[zero] support shard optimizer state dict of zero

colossalai/booster/plugin/low_level_zero_plugin.py

@@ -1,5 +1,8 @@
+import logging
+import os
 import warnings
 from functools import partial
+from pathlib import Path
 from typing import Callable, Iterator, List, Optional, Tuple, Union
 
 import torch
@@ -10,10 +13,16 @@
 from torch.utils._pytree import tree_map
 from torch.utils.data import DataLoader
 
-from colossalai.checkpoint_io import CheckpointIO, GeneralCheckpointIO
+from colossalai.checkpoint_io import CheckpointIndexFile, CheckpointIO
+from colossalai.checkpoint_io.utils import (
+    get_optimizer_base_filenames,
+    get_shard_filename,
+    save_param_groups,
+    save_state_dict,
+)
 from colossalai.interface import ModelWrapper, OptimizerWrapper
 from colossalai.utils import get_current_device
-from colossalai.zero import zero_model_wrapper, zero_optim_wrapper
+from colossalai.zero import LowLevelZeroOptimizer, zero_model_wrapper, zero_optim_wrapper
 
 from .dp_plugin_base import DPPluginBase
 from .torch_ddp_plugin import TorchDDPCheckpointIO
@@ -32,21 +41,104 @@ def _convert_floating_point(x, dtype: torch.dtype = torch.float16):
 
 class LowLevelZeroCheckpointIO(TorchDDPCheckpointIO):
 
-    def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather_dtensor: bool):
-        """
-        Save optimizer to checkpoint but only on master process.
+    def save_unsharded_optimizer(self, optimizer: OptimizerWrapper, checkpoint: str, gather_dtensor: bool = False):
+        """Save optimizer to checkpoint but only on master process.
+
+        Args:
+            optimizer (OptimizerWrapper): Optimizer to save state_dict
+            checkpoint (str): Path to save checkpoint
+            gather_dtensor (bool): Whether to gather_dtensor, not used
         """
-        # TODO(ver217): optimizer state dict is sharded, and cannot get full state dict now
-        warnings.warn(
-            'LowLevelZeroPlugin does not support save full optimizer checkpoint now. Save it on every process.')
-        checkpoint = f'{checkpoint}.rank{self.coordinator.rank}'
-        GeneralCheckpointIO.save_unsharded_optimizer(self, optimizer, checkpoint, gather_dtensor)
 
-    def load_optimizer(self, optimizer: Optimizer, checkpoint: str):
-        warnings.warn(
-            'LowLevelZeroPlugin can only load optimizer checkpoint saved by itself with the same number of processes.')
-        checkpoint = f'{checkpoint}.rank{self.coordinator.rank}'
-        super().load_optimizer(optimizer, checkpoint)
+        # the `state_dict` in LowLevelZeroOptimizer has communication
+        # if only the master rank collect state_dict and save,
+        # the communication on each rank would not match
+        state_dict = optimizer.state_dict()
+        if self.coordinator.is_master():
+            save_state_dict(state_dict, checkpoint, use_safetensors=False)
+
+    def save_sharded_optimizer(self,
+                               optimizer: OptimizerWrapper,
+                               checkpoint: str,
+                               gather_dtensor: bool = False,
+                               prefix: str = None,
+                               size_per_shard: int = 1024):
+        """
+        Save sharded Zero-optimizer checkpoint under the given checkpointing path.
+        The following files will be created under the path:
+        - An index file (pytorch_optim.bin.index.json) containing a map between optimizer states and file names
+        - A group file (pytorch_optim_group.bin) recording information of param_groups
+        - Multiple files (pytorch_optim-000XX.bin) that store state tensors of optimizer in a sharding way
+
+        Args:
+            optimizer (OptimizerWrapper): Optimizer to save sharded state_dict
+            checkpoint (str): Path to save optimizer state_dict
+            gather_dtensor (bool): Whether to gather_dtensor, not used
+            prefix (str): Perfix of file to save
+            size_per_shard (int): Max file size of each file that store state tensors
+        """
+        if os.path.isfile(checkpoint):
+            logging.error(f"Provided path ({checkpoint}) should be a directory, not a file")
+            return
+
+        Path(checkpoint).mkdir(parents=True, exist_ok=True)
+
+        # state_dict only provide only 'param_groups'
+        state_dict = optimizer.optim.state_dict()
+        # state shard would be handled by the low-level zero optimizer
+        sharded_state = optimizer.state_dict_shard(max_shard_size=size_per_shard)
+
+        # Preparing file paths and index file.
+        states_name, save_index_file, param_group_file = get_optimizer_base_filenames(prefix)
+        index_file = CheckpointIndexFile(checkpoint)
+
+        # Store the information of param groups to param_group_file.
+        index_file.append_meta_data("param_groups", param_group_file)
+        group_file_path = os.path.join(checkpoint, param_group_file)
+        save_param_groups(state_dict, group_file_path)
+
+        # Save shards of optimizer states.
+        total_size = 0
+        for idx, shard_pair in enumerate(sharded_state):
+            shard, current_size = shard_pair
+            shard_file = get_shard_filename(states_name, idx)
+            total_size = total_size + current_size
+            for param_id in shard.keys():
+                index_file.append_weight_map(str(param_id), shard_file)
+
+            checkpoint_file_path = os.path.join(checkpoint, shard_file)
+            if self.coordinator.is_master():
+                save_state_dict(shard, checkpoint_file_path, use_safetensors=False)
+
+        # Wrap up index file.
+        index_file.append_meta_data("total_size", total_size)
+        if self.coordinator.is_master():
+            index_file.write_index_file(save_index_file)
+        logging.info(f"The optimizer is going to be split to checkpoint shards. "
+                     f"You can find where each parameters has been saved in the "
+                     f"index located at {save_index_file}.")
+
+    def load_sharded_optimizer(self, optimizer: OptimizerWrapper, index_file_path: str, prefix: str):
+        """Load sharded optimizer with the given path to index file.
+
+        Args:
+            optimizer (OptimizerWrapper): Optimizer to load state_dict
+            index_file_path (str): Path to the index file
+            prefix (str): Not used.
+        """
+        super().load_sharded_optimizer(optimizer, index_file_path, prefix)
+        current_rank_state_dict = optimizer.optim.state_dict()['state']
+        for param_idx, state in current_rank_state_dict.items():
+            for k, v in state.items():
+                if isinstance(v, torch.Tensor) and k != 'step':
+                    padding_size = (self.coordinator.world_size -
+                                    v.numel() % self.coordinator.world_size) % self.coordinator.world_size
+                    with torch.no_grad():
+                        v = v.flatten()
+                        if padding_size > 0:
+                            v = torch.nn.functional.pad(v, [0, padding_size])
+                        v_list = v.split(v.numel() // self.coordinator.world_size)
+                        current_rank_state_dict[param_idx][k] = v_list[self.coordinator.rank].detach()
 
 
 class LowLevelZeroModel(ModelWrapper):
@@ -74,36 +166,6 @@ def forward(self, *args, **kwargs):
         return super().forward(*args, **kwargs)
 
 
-class LowLevelZeroOptimizer(OptimizerWrapper):
-
-    def __init__(self,
-                 module: nn.Module,
-                 optimizer: Optimizer,
-                 zero_optim_config: dict,
-                 optim_kwargs: dict,
-                 verbose: bool = False) -> None:
-        optimizer = zero_optim_wrapper(module,
-                                       optimizer,
-                                       optim_config=zero_optim_config,
-                                       **optim_kwargs,
-                                       verbose=verbose)
-        super().__init__(optimizer)
-
-    def backward(self, loss: Tensor, *args, **kwargs):
-        self.optim.backward(loss)
-
-    def clip_grad_by_norm(self,
-                          max_norm: Union[float, int],
-                          norm_type: Union[float, int] = 2,
-                          error_if_nonfinite: bool = False,
-                          *args,
-                          **kwargs) -> Tensor:
-        warnings.warn(f'LowLevelZero controls grad clipping by itself, so you should not use clip_grad_by_norm')
-
-    def clip_grad_by_value(self, clip_value: float, *args, **kwargs) -> None:
-        raise NotImplementedError('LowLevelZero does not support clip_grad_by_value')
-
-
 class LowLevelZeroPlugin(DPPluginBase):
     """
     Plugin for low level zero.
@@ -211,8 +273,11 @@ def configure(
 
         if optimizer is not None and \
                 not isinstance(optimizer, OptimizerWrapper):
-            optimizer = LowLevelZeroOptimizer(model.unwrap(), optimizer, self.zero_optim_config, self.optim_kwargs,
-                                              self.verbose)
+            optimizer = zero_optim_wrapper(model.unwrap(),
+                                           optimizer,
+                                           optim_config=self.zero_optim_config,
+                                           **self.optim_kwargs,
+                                           verbose=self.verbose)
 
         return model, optimizer, criterion, dataloader, lr_scheduler
 

colossalai/zero/low_level/low_level_optim.py

@@ -2,7 +2,7 @@
 import copy
 from contextlib import contextmanager
 from functools import partial
-from typing import Optional
+from typing import Dict, Iterator, Optional, Tuple
 
 import torch
 import torch.distributed as dist
@@ -447,18 +447,23 @@ def _unscale_and_clip_grads(self, grad_groups_flat, total_norm):
     # Gradient Synchronization #
     ############################
 
+    # this method is used to sync gradient manually
+    def sync_grad(self):
+        for group_id in range(self.num_param_groups):
+            param_group = self._working_param_groups[group_id]
+            for param in param_group:
+                if param.requires_grad and param.grad is not None:
+                    self._add_to_bucket(param, group_id)
+
+        self._run_reduction()
+
     def _reduce_grad(self, partition_grad):
         # if not overlapping communication (no reduction hook is attached) when zero1
         # we need to manually reduce these gradients
         if not partition_grad and not self._overlap_communication:
-            for group_id in range(len(self._working_param_groups)):
-                param_group = self._working_param_groups[group_id]
-                for param in param_group:
-                    if param.grad is not None:
-                        self._add_to_bucket(param, group_id)
-
-        # run reduction
-        self._run_reduction()
+            self.sync_grad()
+        else:
+            self._run_reduction()
 
     # this context comes from pytorch DDP
     @contextmanager
@@ -473,7 +478,8 @@ def no_sync(self):
     ##############
     # State Dict #
     ##############
-    def _pack_state(self, state: dict) -> dict:
+
+    def _pack_state(self, state: Dict) -> Dict:
         # comes from pytorch optimizer.state_dict()
         param_mappings = {}
         start_index = 0
@@ -487,17 +493,17 @@ def pack_group(group):
             start_index += len(packed['params'])
             return packed
 
-        param_groups = [pack_group(g) for g in self.param_groups]
+        param_groups = [pack_group(g) for g in self.optim.param_groups]
         # Remap state to use order indices as keys
         packed_state = {(param_mappings[id(k)] if isinstance(k, torch.Tensor) else k): v for k, v in state.items()}
 
         return {'state': packed_state, 'param_groups': param_groups}
 
-    def state_dict(self) -> dict:
+    def state_dict(self) -> Dict:
         """Return a state_dict same with DDP
 
         Returns:
-            dict: the pytorch form state_dict
+            Dict: the pytorch form state_dict
         """
         zero_state = dict()
         for param, state in self.optim.state.items():
@@ -514,7 +520,7 @@ def state_dict(self) -> dict:
 
         return states_dict
 
-    def load_state_dict(self, state_dict: dict):
+    def load_state_dict(self, state_dict: Dict):
         """Load state dict, requires the state_dict be the pytorch form
 
         Args:
@@ -534,3 +540,46 @@ def load_state_dict(self, state_dict: dict):
 
         self.optim.load_state_dict(zero_state_dict)
         zero_state_dict = dict()
+
+    def state_dict_shard(self, max_shard_size: int = 1024) -> Iterator[Tuple[Dict, int]]:
+        """Returns dictionaries containing a whole state of the module one by one. The max size of dictionary shard is specified by ``max_shard_size``.
+           Only include the 'state' in state_dict.
+
+        Args:
+            max_shard_size (int, optional): max size of state shard (in MB). Defaults to 1024.
+
+        Yields:
+            Iterator[OrderedDict]: A generator of state dict shard
+        """
+        ret_block = dict()
+        ret_block_size = 0
+
+        local_states = self.optim.state_dict()['state']
+        for param_idx, states in local_states.items():
+            current_block_size = 0
+            current_block = copy.deepcopy(states)
+
+            # find the working param of current param_id
+            for group_id, pg in self._master_param_groups_of_current_rank.items():
+                if (group_id + 1) * len(pg) < param_idx:
+                    continue
+                master_param = pg[param_idx - (group_id) * len(pg)]
+                working_param = self._param_store.master_to_working_param[id(master_param)]
+
+            for k, v in states.items():
+                if isinstance(v, torch.Tensor) and k != 'step':
+                    state_tensor = [torch.zeros_like(v) for _ in range(self._world_size)]
+                    dist.all_gather(state_tensor, v, group=self.dp_pg)
+                    state_tensor = torch.stack(state_tensor).view(-1)[:working_param.numel()].reshape_as(working_param)
+                    current_block_size += state_tensor.numel()
+                    current_block[k] = state_tensor
+
+            if ret_block_size + current_block_size > max_shard_size and len(ret_block) > 0:
+                yield ret_block, ret_block_size
+                ret_block = dict()
+                ret_block_size = 0
+
+            ret_block[param_idx] = current_block
+            ret_block_size += current_block_size
+
+        yield ret_block, ret_block_size

colossalai/zero/low_level/readme.md

@@ -0,0 +1,54 @@
+# Low Level ZeRO
+>Low Level ZeRO == ZeRO-DP stage 1 and 2, we would denote it as ZeRO.
+
+## Design:
+### Notion
+`p32` denotes the param copy in the optimizer
+`p` denotes the model param
+`g` denotes the gradient
+
+### INIT
+In low level zero(1, 2), `p32` is split. Different from the previous implement, we split each `p32` evenly by world_size. Thus, rank0 got a param list as `[p00, p10]`, rank1 got a param list as `[p-01, p-11]`, etc.
+<img width="840" alt="image" src="https://github.com/hpcaitech/ColossalAI/assets/74758262/f7758d7d-c5e5-44a4-a121-3aba8b05c904">
+
+For the detailed implementation, we first pad `p` for it can be split by world_size if needed. Then, we would view it to the shape `[world_size, -1]`, and each rank got its own part `p32` by cloning.
+
+### BWD
+To leverage the communication, a gradient would be added to a bucket first. When the bucket is full, each `g` in it would be reshaped as `[world_size, -1]`. And the `[local_rank]` parts would be united.
+The data structure looks like this:
+```
+{
+0: [g-00, g-10],
+1: [g-01, g-11],
+2: [g-02, g-12]
+}
+```
+After that, the gradients would be flattened by rank, and the data structure looks like this:
+```
+# g-0 means flatten([g-00, g-10])
+{
+0: [g-0],
+1: [g-1],
+2: [g-2]
+}
+```
+For zero1, we iterate the dictionary and do `all_reduce`. For zero2, we can just do `reduce-scatter`.
+
+### Optim
+For each rank gets its own `p32` and the counterpart `g`, it is quite easy to do `optim.step()`.
+
+However, we have to consider a situation of layer drop, for instance:
+```
+class MlpModel(nn.Module):
+    def __init__(self):
+        super(MlpModel, self).__init__()
+        self.linear1 = nn.Linear(128, 256)
+        self.drop_linear = nn.Linear(256, 256)
+        self.linear2 = nn.Linear(256, 512)
+
+    def forward(self, x):
+        x = self.linear1(x)
+        x = self.linear2(x)
+        return x
+```
+And the solution is to build a mapping of `p32`, `p`, and `g`. Before `optim.step()`, we collect `p` which `requires_grad=True` and `p.grad != None` as a real working param. And select the counterpart `p32` and `g`.