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"""
fused conv2d_bias_add_hardswish op, for residual block
"""
from aitemplate.compiler.ops.conv.common_conv2d_bias_add_activation import (
conv2d_bias_add_activation,
)
# pylint: disable=C0103
[docs]class conv2d_bias_add_hardswish(conv2d_bias_add_activation):
r"""Conv2d_bias_add_hardswish.
Applies a 2D convolution on input in shape (N, H, W, C_in), adds a bias in shape (C_out), adds the residual in shape (N, H_out, W_out, C_out), performs hardswish operation and produces output in shape (N, H_out, W_out, C_out). N is batch size, H, W are the height and width of the input images in pixels, and C is the number of channels.
Args:
input: input tensor of shape :math:`(N , H , W, \text{in\_channels})`
weight: filters of shape :math:`(\text{out\_channels} , K_h, K_w, \frac{\text{in\_channels}}{\text{groups}})`
bias: optional bias tensor of shape :math:`(\text{out\_channels})`
residual: residual to add after conv2d_bias
This operator uses "channels_last" data format. Below is an example and its equivalence in PyTorch:
.. highlight:: python
.. code-block:: python
X = Tensor(shape=[N, H, W, C_in], dtype="float16", name="images", is_input=True)
W = Tensor(shape=[C_out, K_h, K_w, C_in], dtype="float16", name="weight", is_input=True)
B = Tensor(shape=[C_out], dtype="float16", name="bias", is_input=True)
R = Tensor(shape=[N, H_out, W_out, C_out], dtype="float16", name="residual", is_input=True)
OP = aitemplate.compiler.ops.conv2d_bias_add_hardswish(stride=1, pad=1, dilate=1)
Y = OP(X, W, B, R)
.. highlight:: python
.. code-block:: python
X_pt = NHWC2NCHW(X_ait)
W_pt = NHWC2NCHW(W_ait)
B_pt = NHWC2NCHW(B_ait)
R_pt = NHWC2NCHW(R_ait)
Y_pt = torch.nn.functional.conv2d(X_pt, W_pt, bias=B_pt)
Z_pt = Y_pt + R_pt
Result_pt = torch.nn.functional.hardswish(Z_pt)
Result = NCHW2NHWC(Result_pt)
"""
def __init__(self, stride, pad, dilate=1, group=1) -> None:
"""Conv2d_bias_add_hardswish constructor.
Parameters
----------
stride : int
Stride of the convolution
pad : int
Size of padding to add to the input
dilate : int, optional
Size of spacing between kernel elements, by default 1
group : int, optional
Number of input channels to process to compute one output channel, by default 1
"""
super().__init__("hardswish", stride, pad, dilate=dilate, group=group)
def _get_op_attributes(self):
attr = super()._get_op_attributes()
del attr["activation"]
return attr