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add stride into KJT pytree #2587

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add stride into KJT pytree #2587

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16 changes: 11 additions & 5 deletions torchrec/sparse/jagged_tensor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3031,13 +3031,17 @@ def dist_init(

def _kjt_flatten(
t: KeyedJaggedTensor,
) -> Tuple[List[Optional[torch.Tensor]], List[str]]:
return [getattr(t, a) for a in KeyedJaggedTensor._fields], t._keys
) -> Tuple[List[Optional[torch.Tensor]], Tuple[List[str], int]]:
# for variable batch scenario, the stride cannot be computed from lengths/len(keys),
# instead, it should be computed from stride_per_key_per_rank, which is not included
# in the flatten spec. The stride is needed for the EBC output shape, so we need to
# store it in the context.
return [getattr(t, a) for a in KeyedJaggedTensor._fields], (t._keys, t.stride())


def _kjt_flatten_with_keys(
t: KeyedJaggedTensor,
) -> Tuple[List[Tuple[KeyEntry, Optional[torch.Tensor]]], List[str]]:
) -> Tuple[List[Tuple[KeyEntry, Optional[torch.Tensor]]], Tuple[List[str], int]]:
values, context = _kjt_flatten(t)
# pyre can't tell that GetAttrKey implements the KeyEntry protocol
return [ # pyre-ignore[7]
Expand All @@ -3046,9 +3050,11 @@ def _kjt_flatten_with_keys(


def _kjt_unflatten(
values: List[Optional[torch.Tensor]], context: List[str] # context is the _keys
values: List[Optional[torch.Tensor]],
context: List[str], # context is (_keys, _stride)
) -> KeyedJaggedTensor:
return KeyedJaggedTensor(context, *values)
keys, stride = context
return KeyedJaggedTensor(keys, *values, stride=stride)


def _kjt_flatten_spec(
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