*Memos:
- My post explains Matrix and Element-wise multiplication in PyTorch.
- My post explains Dot and Matrix-vector multiplication in PyTorch.
- My post explains matmul() and dot()
- My post explains mv(), mm() and bmm().
- My post explains add().
- My post explains sub() and mul().
- My post explains div().
- My post explains remainder() and fmod().
<Dot multiplication>
- dot() can multiply 1D tensors:
import torch
tensor1 = torch.tensor([2, 7, 4]) # 1D tensor
tensor2 = torch.tensor([6, 3, 5]) # 1D tensor
torch.dot(tensor1, tensor2)
tensor1.dot(tensor2)
# tensor(53)
-
matmul() or
@can multiply 1D or more D tensors:
import torch
tensor1 = torch.tensor([2, 7, 4]) # 1D tensor
tensor2 = torch.tensor([6, 3, 5]) # 1D tensor
torch.matmul(tensor1, tensor2)
tensor1.matmul(tensor2)
tensor1 @ tensor2
# tensor(53)
<Matrix-vector multiplication>
- mv() can multiply a 2D and 1D tensor:
import torch
tensor1 = torch.tensor([[2, 7, 4], [8, 3, 2]]) # 2D tensor
tensor2 = torch.tensor([5, 0, 8]) # 1D tensor
torch.mv(tensor1, tensor2)
tensor1.mv(tensor2)
# tensor([42, 56])
-
matmul()or@can multiply 1D or more D tensors:
import torch
tensor1 = torch.tensor([[2, 7, 4], [8, 3, 2]]) # 2D tensor
tensor2 = torch.tensor([5, 0, 8]) # 1D tensor
torch.matmul(tensor1, tensor2)
tensor1.matmul(tensor2)
tensor1 @ tensor2
# tensor([42, 56])
<Matrix multiplication>
- mm() can multiply 2D tensors:
import torch
tensor1 = torch.tensor([[2, 7, 4], [8, 3, 2]]) # 2D tensor
tensor2 = torch.tensor([[5, 0, 8, 6], # 2D tensor
[3, 6, 1, 7],
[1, 4, 9, 2]])
torch.mm(tensor1, tensor2)
tensor1.mm(tensor2)
# tensor([[35, 58, 59, 69], [51, 26, 85, 73]])
- bmm() can multiply 3D tensors:
import torch
tensor1 = torch.tensor([[[2, 7]], [[8, 3]]]) # 3D tensor
tensor2 = torch.tensor([[[5, 9], [3, 6]], # 3D tensor
[[7, 2], [1, 4]]])
torch.bmm(tensor1, tensor2)
tensor1.bmm(tensor2)
# tensor([[[31, 60]], [[59, 28]]])
-
matmul()or@can multiply 1D or more D tensors by dot or matrix multiplication:
import torch
tensor1 = torch.tensor([[2, 7], [8, 3]]) # 2D tensor
tensor2 = torch.tensor([[[[5, 9], [3, 6]], [[7, 2], [1, 4]]],
[[[6, 0], [4, 6]], [[2, 9], [8, 1]]]])
# 4D tensor
torch.matmul(tensor1, tensor2)
tensor1.matmul(tensor2)
tensor1 @ tensor2
# tensor([[[[31, 60], [49, 90]], [[21, 32], [59, 28]]],
# [[[40, 42], [60, 18]], [[60, 25], [40, 75]]]])
<Element-wise calculation>
-
mul() or
*can do multiplication with 0D or more D tensors. *mul()and multiply() are the same becausemultiply()is the alias ofmul():
import torch
tensor1 = torch.tensor([2, 7, 4]) # 1D tensor
tensor2 = torch.tensor([6, 3, 5]) # 1D tensor
torch.mul(tensor1, tensor2)
tensor1.mul(tensor2)
tensor1 * tensor2
# tensor([12, 21, 20])
-
div() or
/can do division with 0D or more D tensors: *Memos: -
divide() is the alias of
div(). -
true_divide() is the alias of
div()withrounding_mode=None. -
floor_divide() is the same as
div()withrounding_mode="trunc"as long as I experimented:
import torch
tensor1 = torch.tensor([2, 7, 4]) # 1D tensor
tensor2 = torch.tensor([6, 3, 5]) # 1D tensor
torch.div(tensor1, tensor2)
tensor1.div(tensor2)
tensor1 / tensor2
# tensor([0.3333, 2.3333, 0.8000])
-
remainder() or
%can do modulo(mod) calculation with 0D or more D tensors:
import torch
tensor1 = torch.tensor([2, 7, 4]) # 1D tensor
tensor2 = torch.tensor([6, 3, 5]) # 1D tensor
torch.remainder(tensor1, tensor2)
tensor1.remainder(tensor2)
tensor1 % tensor2
# tensor([2, 1, 4])
-
add() or
+can do addition with 0D or more D tensors:
import torch
tensor1 = torch.tensor([2, 7, 4]) # 1D tensor
tensor2 = torch.tensor([6, 3, 5]) # 1D tensor
torch.add(tensor1, tensor2)
tensor1.add(tensor2)
tensor1 + tensor2
# tensor([8, 10, 9])
-
sub() or
-can do subtraction with 0D or more D tensors. *sub()and subtract() are the aliases ofsub():
import torch
tensor1 = torch.tensor([2, 7, 4]) # 1D tensor
tensor2 = torch.tensor([6, 3, 5]) # 1D tensor
torch.subtract(tensor1, tensor2)
tensor1.subtract(tensor2)
tensor1 - tensor2
# tensor([-4, 4, -1])
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