[gemini] zero supports gemini (#1093)

* add placement policy

* add gemini mgr

* update mem stats collector

* update zero

* update zero optim

* fix bugs

* zero optim monitor os

* polish unit test

* polish unit test

* add assert

colossalai/gemini/gemini_mgr.py

@@ -0,0 +1,113 @@
+import functools
+from .memory_tracer.memstats_collector import MemStatsCollectorV2
+from typing import List, Optional, Tuple
+from time import time
+from colossalai.tensor.chunk import Chunk, ChunkManager
+from .placement_policy import PlacementPolicy, PlacementPolicyFactory
+
+
+class GeminiManager:
+    """
+    Stateful Tensor Manager, inspired from PatrickStar
+
+    PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management
+    https://arxiv.org/abs/2108.05818
+    """
+
+    def __init__(self, placement_policy: str, chunk_manager: ChunkManager) -> None:
+        # TODO: remove assert
+        assert placement_policy == 'cuda', 'placement_policy can only be "cuda" now'
+        assert placement_policy in PlacementPolicyFactory.get_polocy_names()
+        policy_cls = PlacementPolicyFactory.create(placement_policy)
+        self._chunk_manager = chunk_manager
+        self._mem_stats_collector = MemStatsCollectorV2(chunk_manager) if policy_cls.need_mem_stats else None
+        self._placement_policy = policy_cls(chunk_manager, self._mem_stats_collector)
+        self._compute_list: List[Tuple[Chunk, ...]] = []
+        self._compute_idx: int = -1
+
+        self._cpu_gpu_move_volume = 0
+        self._layout_time = 0
+        self._evict_time = 0
+        self._warmup = True
+
+    def pre_iter(self):
+        if self._mem_stats_collector and self._warmup:
+            self._mem_stats_collector.start_collection()
+
+    def post_iter(self):
+        """This function must be called when each iteration finishes
+        """
+        if self._mem_stats_collector and self._warmup:
+            self._mem_stats_collector.finish_collection()
+        self._warmup = False
+        self._compute_idx = -1
+        self._cpu_gpu_move_volume = 0
+        self._layout_time = 0
+        self._evict_time = 0
+
+    def adjust_layout(self, chunks: Tuple[Chunk, ...], group_name: str) -> None:
+        """ Adjust the layout of statefuil tensor according to the information provided
+        by mem_stats_collector, which should belongs to a Sharded Model.
+        """
+        # find stateful tensor in state COMPUTE
+        start = time()
+        self._record_chunks_order(chunks)
+        cuda_demand, hold_cuda_tensor_list = self._get_layout_info(self._compute_idx, self._warmup, chunks, group_name)
+        self._layout_time += time() - start
+        vol, evict_time = self._placement_policy.evict_tensors(hold_cuda_tensor_list,
+                                                               cuda_demand=cuda_demand,
+                                                               warmup=self._warmup,
+                                                               compute_list=self._compute_list,
+                                                               compute_idx=self._compute_idx)
+        self._cpu_gpu_move_volume += vol
+        self._evict_time += evict_time
+        # move COMPUTE tensors to CUDA
+        self._cpu_gpu_move_volume += cuda_demand
+
+    @property
+    def cpu_gpu_move_volume(self):
+        return self._cpu_gpu_move_volume
+
+    # @functools.lru_cache(maxsize=None)
+    # TODO: test lru
+    def _get_layout_info(self, compute_idx: int, warmup: bool, chunks: Tuple[Chunk, ...], group_name: str):
+        cuda_demand = 0
+        for chunk in chunks:
+            if chunk.device_type == 'cpu' or chunk.is_free:
+                cuda_demand += chunk.mem
+        can_evict_chunks = []
+        for chunk in self._chunk_manager.chunk_groups[group_name]:
+            if not chunk.is_free and chunk.device_type == 'cuda' and chunk.can_move_device:
+                can_evict_chunks.append(chunk)
+        return cuda_demand, can_evict_chunks
+
+    def _record_chunks_order(self, chunks: Tuple[Chunk, ...]) -> None:
+        self._compute_idx += 1
+        if self._warmup and self._placement_policy.need_mem_stats:
+            self._compute_list.append(chunks)
+
+    @property
+    def default_device(self):
+        return self._placement_policy.get_default_device()
+
+    def sample_overall_data(self):
+        if self._mem_stats_collector:
+            self._mem_stats_collector.sample_overall_data()
+
+    def sample_model_data(self):
+        if self._mem_stats_collector:
+            self._mem_stats_collector.sample_model_data()
+
+    @property
+    def chunk_manager(self):
+        return self._chunk_manager
+
+    @property
+    def cuda_margin_mem(self) -> Optional[float]:
+        if self._mem_stats_collector:
+            return self._mem_stats_collector.cuda_margin_mem
+        return None
+
+    @property
+    def is_cuda_margin_mem_avail(self) -> bool:
+        return self._placement_policy.need_mem_stats

colossalai/gemini/memory_tracer/memstats_collector.py

@@ -1,5 +1,6 @@
 from colossalai.gemini.memory_tracer import SyncCudaMemoryMonitor
-from colossalai.utils.memory import colo_device_memory_used
+from colossalai.utils.memory import colo_device_memory_used, colo_device_memory_capacity
+from colossalai.utils import get_current_device
 from colossalai.gemini.stateful_tensor import StatefulTensor
 from colossalai.tensor import ChunkManager
 
@@ -145,3 +146,7 @@ class MemStatsCollectorV2(MemStatsCollector):
             cpu_mem = self._chunk_manager.total_mem['cpu']
             self._model_data_cuda_list.append(cuda_mem)
             self._model_data_cpu_list.append(cpu_mem)
+
+    @property
+    def cuda_margin_mem(self) -> float:
+        return colo_device_memory_capacity(get_current_device()) - max(self.overall_mem_stats('cuda'))

colossalai/gemini/placement_policy.py

@@ -0,0 +1,157 @@
+from abc import ABC, abstractmethod
+from time import time
+from typing import List, Optional, Tuple, Dict
+import torch
+from colossalai.utils import get_current_device
+from colossalai.utils.memory import colo_device_memory_capacity
+
+from colossalai.gemini.memory_tracer.memstats_collector import MemStatsCollectorV2
+from typing import Type
+import functools
+from colossalai.tensor.chunk import Chunk, ChunkManager
+
+
+class PlacementPolicy(ABC):
+    need_mem_stats: bool = False
+
+    def __init__(self, chunk_manager: ChunkManager, mem_stats_collector: Optional[MemStatsCollectorV2] = None) -> None:
+        self.chunk_manager = chunk_manager
+        self.mem_stats_collector: Optional[MemStatsCollectorV2] = mem_stats_collector
+
+    @abstractmethod
+    def evict_tensors(self, can_evict_chunks: List[Chunk], **kwargs) -> None:
+        raise NotImplementedError
+
+    @staticmethod
+    def get_default_device() -> torch.device:
+        return torch.device('cpu')
+
+
+class CPUPlacementPolicy(PlacementPolicy):
+
+    def __init__(self, chunk_manager: ChunkManager, mem_stats_collector: Optional[MemStatsCollectorV2] = None) -> None:
+        super().__init__(chunk_manager, mem_stats_collector=mem_stats_collector)
+
+    def evict_tensors(self, can_evict_chunks: List[Chunk], **kwargs) -> int:
+        volume = 0
+        for chunk in can_evict_chunks:
+            self.chunk_manager.move_chunk(chunk, torch.device('cpu'))
+            volume += chunk.mem
+        return volume, 0
+
+
+class CUDAPlacementPolicy(PlacementPolicy):
+
+    def __init__(self, chunk_manager: ChunkManager, mem_stats_collector: Optional[MemStatsCollectorV2] = None) -> None:
+        assert torch.cuda.is_available(), 'Cannot use CUDATensorPlacementPolicy when CUDA is not available'
+        super().__init__(chunk_manager, mem_stats_collector=mem_stats_collector)
+
+    def evict_tensors(self, can_evict_chunks: List[Chunk], **kwargs) -> int:
+        return 0, 0
+
+    @staticmethod
+    def get_default_device() -> torch.device:
+        return get_current_device()
+
+
+class AutoPlacementPolicy(PlacementPolicy):
+
+    need_mem_stats: bool = True
+
+    def __init__(self, chunk_manager: ChunkManager, mem_stats_collector: Optional[MemStatsCollectorV2] = None) -> None:
+        super().__init__(chunk_manager, mem_stats_collector=mem_stats_collector)
+        # model data will use 1-self._warmup_non_model_data_ratio CUDA memory in warmup phase
+        # TODO(ver217): make these args configurable
+        self._warmup_non_model_data_ratio: float = 0.8
+        self._steady_cuda_cap_ratio: float = 0.9
+
+    def evict_tensors(self,
+                      can_evict_chunks: List[Chunk],
+                      cuda_demand: int = 0,
+                      warmup: bool = True,
+                      compute_list: List[Tuple[Chunk, ...]] = [],
+                      compute_idx: int = 0,
+                      **kwargs) -> int:
+        """
+        Evict tensors from CUDA device.
+
+        Args:
+            hold_cuda_tensor_list (List[StatefulTensor]): the list of tensor in state of HOLD-like
+            cuda_demand (int, optional): the volume of data needed on cuda device. Defaults to 0.
+            warmup (bool, optional): a flag indicates whether in the phase of warmup. Defaults to True.
+            compute_list (List[StatefulTensor], optional): TODO. Defaults to [].
+            compute_idx (int, optional): the idx of computing device. Defaults to 0.
+
+        Raises:
+            RuntimeError:
+
+        Returns:
+            int: the volume of memory that is evicted
+        """
+        start = time()
+        cuda_capacity = colo_device_memory_capacity(get_current_device())
+        used_cuda_model_data = self.chunk_manager.total_mem['cuda']
+        if warmup:
+            # We designate a part of CUDA memory for model data in warmup iterations.
+            max_cuda_non_model_data_per_period = cuda_capacity * self._warmup_non_model_data_ratio
+        else:
+            # max non-model-data cuda memory consumption of this sampling moment and the next sampling moment.
+            max_cuda_non_model_data_per_period = self.mem_stats_collector.next_period_non_model_data_usage('cuda')
+            cuda_capacity *= self._steady_cuda_cap_ratio
+        total_cuda_model_data = cuda_capacity - max_cuda_non_model_data_per_period
+        avail_cuda_model_data = total_cuda_model_data - used_cuda_model_data
+        freed_cuda_model_data = 0
+        end = time()
+        if avail_cuda_model_data < cuda_demand:
+            # Move cuda_demand - avail_cuda_model_data volume of tensors
+            # to_free_cuda_model_data = cuda_demand - avail_cuda_model_data
+            to_free_cuda_model_data = cuda_demand - avail_cuda_model_data
+            to_free_chunks = can_evict_chunks
+            if not warmup:
+                to_free_chunks = self._sort_can_evict_chunks(tuple(to_free_chunks), compute_idx, tuple(compute_list))
+                # print(self._sort_can_evict_chunks.cache_info())
+            end = time()
+            for chunk in to_free_chunks:
+                if freed_cuda_model_data >= to_free_cuda_model_data:
+                    break
+                freed_cuda_model_data += chunk.mem
+                self.chunk_manager.move_chunk(chunk, torch.device('cpu'))
+            if freed_cuda_model_data < to_free_cuda_model_data:
+                raise RuntimeError(
+                    f"Adjust layout failed! No enough CUDA memory! Need {to_free_cuda_model_data}, freed {freed_cuda_model_data}"
+                )
+        return freed_cuda_model_data, end - start
+
+    @staticmethod
+    @functools.lru_cache(maxsize=None)
+    def _sort_can_evict_chunks(can_evict_chunks: tuple, compute_idx: int, compute_list: tuple) -> list:
+        next_compute_idx = {chunk: len(compute_list) for chunk in can_evict_chunks}
+        for i in range(len(compute_list) - 1, compute_idx, -1):
+            for chunk in compute_list[i]:
+                if chunk in next_compute_idx:
+                    next_compute_idx[chunk] = i
+        next_compute_idx = sorted(next_compute_idx.items(), key=lambda pair: pair[1], reverse=True)
+        return [t for (t, idx) in next_compute_idx]
+
+
+class PlacementPolicyFactory:
+    policies: Dict[str, PlacementPolicy] = {
+        'cpu': CPUPlacementPolicy,
+        'cuda': CUDAPlacementPolicy,
+        'auto': AutoPlacementPolicy
+    }
+
+    @staticmethod
+    def create(policy_name: str) -> Type[PlacementPolicy]:
+        if policy_name not in PlacementPolicyFactory.policies:
+            raise TypeError(f"Unknown tensor placement policy {policy_name}")
+        return PlacementPolicyFactory.policies[policy_name]
+
+    @staticmethod
+    def get_polocy_names():
+        return tuple(PlacementPolicyFactory.policies.keys())
+
+    @staticmethod
+    def get_default_device(policy_name: str) -> torch.device:
+        policy_cls = PlacementPolicyFactory.create(policy_name)
+        return policy_cls.get_default_device()

colossalai/nn/parallel/data_parallel.py

@@ -4,8 +4,11 @@ from colossalai.core import global_context as gpc
 from colossalai.context import ParallelMode
 from functools import partial
 from colossalai.zero.utils.zero_hook_v2 import ZeROHookV2
-from colossalai.tensor.chunk import ChunkManager, TensorState
+from colossalai.tensor.chunk import ChunkManager, TensorState, Chunk
 from colossalai.tensor.param_op_hook import use_param_op_hooks
+from colossalai.gemini.gemini_mgr import GeminiManager
+from typing import Dict
+from colossalai.logging import get_dist_logger
 
 
 def free_storage(data: torch.Tensor) -> None:
@@ -89,12 +92,14 @@ class ColoDDP(torch.nn.Module):
 
 class ColoDDPV2(ColoDDP):
 
-    def __init__(self, module: torch.nn.Module, chunk_manager: ChunkManager) -> None:
+    def __init__(self, module: torch.nn.Module, gemini_manager: GeminiManager) -> None:
         super().__init__(module)
-        self.chunk_manager = chunk_manager
-        self.param_op_hook = ZeROHookV2(chunk_manager)
+        self.gemini_manager = gemini_manager
+        self.chunk_manager = gemini_manager.chunk_manager
+        self.param_op_hook = ZeROHookV2(gemini_manager)
         self.fp32_params = []
         self.overflow_counter = 0
+        self.grads_device: Dict[torch.Tensor, torch.device] = {}
         # TODO: get param order and filter unused params
         for p in module.parameters():
             assert p.dtype == torch.half
@@ -102,22 +107,32 @@ class ColoDDPV2(ColoDDP):
             self.chunk_manager.append_tensor(p, 'fp16_param')
             self.chunk_manager.append_tensor(fp32_p, 'fp32_param')
             self.fp32_params.append(fp32_p)
+            self.grads_device[p] = self.gemini_manager.default_device
+        self._logger = get_dist_logger()
 
     def forward(self, *args, **kwargs):
         self.module.zero_grad(set_to_none=True)
+        self.gemini_manager.pre_iter()
         with use_param_op_hooks(self.param_op_hook):
             outputs = self.module(*args, **kwargs)
         self.chunk_manager.exec_lazy_release()
         return outputs
 
-    def _post_backward(self):
-        self.chunk_manager.exec_lazy_release()
+    def _setup_grads_ptr(self):
         for p in self.module.parameters():
             if self.chunk_manager.get_chunk(p).is_free or not p.requires_grad:
                 p.grad = None
             else:
                 p.grad = p.data
 
+    def _post_backward(self):
+        self.chunk_manager.exec_lazy_release()
+        self._setup_grads_ptr()
+        self._logger.info(
+            f'layout time: {self.gemini_manager._layout_time}, evict time: {self.gemini_manager._evict_time}, PCIE move vol: {self.gemini_manager._cpu_gpu_move_volume}B'
+        )
+        self.gemini_manager.post_iter()
+
     def backward(self, loss: torch.Tensor):
         with self.param_op_hook.switch_to_backward(), use_param_op_hooks(self.param_op_hook):
             loss.backward()
@@ -141,7 +156,12 @@ class ColoDDPV2(ColoDDP):
             self.chunk_manager.release_chunk(chunk)
             if reduced and not chunk.is_free:
                 self.overflow_counter += chunk.has_inf_or_nan
+                self.chunk_manager.move_chunk(chunk, self.grads_device[p])
         return empty_grad
 
     def zero_grad(self, set_to_none: bool = False) -> None:
         self.module.zero_grad(set_to_none=True)
+
+    def _set_chunk_grad_device(self, chunk: Chunk, device: torch.device) -> None:
+        for tensor in chunk.get_tensors():
+            self.grads_device[tensor] = device

colossalai/tensor/chunk.py

@@ -178,6 +178,9 @@ class Chunk:
     def __eq__(self, __o: object) -> bool:
         return self is __o
 
+    def get_tensors(self) -> List[torch.Tensor]:
+        return list(self.tensors_info.keys())
+
 
 class ChunkManager:
 
@@ -234,6 +237,10 @@ class ChunkManager:
 
     def access_chunk(self, chunk: Chunk) -> None:
         if chunk in self.accessed_chunks:
+            if chunk.device_type != 'cuda':
+                self.total_mem[chunk.device_type] -= chunk.mem
+                chunk.move_device(get_current_device())
+                self.total_mem[chunk.device_type] += chunk.mem
             return
         if not chunk.is_free:
             self.total_mem[chunk.device_type] -= chunk.mem

colossalai/zero/utils/zero_hook_v2.py

@@ -5,6 +5,7 @@ from enum import Enum
 from typing import List
 from contextlib import contextmanager
 from functools import partial
+from colossalai.gemini.gemini_mgr import GeminiManager
 
 
 class TrainingPhase(Enum):
@@ -14,9 +15,10 @@ class TrainingPhase(Enum):
 
 class ZeROHookV2(ParamOpHook):
 
-    def __init__(self, chunk_manager: ChunkManager) -> None:
+    def __init__(self, gemini_manager: GeminiManager) -> None:
         super().__init__()
-        self._chunk_manager = chunk_manager
+        self._gemini_manager = gemini_manager
+        self._chunk_manager = gemini_manager.chunk_manager
         self._training_phase = TrainingPhase.FORWARD
 
     def pre_op(self, params):
@@ -24,9 +26,11 @@ class ZeROHookV2(ParamOpHook):
         for p in params:
             self._chunk_manager.trans_tensor_state(p, TensorState.COMPUTE)
         self._chunk_manager.exec_lazy_release()
-        # TODO: evict chunks
+        self._gemini_manager.sample_overall_data()
+        self._gemini_manager.adjust_layout(chunks, 'fp16_param')
         for chunk in chunks:
             self._chunk_manager.access_chunk(chunk)
+        self._gemini_manager.sample_model_data()
 
     def post_op(self, params):
         for p in params:

colossalai/zero/zero_optimizer.py

@@ -7,6 +7,7 @@ from typing import Dict
 from colossalai.amp.naive_amp.grad_scaler import DynamicGradScaler
 from colossalai.logging import get_dist_logger
 from colossalai.nn.optimizer import ColossalaiOptimizer
+from colossalai.utils import get_current_device, disposable
 
 
 class OptimState(Enum):
@@ -19,6 +20,7 @@ class ZeroOptimizer(ColossalaiOptimizer):
     def __init__(self,
                  optim: Optimizer,
                  module: ColoDDPV2,
+                 gpu_margin_mem_ratio: float = 0.0,
                  initial_scale: float = 2**32,
                  min_scale: float = 1,
                  growth_factor: float = 2,
@@ -29,6 +31,8 @@ class ZeroOptimizer(ColossalaiOptimizer):
         super().__init__(optim)
         assert isinstance(module, ColoDDPV2)
         self.module = module
+        self.gemini_manager = module.gemini_manager
+        self.chunk_manager = self.gemini_manager.chunk_manager
         self.optim_state = OptimState.UNSCALED
         self.fp16_param_to_fp32_param: Dict[torch.Tensor, torch.Tensor] = {}
         for p, fp32_p in zip(module.parameters(), module.fp32_params):
@@ -45,6 +49,18 @@ class ZeroOptimizer(ColossalaiOptimizer):
         self._found_overflow: torch.Tensor = torch.zeros(1, dtype=torch.int64, device=torch.cuda.current_device())
         self._logger = get_dist_logger()
 
+        self.gpu_margin_mem_ratio: float = float(gpu_margin_mem_ratio)
+        assert 0.0 <= self.gpu_margin_mem_ratio <= 1.0, f'gpu_margin_mem_ratio must >=0.0 and <=1.0'
+        # Only move fp32 shards from CPU to GPU when user allows and inner optimizer is valid
+        # Inner optimizer must support optimizing hybrid (CPU and CUDA) tensors,
+        # and it must set `num_fp32_shards_per_param` correctly
+        self._should_move_fp32_params_h2d: bool = self.gemini_manager.is_cuda_margin_mem_avail and self.gpu_margin_mem_ratio > 0.0 and getattr(
+            optim, 'num_fp32_shards_per_param', 0) >= 2
+        if self.gpu_margin_mem_ratio > 0.0 and not self.gemini_manager.is_cuda_margin_mem_avail:
+            self._logger.warning(f'gpu_margin_mem_ratio is meaningless when placement_policy is not "auto"', ranks=[0])
+
+        self._register_states = disposable(self._register_states_)
+
     def _update_params_ptr(self):
         for group in self.optim.param_groups:
             for p in group['params']:
@@ -82,6 +98,7 @@ class ZeroOptimizer(ColossalaiOptimizer):
         return self.optim.zero_grad(set_to_none=True)
 
     def step(self, *args, **kwargs):
+        self._maybe_move_fp32_params()
         # unscale grads if scaled
         if self.optim_state == OptimState.SCALED:
             self._unscale_grads()
@@ -94,6 +111,7 @@ class ZeroOptimizer(ColossalaiOptimizer):
             return
         self._update_params_ptr()
         ret = self.optim.step(*args, **kwargs)
+        self._register_states()
         self._update_fp16_params()
         return ret
 
@@ -109,3 +127,29 @@ class ZeroOptimizer(ColossalaiOptimizer):
 
     def backward_by_grad(self, tensor: torch.Tensor, grad: torch.Tensor):
         self.module.backward_by_grad(tensor, grad)
+
+    def _maybe_move_fp32_params(self):
+        if self._should_move_fp32_params_h2d:
+            self._should_move_fp32_params_h2d = False
+            available_cuda_margin_mem = self.gemini_manager.cuda_margin_mem * self.gpu_margin_mem_ratio
+            fp32_params_available_cuda_margin_mem = available_cuda_margin_mem / self.optim.num_fp32_shards_per_param
+            fp32_params_used_cuda_margin_mem = 0
+            for fp16_param_chunk, fp32_param_chunk in zip(self.chunk_manager.chunk_groups['fp16_param'],
+                                                          self.chunk_manager.chunk_groups['fp32_param']):
+                if fp32_param_chunk.is_free:
+                    continue
+                if fp32_params_used_cuda_margin_mem + fp32_param_chunk.mem < fp32_params_available_cuda_margin_mem:
+                    self.chunk_manager.move_chunk(fp32_param_chunk, get_current_device())
+                    # stores grad now
+                    self.chunk_manager.move_chunk(fp16_param_chunk, get_current_device())
+                    self.module._set_chunk_grad_device(fp16_param_chunk, get_current_device())
+                    fp32_params_used_cuda_margin_mem += fp32_param_chunk.mem
+            self.module._setup_grads_ptr()
+
+    def _register_states_(self):
+        for group in self.optim.param_groups:
+            for p in group['params']:
+                state = self.optim.state[p]
+                for val in state.values():
+                    if isinstance(val, torch.Tensor):
+                        self.chunk_manager.add_extern_static_tensor(val)

tests/test_tensor/test_zero.py

@@ -14,6 +14,7 @@ from tests.components_to_test.registry import non_distributed_component_funcs
 from torch.nn.parallel import DistributedDataParallel as DDP
 from colossalai.nn.parallel import ColoDDPV2
 from colossalai.testing import parameterize
+from colossalai.gemini.gemini_mgr import GeminiManager
 
 
 def check_param_equal(model, torch_model):
@@ -44,7 +45,8 @@ def run_gpt(use_chunk, use_zero):
     model = model.half()
     chunk_size = 38 * 1024**2 if use_chunk else None
     chunk_manager = ChunkManager(chunk_size, enable_distributed_storage=use_zero)
-    model = ColoDDPV2(model, chunk_manager)
+    gemini_manager = GeminiManager('cuda', chunk_manager)
+    model = ColoDDPV2(model, gemini_manager)
     torch_model = DDP(torch_model, device_ids=[gpc.get_global_rank()], process_group=gpc.get_group(ParallelMode.DATA))
     print(chunk_manager)
     check_param_equal(model, torch_model)

tests/test_tensor/test_zero_optim.py

@@ -18,6 +18,7 @@ from colossalai.nn.optimizer import HybridAdam
 from colossalai.zero import ZeroOptimizer
 from colossalai.testing import parameterize
 from colossalai.amp import convert_to_apex_amp
+from colossalai.gemini.gemini_mgr import GeminiManager
 
 
 def check_param_equal(model, torch_model):
@@ -53,7 +54,8 @@ def run_gpt(use_chunk, use_zero):
 
     chunk_size = 38 * 1024**2 if use_chunk else None
     chunk_manager = ChunkManager(chunk_size, enable_distributed_storage=use_zero)
-    model = ColoDDPV2(model, chunk_manager)
+    gemini_manager = GeminiManager('cuda', chunk_manager)
+    model = ColoDDPV2(model, gemini_manager)
     optim = HybridAdam(model.parameters(), lr=1e-3)
     optim = ZeroOptimizer(optim, model, initial_scale=32)