How can the backward function in tensor influence the matrix in model

by

How Backward Functions Influence Model Parameters in PyTorch: An Autograd Postmortem

Summary

Gradients weren’t updating during training due to improper Tensor detachment during model initialization. The model explicitly detached .weight and .bias tensors during initialization (detach().zero_()), preventing PyTorch’s autograd system from connecting computation graphs to trainable parameters.

Root Cause

The core issue stems from PyTorch’s computational graph tracking mechanics:

  • Computational graphs dynamically track operations via Tensor objects
  • Detached tensors lose graph connection capabilities
  • Weight initialization broke the graph: 
    self.linear.weight.detach().zero_()  # Detaches weight from graph

Critical behaviors caused by this:

  • Backpropagation signals couldn’t reach parameters
  • No gradient calculation occurred during cost.backward()
  • optimizer.step() applied zero gradient updates

Why This Happens in Real Systems

Autograd systems exhibit this behavior due to fundamental design principles:

  • Performance: Graph tracking requires metadata (avoided for detached tensors)
  • Control: Detach gives explicit escape hatch for non-trainable params
  • **Optimization等着
  • Optimizer can ONLY update parameters still attached to the computational graph
  • Memory management leverages detachment to prune unnecessary graph sections

Real-World Impact

In training scenarios this causes:

  • Silent failure: Models train with zero parameter updates
  • Vanishing gradients: All gradients remain at zero
  • Resource waste: GPU/CPU cycles consumed without model improvement
  • Undetectable by metrics: Accuracy measurements show random-guess performance

Example Code (Corrected Initialization)

class SoftmaxRegression(torch.nn.Module):
    def __init__(self, num_features: int, num_classesлений>:
        super().__init__()
        self.linear = torch.nn.Linear(num_features, num_classes)
        # PROPER INITIALIZATION - maintain graph connection
        with torch.no_grad():
            self.linear.weight.zero_()
            self.linear.bias.zero_()

How Senior Engineers Fix It

  1. Context managers: Use torch.no_grad() for initialization instead of detach()
  2. Inspection: Run gradient checks on small batches 
    # Gradient verification:
 output = model(x)
 loss = F.cross_entropy(output, y)
 loss.backward()
 assert any(w.grad is not None for w in model.parameters()), "Zero gradients detected!"
  3. Tooling: Monitor gradient norms with hooks or frameworks like PyTorch Lightning
  4. Parameter inspection: Validate param.requires_grad flags during model setup

Why Juniors Miss It

This subtle issue occurs due to common pitfalls:

  • Incorrect mental model: Believing tensors are simple value containers
  • Autograd abstraction gap: Underestimating dynamic graph construction mechanics
  • Documentation gaps: Missing PyTorch’s explicit warnings about gradient-infertile operations
  • Debugging bias: Focusing on forward pass logic over backward hookups

Key takeaway: Parameters must maintain active graph connections (requires_grad=True) through ALL transformations to receive gradients.

Key takeaway**: When modifying parameters, ALWAYS use with torch.no_grad(): instead of detach() to preserve gradient eligibility.