hpcaitech · feifeibear · Apr 6, 2022 · Apr 1, 2022 · Apr 1, 2022 · Apr 1, 2022
@@ -1,3 +1,4 @@
 from .async_memtracer import AsyncMemoryMonitor
+from .memstats_collector import MemStatsCollector
 
-__all__ = ['AsyncMemoryMonitor']
+__all__ = ['AsyncMemoryMonitor', 'MemStatsCollector']
@@ -11,15 +11,21 @@ class SamplingCounter:
 
     def __init__(self) -> None:
         self._samplint_cnt = 0
+        self._max_sampling_cnt = None
 
     def advance(self):
         self._samplint_cnt += 1
 
+    def next(self):
+        assert self._max_sampling_cnt is not None
+        return (self._samplint_cnt + 1) % self._max_sampling_cnt
+
     @property
     def sampling_cnt(self):
         return self._samplint_cnt
 
     def reset(self):
+        self._max_sampling_cnt = self._samplint_cnt
         self._samplint_cnt = 0
 
 
@@ -56,7 +62,7 @@ def overall_mem_stats(self, device_type: str):
         else:
             raise TypeError
 
-    def model_data_cuda_list(self, device_type: str, unit: str = 'B') -> List[int]:
+    def model_data_list(self, device_type: str, unit: str = 'B') -> List[int]:
         if unit == 'GB':
             scale = 1e9
         elif unit == 'MB':
@@ -75,7 +81,7 @@ def model_data_cuda_list(self, device_type: str, unit: str = 'B') -> List[int]:
         else:
             raise TypeError
 
-    def non_model_data_cuda_list(self, device_type: str, unit: str = 'B') -> List[int]:
+    def non_model_data_list(self, device_type: str, unit: str = 'B') -> List[int]:
         """Non model data stats
         """
         if unit == 'GB':
@@ -96,6 +102,14 @@ def non_model_data_cuda_list(self, device_type: str, unit: str = 'B') -> List[in
         else:
             raise TypeError
 
+    def current_non_model_data(self, device_type: str) -> int:
+        """get the non model data of current sampling moment
+        """
+        return self.non_model_data_list(device_type)[self._sampling_cnter.sampling_cnt]
+
+    def next_non_model_data(self, device_type: str):
+        return self.non_model_data_list(device_type)[self._sampling_cnter.next()]
+
     @property
     def sampling_time(self):
         return [t - self._sampling_time[0] for t in self._sampling_time]

@@ -0,0 +1,69 @@
+import torch
+from colossalai.context.singleton_meta import SingletonMeta
+from colossalai.utils.cuda import get_current_device
+from colossalai.zero.sharded_param.sharded_param import ShardedParamV2
+from colossalai.zero.sharded_param.tensorful_state import StatefulTensor, TensorState
+from colossalai.zero.shard_utils.tensor_utils import colo_model_data_tensor_move_inline, colo_tensor_mem_usage
+from colossalai.utils.memory_utils.utils import colo_cuda_memory_capacity
+from typing import Set
+from colossalai.utils.memory_tracer import MemStatsCollector
+
+
+class StatefulTensorMgr(SingletonMeta):
+    _stateful_tensor_list: Set[ShardedParamV2] = set()
+
+    def register_param(self, param: ShardedParamV2) -> None:
+        for t in param.get_payload_tensors():
+            assert isinstance(t, StatefulTensor)
+            self._stateful_tensor_list.add(t)
+
+    def evict_tensors(self) -> None:
+        pass
+
+    def adjust_layout(self, mem_stats_collector: MemStatsCollector) -> None:
+        """ Adjust the layout of statefuil tensor according to the information provided
+        by mem_stats_collector, which should belongs to a Sharded Model.
+
+        Args:
+            mem_stats_collector (MemStatsCollector): a collector, usually owned by a Sharded Model.
+            It contains non-model footprint of a DNN model.
+        """
+        # find stateful tensor in state COMPUTE
+        move_to_cuda_tensor_list = []
+        cuda_demand = 0
+        used_cuda_model_data = 0
+        hold_cuda_tensor_list = []
+        for tensor in self._stateful_tensor_list:
+            if tensor.state == TensorState.FREE:
+                continue
+
+            if tensor.device.type == 'cuda':
+                used_cuda_model_data += colo_tensor_mem_usage(tensor.payload)[0]
+                if tensor.state in [TensorState.HOLD, TensorState.HOLD_AFTER_BWD, TensorState.HOLD_AFTER_FWD]:
+                    hold_cuda_tensor_list.append(tensor)
+            else:
+                if tensor.state == TensorState.COMPUTE:
+                    move_to_cuda_tensor_list.append(tensor)
+                    cuda_demand += colo_tensor_mem_usage(tensor.payload)[0]
+
+        # max non-model-data cuda memory consumption of this sampling moment and the next sampling moment.
+        max_cuda_non_model_data_per_period = max(mem_stats_collector.current_non_model_data('cuda'),
+                                                 mem_stats_collector.next_non_model_data('cuda'))
+        cuda_capacity = colo_cuda_memory_capacity()
+        cuda_model_data_period = cuda_capacity - max_cuda_non_model_data_per_period
+        if cuda_model_data_period < used_cuda_model_data + cuda_demand:
+            # move cuda_model_data_period - cuda_demand - used_cuda_model_data volume of tensor
+            # Here use a naive eviction strategy.
+            acc_size = 0
+            for t in hold_cuda_tensor_list:
+                if acc_size > cuda_demand:
+                    break
+                colo_model_data_tensor_move_inline(t, torch.device('cpu'))
+                t_size = colo_tensor_mem_usage(t)
+                acc_size += t_size
+            if acc_size < cuda_demand:
+                raise RuntimeError("Adjust layout failed! No enough CUDA memory!")
+
+        # move COMPUTE tensors to CUDA
+        for t in move_to_cuda_tensor_list:
+            colo_model_data_tensor_move_inline(t, get_current_device())
@@ -3,6 +3,7 @@
 from typing import Optional, Tuple
 from colossalai.zero.shard_utils.tensor_utils import colo_tensor_mem_usage
 from .tensorful_state import StatefulTensor, TensorState
+from typing import List
 
 
 class ShardedParamV2(object):
@@ -22,6 +23,11 @@ def __init__(self, param: torch.nn.Parameter, rm_torch_payload=False) -> None:
         if rm_torch_payload:
             self.remove_torch_payload()
 
+    def get_payload_tensors(self) -> List[StatefulTensor]:
+        """returns stateful tensors kept by this class.
+        """
+        return [self._sharded_data_tensor, self.saved_grad]
+
     def remove_torch_payload(self):
         self.param.data = torch.empty([], dtype=self.param.dtype, device=self.param.device)