PyTorch: torch.matmul() The Key to Efficient Matrix Multiplication Operations

PyTorch, a machine learning library optimized for the Python programming language, opens up a world of possibilities for data scientists and developers alike. 

Within PyTorch, we encounter the concept of "tensors" which are akin to NumPy arrays, allowing us to perform various mathematical operations like addition, subtraction, multiplication, and division.

	import torch
	X = torch.tensor([1,2,3])
	Y = torch.tensor(2)
	# addition
	_add = X + Y
	# substraction
	_sub = Y - X
	# multiplication
	_multi = X * Y
	# division
	_div = Y / X

view raw tensor_operations.py hosted with ❤ by GitHub

In this blog, we will focus on the crucial operation of multiplication.

Above, we employ the asterisk (*) symbol and PyTorch's built-in functions torch.matmul() or torch.mm() to showcase the difference between them. Let's delve deeper into the comparison:

Using the Asterisk Symbol ():

	# run on IPython
	import torch
	X = torch.tensor([1, 2, 3])
	Y = torch.tensor(2)
	%%time
	X * Y
	# Output:
	# CPU times: user 821 µs, sys: 760 µs, total: 1.58 ms
	# Wall time: 23.2 ms
	# tensor([2, 4, 6])

view raw torch_aterisk.py hosted with ❤ by GitHub

In the first case, we use the traditional multiplication operation with the asterisk () symbol and measure the processing time, the execution time reaches ~23.2ms. Python computes this operation by iterating through the elements, which can be time-consuming for large tensors.

Using PyTorch's Built-in Function torch.matmul() or torch.mm():

	# run on IPython
	import torch
	X = torch.tensor([1, 2, 3])
	Y = torch.tensor(2)
	%%time
	torch.matmul(X, Y)
	# Output:
	# CPU times: user 245 µs, sys: 52 µs, total: 297 µs
	# Wall time: 2.44 ms
	# tensor([2, 4, 6])

view raw torch_torch_matmul.py hosted with ❤ by GitHub

In the second case, we harness the power of PyTorch's built-in function torch.matmul() or torch.mm() to perform the matrix multiplication and also measure the processing time, the execution time reaches ~2.44ms. These built-in functions leverage optimized algorithms, making the computation significantly faster.

Upon analysis, we discover that using built-in functions can lead to computational time savings of up to 10x - 12x faster compared to the traditional multiplication. Therefore, it is highly recommended to utilize PyTorch's built-in functions for improved efficiency and faster execution of your code.

Seamless Using PyTorch

By embracing PyTorch's capabilities, you can unlock the potential for seamless, efficient matrix operations in your machine learning projects. Whether you're working on deep learning models or advanced data analysis, PyTorch empowers you to achieve optimal performance and stay ahead in the rapidly evolving field of artificial intelligence.

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