ipex-llm/python/llm/example/CPU/Speculative-Decoding/starcoder

Starcoder

In this directory, you will find examples on how you could run Starcoder BF16 inference with self-speculative decoding using IPEX-LLM on Intel CPUs. For illustration purposes,we utilize the bigcode/starcoder and bigcode/tiny_starcoder_py as reference Starcoder models.

0. Requirements

To run these examples with IPEX-LLM on Intel CPUs, we have some recommended requirements for your machine, please refer to here for more information.

Example: Predict Tokens using generate() API

In the example speculative.py, we show a basic use case for a Starcoder model to predict the next N tokens using generate() API, with IPEX-LLM speculative decoding optimizations on Intel CPUs.

1. Install

We suggest using conda to manage environment:

conda create -n llm python=3.9
conda activate llm
pip install --pre --upgrade ipex-llm[all]
pip install intel_extension_for_pytorch==2.1.0
pip install transformers==4.31.0

2. Configures high-performing processor environment variables

source ipex-llm-init -t
export OMP_NUM_THREADS=48 # you can change 48 here to #cores of one processor socket

3. Run

We recommend to use numactl to bind the program to a specified processor socket:

numactl -C 0-47 -m 0 python ./speculative.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --prompt PROMPT --n-predict N_PREDICT

For example, 0-47 means bind the python program to core list 0-47 for a 48-core socket.

Arguments info:

• --repo-id-or-model-path REPO_ID_OR_MODEL_PATH: argument defining the huggingface repo id for the Starcoder model (e.g. bigcode/starcoder and bigcode/tiny_starcoder_py) to be downloaded, or the path to the huggingface checkpoint folder. It is default to be bigcode/starcoder.
• --prompt PROMPT: argument defining the prompt to be infered (with integrated prompt format for chat). A default prompt is provided.
• --n-predict N_PREDICT: argument defining the max number of tokens to predict. It is default to be 128.

Sample Output

bigcode/starcoder

def dfs_print_Fibonacci_sequence(n):
    if n == 0:
        return
    elif n == 1:
        print(0)
        return
    elif n == 2:
        print(0)
        print(1)
        return
    else:
        print(0)
        print(1)
        dfs_print_Fibonacci_sequence(n-2)
        print(dfs_Fibonacci_sequence(n-1))

def dfs_Fibonacci_sequence(n):
    if n == 0:
        return 0
    elif n == 1:
        return 1
    else:
        return dfs_Fibonacci_sequence
Tokens generated 128
E2E Generation time xx.xxxxs
First token latency xx.xxxxs

bigcode/tiny_starcoder_py

def dfs_print_Fibonacci_sequence(n):
    if n == 0:
        return
    print(n)
    for i in range(2, n):
        print(dfs_print_Fibonacci_sequence(i))


def dfs_print_Fibonacci_sequence_2(n):
    if n == 0:
        return
    print(n)
    for i in range(2, n):
        print(dfs_print_Fibonacci_sequence_2(i))


def dfs_print_Fibonacci_sequence_3(n):
    if n == 0:
        return
    print(n)
    for i in
Tokens generated 128
E2E Generation time xx.xxxxs
First token latency xx.xxxxs