[gemini] add stateful tensor container (#867)

colossalai/gemini/stateful_tensor_container.py

@@ -0,0 +1,131 @@
+import queue
+import heapq
+from abc import ABC, abstractmethod
+from typing import Optional, List, Dict
+from colossalai.gemini.stateful_tensor import StatefulTensor, TensorState
+
+
+def evict_check(st: StatefulTensor) -> bool:
+    if st.state is not TensorState.COMPUTE and st.device.type == 'cuda':
+        return True
+    return False
+
+
+# Here ST means Stateful Tensor
+class BaseSTContainer(ABC):
+    """A type of container that store all potential stateful tensors which can be evicted from
+    CUDA. This kind of stateful tensor should satisfy two conditions. One is that it hasn't been
+    evicted, meaning the type of its device is CUDA, the other is that it isn't pinned in CUDA
+    memory, meaning its state isn't COMPUTE.
+
+    This container should get a stateful tensor when it become HOLD_LIKE from COMPUTE.
+    And it pops stateful tensors in function, `evict_tensors`.
+
+    In order to acquire an optimal eviction policy, users may need to offer computation step
+    index of each stateful tensor. So we can use a heap to maintain all potential evictable
+    statefule tensors. When poping, we can get the stateful tensor that used furthest in
+    current computation step.
+    """
+
+    def __init__(self, compute_step_dict: Dict[StatefulTensor, List[int]], total_step: int):
+        self.compute_step_dict = compute_step_dict
+        self.total_step = total_step
+
+    @abstractmethod
+    def empty(self) -> bool:
+        pass
+
+    @abstractmethod
+    def create(self, stateful_tensor_list: List[StatefulTensor]) -> None:
+        pass
+
+    @abstractmethod
+    def push(self, stateful_tensor: StatefulTensor, cur_step: int) -> None:
+        pass
+
+    @abstractmethod
+    def pop(self) -> Optional[StatefulTensor]:
+        pass
+
+
+class QueueSTContainer(BaseSTContainer):
+    """Queue type stateful tensor container. This is used in 'cpu' tensor placement policy.
+    It pops potential evictable stateful tensors in FIFO.
+    """
+
+    def __init__(self, compute_step_dict: Dict[StatefulTensor, List[int]], total_step: int):
+        super().__init__(compute_step_dict, total_step)
+        self.container = None
+
+    def empty(self) -> bool:
+        assert self.container is not None
+        return self.container.empty()
+
+    def create(self, stateful_tensor_list: List[StatefulTensor]) -> None:
+        self.container = queue.SimpleQueue()
+        for stateful_tensor in stateful_tensor_list:
+            self.container.put(stateful_tensor)
+
+    def push(self, stateful_tensor: StatefulTensor, cur_step: int) -> None:
+        self.container.put(stateful_tensor)
+
+    def pop(self) -> Optional[StatefulTensor]:
+        ret = None
+        while not self.empty():
+            out_tensor = self.container.get()
+            if evict_check(out_tensor):
+                ret = out_tensor
+                break
+
+        return ret
+
+
+class HeapSTContainer(BaseSTContainer):
+    """Heap type stateful tensor container. This is used in 'auto' tensor placement policy.
+    It pops potential evictable stateful tensors in the order of the distance between current
+    step and next used step.
+    """
+
+    def __init__(self, compute_step_dict: Dict[StatefulTensor, List[int]], total_step: int):
+        super().__init__(compute_step_dict, total_step)
+        self.container = None
+
+    def empty(self) -> bool:
+        assert self.container is not None
+        return self.container == []
+
+    def create(self, stateful_tensor_list: List[StatefulTensor]) -> None:
+        self.container = []
+        for stateful_tensor in stateful_tensor_list:
+            # we want to pop the tensor which has the greatest next_step
+            # so the weight is next_step multiplied by -1
+            weight = -self.__get_next_compute_step(stateful_tensor, -1)
+            self.container.append((weight, stateful_tensor))
+        heapq.heapify(self.container)
+
+    def push(self, stateful_tensor: StatefulTensor, cur_step: int) -> None:
+        # we want to pop the tensor which has the greatest next_step
+        # so the weight is next_step multiplied by -1
+        weight = -self.__get_next_compute_step(stateful_tensor, cur_step)
+        heapq.heappush(self.container, (weight, stateful_tensor))
+
+    def pop(self) -> Optional[StatefulTensor]:
+        ret = None
+        while not self.empty():
+            _, out_tensor = heapq.heappop(self.container)
+            if evict_check(out_tensor):
+                ret = out_tensor
+                break
+        return ret
+
+    def __get_next_compute_step(self, stateful_tensor: StatefulTensor, cur_step: int):
+        # compute the id of next step
+        # if the tensor is not used in the furture
+        # next_step is set to the maximum
+        next_step = self.total_step
+        step_list = self.compute_step_dict[stateful_tensor]
+        for step in step_list:
+            if step > cur_step:
+                next_step = step
+                break
+        return next_step

tests/test_gemini/test_stateful_tensor_container.py

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+import pytest
+import torch
+
+from colossalai.gemini.stateful_tensor import TensorState, StatefulTensor
+from colossalai.gemini.stateful_tensor_container import QueueSTContainer, HeapSTContainer
+
+
+@pytest.mark.dist
+def test_stateful_tensor_container():
+    st1 = StatefulTensor(torch.randn(1, device='cuda'))
+    st2 = StatefulTensor(torch.randn(2, device='cuda'))
+    st3 = StatefulTensor(torch.randn(3, device='cuda'))
+    stateful_tensor_list = [st1, st2, st3]
+    step_list = [st1, st2, st3, st3, st2, st1]
+
+    compute_step_dict = dict()
+    compute_step_dict[st1] = [0, 5]
+    compute_step_dict[st2] = [1, 4]
+    compute_step_dict[st3] = [2, 3]
+
+    def run_queue_test():
+        # test queue container
+        queue_container = QueueSTContainer(compute_step_dict, 6)
+        queue_container.create(stateful_tensor_list)
+
+        res_list = []
+
+        for i in range(6):
+            stateful_tensor = step_list[i]
+            stateful_tensor.trans_state(TensorState.COMPUTE)
+            st_out = queue_container.pop()
+            st_out.move_to(torch.device('cpu'))
+
+            res_list.append(st_out.payload.size(0))
+
+            stateful_tensor.move_to(torch.device('cuda'))
+            queue_container.push(stateful_tensor, i)
+            stateful_tensor.trans_state(TensorState.HOLD)
+
+        assert res_list == [2, 3, 1, 2, 3, 2]
+
+    run_queue_test()
+
+    def run_heap_test():
+        # test heap container
+        st1.move_to(torch.device('cuda'))
+        st2.move_to(torch.device('cuda'))
+        st3.move_to(torch.device('cuda'))
+
+        heap_container = HeapSTContainer(compute_step_dict, 6)
+        heap_container.create(stateful_tensor_list)
+
+        res_list = []
+
+        for i in range(6):
+            stateful_tensor = step_list[i]
+            stateful_tensor.trans_state(TensorState.COMPUTE)
+            st_out = heap_container.pop()
+
+            if st_out is not None:
+                res_list.append(st_out.payload.size(0))
+                st_out.move_to(torch.device('cpu'))
+
+            stateful_tensor.move_to(torch.device('cuda'))
+            heap_container.push(stateful_tensor, i)
+            stateful_tensor.trans_state(TensorState.HOLD)
+
+        assert res_list == [3, 1, 2, 3, 2]
+
+    run_heap_test()
+
+
+if __name__ == '__main__':
+    test_stateful_tensor_container()