mirror of https://github.com/hpcaitech/ColossalAI
274 lines
11 KiB
Python
274 lines
11 KiB
Python
from .op_wrapper import _COLOSSAL_OPS
|
|
from .const import TensorType
|
|
from copy import copy
|
|
import torch
|
|
from torch.overrides import get_default_nowrap_functions
|
|
|
|
from colossalai.tensor import ColoTensorSpec
|
|
from colossalai.tensor import distspec, ProcessGroup
|
|
from colossalai.tensor.dist_spec_mgr import DistSpecManager
|
|
from colossalai.tensor.distspec import _DistSpec, DistPlacementPattern
|
|
from typing import Optional
|
|
|
|
|
|
def _convert_output(output, pg: ProcessGroup):
|
|
if type(output) == torch.Tensor:
|
|
return ColoTensor.from_torch_tensor(output, ColoTensorSpec(pg))
|
|
elif isinstance(output, (list, tuple)):
|
|
return type(output)(_convert_output(o, pg) for o in output)
|
|
else:
|
|
return output
|
|
|
|
|
|
def _scan_for_pg_from_args(args, kwargs) -> ProcessGroup:
|
|
for elem in args:
|
|
if isinstance(elem, ColoTensor):
|
|
pg = elem.get_process_group()
|
|
return pg
|
|
elif isinstance(elem, (list, tuple)):
|
|
pg = _scan_for_pg_from_args(elem, {})
|
|
if pg is not None:
|
|
return pg
|
|
for k, v in kwargs:
|
|
if isinstance(v, ColoTensor):
|
|
pg = v.get_process_group()
|
|
return pg
|
|
return None
|
|
|
|
|
|
class ColoTensor(torch.Tensor):
|
|
""" Data Structure for Tensor in Colossal-AI. It is a subclass of torch.Tensor.
|
|
Args:
|
|
data (torch.Tensor): a torch tensor used as the payload the colotensor.
|
|
spec (ColoTensorSpec, optional): the tensor spec of initialization. Defaults to ColoTensorSpec(distspec.replicate()).
|
|
|
|
The signature of the function has to be consistent with the __new__ except for the 1st arg.
|
|
The class should be initialized with a torch tensor in the following ways.
|
|
1. directly init.
|
|
>>> pg = ProcessGroup()
|
|
>>> colo_t1 = ColoTensor(torch.randn(2,3), spec = ColoTensorSpec(pg, distspec.replicate())
|
|
>>> # If initializaed in a shard model, the tensor passed in is one shard of the global tensor.
|
|
>>> shard_spec = distspec.shard(process_group=ProcessGroup(tp=world_size),
|
|
>>> dims=[0],
|
|
>>> num_partitions=[world_size])
|
|
>>> tensor_spec = ColoTensorSpec(pg, shard_spec)
|
|
>>> colo_t2 = ColoTensor.from_torch_tensor(t_ref.clone(), tensor_spec)
|
|
2. use static method from_torch_tensor
|
|
>>> colo_t = ColoTensor.from_torch_tensor(torch.randn(2,3), spec = ColoTensorSpec(pg, distspec.replicate())
|
|
"""
|
|
|
|
def __new__(cls, data: torch.Tensor, spec: ColoTensorSpec) -> 'ColoTensor':
|
|
"""__new__
|
|
The signature of the __new__ has to be consistent with the torch.Tensor.
|
|
Args:
|
|
data (torch.Tensor): a torch tensor used as the payload the colotensor.
|
|
spec (TensorSpec, optional): the tensor spec of initialization.
|
|
Returns:
|
|
ColoTensor: a ColoTensor wrappers the data.
|
|
"""
|
|
if data is None:
|
|
data = torch.empty(0)
|
|
return torch.Tensor._make_subclass(cls, data, data.requires_grad)
|
|
|
|
def __init__(self, data: torch.Tensor, spec: Optional[ColoTensorSpec] = None) -> None:
|
|
# If not set spec, use a DP process group and replicate dist spec
|
|
if spec is None:
|
|
self.has_initialized = False
|
|
self.dist_spec = distspec.replicate()
|
|
self.compute_spec = None
|
|
self.process_group = ProcessGroup()
|
|
else:
|
|
self.has_initialized = True
|
|
self.dist_spec = spec.dist_attr
|
|
self.compute_spec = spec.compute_attr
|
|
if spec.pg is None:
|
|
self.process_group = ProcessGroup()
|
|
else:
|
|
self.process_group = spec.pg
|
|
|
|
self._type = TensorType.NONMODEL
|
|
self._graph_node = None
|
|
|
|
def has_compute_spec(self) -> bool:
|
|
return self.compute_spec is not None
|
|
|
|
def is_model_data(self) -> bool:
|
|
return self._type == TensorType.MODEL
|
|
|
|
def get_process_group(self) -> 'ProcessGroup':
|
|
return self.process_group
|
|
|
|
def set_process_group(self, pg: ProcessGroup):
|
|
"""set_process_group
|
|
change the pg of the ColoTensor. Note that the valid use cases is limited.
|
|
Only existing pg is DP and dist spec is REPLICaTE is valid.
|
|
Args:
|
|
pg (ProcessGroup): target pg
|
|
|
|
Raises:
|
|
RuntimeError:
|
|
RuntimeError:
|
|
"""
|
|
assert isinstance(pg, ProcessGroup), f"pg as type {type(pg)} is invalid"
|
|
if self.process_group.tp_world_size() != 1:
|
|
raise RuntimeError("can not set_process_group on a ColoTensor whose process_group has tp world group")
|
|
|
|
if self.dist_spec.placement.value != 'r':
|
|
raise RuntimeError("can not set_process_group on a ColoTensor whose dist spec is not REPLICATE")
|
|
|
|
self.process_group = pg
|
|
|
|
def get_tp_world_size(self) -> int:
|
|
return self.process_group.tp_world_size()
|
|
|
|
def set_dist_spec(self, dist_spec: _DistSpec):
|
|
"""set_dist_spec
|
|
set dist spec and change the payloads.
|
|
Args:
|
|
dist_spec (_DistSpec): target dist spec.
|
|
"""
|
|
assert isinstance(dist_spec, _DistSpec)
|
|
assert self.process_group is not None
|
|
self._convert_to_dist_spec(dist_spec)
|
|
|
|
def set_tensor_spec(self, dist_spec, compute_spec):
|
|
if dist_spec:
|
|
assert isinstance(dist_spec, _DistSpec), f"{type(dist_spec)}"
|
|
self.set_dist_spec(dist_spec)
|
|
if compute_spec:
|
|
self.compute_spec = compute_spec
|
|
|
|
def has_compute_pattern(self, compute_pattern):
|
|
return self.compute_spec.compute_pattern == compute_pattern
|
|
|
|
@classmethod
|
|
def __torch_function__(cls, func, types, args=(), kwargs=None):
|
|
if kwargs is None:
|
|
kwargs = {}
|
|
|
|
if not all(issubclass(cls, t) for t in types):
|
|
return NotImplemented
|
|
global _COLOSSAL_OPS
|
|
if func in _COLOSSAL_OPS:
|
|
func = _COLOSSAL_OPS[func]
|
|
|
|
with torch._C.DisableTorchFunction():
|
|
ret = func(*args, **kwargs)
|
|
if func in get_default_nowrap_functions():
|
|
return ret
|
|
else:
|
|
pg = _scan_for_pg_from_args(args, kwargs)
|
|
return _convert_output(ret, pg)
|
|
|
|
def __repr__(self):
|
|
return f'ColoTensor:\n{super().__repr__()}\n{self.dist_spec}\n{self.process_group}'
|
|
|
|
def _convert_to_dist_spec(self, dist_spec: _DistSpec) -> None:
|
|
"""_convert_to_dist_spec
|
|
Note the function will not handle the logic of backward propagation!
|
|
It is used during model tensor initializations as an internal function.
|
|
Args:
|
|
dist_spec (_DistSpec): the target dist. spec.
|
|
"""
|
|
with DistSpecManager.no_grad():
|
|
self.data = DistSpecManager.handle_trans_spec(self, self.dist_spec, dist_spec, self.process_group)
|
|
self.dist_spec = dist_spec
|
|
|
|
def convert_to_dist_spec(self, dist_spec: _DistSpec) -> 'ColoTensor':
|
|
ret = DistSpecManager.handle_trans_spec(self, self.dist_spec, dist_spec, self.process_group)
|
|
return ColoTensor.from_torch_tensor(ret, ColoTensorSpec(self.process_group, dist_attr=dist_spec))
|
|
|
|
def to_replicate_(self):
|
|
"""to_replicate_
|
|
an inline member function, converting dist spec of the tensor to REPLICATE
|
|
"""
|
|
self.data = DistSpecManager.handle_trans_spec(self, self.dist_spec, distspec.replicate(), self.process_group)
|
|
self.dist_spec = distspec.replicate()
|
|
|
|
def to_replicate(self) -> 'ColoTensor':
|
|
"""to_replicate
|
|
converting dist spec of the tensor to REPLICATE
|
|
"""
|
|
return self.convert_to_dist_spec(distspec.replicate())
|
|
|
|
@staticmethod
|
|
def from_torch_tensor(tensor: torch.Tensor, spec: Optional[ColoTensorSpec] = None) -> 'ColoTensor':
|
|
tensor = tensor.as_subclass(ColoTensor)
|
|
tensor.__init__(tensor, spec=spec)
|
|
return tensor
|
|
|
|
def __deepcopy__(self, memo):
|
|
if id(self) in memo:
|
|
return memo[id(self)]
|
|
else:
|
|
with torch._C.DisableTorchFunction():
|
|
data = self.data.clone()
|
|
tensor = ColoTensor(data, spec=copy(ColoTensorSpec(self.process_group, self.dist_spec, self.compute_spec)))
|
|
memo[id(self)] = tensor
|
|
return tensor
|
|
|
|
##### override builtin functions which must use tensor in replicate placement ####
|
|
|
|
def view_local(self, *args) -> 'ColoTensor':
|
|
return super().view(*args)
|
|
|
|
def size_local(self, *args, **kwargs) -> torch.Size:
|
|
return super().size(*args, **kwargs)
|
|
|
|
def view_global(self, *args) -> 'ColoTensor':
|
|
"""override the torch buildin view()
|
|
the args passed in must be in a replicate placement.
|
|
Returns:
|
|
ColoTensor: a tensor after viewed.
|
|
"""
|
|
if self.is_replicate():
|
|
return super().view(*args)
|
|
# TODO(jiaruifang) check why this not work
|
|
# self.data = self.to_replicate()
|
|
self.data = DistSpecManager.handle_trans_spec(self.data, self.dist_spec, distspec.replicate(),
|
|
self.process_group)
|
|
self.dist_spec = distspec.replicate()
|
|
return super().view(*args)
|
|
|
|
def size_global(self, args: Optional[int] = None):
|
|
"""override the torch buildin size()
|
|
the shape passed in must be in a replicate placement.
|
|
Returns:
|
|
ColoTensor: a tensor after viewed.
|
|
"""
|
|
if self.is_replicate():
|
|
if args is not None:
|
|
return super().size(args)
|
|
else:
|
|
return super().size()
|
|
|
|
spec = self.dist_spec
|
|
dims = spec.dims
|
|
num_partitions = spec.num_partitions
|
|
# import inspect
|
|
# print(*['{:40}| {}:{}\n'.format(x.function, x.filename, x.lineno) for x in inspect.stack()])
|
|
|
|
size_list = list(super().size())
|
|
for dim, num_partition in zip(dims, num_partitions):
|
|
size_list[dim] *= num_partition
|
|
if args is not None:
|
|
return size_list[args]
|
|
else:
|
|
return torch.Size(size_list)
|
|
|
|
# Some API for dist spec check
|
|
|
|
def is_replicate(self):
|
|
return self.dist_spec.placement == DistPlacementPattern.REPLICATE \
|
|
or (len(self.dist_spec.num_partitions) == 1
|
|
and self.dist_spec.num_partitions[0] == 1) \
|
|
or (self.process_group.tp_world_size() == 1)
|
|
|
|
def is_shard_1dcol(self):
|
|
return self.dist_spec.placement == DistPlacementPattern.SHARD \
|
|
and len(self.dist_spec.dims) == 1 and self.dist_spec.dims[0] == -1
|
|
|
|
def is_shard_1drow(self):
|
|
return self.dist_spec.placement == DistPlacementPattern.SHARD \
|
|
and len(self.dist_spec.dims) == 1 and self.dist_spec.dims[0] == 0
|