pytorch digraph

PyTorch digraph refers to the computational graph structure used by PyTorch to represent and track the operations performed on tensors. A computational graph is a directed acyclic graph (DAG) where nodes represent operations and edges represent data flow between operations.

In PyTorch, you can create a digraph by defining the operations using PyTorch functions and then applying them to tensors. Each operation creates a node in the graph, and the tensors are passed as inputs and outputs between nodes.

Here's an example of creating a simple digraph in PyTorch:

import torch

# Create tensors
x = torch.tensor(2.0)
y = torch.tensor(3.0)

# Perform operations
z = x + y
w = z * y

# Print the computational graph
print(w.grad_fn)

In this example, the digraph represents the computation `w = (x + y) * y`. The `grad_fn` attribute of the final tensor `w` will point to the operation that created it, which is `MulBackward0` in this case.

The digraph in PyTorch is dynamic, meaning it can change during runtime based on the input data and operations performed. This flexibility allows for efficient automatic differentiation, which is crucial for training deep learning models.