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[Tensor] activation is an attr of ColoTensor (#897)

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This commit is contained in:
Jiarui Fang
2022-04-28 14:43:22 +08:00
committed by GitHub
parent e76f76c08b
commit 676f191532
4 changed files with 51 additions and 35 deletions
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41
colossalai/tensor/colo_tensor.py
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@@ -7,6 +7,13 @@ from colossalai.core import global_context as gpc
from colossalai.nn.layer.utils import divide
from colossalai.tensor import TensorSpec, ComputePattern, ShardPattern
from colossalai.nn.layer.parallel_1d._utils import split_forward_gather_backward, gather_forward_split_backward
from enum import Enum
class TensorType(Enum):
MODEL = 0
NONMODEL = 1 # mainly activations
class ColoTensor(object):
""" Data Structure for Tensor in Colossal-AI
@@ -28,6 +35,7 @@ class ColoTensor(object):
device=None,
torch_tensor=torch.empty(0),
shard_spec: TensorSpec = TensorSpec(),
is_model_data: bool = False,
):
self._size = size
self._dtype = dtype
@@ -37,6 +45,10 @@ class ColoTensor(object):
self._torch_tensor = torch_tensor
self._shard_spec = shard_spec
self._shard_pattern = ShardPattern.NA
if is_model_data:
self._type = TensorType.MODEL
else:
self._type = TensorType.NONMODEL
def __getitem__(self, key):
return ColoTensor.init_from_torch_tensor(self.torch_tensor()[key])
@@ -85,13 +97,14 @@ class ColoTensor(object):
return product(self._size)
@staticmethod
def init_from_torch_tensor(tensor: torch.Tensor, save_payload=True) -> 'ColoTensor':
def init_from_torch_tensor(tensor: torch.Tensor, save_payload=True, is_model_data=False) -> 'ColoTensor':
colo_t = ColoTensor(*tensor.size(),
dtype=tensor.dtype,
requires_grad=tensor.requires_grad,
pin_memory=tensor.is_pinned(),
device=tensor.device,
torch_tensor=tensor if save_payload else torch.empty(0))
torch_tensor=tensor if save_payload else torch.empty(0),
is_model_data=is_model_data)
return colo_t
def del_torch_tensor(self, save_shape=False) -> None:
@@ -120,31 +133,28 @@ class ColoTensor(object):
self._shard_spec = spec
if shard == True:
self.shard()
def set_shard_pattern(self, shard_pattern: ShardPattern):
self._shard_pattern = shard_pattern
def shard(self):
assert self._shard_spec is not None, 'You should call set_spec() before _shard() ColoTensor.'
if self._shard_pattern is not ShardPattern.NA: # reshard
if self._shard_pattern is not ShardPattern.NA: # reshard
self.gather()
# Model Parameters
if ComputePattern.TP1DRow in self._shard_spec.compute_patterns:
parallel_action = self._shard_spec.get_action_by_compute_pattern(
ComputePattern.TP1DRow)
parallel_action = self._shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DRow)
self._shard_1d(parallel_action=parallel_action, dim=-1)
self._shard_pattern = ShardPattern.Col # We bind our ComputePattern on weight, which has to be transposed when linear().
self._shard_pattern = ShardPattern.Col # We bind our ComputePattern on weight, which has to be transposed when linear().
elif ComputePattern.TP1DCol in self._shard_spec.compute_patterns:
parallel_action = self._shard_spec.get_action_by_compute_pattern(
ComputePattern.TP1DCol)
parallel_action = self._shard_spec.get_action_by_compute_pattern(ComputePattern.TP1DCol)
self._shard_1d(parallel_action=parallel_action, dim=0)
self._shard_pattern = ShardPattern.Row
def gather(self):
assert self.is_activation(), 'Currently we only support gather Activation ColoTensor.'
assert not self.is_gathered(), 'Only sharded ColoTensor can be gathered.'
parallel_action = self._shard_spec.get_action_by_compute_pattern(
ComputePattern.Activation)
parallel_action = self._shard_spec.get_action_by_compute_pattern(ComputePattern.DP)
if self._shard_pattern == ShardPattern.Row:
dim = 0
elif self._shard_pattern == ShardPattern.Col:
@@ -159,9 +169,8 @@ class ColoTensor(object):
return self._shard_spec is not None and self._shard_spec.num_action > 0
def is_activation(self) -> bool:
return self._shard_spec is not None and self._shard_spec.num_action == 1 \
and ComputePattern.Activation in self._shard_spec.compute_patterns
return self._type == TensorType.NONMODEL
def _shard_1d(self, parallel_action, dim=-1):
num_partition = gpc.get_world_size(parallel_action.parallel_mode)
local_rank = gpc.get_local_rank(parallel_action.parallel_mode)
@@ -169,8 +178,8 @@ class ColoTensor(object):
# Reshape to get shard for this rank and we don't want autograd
# recording here for the narrow op and 'local_shard' should be a
# leaf variable in the autograd graph.
self._torch_tensor = self._torch_tensor.narrow(dim, local_rank * chunk_size, chunk_size).detach(
).contiguous() # TODO Shall we clone() here since detach() will point to the old tensor?
self._torch_tensor = self._torch_tensor.narrow(dim, local_rank * chunk_size, chunk_size).detach().contiguous(
) # TODO Shall we clone() here since detach() will point to the old tensor?
self._torch_tensor.requires_grad = self._requires_grad
self._size = self._torch_tensor.size()