### **Self-Speculative Decoding**

**Self-Speculative Decoding** is a technique that combines early exiting with assisted decoding to enhance generation efficiency. Here's how it works:

1. **Early Exiting**: An LLM (Language Model) is trained to exit early, utilizing its language modeling head with earlier hidden states as input. This effectively skips certain layers in the model, which generates a lower-quality output. This lower-quality output, generated with fewer layers, is used as an assistant output.
   
2. **Self-Speculation**: The model then applies a process known as **self-speculation**. Using the remaining layers of the model, it refines the early exit output, ensuring that the final result is of the same quality (or has the same distribution) as if the model had used all of its layers from the start.

3. **Efficiency**: Since the assistant model in self-speculative decoding is essentially a portion of the target model (the same model but with early exit), caches and weights are shared. This significantly reduces memory requirements without compromising the final output's quality.

4. **Parameter for Early Exit**: To enable self-speculative decoding, the `assistant_early_exit` argument is passed with an integer indicating how many layers to skip. The earlier layers used for early exit will serve as the assistant output, and the remaining layers will refine it.

### **Advantages of Self-Speculative Decoding**:
- **Memory Efficiency**: Since the assistant model shares weights and caches with the main model, memory usage is reduced.
- **Improved Latency**: By generating a preliminary output with fewer layers and refining it with the remaining layers, the decoding process becomes faster while maintaining the quality of the output.
- **Same Quality as Full Model**: Despite using early exit, the final generated output has the same quality as it would with the full model, ensuring no loss in generation quality.

### **Example Code for Self-Speculative Decoding**:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Define the model and checkpoint
prompt = "Alice and Bob"
checkpoint = "facebook/layerskip-llama3.2-1B"

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
inputs = tokenizer(prompt, return_tensors="pt")

model = AutoModelForCausalLM.from_pretrained(checkpoint)

# Generate the output with self-speculative decoding (using early exit)
outputs = model.generate(
    **inputs,
    assistant_early_exit=4,  # Set to 4 layers to exit early
    do_sample=False,
    max_new_tokens=20
)

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

### **Explanation of Parameters**:
- **`assistant_early_exit`**: This parameter defines how many layers should be skipped to generate the early exit output. The assistant model will be the same as the target model but with fewer layers.
- **`do_sample`**: This controls whether the output is generated using sampling. In this example, we set it to `False` to use deterministic generation.
- **`max_new_tokens`**: Defines the maximum number of new tokens to generate after the prompt.

### **Potential Output**:
```plaintext
['Alice and Bob are sitting in a bar. Alice is drinking a beer and Bob is drinking a']
```

### **How Self-Speculative Decoding Works**:
1. **Early Exit**: The model generates a quick, low-quality output using only the first few layers (in this case, 4 layers are skipped). This is faster and requires less memory.
2. **Refinement**: The remaining layers refine the output generated from the early exit, improving its quality without needing to recompute from scratch.
3. **Final Output**: The result after self-speculation is the same quality as if the model had used all layers from the beginning.

### **Conclusion**:
Self-speculative decoding is a powerful technique to speed up the decoding process without sacrificing the quality of generated text. By using early exit for preliminary output and refining it with the remaining layers, this approach ensures memory efficiency and lower latency while maintaining output quality.