### **Under the Hood: How Cache Objects Work in Attention Mechanism**  

When utilizing a **cache object** in the input, the **Attention module** integrates past and present information seamlessly. Understanding this mechanism is crucial for implementing **efficient text generation loops**.

---

## **How Attention Uses Cached Key-Value Pairs**  
### **Concatenation of Past and Present Key-Values**  
1️⃣ The **Attention module** concatenates the **current key-values** with the **past key-values** stored in the cache.  
2️⃣ This results in an **attention weight matrix** of shape:  
   \[
   (\text{new_tokens_length}, \text{past_kv_length} + \text{new_tokens_length})
   \]
3️⃣ Essentially, the model **combines** past and current key-values to compute attention scores, ensuring that it considers **both the previous context and new input**.  

✅ **This avoids recomputation** and makes generation much faster.

---

## **Ensuring Correct Attention Mask Shape**  
When calling `forward()` iteratively instead of using `.generate()`, you must ensure that the **attention mask** accounts for both **past** and **new key-values**.  
- **Expected Shape**:  
  \[
  (\text{batch_size}, \text{past_kv_length} + \text{new_tokens_length})
  \]
- The `.generate()` method handles this **internally**, but if you implement a **custom generation loop**, you must prepare the **attention mask manually**.

---

## **Understanding `cache_position` in Custom Generation Loops**  
📌 **What is `cache_position`?**  
- When reusing an **existing** cache object while calling `forward()`, you must pass a **valid `cache_position`**.  
- `cache_position` **tracks token positions** in the sequence.  
- Unlike regular sequence indices, `cache_position` is **not affected by padding** and **increments by 1 for each new token**.

📌 **Example: How `cache_position` Works**  
- Suppose the **key-value cache contains 10 tokens** (including padding).  
- The **cache position** for the next token should be:  
  ```python
  torch.tensor([10])
  ```

---

## **Example: Custom Generation Loop Using Cache**
The following example shows **how to implement your own generation loop** with cached key-values using the `DynamicCache` class.

### **Code Example**
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, DynamicCache

# Load model and tokenizer
model_id = "meta-llama/Llama-2-7b-chat-hf"
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="cuda:0")
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Initialize past key-value cache
past_key_values = DynamicCache()

# Prepare input prompt
messages = [{"role": "user", "content": "Hello, what's your name?"}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=True).to("cuda:0")

# Initialize generation variables
generated_ids = inputs.input_ids
cache_position = torch.arange(inputs.input_ids.shape[1], dtype=torch.int64, device="cuda:0")
max_new_tokens = 10

# Custom token generation loop
for _ in range(max_new_tokens):
    outputs = model(**inputs, cache_position=cache_position, past_key_values=past_key_values, use_cache=True)
    
    # Greedily sample the next token
    next_token_ids = outputs.logits[:, -1:].argmax(-1)
    generated_ids = torch.cat([generated_ids, next_token_ids], dim=-1)
    
    # Update attention mask for new token
    attention_mask = inputs["attention_mask"]
    attention_mask = torch.cat([attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1)
    
    # Prepare inputs for the next step
    inputs = {"input_ids": next_token_ids, "attention_mask": attention_mask}
    cache_position = cache_position[-1:] + 1  # Increment cache position

# Decode and print final output
print(tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0])
```

### **Output Example**
```
[INST] Hello, what's your name? [/INST]  Hello! My name is LLaMA,
```

---

## **Key Takeaways**
✔ **Cached Key-Values Speed Up Generation** → Avoids redundant computations.  
✔ **Attention Mask Must Account for Past + New Tokens** → Ensure correct shape.  
✔ **`cache_position` Tracks Token Positions** → Updates per token, unaffected by padding.  
✔ **Custom Loops Require Manual Cache Handling** → Unlike `.generate()`, which automates this.  

📌 **By properly managing cache objects, you can achieve ultra-fast text generation for chatbots, summarization, and other NLP tasks!** 🚀