[FSDP][optim_state_dict][2/N] Add _get_fqn_to_fsdp_param_info to map from original FQN to flat_param (#89899)

fegin · pytorchmergebot · commit 44779d9bc624 · 2022-12-07T19:40:47.000Z
**Motivation:** Add a helper to map from the FQN to the corresponding flat_param. The helper will directly get flat_param from fsdp_state and flat_handler as flat_param is not registered to the module if `use_orig_params` is True. Pull Request resolved: #89899 Approved by: https://github.com/awgu
diff --git a/torch/distributed/fsdp/_optim_utils.py b/torch/distributed/fsdp/_optim_utils.py
@@ -1,5 +1,6 @@
 import copy
 import functools
+from dataclasses import dataclass
 from typing import (
     Any,
     cast,
@@ -21,14 +22,27 @@
 import torch.distributed.fsdp.fully_sharded_data_parallel as fsdp_file
 import torch.nn as nn
 from torch.distributed._shard.sharded_tensor import ShardedTensor
-from torch.distributed.fsdp._common_utils import _get_param_to_fqns
+from torch.distributed.fsdp._common_utils import (
+    _apply_to_modules,
+    _get_param_to_fqns,
+    _module_handles,
+    clean_tensor_name,
+)
 from torch.distributed.fsdp._fsdp_extensions import _ext_chunk_tensor
 from torch.distributed.fsdp._runtime_utils import _clear_grads_if_needed, _lazy_init
 from torch.distributed.fsdp._shard_utils import _gather_state_dict
 from torch.distributed.fsdp.api import ShardingStrategy
 from torch.distributed.fsdp.flat_param import FlatParameter, FlatParamHandle
 
 
+@dataclass
+class FSDPParamInfo:
+    # The typing will be changed to FSDPState in the future.
+    state: nn.Module
+    flat_param: FlatParameter
+    param_indices: Dict[str, int]
+
+
 def sorted_items(dictionary: Dict[str, Any]) -> Iterator[Tuple[str, Any]]:
     keys = sorted(dictionary.keys())
     for k in keys:
@@ -84,7 +98,7 @@ class _OptimStateKey(NamedTuple):
     """
 
     unflat_param_names: Tuple[str, ...]
-    is_flat_param: bool
+    is_fsdp_managed: bool
 
 
 def _unflatten_optim_state(
@@ -293,23 +307,21 @@ def _flatten_optim_state_dict(
             '`optim_state_dict` must have the keys "state" and '
             '"param_groups" to be a valid optimizer state dict'
         )
-    flat_param_to_fsdp_module = _get_flat_param_to_fsdp_module(model)
     param_to_fqns = _get_param_to_fqns(model)
+    fqn_to_fsdp_param_info = _get_fqn_to_fsdp_param_info(model)
 
     # Construct the "state" part
     flat_osd_state: Dict[_OptimStateKey, Any] = {}
     unflat_osd_state = unflat_osd["state"]
     for param, unflat_param_names in param_to_fqns.items():
-        if isinstance(param, FlatParameter):  # flatten FSDP parameters' states
-            assert (
-                param in flat_param_to_fsdp_module
-            ), f"Check the `flat_param_to_fsdp_module` construction\nparam: {param}"
-            fsdp_module = flat_param_to_fsdp_module[param]
+        fqn = unflat_param_names[0]
+        if fqn in fqn_to_fsdp_param_info:
+            fsdp_param_info = fqn_to_fsdp_param_info[fqn]
             flat_state = _flatten_optim_state(
                 unflat_osd_state,
                 unflat_param_names,
-                fsdp_module,
-                param,
+                fsdp_param_info.state,
+                fsdp_param_info.flat_param,
                 shard_state,
             )
             key = _OptimStateKey(tuple(unflat_param_names), True)
@@ -670,7 +682,7 @@ def _process_pos_dim_tensor_state(
             if not is_pos_dim_tensor_state:
                 no_tensor_osd["state"][key][state_name] = value
                 continue
-            if key.is_flat_param:  # FSDP parameter
+            if key.is_fsdp_managed:  # FSDP parameter
                 sharded_size = FlatParamHandle._get_sharded_size(
                     value, rank=0, world_size=world_size
                 )
@@ -753,7 +765,7 @@ def _broadcast_pos_dim_tensor_states(
             else:
                 unsharded_tensor = None
             shape, dtype = value.shape, value.dtype
-            if key.is_flat_param:  # FSDP parameter
+            if key.is_fsdp_managed:  # FSDP parameter
                 _broadcast_sharded_pos_dim_tensor_state(
                     unsharded_tensor,
                     param_state,
@@ -1079,6 +1091,7 @@ def _map_param_id_to_optim_keys(
     group: Optional[dist.ProcessGroup],
     param_id_to_param: List[nn.Parameter],
     param_to_fqns: Dict[nn.Parameter, List[str]],
+    fqn_to_fsdp_param_info: Dict[str, FSDPParamInfo],
 ) -> Tuple[Dict[int, _OptimStateKey], Dict[_OptimStateKey, int]]:
     """
     Construct the local mapping between the `_OptimStateKey` and parameter IDs
@@ -1087,18 +1100,21 @@ def _map_param_id_to_optim_keys(
     """
     rank = dist.get_rank(group)
     optim_state_key_to_param_id: Dict[_OptimStateKey, int] = {}  # local
-    r0_param_id_to_optim_state_key: Dict[
-        int, _OptimStateKey
-    ] = {}  # rank 0
+    r0_param_id_to_optim_state_key: Dict[int, _OptimStateKey] = {}  # rank 0
 
     for param_id, param in enumerate(param_id_to_param):
         # Do not include parameters without state to avoid empty mappings
         # just like in normal `torch.optim.Optimizer.state_dict()`
         if param_id 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
+        is_fsdp_managed = fqns[0] in fqn_to_fsdp_param_info
         optim_state_key = _OptimStateKey(
-            unflat_param_names=tuple(param_to_fqns[param]),
-            is_flat_param=isinstance(param, FlatParameter),
+            unflat_param_names=tuple(fqns),
+            is_fsdp_managed=is_fsdp_managed,
         )
         if rank == 0:
             r0_param_id_to_optim_state_key[param_id] = optim_state_key
@@ -1220,6 +1236,7 @@ def _optim_state_dict(
         if using_optim_input
         else _get_param_id_to_param(optim)
     )
+    fqn_to_fsdp_param_info = _get_fqn_to_fsdp_param_info(model)
 
     (
         param_id_to_optim_state_key,
@@ -1229,20 +1246,23 @@ def _optim_state_dict(
         group,
         param_id_to_param,
         param_to_fqns,
+        fqn_to_fsdp_param_info,
     )
-    flat_param_to_fsdp_state = _get_flat_param_to_fsdp_module(model)
 
     # Iterate in rank 0's flattened parameter ID order to ensure aligned
     # all-gathers across ranks
     for optim_state_key in param_id_to_optim_state_key.values():
         param_id = optim_state_key_to_param_id[optim_state_key]
-        if optim_state_key.is_flat_param:
-            param = param_id_to_param[param_id]
-            fsdp_state = flat_param_to_fsdp_state[param]
+        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(
-                cast(FlatParameter, param),
+                fsdp_param_info.flat_param,
                 optim_state_dict["state"][param_id],
-                fsdp_state,
+                fsdp_param_info.state,
                 to_save,
                 shard_state,
             )
@@ -1269,3 +1289,43 @@ def _optim_state_dict(
         )
 
     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. ``FlatParameter._fqns`` only stores the first
+    FQN of a shared parameter. So the keys in the mapping are guaranteed to map
+    to unique parameters.
+    """
+
+    def module_fn(module, prefix, fqn_to_param_info):
+        # TODO: make it work with composable API.
+        if not isinstance(module, fsdp_file.FullyShardedDataParallel):
+            return
+        _lazy_init(module, module)
+        handles = _module_handles(module, module)
+        if not handles:
+            return
+        flat_param = handles[0].flat_param
+        fsdp_param_info = FSDPParamInfo(module, flat_param, {})
+        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].flat_param == flat_param
+            fqn_to_param_info[fqn] = fsdp_param_info
+            fsdp_param_info.param_indices[fqn] = idx
+
+    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_to_param_info,
+    )
