def _unwrap_batched(
batched_outputs: Union[Tensor, Tuple[Tensor, ...]],
out_dims: out_dims_t,
vmap_level: int,
batch_size: int,
func: Callable,
allow_none_pass_through: bool = False,
) -> Tuple:
num_outputs = _num_outputs(batched_outputs)
out_dims_as_tuple = _as_tuple(
out_dims,
num_outputs,
lambda:
f"vmap({_get_name(func)}, ..., out_dims={out_dims}): `out_dims` must "
f"have one dim per output (got {num_outputs} outputs) of {_get_name(func)}.",
)
# NOTE [Ignored _remove_batch_dim, _add_batch_dim]
# There is something wrong with our type bindings for functions that begin
# with '_', see #40397.
if isinstance(batched_outputs, Tensor):
out_dim = out_dims_as_tuple[0]
return torch._remove_batch_dim(batched_outputs, vmap_level, batch_size,
out_dim) # type: ignore[return-value]
if allow_none_pass_through:
return tuple(
(torch._remove_batch_dim(out, vmap_level, batch_size, out_dim
) if out is not None else None)
for out, out_dim in zip(batched_outputs, out_dims_as_tuple))
else:
return tuple(
torch._remove_batch_dim(out, vmap_level, batch_size, out_dim)
for out, out_dim in zip(batched_outputs, out_dims_as_tuple))
def _unwrap_batched(batched_outputs, vmap_level, batch_size):
# NOTE [Ignored _remove_batch_dim, _add_batch_dim]
# There is something wrong with our type bindings for functions that begin
# with '_', see #40397.
if isinstance(batched_outputs, Tensor):
return torch._remove_batch_dim(batched_outputs, vmap_level, batch_size, 0) # type: ignore
return tuple(torch._remove_batch_dim(out, vmap_level, batch_size, 0) # type: ignore
for out in batched_outputs)
def _unwrap_batched(batched_outputs, out_dims, vmap_level, batch_size, fn_name):
num_outputs = _num_outputs(batched_outputs)
out_dims_as_tuple = _as_tuple(
out_dims, num_outputs,
lambda: OUT_DIMS_AND_NUM_OUTPUTS_MISMATCH.format(
fn=fn_name, out_dims=out_dims, num_outputs=num_outputs))
# NOTE [Ignored _remove_batch_dim, _add_batch_dim]
# There is something wrong with our type bindings for functions that begin
# with '_', see #40397.
if isinstance(batched_outputs, Tensor):
out_dim = out_dims_as_tuple[0]
return torch._remove_batch_dim(batched_outputs, vmap_level, batch_size, out_dim) # type: ignore
return tuple(torch._remove_batch_dim(out, vmap_level, batch_size, out_dim) # type: ignore
for out, out_dim in zip(batched_outputs, out_dims_as_tuple))
def _unwrap_batched(batched_outputs: Union[Tensor, Tuple[Tensor, ...]],
out_dims: out_dims_t, vmap_level: int, batch_size: int,
fn_name: str) -> Tuple:
num_outputs = _num_outputs(batched_outputs)
out_dims_as_tuple = _as_tuple(
out_dims, num_outputs,
lambda: f'vmap({fn_name}, ..., out_dims={out_dims}): `out_dims` must '
f'have one dim per output (got {num_outputs} outputs) of {fn_name}.')
# NOTE [Ignored _remove_batch_dim, _add_batch_dim]
# There is something wrong with our type bindings for functions that begin
# with '_', see #40397.
if isinstance(batched_outputs, Tensor):
out_dim = out_dims_as_tuple[0]
return torch._remove_batch_dim(batched_outputs, vmap_level, batch_size,
out_dim) # type: ignore
return tuple(
torch._remove_batch_dim(out, vmap_level, batch_size,
out_dim) # type: ignore
for out, out_dim in zip(batched_outputs, out_dims_as_tuple))
