In PyTorch, the torch.bmm function is used to perform batch matrix multiplication (Batch Matrix Multiplication). It accepts three-dimensional tensors as input and performs batch matrix multiplication operations.
Specifically, suppose we have two input tensors A and B, whose dimensions are respectively
（b，n，m）
and
（b，m，p）
where b represents the batch size, n, m and p represent the number of rows and columns of the matrix respectively.
Then the operation of torch.bmm can be expressed as:
C = torch.bmm(A, B)
The dimensions of the resulting tensor C are
（b，n，p）
where each element C[i] is the product of matrices A[i] and B[i].
When performing batch matrix multiplication, torch.bmm will multiply the matrices in each batch, so it is necessary to ensure that the batch_size dimensions of the two input tensors A and B are the same.
This batch matrix multiplication operation is often used in deep learning to handle multiple samples or data batches, especially in models such as recurrent neural networks (RNN) and attention mechanisms. torch.bmm provides an efficient way to perform such batch matrix multiplication operations.