Merge pull request #71 from nopperl/topology-agnostic-loading

Implement pipeline parallel size-agnostic optimizer state loading

Author: NouamaneTazi

src/nanotron/optim/zero.py

@@ -348,16 +348,17 @@ def find_optim_index_from_param_name(
     # NOTE: (pp_rank, dp_rank, tp_rank) or (pp_rank, tp_rank)
     ckp_sharded_optim_states: Union[Tuple[Tuple[int, int, int], torch.Tensor], Tuple[Tuple[int, int], torch.Tensor]],
     is_zero1: bool,
+    pp_rank=0,
 ) -> int:
     param_name = param_name.replace("module.", "")
     # NOTE: since all shards have the same optim state names
-    # so we take the first shard
+    # so we take the first shard (except optionally the pp dimension)
     if is_zero1 is True:
         # NOTE: (pp_rank, dp_rank, tp_rank)
-        OPTIM_STATE_INDEX_TO_PARAM_NAME = ckp_sharded_optim_states[(0, 0, 0)]["names"]
+        OPTIM_STATE_INDEX_TO_PARAM_NAME = ckp_sharded_optim_states[(pp_rank, 0, 0)]["names"]
     else:
         # NOTE: (pp_rank, tp_rank)
-        OPTIM_STATE_INDEX_TO_PARAM_NAME = ckp_sharded_optim_states[(0, 0)]["names"]
+        OPTIM_STATE_INDEX_TO_PARAM_NAME = ckp_sharded_optim_states[(pp_rank, 0)]["names"]
 
     return next((k for k, v in OPTIM_STATE_INDEX_TO_PARAM_NAME.items() if v == param_name), None)
 

src/nanotron/serialize/optimizer.py

@@ -144,7 +144,9 @@ def load_optimizer(
     ckp_dp_size = ckp_optimizer_config["parallelism"]["dp_size"]
     ckpt_expert_parallel_size = ckp_optimizer_config["parallelism"]["expert_parallel_size"]
 
-    if int(ckp_tp_size) != int(parallel_context.tp_pg.size()):
+    if int(ckp_tp_size) != int(parallel_context.tp_pg.size()) or int(ckp_pp_size) != int(
+        parallel_context.pp_pg.size()
+    ):
         assert (
             param_shard_metadata is not None
         ), f"You have to pass how the original parameters are sharded in order to resume in a different tensor parallel size, ckp_tp_size: {ckp_tp_size}, current tp_size: {parallel_context.tp_pg.size()}"
@@ -182,14 +184,17 @@ def get_checkpoint_state_metadata(param_name: str, pp_rank: int, tp_rank: int) -
 
         model_state_dict = model.state_dict()
         new_optim_state_dict = optimizer.state_dict()
+        # TODO: this does not handle the edge case of different pipeline parallel optimizer state shards saving different state keys
         OPTIMIZER_STATE_NAMES = sorted(ckp_sharded_optim_states[(0, 0)]["state"][0].keys() - ["step"])
         # NOTE: because we can only resume training with the same optimizer type
         # (0, 0) = (pp_rank, tp_rank)
         # NOTE: also we don't merge "step" because it's just a scalar
-
-        param_names = sorted(model_state_dict.items(), key=lambda x: x[0])
-        for param_name, _ in tqdm(
-            param_names,
+        param_names = list(model_state_dict.keys())
+        new_optim_state_param_names = {}
+        # NOTE: iterates through all model parameters in the local pipeline parallel rank (hence, might not be the full model).
+        # Since model parameters and optimizer states are aligned, loads only the optimizer states for these parameters from the checkpoint shards.
+        for param_index, param_name in tqdm(
+            enumerate(param_names),
             disable=dist.get_rank(parallel_context.world_pg) != 0,
             desc="Topology-agnostic optimizer loading",
         ):
@@ -201,28 +206,49 @@ def get_checkpoint_state_metadata(param_name: str, pp_rank: int, tp_rank: int) -
             if not isinstance(param, NanotronParameter):
                 raise NotImplementedError("Parameters are required to be NanotronParameter")
 
+            # NOTE: for tied parameters, the metadata is stored using the parameter name,
+            # while the data is stored using the name of the main tied parameter,
+            # which may be different (e.g. `model.token_position_embeddings.pp_block.token_embedding.weight`
+            # for `model.lm_head.pp_block.weight`).
+            base_name = param.get_tied_info().name if param.is_tied else param_name
+            if param_name != base_name:
+                # NOTE: skip tied parameter if main tied parameter has already been loaded
+                # (not always the case if pipeline parallel)
+                if base_name in new_optim_state_param_names.values():
+                    continue
+            new_optim_state_param_names[param_index] = base_name
+
             if param.is_sharded:
                 # NOTE: optimizer states's shape is equal to the parameter's shape
                 # NOTE: sometines an unsharded parameter's shape differ
                 # from an unsharded optimizer state's shape
                 new_shard_metadata = param.get_sharded_info()
                 new_unshared_shape = new_shard_metadata.unsharded_shape
-
-                # NOTE: merging optimizer states
-                optim_state_index = find_optim_index_from_param_name(
-                    param_name, ckp_sharded_optim_states, is_zero1=False
-                )
-
-                new_optim_state_dict["state"][optim_state_index] = {}
+                new_optim_state_dict["state"][param_index] = {}
+                # NOTE: restore each state tensor (e.g. exg_avg) by iterating through
+                # the optimizer state shards saved using the previous topology
                 for state_key in OPTIMIZER_STATE_NAMES:
                     # TODO(xrsrke): free the memory of the shards that isn't
                     # corresponding to the current rank
                     buffer = torch.zeros_like(param, device="cuda")
                     unsharded_buffer = torch.empty(new_unshared_shape, device="cuda")
 
                     for (pp_rank, tp_rank), ckp_optim_state in ckp_sharded_optim_states.items():
-                        ckp_shard_metadata = get_checkpoint_state_metadata(param_name, pp_rank, tp_rank)
-                        ckp_shard_data = ckp_optim_state["state"][optim_state_index][state_key]
+                        old_optim_state_index = find_optim_index_from_param_name(
+                            base_name, ckp_sharded_optim_states, is_zero1=False, pp_rank=pp_rank
+                        )
+                        if old_optim_state_index is None:
+                            continue  # NOTE: param is not in this pp shard
+                        ckp_shard_data = ckp_optim_state["state"][old_optim_state_index][state_key]
+                        # NOTE: the metadata for the main parameter of a tied parameter might be in a
+                        # different pipeline parallel shard.
+                        if param.is_tied:
+                            metadata_pp_rank = next(
+                                iter(param_shard_metadata[param_name.replace("module.", "")].keys())
+                            )[0]
+                        else:
+                            metadata_pp_rank = pp_rank
+                        ckp_shard_metadata = get_checkpoint_state_metadata(param_name, metadata_pp_rank, tp_rank)
 
                         # NOTE: if the checkpoint is from a Zero-1 optimizer,
                         # so it's flattened, so we need to reshape it
@@ -232,7 +258,7 @@ def get_checkpoint_state_metadata(param_name: str, pp_rank: int, tp_rank: int) -
                             orig_shape = [int(dim) for dim in orig_shape]
                             ckp_shard_data = ckp_shard_data.view(orig_shape)
 
-                        new_optim_state_dict["state"][optim_state_index][state_key] = merge_and_shard_tp_tensors(
+                        new_optim_state_dict["state"][param_index][state_key] = merge_and_shard_tp_tensors(
                             buffer,
                             unsharded_buffer,
                             [
@@ -243,17 +269,16 @@ def get_checkpoint_state_metadata(param_name: str, pp_rank: int, tp_rank: int) -
 
                         if ckp_optim_type == ZeroDistributedOptimizer.__name__:
                             # NOTE: flatten the optimizer states
-                            new_optim_state_dict["state"][optim_state_index][state_key] = new_optim_state_dict[
-                                "state"
-                            ][optim_state_index][state_key].flatten()
-
-                new_optim_state_dict["state"][optim_state_index]["step"] = ckp_optim_state["state"][optim_state_index][
-                    "step"
-                ]
-
-        # NOTE: since all shards have the same optim state names
-        # so we take the first shard
-        new_optim_state_dict["names"] = ckp_sharded_optim_states[(0, 0)]["names"]
+                            new_optim_state_dict["state"][param_index][state_key] = new_optim_state_dict["state"][
+                                param_index
+                            ][state_key].flatten()
+                        # NOTE: a bit awkward, but while we're already reading this (pp,tp) shard for whatever state_key,
+                        # try to get the step value as well.
+                        step = ckp_optim_state["state"][old_optim_state_index].get("step")
+                        if step is not None:
+                            new_optim_state_dict["state"][param_index]["step"] = step
+
+        new_optim_state_dict["names"] = new_optim_state_param_names
         state_dict = new_optim_state_dict
     else:
         # TODO @thomasw21: Load optimizer type and check that it's compatible otherwise we might be be loading something else completely