# Copyright 2022 Huawei Technologies Co., Ltd
#
# 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
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# See the License for the specific language governing permissions and
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# ============================================================================
"""basic"""
import math
import mindspore.ops as P
import mindspore.common.dtype as mstype
from mindspore.common.tensor import Tensor
from mindspore.common.initializer import initializer, Uniform
from mindspore.common.parameter import Parameter
from mindspore.ops.primitive import constexpr
from mindspore._checkparam import Validator
from ..cell import Cell
__all__ = ['BiDense']
@constexpr
def check_dense_inputs_same_shape(input1, input2, prim_name=None):
msg_prefix = f"For '{prim_name}', the" if prim_name else "The"
if input1[:-1] != input2[:-1]:
raise ValueError(f"{msg_prefix} dimensions except the last of 'input1' must be same as 'input2', but got "
f"{input1} of 'input1' and {input2} of 'input2'")
@constexpr
def _check_is_tensor(param_name, input_data, cls_name):
"""Internal function, used to check whether the input data is Tensor."""
if input_data is not None and not isinstance(P.typeof(input_data), mstype.tensor_type):
raise TypeError(f"For '{cls_name}', the '{param_name}' must be '{mstype.tensor_type}', "
f"but got '{P.typeof(input_data)}'")
@constexpr
def check_last_dimension(input_dim, input_channels, input_name, input_channels_name, prim_name=None):
msg_prefix = f"For '{prim_name}', the" if prim_name else "The"
if input_dim != input_channels:
raise ValueError(f"{msg_prefix} last dimension of '{input_name}' must be same as '{input_channels_name}',"
f" but got {input_dim} of '{input_name}' and {input_channels} of '{input_channels_name}'")
[docs]class BiDense(Cell):
r"""
The bilinear dense connected layer.
Applies dense connected layer for two inputs. This layer implements the operation as:
.. math::
y = x_1^T A x_2 + b,
where :math:`x_1` is the first input tensor, :math:`x_2` is the second input tensor
, :math:`A` is a weight matrix with the same data type as the :math:`x_{*}` created by the layer
, and :math:`b` is a bias vector with the same data type as the :math:`x_{*}` created by the layer
(only if has_bias is True).
Args:
in1_channels (int): The number of channels in the input1 space.
in2_channels (int): The number of channels in the input2 space.
out_channels (int): The number of channels in the output space.
weight_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable weight_init parameter.
The values of str refer to the function `initializer`. Default: None.
bias_init (Union[Tensor, str, Initializer, numbers.Number]): The trainable bias_init parameter.
The values of str refer to the function `initializer`. Default: None.
has_bias (bool): Specifies whether the layer uses a bias vector. Default: True.
Shape:
- **input1** - :math:`(*, H_{in1})` where :math:`H_{in1}=\text{in1_channels}` and
:math:`*` means any number of additional dimensions including none. All but the last dimension
of the inputs should be the same.
- **input2** - :math:`(*, H_{in2})` where :math:`H_{in2}=\text{in2_channels}` and
:math:`*` means any number of additional dimensions including none. All but the last dimension
of the inputs should be the same.
- **output** - :math:`(*, H_{out})` where :math:`H_{out}=\text{out_channels}`
and all but the last dimension are the same shape as the inputs.
Dtype:
- **input1** (Tensor) - The dtype must be float16 or float32 and be same as **input2**.
- **input1** (Tensor) - The dtype must be float16 or float32 and be same as **input1**.
- **output** (Tensor) - With the same dtype as the inputs.
Weights:
- **weight** (Parameter) - The learnable weights with shape
:math:`(\text{out_channels}, \text{in1_channels}, \text{in2_channels})`.
When `weight_init` is `None`, the values are initialized from
:math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where :math:`k = \frac{1}{\text{in1_channels}}`.
- **bias** (Parameter) - The learnable bias of shape :math:`(\text{out_channels})`.
If `has_bias` is `True` and `bias_init` is `None`, the values are initialized from
:math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where :math:`k = \frac{1}{\text{in1_channels}}`.
Raises:
TypeError: If `in1_channels`, `in2_channels` or `out_channels` is not an int.
TypeError: If `has_bias` is not a bool.
ValueError: If length of shape of `weight_init` is not equal to 3 or shape[0] of `weight_init`
is not equal to `out_channels` or shape[1] of `weight_init` is not equal to `in1_channels`
or shape[2] of `weight_init` is not equal to `in2_channels`.
ValueError: If length of shape of `bias_init` is not equal to 1
or shape[0] of `bias_init` is not equal to `out_channels`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x1 = Tensor(np.random.randn(128, 20), mindspore.float32)
>>> x2 = Tensor(np.random.randn(128, 30), mindspore.float32)
>>> net = nn.BiDense(20, 30, 40)
>>> output = net(x1, x2)
>>> print(output.shape)
(128, 40)
"""
def __init__(self,
in1_channels,
in2_channels,
out_channels,
weight_init=None,
bias_init=None,
has_bias=True):
super().__init__()
self.in_channels = Validator.check_positive_int(in1_channels, "in1_channels", self.cls_name)
self.in_channels = Validator.check_positive_int(in2_channels, "in2_channels", self.cls_name)
self.out_channels = Validator.check_positive_int(out_channels, "out_channels", self.cls_name)
self.has_bias = Validator.check_bool(has_bias, "has_bias", self.cls_name)
self.in1_channels = in1_channels
self.in2_channels = in2_channels
self.out_channels = out_channels
self.has_bias = has_bias
bound = 1 / math.sqrt(in1_channels)
if weight_init is None:
weight_init = Uniform(bound)
if isinstance(weight_init, Tensor):
if weight_init.ndim != 3 or weight_init.shape[0] != out_channels or \
weight_init.shape[1] != in1_channels or weight_init.shape[2] != in2_channels:
raise ValueError(f"For '{self.cls_name}', weight init shape error. The ndim of 'weight_init' must "
f"be equal to 3, the first dim must be equal to 'out_channels', the "
f"second dim must be equal to 'in1_channels', and the third dim must be "
f"equal to 'in2_channels'. But got 'weight_init': {weight_init}, "
f"'out_channels': {out_channels}, 'in_channels': {in1_channels}, "
f"'in2_channels': {in2_channels}")
self.weight = Parameter(initializer(weight_init, (out_channels, in1_channels, in2_channels)), 'weight')
if self.has_bias:
if bias_init is None:
bias_init = Uniform(bound)
if isinstance(bias_init, Tensor):
if bias_init.ndim != 1 or bias_init.shape[0] != out_channels:
raise ValueError(f"For '{self.cls_name}', bias init shape error. The ndim of 'bias_init' should "
f"be equal to 1, and the first dim must be equal to 'out_channels'. But got "
f"'bias_init': {bias_init}, 'out_channels': {out_channels}.")
self.bias = Parameter(initializer(bias_init, [out_channels]), name="bias")
self.bias_add = P.BiasAdd()
self.matmul = P.MatMul()
def construct(self, input1, input2):
_check_is_tensor("input1", input1, self.cls_name)
_check_is_tensor("input2", input2, self.cls_name)
input1_shape = input1.shape
input2_shape = input2.shape
check_last_dimension(input1_shape[-1], self.in1_channels, "input1", "in1_channels", self.cls_name)
check_last_dimension(input2_shape[-1], self.in2_channels, "input2", "in2_channels", self.cls_name)
check_dense_inputs_same_shape(input1_shape, input2_shape, self.cls_name)
if len(input1_shape) != 2:
input1 = input1.reshape((-1, input1_shape[-1]))
input2 = input2.reshape((-1, input2_shape[-1]))
batch_size = input1.shape[0]
output = self.matmul(input1, self.weight.transpose(1, 2, 0).view(self.in1_channels, -1))
output = output.view(batch_size, self.in2_channels, self.out_channels)
output = output.transpose(2, 0, 1) * input2
output = output.sum(2).swapaxes(0, 1)
if self.has_bias:
output = self.bias_add(output, self.bias)
if len(input1_shape) != 2:
out_shape = input1_shape[:-1] + (-1,)
output = output.reshape(out_shape)
return output
def extend_repr(self):
s = 'in1_channels={}, in2_channels={}, output_channels={}'.format(
self.in1_channels, self.in2_channels, self.out_channels)
if self.has_bias:
s += ', has_bias={}'.format(self.has_bias)
return s