# 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.
#
"""
conv2d + bias + residual + hardswish
"""
from aitemplate.frontend.nn.conv2d.common_conv2d_bias_add_act import Conv2dBiasAddAct
[docs]class Conv2dBiasAddHardswish(Conv2dBiasAddAct):
r"""Applies 2D convolution with bias + add + hardswish.
Attributes:
weight (Tensor): the learnable weights of the module of shape
:math:`(\text{out_channels}, \text{kernel_size}, \text{kernel_size}, `
:math:`\frac{\text{in_channels}}{\text{groups}})`.
bias (Tensor): the learnable bias of the module of shape
(out_channels).
Args:
input (Tensor): the input tensor to apply 2D convolution on.
residual (Tensor): the residule tensor to add after Conv2dBias.
Examples::
>>> m = nn.Conv2dBiasAddRelu(128, 256, 3, 1)
>>> input = Tensor(shape=[4, 28, 28, 128])
>>> residual = Tensor(shape=[4, 28, 28, 256])
>>> output = m(input, residual)
"""
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride,
padding=0,
dilation=1,
groups=1,
dtype="float16",
):
super().__init__(
"conv2d_bias_add_hardswish",
in_channels,
out_channels,
kernel_size,
stride,
padding,
dilation,
groups,
dtype,
)