### **Prompt Lookup for Assisted Decoding**

**Prompt Lookup** is an alternative approach to assisted decoding that leverages n-gram based lookup instead of model-based assisted decoding. This technique focuses on finding relevant n-grams (sequences of tokens) in a prompt and using them for generating the next tokens, thus improving the efficiency of the decoding process. It can be especially useful when you want to utilize predefined prompt information or cached responses for faster and more contextually relevant text generation.

### **How Prompt Lookup Works:**
1. **N-Gram Based Lookup**: The idea is to use **n-grams** (a contiguous sequence of `n` items from a given text) from the prompt for generating new tokens. This method is based on the concept that certain sequences of words or tokens often appear together in natural language.
2. **Triggering Assisted Decoding**: By setting a specific parameter (`prompt_lookup_num_tokens`), you trigger n-gram based assisted decoding, where the lookup happens in predefined chunks of text (e.g., a list of n-grams or a dictionary).
3. **Contextual Information**: This allows the model to utilize more structured, potentially domain-specific knowledge or previously generated sequences without needing to repeatedly generate them from scratch.

### **Advantages of Prompt Lookup:**
- **Efficiency**: Reduces the need for generating text from scratch and speeds up the decoding process.
- **Contextual Relevance**: By using n-grams from the prompt, the generated text is often more contextually aligned with the input, especially in repetitive or structured tasks.
- **Memory Usage**: It can use a pre-built set of n-grams from a knowledge base, helping to avoid expensive computations on repeated or commonly seen patterns.

### **Example Code for Prompt Lookup Assisted Decoding:**

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Define the model and checkpoint
prompt = "Alice and Bob are planning to"
checkpoint = "google/gemma-2-9b"

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint)

# Define the n-gram based assisted decoding configuration
prompt_lookup_num_tokens = 5  # Example of setting number of tokens for n-gram lookup

# Tokenize the input prompt
inputs = tokenizer(prompt, return_tensors="pt")

# Generate the output with prompt lookup enabled
outputs = model.generate(
    **inputs,
    prompt_lookup_num_tokens=prompt_lookup_num_tokens  # This triggers n-gram based assisted decoding
)

# Decode the generated text and print the result
generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
print(generated_text)
```

### **Explanation of Parameters:**
- **`prompt_lookup_num_tokens`**: This is the key parameter for enabling n-gram based lookup in the decoding process. It specifies how many tokens to consider from the prompt when looking up n-grams.
- **`model.generate`**: The function responsible for generating text. With the **prompt_lookup_num_tokens** parameter, the model will incorporate n-grams from the prompt to generate the next tokens.

### **Potential Output:**
```plaintext
['Alice and Bob are planning to go on a trip next weekend. They decided to']
```

### **How Prompt Lookup Helps in Assisted Decoding:**
- When using model-based assisted decoding, the assistant model generates candidate tokens based on the input. In contrast, prompt lookup makes use of n-grams (token sequences) directly from the prompt to generate the output. By leveraging predefined n-grams, the model avoids redundant computations, thus speeding up the decoding process.
- The **`prompt_lookup_num_tokens`** determines the window size for these n-grams, which can significantly influence the generated content, especially when you need a specific number of tokens from the input to guide the generation.

### **Conclusion:**
Prompt Lookup provides an efficient method for assisted decoding by utilizing n-grams instead of relying solely on a model to generate candidate tokens. This approach speeds up the process and ensures that the generated text stays contextually relevant, making it useful for tasks with predefined sequences or when you want to leverage prior knowledge efficiently.