# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from aitemplate import backend
from aitemplate.backend import registry
from aitemplate.compiler.base import Operator, Tensor
from aitemplate.compiler.dtype import normalize_dtype
[docs]class where(Operator):
"""
Return a tensor of elements selected from either input or other, depending on condition.
Parameters:
condition (A bool Tensor): When True (nonzero), yield input, otherwise yield other
input_tensor (Tensor or Scalar): value (if input is a scalar) or values selected at indices where condition is True
other_tensor (Tensor or Scalar): value (if other is a scalar) or values selected at indices where condition is False
dtype: output dtype if both input_tensor and output_tensor is scalar
Returns:
Tensor: A tensor of shape equal to the shape of condition
"""
def __init__(self) -> None:
super().__init__()
self._attrs["op"] = "where"
def __call__(
self,
condition: Tensor,
input_tensor: Tensor,
other_tensor: Tensor,
dtype: str = "",
) -> Tensor:
assert isinstance(
condition, Tensor
), f"condition needs to be a tensor, but got {type(condition)}"
assert (
condition.dtype() == "bool"
), f"condition needs to be a bool tensor, but got {condition.dtype()}"
output_shape = condition.shape()
args = []
inputs = []
common_dtype = None
for tensor in [input_tensor, other_tensor]:
if isinstance(tensor, int) or isinstance(tensor, float):
tensor = Tensor(shape=[], value=tensor, dtype=common_dtype)
else:
assert isinstance(
tensor, Tensor
), f"Unsupported data type: {type(tensor)}"
assert (
tensor.shape() == output_shape
), f"Tensor shape should be the same, {tensor.shape()} != {output_shape}"
if common_dtype is None:
common_dtype = normalize_dtype(tensor.dtype())
else:
assert common_dtype == normalize_dtype(
tensor.dtype()
), f"Expect tensor of the same dtype, got {common_dtype} and {normalize_dtype(tensor.dtype())}"
inputs.append(tensor)
args.append(tensor)
# In case where both inputs are scalars,
if len(inputs) == 0:
assert dtype != "", "dtype needs to be provided for scalars"
common_dtype = normalize_dtype(dtype)
for arg in args:
arg._attrs["dtype"] = common_dtype
self._attrs["args"] = [condition, *args]
self._attrs["inputs"] = [condition, *inputs]
self._set_depth()
output = Tensor(
shape=output_shape,
src_ops={self},
dtype=common_dtype,
)
self._attrs["outputs"] = [output]
return output
[docs] def gen_function(self) -> str:
target = backend.target.Target.current()
func_key = f"{target.name()}.{self._attrs['op']}.gen_function"
func = registry.get(func_key)
return func(self._attrs)