# Pytorch Foundation: similarities and differences between Torch.mul, Torch.mm and Torch.matmul

Pytorch Foundation: similarities and differences between Torch.mul, Torch.mm and Torch.matmul

# Torch.mul

torch.mul(input, other, ***, out=None) → Tensor

Multiply each input element by another scalar to return a new tensor.
o u t i = o t h e r × i n p u t i out_i = other \times input_i outi​=other×inputi​
input is a tensor, and other will multiply each tensor element. The output is a tensor.

If the input is of type FloatTensor or DoubleTensor, other should be a real number, otherwise it should be an integer

Example

>>> a = torch.randn(3)
>>> a
tensor([ 0.2015, -0.4255,  2.6087])
>>> torch.mul(a, 100)
tensor([  20.1494,  -42.5491,  260.8663])


torch.mul(input, other, ***, out=None) → Tensor

Each element of tensor input must be multiplied by each element of tensor other, and the result will return a tensor

input and other must comply with the broadcast mechanism
o u t i = i n p u t i × o t h e r i out_i = input_i \times other_i outi​=inputi​×otheri​
input and other are tensors. Return is also a tensor

Example

>>> a = torch.randn(4, 1)
>>> a
tensor([[ 1.1207],
[-0.3137],
[ 0.0700],
[ 0.8378]])
>>> b = torch.randn(1, 4)
>>> b
tensor([[ 0.5146,  0.1216, -0.5244,  2.2382]])
>>> torch.mul(a, b)
tensor([[ 0.5767,  0.1363, -0.5877,  2.5083],
[-0.1614, -0.0382,  0.1645, -0.7021],
[ 0.0360,  0.0085, -0.0367,  0.1567],
[ 0.4312,  0.1019, -0.4394,  1.8753]])


# Torch.mm

torch.mm(input, mat2, ***, out=None) → Tensor

Perform matrix multiplication of matrix input and mat2

If input is ( n × m ) of Zhang amount ， ' m a t 2 ' yes The tensor of (n \times m),  mat2 'is (n × m) The tensor of 'mat2' is (m\times p) of Zhang amount ， transport Out take meeting yes The output will be The output will be the tensor of (n\times p)\$

This function does not have a broadcast mechanism. If you want to use the broadcast mechanism, you need torch. Match()

Support striped and sparse two-dimensional tensors as inputs, and autograd with respect to striped inputs

This operator supports TensorFloat32.

>>> mat1 = torch.randn(2, 3)
>>> mat2 = torch.randn(3, 3)
>>> torch.mm(mat1, mat2)
tensor([[ 0.4851,  0.5037, -0.3633],
[-0.0760, -3.6705,  2.4784]])


input is the first tensor matrix and mat2 is the second tensor matrix. output is a tensor

# Torch.matmul

torch.matmul(input, other, ***, out=None) → Tensor

Matrix product of two tensors.

Its behavior depends on the dimension of the tensor as follows:

• If both tensors are one-dimensional, the dot product (scalar) is returned.

• If both parameters are two-dimensional, the matrix matrix product is returned.

• If the first parameter is one-dimensional and the second parameter is two-dimensional, in order to multiply the matrix, a 1 is added in front of its dimension. After matrix multiplication, additional dimensions are deleted.

• If the first parameter is two-dimensional and the second parameter is one-dimensional, the matrix vector product is returned.

• If two parameters are at least one-dimensional and at least one parameter is N-dimensional (where N > 2), a batch matrix multiplication is returned. If the first parameter is one-dimensional, add 1 before its dimension to multiply the batch matrix, and then delete it. If the second parameter is one-dimensional, a 1 is appended to its dimension for the purpose of batch matrix multiplication, and then it is deleted.

• The non matrix (i.e., batch) dimension is broadcast (and therefore must be broadcast).

Example: if input is ( j × 1 × n × n ) (j\times 1 \times n \times n) (j × one × n × n) The tensor of is multiplied by an other tensor ( k × n × n ) (k \times n \times n) (k × n × n) , then the output will be ( j × k × n × n ) (j \times k \times n \times n) (j×k×n×n)

It should be noted that when determining whether the input can be broadcast, the broadcast logic only looks at the batch dimension, not the matrix dimension.

For example, input is a tensor ( j × 1 × n × m ) (j \times 1 \times n \times m) (j × one × n × m) And other is a tensor ( k × m × p ) (k \times m\times p) (k × m × p) , these inputs are valid for broadcasting, even if the last two dimensions (i.e. matrix dimension) are different. out will be a tensor ( j × k × n × p ) (j \times k \times n \times p) (j×k×n×p).

Support tensorfloat32

  >>> # vector x vector
>>> tensor1 = torch.randn(3)
>>> tensor2 = torch.randn(3)
>>> torch.matmul(tensor1, tensor2).size()
torch.Size([])
>>> # matrix x vector
>>> tensor1 = torch.randn(3, 4)
>>> tensor2 = torch.randn(4)
>>> torch.matmul(tensor1, tensor2).size()
torch.Size([3])
>>> # batched matrix x broadcasted vector
>>> tensor1 = torch.randn(10, 3, 4)
>>> tensor2 = torch.randn(4)
>>> torch.matmul(tensor1, tensor2).size()
torch.Size([10, 3])
>>> # batched matrix x batched matrix
>>> tensor1 = torch.randn(10, 3, 4)
>>> tensor2 = torch.randn(10, 4, 5)
>>> torch.matmul(tensor1, tensor2).size()
torch.Size([10, 3, 5])
>>> # batched matrix x broadcasted matrix
>>> tensor1 = torch.randn(10, 3, 4)
>>> tensor2 = torch.randn(4, 5)
>>> torch.matmul(tensor1, tensor2).size()
torch.Size([10, 3, 5])
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Posted by rocketsprocket on Sat, 25 Sep 2021 13:53:12 +0530