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# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
#
# 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 torch import Tensor
def common_broadcast(x: Tensor, y: Tensor) -> tuple[Tensor, Tensor]:
"""
Broadcasts two tensors to have the same shape by adding singleton dimensions where necessary.
Args:
x (Tensor): The first input tensor.
y (Tensor): The second input tensor.
Returns:
tuple[Tensor, Tensor]: A tuple containing the two tensors with broadcasted shapes.
Raises:
AssertionError: If the dimensions of the tensors do not match at any axis within their common dimensions.
"""
ndims1 = x.ndim
ndims2 = y.ndim
common_ndims = min(ndims1, ndims2)
for axis in range(common_ndims):
assert x.shape[axis] == y.shape[axis], "Dimensions not equal at axis {}".format(axis)
if ndims1 < ndims2:
x = x.reshape(x.shape + (1,) * (ndims2 - ndims1))
elif ndims2 < ndims1:
y = y.reshape(y.shape + (1,) * (ndims1 - ndims2))
return x, y
def batch_add(x: Tensor, y: Tensor) -> Tensor:
"""
Adds two tensors element-wise after broadcasting them to a common shape.
Args:
x (Tensor): The first input tensor.
y (Tensor): The second input tensor.
Returns:
Tensor: The element-wise sum of the input tensors after broadcasting.
"""
x, y = common_broadcast(x, y)
return x + y
def batch_mul(x: Tensor, y: Tensor) -> Tensor:
"""
Multiplies two tensors element-wise after broadcasting them to a common shape.
Args:
x (Tensor): The first input tensor.
y (Tensor): The second input tensor.
Returns:
Tensor: The element-wise product of the input tensors after broadcasting.
"""
x, y = common_broadcast(x, y)
return x * y
def batch_sub(x: Tensor, y: Tensor) -> Tensor:
"""
Subtracts two tensors element-wise after broadcasting them to a common shape.
Args:
x (Tensor): The first input tensor.
y (Tensor): The second input tensor.
Returns:
Tensor: The result of element-wise subtraction of the input tensors.
"""
x, y = common_broadcast(x, y)
return x - y
def batch_div(x: Tensor, y: Tensor) -> Tensor:
"""
Divides two tensors element-wise after broadcasting them to a common shape.
Args:
x (Tensor): The first input tensor.
y (Tensor): The second input tensor.
Returns:
Tensor: The result of element-wise division of `x` by `y` after broadcasting.
"""
x, y = common_broadcast(x, y)
return x / y
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