mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
78 lines
2.7 KiB
78 lines
2.7 KiB
from typing import Any, Callable, List, Tuple
|
|
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from typing import Union
|
|
from colossalai.gemini.stateful_tensor import StatefulTensor
|
|
|
|
|
|
def get_gradient_predivide_factor(world_size: int) -> float:
|
|
factor: int = 1
|
|
while world_size % factor == 0 and world_size / factor > factor:
|
|
factor *= 2
|
|
return float(factor)
|
|
|
|
|
|
def free_storage(data: torch.Tensor) -> None:
|
|
"""Free underlying storage of a Tensor."""
|
|
if data.storage().size() > 0:
|
|
# Since we're modifying the Tensor's Storage directly, make sure the Tensor
|
|
# is the sole occupant of the Storage.
|
|
assert data.storage_offset() == 0
|
|
data.storage().resize_(0)
|
|
|
|
|
|
@torch.no_grad()
|
|
def alloc_storage(data: torch.Tensor, size: torch.Size) -> None:
|
|
"""Allocate storage for a tensor."""
|
|
if data.storage().size() == size.numel(): # no need to reallocate
|
|
return
|
|
assert data.storage().size() == 0
|
|
data.storage().resize_(size.numel())
|
|
|
|
|
|
def cast_tensor_to_fp16(tensor: torch.Tensor) -> torch.Tensor:
|
|
if isinstance(tensor, StatefulTensor):
|
|
tensor = tensor.payload
|
|
if torch.is_floating_point(tensor) and tensor.dtype is torch.float32:
|
|
return tensor.half()
|
|
return tensor
|
|
|
|
|
|
def cast_tensor_to_fp32(tensor: Union[torch.Tensor, StatefulTensor]) -> torch.Tensor:
|
|
if isinstance(tensor, StatefulTensor):
|
|
tensor = tensor.payload
|
|
|
|
if torch.is_floating_point(tensor) and tensor.dtype is torch.float16:
|
|
return tensor.float()
|
|
return tensor
|
|
|
|
|
|
def apply_to_tensors(x: Any, fn: Callable):
|
|
if torch.is_tensor(x):
|
|
return fn(x)
|
|
elif isinstance(x, list):
|
|
return [apply_to_tensors(t, fn) for t in x]
|
|
elif isinstance(x, tuple):
|
|
return tuple(apply_to_tensors(t, fn) for t in x)
|
|
elif isinstance(x, dict):
|
|
return {key: apply_to_tensors(val, fn) for key, val in x.items()}
|
|
else:
|
|
return x
|
|
|
|
|
|
def cast_float_arguments(fn: Callable, *args: Any, **kwargs: Any) -> Tuple[Any, Any]:
|
|
return apply_to_tensors(args, fn), apply_to_tensors(kwargs, fn)
|
|
|
|
|
|
def chunk_and_pad(tensor: torch.Tensor, num_chunks: int) -> List[torch.Tensor]:
|
|
"""Chunk a given Tensor into num_chunks parts and add any necessary padding."""
|
|
chunks = list(torch.flatten(tensor).chunk(num_chunks))
|
|
# torch.chunk may return fewer than num_chunks chunks, pad accordingly.
|
|
num_pad_for_partial_chunk = chunks[0].numel() - chunks[-1].numel()
|
|
if num_pad_for_partial_chunk > 0:
|
|
chunks[-1] = F.pad(chunks[-1], [0, num_pad_for_partial_chunk])
|
|
if len(chunks) < num_chunks:
|
|
chunks.extend([torch.zeros_like(chunks[0]) for _ in range(num_chunks - len(chunks))])
|
|
return chunks
|