You Don't Need to Modify PyTorch to Build New Models

The Main Idea

When you create a new neural network architecture, you write your own model code. You don't touch PyTorch itself.

Think of it like cooking: you don't modify your oven to make a new recipe. You just use the oven differently.

Two Separate Layers

🟢 Your Model Code (What You Always Work On)

This is where you build your architecture:

import torch.nn as nn

class MyCustomModel(nn.Module):
    def __init__(self):
        super().__init__()
        # Mix and match PyTorch building blocks
        self.layer1 = nn.Linear(100, 64)
        self.layer2 = nn.Linear(64, 32)
        self.custom_attention = MyAttention()  # ← Your invention!

    def forward(self, x):
        x = self.layer1(x)
        x = self.custom_attention(x)  # ← Your custom logic
        x = self.layer2(x)
        return x

This is where all innovation happens.

🔴 PyTorch Core (What You Almost Never Touch)

This is the engine under the hood:

• How matrix multiplication runs on GPU
• The math behind backpropagation
• Low-level CUDA operations

You only modify this for extreme performance optimization (maybe 0.1% of projects).

Real Example: Custom Attention

Say you invent a new attention mechanism. Here's the famous formula:

You implement it using PyTorch building blocks:

class SimpleAttention(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.scale = dim ** -0.5

    def forward(self, query, key, value):
        # Calculate attention scores
        scores = torch.matmul(query, key.transpose(-1, -2)) * self.scale

        # Apply softmax
        attn_weights = torch.softmax(scores, dim=-1)

        # Apply to values
        output = torch.matmul(attn_weights, value)
        return output

Done! No PyTorch modification needed. You used existing operations (torch.matmul, torch.softmax) in a new way.

How Every Research Paper Works

They all just wrote clever nn.Module classes!

Example: Inventing a "Gated Residual Block"

Say you have a new idea:

class GatedResidualBlock(nn.Module):
    """Your new invention!"""
    def __init__(self, dim):
        super().__init__()
        self.conv = nn.Conv2d(dim, dim, 3, padding=1)
        self.gate = nn.Conv2d(dim, dim, 1)  # Learn what to keep

    def forward(self, x):
        residual = x
        x = self.conv(x)
        gate = torch.sigmoid(self.gate(x))  # 0 to 1
        return residual + gate * x  # Gated skip connection

This is new architecture research, using only existing PyTorch operations.

When DO You Modify PyTorch?

Only when you need blazing speed for a specific operation:

# Someone writes a custom CUDA kernel for faster attention
# (This is C++/CUDA, very advanced)
import my_fast_kernel  # Compiled from C++

class MyModel(nn.Module):
    def forward(self, x):
        # Use the optimized operation
        return my_fast_kernel.fast_attention(x)

But even the fastest models (like GPT-4) are mostly written in Python with nn.Module!

The Simple Truth

┌─────────────────────────────┐
│   Your Model (Python)       │  ← You work here 99% of time
│   class MyModel(nn.Module)  │
├─────────────────────────────┤
│   PyTorch Building Blocks   │  ← You use these
│   nn.Linear, nn.Conv2d...   │
├─────────────────────────────┤
│   PyTorch Engine (C++)      │  ← Rarely touch this
│   GPU operations, autograd  │
└─────────────────────────────┘

Quick Checklist

Want to add:

• ✅ New layer types? → Write a new nn.Module
• ✅ Different connections? → Change your forward()
• ✅ Novel architecture? → Compose existing layers
• ✅ Custom loss? → Write a Python function
• ❌ Faster matrix multiply? → Now you'd modify PyTorch (rare!)

Bottom Line

PyTorch gives you LEGO blocks. You build whatever you want by snapping them together in new ways.

You don't need to modify the LEGO plastic formula to build a castle. 🏰

That's it! Your creativity lives in your code, not in the framework.