Next commit [checkpointio] Unsharded Optimizer Checkpoint for Gemini Plugin (#4141)

* [checkpointio] unsharded optimizer checkpoint for Gemini plugin

* [checkpointio] unsharded optimizer checkpoint for Gemini using all_gather

colossalai/booster/plugin/gemini_plugin.py

@@ -33,44 +33,40 @@ class GeminiCheckpointIO(GeneralCheckpointIO):
         super().__init__()
         self.coordinator = DistCoordinator()
 
-    def load_unsharded_model(self, model: GeminiDDP, checkpoint: str, strict: bool = True):
-        """
-        Load model from checkpoint with automatic unwrapping.
-        """
-        # the model should be unwrapped in self.load_model via ModelWrapper.unwrap
-        return super().load_unsharded_model(model, checkpoint, strict=strict)
-
     def save_unsharded_model(self, model: GeminiDDP, checkpoint: str, gather_dtensor: bool, use_safetensors: bool):
         """
-        Save model to checkpoint but only on master process.
+        Save sharded model to checkpoint but only on master process.
+        The model should be unwrapped in self.load_model via ModelWrapper.unwrap.
+        As there is communication when getting state dict, this must be called on all processes.
         """
-        # the model should be unwrapped in self.load_model via ModelWrapper.unwrap
-        # as there is communication when get state dict, this must be called on all processes
         state_dict = model.state_dict(only_rank_0=True)
         if self.coordinator.is_master():
             save_state_dict(state_dict, checkpoint, use_safetensors)
 
-    def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather_dtensor: bool):
+    def load_unsharded_model(self, model: GeminiDDP, checkpoint: str, strict: bool = True):
         """
-        Save optimizer to checkpoint but only on master process.
+        Load model from checkpoint with automatic unwrapping.
+        The model should be unwrapped in self.load_model via ModelWrapper.unwrap.
         """
-        # TODO(ver217): optimizer state dict is sharded
-        warnings.warn('GeminiPlugin does not support save full optimizer checkpoint now. Save it on every process.')
-        checkpoint = f'{checkpoint}.rank{self.coordinator.rank}'
-        super().save_unsharded_optimizer(optimizer, checkpoint, gather_dtensor)
-
-    def load_optimizer(self, optimizer: Optimizer, checkpoint: str):
-        warnings.warn(
-            'GeminiPlugin 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)
-
-    def save_lr_scheduler(self, lr_scheduler: LRScheduler, checkpoint: str):
+        super().load_unsharded_model(model, checkpoint, strict=strict)
+
+    def save_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str, gather_dtensor: bool):
         """
-        Save model to checkpoint but only on master process.
+        Save unsharded optimizer state dict to checkpoint.
+        After calling optimizer.state_dict(), the complete optimizer states will be collected on master rank.
+        As there is communication when getting state dict, this must be called on all processes.
+        The saving process will only be executed by master rank.
         """
+        state_dict = optimizer.state_dict()
         if self.coordinator.is_master():
-            super().save_lr_scheduler(lr_scheduler, checkpoint)
+            save_state_dict(state_dict, checkpoint, use_safetensors=False)
+
+    def load_unsharded_optimizer(self, optimizer: Optimizer, checkpoint: str):
+        """
+        Loading unsharded optimizer from checkpoint file.
+        For each process, only loading optimizer states of parameters it controls.
+        """
+        super().load_unsharded_optimizer(optimizer, checkpoint)
 
     def save_sharded_model(self,
                            model: GeminiDDP,
@@ -82,6 +78,12 @@ class GeminiCheckpointIO(GeneralCheckpointIO):
         """
         Save sharded model
         """
+        if os.path.isfile(checkpoint_path):
+            logging.error(f"Provided path ({checkpoint_path}) should be a directory, not a file")
+            return
+
+        Path(checkpoint_path).mkdir(parents=True, exist_ok=True)
+
         state_dict_shard = model.state_dict_shard(max_shard_size=max_shard_size, only_rank_0=True, dtype=torch.float32)
         weights_name, save_index_file = get_model_base_filenames(prefix, use_safetensors)
         total_size = 0
@@ -117,6 +119,23 @@ class GeminiCheckpointIO(GeneralCheckpointIO):
         """
         return super().load_sharded_model(model, checkpoint_index_file, strict, use_safetensors, load_sub_module=False)
 
+    def save_sharded_optimizer(self, optimizer: Optimizer, checkpoint: Path, gather_dtensor: bool, prefix: str,
+                               size_per_shard: int):
+        """
+        Save sharded optimizer state dict to checkpoint folder.
+        As there is communication when getting state dict, this must be called on all processes.
+        """
+        Path(checkpoint).mkdir(parents=True, exist_ok=True)
+        super().save_sharded_optimizer(optimizer, checkpoint, gather_dtensor, prefix, size_per_shard)
+
+    def load_sharded_optimizer(self, optimizer: Optimizer, checkpoint_index_file: Path, prefix: str):
+        """
+        Loading sharded optimizer from checkpoint folder, with index file given.
+        For each process, only loading optimizer states of parameters it controls.
+        """
+        # TODO(Baizhou): To be implemented.
+        pass
+
 
 class GeminiModel(ModelWrapper):
 
@@ -193,7 +212,7 @@ class GeminiPlugin(DPPluginBase):
             which will be used when using hybrid CPU optimizer.
             This argument is meaningless when `placement_policy` of `GeminiManager` is not "auto".
             Defaults to 0.0.
-        initial_scale (float, optional): Initial scale used by DynamicGradScaler. Defaults to 2**32.
+        initial_scale (float, optional): Initial scale used by DynamicGradScaler. Defaults to 2**16.
         min_scale (float, optional): Min scale used by DynamicGradScaler. Defaults to 1.
         growth_factor (float, optional): growth_factor used by DynamicGradScaler. Defaults to 2.
         backoff_factor (float, optional): backoff_factor used by DynamicGradScaler. Defaults to 0.5.
@@ -219,7 +238,7 @@ class GeminiPlugin(DPPluginBase):
         min_chunk_size_m: float = 32,
         memstats: Optional[MemStats] = None,
         gpu_margin_mem_ratio: float = 0.0,
-        initial_scale: float = 2**32,
+        initial_scale: float = 2**16,
         min_scale: float = 1,
         growth_factor: float = 2,
         backoff_factor: float = 0.5,

colossalai/checkpoint_io/checkpoint_io_base.py

@@ -152,6 +152,7 @@ class CheckpointIO(ABC):
                 names to compose the keys in state_dict. Defaults to None.
             size_per_shard (int, optional): Maximum size of checkpoint shard file in MB. This is useful only when ``shard=True``. Defaults to 1024.
         """
+
         index_file_exists, index_file_path = has_index_file(checkpoint)
 
         if Path(checkpoint).is_dir() and not index_file_exists:
@@ -186,6 +187,7 @@ class CheckpointIO(ABC):
             prefix (str): prefix for the optimizer checkpoint when shard = True. Default: None.
             size_per_shard (int): size per shard in MB. Default: 1024. This value is only used when shard is set to True.
         """
+
         if shard:
             self.save_sharded_optimizer(optimizer, checkpoint, gather_dtensor, prefix, size_per_shard)
         else:

colossalai/checkpoint_io/general_checkpoint_io.py

@@ -28,6 +28,7 @@ from .utils import (
     shard_model_checkpoint,
     shard_optimizer_checkpoint,
     sharded_optimizer_loading_epilogue,
+    unwrap_optimizer,
 )
 
 __all__ = ['GeneralCheckpointIO']
@@ -59,7 +60,7 @@ class GeneralCheckpointIO(CheckpointIO):
 
         # If optimizer is wrapped, unwrap it.
         if isinstance(optimizer, OptimizerWrapper):
-            optimizer = optimizer.optim
+            optimizer = unwrap_optimizer(optimizer)
 
         # Read checkpoint index file.
         ckpt_index_file = CheckpointIndexFile.from_file(index_file_path)
@@ -96,6 +97,11 @@ class GeneralCheckpointIO(CheckpointIO):
         - 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
         """
+
+        # If optimizer is wrapped, unwrap it.
+        if isinstance(optimizer, OptimizerWrapper):
+            optimizer = unwrap_optimizer(optimizer)
+
         if os.path.isfile(checkpoint):
             logging.error(f"Provided path ({checkpoint}) should be a directory, not a file")
             return
@@ -121,9 +127,8 @@ class GeneralCheckpointIO(CheckpointIO):
             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)
-
+            for key in shard.keys():
+                index_file.append_weight_map(key, shard_file)
             checkpoint_file_path = os.path.join(checkpoint, shard_file)
             save_state_dict(shard, checkpoint_file_path, use_safetensors=False)
 
@@ -177,7 +182,6 @@ class GeneralCheckpointIO(CheckpointIO):
             total_size = total_size + shard_pair[1]
             for key in shard.keys():
                 index_file.append_weight_map(key, shard_file)
-
             checkpoint_file_path = os.path.join(checkpoint_path, shard_file)
             save_state_dict(shard, checkpoint_file_path, use_safetensors)
 

colossalai/checkpoint_io/utils.py

@@ -10,6 +10,8 @@ import torch
 import torch.nn as nn
 from torch.optim import Optimizer
 
+from colossalai.interface import OptimizerWrapper
+from colossalai.nn.optimizer import ColossalaiOptimizer
 from colossalai.tensor.d_tensor import is_distributed_tensor
 
 SAFE_WEIGHTS_NAME = "model.safetensors"
@@ -88,6 +90,19 @@ def is_safetensor_checkpoint(checkpoint_file_path: str) -> bool:
 # ======================================
 # Helper functions for saving shard file
 # ======================================
+def unwrap_optimizer(optimizer: OptimizerWrapper):
+    '''
+    Unwrap a wrapped optimizer.
+    This method should be used before saving/loading it to/from sharded checkpoints.
+    '''
+
+    # TODO(Baizhou): ColossalaiOptimizer will be replaced with OptimizerWrapper in the future
+    unwrapped_optim = optimizer.optim
+    if isinstance(unwrapped_optim, ColossalaiOptimizer):
+        unwrapped_optim = unwrapped_optim.optim
+    return unwrapped_optim
+
+
 def shard_model_checkpoint(state_dict: torch.Tensor, max_shard_size: int = 1024) -> Iterator[Tuple[OrderedDict, int]]:
     """
     Splits a model state dictionary in sub-checkpoints so that the final size of each sub-checkpoint does not exceed a
@@ -103,7 +118,7 @@ def shard_model_checkpoint(state_dict: torch.Tensor, max_shard_size: int = 1024)
             weight_size = calculate_tensor_size(weight)
 
             # If this weight is going to tip up over the maximal size, we split.
-            if current_block_size + weight_size > max_shard_size:
+            if current_block_size + weight_size > max_shard_size and current_block_size > 0:
                 ret_block = current_block
                 ret_block_size = current_block_size
                 current_block = {}
@@ -140,9 +155,10 @@ def shard_optimizer_checkpoint(state_dict: dict, max_shard_size: int = 1024) ->
         isDTensor = False
         for state_tensor in state.values():
 
-            # When state_tensor is None (e.g., a SGD optimizer with momentum set to 0),
+            # When state_tensor is not of Tensor class,
+            # e.g., a SGD optimizer with momentum set to 0 can have None as state
             # The calculation of tensor size should be skipped to avoid error.
-            if state_tensor is None:
+            if not isinstance(state_tensor, torch.Tensor):
                 continue
 
             # If the states are stored as DTensors, mark isDTensor as true.
@@ -152,7 +168,7 @@ def shard_optimizer_checkpoint(state_dict: dict, max_shard_size: int = 1024) ->
 
         if not isDTensor:
 
-            if current_block_size + state_size > max_shard_size:
+            if current_block_size + state_size > max_shard_size and current_block_size > 0:
                 ret_block = current_block
                 ret_block_size = current_block_size
                 current_block = {}

colossalai/interface/optimizer.py

@@ -119,3 +119,9 @@ class OptimizerWrapper:
         """
         raise NotImplementedError(
             "The method unscale_grad is only available for optimizers with mixed precision training")
+
+    def unwrap(self):
+        """
+        Unwrap the optimizer for checkpoint saving/loading.
+        """
+        return self.optim

colossalai/testing/comparison.py

@@ -5,6 +5,7 @@ import torch.distributed as dist
 from torch import Tensor
 from torch.distributed import ProcessGroup
 from torch.testing import assert_close
+from torch.utils._pytree import tree_flatten
 
 
 def assert_equal(a: Tensor, b: Tensor):
@@ -16,7 +17,12 @@ def assert_not_equal(a: Tensor, b: Tensor):
 
 
 def assert_close_loose(a: Tensor, b: Tensor, rtol: float = 1e-3, atol: float = 1e-3):
-    assert_close(a, b, rtol=rtol, atol=atol)
+    assert_close(a,
+                 b,
+                 rtol=rtol,
+                 atol=atol,
+                 msg=f"Tensor not close, shape: {a.shape} vs {b.shape}, \
+                                                   dtype: {a.dtype} vs {b.dtype}")
 
 
 def assert_equal_in_group(tensor: Tensor, process_group: ProcessGroup = None):
@@ -33,25 +39,51 @@ def assert_equal_in_group(tensor: Tensor, process_group: ProcessGroup = None):
 
 
 def check_state_dict_equal(d1: OrderedDict, d2: OrderedDict, ignore_device: bool = True):
-    for k, v in d1.items():
-        if isinstance(v, dict):
-            check_state_dict_equal(v, d2[k])
-        elif isinstance(v, list):
-            for i in range(len(v)):
-                if isinstance(v[i], torch.Tensor):
+    assert len(list(d1.keys())) == len(list(d2.keys())), \
+           f"Number of keys unequal: {len(list(d1.keys()))} vs {len(list(d2.keys()))}"
+    for k, v1 in d1.items():
+        assert k in d2
+        v2 = d2[k]
+        if isinstance(v1, dict):
+            assert isinstance(v2, dict)
+            check_state_dict_equal(v1, v2, ignore_device)
+        elif isinstance(v1, list):
+            assert isinstance(v2, list)
+            for v1_i, v2_i in zip(v1, v2):
+                if isinstance(v1_i, torch.Tensor):
+                    assert isinstance(v2_i, torch.Tensor)
                     if not ignore_device:
-                        v[i] = v[i].to("cpu")
-                        d2[k][i] = d2[k][i].to("cpu")
-                    assert torch.equal(v[i], d2[k][i])
+                        v1_i = v1_i.to("cpu")
+                        v2_i = v2_i.to("cpu")
+                    assert_close_loose(v1_i, v2_i)
+                elif isinstance(v1_i, dict):
+                    assert isinstance(v2_i, dict)
+                    check_state_dict_equal(v1_i, v2_i, ignore_device)
                 else:
-                    assert v[i] == d2[k][i]
-        elif isinstance(v, torch.Tensor):
+                    assert v1_i == v2_i, f"{v1_i} not equals to {v2_i}"
+        elif isinstance(v1, torch.Tensor):
+            assert isinstance(v2, torch.Tensor)
             if not ignore_device:
-                v = v.to("cpu")
-                d2[k] = d2[k].to("cpu")
-            assert torch.equal(v, d2[k])
+                v1 = v1.to("cpu")
+                v2 = v2.to("cpu")
+            assert_close_loose(v1, v2)
         else:
-            assert v == d2[k]
+            assert v1 == v2, f"{v1} not equals to {v2}"
+
+
+def check_state_dict_equal_pytree(d1: OrderedDict, d2: OrderedDict, ignore_device: bool = True):
+    flat_d1, _ = tree_flatten(d1)
+    flat_d2, _ = tree_flatten(d2)
+    assert len(flat_d1) == len(flat_d2)
+    for v1, v2 in zip(flat_d1, flat_d2):
+        if isinstance(v1, torch.Tensor):
+            assert isinstance(v2, torch.Tensor)
+            if not ignore_device:
+                v1 = v1.to("cpu")
+                v2 = v2.to("cpu")
+            assert_close_loose(v1, v2)
+        else:
+            assert v1 == v2, f"{v1} not equals to {v2}"
 
 
 def assert_hf_output_close(out1: Any,

colossalai/zero/gemini/gemini_optimizer.py

 # this code is inspired by the DeepSpeed library and implemented with our own design from scratch
+import copy
+import gc
 import math
 import warnings
 from typing import Any, Dict, Set, Tuple
@@ -101,6 +103,11 @@ class ZeroOptimizer(ColossalaiOptimizer):
         self.clipping_flag = clipping_norm > 0.0
         self.max_norm = clipping_norm
         self.verbose = verbose
+        self.param_groups_backup = list()
+
+        # Mapping from integer id to real/fake param tensor, used for checkpointing.
+        self.id_to_real_params: Dict[int, Parameter] = dict()
+        self.id_to_fake_params: Dict[int, Parameter] = dict()
 
         if self.clipping_flag:
             assert norm_type == 2.0, "ZeroOptimizer only supports L2 norm now"
@@ -301,25 +308,352 @@ class ZeroOptimizer(ColossalaiOptimizer):
             end = min(local_chunk.shard_size, param_info.end - local_chunk.shard_begin)
             return begin, end
 
+        param_id = -1
         for group in self.optim.param_groups:
             fake_params_list = list()
-
+            group_backup = {k: v for k, v in group.items() if k != 'params'}
+            group_ids = []
             for param in group['params']:
+
+                # Record the mapping of id to current param.
+                param_id += 1
+                self.id_to_real_params[param_id] = param
+                group_ids.append(param_id)
+
+                # If current param is controlled by current process, add it to fake_param.
                 if is_ddp_ignored(param):
                     continue
                 chunk16 = self.chunk_manager.get_chunk(param)
                 range_pair = get_range_pair(chunk16, param)
                 if range_pair[0] >= range_pair[1]:
                     continue
-
                 grad_device = self.module.grads_device[param]
                 fake_param = torch.nn.Parameter(torch.empty([0], device=grad_device))
                 self.param_to_chunk32[fake_param] = chunk16.paired_chunk
                 self.param_to_range[fake_param] = range_pair
-
+                self.id_to_fake_params[param_id] = fake_param
                 fake_params_list.append(fake_param)
 
+            # Update self.optim.param_groups as well as backup group.
             group['params'] = fake_params_list
+            group_backup['params'] = group_ids
+            self.param_groups_backup.append(group_backup)
+
+    def get_offsets(self, param_id: int) -> tuple:
+        '''
+        Args:
+            param_id(int): The id of parameter.
+
+        Returns:
+            chunk_offset(int): Offset of parameter inside the chunk.
+            shard_offset(int): Offset of its optimizer state shard
+                                relative to the whole optimizer state.
+            shard_size(int): Length of parameter shard owned by current process.
+        '''
+
+        if param_id not in self.id_to_fake_params:
+            return -1, -1, -1
+        fake_param = self.id_to_fake_params[param_id]
+        chunk = self.param_to_chunk32[fake_param].paired_chunk
+        param = self.id_to_real_params[param_id]
+        param_info = chunk.tensors_info[param]
+
+        begin_in_chunk, end_in_chunk = self.param_to_range[fake_param]
+        chunk_offset = begin_in_chunk
+        shard_offset = begin_in_chunk + chunk.shard_begin - param_info.offset
+        shard_size = end_in_chunk - begin_in_chunk
+        assert chunk_offset >= 0 and shard_offset >= 0
+
+        return chunk_offset, shard_offset, shard_size
+
+    def collect_states(self, param_id: int, only_rank_0: bool = True) -> dict:
+        """
+        Args:
+            param_id (int): id of the parameter whose state is to be gathered at master rank.
+            only_rank_0(bool): if True, states will be collected only on master rank, otherwise collected on every rank.
+
+        Returns:
+            collected_states(dict): the gathered optimzier state of parameter with given id
+                                    if this method is called by master rank, otherwise an empty dict.
+
+        This method can work only when called by all processes simultaneously.
+        """
+
+        # Get param & chunk & process group.
+        param = self.id_to_real_params[param_id]
+        fake_param = self.id_to_fake_params.get(param_id, None)
+        chunk = self.chunk_manager.get_chunk(param)
+        process_group = chunk.torch_pg
+        rank = dist.get_rank(process_group)
+        master_rank = 0
+        collected_states = {}
+
+        # Fetch names of states through all_gather.
+        local_state_names = None
+        if fake_param is not None:
+            local_state_names = list(self.optim.state[fake_param].keys())
+        gathered_state_names = [None for _ in range(dist.get_world_size(process_group))]
+        dist.barrier()
+        dist.all_gather_object(gathered_state_names, local_state_names)
+        state_names = None
+        for names in gathered_state_names:
+            if names is not None:
+                # Assume different devices share the same set of state names if they have.
+                state_names = copy.deepcopy(names)
+                break
+
+        # Directly return if this parameter doesn't have optimizer states.
+        # e.g. parameter freezed/layer dropped
+        if state_names is None:
+            return collected_states
+
+        # Boolean variable is_collector indicates that whether the current rank
+        # needs to gather the whole optimizer states.
+        # Only master rank is collector when only_rank_0 is True.
+        # Every rank is collector when only_rank_0 is False.
+        is_collector = (rank == master_rank) or (not only_rank_0)
+
+        # If the chunk is kept gathered,
+        # the parameteres are treated the same as that of those in strict DDP during training.
+        # So states can be directly fetched from current device.
+        if chunk.keep_gathered:
+            assert param_id in self.id_to_fake_params
+            if is_collector:
+                states = self.optim.state[fake_param]
+                for state_name in state_names:
+                    if state_name == 'step':
+                        # To keep aligned with pytorch, state 'step' is stored as a pytorch tensor with type float32.
+                        collected_states[state_name] = torch.tensor(states['step'],
+                                                                    dtype=torch.float32,
+                                                                    requires_grad=False).cpu()
+                    else:
+                        collected_states[state_name] = states[state_name].detach().clone().to(torch.float32).cpu()
+            return collected_states
+
+        # Check whether the param with given id is managed by current process.
+        own_param = param_id in self.id_to_fake_params
+
+        # Collector gets prepared for state collecting.
+        if is_collector:
+            for state_name in state_names:
+                if state_name == 'step':
+                    # To keep aligned with pytorch, state 'step' is stored as a pytorch tensor with type float32.
+                    collected_states[state_name] = torch.tensor(0.0, dtype=torch.float32, requires_grad=False).cpu()
+                else:
+                    collected_states[state_name] = torch.zeros(param.numel(), dtype=torch.float32,
+                                                               requires_grad=False).cpu()
+
+        # Materials for gathering, including compacted state tensors, and the offset of shard inside each state.
+        compacted_states = self.pack_optimizer_states_to_tensor(param_id, state_names) if own_param else None
+        _, shard_offset, shard_size = self.get_offsets(param_id)
+
+        # Collectors gather state shards through all_gathering.
+        gathered_state_shards = [None for _ in range(dist.get_world_size(process_group))]
+
+        dist.barrier()
+        dist.all_gather_object(gathered_state_shards, [compacted_states, shard_offset, shard_size])
+
+        if is_collector:
+            for state_shard in gathered_state_shards:
+                compacted_states = state_shard[0]
+                shard_offset = state_shard[1]
+                shard_size = state_shard[2]
+                if compacted_states is None:
+                    continue
+                self.load_from_compacted_states(compacted_states, collected_states, state_names, shard_offset,
+                                                shard_size)
+
+        # Clean gathered states
+        for state_shard in gathered_state_shards:
+            del state_shard[0]
+            gc.collect()
+
+        # Reshape tensors
+        if is_collector:
+            for state_name, state_tensor in collected_states.items():
+                if state_tensor.numel() == param.numel():
+                    collected_states[state_name] = torch.reshape(state_tensor, param.shape)
+
+        return collected_states
+
+    def pack_optimizer_states_to_tensor(self,
+                                        param_id: int,
+                                        state_names: list,
+                                        device: torch.device = torch.device('cuda'),
+                                        dtype: torch.dtype = torch.float32) -> torch.Tensor:
+        '''
+        With param id given, pack its optimizer states into a compact tensor and return.
+        '''
+        if param_id not in self.id_to_fake_params:
+            return None
+
+        fake_param = self.id_to_fake_params[param_id]
+        param_range = self.param_to_range[fake_param]
+        states = self.optim.state[fake_param]
+        shard_size = param_range[1] - param_range[0]
+        compacted_size = 0
+        for name in state_names:
+            if name == 'step':
+                compacted_size += 1
+            else:
+                compacted_size += shard_size
+        compacted_states = torch.zeros(compacted_size, dtype=dtype, device=device, requires_grad=False)
+
+        next_state_offset = 0
+        for state_name, state_tensor in states.items():
+            # State 'step' needs special operation.
+            if state_name == 'step':
+                if isinstance(state_tensor, torch.Tensor):
+                    compacted_states[next_state_offset] = state_tensor[0].item()
+                else:
+                    assert isinstance(state_tensor, int)
+                    compacted_states[next_state_offset] = state_tensor
+                next_state_offset += 1
+            else:
+                assert state_tensor.numel() == shard_size
+                compacted_states[next_state_offset:next_state_offset + shard_size].copy_(state_tensor)
+                next_state_offset += shard_size
+
+        return compacted_states
+
+    def load_from_compacted_states(self, compacted_states: torch.Tensor, collected_states: dict, state_names: list,
+                                   shard_start: int, shard_size: int):
+        '''
+        Given a tensor carrying compacted optimizer states,
+        update these states to collected_states.
+        '''
+        shard_end = shard_start + shard_size
+        next_state_offset = 0
+
+        for state_name in state_names:
+            if state_name == 'step':
+                collected_states['step'].data = torch.tensor(compacted_states[next_state_offset].item(),
+                                                             dtype=torch.float32,
+                                                             requires_grad=False).cpu()
+                next_state_offset += 1
+            else:
+                target_segment = collected_states[state_name][shard_start:shard_end]
+                target_segment.copy_(compacted_states[next_state_offset:next_state_offset + shard_size])
+                next_state_offset += shard_size
+
+    def state_dict(self, only_rank_0: bool = True) -> dict:
+        """
+        Args:
+            only_rank_0 (bool): a boolean value indicating whether the state_dict is collected
+            only on rank 0, dafault to True.
+
+        Returns:
+            The complete state of the optimizer as a :class:`dict`.
+            It contains two entries:
+
+            * state - a dict holding current optimization state. Its content
+                differs between optimizer classes.
+            * param_groups - a list containing all parameter groups where each
+                parameter group is a dict.
+
+        Warning: This method will gather and return the whole optimizer state_dict,
+                 so it should be called only when memory resources are abundant.
+        """
+        state_dict = {}
+        state_dict['param_groups'] = copy.deepcopy(self.param_groups_backup)
+
+        torch_special_hyperparameters = {
+            'amsgrad': False,
+            'maximize': False,
+            'foreach': None,
+            'capturable': False,
+            'differentiable': False,
+            'fused': False
+        }
+
+        for group in state_dict['param_groups']:
+            for k, v in torch_special_hyperparameters.items():
+                if k not in group:
+                    group[k] = v
+
+        # Collect optimizer states.
+        state_dict['state'] = dict()
+        for param_id in self.id_to_real_params.keys():
+            dist.barrier()
+            state_dict['state'][param_id] = self.collect_states(param_id=param_id, only_rank_0=only_rank_0)
+        return state_dict
+
+    def load_param_groups(self, saved_param_groups: list):
+        """
+        Load saved_param_groups into
+        self.param_groups and self.param_groups_backup
+        """
+        self.param_groups_backup = copy.deepcopy(saved_param_groups)
+
+        # discard the older param_groups
+        self.optim.param_groups = []
+
+        for group in saved_param_groups:
+            fake_params_list = list()
+            updated_group = {k: v for k, v in group.items() if k != 'params'}
+            for param_id in group['params']:
+                if param_id not in self.id_to_fake_params:
+                    continue
+                fake_param = self.id_to_fake_params[param_id]
+                fake_params_list.append(fake_param)
+            updated_group['params'] = fake_params_list
+            self.optim.param_groups.append(updated_group)
+
+    def load_single_param_states(self, param_id: int, saved_states: dict):
+        """
+        Load saved optimizer states into parameter with given id.
+        """
+
+        def cast(param, state_range, value, key=None):
+            """
+            Make a copy of the needed segment of value and cast it to device of param.
+            """
+            assert isinstance(value, torch.Tensor)
+            ret_val = value
+            if (key == "step"):
+                assert value.numel() == 1
+                ret_val = int(value.item())
+            else:
+                state_start, state_end = state_range
+                ret_val = torch.zeros(state_end - state_start,
+                                      dtype=torch.float32,
+                                      device=param.device,
+                                      requires_grad=False)
+                ret_val.copy_(value.flatten()[state_start:state_end])
+            return ret_val
+
+        assert param_id in self.id_to_fake_params
+        fake_param = self.id_to_fake_params[param_id]
+        _, state_offset, param_size = self.get_offsets(param_id)
+        state_range = (state_offset, state_offset + param_size)
+
+        # Copy states assigned to param (and cast tensors to appropriate types).
+        updated_states = dict()
+        for k, v in saved_states.items():
+            updated_states[k] = cast(fake_param, state_range, v, k)
+            del v    # clean loaded states
+        self.optim.state[fake_param].update(updated_states)
+
+    def load_state_dict(self, state_dict: dict):
+        """Loads optimizer state from whole optimizer state_dict.
+           During loading, filter out the part of states not considered by current process.
+
+        Args:
+            state_dict (dict): optimizer state. Should be an object returned
+                from a call to :meth:`state_dict`.
+        """
+        assert 'param_groups' in state_dict
+        self.load_param_groups(state_dict['param_groups'])
+
+        state = state_dict['state']
+
+        for param_id, param_states in state.items():
+            if param_id in self.id_to_fake_params:
+                self.load_single_param_states(param_id, param_states)
+
+        # Epilogue for pytorch optimizer.
+        self.optim._hook_for_profile()    # To support multiprocessing pickle/unpickle.
+        self.optim.defaults.setdefault('differentiable', False)
 
 
 class GeminiAdamOptimizer(ZeroOptimizer):

tests/test_checkpoint_io/test_gemini_checkpoint_io.py

@@ -8,15 +8,18 @@ from utils import shared_tempdir
 import colossalai
 from colossalai.booster import Booster
 from colossalai.booster.plugin import GeminiPlugin
-from colossalai.booster.plugin.gemini_plugin import GeminiCheckpointIO
 from colossalai.nn.optimizer import HybridAdam
-from colossalai.testing import check_state_dict_equal, parameterize, rerun_if_address_is_in_use, spawn
-from colossalai.zero import ZeroDDP
-from colossalai.zero.gemini.chunk import ChunkManager, search_chunk_configuration
-from colossalai.zero.gemini.gemini_mgr import GeminiManager
+from colossalai.testing import (
+    check_state_dict_equal,
+    clear_cache_before_run,
+    parameterize,
+    rerun_if_address_is_in_use,
+    spawn,
+)
 from tests.kit.model_zoo import model_zoo
 
 
+@clear_cache_before_run()
 @parameterize('placement_policy', ['cuda', 'cpu'])
 @parameterize('model_name', ['transformers_bert_for_sequence_classification'])
 @parameterize('use_safetensors', [False, True])
@@ -29,33 +32,33 @@ def exam_state_dict_with_origin(placement_policy, model_name, use_safetensors: b
         pretrained_path = os.path.join(tempdir, 'pretrained')
         bert_model.config.save_pretrained(save_directory=pretrained_path)
 
-        # TODO(ver217): use boost api
-        config_dict, *_ = search_chunk_configuration(bert_model, search_range_m=1, search_interval=100)
-        chunk_manager = ChunkManager(config_dict)
-        gemini_manager = GeminiManager(placement_policy, chunk_manager)
-        bert_model = ZeroDDP(bert_model, gemini_manager)
-        bert_model.train()
-
-        ckpt_io = GeminiCheckpointIO()
+        plugin = GeminiPlugin(placement_policy=placement_policy)
+        booster = Booster(plugin=plugin)
+        bert_model, _, _, _, _ = booster.boost(bert_model)
         model_size = sum(p.numel() * p.element_size() for p in bert_model.parameters()) / 1024**2
-        ckpt_io.save_model(bert_model, (pretrained_path),
+
+        booster.save_model(bert_model,
+                           pretrained_path,
                            True,
                            True,
                            '', (model_size / 3),
                            use_safetensors=use_safetensors)
         dist.barrier()
+
         new_bert_model = BertForSequenceClassification.from_pretrained(pretrained_path)
-        check_state_dict_equal(bert_model.state_dict(only_rank_0=False, dtype=torch.float32),
+        check_state_dict_equal(bert_model.unwrap().state_dict(only_rank_0=False, dtype=torch.float32),
                                new_bert_model.state_dict(), False)
 
 
+@clear_cache_before_run()
 @parameterize('placement_policy', ['cuda', 'cpu'])
-@parameterize('shard', [True, False])
+@parameterize('shard', [False])
 @parameterize('model_name', ['transformers_gpt'])
-def exam_state_dict(placement_policy, shard: bool, model_name: str):
+@parameterize('size_per_shard', [32])
+def exam_state_dict(placement_policy, shard: bool, model_name: str, size_per_shard: int):
     (model_fn, data_gen_fn, output_transform_fn, _, _) = next(iter(model_zoo.get_sub_registry(model_name).values()))
     criterion = lambda x: x.mean()
-    plugin = GeminiPlugin(placement_policy=placement_policy)
+    plugin = GeminiPlugin(placement_policy=placement_policy, precision="fp16", initial_scale=(2**14))
     booster = Booster(plugin=plugin)
 
     model = model_fn()
@@ -78,18 +81,32 @@ def exam_state_dict(placement_policy, shard: bool, model_name: str):
     with shared_tempdir() as tempdir:
         model_ckpt_path = f"{tempdir}/model"
         optimizer_ckpt_path = f"{tempdir}/optimizer"
-        booster.save_model(model, model_ckpt_path)
-        if not shard:
-            # TODO(ver217): optimizer checkpointing is not supported for sharded checkpoint
-            booster.save_optimizer(optimizer, optimizer_ckpt_path)
+        booster.save_model(model, model_ckpt_path, shard=shard, size_per_shard=size_per_shard)
+
+        booster.save_optimizer(optimizer, optimizer_ckpt_path, shard=shard, size_per_shard=size_per_shard)
         dist.barrier()
 
         booster.load_model(new_model, model_ckpt_path)
         check_state_dict_equal(model.unwrap().state_dict(only_rank_0=False),
                                new_model.unwrap().state_dict(only_rank_0=False), False)
-        if not shard:
-            booster.load_optimizer(new_optimizer, optimizer_ckpt_path)
-            check_state_dict_equal(optimizer.state_dict(), new_optimizer.state_dict(), False)
+
+        booster.load_optimizer(new_optimizer, optimizer_ckpt_path)
+        check_state_dict_equal(optimizer.unwrap().state_dict(only_rank_0=False),
+                               new_optimizer.unwrap().state_dict(only_rank_0=False), False)
+
+        # Check the new model/optimizer can successfully run.
+        data = data_gen_fn()
+        data = {
+            k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items()
+        }
+        output = new_model(**data)
+        output = output_transform_fn(output)
+        output_key = list(output.keys())[0]
+        loss = criterion(output[output_key])
+        booster.backward(loss, new_optimizer)
+        new_optimizer.step()
+        booster.save_model(new_model, model_ckpt_path, shard=shard)
+        booster.save_optimizer(new_optimizer, optimizer_ckpt_path, shard=shard)
 
 
 def run_dist(rank, world_size, port):
@@ -100,7 +117,7 @@ def run_dist(rank, world_size, port):
 
 
 @pytest.mark.dist
-@pytest.mark.parametrize('world_size', [2])
+@pytest.mark.parametrize('world_size', [1, 2])
 @rerun_if_address_is_in_use()
 def test_gemini_ckpIO(world_size):
     spawn(run_dist, world_size)

tests/test_checkpoint_io/test_gemini_torch_compability.py

+import pytest
+import torch
+import torch.distributed as dist
+from torch.optim import Adam
+from utils import shared_tempdir
+
+import colossalai
+from colossalai.booster import Booster
+from colossalai.booster.plugin import GeminiPlugin, TorchDDPPlugin
+from colossalai.nn.optimizer import HybridAdam
+from colossalai.testing import (
+    check_state_dict_equal,
+    clear_cache_before_run,
+    parameterize,
+    rerun_if_address_is_in_use,
+    spawn,
+)
+from tests.kit.model_zoo import model_zoo
+
+
+@clear_cache_before_run()
+@parameterize('shard', [False])
+@parameterize('model_name', ['transformers_gpt'])
+def exam_torch_load_from_gemini(shard: bool, model_name: str):
+
+    (model_fn, data_gen_fn, output_transform_fn, _, _) = next(iter(model_zoo.get_sub_registry(model_name).values()))
+    criterion = lambda x: x.mean()
+    plugin = GeminiPlugin(precision="fp16", initial_scale=(2**14))
+    booster = Booster(plugin=plugin)
+
+    model = model_fn()
+    optimizer = HybridAdam(model.parameters(), lr=0.001)
+    model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion)
+
+    data = data_gen_fn()
+    data = {k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items()}
+    output = model(**data)
+    output = output_transform_fn(output)
+    output_key = list(output.keys())[0]
+    loss = criterion(output[output_key])
+
+    booster.backward(loss, optimizer)
+    optimizer.step()
+
+    with shared_tempdir() as tempdir:
+        model_ckpt_path = f"{tempdir}/model"
+        optimizer_ckpt_path = f"{tempdir}/optimizer"
+
+        booster.save_model(model, model_ckpt_path, shard=shard)
+        booster.save_optimizer(optimizer, optimizer_ckpt_path, shard=shard)
+        dist.barrier()
+
+        new_model = model_fn()
+        new_optimizer = Adam(new_model.parameters(), lr=0.001)
+        new_plugin = TorchDDPPlugin()
+        new_booster = Booster(plugin=new_plugin)
+        new_model, new_optimizer, criterion, _, _ = new_booster.boost(new_model, new_optimizer, criterion)
+
+        # Loading HybridAdam states to torch.Adam
+        new_booster.load_model(new_model, model_ckpt_path, strict=True)
+
+        # Add prefix to get aligned with pytorch parameter names.
+        check_state_dict_equal(
+            model.unwrap().state_dict(only_rank_0=False, prefix='module.module.', dtype=torch.float32),
+            new_model.state_dict(), False)
+
+        new_booster.load_optimizer(new_optimizer, optimizer_ckpt_path)
+        check_state_dict_equal(optimizer.unwrap().state_dict(only_rank_0=False), new_optimizer.state_dict(), False)
+
+        # Check the new model/optimizer can successfully run.
+        data = data_gen_fn()
+        data = {
+            k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items()
+        }
+        output = new_model(**data)
+        output = output_transform_fn(output)
+        output_key = list(output.keys())[0]
+        loss = criterion(output[output_key])
+        new_booster.backward(loss, new_optimizer)
+        new_optimizer.step()
+        new_booster.save_model(new_model, model_ckpt_path, shard=shard)
+        new_booster.save_optimizer(new_optimizer, optimizer_ckpt_path, shard=shard)
+
+
+@clear_cache_before_run()
+@parameterize('shard', [False])
+@parameterize('model_name', ['transformers_gpt'])
+def exam_gemini_load_from_torch(shard: bool, model_name: str):
+
+    (model_fn, data_gen_fn, output_transform_fn, _, _) = next(iter(model_zoo.get_sub_registry(model_name).values()))
+    criterion = lambda x: x.mean()
+    plugin = TorchDDPPlugin()
+    booster = Booster(plugin=plugin)
+
+    model = model_fn()
+    optimizer = Adam(model.parameters(), lr=0.001)
+    model, optimizer, criterion, _, _ = booster.boost(model, optimizer, criterion)
+
+    data = data_gen_fn()
+    data = {k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items()}
+    output = model(**data)
+    output = output_transform_fn(output)
+    output_key = list(output.keys())[0]
+    loss = criterion(output[output_key])
+
+    booster.backward(loss, optimizer)
+    optimizer.step()
+
+    with shared_tempdir() as tempdir:
+        model_ckpt_path = f"{tempdir}/model"
+        optimizer_ckpt_path = f"{tempdir}/optimizer"
+
+        booster.save_model(model, model_ckpt_path, shard=shard)
+        booster.save_optimizer(optimizer, optimizer_ckpt_path, shard=shard)
+        dist.barrier()
+
+        new_model = model_fn()
+        new_optimizer = HybridAdam(new_model.parameters(), lr=0.001)
+        new_plugin = GeminiPlugin()
+        new_booster = Booster(plugin=new_plugin)
+        new_model, new_optimizer, criterion, _, _ = new_booster.boost(new_model, new_optimizer, criterion)
+
+        # Loading torch.Adam states to HybridAdam
+        new_booster.load_model(new_model, model_ckpt_path, strict=True)
+
+        # Add prefix to get aligned with pytorch parameter names.
+        check_state_dict_equal(
+            new_model.unwrap().state_dict(only_rank_0=False, prefix='module.module.', dtype=torch.float32),
+            model.state_dict(), False)
+
+        new_booster.load_optimizer(new_optimizer, optimizer_ckpt_path)
+        old_state_dict = optimizer.state_dict()
+        new_state_dict = new_optimizer.unwrap().state_dict(only_rank_0=False)
+
+        # Comparison of param_groups needs special care here,
+        # since not all hyperparameters in Adam are used by HybridAdam
+        hyperparameters_to_examine = ['params', 'lr', 'betas', 'eps', 'weight_decay']
+        for old_group, new_group in zip(old_state_dict['param_groups'], new_state_dict['param_groups']):
+            for k in hyperparameters_to_examine:
+                assert k in old_group and k in new_group, \
+                        f"Old group's keys: {list(old_group.keys())}, New group's keys: {list(new_group.keys())}"
+                assert old_group[k] == new_group[k]
+        check_state_dict_equal(old_state_dict['state'], new_state_dict['state'], False)
+
+        # Check the new model/optimizer can successfully run.
+        data = data_gen_fn()
+        data = {
+            k: v.to('cuda') if torch.is_tensor(v) or 'Tensor' in v.__class__.__name__ else v for k, v in data.items()
+        }
+        output = new_model(**data)
+        output = output_transform_fn(output)
+        output_key = list(output.keys())[0]
+        loss = criterion(output[output_key])
+        new_booster.backward(loss, new_optimizer)
+        new_optimizer.step()
+        new_booster.save_model(new_model, model_ckpt_path, shard=shard)
+        new_booster.save_optimizer(new_optimizer, optimizer_ckpt_path, shard=shard)
+
+
+def run_dist(rank, world_size, port):
+    config = {}
+    colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    exam_torch_load_from_gemini()
+    exam_gemini_load_from_torch()
+
+
+@pytest.mark.dist
+@pytest.mark.parametrize('world_size', [1, 2])
+@rerun_if_address_is_in_use()
+def test_gemini_ckpIO(world_size):
+    spawn(run_dist, world_size)