129 lines
4.0 KiB
Python
129 lines
4.0 KiB
Python
"""
|
|
Specialization of einops for torch.
|
|
|
|
Unfortunately, torch's jit scripting mechanism isn't strong enough,
|
|
and to have scripting supported at least for layers,
|
|
a number of additional moves is needed.
|
|
|
|
Design of main operations (dynamic resolution by lookup) is unlikely
|
|
to be implemented by torch.jit.script,
|
|
but torch.compile seems to work with operations just fine.
|
|
"""
|
|
|
|
import warnings
|
|
from typing import Dict, List, Tuple
|
|
|
|
import torch
|
|
from einops.einops import TransformRecipe, _reconstruct_from_shape_uncached
|
|
|
|
|
|
class TorchJitBackend:
|
|
"""
|
|
Completely static backend that mimics part of normal backend functionality
|
|
but restricted to be within torchscript.
|
|
"""
|
|
|
|
@staticmethod
|
|
def reduce(x: torch.Tensor, operation: str, reduced_axes: List[int]):
|
|
if operation == "min":
|
|
return x.amin(dim=reduced_axes)
|
|
elif operation == "max":
|
|
return x.amax(dim=reduced_axes)
|
|
elif operation == "sum":
|
|
return x.sum(dim=reduced_axes)
|
|
elif operation == "mean":
|
|
return x.mean(dim=reduced_axes)
|
|
elif operation == "prod":
|
|
for i in list(sorted(reduced_axes))[::-1]:
|
|
x = x.prod(dim=i)
|
|
return x
|
|
else:
|
|
raise NotImplementedError("Unknown reduction ", operation)
|
|
|
|
@staticmethod
|
|
def transpose(x, axes: List[int]):
|
|
return x.permute(axes)
|
|
|
|
@staticmethod
|
|
def stack_on_zeroth_dimension(tensors: List[torch.Tensor]):
|
|
return torch.stack(tensors)
|
|
|
|
@staticmethod
|
|
def tile(x, repeats: List[int]):
|
|
return x.repeat(repeats)
|
|
|
|
@staticmethod
|
|
def add_axes(x, n_axes: int, pos2len: Dict[int, int]):
|
|
repeats = [-1] * n_axes
|
|
for axis_position, axis_length in pos2len.items():
|
|
x = torch.unsqueeze(x, axis_position)
|
|
repeats[axis_position] = axis_length
|
|
return x.expand(repeats)
|
|
|
|
@staticmethod
|
|
def is_float_type(x):
|
|
return x.dtype in [torch.float16, torch.float32, torch.float64, torch.bfloat16]
|
|
|
|
@staticmethod
|
|
def shape(x):
|
|
return x.shape
|
|
|
|
@staticmethod
|
|
def reshape(x, shape: List[int]):
|
|
return x.reshape(shape)
|
|
|
|
|
|
# mirrors einops.einops._apply_recipe
|
|
def apply_for_scriptable_torch(
|
|
recipe: TransformRecipe, tensor: torch.Tensor, reduction_type: str, axes_dims: List[Tuple[str, int]]
|
|
) -> torch.Tensor:
|
|
backend = TorchJitBackend
|
|
(
|
|
init_shapes,
|
|
axes_reordering,
|
|
reduced_axes,
|
|
added_axes,
|
|
final_shapes,
|
|
n_axes_w_added,
|
|
) = _reconstruct_from_shape_uncached(recipe, backend.shape(tensor), axes_dims=axes_dims)
|
|
if init_shapes is not None:
|
|
tensor = backend.reshape(tensor, init_shapes)
|
|
if axes_reordering is not None:
|
|
tensor = backend.transpose(tensor, axes_reordering)
|
|
if len(reduced_axes) > 0:
|
|
tensor = backend.reduce(tensor, operation=reduction_type, reduced_axes=reduced_axes)
|
|
if len(added_axes) > 0:
|
|
tensor = backend.add_axes(tensor, n_axes=n_axes_w_added, pos2len=added_axes)
|
|
if final_shapes is not None:
|
|
tensor = backend.reshape(tensor, final_shapes)
|
|
return tensor
|
|
|
|
|
|
def allow_ops_in_compiled_graph():
|
|
if hasattr(torch, "__version__") and torch.__version__[0] < "2":
|
|
# torch._dynamo and torch.compile appear in pytorch 2.0
|
|
return
|
|
try:
|
|
from torch._dynamo import allow_in_graph
|
|
except ImportError:
|
|
warnings.warn("allow_ops_in_compiled_graph failed to import torch: ensure pytorch >=2.0", ImportWarning)
|
|
return
|
|
|
|
from .einops import rearrange, reduce, repeat, einsum
|
|
from .packing import pack, unpack
|
|
|
|
allow_in_graph(rearrange)
|
|
allow_in_graph(reduce)
|
|
allow_in_graph(repeat)
|
|
allow_in_graph(einsum)
|
|
allow_in_graph(pack)
|
|
allow_in_graph(unpack)
|
|
|
|
# CF: https://github.com/pytorch/pytorch/blob/2df939aacac68e9621fbd5d876c78d86e72b41e2/torch/_dynamo/__init__.py#L222
|
|
global _ops_were_registered_in_torchdynamo
|
|
_ops_were_registered_in_torchdynamo = True
|
|
|
|
|
|
# module import automatically registers ops in torchdynamo
|
|
allow_ops_in_compiled_graph()
|