Compute Graph

The compute graph represents operator dependencies during tensor computation.

Concept

• Each operation is a node in the graph.
• Nodes maintain pointers to input tensors.
• Backward passes traverse the graph in reverse.

Graph structure

• Nodes are created by tensor operations.
• Graphs are acyclic for feedforward workloads.
• Intermediate tensors can be shared across downstream nodes.

Performance implications

• Graph depth and width determine backward memory usage.
• Shared intermediate results reduce recomputation but increase lifetime.
• Avoid unnecessary graph nodes in production inference.

When to use

• Use the graph for custom gradients and model introspection.
• Use it in debugging or research workflows.

When not to use

• Do not keep the graph alive beyond a single backward pass unless required.
• Do not enable autograd for pure inference workloads.