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Super Kai (Kazuya Ito)

Posted on • Updated on

# Functions and operators for Dot and Matrix multiplication and Element-wise calculation in PyTorch

*Memos:

### <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 because `multiply()` is the alias of `mul()`:
``````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()` with `rounding_mode=None`.
• floor_divide() is the same as `div()` with `rounding_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

tensor1 + tensor2
# tensor([8, 10, 9])
``````
• sub() or `-` can do subtraction with 0D or more D tensors. *`sub()` and subtract() are the aliases of `sub()`:
``````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])
``````