16. Building a Complete GPT-Style Decoder-Only Transformer Language Model from Scratch • Deep Learning & PyTorch Neural Networks Architecture • Rishabh Ki Kaksha

Deep Learning & PyTorch Neural Networks Architecture › 16. Building a Complete GPT-Style Decoder-Only Transformer Language Model from Scratch

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Course Syllabus

Module 1: PyTorch Tensors, Autograd & Computational Graphs

01. PyTorch Tensor Architecture: Strides, Storage Buffers, Memory Layout (Contiguous vs Non-Contiguous) 02. Dynamic Computational Graphs & The PyTorch Autograd Engine (grad_fn, requires_grad, backward) 03. GPU Acceleration with CUDA: Tensor Device Placement (to("cuda")), CUDA Streams & Pinned Memory

Module 2: Multi-Layer Perceptrons (MLPs) & Custom Training Loops

04. Building Custom nn.Module Classes, Custom Parameters & Activation Functions (ReLU, GELU, Swish) 05. Custom Datasets & High-Performance DataLoaders (num_workers, collate_fn, pin_memory) 06. The Enterprise Training Loop: Forward Pass, Loss Computation, Zero Grad, Backward Pass & Optimizer Step 07. Neural Regularization Techniques: Dropout, Batch Normalization (BatchNorm1d) & Layer Normalization (LayerNorm)

Module 3: Convolutional Neural Networks (CNNs) & Computer Vision Architecture

08. Convolutional Operations: Kernels, Filters, Strides, Padding & Feature Map Dilation 09. Modern CNN Architectures: Residual Connections (ResNet), Squeeze-and-Excitation & ConvNeXt 10. Transfer Learning & Vision Fine-Tuning with Torchvision Pretrained Models

Module 4: Recurrent Architectures: RNNs, LSTMs & GRUs for Sequential Data

11. Sequential Modeling: Vanilla RNNs & The Vanishing / Exploding Gradient Problem 12. Long Short-Term Memory (LSTM) Networks: Forget Gate, Input Gate, Candidate State & Output Gate Math 13. Gated Recurrent Units (GRUs) & Bidirectional Sequence Processing in PyTorch

Module 5: Modern Transformer Architecture from Scratch in PyTorch

14. Scaled Dot-Product Attention: Query, Key, Value Matrices & Attention Softmax Calculation 15. Multi-Head Attention (MHA) & Causal Masking Implementation from Scratch in Pure PyTorch 16. Building a Complete GPT-Style Decoder-Only Transformer Language Model from Scratch

Module 6: PyTorch Distributed (DDP), Mixed Precision (AMP) & Model Checkpointing

17. Automatic Mixed Precision (torch.cuda.amp.autocast & GradScaler) for 2x Faster GPU Training 18. Distributed Data Parallel (DDP) Architecture: Multi-GPU Synchronization with torch.distributed 19. Robust Checkpointing: Saving State Dicts, Resuming Training & Early Stopping Callbacks 20. Model Export & Production Serialization: TorchScript (JIT Tracing), ONNX & TensorRT Compilation

Interactive Lesson Studio

# 16. Building a Complete GPT-Style Decoder-Only Transformer Language Model from Scratch

> Deep Learning & PyTorch Notes by Rishabh - Topic 16

---

### 1. Complete Architecture of Mini-GPT 🤖

```
[ Input Tokens ] ➔ [ Token Embedding + Position Embedding ]
                                ⬇
┌─────────────────────────────────────────────────────────────┐
│ TRANSFORMER BLOCK (Repeated N Times, e.g. 12 Layers)         │
│   ├── LayerNorm ➔ Multi-Head Causal Attention ➔ Residual Add │
│   └── LayerNorm ➔ Feed-Forward Network (SwiGLU/GELU) ➔ Add   │
└─────────────────────────────────────────────────────────────┘
                                ⬇
           [ Final LayerNorm ➔ Linear LM Head (Vocab) ] ➔ Softmax Next-Token!
```

---

### 2. PyTorch MiniGPT Implementation from Scratch

```python
import torch
import torch.nn as nn

class TransformerBlock(nn.Module):
    def __init__(self, d_model: int, n_heads: int):
        super().__init__()
        self.ln1 = nn.LayerNorm(d_model)
        self.attn = MultiHeadAttention(d_model, n_heads)
        self.ln2 = nn.LayerNorm(d_model)
        self.mlp = nn.Sequential(
            nn.Linear(d_model, 4 * d_model),
            nn.GELU(),
            nn.Linear(4 * d_model, d_model)
        )

def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = x + self.attn(self.ln1(x)) # Pre-LN + Attention Residual
        x = x + self.mlp(self.ln2(x))  # Pre-LN + MLP Residual
        return x

class MiniGPT(nn.Module):
    def __init__(self, vocab_size: int, d_model: int = 768, n_layers: int = 12, n_heads: int = 12, max_len: int = 2048):
        super().__init__()
        self.token_emb = nn.Embedding(vocab_size, d_model)
        self.pos_emb = nn.Embedding(max_len, d_model)
        self.blocks = nn.ModuleList([TransformerBlock(d_model, n_heads) for _ in range(n_layers)])
        self.ln_f = nn.LayerNorm(d_model)
        self.lm_head = nn.Linear(d_model, vocab_size, bias=False)

def forward(self, idx: torch.Tensor) -> torch.Tensor:
        B, T = idx.shape
        positions = torch.arange(0, T, device=idx.device).unsqueeze(0)
        x = self.token_emb(idx) + self.pos_emb(positions)
        for block in self.blocks:
            x = block(x)
        x = self.ln_f(x)
        logits = self.lm_head(x) # [Batch, SeqLen, VocabSize]
        return logits
```

---

### 3. Interview Ke 3 Sawal Is Topic Se

1. **Weight Tying (Embedding matrix aur LM Head matrix ka weights share karna) kyu use hota hai?** -> Parameters 30% reduce karta hai aur vocabulary representation alignment improve karta hai.
2. **SwiGLU activation GELU se Llama models me kyu behtar hai?** -> Gated Linear Unit with Swish function representation capacity boost karti hai.
3. **Encoder-Decoder (T5) vs Decoder-Only (GPT/Llama) me Decoder-Only dominant kyu bana?** -> Zero-shot few-shot in-context learning capability aur uniform KV-caching efficiency ki wajah se.

---
**Next Topic:** 17. Automatic Mixed Precision (torch.cuda.amp.autocast & GradScaler) for 2x Faster GPU Training

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