstance(module, module_classes):
+                # Shallow copy to avoid coupling changes across modules
+                target_module_to_kwargs[module] = copy.copy(root_kwargs)
+        return target_module_to_kwargs
+
+    def __call__(self, module, recurse, *args, **kwargs):
+        # nonwrapped_numel is not used.
+        return _module_wrap_policy(
+            module, recurse, nonwrapped_numel=-1, module_classes=self._module_classes
+        )
+
+    def __repr__(self) -> str:
+        return super().__repr__() + f"({self._module_classes_str})"
+
+
+class CustomPolicy(_Policy):
+    """
+    This policy takes in a lambda function that maps a given ``nn.Module`` to
+    either ``False``, ``True``, or a kwarg dictionary.
+    - If the function returns ``False`` or an empty dictionary, then the module
+      does not have the API applied.
+    - If the function returns ``True``, then the module has the API applied
+      with the root's kwargs.
+    - If the function returns a non-empty dictionary, then the module has the
+      API applied, and the dictionary overrides the root's kwargs.
+
+    Example::
+
+        >>> # xdoctest: +SKIP("undefined variables")
+        >>> model = init_transformer_model(...)
+        >>> def lambda_fn(module: nn.Module):
+        >>>     if module is model.lm_head:
+        >>>         return {"sharding_strategy": ShardingStrategy.SHARD_GRAD_OP}
+        >>>     elif isinstance(module, TransformerBlock):
+        >>>         return True
+        >>>     return False
+        >>> policy = CustomPolicy(lambda_fn)
+        >>> fsdp_model = FSDP(model, auto_wrap_policy=policy)
+    """
+
+    def __init__(self, lambda_fn: Callable[[nn.Module], Union[bool, Dict[str, Any]]]):
+        self._lambda_fn = lambda_fn
+
+    def _run_policy(
+        self,
+        root_module: nn.Module,
+        ignored_modules: Set[nn.Module],
+        root_kwargs: Dict[str, Any],
+    ) -> Dict[nn.Module, Dict[str, Any]]:
+        target_module_to_kwargs: Dict[nn.Module, Dict[str, Any]] = {}
+        for module in root_module.modules():
+            if module in ignored_modules:
+                continue
+            res = self._lambda_fn(module)
+            if not isinstance(res, (dict, bool)):
+                raise ValueError(
+                    "The lambda_fn passed to CustomPolicy should return "
+                    f"False/True or a kwarg dict, but it returned {res}"
+                )
+            if not res:
+                continue
+            kwargs = copy.copy(root_kwargs)
+            if isinstance(res, dict):
+                # Override the root kwargs with the ones specified by the
+                # lambda function
+                kwargs.update(res)
+            target_module_to_kwargs[module] = kwargs
+        return target_module_to_kwargs
+
+
+def lambda_auto_wrap_policy(
+    module: nn.Module, recurse: bool, nonwrapped_numel: int, lambda_fn: Callable
+) -> bool:
+    """
+    A convenient auto wrap policy to wrap submodules based on an arbitrary user
+    function. If `lambda_fn(submodule) == True``, the submodule will be wrapped as
+    a `wrapper_cls` unit.
+
+    Return if a module should be wrapped during auto wrapping.
+
+    The first three parameters are required by :func:`_recursive_wrap`.
+
+    Args:
+        module (nn.Module): Current module being considered.
+        recurse (bool): If ``False``, then this function must decide whether
+            ``module`` should be wrapped as an FSDP instance or not. If
+            ``True``, then the function is still recursing down the module
+            tree as a part of the DFS.
+        nonwrapped_numel (int): Parameter numel not yet wrapped.
+
+        lambda_fn (Callable[[nn.Module], bool]): If this returns ``True``, then
+            this module will be wrapped.
+    """
+    if recurse:
+        return True  # always recurse
+    return lambda_fn(module)
+
+
+def transformer_auto_wrap_policy(
+    module: nn.Module,
+    recurse: bool,
+    nonwrapped_numel: int,
+    transformer_layer_cls: Set[Type[nn.Module]],
+) -> bool:
+    """
+    See :func:`_module_wrap_policy`, where ``transformer_layer_cls`` is the
+    same as ``module_classes``. Note that shared parameters must be wrapped in
+    the same FSDP instance, so this auto wrap policy can help wrap shared
+    embeddings into the same FSDP instance for transformer models.
+    """
+    return _module_wrap_policy(module, recurse, nonwrapped_numel, transformer_layer_cls)
+
+
+def _wrap_module_cls_individually(
+    module: nn.Module, module_classes: Sequence[type], recurse: bool, *args, **kwargs
+):
+    if recurse:
+        # always recurse
+        return True
+    else:
+        # if not recursing, decide whether we should wrap based on whether the type of module
+        # is in `module_classes`.
+        return isinstance(module, tuple(module_classes))
+
+
+def _or_policy(
+    module: nn.Module,
+    recurse: bool,
+    nonwrapped_numel: int,
+    policies,
+) -> bool:
+    """
+    A policy that wraps ``module`` if any policy in the passed in iterable of
+    ``policies`` returns ``True``.
+    """
+    return any(
+        policy(module=module, recurse=recurse, nonwrapped_numel=nonwrapped_numel)
+        for policy in policies
+    )
+
+
+def size_based_auto_wrap_policy(
+    module: nn.Module,
+    recurse: bool,
+    nonwrapped_numel: int,
+    # Additional custom arguments
+    min_num_params: int = int(1e8),
+    force_leaf_modules: Optional[Set[Type[nn.Module]]] = None,
+    exclude_wrap_modules: Optional[Set[Type[nn.Module]]] = None,
+) -> bool:
+    """
+    A size-based auto wrap policy.
+
+    Args:
+        module (nn.Module): Current module being considered.
+        recurse (bool): If ``False``, then this function must decide whether
+            ``module`` should be wrapped as an FSDP instance or not. If
+            ``True``, then the function is still recursing down the module
+            tree as a part of the DFS.
+        nonwrapped_numel (int): Parameter numel not yet wrapped.
+
+        min_num_params (int): Customizable policy input that controls the size
+            threshold over which a module is ready to be wrapped. This is in
+            units of numel.
+        force_leaf_modules (Set[Type[nn.Module]]): Set of module types to keep
+            as leaves, i.e. their children will never be wrapped.
+        exclude_wrap_modules (Set[Type[nn.Module]]): Set of module types to be
+            excluded in wrapping.
+
+    Returns:
+        Whether ``module`` should be wrapped.
+    """
+    force_leaf_modules = (
+        size_based_auto_wrap_policy.FORCE_LEAF_MODULES  # type: ignore[attr-defined]
+        if force_leaf_modules is None
+        else force_leaf_modules
+    )
+    exclude_wrap_modules = (
+        size_based_auto_wrap_policy.EXCLUDE_WRAP_MODULES  # type: ignore[attr-defined]
+        if exclude_wrap_modules is None
+        else exclude_wrap_modules
+    )
+
+    # Keep the argument `min_num_params` for BC for now, but it represents the
+    # minimum non-wrapped *numel* before triggering a wrapping
+    min_nonwrapped_numel = min_num_params
+    is_large = nonwrapped_numel >= min_nonwrapped_numel
+    if recurse:
+        # We should recurse if the module is big enough but not in force_leaf_modules list.
+        return is_large and not isinstance(module, tuple(force_leaf_modules))
+    else:
+        # If we are not recursing, determine if we should wrap.
+        return is_large and not isinstance(module, tuple(exclude_wrap_modules))
+
+
+# Set those defaults to the size_based_auto_wrap_policy function. Make them easy to be imported.
+size_based_auto_wrap_policy.EXCLUDE_WRAP_MODULES = {nn.ModuleList, nn.ModuleDict}  # type: ignore[attr-defined]
+size_based_auto_wrap_policy.FORCE_LEAF_MODULES = {nn.MultiheadAttention}  # type: ignore[attr-defined]
+
+
+@contextlib.contextmanager
+def enable_wrap(
+    *, wrapper_cls: Any, **wrapper_kwargs: Any
+) -> Generator[None, None, None]:
+    """
+    Context manager to wrap modules using a wrapper.
+
+    Useful for when you'd like to apply the same configuration arguments to all
+    child modules that you wrap. A particularly important use case is wrapping
+    large layers so that they get sharded (in-place) during initialization, to
+    avoid running out of system memory. Large layers can indicate that they
+    should be sharded via the ``wrap`` annotation and this context manager can
+    provide the exact configuration for these nested instances.
+
+    Usage::
+
+        with enable_wrap(wrapper_cls, **params):
+            # Wraps layer in FSDP by default if within context
+            self.l1 = wrap(torch.nn.Linear(5, 5))
+
+    Args:
+        wrapper_cls:
+            Class that `wrap` annotation will `wrap` modules with, such as
+            `FullyShardedDataParallel`.
+        **wrapper_kwargs:
+            Configuration settings that will be passed to all ``wrap``
+            instances inside the context
+    """
+    kwargs = {
+        "wrapper_cls": wrapper_cls,
+        **wrapper_kwargs,
+    }
+    with _ConfigAutoWrap(**kwargs):
+        yield
+
+
+def wrap(module: nn.Module, **wrap_overrides: Any) -> nn.Module:
+    """
+    Annotate that a module should be wrapped. Annotated modules will only be
+    wrapped if inside of an :func:`enable_wrap` context manager. This allows
+    a module to be initialized both with and without a wrapper without code
+    change.
+
+    The class that this function wraps the passed in ``nn.Module`` with is the
+    passed in ``wrapper_cls`` argument into ``enable_wrap``. Both
+    ``enable_wrap`` and ``wrap`` can take in kwargs specifying how to construct
+    the ``wrapper_cls`` instance. In the case of duplicate kwargs in
+    ``enable_wrap`` and ``wrap``, the argument passed into ``wrap`` will be
+    respected.
+
+    Usage::
+
+        with enable_wrap(wrapper_cls=FSDP, **fsdp_config):
+            # Wraps layer in FSDP by default if within context
+            self.l1 = wrap(torch.nn.Linear(5, 5))
+
+    Args:
+        module (nn.Module): module to wrap (if in :func:`enable_wrap` context)
+        **wrap_overrides: configuration overrides that will take priority over
+            the values provided by the :func:`enable_wrap` context
+    """
+    if _ConfigAutoWrap.in_autowrap_context:
+        assert _ConfigAutoWrap.wrapper_cls is not None
+
+        wrap_overrides = {**_ConfigAutoWrap.kwargs, **wrap_overrides}
+        return _wrap(
+            module,
+            _ConfigAutoWrap.wrapper_cls,
+            **wrap_overrides,
+        )
+    return module
+
+
+def _wrap(module: nn.Module, wrapper_cls: Callable, **kwargs) -> nn.Module:
+    assert wrapper_cls is not None
+    if hasattr(module, "_wrap_overrides"):
+        # If module has a _wrap_overrides attribute, we force overriding the
+        # FSDP config with these attributes for this module. Currently this
+        # is only used to disable mixed precision for BatchNorm when
+        # auto_wrapping.
+        overrides = {**kwargs, **module._wrap_overrides}  # type: ignore[arg-type]
+        return wrapper_cls(module, **overrides)
+
+    return wrapper_cls(module, **kwargs)
+
+
+def _recursive_wrap(
+    module: nn.Module,
+    auto_wrap_policy: Callable,
+    wrapper_cls: Callable,
+    ignored_modules: Set[nn.Module],
+    ignored_params: Set[nn.Parameter],
+    only_wrap_children: bool = False,
+    **kwargs: Any,
+) -> Tuple[nn.Module, int]:
+    """
+    Wraps submodules of ``module`` for which ``auto_wrap_policy`` returns
+    ``True`` with ``wrapper_cls``.
+
+    Args:
+        module (nn.Module): Module to recursively wrap.
+        auto_wrap_policy (Callable): A callable representing a policy that
+            determines which modules to recursively wrap with ``wrapper_cls``.
+        ignored_modules (Set[torch.nn.Module]): Modules to ignore when
+            wrapping.
+        ignored_params (Set[torch.nn.Parameter]): Parameters to ignore when
+            wrapping; these should be the parameters contained in the modules
+            in ``ignored_modules``.
+    Returns:
+        (nn.Module, int):
+            ``module`` after wrapping and the numel recursively wrapped.
+    """
+    assert auto_wrap_policy is not None, "Must specify auto_wrap_policy."
+    assert wrapper_cls is not None, "Must specify wrapper_cls"
+    # Make sure no child is already wrapped.
+    for _, child in module.named_modules():
+        if child in ignored_modules:
+            continue
+        try:
+            assert not isinstance(child, cast(type, wrapper_cls))
+        except TypeError:
+            # wrapper_cls is a function as opposed to a class type, just bypass above check.
+            pass
+
+    # We count all params, assuming none of them are already wrapped.
+    nonwrapped_numel = sum(
+        p.numel() for p in module.parameters() if p not in ignored_params
+    )
+
+    assert auto_wrap_policy is not None
+    if auto_wrap_policy(module=module, recurse=True, nonwrapped_numel=nonwrapped_numel):
+        total_wrapped_numel = 0
+        # Iterate through the children, recursively wrap if necessary
+        for name, child in module.named_children():
+            if child in ignored_modules:
+                continue
+            wrapped_child, num_wrapped_params = _recursive_wrap(
+                module=child,
+                auto_wrap_policy=auto_wrap_policy,
+                wrapper_cls=wrapper_cls,
+                ignored_modules=ignored_modules,
+                ignored_params=ignored_params,
+                **kwargs,
+            )
+            setattr(module, name, wrapped_child)
+            # Keep track of how many parameters have been wrapped
+            total_wrapped_numel += num_wrapped_params
+        # decide if we need to wrap the current module,
+        # since the left over parameters exceed the number of params to wrap
+        remainder = nonwrapped_numel - total_wrapped_numel
+        if not only_wrap_children and auto_wrap_policy(
+            module=module, recurse=False, nonwrapped_numel=remainder
+        ):
+            # Leaf node or final wrapping of the remainder both happen here.
+            return _wrap(module, wrapper_cls, **kwargs), nonwrapped_numel
+        else:
+            return module, total_wrapped_numel
+    return module, 0
+
+
+class _ConfigAutoWrap:
+    """
+    Helper class to wrap modules based on default config args via a context manager.
+    See :func:`enable_wrap` for more information.
+    """
+
+    in_autowrap_context: bool = False  # Context flag
+    wrapper_cls: Optional[Callable] = None  # The wrapper class
+    kwargs: Dict[str, Any] = {}  # Wrapper's args
+
+    def __init__(self, **kwargs: Dict[str, Any]):
+        self.kwargs = kwargs
+
+    @staticmethod
+    def enable_autowrap_context(kwargs: Any) -> None:
+        if _ConfigAutoWrap.in_autowrap_context:
+            raise NotImplementedError(
+                "You are already within an autowrap context and we currently do not supported nested autowrap."
+            )
+        _ConfigAutoWrap.in_autowrap_context = True
+        # Get and save the wrapper cls for the context.
+        assert (
+            "wrapper_cls" in kwargs.keys()
+        ), "Expected to pass in wrapper_cls arg into _ConfigAutoWrap."
+        _ConfigAutoWrap.wrapper_cls = cast(Callable, kwargs["wrapper_cls"])
+        del kwargs["wrapper_cls"]
+        # Save the rest.
+        _ConfigAutoWrap.kwargs = kwargs
+
+    @staticmethod
+    def disable_autowrap_context() -> None:
+        _ConfigAutoWrap.in_autowrap_context = False
+        _ConfigAutoWrap.wrapper_cls = None
+        _ConfigAutoWrap.kwargs = {}
+
+    def __enter__(self) -> None:
+        self.enable_autowrap_context(self.kwargs)
+
+    def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> None:
+        self.disable_autowrap_context()

infer_4_47_1/lib/python3.10/site-packages/torch/distributed/rpc/_testing/__init__.py

@@ -0,0 +1,20 @@
+# mypy: allow-untyped-defs
+
+import torch
+
+
+def is_available():
+    return hasattr(torch._C, "_faulty_agent_init")
+
+
+if is_available() and not torch._C._faulty_agent_init():
+    raise RuntimeError("Failed to initialize torch.distributed.rpc._testing")
+
+if is_available():
+    # Registers FAULTY_TENSORPIPE RPC backend.
+    from torch._C._distributed_rpc_testing import (
+        FaultyTensorPipeAgent,
+        FaultyTensorPipeRpcBackendOptions,
+    )
+
+    from . import faulty_agent_backend_registry