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9f818c5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: OpenMDW-1.1
# Functions for performing operations with broadcasting to the right axis
#
# Example
# input1: tensor of size (N1, N2)
# input2: tensor of size (N1, N2, N3, N4)
# batch_mul(input1, input2) = input1[:, :, None, None] * input2
#
# If the common dimensions don't match, we raise an assertion error.
from torch import Tensor
def common_broadcast(x: Tensor, y: Tensor) -> tuple[Tensor, Tensor]:
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)) # x broadcast-padded to ndims2: [*x.shape,1,...]
elif ndims2 < ndims1:
y = y.reshape(y.shape + (1,) * (ndims1 - ndims2)) # y broadcast-padded to ndims1: [*y.shape,1,...]
return x, y
def batch_add(x: Tensor, y: Tensor) -> Tensor:
x, y = common_broadcast(x, y)
return x + y # broadcast result shape
def batch_mul(x: Tensor, y: Tensor) -> Tensor:
x, y = common_broadcast(x, y)
return x * y # broadcast result shape
def batch_sub(x: Tensor, y: Tensor) -> Tensor:
x, y = common_broadcast(x, y)
return x - y # broadcast result shape
def batch_div(x: Tensor, y: Tensor) -> Tensor:
x, y = common_broadcast(x, y)
return x / y # broadcast result shape
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