move examples to examples dir

example_user.py

@@ -1,78 +0,0 @@
-# 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
-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)
-
-peers: List[PeerHandle] = [
-    GRPCPeerHandle(
-        "node1",
-        "localhost:8080",
-        DeviceCapabilities(model="test1", chip="test1", memory=10000)
-    ),
-    GRPCPeerHandle(
-        "node2",
-        "localhost:8081",
-        DeviceCapabilities(model="test2", chip="test2", memory=20000)
-    )
-]
-shards: List[Shard] = [
-    Shard(model_id=path_or_hf_repo, start_layer=0, end_layer=15, n_layers=32),
-    Shard(model_id=path_or_hf_repo, start_layer=16, end_layer=31, n_layers=32),
-    # Shard(model_id=path_or_hf_repo, start_layer=0, end_layer=30, n_layers=32),
-    # Shard(model_id=path_or_hf_repo, start_layer=31, end_layer=31, 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, shard in zip(peers, shards):
-        await peer.connect()
-        await peer.reset_shard(shard)
-
-    tokens = []
-    last_output = prompt
-
-    for _ in range(20):
-        for peer, shard in zip(peers, shards):
-            if isinstance(last_output, str):
-                last_output = await peer.send_prompt(shard, last_output)
-                print("prompt output:", last_output)
-            else:
-                last_output = await peer.send_tensor(shard, last_output)
-                print("tensor output:", last_output)
-
-        if not last_output:
-            break
-
-        tokens.append(last_output.item())
-
-    print(tokenizer.decode(tokens))
-
-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))