exo/examples/llama3_distributed.py

# In this example, a user is running a home cluster with 3 shards.
# They are prompting the cluster to generate a response to a question.
# The cluster is given the question, and the user is given the response.

from exo.inference.mlx.sharded_utils import get_model_path, load_tokenizer
from exo.inference.shard import Shard
from exo.networking.peer_handle import PeerHandle
from exo.networking.grpc.grpc_peer_handle import GRPCPeerHandle
from exo.topology.device_capabilities import DeviceCapabilities, DeviceFlops
from typing import List
import asyncio
import argparse
import uuid

models = {
  "mlx-community/Meta-Llama-3-8B-Instruct-4bit": Shard(model_id="mlx-community/Meta-Llama-3-8B-Instruct-4bit", start_layer=0, end_layer=0, n_layers=32),
  "mlx-community/Meta-Llama-3-70B-Instruct-4bit": Shard(model_id="mlx-community/Meta-Llama-3-70B-Instruct-4bit", start_layer=0, end_layer=0, n_layers=80)
}

path_or_hf_repo = "mlx-community/Meta-Llama-3-8B-Instruct-4bit"
model_path = get_model_path(path_or_hf_repo)
tokenizer_config = {}
tokenizer = load_tokenizer(model_path, tokenizer_config)

# we intentionally leave out peer1 to demonstrate equality of nodes in exo.
# there is no "master" node in exo, all nodes are equal and can take on any role.
# peer1 = GRPCPeerHandle(
#     "node1",
#     "localhost:8080",
#     DeviceCapabilities(model="placeholder", chip="placeholder", memory=0)
# )
peer2 = GRPCPeerHandle("node2", "localhost:8081", DeviceCapabilities(model="placeholder", chip="placeholder", memory=0, flops=DeviceFlops(fp32=0, fp16=0, int8=0)))
shard = models[path_or_hf_repo]
request_id = str(uuid.uuid4())


async def run_prompt(prompt: str):
  if tokenizer.chat_template is None:
    tokenizer.chat_template = tokenizer.default_chat_template
  if (hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None):
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

  await peer2.connect()

  try:
    await peer2.send_prompt(shard, prompt, request_id)
  except Exception as e:
    print(e)

  import time
  # poll 10 times per second for result (even though generation is faster, any more than this it's not nice for the user)
  previous_length = 0
  n_tokens = 0
  start_time = time.perf_counter()
  while True:
    try:
      result, is_finished = await peer2.get_inference_result(request_id)
    except Exception as e:
      continue
    await asyncio.sleep(0.1)

    # Print the updated string in place
    updated_string = tokenizer.decode(result)
    n_tokens = len(result)
    print(updated_string[previous_length:], end='', flush=True)
    previous_length = len(updated_string)

    if is_finished:
      print("\nDone")
      break
  end_time = time.perf_counter()
  print(f"\nDone. Processed {n_tokens} tokens in {end_time - start_time:.2f} seconds ({n_tokens / (end_time - start_time):.2f} tokens/second)")


if __name__ == "__main__":
  parser = argparse.ArgumentParser(description="Run prompt")
  parser.add_argument("--prompt", type=str, help="The prompt to run")
  args = parser.parse_args()

  asyncio.run(run_prompt(args.prompt))