Revert "[feature] new zero implementation"

colossalai/gemini/__init__.py

@@ -1,6 +1,10 @@
-from .chunk import TensorInfo, TensorState
+from .chunk import TensorInfo, Chunk, TensorState
+from .chunk_mgr import ChunkManager
 from .stateful_tensor_mgr import StatefulTensorMgr
 from .tensor_placement_policy import TensorPlacementPolicyFactory
 from .gemini_mgr import GeminiManager
 
-__all__ = ['StatefulTensorMgr', 'TensorPlacementPolicyFactory', 'GeminiManager', 'TensorInfo', 'TensorState']
+__all__ = [
+    'StatefulTensorMgr', 'TensorPlacementPolicyFactory', 'GeminiManager', 'ChunkManager', 'TensorInfo', 'Chunk',
+    'TensorState'
+]

colossalai/gemini/chunk.py

@@ -0,0 +1,316 @@
+import torch
+import torch.distributed as dist
+from dataclasses import dataclass
+from enum import Enum
+from typing import Optional, Dict, List
+
+from colossalai.utils import get_current_device
+from colossalai.tensor import ProcessGroup as ColoProcessGroup
+
+
+class TensorState(Enum):
+    FREE = 0
+    COMPUTE = 1
+    HOLD = 2
+    HOLD_AFTER_BWD = 3
+    READY_FOR_REDUCE = 4
+
+
+STATE_TRANS = ((TensorState.FREE, TensorState.HOLD), (TensorState.FREE, TensorState.COMPUTE),
+               (TensorState.HOLD, TensorState.FREE), (TensorState.HOLD, TensorState.COMPUTE),
+               (TensorState.COMPUTE, TensorState.HOLD), (TensorState.COMPUTE, TensorState.HOLD_AFTER_BWD),
+               (TensorState.COMPUTE, TensorState.READY_FOR_REDUCE), (TensorState.HOLD_AFTER_BWD, TensorState.COMPUTE),
+               (TensorState.HOLD_AFTER_BWD, TensorState.READY_FOR_REDUCE), (TensorState.READY_FOR_REDUCE,
+                                                                            TensorState.HOLD))
+
+
+@dataclass
+class TensorInfo:
+    state: TensorState
+    offset: int
+    end: int
+
+
+class ChunkFullError(Exception):
+    pass
+
+
+def is_storage_empty(tensor: torch.Tensor) -> bool:
+    return tensor.storage().size() == 0
+
+
+def free_storage(tensor: torch.Tensor) -> None:
+    if not is_storage_empty(tensor):
+        tensor.storage().resize_(0)
+
+
+def alloc_storage(tensor: torch.Tensor) -> None:
+    if is_storage_empty(tensor):
+        tensor.storage().resize_(tensor.numel())
+
+
+class Chunk:
+    """
+    A chunk is a contiguous memory space which contains multiple tensors.
+
+    Args:
+        chunk_size (int): the number of elements in a chunk
+        src_rank (int): the process which owns the chunk
+        dtype (torch.dtype): the data type of the chunk
+        init_device (torch.device): optional, the device where the tensor is initialized. The default value is None, which is the current GPU.
+        force_data_on_cuda (bool): optional, if True, chunk.data is always on cuda. Defaults to False.
+    """
+
+    def __init__(self,
+                 chunk_size: int,
+                 src_rank: int,
+                 process_group: ColoProcessGroup,
+                 dtype: torch.dtype,
+                 init_device: Optional[torch.device] = None,
+                 force_data_on_cuda: bool = False) -> None:
+        self.size = chunk_size
+        self.utilized_size = 0
+        self.src_rank = src_rank
+        self.process_group = process_group
+        self.is_src_rank = process_group.dp_local_rank() == src_rank
+        self.global_src_rank = process_group.get_ranks_in_dp()[src_rank]
+        self.dtype = dtype
+        device = init_device or get_current_device()
+        if force_data_on_cuda:
+            self.data = torch.empty(chunk_size, dtype=dtype, device=get_current_device())
+            self._cpu_data = torch.empty(chunk_size, dtype=dtype)
+            if device.type == 'cuda':
+                free_storage(self._cpu_data)
+            else:
+                free_storage(self.data)
+        else:
+            self.data = torch.empty(chunk_size, dtype=dtype, device=device)
+            self._cpu_data = None
+
+        # we only keep the chunk in full in the process by which the tensor is owned
+        if not self.is_src_rank:
+            free_storage(self._payload)
+
+        # each tensor is associated with a TensorInfo to track meta info
+        self.tensors_info: Dict[torch.Tensor, TensorInfo] = {}
+        self.mem = self.size * self.data.element_size()
+
+    def append(self, tensor: torch.Tensor) -> None:
+        """
+        Add a tensor to the chunk.
+
+        Args:
+            tensor (torch.Tensor): a tensor to be added to the chunk
+        """
+        assert tensor.dtype == self.dtype
+        new_utilized_size = self.utilized_size + tensor.numel()
+
+        # raise exception when the chunk size is exceeded
+        if new_utilized_size > self.size:
+            raise ChunkFullError
+
+        # set tensor state
+        tensor_state = TensorState.FREE
+
+        # if the process owns the rank, then copy the tensor to its chunk buffer
+        # otherwise set its storage size to 0 to reduce memory consumption
+        if self.is_src_rank:
+            self._payload[self.utilized_size:new_utilized_size].copy_(tensor.flatten())
+            tensor_state = TensorState.HOLD
+            assert type(self._payload) == torch.Tensor, "copy_tensor_to_chunk_slice must use a torch tensor"
+            tensor.data = self._payload[self.utilized_size:new_utilized_size].view(tensor.shape)
+        else:
+            tensor.storage().resize_(0)
+        self.tensors_info[tensor] = TensorInfo(tensor_state, self.utilized_size, new_utilized_size)
+        self.utilized_size = new_utilized_size
+
+    def release(self) -> None:
+        """
+        Release the memory space on processes which do not own the chunk.
+        """
+        if not self.is_src_rank:
+            free_storage(self._payload)
+            self._update_tensors_state(TensorState.FREE)
+
+    def _update_tensors_ptr(self) -> None:
+        assert type(self._payload) == torch.Tensor
+        for tensor, tensor_info in self.tensors_info.items():
+            tensor.data = self._payload[tensor_info.offset:tensor_info.end].view(tensor.shape)
+
+    def _update_tensors_state(self, next_state: TensorState, prev_state: Optional[TensorState] = None):
+        for tensor_info in self.tensors_info.values():
+            if prev_state is None or tensor_info.state == prev_state:
+                tensor_info.state = next_state
+
+    def access(self) -> None:
+        """
+        Broadcast the chunk to synchronize the tensors across data parallel processes.
+        """
+        # recover the chunk on non-owner processes
+        # and broadcast the chunk from the source to all processes
+        if not self.is_src_rank:
+            alloc_storage(self._payload)
+        self.move_device(get_current_device(), update_ptr=False)
+        dist.broadcast(self.data, self.global_src_rank, group=self.process_group.dp_process_group())
+
+        # update tensor meta info
+        self._update_tensors_ptr()
+        if not self.is_src_rank:
+            self._update_tensors_state(TensorState.HOLD, prev_state=TensorState.FREE)
+
+    def move_device(self, device: torch.device, update_ptr: bool = True) -> None:
+        """
+        Move the chunk to a target device.
+
+        Args:
+            device (torch.device): the target device for data movement.
+        """
+        if self._payload.device == device:
+            return
+        if self._cpu_data is None:
+            self.data.data = self.data.to(device)
+        else:
+            if device.type == 'cuda':
+                # cpu -> cuda
+                src = self._cpu_data
+                dest = self.data
+            else:
+                # cuda -> cpu
+                src = self.data
+                dest = self._cpu_data
+            alloc_storage(dest)
+            dest.copy_(src)
+            free_storage(src)
+
+        if update_ptr:
+            self._update_tensors_ptr()
+
+    def reduce(self, is_all_reduce: bool = False) -> None:
+        """
+        Reduce or all-reduce the chunk.
+
+        Args:
+            is_all_reduce (bool): optional, whether to all-reduce the chunk. The default is false.
+        """
+        self.move_device(get_current_device(), update_ptr=False)
+        if is_all_reduce:
+            dist.all_reduce(self.data, group=self.process_group.dp_process_group())
+        else:
+            dist.reduce(self.data, self.global_src_rank, group=self.process_group.dp_process_group())
+        self._update_tensors_ptr()
+        self._update_tensors_state(TensorState.HOLD)
+
+    def tensor_trans_state(self, tensor: torch.Tensor, tensor_state: TensorState) -> None:
+        """
+        Make a transition of the tensor into the next state.
+
+        Args:
+            tensor (torch.Tensor): a torch Tensor object.
+            tensor_state (TensorState): the target state for transition.
+        """
+
+        # As the gradient hook can be triggered either before or after post-backward
+        # tensor's state can be compute -> hold_after_bwd -> ready_for_reduce
+        # or compute -> ready_for_reduce -> hold_after_bwd
+        # the second one is invalid, we just ignore ready_for_reduce -> hold_after_bwd
+        # this function only apply valid state transformation
+        # invalid calls will be ignored and nothing changes
+        if (self.tensors_info[tensor].state, tensor_state) not in STATE_TRANS:
+            # print(
+            #     f'WARNING: Rank{self.process_group.rank()} apply invalid state trans: {self.tensors_info[tensor].state} to {tensor_state}'
+            # )
+            return
+        self.tensors_info[tensor].state = tensor_state
+
+    def copy_tensor_to_chunk_slice(self, tensor: torch.Tensor, data_slice: torch.Tensor) -> None:
+        """
+        Copy data slice to the memory space indexed by the input tensor in the chunk.
+
+        Args:
+            tensor (torch.Tensor): the tensor used to retrive meta information
+            data_slice (torch.Tensor): the tensor to be copied to the chunk
+        """
+        tensor_info = self.tensors_info[tensor]
+        self._payload[tensor_info.offset:tensor_info.end].copy_(data_slice.flatten())
+        tensor.data = self._payload[tensor_info.offset:tensor_info.end].view(tensor.shape)
+
+    @property
+    def can_release(self) -> bool:
+        """
+        Check whether the chunk can be released.
+        """
+        for tensor_info in self.tensors_info.values():
+            if tensor_info.state != TensorState.HOLD:
+                return False
+        return True
+
+    @property
+    def can_move_device(self) -> bool:
+        """
+        Check whether the chunk can be moved across devices.
+        """
+        for tensor_info in self.tensors_info.values():
+            if tensor_info.state in (TensorState.COMPUTE, TensorState.READY_FOR_REDUCE):
+                return False
+        return True
+
+    @property
+    def can_reduce(self) -> bool:
+        """
+        Check whether the chunk can be reduced.
+        """
+        for tensor_info in self.tensors_info.values():
+            if tensor_info.state != TensorState.READY_FOR_REDUCE:
+                return False
+        return True
+
+    @property
+    def is_empty(self) -> bool:
+        """
+        Check whether the chunk is empty.
+        """
+        return is_storage_empty(self._payload)
+
+    def __repr__(self) -> str:
+        return f'Chunk: src rank={self.src_rank} ,size={self.size}, utilization={self.utilized_size/self.size*100:.2f}%, freed={self.is_empty}, tensor states={[info.state.name for info in self.tensors_info.values()]}'
+
+    @property
+    def has_inf_or_nan(self) -> bool:
+        """
+        Check if the chunk has inf or nan values.
+        """
+        return torch.isinf(self._payload[:self.utilized_size]).any().item() or \
+            torch.isnan(self._payload[:self.utilized_size]).any().item()
+
+    def copy_(self, dest_chunk: 'Chunk'):
+        """
+        Copy the data of this chunk to a destination chunk.
+        """
+        assert not self.is_empty
+        assert not dest_chunk.is_empty
+        assert self.size == dest_chunk.size
+        assert self.utilized_size == dest_chunk.utilized_size
+        self._payload.copy_(dest_chunk._payload)
+        self._update_tensors_ptr()
+
+    @property
+    def device_type(self) -> str:
+        """
+        Get the device type of the chunk.
+        """
+        return self._payload.device.type
+
+    def __hash__(self) -> int:
+        return hash(id(self))
+
+    def __eq__(self, __o: object) -> bool:
+        return self is __o
+
+    def get_tensors(self) -> List[torch.Tensor]:
+        return list(self.tensors_info.keys())
+
+    @property
+    def _payload(self) -> torch.Tensor:
+        if self._cpu_data is None or is_storage_empty(self._cpu_data):
+            return self.data
+        return self._cpu_data