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refactor _Dim into Dim #2847

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refactor _Dim into Dim #2847

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14 changes: 6 additions & 8 deletions torchrec/ir/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,6 @@

from torch import nn
from torch.export import Dim, ShapesCollection
from torch.export.dynamic_shapes import _Dim as DIM
from torch.export.unflatten import InterpreterModule
from torch.fx import Node
from torchrec.ir.types import SerializerInterface
Expand Down Expand Up @@ -169,22 +168,21 @@ def decapsulate_ir_modules(
return module


def _get_dim(name: str, min: Optional[int] = None, max: Optional[int] = None) -> DIM:
def _get_dim(name: str, min: Optional[int] = None, max: Optional[int] = None) -> Dim:
"""
Returns a Dim object with the given name and min/max. If the name is not unique, it will append a suffix to the name.
"""
dim = f"{name}_{DYNAMIC_DIMS[name]}"
DYNAMIC_DIMS[name] += 1
# pyre-ignore[7]: Expected `DIM` but got `Dim`.
return Dim(dim, min=min, max=max)


def mark_dynamic_kjt(
kjt: KeyedJaggedTensor,
shapes_collection: Optional[ShapesCollection] = None,
variable_length: bool = False,
vlen: Optional[DIM] = None,
llen: Optional[DIM] = None,
vlen: Optional[Dim] = None,
llen: Optional[Dim] = None,
) -> ShapesCollection:
"""
Makes the given KJT dynamic. If it's not variable length, it will only have
Expand All @@ -203,9 +201,9 @@ def mark_dynamic_kjt(
kjt (KeyedJaggedTensor): The KJT to make dynamic.
shapes_collection (Optional[ShapesCollection]): The collection to update.
variable_length (bool): Whether the KJT is variable length.
vlen (Optional[DIM]): The dynamic length for the values. If it's None, it will use the default name "vlen".
llen (Optional[DIM]): The dynamic length for the lengths, it's only used when variable_length is true. If it's None, it will use the default name "llen".
batch_size (Optional[DIM]): The dynamic length for the batch_size, it's only used when variable_length and mark_batch_size are both true.
vlen (Optional[Dim]): The dynamic length for the values. If it's None, it will use the default name "vlen".
llen (Optional[Dim]): The dynamic length for the lengths, it's only used when variable_length is true. If it's None, it will use the default name "llen".
batch_size (Optional[Dim]): The dynamic length for the batch_size, it's only used when variable_length and mark_batch_size are both true.
"""

def _has_dim(t: Optional[torch.Tensor]) -> bool:
Expand Down
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