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- # 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 inference.mlx.sharded_utils import get_model_path, load_tokenizer
- from inference.shard import Shard
- from networking.peer_handle import PeerHandle
- from networking.grpc.grpc_peer_handle import GRPCPeerHandle
- from topology.device_capabilities import DeviceCapabilities
- from typing import List
- import asyncio
- import argparse
- 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)
- peer1 = GRPCPeerHandle(
- "node1",
- "localhost:8080",
- DeviceCapabilities(model="test1", chip="test1", memory=10000)
- )
- peer2 = GRPCPeerHandle(
- "node2",
- "localhost:8081",
- DeviceCapabilities(model="test1", chip="test1", memory=10000)
- )
- shard = Shard(model_id=path_or_hf_repo, start_layer=0, end_layer=0, n_layers=32)
- 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
- )
- for peer in [peer1, peer2]:
- await peer.connect()
- await peer.reset_shard(shard)
- try:
- await peer1.send_prompt(shard, prompt, "request-id-1")
- except Exception as e:
- print(e)
- import sys
- 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:
- result, is_finished = await peer2.get_inference_result("request-id-1")
- 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))
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