baddbmm
- paddle. baddbmm ( input: Tensor, x: Tensor, y: Tensor, beta: float = 1.0, alpha: float = 1.0, out_dtype: paddle.dtype | None = None, name: str | None = None, *, out: Tensor | None = None ) Tensor [source]
-
Perform batch matrix multiplication for input \(x\) and \(y\). \(input\) is added to the final result. The equation is: .. math:
out = \beta \times input + \alpha \times x \times y
where \(\beta\) and \(\alpha\) are scaling factors. :param input: The input tensor to be added to the final result. It should be a 2-D or 3-D tensor.
Data type should be float16, float32, float64, uint16.
- Parameters
-
x (Tensor) – The first batch of matrices to be multiplied. It should be a 3-D tensor with shape [b, n, p]. Data type should be float16, float32, float64, uint16. Alias:
batch1.y (Tensor) – The second batch of matrices to be multiplied. It should be a 3-D tensor with shape [b, p, m]. Data type should be float16, float32, float64, uint16. Alias:
batch2.beta (float, optional) – The scaling factor for input. Default: 1.0.
alpha (float, optional) – The scaling factor for x @ y. Default: 1.0.
out_dtype (paddle.dtype|None, optional) – The desired data type of the returned tensor. If None, the output tensor will have the same data type as input. Default: None.
name (str|None, optional) – Name for the operation (optional, default is None). For more information, please refer to api_guide_Name.
- Keyword Arguments
-
out (Tensor, optional) – The output Tensor. If set, the result will be stored in this Tensor. Default: None.
- Returns
-
The output tensor should be a 3-D tensor with shape [b, n, m].
- Return type
-
Tensor
Examples
>>> import paddle >>> x = paddle.ones([2, 2, 2]) >>> y = paddle.ones([2, 2, 2]) >>> input = paddle.ones([2, 2, 2]) >>> out = paddle.baddbmm(input=input, x=x, y=y, beta=0.5, alpha=5.0) >>> out Tensor(shape=[2, 2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[[10.50000000, 10.50000000], [10.50000000, 10.50000000]], [[10.50000000, 10.50000000], [10.50000000, 10.50000000]]])
